• Energy & Environment ›

Pollution in India - statistics & facts

India’s air pollution crisis, water and land pollution, key insights.

Detailed statistics

Most polluted countries based on PM2.5 concentration globally 2022

Share of population exposed to hazardous levels of PM2.5 worldwide 2022, by country

Share of ocean plastic waste inputs worldwide 2019, by country

Editor’s Picks Current statistics on this topic

Current statistics on this topic.

Per capita CO₂ emissions in India 1970-2022

Waste Management

Number of waste items found along beaches in India 2022, by type

Related topics

Recommended.

  • Environment of India
  • Emissions in India
  • Waste management in India
  • Water accessibility in India
  • Global plastic waste

Recommended statistics

  • Premium Statistic Most polluted countries based on PM2.5 concentration globally 2022
  • Premium Statistic Most polluted capital cities based on PM2.5 concentration globally 2022
  • Premium Statistic Share of population exposed to hazardous levels of PM2.5 worldwide 2022, by country
  • Premium Statistic Lowest clean oceans scores worldwide 2022, by country
  • Premium Statistic Share of ocean plastic waste inputs worldwide 2019, by country

Average PM2.5 concentration in the most polluted countries worldwide in 2022 (in micrograms per cubic meter of air)

Most polluted capital cities based on PM2.5 concentration globally 2022

Average PM2.5 concentration in the most polluted capital cities worldwide in 2022 (in micrograms per cubic meter of air)

Share of national population exposed to hazardous concentrations of air pollution worldwide as of 2022, by country

Lowest clean oceans scores worldwide 2022, by country

Lowest scores of clean estuarine, coastal, and open ocean waters worldwide as of 2022, by country

Distribution of global plastic waste emitted to the ocean in 2019, by select country

Air pollution

  • Premium Statistic Most polluted cities based on PM2.5 concentration globally 2022
  • Premium Statistic Average air quality index India 2022, by select city
  • Premium Statistic Most polluted cities in India 2022, by PM2.5 concentration
  • Premium Statistic Average monthly PM2.5 concentration in India 2020-2022, by select city
  • Premium Statistic Concentration of PM10 in India 2001-2021, by select city
  • Premium Statistic Concentration of nitrogen dioxide in India 2001-2021, by select city

Most polluted cities based on PM2.5 concentration globally 2022

Average PM2.5 concentration in the most polluted cities worldwide in 2022 (in micrograms per cubic meter of air)

Average air quality index India 2022, by select city

Average air quality index across India in 2022, by select city

Most polluted cities in India 2022, by PM2.5 concentration

Average PM2.5 concentration in the most polluted cities in India in 2022 (in micrograms per cubic meter of air)

Average monthly PM2.5 concentration in India 2020-2022, by select city

Average monthly PM2.5 concentration in selected cities in India from 2020 to 2022 (in micrograms per cubic meter of air)

Concentration of PM10 in India 2001-2021, by select city

Particulate matter (PM10) concentration in ambient air in selected cities in India from 2001 to 2021 (in micrograms per cubic meter)

Concentration of nitrogen dioxide in India 2001-2021, by select city

Nitrogen dioxide (NO₂) concentration in ambient air in selected cities in India from 2001 to 2021 (in micrograms per cubic meter)

Land pollution

  • Premium Statistic Land degradation shares in India 2019, by state
  • Premium Statistic Municipal solid waste generated in India FY 2021, by state
  • Premium Statistic Hazardous waste generation in India FY 2022, by state
  • Premium Statistic Number of landfills in India FY 2021, by state

Land degradation shares in India 2019, by state

Share of land under degradation in India as of 2019, by state

Municipal solid waste generated in India FY 2021, by state

Municipal solid waste generated in India in FY 2021, by state (in metric tons per day)

Hazardous waste generation in India FY 2022, by state

Hazardous waste generated in India in FY 2022, by state (in metric tons)

Number of landfills in India FY 2021, by state

Number of landfills in India in FY 2021, by state

Water pollution

  • Premium Statistic Number of districts with contaminated water in India 2022, by contamination type
  • Premium Statistic Population affected by groundwater contamination in India 2021, by contaminant
  • Premium Statistic Number of grossly polluting industries in India 2022, by state
  • Premium Statistic Number of waste items found along beaches in India 2022, by type

Number of districts with contaminated water in India 2022, by contamination type

Number of districts with contaminated water in India in 2022, by contamination type

Population affected by groundwater contamination in India 2021, by contaminant

Number of people affected by groundwater contamination in India in 2021, by contaminant

Number of grossly polluting industries in India 2022, by state

Number of grossly polluting industries discharging effluents into lakes and rivers in India in 2022, by state

Leading waste found along the Indian coastline during the International Coastal Ocean Cleanup in 2022, by type

Public opinion

  • Premium Statistic Opinion on ailments due to toxic air in Delhi India 2022
  • Premium Statistic Views on initiatives to reduce air pollution in Delhi in India 2022
  • Premium Statistic Symptoms experienced due to air pollution across Delhi NCR in India 2023
  • Premium Statistic Symptoms experienced due to air pollution across Mumbai in India 2023

Opinion on ailments due to toxic air in Delhi India 2022

Opinion on ailments due to toxic air in Delhi in India 2022

Views on initiatives to reduce air pollution in Delhi in India 2022

Views on initiatives to be taken to reduce air pollution in winter in Delhi in India in 2022

Symptoms experienced due to air pollution across Delhi NCR in India 2023

Symptoms experienced due to air pollution across Delhi NCR in India as of November 2023

Symptoms experienced due to air pollution across Mumbai in India 2023

Symptoms experienced due to air pollution across Mumbai in India as of November 2023

Further reports Get the best reports to understand your industry

Get the best reports to understand your industry.

Mon - Fri, 9am - 6pm (EST)

Mon - Fri, 9am - 5pm (SGT)

Mon - Fri, 10:00am - 6:00pm (JST)

Mon - Fri, 9:30am - 5pm (GMT)

To revisit this article, visit My Profile, then View saved stories .

  • Backchannel
  • Wired World
  • Artificial Intelligence
  • Newsletters
  • Wired Insider

Oliver Franklin-Wallis

Inside India’s Gargantuan Mission to Clean the Ganges River

people burning near Ganges River

In the mornings in Varanasi, the air on the banks of the Ganges fills with the scent of burning bodies. On the steps of the Manikarnika ghat —the holiest of the city’s stepped riverbanks, upon which Hindu dead are cremated—the fires are already lit, and mourners assemble by the hundred to accompany their loved ones at the end. Pyres of sandalwood (for the rich) and mango wood (for everyone else) are already burning; on one, a corpse wrapped in white is visible in the flames.

Down at the river, where I’m watching from a boat, some families are engaged in the ceremonial washing of their dead, the corpses shrouded in white linen and decorated with flowers. A few meters away, a man from another family (usually, the honor is bestowed on the eldest son) wades into the water, casting in the ashes of an already cremated relative so that the Ganges might carry their spirit onwards to the next life or even moksha, the end of the rebirth cycle, and transcendence.

The funeral ceremonies, held against the backdrop of the ancient city, are undeniably beautiful; but the same can’t be said of the river itself. The water’s surface is flaked with ashes; ceremonial flowers linger in the eddies. Just downstream, a couple of men are diving for discarded jewelry. Not 50 meters upstream, another group, having finished their rites, are bathing in the filthy water. An older man, clad in white, finishes his bathing with a traditional blessing: He cups the fetid Ganges water in one hand and takes a sip.

The Ganges is one of the most densely populated river basins in the world, providing water for an estimated 600 million people. But to Hindus, it is more than a waterway: It is Ma Ganga, the mother river, formed—according to the sacred text the Bhagavata Purana —when Lord Vishnu himself punctured a hole in the universe and divine water flooded into the world. Water from the Ganges is widely used in Hindu prayer and ceremony; you can buy plastic bottles of it from stalls all over the subcontinent—or order one on Amazon in the UK for as little as £3.

And yet despite its sacred status, the Ganges is one of the most contaminated major rivers on earth. The UN has called it “woefully polluted.” As India’s population has exploded—in April 2023, it overtook China to become the world’s most populous country—hundreds of millions of people have settled along the Ganges’ floodplain. India’s sanitation system has struggled to keep up. The Ganges itself has become a dumping ground for countless pollutants: toxic pesticides, industrial waste, plastic, and, more than anything, billions upon billions of liters of human effluent.

It’s March 2022, and I’ve come to India while reporting my book, Wasteland , about the global waste industry. And few issues in waste are more critical (yet less sexy) than sanitation. In the global north, sewage is a problem that many of us assumed was more or less fixed in Victorian times. But access to clean water and adequate sanitation remains an urgent global issue. Some 1.7 billion people worldwide still do not have access to modern sanitation facilities.

Los Angeles Just Proved How Spongy a City Can Be

Andy Greenberg

This Tiny Website Is Google’s First Line of Defense in the Patent Wars

Paresh Dave

Neuralink’s First Brain Implant Is Working. Elon Musk’s Transparency Isn’t

Emily Mullin

Every day, an estimated 494 million people without access to flushing toilets and closed sewers are forced to defecate in the open, in gutters, or in plastic bags. The World Health Organization estimates that one in 10 people consumes wastewater (aka sewage) every year, either via unclean drinking water or contaminated food. In India, the result is that 37 million people are thought to be affected by water-borne illnesses such as typhoid, dysentery, and hepatitis every year. Worldwide, poor sanitation kills more children annually than AIDS, malaria, and measles combined .

Sanitation is one of those amenities that most of us in the global north don’t think about until something goes wrong. In the UK, sewers have lately dominated news headlines for the wrong reasons: Many of Britain’s rivers and beaches are being polluted by sewage overflow and farming runoff. According to the UK’s Environment Agency, water companies discharged sewage into English rivers on 301,091 occasions in 2022, totaling more than 1.7 million hours ; on Britain’s beaches, sewage is reportedly making swimmers sick. Britain’s sanitation woes have been caused by years of neglect: systemic underinvestment by profit-chasing ownership; austerity-starved and ineffectual regulation; and the ever-widening expansion of our concrete urban spaces, which divert water away from natural soaks like soil and wetlands and into our watercourses.

people in Ganges River

In India—like much of the global south—the issue is the opposite: In most cases, the sewers were never there in the first place. In this respect, the Ganges’ pollution is a strange mark of success. When Prime Minister Narendra Modi was first elected in 2014, among the first things he did was launch the Clean India Campaign, a nationwide effort to install sanitation and modern waste facilities in a country that had previously lacked them.

Even those critical of Modi’s government—denounced for alleged Islamophobic policies and oppression of the press, among many other things—have to admit that the numbers since have been astonishing. Between 2014 and 2019, by one official estimate, India installed 110 million toilets, providing sanitation for an estimated half a billion people. Little more than a decade ago, India was known for having the highest rate of open defecation (that is, shitting in the open) in the world. Thanks to this massive expansion of public and private toilets, that rate has reportedly plummeted. The issue is that with so many new toilets, the sewage needs to go somewhere.

In that sense, India is like many rapidly urbanizing countries in the global south. But India is also unique, in that Hindu culture places rivers at the center of religious beliefs. And it’s for this reason the Modi government, alongside its Clean India Campaign, launched an expensive infrastructure plan to clean up the national river: the Namami Gange (“Obeisance to the Ganges”) program. It is by no means the first attempt. Previous governments have been launching “‘action plans”’ to clean the Ganges since at least the 1980s. But past efforts, beset by alleged corruption and mismanagement, rarely got far.

To date, the Namami Gange program has cost over 328 billion rupees ($3.77 billion) and promised the construction of more than 170 new sewage facilities and 5,211 kilometers of sewer lines—enough to cross the Atlantic Ocean. It is a fascinating test case in the global effort to clean up our rivers and seas. After all, if you can’t clean a river sacred to hundreds of millions of people, what hope do the rest of us have?

The offices of Varanasi’s water board, are a traffic-clogged drive west from the cremation ghats and the old city, in one of Varanasi’s increasingly busy commercial neighborhoods. When I arrive there is construction work and activity everywhere. In his air-conditioned office, Raghuvendra Kumar, Jal Kal’s general manager, explains that this is one of the challenges that the Namami Gange project has faced. “This city does not sleep,” he explains.

Kumar, a neat man with a side parting, in a black leather jacket and surgical mask (when we speak, India is not long out of a Covid spike), has been at Jal Kal since 2018. “When I joined, the situation in the city was much worse, because the work was still in progress,” Kumar says. “Sewers were flowing everywhere. It flowed into the streets.”

Varanasi is among the oldest inhabited cities in the world. It is situated at the confluence of two rivers: the Varuna and Assi, both tributaries of the Ganges, which join the river course here. The city’s spiritual and tourist center, on the western bank of the river, is a warren of alleyways, many too narrow to move cars down and often blocked by stray cows and market stalls. The city’s original trunk sewer (the main sewer, into which smaller pipes feed) was built by the British in the early 20th century, but local officials explain that the precursor can be traced back to the Mughal Empire.

Until a few years ago, much of the city’s sewage was released untreated into the Ganges via public drains, or nullahs , which discharged along the same bank as the ghats, where people habitually bathe. Since 2016, the center of the city has seen the installation of several kilometers of new sewer lines, connecting pipes that once spewed straight into the river to a new intercepting sewer, which now carries much of the flow off to one of three new sewage treatment plants. Out of 23 known drains that previously carried raw sewage into the Ganges, Kumar says that 20 have been capped, with the rest in progress. Later, on the same boat that took me past the cremation sites, I see it myself: The city’s most notorious drain, Sisamau, is now capped. Only a steady trickle remains.

In a city that has seen near-constant civic engineering work going on for the last two decades, the sewer project has not always been popular. (“Changing the mindset of the people is a very difficult task,” Kumar says.) To improve uptake of the new waste regime, Jal Kal and the state’s Pollution Control Board put out a series of local adverts; the city ran public announcements over loudspeakers from garbage collection vehicles, warning against open defecation and asking inhabitants not to pollute the river and new drains with garbage. “In the last three to five years, it has come into the habit of the citizens that we have to improve our lifestyle, we have to change our behavior,” Kumar says. “And now it has become the habit of the people.”

It’s not the only change that has taken place in Varanasi. The temple flowers that once clogged the banks of the Ganges after cremations and religious festivals are now collected on the banks in marked bins and in the river using floating barriers; the remains are composted or collected by a local startup, Phool, which converts them into incense sticks. The city’s wider green policies have helped cut pollution levels: Varanasi has passed laws banning certain plastics within the holy city and launched a scheme mandating that more than 580 diesel-powered boats on the river be converted to run on compressed natural gas, reducing oil slicks on the water’s surface. The city also set about “beautifying” the ghats, employing teams of workers to collect leftover waste for recycling, and artists to paint murals celebrating the Namami Gange campaign. And most importantly, 361 public toilets have been built , connected to the new sewers, to reduce the rate of open defecation.

aerial of person burning near Ganges River

Among the Namami Gange projects inaugurated by Modi himself are a new sewage treatment plant in Dinapur, to the northeast of the city, designed to process up to 140 million liters of effluent per day. Similarly, as the city has expanded, so by necessity has the sanitation system. The day after I visit Jal Kal, I am given a tour of a brand-new sewage plant in Ramnagar, on the river’s west bank, where the population is booming. On the road to the plant I’m surrounded by building works, formal and informal; at one point, we pass a group digging up bricks from a newly laid road, presumably for housing construction.

I’m met by Shashikari Shastri, an engineer in charge, who shows me around. The sewage treatment plant is a modern and pleasant place (at least, as pleasant as sewage works get), with pale green buildings and neat rows of trees in the flower beds.

Most sewage treatment plants work in a similar way. To grossly simplify: The bigger solids (i.e., feces) are screened out in large, often open tanks, and those solids that remain are allowed to settle on the bottom of the tank or float to the surface, and are removed. The remaining water is then passed into a series of tanks and mixed with bacteria, which digest the leftover organic matter and kill off remaining pathogens. The ponds are aerated to encourage digestion. (The result tends to be bubbling lanes of sewage which, if you close your eyes, could sound like water fountains, were it not for the smell.) At this stage, any lingering solids are again settled out. Different technologies exist for third and even fourth steps to clean the water further—UV light, chlorination, etc.

The older sewage treatment plants in Varanasi work using an activated sludge technique, in which some of the solids removed during the settling process are reinjected as a kind of bacterial starter. Ramnagar, however, uses a modern A20 (anaerobic-anoxic) design, in which the effluent is passed through additional tanks to reduce dissolved nitrogen and phosphorus. “Our focus is to minimize eutrophication, because last year lots of algae and eutrophication was found [in the Ganges],” Shastri explains. Eutrophication is when a body of water becomes overly enriched with nutrients and minerals, leading to an explosion of algae, which can choke the river of aquatic life.

We arrive eventually at the outlet pipe, a cascading series of tiled waterfalls at the river’s edge. By now, Shastri says, the treated water is far cleaner than when it arrived. This is measured using biological oxygen demand (BOD)—the amount of dissolved oxygen in the water that bacteria need to remove any unwanted organic matter, a proxy measure for how much waste is in the water. “The BOD at the inlet is 180 mg/liter,” Shastri explains. “At the outlet, it’s 5 to10 mg/liter.” Down on the sand, children are playing. Another group is mining sand (illegally, most likely) for building materials.

The sewage treatment plant—like several that I visited along the Ganges reporting my book—is an impressive place, if small. (Despite asking, I was not permitted access to the city’s largest plant, in Dinapur, during my time there.) Still, I couldn’t help but feel that its minuscule size was woefully inadequate for the task in hand.

Size is not the only issue. The rosy image of the Namami Gange campaign, painted by the city’s civil servants, does not always match the reality on the ground. While almost everyone I spoke to in Varanasi was positive about the effect of the campaign on the river and the city, it’s clear that despite the rapid pace of building, the Ganges is still far from clean.

One afternoon in Varanasi, my fellow reporter Rahul Singh and I walked over to the banks of the Assi River (or “Assi nullah [sewer]” as many people still colloquially refer to it). Despite the Namami Gange project’s efforts, the banks of the Assi were buried ankle-deep in plastic waste: microsachets, bottles, packets, pots. I met one of the city’s waste pickers collecting PET bottles, which he can sell for 10 rupees (less than 10p) per kilogram. A little further upstream, floating barriers have been installed in the water to help catch the garbage; so much trash has built up on them that it has created reef-like islands midstream.

When the Assi reaches the Ganges, it passes through a pumping plant, designed to filter out solid rubbish before transferring the wastewater downstream to a sewage treatment plant. But when I visited, the pumping station was barely manned and operating at a fraction of its capacity. One of the metal screens for trapping garbage was broken; inside the facility, plastic and other waste trickled slowly off a conveyor belt and into sacks to be carted away for recycling or incineration. One of the staff (who I agreed could remain nameless) told me the plant extracts a ton of plastic waste per day.

The creaking reality of some of the infrastructure goes against the government’s line on the Namami Gange campaign, which it tends to portray in rapturous, nationalistic tones. The reality is that nearly 10 years after Modi first unveiled the project, the Ganges in Varanasi, and along much of its stretch, remains polluted.

According to the government-run Pollution Control Board’s own figures, in 2020, samples of the river water collected in Varanasi far exceeded India’s own recommended limits for fecal coliform and fecal streptococci bacteria—the latter exceeding the limit by more than 20-fold. The same was true when I visited the industrial city of Kanpur, known for its chromium and heavy metals pollution. It’s not just the Ganges, either: The Yamuna, in Delhi, registered fecal streptococci readings at 10,800 times the recommended limit. All across India, there are reports of rivers foaming with toxic waste or lakes catching fire.

people in Ganges River holding up statues hands

This is the reality of a country like India, that is growing at such an astonishing rate: The risk for India’s civic planners is that by the time new infrastructure—sewage plants, waste facilities, roads—are built, the population is already greater than their capacity. (It is also, it should be said, not solely an Indian problem. Every major industrial country—from China in the last two decades, to the US and other Western countries several decades ago—has faced river pollution crises.) But the continued failure of the government’s schemes to clean the Ganges is a wedge issue for religious campaigners, to whom the issue of cleaning the Ganges is more than practical or political. It’s moral.

One evening in Varanasi, I head back to the ghats, to meet with one of the Namami Gange project’s most outspoken critics. Vishwambhar Nath Mishra is an intense man in his fifties, with white hair and a thick mustache. Mishra is a professor of electronics engineering at Banaras Hindu University, and also mahant (high priest) of Varanasi’s Sankat Mochan Hanuman Temple, a position he inherited from his late father, Veer Bhadra Mishra. Mishra’s father was a lifelong campaigner for the Ganges, and back in the 1980s he set up the Sankat Mochan Foundation, an NGO focused on protecting the river; when we meet, in a room near the foundation, there is a picture of the elder Mishra on the wall, smiling happily. When Mishra Sr. died in 2013, Vishwambhar inherited the foundation, along with his religious duties.

For Mishra, that combination—of engineering, campaigning, and religion—gives him a unique perspective on the requirements of cleaning the Ganges. “The use of this river is entirely different from other river systems,” Mishra says. “People come from distant places and worship Ganga like their mother. A few [of those] people come and gently touch Ganga water and put it on their forehead. A few people come and take a religious bathe in the river. And a few take sips of Ganga water.” This sip is a sacred ritual part of the daily bath in the river taken by many devout Indians.

“Now, if people are sipping on the water, that means the quality has to be potable water quality; there has to be no compromise,” Mishra says. For him, it’s personal. As a religious leader, one person expected to sip Ganges water during their daily bath is Mishra himself.

Mishra’s weapon in the fight for the Ganges is a simple one: data. In 1993, the Sankat Mochan Foundation established one of the few independent labs to analyze the quality of the Ganges’ water in Varanasi. “That’s why they [the government] are scared,” Mishra says. “We have a database that speaks the reality of how healthy the river is.” Ever since, the foundation has been keeping track of the water—bacteria levels, oxygen demand—and has seen the river’s health decline with India’s growth.

According to Mishra and his fellow activists, the government’s own figures when it comes to sewage in Varanasi don’t add up. The largest sewage treatment plant, at Dinapur, has a stated processing capacity of 140 million liters a day (MLD). “Now as a matter of fact, I know that in [the Dinapur plant], they are able to carry only 60 MLD of sewage,” Mishra says, growing more animated as he talks. “At Goitha, where the capacity is 120 MLD, a few months back when I asked those people, they are able to transport only 10 to20 MLD of sewage. That’s all. So as a scientific man, you can just calculate the efficiency.” Similarly, Mishra claims that the government’s assertions that drains are no longer discharging into the river is not true. “Five years ago we found 33 locations discharging [sewage] … That has reduced to 15 or 16,” he says. (The Uttar Pradesh Pollution Control Board did not respond to requests for comment.)

Whereas India’s religious and environmental campaigners like Mishra hope to make the Ganges drinkable again, the Indian government has to date only declared an intent to make the Ganges in Varanasi a Class B river—fit for bathing only. Even by that standard, Mishra says, the project is failing. “We have scientific parameters that if Ganga is a Class B River, then total fecal coliform count should be less than 500 per 100 ml,” Mishra says. (Fecal coliform bacteria are a strong indicator of other pathogens being present.) Mishra shows me a ream of paper, upon which he has printed charts of the lab’s water quality data at numerous locations, going back months. “Right now [in March 2022], where we are sitting at Tulshi ghat, the figure is 41,400 per 100 ml. At the end of [Varanasi], where a big channel is discharging, it is 51 million.”

(While I could not independently confirm these numbers, even the Indian government’s data shows that pathogen levels in the Ganges at Varanasi are many multiples higher than its safety targets.)

Back in 2014, before the launch of the Namami Gange program, Mishra sat with Modi to discuss his hopes to clean the Ganges. Mishra’s foundation has since presented its own proposals for treatment projects, but has been ignored. The Pollution Control Board and state government dispute the foundation’s data; Mishra, meanwhile, says that the government’s figures, which are averages of samples taken from across the width of the river, do not reflect the reality experienced by bathers on the ghats, where sewers discharge into the Ganges and the water is slower. “They will never recognize our laboratory because they know that it will be a big trouble for them. But we have all the data since 1993.”

Mishra also claims that commercial interests are preventing the government from taking even more decisive action to cut pollution. “Ganga happens to be a very fertile cow. So, everybody’s milking in the name of Ganga,” he says. ( Allegations of corruption have plagued India’s many Ganges cleanup campaigns, although Mishra didn’t share any specific evidence of corruption. India’s Ministry of Jal Shakti, or water ministry, did not respond to WIRED’s requests for comment.)

Most politicians and engineers in India, when asked, will tell you that a totally pure Ganges, of the sort that Mishra is aiming for, is almost certainly impossible. (“Religious people don’t follow logic,” SK Barman, a project manager for the state water company’s Ganga Pollution Prevention Unit, told me. “We have to achieve salvation somehow. Moksha, moksha, moksha.”) But in driving the conversation, it’s also clear that without Mishra and the countless other environmental activists across India campaigning for the Ganges restoration, the issue would be worse.

A year since I was last in Varanasi, it’s clear that India’s sanitation drive is still far from where the government’s narrative would have the public believe. According to a public information request by the Indian news organization Down to Earth, in 2023, 71 percent of the Ganges’ river monitoring stations were reporting “alarmingly high” levels of fecal coliform bacteria. Over 66 percent of drains in the state of Uttar Pradesh, where Varanasi sits, still empty into the Ganges and its tributaries.

There is no doubt that the Namami Gange project has made progress, and not just in the number of toilets installed and treatment plants made operational. Nearly every member of the public I spoke to in India—in Varanasi, Kanpur, and in New Delhi—confirmed that anecdotally, pollution issues are improving. It wasn’t that long ago that dead bodies would be regularly found in the river, and sewage in the rainy season flowed up onto the ghats. Today, there are increased sightings of aquatic life, such as the Ganges river dolphin.

And at 2022’s state elections, Modi’s BJP party remained in power—a significant sign ahead of 2024’s presidential election. In March 2023, Modi’s government confirmed Namami Gange Mission II, an additional $2.56 billion of expenditure on expanding the program and continuing to complete already commissioned infrastructure.

As for Mishra and the other activists advocating for a clean holy river, their campaign continues, no matter how unpopular it makes him with the government and Modi-leaning press. “I have heard, ‘Why? Why don’t you say the Ganga is clean?’ Mishra says. “I cannot say that. We are totally committed to the Ganga, and we cannot mislead people. For me, the Ganga is the medium of my life.”

It’s a holy mission, I say.

“It’s a holy mission, and it’s a scientific mission.”

This article appears in the January/February 2024 issue of WIRED UK magazine.

You Might Also Like …

📨 Make the most of chatbots with our AI Unlocked newsletter

Read WIRED’s exclusive 6-part excerpt of 2054, A Novel

Confessions of an AI clickbait kingpin

Polyamory has entered the chat

This website tracked hate crimes in India . Then the government took it offline

YouTube, Discord, and Lord of the Rings led police to a teen accused of a swatting spree

🔌 Charge right into travel season with the best travel adapters , power banks , and USB hubs

Critical Infrastructure Is Sinking Along the US East Coast

Matt Reynolds

Scabies Is Making a Comeback

Dhruv Mehrotra

Create an account

Create a free IEA account to download our reports or subcribe to a paid service.

Air quality and climate policy integration in India

Frameworks to deliver co-benefits

About this report

Executive summary, energy is at the centre of india’s environmental challenges.

Air pollution has emerged as one of India’s gravest environmental problems in recent years. In many locations, concentrations of particulate matter considerably exceed recommended national and international standards resulting in severe implications for population health. In 2019 alone, India experienced an estimated 1.2 million air pollution-related premature deaths. 

Average annual PM2.5 concentration levels in India, 2019 and 2030, Stated Policies Scenario

India Pm2 5 3 155mm 01 1

TERI and IEA analysis.

Energy use is at the heart of India’s air pollution and climate change challenges and today’s energy choices matter for future development, as they have direct and far-reaching implications for the lives of a growing and rapidly urbanising population. In the IEA’s Stated Policies Scenario, which reflects the impact of existing and today’s announced policy frameworks, India accounts for nearly one quarter of global energy demand growth to 2040, more than any other country over the same period. While enhancing economic prosperity, India’s increased energy requirements would entail substantial negative environmental externalities: in the coming years, India will become the largest contributor to global CO 2 emissions growth. Addressing air pollution and curbing CO 2 emissions in a timely and efficient way is vital for future, owing to its close linkage with socioeconomic and human development.

Policy integration and alignment is needed to boost India’s clean energy transition

Given the complex nature of clean energy transitions, coherent policy packages are needed to deliver the necessary rate of change across the entire energy system. Energy-related air pollutant and CO 2 emissions arise from the same sources. Thus, if well designed, energy policies that seek to tackle air pollution or climate change can deliver important co-benefits for other targets.

Here, timing and technological choices are crucial for making measures such as renewables deployment, enhanced emissions standards, energy efficiency measures in industry and the thermal power segment, as well as residential clean energy access a consistent policy framework, fostering clean energy transition. While air pollution measures enable short-term CO 2 emissions stabilisation, climate policies prevent long-term technology lock-in and deliver lasting air pollution reductions. A power sector transition from fossil to renewable energy, for example, addresses both concerns and deliver significant air pollution and CO 2 emissions reductions. To demonstrate co-benefit potential, this report provides quantitative analysis on how flagship energy policies’ contribute to both air pollution reduction and climate change mitigation. Four key sectors are assessed: captive power plants, industrial energy efficiency, electrification of road transport and expanded access to clean cooking.

Well aligned air quality and climate policies generate co-benefits across the entire energy sector

In the power sector, IEA analysis finds that a timely and full implementation of the 2015 emissions standards notification for thermal power plants is crucial if India is to reduce air pollutants in the short term. The installation of emissions control technologies in thermal power plants would enable a 95% reduction in combustion-related emissions of SO 2 and an 80% reduction in PM 2.5 emissions from the power sector by 2030 compared to 2019 levels. Strong renewables deployment could further abate air pollutants and mitigate CO 2 emissions in the long-run.

Emissions of SO2, NOX and PM2.5 from the Indian power sector in the Stated Policies Scenario, 2019-2040

Captive power plants, also known as auto-producers, are employed by heavy industry and service companies to generate electricity in the face of concerns regarding cost and reliability of grid supply. This fossil-fuel intensive sector is understood to make up around 14% of India’s power supply in 2019, and with its greater share of coal and oil use, accounted for 16-18% of all power-related CO 2 and air pollution emissions. To meet India’s decarbonisation targets, the power sector is expected to shift away from coal towards cleaner sources of power. While the government has introduced stricter pollution limits, the majority of thermal power plants are unlikely to meet the air pollution standards by the mid-2020s.

Even if the captive power sector continues to rely on coal at 2019 levels (87%), full implementation of emission control measures until 2030 would result in significant reductions of SO 2 , NO X and PM 2.5 emissions by 2040, despite strong growth in generation. Should solar PV provide one third of captive power supply in 2040, similar to its share in the national mix if current targets are met, PM 2.5 and NO X emissions would be reduced by another half. Failing to implement air pollution control technologies, however, would see emissions across all air pollutants more than doubling and SO 2 emissions reaching more than 1.5 Mt in 2040, a level 16 times higher than under the implementation of emission control measures. Given its scale and strong reliance on fossil fuels, the captive power segment must be included in all power-sector policy decisions if India seeks to accomplish both a power sector transition and industrial development.

Supported by a range of economic development policies, the contribution of industry to gross domestic product nearly quadrupled over the past two decades resulting in significant energy demand growth in the sector. By 2019, industry was India’s main energy-consuming sector, accounting for more than one third of total final energy consumption, with three sectors, iron and steel, chemicals and cement accounting for almost 45%. IEA projects that strong growth in economic output could more than double industry energy demand by 2040, increasing the sector’s share of final energy consumption to 40%.

India’s Bureau of Energy Efficiency estimates that the industry sector could contribute 60% of possible energy savings until 2031 and several programmes have been introduced to meet this target. The Perform, Achieve and Trade (PAT) scheme is the cornerstone of these efforts. Established in 2012, the first PAT cycle targeted large energy-intensive industries. Following initial success, the scheme was extended, and phases PAT II-VI have been rolled out annually in two-year cycles since 2016. By improving energy efficiency and avoiding additional energy consumption in industry, the PAT scheme indirectly delivers environmental benefits as lower energy consumption results in fewer CO 2 and air pollutant emissions. Nonetheless, it could be argued that the PAT scheme was not ambitious enough and targets could be set higher, such as by means of benchmark setting as well as broader coverage of large energy consumers and sectors. 

Energy intensity of production in the Stated Policies Scenario, 2023-2040

Industrial energy consumption in the states policies scenario, 2015-2040.

IEA analysis estimates that a continued and expanded PAT scheme could result in more than 80 Mt of avoided CO 2 emissions in 2030 and 265 Mt CO 2 in 2040, of which iron and steel would contribute about 70% and cement more than a quarter. Converting the PAT scheme’s energy saving to carbon saving certificates could further trigger fuel switching, which would contribute additional CO 2 emissions reductions. Furthermore, expanding the PAT scheme could save more than 10% SO 2 and NO X pollution from large industry by 2040.

The past two decades have seen seven-fold increase in the number of passenger cars on India’s roads leading to significant increases in urban air pollution levels and associated health problems. In response, India has adopted tighter emissions standards (Bharat Stage VI) effective for all vehicles manufactured after March 2020 and introduced the ambition to reach a 30%-share of EVs in total vehicle sales by 2030.

In terms of air pollution, road transportation was responsible for more than 40% of total NO X emissions (3.3 Mt) and around 7% combustion-related PM 2.5 emissions in 2019. Implementing existing policies, including stricter fuel and vehicle standards, could lead to road transport-related NO X emissions peaking around 2025 and then declining by over 60% by 2040. This would reduce road transport’s share of combustion-related NO X emissions to one quarter by 2040. India’s transport sector directly emitted almost 320 Mt CO 2 in 2019 or 14% of the country’s carbon emissions, more than 90% of which arose from road transport.

Growing demand for road transport sees projected emissions from the sector rising to more than 450 Mt CO 2 by 2030, and almost 600 Mt CO 2 by 2040. Furthermore, the growing use of EVs leads to indirect emissions in the power sector. While these emissions amounted to less than 0.5 Mt CO 2 in 2019, their importance is expected to grow with the uptake of EVs. By 2030, CO 2 emitted by EV electricity consumption could reach 20 Mt CO 2 , and more than double by 2040. The speed at which the power sector reduces its CO 2 intensity is closely related to the net mitigation potential of large-scale EV deployment. A power mix that remains as carbon-intensive in 2040 as it was in 2019 could lead to the EV fleet emitting 20Mt CO 2 more than an equivalent fleet of conventional cars, while decarbonising the power mix as currently planned could deliver savings of more than 35 Mt CO 2 .

Emissions of NOX from the use of EVs in the Stated Policies Scenario, 2025-2040

Emissions of so2 from the use of evs in the stated policies scenario, 2025-2040, emissions of co2 from the use of evs in the stated policies scenario, 2025-2040.

Around 660 million people, just under half of India’s population, relied primarily on traditional use of biomass for cooking and heating in 2019. Burned indoors in poorly ventilated spaces, these fuels directly expose households to indoor air pollution, often with severe consequences for their health. A quarter of the almost 2.5 million premature death cases resulting from indoor air pollution globally occurred in India. Over the past decade, the country developed a comprehensive policy framework to promote clean cooking solutions notably targeting poor, rural households resulting in the share of the population relying on biomass, kerosene or coal declining by almost 20% over the period 2010-19, with half of the total population using cleaner fuels such as LPG.

Heavy reliance on traditional biomass for cooking and heating results in high levels of harmful PM 2.5 pollution and to a lesser extent contributes to NO X and SO 2 emissions. Indoor air pollution occurs disproportionately in rural areas, where more than 90% of the population live without access to clean cooking. Stated policy efforts to improve access to clean cooking fuel are expected to reduce the share of the population using traditional biomass to less than 25% and enable a 40% reduction in indoor particulate matter air pollution by 2040. Replacing traditional biomass with LPG would increase CO 2 , but reduce methane emissions and could avoid a substantial amount of black carbon, a potent climate forcer. Nonetheless, the high inequality in geographical distribution would remain, with 95% of India’s population without clean cooking access living in rural areas in the future.

Displaced PM2.5 emissions through the use of clean cooking fuels instead of traditional biomass

Displaced so2 and nox emissions through the use of clean cooking fuels instead of traditional biomass.

Fully shifting away from highly polluting to clean cooking fuels by 2030 would almost entirely remove indoor PM 2.5 emissions and reduce associated premature deaths to 0.1 million cases annually. Efforts to increase the availability and affordability of LPG in rural areas are needed along with improved gas stove availability and gas distribution infrastructure in urban areas. In major cities, overcoming reliability issues and improving the wattage of supply through investments in the power distribution infrastructure would allow electricity to actively contribute to clean cooking access in high- and middle-income urban areas. Finally, regional diversity will require policy makers to understand and account for geographic and cultural differences or similarities, and design solutions to improve access to cooking energy accordingly.

Ways forward

Present analysis of India’s energy and climate policies shows that current air pollution and climate measures, if fully implemented, could improve air quality but remain insufficient to deliver the levels recommended by the World Health Organisation. A wholly integrated policy response could enable additional, technical savings of 3.2 Mt SO 2 , 5.1 Mt NO X and 4.2 Mt PM 2.5  in 2040, of which more than two-thirds would be realised by stricter air pollution measures. Policy makers must recognise that in many sectors, there are synergies between air pollution and climate policy objectives. Otherwise, benefits from both air pollution and climate policy measures will be undervalued. Acknowledging these synergies in the design and implementation of future policy frameworks will provide a more impactful response to the most pressing national health and environmental challenges and offer great potential for India’s contribution in the global fight against climate change.

Additional technical potential for air pollution reduction, 2040

Related files.

  • Launch presentation Download "Launch presentation"

Documentation

Cite report.

IEA (2021), Air quality and climate policy integration in India , IEA, Paris https://www.iea.org/reports/air-quality-and-climate-policy-integration-in-india, Licence: CC BY 4.0

Share this report

  • Share on Twitter Twitter
  • Share on Facebook Facebook
  • Share on LinkedIn LinkedIn
  • Share on Email Email
  • Share on Print Print

Subscription successful

Thank you for subscribing. You can unsubscribe at any time by clicking the link at the bottom of any IEA newsletter.

India’s States Take Action Against Regional Air Pollution

Image

STORY HIGHLIGHTS

  • Air quality management in India is a regional issue.
  • Fortunately, there is now greater political will among India’s states for tackling air pollution.
  • The World Bank is helping Uttar Pradesh and Bihar move beyond city-wise planning to develop their first state-wide Clean Air Action Plans that can form the building blocks for wider cooperation across the airshed.

Over the past few decades, India’s economic growth has led to the rapid deterioration of air quality. Delhi’s inordinately high pollution levels and consistent ranking among the world’s most polluted capitals has grabbed media attention, bringing air pollution under regular public scrutiny.

However, contrary to common perception, air pollution in India is not just an urban or a single city issue. The country’s air quality has remained dangerously poor at the regional scale, particularly so across the seven states of the Indo-Gangetic Plain that form a major part of the north Indian airshed.  

Although it is well known that Delhi and the National Capital Region (NCR) are impacted by the burning of crop residue in the neighboring states of Punjab and Haryana, India’s current planning framework for air quality management continues to be limited.  

City action plans are restricted to their own jurisdictions and do not identify the substantive sources of air pollution that arise beyond municipal limits. In fact, about a third of the smog that engulfs Kanpur, Patna, and other cities in the Indo-Gangetic plain originates from the burning of biomass for cooking and heating in both urban and rural areas.

Since air pollution is clearly a transboundary issue that crosses the borders of cities, states and even nations in some cases, city action plans alone will not be enough to reduce emissions over the wider airshed.  

Every state will first need to do their part to help themselves, with the spill-over benefits impacting neighboring states as well.  

Bihar and Uttar Pradesh take the lead

The good news is that there is now greater political will among the states for tackling the challenge. In the densely populated states of Uttar Pradesh and Bihar, among the poorest in the country, the World Bank is working with state governments to develop state-wide Clean Air Action Plans that prioritize what needs to be done to achieve air quality targets by 2024 and 2030.

Bihar – which receives pollutants from both within the state and beyond, leading to pollution levels that are much higher than national standards - has now pioneered the establishment of a network of air quality monitoring stations across the state.

Bringing local knowledge and international best practices together, Bihar’s Pollution Control Board has diversified the location of its monitoring stations to include both urban and rural areas to determine how much pollution emanates from which source - agriculture, industry, households, transportation and construction. Earlier, only the major cities of Patna, Gaya and Muzaffarpur had monitoring stations and just 4 of the state’s 38 districts were covered.

“We are now collecting data from 24 stations across the state in both urban and rural areas, and collating it centrally,” said Mr. S. Chandrasekar, Member Secretary, Bihar State Pollution Control Board. “Going forward, our plan will be to move beyond the cities and bring different sectors together, helping them coordinate their work so people in the state and beyond can benefit.”

Furthermore, since the burning of biomass by households is among the largest sources of air pollution in Bihar, the World Bank’s Jeevika rural livelihoods project, in collaboration with The Energy Research Institute (TERI), is helping pilot a program that promotes the use of smokeless solar-powered cookstoves among rural women. The women say that switching from biomass burning cookstoves to cleaner ones has made a difference to their health and led to savings on fuel. 

Uttar Pradesh , too, has proactively begun to address the issue of air pollution across the state. The Uttar Pradesh Pollution Control Board is developing a Clean Airshed Plan with the Indian Institute of Technology (IIT) Kanpur and the World Bank as key partners. Data is being collected and models are being run to arrive at the most cost-effective interventions in each sector and determine which areas should be focused on first. Efforts are also on to devise the policies, institutions and compliance mechanisms that will be needed to reach the state’s clean air commitments and targets.

“Uttar Pradesh is in the heart of the Indo-Gangetic Plain, which is regarded as the ‘hotspot’ for air pollution in South Asia,” said Mr. Ashish Tiwari, Secretary, Department of Environment, Forest and Climate Change, Government of Uttar Pradesh. “Our state, therefore, has the dual challenge of not only managing our own local sources of pollution in the micro airshed but also addressing the emissions from adjoining states such as Punjab, Haryana and the National Capital Region of Delhi. Our state action plan will give us a roadmap for tackling long range pollutants as well so citizens across the airshed can breathe clean air. The plan will also provide the foundation for a robust air pollution mitigation strategy based on regional cooperation.”

Air Quality Management Program

While the first steps to cleaner air are well underway in these two Indo-Gangetic states, a number of follow-on actions will be needed.

States will need to devise new modes of coordination between city and state administrations, as well as between line departments, with mechanisms for a multi-sectoral dialogue so that actions can be coordinated and prioritized. This may involve the creation of new state authorities that cover wider jurisdictions, as well as a regulatory rethink, with extensive consultations among stakeholders.

Beyond the initiatives of individual states, regional coordination mechanisms will also need to be established, with support from the central government. State-wise clean air plans can then become an important basis for inter-state as well as regional collaboration.      

“We are working with India at this important juncture as it adopts a broader airshed management approach. A measurable reduction in emissions will lead to improved health and a better quality of life for the people,” said Karin Shepardson, Lead Environment Specialist at the World Bank.

  • Clearing the Air : A Tale of Three Cities (English)
  • Air Pollution: Locked Down by COVID-19 but Not Arrested
  • India’s Youth Rise Up to Fix Country’s Toxic Air Problem
  • Tackling poor air quality: Lessons from three cities
  • A silver lining in India's smog?
  • World Bank in India
  • World Bank India on Twitter
  • World Bank India on Facebook

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Open access
  • Published: 04 October 2023

An environmental justice analysis of air pollution in India

  • Priyanka N. deSouza 1 , 2 ,
  • Ekta Chaudhary 2 ,
  • Sagnik Dey 2 , 3 , 4 ,
  • Soohyeon Ko 5 , 6 ,
  • Jeremy Németh 1 ,
  • Sarath Guttikunda 7 , 8 ,
  • Sourangsu Chowdhury 9 ,
  • Patrick Kinney 10 ,
  • S. V. Subramanian 11 , 12 ,
  • Michelle L. Bell 13 &
  • Rockli Kim 6 , 14  

Scientific Reports volume  13 , Article number:  16690 ( 2023 ) Cite this article

4871 Accesses

49 Altmetric

Metrics details

  • Environmental impact
  • Sustainability

An Author Correction to this article was published on 09 November 2023

This article has been updated

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM 2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM 2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM 2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM 2.5 levels corresponding to 0.127 μg/m 3 (95% CI 0.062 μg/m 3 , 0.192 μg/m 3 ) and 0.199 μg/m 3 (95% CI 0.116 μg/m 3 , 0.283 μg/m 3 , respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM 2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM 2.5 levels and different SES parameters.

Ambient air pollution is the world’s single largest environmental health risk and is estimated to have been responsible for 6.7 million premature deaths in 2019 1 . Fine particulate matter (PM 2.5 ) concentrations in India are among the highest in the world 2 , 3 , 4 . According to the 2019 Global Burden of Disease, PM 2.5 was estimated to be responsible for 1.67 million deaths (0.98 million deaths from ambient pollution and 0.61 million deaths from household pollution), or 17.8% of the total deaths recorded in India, with economic losses alone corresponding to ~ 1.36% of the India’s Gross Domestic Product in 2017 5 .

In the United States, and elsewhere, a rich body of environmental justice (EJ) research documents the substantial and persistent disparities in exposure to pollution by markers of privilege 6 , 7 , 8 , 9 , 10 . Such work has resulted in deliberate efforts to incorporate concerns of equity into environmental policymaking 11 . Little work has been done examining if socially disadvantaged and marginalized communities are also disproportionately burdened by particulate matter pollution in low- and middle-income countries like India 12 , although such work could inspire similar policy efforts.

In India, limited existing evidence has shown that pollution from coal fired power plants is higher among marginalized populations belonging to lower castes and among the poor 13 . Recent work has also found disparities in air pollution-related mortality from power generation plants, with poorer, coal-dependent states in eastern India bearing the brunt of PM 2.5 -mortality from electricity generation 14 . Other research has found that PM 2.5 levels are higher in districts with a higher percentage of lower caste or Scheduled Caste (SC) residents, young children, and households in poor condition 15 . Scheduled Castes and Scheduled Tribes, and Other Backward Classes are officially designated groups of people who are among the most socioeconomically disadvantaged in India; and that the greatest increase in PM 2.5 concentrations were in less urbanized districts with a high percentage of SCs, women, children, persons with disabilities, and households without toilets 15 .

The EJ studies described here draw on data for socioeconomic status (SES) from the Census as well as the Houselisting and Housing Census data of India 13 , 15 . As the Census is conducted once every 10 years, some of the variables, such as asset ownership, likely do not reflect the current distribution of wealth. In addition, the Census datasets contain limited information on SES information relevant to evaluating environmental justice concerns in India. For example, religion is typically not recorded. Finally, existing research that has utilized these datasets have been conducted at the district-level which may not be a fine-enough spatial scale to capture the substantial heterogeneity in PM 2.5 and SES status in India. We aim to fill these gaps in the current study by evaluating how total (main analysis), anthropogenic PM 2.5 levels, and source-specific PM 2.5 concentrations (supplementary analyses) vary over a rich array of context-specific SES variables relating to caste, religion, income, education, household assets and wealth associated with social advantage in India, using data from the National Family and Health Survey (NFHS-4) conducted between 2015-2016.

The NFHS are nationally representative surveys measuring indicators of population, health and nutrition, with a focus on maternal and infant health. In the NFHS, women between 15 and 49 years of age from ~ 25-30 households, sampled randomly from each of 28,526 clusters (villages in rural areas and census enumeration blocks in urban areas), which were in turn randomly sampled from each district in India were interviewed in detail. Using a precision-weighted method (described in more detail in “ Methods ” Section) that accounts for the NFHS-4 survey design and sampling variability 16 , 17 , we estimated the prevalence of the following SES factors at the cluster-level from the survey responses for the time period 2010-2015: (1) Households that were in the lowest wealth quintile, (2) Households that had Below Poverty Line (BPL) ration cards, (3) Households that had electricity, (4) Households that had improved sanitation, (5) Households that used solid fuels for their energy needs, (6) Households that had access to safe drinking water, (7) Households headed by a Muslim, (8) Households headed by a college-educated individual, (9) Households headed by a woman, (10) Households headed by an individual belonging to a Scheduled Caste (SC), (11) Households headed by an individual belonging to a Scheduled Tribe (ST), (12) Households headed by an individual belonging to an Other Backward Class (OBC), (13) Mothers married young (< 18 years of age), and (14) Underweight mothers (BMI < 18.5 kg/m 2 ), an indicator of food-access (Figs. S1 – 4 ). We also used population density available for each cluster in our analysis (Fig. S5 ).

Total-PM 2.5 concentrations, averaged over the years 2010-2015 were obtained from a well-validated satellite-derived dataset 18 . The PM 2.5 sources considered in this analysis are Agricultural Residue Burning (ARB), Domestic Burning (DOM), Industrial (IND), International (INT), OTH (other), POW (power), road dust (RD), and Transport (TRA). We derived anthropogenic PM 2.5 values by deducting soil dust from natural sources from total PM 2.5 levels. Source- and species- specific PM 2.5 concentrations were obtained from the output of the Community Multiscale Air Quality (CMAQ) model, described elsewhere for the year 2016, alone 19 . We chose to consider anthropogenic concentrations, in addition to total levels, because policymakers have control over the former exposure.

We first visually examined relationships between the PM levels and SES variables considered by plotting mean PM concentrations for clusters categorized into deciles based on the prevalence of different SES variables. We used multilevel models to quantify the geographic variation of total, anthropogenic and source-specific PM 2.5 across different spatial scales. We evaluated associations between each exposure of interest and the SES factors described in unadjusted and fully-adjusted multilevel models. We also evaluated how disparities in exposure to PM 2.5 from power generation (POW) varied relative to the benefits consumers receive. We used average nighttime luminosity as a proxy for energy consumption from power generation. Finally, we evaluated associations between the change in PM 2.5 levels between 2010 and 2015 with changes in different SES factors (for more details refer to “ Methods ” section).

Descriptive statistics of the SES parameters and PM 2.5 concentrations for the 28,072 clusters with non-missing SES and total PM 2.5 levels are displayed in Table S1 in Supplementary Information . PM 2.5 levels are high in India, with mean concentrations of 53.4 μg/m 3 (median: 47 μg/m 3 ; range: 3.5–131.7 μg/m 3 ). Descriptive statistics for 27,534 clusters that have non-missing SES, anthropogenic, and source-specific PM 2.5 levels are displayed in Table S2 . Pair-wise Pearson correlation coefficients between the different parameters considered are displayed in Fig. S12 .

Evaluating disparities in PM concentrations along different EJ dimensions

One-way ANOVA tests revealed that total-PM 2.5 concentrations varied significantly over all clusters classified into deciles based on the prevalence of all SES parameters, considered. When repeating this analysis for urban clusters, alone, we generally observed similar results with one exception: total-PM 2.5 levels did not vary significantly over urban clusters categorized into deciles based on the prevalence of poor residents.

Mean total-PM 2.5 concentrations were on average higher in clusters corresponding to a high prevalence of SCs (Decile 10: 60.7 μg/m 3 , Decile 1: 21.2 μg/m 3 ), OBCs (Decile 10: 54.1 μg/m 3 , Decile 1: 28.0 μg/m 3 ), Muslims (Decile 10: 56.1 μg/m 3 , Decile 1: 38.3 μg/m 3 ), poor households (Decile 10: 59.3 μg/m 3 , Decile 1: 58.8 μg/m 3 ), households with no formal education (Decile 10: 56.5 μg/m 3 , Decile 1: 44.9 μg/m 3 ), underweight mothers (Decile 10: 55.7 μg/m 3 , Decile 1: 35.4 μg/m 3 ) and mothers who were married young (Decile 10: 59.1 μg/m 3 , Decile 1: 40.7 μg/m 3 ). Mean total-PM 2.5 levels were lower in clusters with a high percentage of STs (Decile 10: 31.6 μg/m 3 , Decile 1: 72.3 μg/m 3 ), and electrified households (Decile 10: 58.1 μg/m 3 , Decile 1: 72.0 μg/m 3 ). STs tend to live in remote rural areas 20 , which explains the trend observed in PM 2.5 levels.

Contrary to expectations, total-PM 2.5 levels were higher in clusters with a higher prevalence of households with safe drinking water (Decile 10: 76.7 μg/m 3 , Decile 1: 43.4 μg/m 3 ), in clusters with a high prevalence of college-educated household heads (Decile 1: 49.3 μg/m 3 , Decile 10: 55.2 μg/m 3 ), and in clusters with a lower prevalence of households headed by women (Decile 10: 51.2 μg/m 3 , Decile 1: 59.4 μg/m 3 ). Total-PM 2.5 concentrations were also lower in clusters with a higher prevalence of households living below the poverty line with ration cards (Decile 10: 39.7 μg/m 3 , Decile 1: 57.6 μg/m 3 ). There is a negative correlation between the prevalence of poverty and households with safe drinking water (− 0.16), and almost no correlation (0.00) between the prevalence of households headed by women and poverty (Fig. S12 ). Although the prevalence of poverty and the prevalence of households living BPL with ration cards were correlated (0.37), not all households BPL can avail of a ration card due to limitations imposed by state quotas. The quotas rely on data from National Sample Survey (NSS) Household Consumption Survey for 2011–2012 which are outdated. Research has shown that it is often the most underprivileged who cannot access ration cards even though they are BPL 21 . Thus, the prevalence of households BPL, likely, does not capture the poorest of the poor in India. When we repeated this analysis, disaggregated by urban/rural designation, we observed similar trends among urban and rural clusters (Fig.  1 ).

figure 1

Total-PM 2.5 concentrations by decile of the different SES prevalence parameters considered, disaggregated by urban/rural clusters. Total-PM 2.5 concentrations corresponding to the first and tenth decile are highlighted. The boxes correspond to the first and third quartiles of the distribution of Total-PM 2.5 concentrations corresponding to each group.

We observed similar relationships between anthropogenic-PM 2.5 levels and SES parameters (Fig. S13 ). One-way ANOVA tests revealed that anthropogenic-PM 2.5 concentrations varied significantly over all clusters classified into deciles based on the prevalence of all SES parameters except college-educated household heads. The same was true for the variation of industrial-PM 2.5 , agricultural residue burning-PM 2.5 , and other-PM 2.5 levels. Transport-PM 2.5 varied significantly over all clusters categorized by the prevalence of all SES variables. The same was true for power-PM 2.5 concentrations except over clusters classified into deciles on the basis of the prevalence of Muslim household and heads, and households with safe drinking water; And for domestic burning-PM 2.5 levels except over clusters classified into deciles on the basis of the prevalence of households BPL; And for road dust-PM 2.5 except for clusters classified into deciles on the basis of the prevalence households with a female head; And for international-PM 2.5 except over clusters classified into deciles on the basis of the prevalence of households with underweight mothers. A full description of the variation of source-specific PM 2.5 across clusters classified on the basis of the prevalence of different SES parameters can be found in section S2 in the SI .

When evaluating the distribution of SES parameters for different concentrations of total-PM 2.5 , we observed that at higher total-PM 2.5 levels, there was a greater prevalence of SCs (prevalence at Decile 1: 0.10, Decile 10: 0.23), OBCs (Decile 1: 0.30, Decile 10: 0.48), Muslims (Decile 1: 0.10, Decile 10: 0.13), poor households (Decile 1: 0.10, Decile 10: 0.25), households that used solid fuels (Decile 1: 0.53, Decile 10: 0.59), households that were headed by someone with no formal education (Decile 1: 0.25, Decile 10: 0.30), households with a college educated head (Decile 1: 0.08, Decile 10: 0.11), underweight/thin mothers (Decile 1: 0.12, Decile 10: 0.21), and mothers who had married young (Decile 1: 0.30, Decile 10: 0.45).

The prevalence of the following SES parameters were lower for clusters experiencing high levels of total-PM 2.5 : household headed by an ST (Decile 1: 0.43, Decile 10: 0.01), households below the poverty line with ration cards (Decile 1: 0.39, Decile 10: 0.25), households with improved sanitation (Decile 1: 0.77, Decile 10: 0.51), and electrified households (Decile 1: 0.94, Decile 10: 0.79) (Fig. S22 ).

Evaluating variation in PM 2.5 across multiple geographic scales

When we evaluated the partitioning of variation in total PM 2.5 concentrations by the different geographic scales using multilevel models that only controlled for the logarithm of population density and urban/rural, we found that most of the variation (> 80%) was observed at the state-level, ~ 15% of the variation in total PM 2.5 concentrations was observed at the district level, while the remaining was at the cluster-level (Table S2 ). Further adjusting for SES variables only explained a small proportion of variance (~ 1%) in PM 2.5 concentrations at each spatial scale (Table S3 ). We found similar results when evaluating the partitioning of variation for anthropogenic PM 2.5 levels (Table S4 ), and source-specific PM 2.5 values (Table S5 ). The large variation of PM 2.5 concentrations at the state-spatial scale indicates that tackling large regional sources should be a priority in tackling pollution in India. These results could also suggest that more detailed ground-based PM 2.5 measurements and emission inventories are needed to capture fine-scale PM variations in India.

Evaluating associations between PM 2.5 levels and different EJ dimensions

We used unadjusted and fully-adjusted multilevel models to evaluate associations between total PM exposures considered in this study and the various SES parameters. In order to compare associations across the different SES variables, we standardized each variable in the model using z-scores. More information can be found in “ Methods ” Section.

From the fully-adjusted multilevel models, we found that an increasing prevalence of SC, OBC households were associated with small but significant increases in total-PM 2.5 concentrations. An increasing prevalence of poor, electrified, ST, Muslim households, households BPL with ration cards, underweight or thin mothers were associated with decreasing levels of total-PM 2.5 . Specifically, we observed that a 1 standard deviation (SD) increase in the prevalence of households with an OBC head was associated with the largest increase in total-PM 2.5 concentrations of 0.199 μg/m 3 (95% CI 0.116 μg/m 3 , 0.283 μg/m 3 ). The next largest positive association was observed with the SES parameter: the prevalence of SC households: 0.127 μg/m 3 (95% CI 0.062 μg/m 3 , 0.129 μg/m 3 ), followed by the prevalence of mothers married young: 0.106 μg/m 3 (95% CI − 0.003 μg/m 3 , 0.215 μg/m 3 ).

The largest negative association was observed with the SES variable: the prevalence of ST households: − 0.383 μg/m 3 (95% CI − 0.497 μg/m 3 , − 0.269 μg/m 3 ) and the prevalence of households living in poverty: − 0.260 μg/m 3 (95% CI − 0.376 μg/m 3 , − 0.144 μg/m 3 ). The latter association diverges from our initial hypothesis that lower total-PM 2.5 concentrations would be present in richer clusters (Table 1 ).

When evaluating these associations, disaggregated by urban/rural, we observed substantial differences. For instance, we observed positive associations between total-PM 2.5 and the prevalence of SC: 0.198 μg/m 3 (95% CI 0.114 μg/m 3 , 0.282 μg/m 3 ) and OBC household heads: 0.245 μg/m 3 (95% CI 0.139 μg/m 3 , 0.350 μg/m 3 ) in rural clusters, respectively, but not urban clusters: − 0.030 μg/m 3 (95% CI − 0.125 μg/m 3 , 0.065 μg/m 3 ) and − 0.032 μg/m 3 (95% CI − 0.231 μg/m 3 , 0.166 μg/m 3 ), respectively. The negative association between total-PM 2.5 levels and the prevalence of ST household heads was significant in rural clusters: − 0.371 μg/m 3 (95% CI − 0.515 μg/m 3 , − 0.228 μg/m 3 ), but not urban clusters: − 0.032 μg/m 3 (95% CI − 0.231 μg/m 3 , 0.166 μg/m 3 ), although the association in urban clusters demonstrated the same general trend observed in rural clusters.

We observed significant negative associations between total-PM 2.5 levels in urban clusters with a higher prevalence of female-headed households: − 0.136 μg/m 3 (95% CI − 0.268 μg/m 3 , − 0.003 μg/m 3 ), but not in rural clusters: 0.056 μg/m 3 (95% CI − 0.053 μg/m 3 , 0.165 μg/m 3 ). We observed significant negative associations between total-PM 2.5 levels in rural clusters with a higher prevalence of households living in poverty: − 0.298 (95% CI − 0.441, − 0.156), but not in urban areas: 0.081 (95% CI − 0.056, 0.219).

We noted significant associations between total-PM 2.5 concentrations and the prevalence of households with improved sanitation : 0.123 μg/m 3 (95% CI 0.010 μg/m 3 , 0.236 μg/m 3 ) and households with safe drinking water: − 0.127 μg/m 3 (95% CI − 0.216 μg/m 3 , − 0.039 μg/m 3 ) in urban clusters, compared with − 0.137 μg/m 3 (95% CI − 0.256 μg/m 3 , − 0.018 μg/m 3 ) and 0.111 μg/m 3 (95% CI 0.041 μg/m 3 , 0.181 μg/m 3 ), respectively, in rural locations (Table 1 ). Our results suggest that different dimensions of inequality operate differently in urban and rural clusters.

When evaluating associations between the various SES parameters considered with anthropogenic-PM 2.5 levels, we observed similar trends in associations to those estimated with total-PM 2.5 levels (Table S6 ), with some differences. Namely, the general trend of associations between anthropogenic-PM 2.5 concentrations and the prevalence of households with a Muslim head was positive: 0.010 μg/m 3 (95% CI − 0.037 μg/m 3 , 0.056 μg/m 3 ), whereas it was negative when considering total-PM 2.5 levels. The same trend was observed when evaluating associations with the prevalence of households with improved sanitation: Associations with anthropogenic-PM 2.5 were − 0.029 μg/m 3 (95% CI − 0.089 μg/m 3 , 0.032 μg/m 3 ), while those with total-PM 2.5 were 0.112 μg/m 3 (95% CI 0.037 μg/m 3 , 0.144 μg/m 3 ).

We also reported associations with source-specific PM 2.5 exposures using unadjusted (Table S7 ), fully-adjusted models (Table S8 ), and disaggregated by urban/rural designation (Table S9 ). We observed substantial differences in the magnitude and direction of associations observed with the different SES parameters considered. For example, PM 2.5 from domestic burning is significantly lower in clusters with a higher prevalence of household heads with no formal education; However, we observed the opposite trend for PM 2.5 from agricultural residue burning (for more details refer to section S4). The differences in associations are a result of the different distribution of sources (agricultural land, for example), as well as SES parameters (access to reliable electricity, among others). The latter is an environmental justice concern as research has shown that villages inhabited solely by SCs are significantly less likely to be electrified 22 than other villages; while the former is not. Another possible reason for the difference in results is that so far, we assume a linear relationship between PM exposures and the different SES parameters considered. In future sections, we relax this assumption.

Evaluating associations between PM levels and different EJ dimensions after accounting for potential non-linearities

After accounting for potential non-linearities between the different SES parameters and total-PM 2.5 , there are several important nuances regarding associations between PM 2.5 and the SES under consideration (Fig.  2 ). For example, when evaluating non-linear associations between total-PM 2.5 and religion, caste, and gender-related SES, we find that although total-PM 2.5 decreases on average with a unit increase in prevalence of ST households, total-PM 2.5 concentrations increase in clusters with the highest prevalence of ST households. We observe a similar result in associations between totla-PM 2.5 concentrations and clusters with a high prevalence of Muslim households (Fig.  2 ). Note that clusters with the highest prevalence of Muslim households have the lowest total-PM 2.5 levels.

figure 2

Partial response plot (red line) and 95% CI (between the blue dashed lines) for the association between total-PM 2.5 and the prevalence of ( A ) SC households, ( B ) ST households, ( C ) OBC households, ( D ) Muslim households, ( E ) Households with a female head, ( F ) Mothers married young < 18 y of age), ( G ) Households BPL, ( H ) Poor Households, ( I ) Households with improved sanitation, ( J ) Electrified households, ( K ) Households using solid fuels, ( L ) Households with safe drinking water, ( M ) Underweight mothers, ( N ) Household head without formal education, ( O ) Household head with college-educated head, and ( P ) Population density in fully-adjusted models. We also display partial residual points and rug plots to provide readers with an understanding of the distribution of variables considered.

When evaluating associations between total-PM 2.5 and the prevalence of poverty, we note that total-PM 2.5 levels increase after initially decreasing for clusters corresponding to the highest prevalence of poverty (Fig.  2 ). We observed that the associations between total-PM 2.5 and various household characteristics such as access to drinking water, access to improved sanitation, and access to electricity were fairly constant across different levels of these SES parameters. Total-PM 2.5 concentrations first increased with the increasing prevalence of underweight mothers and then decreased (Fig.  2 ). Finally, we observed increases in total-PM 2.5 in clusters with a higher prevalence of college-educated household heads (Fig.  2 ). These plots suggest that it is important to take into consideration non-linearity when evaluating associations between total-PM 2.5 and SES in India. We note similar nonlinearities when evaluating associations between anthropogenic-PM 2.5 levels and SES (Fig. S23 ), and between source-specific PM 2.5 levels and SES (Figs. S24 – S31 ) in supplementary analyses.

Evaluating associations between the ratio of power-PM levels and nighttime luminosity and different EJ dimensions

A higher prevalence of ST households in a cluster was significantly associated with a decrease in total-PM 2.5 concentrations (Table 1 ); but was not significantly associated with changes in PM 2.5 from power-generation (Table S8 ). However, we noticed significant disparities in exposure to PM 2.5 from power generation relative to the benefits consumers receive (nighttime luminosity as a proxy for electricity use).

Specifically, the association between PM 2.5 from power generation (burden) relative to nighttime luminosity (benefit) and the prevalence of ST households was significant: 15.187 (95% CI 3.829, 26.539) (Table 2 ). We observe similar results for clusters with a high prevalence of households in poverty: 15.970 (95% CI 3.459, 28.629). Unsurprisingly, we find that exposure relative to the benefits of PM 2.5 from power generation is low in clusters with a high prevalence of electrified households, and safe drinking water (proxies of power consumption) (Table 2 ). Our analysis thus explores environmental justice concerns beyond looking at the distributional impacts of PM 2.5 , to the distributional impacts relative to the benefit from a key source of PM 2.5 .

Evaluating associations between the difference and percentage difference in PM 2.5 concentrations in each cluster between 2015 and 2020 and different EJ dimensions

Overall, the mean difference in PM 2.5 concentrations between 2010 and 2015 was 0.66 (min: − 24.4 μg/m 3 , max: 16.5 μg/m 3 , median: 0.8 μg/m 3 , standard deviation: 5.0 μg/m 3 ). The mean percentage difference was 2.6% (min: − 22.3%, max: 24.9%, median: 1.9%, standard deviation: 9.5%).

We observed that there was a significant increase in PM 2.5 increase levels 2015 relative to 2010 of 0.024 μg/m 3 (95% CI 0.013 μg/m 3 , 0.047 μg/m 3 ) for every standard deviation increase in the prevalence of Muslim households (Table 3 ). We observed the same general trend when evaluating associations between the percentage difference in PM 2.5 levels between 2010 and 2015, relative to 2010 levels, instead of the absolute difference in PM 2.5 levels in urban but not rural areas. In rural areas, there were significant decreases in PM 2.5 levels in clusters with a higher prevalence of SC: − 0.040 μg/m 3 (95% CI − 0.072 μg/m 3 , − 0.009 μg/m 3 ), ST: − 0.054 μg/m 3 (95% CI − 0.108 μg/m 3 , 0.000 μg/m 3 ), OBC: − 0.042 μg/m 3 (95% CI − 0.082 μg/m 3 , − 0.003 μg/m 3 ), and mothers married < 18 years of age: − 0.062 μg/m 3 (95% CI − 0.112 μg/m 3 , − 0.013 μg/m 3 ); while we observed an increase in clusters with a higher prevalence of underweight mothers: 0.048 μg/m 3 (95% CI 0.009 μg/m 3 , 0.088 μg/m 3 ). We observed similar results in rural areas when evaluating associations between the percentage difference in PM 2.5 levels, instead (Table 3 ). Our results that in recent years, overall, religion is becoming an increasingly important lens in India to evaluate EJ patterns.

We also noted a significant decrease in levels of − 0.051 μg/m 3 (95% CI − 0.086 μg/m 3 , − 0.016 μg/m 3 ) for every standard deviation increase in the prevalence of households BPL. We observed similar results when using the percentage difference in PM 2.5 levels: − 0.071% (95% CI − 0.142%, − 0.001%) (Table 3 ). However, we noted a significant increase in the percentage difference in PM 2.5 concentrations for every increase in the prevalence of poor households, overall: 0.135% (95% CI 0.044%, 0.226%), and in rural areas: 0.140% (95% CI 0.029%, 0.250%). We observed the opposite results in urban areas: − 0.146% (95% CI − 0.257%, − 0.034%) (Table 3 ). Our results suggest that villages in rural areas with a high prevalence of poorer residents, without access to services such as ration cards are vulnerable to increases in PM 2.5 concentrations, relative to base levels.

Finally, we observed significant decreases in the percentage difference in PM 2.5 levels for every increase in the prevalence of solid fuels, overall: − 0.131% (95% CI − 0.215%, − 0.048%) and in rural areas: − 0.150% (95% CI − 0.225%, − 0.075%), and opposite results in urban clusters: 0.236% (95% CI 0.132%, 0.341%) (Table 3 ).

We curated a dataset of total, anthropogenic, and source-specific PM 2.5 levels and SES variables associated with social advantage in India for a nationally representative set of clusters in India, which are villages in rural areas and census enumeration blocks in urban areas for the year 2015. Evaluating the variation in total-PM 2.5 across multiple geographic scales, revealed that most variation occurred at the state-level, indicating that tackling large regional sources should be a priority in tackling pollution in India. This result could also suggest that more detailed ground-based PM 2.5 measurements and emission inventories are needed to capture fine-scale PM variations in India.

In many regions of the world there is a growing understanding in EJ research that although identifying risk factors such as race, education and income is important, it is just as important to identify structural factors that result in such disparities 23 , 24 . Our research suggests that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM 2.5 exposures. Specifically, we observed that total-PM 2.5 levels were significantly higher in clusters with a higher prevalence of SC, OBC households, and underweight and lower in clusters with a high prevalence of Muslim, ST, poor, and electrified households. However, different directions in associations were observed when disaggregating our analysis by urban/rural designation. For example, the general trend of associations between the prevalence of poor households and total-PM 2.5 was positive in urban areas, but negative in rural locations. When considering other PM 2.5 exposures, we also noted differences in the direction and magnitude of associations with different SES factors. Our results suggest that different dimensions of inequality operate differently in urban and rural areas, and for different sources. Future theoretical frameworks developed to conceptualize EJ in India, need to take these empirical differences into consideration.

Our relaxation of the assumption of linearity between the PM 2.5 exposures considered and the different SES parameters can also potentially add nuance to the conceptualization of EJ in India. Specifically, we observed that total-PM 2.5 levels were significantly lower, overall, in clusters with a higher prevalence of poor and Muslim households. However, when we accounted for potential non-linearities in the relationship between PM 2.5 and the SES parameters considered, we observed that PM 2.5 levels were highest in clusters with the highest prevalence of poverty and among the highest prevalence of Muslim households. Our analyses showed that clusters with a high prevalence of Muslim residents were observing significant increases in PM 2.5 concentrations, suggesting that religion is becoming an important axis of inequality in India.

Although, most of this work conceptualized EJ in terms of evaluating disparities in exposure to PM 2.5 , we also considered a different definition of EJ; i.e. evaluating disparities in PM 2.5 exposure from a key-source: power generation relative to the benefits received (using nighttime luminosity as a proxy). We observed that ST and poor households were exposed to significantly higher exposures from power-generation relative to the benefits they received. These results point to the urgency of expanding the theoretical discourse on EJ in India.

To summarize, this research presents a comprehensive overview of disparities in exposure to air pollution along several dimensions of environmental justice in India. Our approach has some limitations. Specifically, the PM 2.5 exposures considered have several uncertainties. For example, previous work has shown that exposure estimates derived from satellite data diverge from each other, especially in rural areas where ground-based monitors are sparse 4 . In addition, we assigned ambient exposures to all individuals based on their cluster of residence. Due to the lack of data, we did not account for differences in housing characteristics, occupational exposures, activity patterns that could influence exposure to ambient PM 2.5 concentrations.

Data and methods

Socioeconomic status (ses) and demographics.

We drew data from the fourth round of National Family Health Survey (NFHS-4) of India (equivalent to Demographic and Health Survey) conducted between Jan 2015 and Nov 2016 25 . NFHS are nationally representative household sample surveys measuring indicators of population, health and nutrition, with special emphasis on maternal and child health.

The NFHS-4 has a two-stage design, in which a number of clusters (villages in rural areas and census enumeration blocks in urban areas) are first selected from each of the 640 districts that existed at the time of the 2011 Census of India. Each of the 28,526 clusters was categorized as urban or rural. A household listing operation was then carried out by visiting each of the selected clusters and listing all residential households. Clusters with more than 300 households were divided into segments of 100–150 households. The resulting list of households served as a sampling frame for selection of households in the second stage. A fixed number of 22 households were selected from each cluster based on equal probability systematic sampling. Women aged 15–49 years were selected from these households for in-depth surveys. NFHS uses extensive interviewer training, standardized measurement methods, and an identical questionnaire to ensure standardization and comparability across diverse sites and times.

The GPS coordinates data for the NFHS-4 clusters were obtained via a special request. These survey cluster coordinates were collected in the field using GPS receivers, usually during the survey sample listing process. In general, the GPS readings for most clusters were accurate to less than 15 m. To ensure that respondent confidentiality was maintained, the GPS latitude/longitude positions were displaced for all clusters. The displacement was randomly carried out so that rural clusters contained a minimum of 0 and a maximum of 5 km of positional error. For 1% of the rural clusters, the displacement occurred up to 10 km. The displacement was restricted so that the points stayed within the second administrative level of the district.

We chose context-specific SES covariates to evaluate the EJ implications of pollution in India. In addition to choosing risk-factors related to income, education, household assets and wealth, which are commonly associated with social advantage, we also looked at caste- and religion-specific variables. Caste has a deep sociological history in Indian society. Lower castes now referred to as Schedule Caste (SCs), Scheduled Tribes (STs) and Other Backward Classes (OBCs) have historically been denied access to important public services. There is still strong evidence of discrimination against these groups in both the education sector and the labor market 26 , 27 , 28 . There is also evidence that religious minorities like Muslims have been marginalized in India 29 . We thus included these covariates as key EJ dimensions in India.

From Household Recode NFHS data, we extracted the following binary household-level covariates: (1) Poor: Household was in the lowest wealth quintile, (2) Household had a Below Poverty Line (BPL) ration card, (3) Household had electricity, (4) Household had improved sanitation, (5) Household used solid fuels for their energy needs, (6) Household had access to safe drinking water, (7) Household head was Muslim, (8) Household head had been to college, (9) Household head was uneducated, (10) Household head was female, (11) Household head belonged to a Scheduled Caste (SC), (12) Household head belonged to a Scheduled Tribe (ST), and (13) Household head belonged to an Other Backward Class (OBC).

From Individual-level NFHS-4 data from the women interviewed, we extracted the following binary level covariates: (1) Mother is uneducated, (2) Mother is literate, (3) Mother was married before 18 years of age, and (4) Mother is underweight (BMI < 18.5 kg/m 2 ), an indicator of food-access.

NFHS-4 provides addition geospatial covariates for each cluster, on population density for the year 2015 (#/km 2 ) within the 2 km (urban) or 10 km (rural) buffer surrounding the NFHS-4 survey cluster location. The estimate of population density is Population counts for each cluster used to produce these estimates were derived from the Gridded Population of the World, Version 4 (GPWv4). Although population density is traditionally measured as persons per square kilometer (or, square mile), a natural logarithmic transformation of this measure is used in our multivariate analysis to account for its skewed distribution, as recommended in previous EJ research. We also derived average nighttime luminosity in the form of a nightlight index (dimensionless) from the NFHS-4 geospatial data for the year 2015. (Figs. S1 – S6 in Supplementary Information displays the spatial distribution of various SES parameters.)

We removed clusters for which we did not have information on context-specific SES covariates or population density and were left with 28,072 of a total of 28,526 clusters. Most of these clusters are in Jammu and Kashmir and Assam (Fig. S11 ).

Deriving cluster-specific SES covariates

To account for the complex survey design and sampling variability, we derived cluster-specific predicted probabilities of each variable from NFHS household and individual data described in “ Evaluating Disparities in PM concentrations along different EJ dimensions ” Section using four-level multilevel models 16 , 30 . The four levels of geographic units are individuals (or households) at level-1 (i), clusters at level-2 (j), districts at level-3 (k) and states at level-4 (l). The model is presented below:

\(\beta_{0}\) is the constant and represents the median log odds of each covariate across all of India; \(u_{0jkl}\) , \(v_{0kl}\) , and \(f_{0l}\) are the residuals at the cluster, district, and state levels, respectively. The residuals are assumed to be normally distributed with a mean 0 and a variance of \(\sigma_{u0}^{2}\) , \(\sigma_{v0}^{2}\) , and \(\sigma_{f0}^{2}\) . These variance terms can be interpreted as within-district between-cluster variation ( \(\sigma_{u0}^{2}\) ), within-state between-district variation ( \(\sigma_{v0}^{2}\) ), and between-state variation ( \(\sigma_{f0}^{2}\) ).

From the model described in Eq.  1 , the cluster-specific logit values were converted to probabilities by taking the average over the simulations, i.e.. \(exp \left( {\beta_{0} + u_{0jkl} + v_{0kl} + f_{0l} } \right) /\left( {1 + exp \left( {\beta_{0} + u_{0jkl} + v_{0kl} + f_{0l} } \right) } \right)\) . For estimation, we used Monte Carlo Markov Chain (MCMC) methods with a burn-in of 5000 cycles, and monitoring of 50,000 iterations of chains. For all estimates, we used 2nd order penalized quasi-likelihood (PQL) for the estimation of starting values, but for few variables (Households with electricity, Poor households, Households with a Muslim head, Household with an ST head) the convergence failed, and we used 1st order marginalized quasi-likelihood (MQL) instead.

In this manner, cluster-specific predictions of the various covariates can be made by “shrunken” higher level residuals that consider the ratio of the between-state, between-district and between-cluster variance to the total variance, which includes the within-state, within-district and within-cluster sampling variance attributable to the sample size of districts with states, clusters within districts, and individuals within clusters. Hence, more shrinkage occurs i.e. cluster-specific means are pulled more towards district-means (and state-means) if there are fewer individuals within a cluster, and consequently higher sampling variances, and/or when the estimated variance of the clusters is small.

Total PM 2.5 exposure

The main exposure variable in this study was long-term ambient PM 2.5 between the years 2010–2015. Because India lacks surface PM 2.5 monitoring sites at the spatial resolution required for the study, and the NFHS surveys do not record PM 2.5 concentration in each cluster, we used satellite-derived annual averaged PM 2.5 estimates derived by Hammer et al. 18 as the main exposure of interest as this dataset has been validated and used in several global studies 4 , 31 . Satellite aerosol optical depths (AODs) were combined from multiple satellite products: MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC with simulation-based results based on their relative uncertainties. These AODs were related to near-surface monthly PM 2.5 concentrations at a 0.01° × 0.01° (~ 1 km × 1 km at the equator) resolution over the globe using the ratio of simulated AOD and PM 2.5 from the GEOS-Chem model. We clipped these estimates to India. On an annual scale the PM 2.5 estimates are highly consistent with globally distributed ground monitors (R 2  = 0.90–0.92). We previously evaluated this dataset based on ground-based monitors in India and found the India-specific R 2 was 0.55 (RMSE: 27.5) 4 . We extracted mean PM 2.5 levels in the 2 km/5 km buffer for urban and rural household clusters respectively (Fig. S7 ).

Note, we opted to use a satellite-derived exposure product for this analysis, instead of model-based products that we use to map source-specific and anthropogenic PM 2.5 concentrations discussed below, as the model-based results are available for a single year, alone. We evaluate associations between the difference in PM 2.5 concentrations between 2010 and 2015 and SES parameters in this paper. Moreover, the model-based PM 2.5 exposure products are at a coarser resolution (36 km × 36 km), compared to the satellite-derived concentrations (1 km × 1 km). The spatial resolution of the satellite-derived concentrations is more well aligned with the spatial resolution of the NFHS clusters.

Anthropogenic and source-specific PM 2.5 exposure

We estimated annual-averaged anthropogenic PM 2.5 concentrations for the year 2016 using the Community Multiscale Air Quality (CMAQ) model 19 . The model set up WRF v.3.9.1 32 & CMAQ v.5.3.1 33 was used to estimate species-specific PM 2.5 concentrations (elemental carbon: EC, ammonium: NH 4 , nitrate: NO 3 , organic carbon: OC, sulfate: SO 4 , soil, and others, including chloride (Cl), sodium (Na), magnesium (Mg), potassium (K), calcium (Ca), soil, and water molecules, and other unspecified species), as well as source-specific PM 2.5 levels from agricultural residue burning (ARB), industry (IND), power (POW), transport (TRA), domestic burning (DOM), road dust (RDUST), international contributions (INT), others (OTH), that include refuse burning, construction, crematoria, NH 3 , biogenic emissions, refineries, and evaporative non-methane volatile organic compounds at a 36 km × 36 km scale 19 (Fig. S9 ). We derived anthropogenic PM 2.5 concentrations by subtracting soil dust levels from total PM 2.5 concentrations derived from the speciated PM 2.5 analysis. (Fig. S8 ). When conducting analyses involving anthropogenic PM 2.5 , and source-specific PM 2.5 levels, we removed clusters for which we did not have information on these exposures due to issues with clipping the exposure dataset and were left with 27,535 clusters (Fig. S11 ). A coefficient of determination between ground-based observations and simulated monthly averaged PM 2.5 concentrations of ~ 0.81 was reported. For more details refer to 19 . We also estimated the ratio of exposure to PM 2.5 from power generation (POW) relative to the NFHS-4 nighttime luminosity index as a measure of inequalities of exposure to POW relative to the benefits that different consumers receive (Fig. S10 ).

Statistical methods

We evaluated disparities in exposure based on local demographic characteristics. To do so, we rank ordered all clusters based on the prevalence of the different SES parameters considered in this study. We compared the distribution of pollution levels in the top and bottom decile of clusters based on each SES parameter (Fig.  1 ). Note, we do not present population-weighted exposures because our prevalence parameters are based on the number of households or the number of mothers in each cluster, whereas we only have data on the total population in each cluster. We evaluated high-end exposure disparities to pollution by analyzing the distribution of demographic characteristics of clusters above the 90th percentiles of air pollution exposure among all clusters and comparing it to the national distribution (Fig.  2 ).

We analyzed the PM 2.5 exposures as a continuous variable, with multilevel linear models including random effects for cluster, district and state-spatial scales. First, we used null models, only including fixed effects for urban/rural to estimate the crude variation in the pollutant exposures at each geographic level. The proportion of variance attributed to each level, z, was computed as follows: 100 × var z /(var cluster  + var district  + var state ). We next added the logarithm of population density to our model and repeated this calculation.

We then used multilevel regression models, again only including urban/rural fixed effects and the logarithm of population density, using each of the PM exposures as the outcome and each SES variable as the exploratory parameters to evaluate associations between pollution and SES. We report the % variance change at each level from introducing the SES variables into the models.

We then ran fully adjusted models where we evaluated associations between the exposures of interest and SES factors after also adjusting for all other SES parameters. In all models, we scaled all independent variables by using z-scores to present effect estimates of linear associations per one standard deviation (SD) increase and facilitate comparability of estimates across all variables used. In the fully-adjusted models, we did not include the prevalence of literate mothers in the analysis, to ensure that the variance inflation factors of all coefficients included were less than four. We also report results from this analysis, disaggregated by urban/rural designation.

We tested for potential non-linearities between the exposures of interest and each SES under consideration and time of operation in the following manner: We used penalized splines (p-spline) to flexibly model the associations between the exposures of interest and the SES under consideration in the fully-adjusted model using a generalized additive model (GAM). We used the minimized generalized cross-validation score (GCV) criterion to select the optimal degrees of freedom (df). We plotted the relationships observed. The GAM fitting and analysis were conducted with the mgcv package in the programming language R.

We used fully-adjusted models to evaluate associations between the ratio of PM 2.5 concentrations from power generation and the average nighttime luminosity (as a proxy for the benefits from power-generation) and each SES parameter. In this manner we evaluated the variation in exposure to concentrations from an important source, relative to benefits received across different SES levels. Finally, we evaluated associations between the difference in PM 2.5 concentrations between the years 2015 and 2010, and the percentage difference with each SES parameter considered using fully-adjusted models. We repeated these analyses using data from urban and rural clusters, separately.

We mapped the geographic distribution of all analyzed variables. All models were run in R 4.2.1. Maps were plotted using QGIS 3.10.1.

Data availability

NFHS-4 data is available on submitting a request via the DHS website https://dhsprogram.com/ .

Change history

09 november 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-023-46982-4

Home | State of Global Air. https://www.stateofglobalair.org/ .

Balakrishnan, K. et al. The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The global burden of disease study 2017. Lancet Planet. Health 3 , e26–e39 (2019).

Article   Google Scholar  

Guttikunda, S. K., Nishadh, K. A. & Jawahar, P. Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Clim. 27 , 124–141 (2019).

deSouza, P. N. et al. Robust relationship between ambient air pollution and infant mortality in India. Sci. Total Environ. 815 , 152755 (2022).

Article   ADS   CAS   PubMed   Google Scholar  

Pandey, A. et al. Health and economic impact of air pollution in the states of India: The global burden of disease study 2019. Lancet Planet. Health 5 , e25–e38 (2021).

Miranda, M. L., Edwards, S. E., Keating, M. H. & Paul, C. J. Making the environmental justice grade: The relative burden of air pollution exposure in the United States. Int. J. Environ. Res. Public. Health 8 , 1755–1771 (2011).

Article   PubMed   PubMed Central   Google Scholar  

Jbaily, A. et al. Air pollution exposure disparities across US population and income groups. Nature 601 , 228–233 (2022).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Colmer, J., Hardman, I., Shimshack, J. & Voorheis, J. Disparities in PM2.5 air pollution in the United States. Science 369 , 575–578 (2020).

Boing, A. F., deSouza, P., Boing, A. C., Kim, R. & Subramanian, S. V. Air pollution, socioeconomic status, and age-specific mortality risk in the United States. JAMA Netw. Open 5 , e2213540 (2022).

deSouza, P. N. et al. Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: Implications for environmental justice. Environ. Res. Commun. 3 , 071003 (2021).

Mohai, P., Pellow, D. & Roberts, J. T. Environmental justice. Annu. Rev. Environ. Resour. 34 , 405–430 (2009).

Hajat, A., Hsia, C. & O’Neill, M. S. Socioeconomic disparities and air pollution exposure: A global review. Curr. Environ. Health Rep. 2 , 440–450 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kopas, J. et al. Environmental justice in India: Incidence of air pollution from coal-fired power plants. Ecol. Econ. 176 , 106711 (2020).

Sengupta, S. et al. Inequality in air pollution mortality from power generation in India. Environ. Res. Lett. 18 , 014005 (2022).

Article   ADS   Google Scholar  

Chakraborty, J. & Basu, P. Air quality and environmental injustice in India: Connecting particulate pollution to social disadvantages. Int. J. Environ. Res. Public. Health 18 , 304 (2021).

Kim, R. et al. Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India. Proc. Natl. Acad. Sci. 118 , e2025865118 (2021).

Subramanian, S. V. et al. Progress on sustainable development goal indicators in 707 districts of India: a quantitative mid-line assessment using the National Family Health Surveys, 2016 and 2021. Lancet Reg: Health Southeast Asia 0 , (2023).

Hammer, M. S. et al. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. https://doi.org/10.1021/acs.est.0c01764 (2020).

Article   PubMed   Google Scholar  

Singh, N., Agarwal, S., Sharma, S., Chatani, S. & Ramanathan, V. Air pollution over India: Causal factors for the high pollution with implications for mitigation. ACS Earth Space Chem. 5 , 3297–3312 (2021).

Article   ADS   CAS   Google Scholar  

Mohindra, K. & Labonté, R. A systematic review of population health interventions and Scheduled Tribes in India. BMC Public Health 10 , 438 (2010).

Narayan, S. Time for universal public distribution system: Food mountains and pandemic hunger in India. Indian J. Hum. Dev. 15 , 503–514 (2021).

Aklin, M., Cheng, C.-Y. & Urpelainen, J. Inequality in policy implementation: Caste and electrification in rural India. J. Public Policy 41 , 331–359 (2021).

Tan, S. B., deSouza, P. & Raifman, M. Structural racism and COVID-19 in the USA: A county-level empirical analysis. J. Racial Ethn. Health Dispar. https://doi.org/10.1007/s40615-020-00948-8 (2021).

Van Horne, Y. O. et al. An applied environmental justice framework for exposure science. J. Expo. Sci. Environ. Epidemiol. https://doi.org/10.1038/s41370-022-00422-z (2022).

IIPS, O. National family health survey (NFHS-4): 2014–15: India. Mumbai Int. Inst. Popul. Sci. (2017).

Banerjee, A., Bertrand, M., Datta, S. & Mullainathan, S. Labor market discrimination in Delhi: Evidence from a field experiment. J. Comp. Econ. 37 , 14–27 (2009).

Thorat, S. & Sadana, N. Discrimination and children’s nutritional status in India. IDS Bull. 40 , 25–29 (2009).

Madheswaran, S. & Attewell, P. Caste discrimination in the Indian urban labour market: Evidence from the national sample survey. Econ. Polit. Wkly. 42 , 4146–4153 (2007).

Google Scholar  

Robinson, R. Religion, socio-economic backwardness & discrimination: The case of Indian Muslims. Indian J. Ind. Relat. 44 , 194–200 (2008).

Rajpal, S., Kim, J., Joe, W., Kim, R. & Subramanian, S. V. Small area variation in child undernutrition across 640 districts and 543 parliamentary constituencies in India. Sci. Rep. 11 , 4558 (2021).

deSouza, P. N. et al. Impact of air pollution on stunting among children in Africa. Environ. Health 21 , 128 (2022).

Skamarock, W. J. G. et al. A Description of the Advanced Research WRF Version 2 (NCAR Tech, 2005).

Appel, K. W. et al. The community multiscale air quality (CMAQ) model versions 5.3 and 5.3.1: System updates and evaluation. Geosci. Model Dev. 14 , 2867–2897 (2021).

Download references

Dey acknowledges funding from IIT Delhi for Institute Chair Fellowship and from Clean Air Fund. Ko and Kim were supported by the National Research Foundation of Korea(NRF) grant No. RS-2023-00219289 funded by the Korea government (MSIT). Subramanian and Kim were supported by grant INV-002992 from the Bill & Melinda Gates Foundation.

Author information

Authors and affiliations.

Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA

Priyanka N. deSouza & Jeremy Németh

Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India

Priyanka N. deSouza, Ekta Chaudhary & Sagnik Dey

Centre of Excellence for Research on Clean Air, IIT Delhi, New Delhi, India

School of Public Policy, IIT Delhi, New Delhi, India

Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea

Soohyeon Ko

Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea

Soohyeon Ko & Rockli Kim

Transportation Research and Injury Prevention (TRIP) Centre, Indian Institute of Technology, New Delhi, 110016, India

Sarath Guttikunda

Urban Emissions, New Delhi, 110019, India

CICERO Center for International Climate Research, Oslo, Norway

Sourangsu Chowdhury

School of Public Health, Boston University, Boston, MA, USA

Patrick Kinney

Harvard Center for Population and Development Studies, Bow Street, Cambridge, MA, 02138, USA

S. V. Subramanian

Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA

School of the Environment, Yale University, New Haven, CT, USA

Michelle L. Bell

Division of Health Policy and Management, College of Health Science, Korea University, Seoul, South Korea

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: P.D. Data curation: P.D., E.C., S.D., S.K., R.K. Methodology: P.D., R.K., S.V.S. Formal analysis: P.D. Investigation: All authors Writing-original draft: P.D. Writing- review and editing:

Corresponding authors

Correspondence to Priyanka N. deSouza or Rockli Kim .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this Article was revised: The original version of this Article contained an error in the spelling of the author Sagnik Dey which was incorrectly given as Sagnk Dey.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

deSouza, P.N., Chaudhary, E., Dey, S. et al. An environmental justice analysis of air pollution in India. Sci Rep 13 , 16690 (2023). https://doi.org/10.1038/s41598-023-43628-3

Download citation

Received : 22 March 2023

Accepted : 26 September 2023

Published : 04 October 2023

DOI : https://doi.org/10.1038/s41598-023-43628-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

pollution project of india

Navigation breadcrumbs

  • Where we work

Air pollution in India 

India has one of the fastest growing economies in the world and air pollution is one of the challenges associated with this growth and development.

Of the world’s 30 cities with the worst air pollution, 21 are in India. The capital, New Delhi, has the poorest air quality among capital cities globally. Concentrations of particulate matter (PM2.5) in New Delhi are nearly 10 times higher than the World Health Organization guidelines. Thermal power plants, pollution from vehicles, industrial emissions, and the burning of wood and dirty fuels for cooking and heating are some of the main causes of air pollution in India.

The effects are devastating. Air pollution is a silent killer, causing more than 2 million deaths a year in India. It also leads to health problems like respiratory and cardiovascular diseases.

If India had achieved safe air quality levels in 2019, its GDP would have increased by $95 billion. Read the full economic analysis from Dalberg . This is because cleaner air would result in lower rates of absenteeism from work, higher productivity at work, higher consumer footfall and fewer premature deaths.

Public awareness of air pollution as a problem is on the rise, especially in Delhi where 85% support stricter air quality laws and enforcement of policies. See the full 2020 YouGov air pollution and Covid-19 survey results .

India has launched an ambitious National Clean Air Program to reduce particulate matter pollution by 30% by 2024. Indian Institute of Technology Kanpur has collaborated with the Department of Environment, Forest & Climate Change [and others], supported by Clean Air Fund, to enable real time measures to mitigate and plug pollution sources. Prof S.N. Tripathi – Expert member of the National Clean Air Program , Ministry of Environment, Forest & Climate Change

Tackling India’s air pollution crisis 

Clean Air Fund’s work in India is relatively new and we will continue to build our partnerships as our grant portfolio grows. We work with government and business, and at every level of society, to help reach India’s clean air goals.

Our work includes: 

  • Supporting air quality monitoring and management initiatives
  • Building capacity on air quality data collection (including concentrations and impact)
  • Facilitating dialogue and information sharing among the air quality movement. 

Engaging the community is also crucial to bringing about cleaner air. Our grant to Health Care Without Harm is building a network of health workers who can act as clean air champions for patients, policy makers and the wider public.

In 2021 we supported the Sesame Workshop India Trust to survey 10,000 children from low resource communities in Delhi about their environmental concerns. The children had the opportunity to bring their concerns to their local leaders.  

Businesses have a significant role to play in tackling the problem too. The India CEO Forum for Clean Air is growing, with 70 members signed up in the first year. Two major Indian businesses, Wipro and Mahindra Group, are both in the Clean Air Alliance , launched in partnership with the World Economic Forum in 2021.

An example of our work in India: Health Care Without Harm

Clean Air Fund has been instrumental in empowering healthcare professionals and amplifying the health voices in the clean air movement. It is a privilege to be a partner and work together towards ensuring clean and healthy air for humankind. Dr Arvind Kumar – Chest and lung surgeon, founder of Health Care Without Harm

See more on India

pollution project of india

Helping India’s health workers become clean air champions

pollution project of india

How solving air pollution can boost India's economy

Featured grants & projects.

  • Skip To Main Content
  • Skip To Navigation
  • Screen Reader Access

pollution project of india

Search form

pollution project of india

  • COVID-19 Research
  • Agricultural Sciences
  • Astronomy & Space Sciences
  • Chemical Sciences
  • Cognitive Sciences and Psychology
  • Computer Sciences and IT
  • Earth, Atmosphere & Environment Sciences
  • Energy Sciences
  • Engineering Sciences
  • Life Sciences & Biotechnology
  • Mathematical Sciences
  • Material Sciences
  • Medical Sciences
  • Pharmaceutical Sciences
  • Physical Sciences
  • Traditional Knowledge
  • Other Areas
  • Institutional
  • International
  • Grants for Seminar and Conferences
  • Startup Grants
  • Ministries & Departments
  • Centres of Excellence
  • Thematic Centres
  • Centres of Higher Learning
  • National Academies
  • Statewise S&T Organisations
  • Industry Related Associations
  • Laboratories
  • International Organisations
  • Civil Societies
  • Science Centres & Planetaria
  • All Programmes & Schemes
  • Research and Development
  • Human Resource and Development
  • Women Schemes
  • International Programmes
  • Societal Development
  • Academia Industry Partnerships
  • School Students
  • Graduate Students
  • Post Graduate Students
  • PhD Scholars
  • Post Doctoral Fellowships
  • Scholarships for Women
  • Faculty and Scientists
  • National Fellows
  • COVID-19 Technology
  • Earth, Atmosphere & Env. Sciences

Air pollution in India: Status and Challenges

It is ironic that we humans pollute the very air that we breathe in but our activities for progress and development that we desire and work for everyday has made air pollution an inevitable hazard. In a country like India, where both population and economic development increased rapidly in past seven decades, air pollution has reached a stage where many cities in India are among the most polluted cities in the world. In a recent World Air Quality 2018 report released by IQAir Group and Greenpeace (AirVisual, 2019), it was reported that fifteen of the top 20 most polluted cities in the world are located in India.

Figure 1: Top 20 polluted cities in the world Source: The Hindu, March 05, 2019 (Koshy, 2019)

By definition, air pollution is contamination of ambient air by chemical species in such concentrations and for such duration that is harmful to general health. Central Pollution Control Board (CPCB) has established National Ambient Air Quality Standards (NAAQS) for some of the most common air pollutants which are mainly particulate matter (PM), carbon monoxide (CO), ground-level ozone, nitrogen dioxide (NOx), sulfur dioxide (SO 2 ) and lead. These are known as “criteria” air pollutants (EPA, 2015). Further, the air pollutants can be primary or secondary depending upon their formation mechanism (CPCB, 2006). Primary pollutants such as CO, NOx and SO 2 are directly emitted from the source while secondary pollutants such as ozone and some aerosols are formed in the atmosphere.

Sources of Air pollution Emissions in India

A diverse range of pollution sources co-exists in urban environments. Conventional sources of air pollution include vehicular emissions, coal-based power plants, fossil fuel consumption in industries and some agricultural activities such as fertilizer application and farm fires. Air pollutants can be natural or may be the result of various anthropogenic activities. Examples include production of brick kilns that use raw wood, agricultural waste or poor quality coal used as a fuel, the roadside burning of organic and plastic waste, cooking that involves the burning of solid biomass or cow dung and the unintentional burning of municipal solid waste at landfills, and construction activities (Kumar et al, 2015) (Figure 3). The local emission inventories point to about 5300 and 7550 tons yr−1 of PM 10 and PM 2.5 release from waste burning in Delhi, respectively, while the corresponding emissions from construction are 3250 and 10,750 tons yr−1 (Guttikunda and Goel, 2013). Other such sources include diesel generators for temporary power generation in cities, traffic congestion, and unregulated small industries.

Figure 2: Average percent contributions of major sources to PM 10 pollution as per CPCB inventory (Source: Guttikunda et al, 2014)

Figure3: There are many unregulated and unaccounted sources of pollution such as landfill, brick kilns, loose soil and waste dumping, biomass burning for cooking using chulhas, dry uncovered surface along the roadside, and traffic congestion (Source: Kumar et al, 2015)

Particulate Matter : A National Threat

According to CPCB annual report 2015-16, out of the 46 million-plus population cities in India, 8 cities (18%) and 38 cities (86%) exceed the NAAQS with respect to NO 2 and PM 10 respectively in the residential/industrial/rural/commercial areas. Hence, in India, fine particulate matter (PM 10 , PM 2.5 ) have emerged as pollutants of major concern.

In a recent survey study (Bernanrd & Kazmin, 2018), The Financial Times collated NASA satellite data of fine particulate matter (PM 2.5 ) and mapped it against population density data from the European Commission to develop a global overview of the number of people affected by this type of dangerous pollution. It was revealed in their study that although historically China has grabbed most headlines for poor air quality, between 1998 and 2016, India has acquired far worse state of pollution than its larger neighbour ever was. At least 140 million people in India are breathing air 10 times or more over the WHO safe limit. WHO prescribes a standard of 10 μgm -3 however, more than 60% of India’s districts have annual concentrations of more than 40 μgm -3 (Figure 4).

Figure 4: PM 2.5 levels have increased in major parts of the country Source: Urbanemissions.info (Guttikunda, 2018)

Health Effects of Air Pollution

Strong links have long been established between exposure to air pollution and cardiovascular diseases, such as strokes and heart disease; cancers; chronic obstructive pulmonary diseases; respiratory diseases, including acute respiratory infections (especially in vulnerable groups like children and elderly); poor birth outcomes, etc. These entail adverse health, economic and developmental consequences (WHO, 2018).

In a study that appeared in The Lancet (Cohen et al, 2017), India had a contribution of 50 % in global estimates of mortality and disability-adjusted life-years attributable to ambient particulate matter pollution for the year 2015. 1 in 8 deaths in India is attributable to air pollution. A study by researchers of the Energy Policy Institute at the University of Chicago (EPIC) concluded that people in India would live 4.3 years longer if the country met the WHO guidelines (EPIC, 2018). 

Impacts of development on meteorology and air pollution

Urbanisation: Massive growth in urbanisation has been found to coincide with the growth in air pollution in many studies. Other than increase in pollution-emitting sources such as vehicles and industries, urbanisation also influences meteorology which in turn affects air quality as urban areas are often associated with altered meteorologies such as higher temperatures and lower wind speeds.

For instance, there is a considerable growth of urban and built-up area during the recent decades over National Capital Region (NCR) of India (17-fold increase in the urban extent). Results indicate a warming of 1.5–2 °C in the surface temperature and 4–5 °C in land surface temperature (LST) during the evening and nighttime due to the changes of land use land cover (LULC) to urban areas during the past five decades

Figure 5 : (Top Panel) Urban expansion as indicated by red colour for Delhi and its satellite. The colour yellow and green are for mixed croplands and irrigated croplands, respectively. (Bottom Panel) Ambient temp increase with the increase in urbanisation over Delhi-NCR every decade Source: Sati and Mohan, 2018

In a city, areas with high urban density are often marked with higher temperatures in comparison to the surrounding areas, a phenomenon known as urban heat island. Urban heat island effect has now been studied in many Indian cities including Delhi where temperatures at highly dense areas were observed to be higher by about 8-10°C in comparison to green areas within the city (Mohan et al 2012, 2013).

Technologies for Air Pollution Monitoring

  • The central nodal monitoring agency, CPCB, has two types of monitoring network. Under manual monitoring network, the air is sampled and then sent to lab for analysis. Under continuous ambient air quality monitoring (CAAQM) real-time data is generated by the instruments which are also displayed online for the public in general. As of 2018, there are 134 locations in the country with manual stations (CPCB, 2018) while 157 locations with CAAQ monitoring (CPCB, 2019). Some of the conventional instruments for air pollution monitoring are Fourier transform infrared (FTIR) instruments, gas chromatographs, and mass spectrometers (Prasad et al, 2011).
  • Nowadays, there are several categories of low cost sensors available for air pollution mitigation (European Commission, 2016). Electrochemical sensors are based on a chemical reaction between gases in the air and the electrode in a liquid inside a sensor. They are used to measure NO 2 , SO 2 , O 3 , NO and CO.
  • Pollutants like NO 2 , O 3 , CO are also measured by metal oxide sensor (resistive sensor, semiconductor) in which gases in the air react on the sensor surface and modify its resistance.
  • VOCs are measured by photo ionization detectors which ionise volatile organic compounds and measures the resulting electrical current.
  • Optical particle counters detect particulate pollution by measuring the light scattered by particles. Optical sensors detect gases like carbon monoxide and carbon dioxide by measuring the absorption of infrared light.

Numerical Modeling for Air Quality Management

Air quality modeling is not only useful for forecasting but also for air quality management. However, for that, prior evaluation is necessary to have confidence in the application of the model. Model performance should be analysed for different simulation design combinations (Mohan and Sati, 2016), sensitivity to different physical parametrisation (Mohan and Bhati, 2011; Gunwani and Mohan, 2017) and different chemical mechanisms for simulation of air pollutants like particulate matter and ozone (Mohan and Gupta 2018, Gupta and Mohan, 2015).

Having an established modeling framework helps in the assessing source contribution of different air pollutants. It also helps in investigating contribution of long-range transport of pollution.

Recent studies based on model simulations show the influence of geographical domain on PM 10 concentrations in Delhi and revealed that the contributions from long range transport towards National Capital Territory of Delhi can be as high as 26% to 97% during summer and 13% to 68% during winter conditions especially during high pollution episodes. It was inferred that the high levels of PM 10 concentration is not only due to local pollution but is also highly influenced by remote sources (Gupta and Mohan, 2013). Air pollution forecast models also provide a framework for the evaluation of different mitigation options for pollution abatement.

Initiatives by the Government of India

In India, the government is looking at innovative solutions for tackling air pollution (WHO, 2016)

  • The Swachh Bharat Abhiyan encourages citizens to adopt cleanliness in all spheres of life and is particularly relevant and timely.
  • The ‘Smart Cities’ initiative assures urban planning, building energy efficient housing and a good network of public transport, all of which are environment-friendly. Citizens’ participation is in-built, thus ensuring sustainability.
  • Promoting more equitable access to clean fuels by removing blanket subsidy on cooking gas to high-income group and including more households from a low-income group in the LPG distribution list are appreciable steps to address household air pollution.
  • The government has also constituted a multisectoral Steering Committee to address air pollution, both household and ambient; WHO India is a member of this forum.
  • Smoke-free legislation to reduce exposure to second-hand smoke is already in place in India viz. Cigarette and other Tobacco Products Act, 2003. India is also a signatory to the WHO Framework Convention on Tobacco Control, 2004.
  • Technologies and strategies in Practice (Gurjar et al, 2016)

Industrial emissions are regulated under the Environment Protection Act, 1986 which involves installation of pollution control equipment to meet the emission guidelines. CPCB has identified 24 critically polluted areas and action plans have been formulated to improve the air quality of these areas. For coal power plants located more than 1000 km from the pit head, ash content of the coal used has to be below 34%.

Environmental clearance from Ministry of Environment and Forest (MoEF) has been made mandatory for establishment of development projects (29 categories) which involves conducting Environmental Impact Assessment (EIA) study, public hearing and submitting the environmental statement.

Moreover, other mitigation measures such as reduction in the sulfur contents of the coal, relocation of industries (i.e. displacement of industries from inner parts of city to outer areas), use of clean fuel [e.g. use of less ash and sulfur content coal, liquid petroleum gas (LPG)] and application of air pollution control devices have been taken into account.

For reducing dust emissions from stone crushers, use of enclosed structures and water spraying system have been adopted. The industrial segment mostly employs electrostatic precipitator filter type air purifiers, which removes fine particles such as dust and smoke, 

from a flowing gas using the force of an induced electrostatic charge, thereby minimally impeding the flow of gases through the unit.

Other technologies in practice are flue gas desulphurisation, bag filters, wet collectors, multi-cyclones, carbon sequestration, industrial fans, gas conditioning systems, catalytic reduction, and fabric filters.

Various measures have been taken by government to reduce vehicular emissions such as introduction of cleaner fuels (e.g. unleaded gasoline, ultra-low sulfur diesel, CNG, LPG), improved engine technologies, introduction of Bharat Norms (equivalent to Euro norms), alternate public transport (Delhi metro rail) to trim down the growing energy demand and emissions. A series of stricter norms for vehicular emission reduction (Bharat Stage I-IV) has been adopted by the Ministry of Road Transport and Highways since 2000.Leaded gasoline was phased out from the entire country from the early 1990s till 2000, and Benzene concentration in gasoline was regulated to 3% in all India. Moreover, moving towards hybrid and electric transport vehicles in a planned manner is a major step.

Challenges towards air pollution management

Poor information exchange on best practice in urban air quality management and the lack of harmonized air pollution policies in the region has contributed to the absence of regional co-operation in addressing urban air quality. The country has areas of high population densities and in turn, higher emissions and an approach of developing strategies for control of air pollution with identification of ‘regional airshed approach’(e.g., Indo-Gangetic Plane) in the country shall be adopted.

There is a clear need for a well coordinated, sponsored initiative to address the fundamental problem of urban air pollution and provide the basis for future regional co-operation leveraging on existing technical expertise, finances and seeking requisite International cooperation.

Cost-effective solutions need also to be developed through advanced research and analysis and integrated into the policy framework in various sectors like transport, health and even the industrial policy. 

As pollution and urban meteorology are interlinked, a framework for mitigation of urbanization-altered temperatures will go hand in hand with mitigation of pollution as well.

Figure 6: Heat Island mitigation measures reduce energy demand which in turn helps in mitigating air pollution (Source: HISAT Policy Report, 2018)

Research needed to address key issues

  • A comprehensive understanding of both conventional and unaccounted sources and their emission characteristics is currently lacking. Representative emission inventories detailing the contribution from city- or industry-specific sources are needed. A holistic overview of emissions would also allow the efficient targeting of key individual sources, which if controlled, would lead to maximised benefits (Kumar et al, 2015).
  • An extensive understanding of local versus peripheral, and peripheral versus regional, sources of emissions and their contribution towards local pollutant concentrations is essential. Development of a modelling framework to assess these aspects is required for air quality forecasting as well as analysis. Numerical modelling can aid in understanding the interplay of possible sources and could assist in evaluating the effect of any policy, infrastructure or technological interventions on cluster-wise emissions and ambient air pollution concentrations.
  • Mostly we are dependent on health studies of western countries for assessing health impacts of pollutants. However, there are a lot of uncertainties with these kinds of dose-response relationships which depend on the population sample. Hence work needs to be done in our country to strengthen cohort studies to find the impacts on the local population.
  • Research also needs to be focussed on the incorporation of satellite data to enhance in-situ monitoring network as well as supplement data inputs for modelling.
  • Development of a single platform to share research and policy recommendations from different agencies involved in developing air pollution technologies and tackling strategies. Government agencies such as CPCB, CSIR, NEERI, SPCBs, MoES institutions and academic institutions such as IITs, IISER Mohali, involved either at policy or research level for air pollution management.
  • The Centre for Atmospheric Sciences at Indian Institute of Technology Delhi establishment in 1979, is a centre of excellence promoting interdisciplinary research in air pollution, climate variability, urban climate, air-sea interaction, numerical modelling of atmosphere and ocean and monsoon studies to understand various physical and social consequences. In addition, IIT Delhi now has a Centre of Excellance for Research in Clean Air.
  • Private organisations and NGOs such as CSE, Greenpeace also undertake various initiatives for research, survey and information dissemination. A common platform to share the work of all these agencies will facilitate coordination required for formulating air pollution management programs. India Science Technology and Innovation portal (ISTI) is a step in this direction.

Recommendations

  • Chemical transport models (CTMs) shall be used as an appropriate tool for policy guidance.
  • A data portal shall be made available in open access format with past and current data of all the sources across the country with strict implementation of Quality audit and quality control (QA/QC).
  • The data available with the monitoring stations of industries and other academic organisations shall be made a part of this national data portal after facilitating similar QA/QC in order to enhance this national network.
  • A standard QA/QC program needs to be evolved for PM10 and PM2.5 for newer sensor-based techniques.
  • Exposure assessment shall be integral to policy making for recommending control strategies. Studies based on the local Indian population are required.
  • City and industrial planning shall involve urban and environmental planners. Smart cities have been planned in India and sustainable development strategies shall require including mitigation strategies.
  • In conclusion, it is important to look beyond monitoring, emission inventories, or source apportionment. Even the best science and technology will not succeed in reducing emissions and improving air quality if it is not considered in a broader framework of economic development of the country, raising awareness of public health risks, and technological progress which is compatible with the nation's cultural, geographical and social context.
  • “Polluter Pays” has long been a policy of air pollution control worldwide. Thus, the major responsibility of the implementation of newer technologies for pollution reduction often comes up as high investment packages for concerned industrial units. The sole responsibility of implementing switching over from older technologies to newer ones for pollution reduction by the concerned industrial units are found to be less viable and delay implementation of easily practicable pollution reduction strategies under the regulatory framework. Technical and financial support with a viable model of sharing the burden of costs including public and private funding shall be worked out with strict timelines for ease of implementing the latest technologies in the wider interest of the general public.
  • Unregulated emission sources such as waste and biomass burning, landfill sites, construction dust, dust from unpaved areas form a major contribution to deteriorating air quality and hence shall be given very high priority for abatement where the technologies are available involving wider participation both from industries and public.
  • AirVisual. (2019) . World most polluted cities in 2018 - pm2. 5 ranking | airvisual. (n.d.). Retrieved June 2, 2019, from https://www.airvisual.com/world-most-polluted-cities
  • Bernard, S. &Kazmin, A. Dirty air: how India became the most polluted country on earth. (n.d.). Financial Times, December 11, 2018. Retrieved May 26, 2019, from https://ig.ft.com/india-pollution
  • Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., … Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736 (17)30505-6
  • CPCB (2006) Central Pollution Control Board. Air Quality Trends and Action Plan for Control of Air Pollution from Seventeen Cities. National Ambient Air Quality Monitoring Series:  NAAQMS/29/2006-07
  • CPCB (2018) National Air Quality Index December-2018
  • CPCB (2019) Continuous Ambient Air Quality Monitoring. https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing
  • EPA (2015, December 10). Managing air quality - air pollutant types [Overviews and Factsheets]. Retrieved June 1, 2019, from US EPA website: https://www.epa.gov/air-quality-management-process/managing-air-quality-air-pollutant-types
  • EPIC (2018). Indians could live up to three years more if there’s a 25% reduction in air pollution | Energy Policy Institute at University of Chicago. (n.d.). Retrieved May 25, 2019, from https://epic.uchicago.edu/news-events/news/indians-could-live-three-year...
  • European commission, Joint Research Centre, Measuring air pollution with low-cost sensors http://ec.europa.eu/environment/air/pdf/Brochure%20lower-cost%20sensors.pdf
  • Gunwani, P., & Mohan, M. (2017). Sensitivity of WRF model estimates to various PBL parameterizations in different climatic zones over India. Atmospheric Research, 194, 43–65. https://doi.org/10.1016/j.atmosres.2017.04.026
  • Gupta, M., & Mohan, M. (2013). Assessment of contribution to PM10 concentrations from long range transport of pollutants using WRF/Chem over a subtropical urban airshed. Atmospheric Pollution Research, 4(4), 405-410. doi: https://doi.org/10.5094/APR.2013.046
  • Gupta, M., & Mohan, M. (2015). Validation of WRF/Chem model and sensitivity of chemical mechanisms to ozone simulation over megacity Delhi. Atmospheric Environment, 122, 220–229. https://doi.org/10.1016/j.atmosenv.2015.09.039
  • Gurjar, B. R., Ravindra, K., &Nagpure, A. S. (2016). Air pollution trends over Indian megacities and their local-to-global implications. Atmospheric Environment, 142, 475–495. https://doi.org/10.1016/j.atmosenv.2016.06.030
  • Guttikunda, S. (2018, May 11). Daily dose of air pollution: breaking down WHO 2018 AAP database for Indian cities. Retrieved May 26, 2019, from Daily Dose of Air Pollution website: http://urbanemissions.blogspot.com/2018/05/breaking-down-who-2018-aap-da...
  • Guttikunda, S. K., & Goel, R. (2013). Health impacts of particulate pollution in a megacity—Delhi, India. Environmental Development, 6, 8–20. https://doi.org/10.1016/j.envdev.2012.12.002
  • Guttikunda, S. K., Goel, R., & Pant, P. (2014). Nature of air pollution, emission sources, and management in the Indian cities. Atmospheric Environment, 95, 501-510. doi:10.1016/j.atmosenv.2014.07.006
  • Koshy, J. (2019, March 5). Fifteen of the 20 most polluted cities in the world are in India. The Hindu. Retrieved from https://www.thehindu.com/sci-tech/energy-and-environment/fifteen-of-the-...
  • Kumar, P., Khare, M., Harrison, R.M., Bloss, W.J., Lewis, A.C., Coe, H., &Morawska, L. (2015). New directions: Air pollution challenges for developing megacities like Delhi.
  • Mohan, M., & Bhati, S. (2011). Analysis of wrf model performance over subtropical region of delhi, india. Advances in Meteorology, 2011, 1–13. https://doi.org/10.1155/2011/621235
  • Mohan, M., & Gupta, M. (2018). Sensitivity of PBL parameterizations on PM10 and ozone simulation using chemical transport model WRF-Chem over a sub-tropical urban airshed in India. Atmospheric Environment, 185, 53–63. https://doi.org/10.1016/j.atmosenv.2018.04.054
  • Mohan, M., & Sati, A. P. (2016). WRF model performance analysis for a suite of simulation design. Atmospheric Research, 169, 280–291. https://doi.org/10.1016/j.atmosres.2015.10.013
  • Mohan, M., Kikegawa, Y., Gurjar, B. R., Bhati, S., Kandya, A., & Ogawa, K. (2012). Urban heat island assessment for a tropical urban airshed in india. 2012. https://doi.org/10.4236/acs.2012.22014
  • Mohan, M. (2018). HISAT Policy Report. Odisha State Pollution Control Board
  • Prasad, R.V., Baig, M.S., Mishra, R.K., Rajalakshmi, P., Desai, U.B., & Merchant, S. (2011). Real time wireless air pollution monitoring system. ICTACT Journal on Communication Technology, 02(02), 370–375. https://doi.org/10.21917/ijct.2011.0051
  • Sati, A. P., & Mohan, M. (2018). The impact of urbanization during half a century on surface meteorology based on WRF model simulations over National Capital Region, India. Theoretical and Applied Climatology, 134(1), 309–323. https://doi.org/10.1007/s00704-017-2275-6
  • WHO (2016) Healthy Environment; Healthy People.
  • http://www.searo.who.int/india/topics/air_pollution/unewsnewsletter_june2016_healthandenvironment.pdf
  • WHO (2018) Addressing the challenge of air pollution in India. Retrieved May 26, 2019, from  http://www.searo.who.int/india/topics/air_pollution/air_pollution_media_note.pdf

Prof. Manju Mohan Professor and Head, Centre for Atmospheric Sciences, India Institute of Technology, Hauz Khas, New Delhi-110016 

Disclaimer:  This work has been submitted by the author. Any opinions, findings, conclusions or recommendations expressed in this article are those of the author only and do not necessarily reflect the views of organization.

  • Comments This field is for validation purposes and should be left unchanged.
  • Climate Change
  • Policy & Economics
  • Biodiversity
  • Conservation

Get focused newsletters especially designed to be concise and easy to digest

  • ESSENTIAL BRIEFING 3 times weekly
  • TOP STORY ROUNDUP Once a week
  • MONTHLY OVERVIEW Once a month
  • Enter your email *
  • Email This field is for validation purposes and should be left unchanged.

5 Biggest Environmental Issues in India in 2024

5 Biggest Environmental Issues in India in 2024

In its latest climate assessment, the Intergovernmental Panel on Climate Change (IPCC) made it very clear that the climate crisis is accelerating at a pace like never before and warned that it is “ now or never ” to limit global warming to 1.5C. From deforestation and droughts to air pollution and plastic waste , there are several factors exacerbating global warming, with consequences felt everywhere in the world. However, some nations suffer more than others. Despite making little to no contribution to climate change, countries in the Global South historically bear the most brunt as they often lack financial resources to tackle the emergency and mitigate the impacts of extreme weather events. Here are some of the biggest environmental issues in India right now and how the country is dealing with them.

1. Air Pollution

Undoubtedly one of the most pressing environmental issues in India is air pollution. According to the 2021 World Air Quality Report, India is home to 63 of the 100 most polluted cities, with New Delhi named the capital with the worst air quality in the world. The study also found that PM2.5 concentrations – tiny particles in the air that are 2.5 micrometres or smaller in length – in 48% of the country’s cities are more than 10 times higher than the 2021 WHO air quality guideline level. 

Vehicular emissions, industrial waste, smoke from cooking, the construction sector, crop burning, and power generation are among the biggest sources of air pollution in India. The country’s dependence on coal, oil, and gas due to rampant electrification makes it the world’s third-largest polluter , contributing over 2.65 billion metric tonnes of carbon to the atmosphere every year.  

The months-long lockdown imposed by the government in March 2020 to curb the spread of Covid-19 led to a halt in human activities. This unsurprisingly, significantly improved air quality across the country. When comparing the Air Quality Index (AQI) data for 2019 and 2020, the daily average AQI in March-April 2019 was 656, the number drastically dropped by more than half to 306 in the same months of 2020.  

You Might Also Like: India’s Coal Dilemma Amid Record-breaking Heatwave

Unfortunately, things did not last long. In 2021, India was among the world’s most polluted countries, second only to Bangladesh. The annual average PM2.5 levels in India was about 58.1 µg/m³ in 2021, “ending a three-year trend of improving air quality” and a clear sign that the country has returned to pre-pandemic levels. Scientists have linked persistent exposure to PM2.5 to many long-term health issues including heart and lung disease, as well as 7 million premature deaths each year. In November 2021, air pollution reached such severe levels that they were forced to shut down several large power plants around Delhi. 

Environmental issues in India

Figure 1: Top 15 Cities with Worst Air Quality in the World (World Air Quality Report 2021).

In recent years, the State Government of the Indian capital has taken some stringent measures to keep a check on air pollution. One of which is the Odd-Even Regulation – a traffic rationing measure under which only private vehicles with registration numbers ending with an odd digit will be allowed on roads on odd dates and those with an even digit on even dates. Starting from January 2023, there will also be a ban on the use of coal as fuel in industrial and domestic units in the National Capital Region (NRC). However, the ban will not apply to thermal power plants, incidentally the largest consumers of coal. Regardless of the measures taken to curb air pollution, as the World Air Quality Report clearly shows – the AQI in India continues to be on a dangerous trajectory.

You Might Also Like: 15 Most Polluted Cities in the World

2. Water Pollution

Among the most pressing environmental issues in India is also water pollution. The Asian country has experienced unprecedented urban expansion and economic growth in recent years. This, however, comes with huge environmental costs. Besides its air, the country’s waterways have become extremely polluted, with around 70% of surface water estimated to be unfit for consumption. Illegal dumping of raw sewage, silt, and garbage into rivers and lakes severely contaminated India’s waters. The near-total absence of pipe planning and an inadequate waste management system are only exacerbating the situation. Every day, a staggering 40 million litres of wastewater enter rivers and other water bodies. Of these, only a tiny fraction is adequately treated due to a lack of adequate infrastructure.

In middle-income countries like India, water pollution can account for the loss of up to half of GDP growth, a World Bank report suggests. Water pollution costs the Indian government between US$6.7 and $7.7 billion a year and is associated with a 9% drop in agricultural revenues as well as a 16% decrease in downstream agricultural yields.

Besides affecting humans, with nearly 40 million Indians suffering from waterborne diseases like typhoid, cholera, and hepatitis and nearly 400,000 fatalities each year, water pollution also damages crops, as infectious bacteria and diseases in the water used for irrigation prevent them from growing. Inevitably, freshwater biodiversity is also severely damaged. The country’s rivers and lakes often become open sewers for residential and industrial waste. Especially the latter – which comprises a wide range of toxic substances like pesticides and herbicides, oil products, and heavy metals – can kill aquatic organisms by altering their environment and making it extremely difficult for them to survive.

Fortunately, the country has started addressing the issue by taking steps to improve its water source quality, often with local startups’ help. One strategy involves the construction of water treatment plants that rely on techniques such as flocculation, skimming, and filtration to remove the most toxic chemicals from the water. The upgrade process at one of the country’s largest plants located in Panjrapur, Maharashtra, will enable it to produce more than 19 million cubic metres of water a day , enough to provide access to clean water to approximately 96 million people. 

The government is also looking at ways to promote water conservation and industrial water reuse by opening several treatment plants across the country. In Chennai, a city in Eastern India, water reclamation rose from 36,000 to 80,000 cubic metres between 2016 and 2019. 

Finally, in 2019, Gujarat – a state of more than 70 million citizens – launched its Reuse of Treated Waste Water Policy , which aims to drastically decrease consumption from the Narmada River. The project foresees the installation of 161 sewage treatment plants all across the state that will supply the industrial and construction sectors with treated water.

3. Food and Water Shortages

According to the Intergovernmental Panel on Climate Change (IPCC), India is the country expected to pay the highest price for the impacts of the climate crisis. Aside from extreme weather events such as flash floods and widespread wildfires, the country often experiences long heatwaves and droughts that dry up its water sources and compromise crops. 

Since March 2022 – which was the hottest and driest month recorded in 120 years – the North West regions have been dealing with a prolonged wave of scorching and record-breaking heat . For several consecutive days, residents were hit by temperatures surpassing 40 degrees Celsius, while in some areas, surface land temperatures reached up to 60C. There is no doubt among experts that this unprecedented heatwave is a direct manifestation of climate change .

The heatwave has also contributed to an economic slowdown due to a loss of productivity, as thousands of Indians are unable to work in the extreme heat. The agriculture sector – which employs over 60% of the population – is often hit hard by these erratic droughts, impacting food stability and sustenance. Currently, farmers are struggling to rescue what remains of the country’s wheat crops, piling on existing fears of a global shortage sparked by the war in Ukraine.

Already among the world’s most water-stressed countries , the heatwave is causing further water shortages across the nations. Even though water tankers are keeping communities hydrated, the supply is not enough to cover the needs of all residents. But heat is not the only factor contributing to water scarcity. In an interview with the Times of India , lead researcher at Pune-based Watershed Organisation Trust Eshwer Kale described the national water policy as very ‘irrigation-centric’. Indeed, over 85% of India’s freshwater is used in agriculture. This has led to a crisis in several states, including Punjab, Haryana, and western Uttar Pradesh. The indiscriminate use of water for irrigation, coupled with the absence of conservation efforts and the huge policy gap in managing water resources has left over 10% of the country’s water bodies in rural areas redundant. A 2019 report predicts that 21 major cities – including New Delhi and India’s IT hub of Bengaluru – will run out of groundwater by 2030, affecting nearly 40% of the population. 

You Might Also Like: Water Scarcity in India

4. Waste Management

Among the most pressing environmental issues in India is also waste. As the second-largest population in the world of nearly 1.4 billion people, it comes as no surprise that 277 million tonnes of municipal solid waste (MSW) are produced there every year. Experts estimate that by 2030, MSW is likely to reach 387.8 million tonnes and will more than double the current value by 2050. India’s rapid urbanisation makes waste management extremely challenging. Currently, about 5% of the total collected waste is recycled, 18% is composted, and the remaining is dumped at landfill sites .

The plastic crisis in India is one of the worst on the planet. According to the Central Pollution Control Board (CPCB), India currently produces more than 25,000 tonnes of plastic waste every day on average, which accounts for almost 6% of the total solid waste generated in the country. India stands second among the top 20 countries having a high proportion of riverine plastic emissions nationally as well as globally. Indus, Brahmaputra, and Ganges rivers are known as the ‘highways of plastic flows’ as they carry and drain most of the plastic debris in the country. Together with the 10 other topmost polluted rivers, they leak nearly 90% of plastics into the sea globally. 

To tackle this issue, in 2020 the government announced that they would ban the manufacture, sale, distribution, and use of single-use plastics from July 1 2022 onwards . Furthermore, around 100 Indian cities are set to be developed as smart cities . Despite being still in its early phase, the project sees civic bodies completely redrawing the long-term vision in solid waste management, with smart technologies but also awareness campaigns to encourage community participation in building the foundation of new collection and disposal systems. 

You Might Also Like: Smart Cities in India

5. Biodiversity Loss

Last but not least on the list of environmental issues in India is biodiversity loss. The country has four major biodiversity hotspots , regions with significant levels of animal and plant species that are threatened by human habitation: the Himalayas, the Western Ghats, the Sundaland (including the Nicobar Islands), and the Indo-Burma region. India has already lost almost 90% of the area under the four hotspots, according to a 2021 report issued by the Centre for Science and Environment (CSE), with the latter region being by far the worst affected.

Environmental Issues in India

Figure 2: Biodiversity Hotspots in India.

Moreover, 1,212 animal species in India are currently monitored by the International Union for Conservation of Nature (IUCN) Red List, with over 12% being classified as ‘endangered’ . Within these hotspots, 25 species have become extinct in recent years.

Due to water contamination, 16% of India’s freshwater fish, molluscs, dragonflies, damselflies, and aquatic plants are threatened with extinction and, according to the WWF and the Zoological Society of London (ZSL) , freshwater biodiversity in the country has experienced an 84% decline. 

Yet, there is more to it. Forest loss is another major driver of biodiversity decline in the country. Since the start of this century, India has lost 19% of its total tree cover . While 2.8% of forests were cut down from deforestation, much of the loss have been a consequence of wildfires, which affected more than 18,000 square kilometres of forest per year – more than twice the annual average of deforestation. 

Forest restoration may be key to India’s ambitious climate goals, but some argue that the country is not doing enough to stop the destruction of this incredibly crucial resource. Indeed, despite committing to create an additional carbon sink of 2.5-3 billion tonnes of CO2 equivalent through additional forest and tree cover by 2030, Narendra Modi’s government faced backlash after refusing to sign the COP26 pledge to stop deforestation and agreeing to cut methane gas emissions. The decision was justified by citing concerns over the potential impact that the deal would have on local trade, the country’s extensive farm sector, and the role of livestock in the rural economy. However, given these activities’ dramatic consequences on biodiversity, committing to end and reverse deforestation should be a priority for India.

If you liked reading about some of the biggest environmental issues in India, you might also like: 14 Biggest Environmental Problems of 2024

About the Author

pollution project of india

Martina Igini

10 of the World’s Most Endangered Animals in 2024

10 of the World’s Most Endangered Animals in 2024

10 of the Most Endangered Species in India in 2023

10 of the Most Endangered Species in India in 2023

10 Deforestation Facts You Should Know About

10 Deforestation Facts You Should Know About

Hand-picked stories weekly or monthly. We promise, no spam!

  • Name This field is for validation purposes and should be left unchanged.

Boost this article By donating us $100, $50 or subscribe to Boosting $10/month – we can get this article and others in front of tens of thousands of specially targeted readers. This targeted Boosting – helps us to reach wider audiences – aiming to convince the unconvinced, to inform the uninformed, to enlighten the dogmatic.

Home

Clean Air Project India

Air pollution is a global concern contributing to a wide range of health risks. According to the World Health Organisation data, around 7 million people die worldwide every year from exposure to the polluted air. More than 80% of people living in urban areas are exposed to concentrations higher than the level recommended by the WHO. India is one of the countries that are severely affected by air pollution. More than half of the world's twenty most polluted cities are located in India. Sensing the urgency, the Government of India launched the National Clean Air Programme (NCAP) with the aim to reduce pollution levels in 122 non-attainment cities by 20-30% by 2024.

CAP India

In view of this, the Global Programme Climate Change and Environment of the Swiss Agency for Development and Cooperation (SDC) has initiated a project 'Clean Air Project in India (CAP India) to support India's efforts for improving air quality.

CAP India was launched in four cities — Lucknow and Kanpur in Uttar Pradesh, Nashik and Pune in Maharashtra — by SDC, and The Energy and Resources Institute (TERI) at the World Sustainable Development Summit 2020.

The CAP India programme will focus on improving data measurement, enhancing capacities of city and state authorities to implement clean air policies and action plans, and raising public awareness for clean air action.

These four cities were selected after conducting a scoping study, which considered several factors such as severity and sources of air pollution, population density and associated health impacts, economic standing of the state, readiness or preparedness of state in terms of policies/regulation etc.

The CAP India project aims to support NCAP by demonstrating viable approaches for cities to address air pollution. NCAP was launched in January 2019 with a goal to meet the prescribed annual average ambient air quality standards across the country.

The CAP India project is planned to be carried out by a consortia of national and international organisations including TERI, Automotive Research Association of India, International Institute for Applied Systems Analysis, IIT-Kanpur, National Environmental Engineering Research Institute, Paul Scherrer Institute, EPFL Switzerland, Indian Institute of Tropical Meteorology, and University of Bern.

The goal of this project is to support India's efforts to improve people's health and well-being through better air quality, while contributing to environment and climate change mitigation.

To support India's efforts to improve air quality, while contributing to public health, environment and climate change mitigation. CAP India is assisting four partner cities — Lucknow, Kanpur, Pune and Nashik — in strengthening their existing clean air action plans based on the state of art source apportionment studies.

The overall goal of the project is to support India's efforts to improve air quality, while contributing to public health, environment and climate change mitigation.

Improved data measurement and analysis on clean air

Enhanced capacities of city and state authorities to implement clean air policies and action plan

Awareness for clean air action

CAP India

Major Activities

Implementation consortium activities.

  • Estimating city-wide source contributions of pollutants using state of art air quality models for four cities.
  • Revising methodologies/protocols for source apportionment based on consultations and review, data collection and design of monitoring networks.
  • Capacity building of state pollution control boards, other government departments, academia, and research institutions through training program on source emission monitoring, inventory preparation, conducting source apportionment study and systematic calibration of analyzers.
  • Strengthening existing action plans using dispersion model results.
  • Developing monitoring, review, and verification (MRV) systems for clean air action plan.

CAP India

  • Demonstrating pilot projects in selected sectors with high mitigation potential in the airshed area of the four cities.
  • Strengthening capacities to prepare and implement action plan.
  • National and international exposure visits to showcase best practices.
  • Raising awareness for clean air action in different sections of local community.
  • Capacity building of media and NGOs for awareness generation on air pollution.
  • Sensitization through health camps, citizen workshops, and action projects with students.

CAP India 4

Research Network Actitivies

  • Improved source apportionment methods/protocols based on measurement.
  • Measurement and analysis of sources of PM and its specific health relevant components.
  • Capacity building of state pollution control boards, other government departments, academia, and research institutions through training program on advanced source apportionment and on-line instrumentation.
  • Strengthening existing action plans using advanced source apportionment studies.

CAP India

TERI and Swiss Agency for Development & Cooperation (SDC) conducted a launch event for the schools in Lucknow, Uttar Pradesh. The event on 5th August saw participation from at least 430 students & teachers from 30 schools. Dr Anand Shukla, Sr Thematic Advisor, SDC highlighted the need to be vocal about local environmental challenges such as air pollution. An interactive session 'Let's learn together' was also conducted by our fellows Ms Neha & Dr Anju Goel to discuss the impacts of air pollution.

Prof. Mukesh Sharma, Department of Civil Engineering, IITKanpur discussed the importance of clean air and how students can play an important role by raising awareness to reduce air pollution. Dr Livleen K Kahlon, Associate Director, Environmental Education and Awareness, TERI said that air pollution from vehicles and power plants has been inhibiting the flying ability of pollinators to find flowers providing them nectar. Air Pollution harms all different lifeforms.

CAP India event launch

The launch of CAP- India in Kanpur was organised on 28 August 2020 through a webinar. The launch event received registration from 227 students and teachers from 9 schools in Kanpur, Uttar Pradesh. An interactive session with students was held to apprise them about air pollution, highlighting facts about Kanpur. This session was an infusion of quiz session, visuals on air pollution, and information dissemination on air pollution and its impacts and solutions.

Proceedings of Kanpur Launch

Supported by: Swiss Agency for Development and Cooperation (SDC)

Implementation Consortium

  • The Energy and Research Institute (TERI), New Delhi, India
  • Automotive Research Association of India (ARAI), Pune, India
  • International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
  • The Ecole polytechnique fédérale de Lausanne (EPFL) Lausanne, Switzerland
  • Indian Institute of Technology, Kanpur (IITK)
  • National Environmental Engineering and Research Institute (NEERI)

Research Partners

  • Paul Scherrer Institute (PSI), Switzerland (Lead)
  • Indian Institute of Tropical Meteorology (IITM)
  • The University of Bern

Related Content

Inter school competitions - under clean air project in india events, clean air project in india: launch event for schools in lucknow events, teri, sdc launch clean air project in india to reduce air pollution in four indian cities press release, subscribe to our newsletter.

  • भारत सरकार GOVERNMENT OF INDIA
  • विज्ञान और प्रौद्योगिकी मंत्रालय MINISTRY OF SCIENCE AND TECHNOLOGY
  • Screen Reader Access

Skip to main content

Search form

Sitemap

विज्ञान एवं प्रौद्योगिकी विभाग

Department of Science & Technology (DST)

  • Home   >>  
  • Clean Air Research Initiative (CARI)  >>  

Clean Air Research Initiative (CARI)

Air Pollution in India is a serious issue with the major sources being fuelwood and biomass burning, fuel adulteration, vehicle emission and traffic congestion. Department of Science & Technology (DST) initiated a solution oriented R&D activity for mitigation of Air Pollution. The programme focusesto identify the technologies that can provide viable deployable solution to mitigate the air pollution and to establish technical resource unit.

1. Traffic Junction Air Pollution Abatement Plan

DST with CSIR-NEERI has developed Wind Augmentation and Air Purifying Unit (WAYU) devices that can be positioned in an industrial complex, residential complexes, and schools in the vicinity of traffic road intersection/divider to tackle air pollution.  This device works basically on two principles i.e.  Wind generation for dilution of air pollutants and active pollutants removal. The device would use low-speed wind generators, appropriate size filters for long operation cycle with reasonable efficiency. It also will have an oxidizer unit for removal of Carbon-monoxide and Hydrocarbons including VOCs.

WAYU can reduce PM10, PM 2.5, Carbon monoxide (CO), volatile organic compounds (VOCs), hydrocarbon (HC) emitted in the atmosphere. This device works basically on two principles mainly wind generation for dilution of air pollutants and active pollutants removal. The device consumes a half unit of electricity each day for running for 10 hours each day. The cost of the device is Rs.60000 per device. Maintenance cost is Rs.1500 per month.

2. Landfill Fire Control Mechanism through Integrated Approach

Landfills are the ultimate disposal option adopted in India. In India, most of the landfills are non-scientific and non-engineered which do not have any leachate and gas collection systems. Open burning of waste and landfill fires are among the largest sources of air pollution in Indian cities and towns. Landfill fires emit nearly 22000 tons per year of pollutants into the air in the city of Mumbai alone. These pollutants include Carbon Monoxide (CO), Hydrocarbons (HC), Particulate Matter (PM), Nitrogen Oxides (NOx) and Sulfur Dioxide (SO 2 ) plus an estimated 10000 TEQ grams of  dioxins/ furans. In view of these facts, this project will understand the cause, source, type and effects of fire in a dumpsite/landfill and to develop an integrated approach for its proper control.

3. Suddha Vaayu: An Electrical Chamber for Detection and Mitigation of Air Pollution

The proposed project is divided into two phases. In the first phase, the prototype of the electrical mitigation chamber will be designed. The proposed prototype consists of three steps of the purification process. In the first step, large particles will be removed using metal mesh or other possible best means. A 5 µm pore diameter fiber filter will be used to remove PM 10 in the second stage. In the third stage, a paper made rundfilter of 2 µm pore diameter will remove PM 2.5. The walls of the mitigation device are coated with silica gel to remove the moisture content from the air. The mitigation device has the capability to run with both external power and energy generated with the solar panels installed on the device. In general 250 watts solar panel with 24 v input is sufficient to power this system. SuddhaVaayu: electrical chamber is expected to have an efficiency of 88-90% to remove PM 2.5 and PM10.

Based on the results from the first phase, multiple pilot chambers will be fabricated and set up at selected locations in Delhi in the second phase. The real-time efficiency of the pilot units will be tested using the integrated air quality sensor platform made and validated as a deliverable of this project.

4. Collecting Particulate Matter in Air Using Filters Placed on the Top of a Moving Car

The proposed project is divided into two phases. In the first phase, the performance of the existing purifier designed by Shudh Vayu LLC will be evaluated using CFD analysis for different scenarios pertaining to operating conditions of the car, meteorology, make and model of the car etc. The prototype consists of three stages of purification process. In the first step large particles will be removed using metal mesh. The second and third stages have foam and HEPA filters, respectively.

The main advantage of this purification system is its principle of pressure difference created due to air flow generated by a moving vehicle. However, as a part of project deliverables, relevant on field tests on the efficiency of the existing filter and the modified filter based on the CFD simulations will be carried out in this project. Additionally, a sensor fusion system which can be mounted on the top of the car will be designed and fabricated which will aid in measuring the real time concentrations of air pollutants. Such a sensor fusion system will aid in real-time air quality mapping of a city. As HEPA filters used in the current design are not washable and re-generatable, large scale usage of the purifier might end up creating solid waste management issues. Thus one of the objectives of this project is developing an affordable and washable air filter, which is of good efficiency.

5. Indigenous Photonic System for Real Time Remote Monitoring of Air Quality

The aim of this project is to test proof of concept of the novel indigenous photonic system for real time remote monitoring of air quality. The project will develop the field deployable novel indigenous air quality monitoring photonic system and evaluate the same in laboratory trials with gold standards. It will also conduct field trials at designated site of existing CPCB air quality monitoring station for inter-comparisons purposes.

6. Mitigation of Air Pollution: Micro-to-Macro Scale Study of Particle Capture by Liquid Droplets

Air pollution comprises of smoke, dust and haze which can be collectively named as aerosols which defines suspension of small particles in air. The sources of particles, aerosol and species that are the constituents of polluted air are manifold, such as, vehicle tail-pipe emissions, road dust kicked up by vehicles, construction sites, dumping of sand and cement, road sweeping, open ovens and furnaces operated by local industries, garbage fires, biomass/ wood burning cook-stoves, brick kilns, factory exhausts, stubble burning, and dust entrained by winds, amongst others. Wind and change in temperature affect the transport of pollutants in air. A mixture of these is distributed in the air people breathe which is known to lead to adverse health effects.

The fundamental aim of the project is to study of particulate matter (PM) capture efficiency by single droplet using two techniques of electrostatic charging of droplet and/or aerosol and addition of additives based on chemical characteristics of the aerosols.

Related Organization

Miscellaneous.

epms

Image of Home

  • Skip to Main Content
  • Screen Reader Access

Image of Default Theme

Environment

image of blog image

Air Pollution:

Rising issue of air pollution has increasingly been becoming a serious concern, particularly in metro cities. A large number of cities and towns do not meet the standards for pollutants specifically for particulate matter. In a few cities including Delhi, the ambient particulate matter concentrations are much above the standards i.e. three to four times or even higher. Air quality regulation and actions for abatement of air pollution is undertaken under various provisions of Air (Prevention and Control of Pollution) Act, 1981 and Environment (Protection) Act, 1985 which prescribes the mechanism and authorities for handling the issue. The major impact is highlighted with reference to health of people. As per the available data for Delhi and NCR for last five years, Particulate Matter (PM10 and PM2.5) concentrations are the major concern for the entire area, however a few violations are observed in NO2 concentrations in Delhi, Meerut and Faridabad. The concentration of SO2 is within the standard limit at all the locations in all the last five years. PM10 are inhalable coarse particles, which are particles with a diameter between 2.5 and 1O micrometers (um) and PM2.5 are fine particles with a diameter of 2.5 um or less.

Air Pollution and Health:

I. Generally, for young and healthy people, moderate air pollution levels are unlikely to have any serious short term effects. However elevated levels and/or long term exposure to air pollution can lead to symptoms and conditions affecting human health. This mainly affects the respiratory and inflammatory systems, but can also lead to more serious conditions such as heart disease. People with lung or heart conditions may be more susceptible to the effects of air pollution.

II. With several international reports about impact of air pollution on health correlating with diseases and death in India, the issue has assumed greater prominence.

III. Global Burden of Disease’ estimates for 2017 that early deaths related to PM2.5 in India are the second highest in the world and ozone related deaths, are the highest in the world. The assumptions on which the model is based are not clear. These numbers are not validated for Indian conditions and there are no conclusive data available to establish direct correlation of death exclusively with air pollution. Health effects of air pollution are cumulative manifestation of factors which include food habits, occupational habits, occupational habits, socio¬economic status, medical history, immunity, heredity etc. of the individuals. Air pollution is one of the triggering factors for respiratory associated ailments and diseases and it is acknowledged that higher the level of air pollution higher is the risk to lungs in a given area. Further in Delhi ozone levels are within the permissible levels; therefore, the estimate of higher number of ozone deaths referred is not clear.

IV. With focus on environmental health issues, MoEF&CC has constituted a high level Apex Committee and a Working Group under the joint chairmanship of ICMR and the Ministry to identify thrust areas in environment health and to evaluate the related projects. In line with recommendation of Working Group, our Ministry in coordination with M/o Health and ICMR has already initiated action towards study on National Environmental Health Profile, with emphasis on impact of air pollution on health.

V. ICMR has initiated a project titled “Effect of Air Pollution on Acute Respiratory Symptom in Delhi: A Multicity Study” with effect from June 2017 at 5 centres viz AIIMS-Pulmonary Medicine Department, AIIMS-Paediatric Department, Vallabhai Patel Chest Institute, Kalawati Saran Children’s Hospital and National Institute of Tuberculosis and Respiratory Diseases for a period of 1 year.

VI. ICMR- National Institute for Research in Environmental Health (NIREH), Bhopal has recently initiated a three year duration study entitled “Aberrant circulating epigenomic signatures: Development and validation of minimal-invasive biomarkers for trans-generational monitoring of air pollution associated cancers” in collaboration with IIT, Kharagpur to develop novel biomarkers bearing epigenetic signatures, for lung cancer.

Sources for Air pollution in Delhi NCR: Various studies conducted to identify the reasons for rise in pollution in country including NCR of Delhi specially during winter months. A study as ‘Comprehensive Study on Air Pollution and Green House Gases in Delhi, 2016’ was conducted by Indian Institute of Technology, Kanpur to identify major air pollution sources in NCT of Delhi, their contributions to ambient air pollution levels and develop an air pollution control plan. The study confirms that Particulate Matter is the main source of pollution and levels of PM10 and PM2.5 are 4-7 times higher than National Ambient Air Quality Standards (NAAQS) in summer and winter months. Based on the air quality measurements in summer and winter months, it is inferred that the contribution of PM10 and PM2.5 from different sources is different in summer and winter. Sources of pollution during winter include secondary particles (25 -30%), vehicles (20 – 25%), biomass burning (17 – 26%), municipal solid waste burning (9 – 8%) and to a lesser extent soil and road dust. Sources of pollution during summer include, coal and fly ash (37 – 26%), soil and road dust (26 – 27%), secondary particles (10 – 15%), biomass burning (7 – 12%), vehicles (6 – 9%) and municipal solid waste burning (8-7%).

Initiatives on Air Pollution Mitigation:

  • National Ambient Air Quality Standards envisaging 12 pollutants have been notified under EPA, 1986 and 115 emission/effluent standards for 104 different sectors of industries, besides 32 general standards for ambient air have also been notified.
  • Government is executing a nation-wide programme of ambient air quality monitoring known as National Air Quality Monitoring Programme (NAMP). The network consists of Six hundred and Ninety-One (691) manual operating stations covering Three Hundred and three (303) cities/towns in twenty-nine (29) states and four (6) Union Territories of the country. In addition, there are 86 real-time Continuous Ambient Air Quality Monitoring stations (CAAQMS) in 57 cities. Delhi has 10 Manual Stations and 18 CAAQMS. 20 additional CAAQMS are at various stages of installation in Delhi.
  • With reference to Vehicular pollution the steps taken include introduction of cleaner / alternate fuels like gaseous fuel (CNG, LPG etc.), ethanol blending, universalization of BS-IV by 2017; leapfrogging from BS-IV to BS-VI fuel standards by 1st April, 2020; ongoing promotion of public transport network of metro, buses, e-rickshaws and promotion of carpooling, streamlining granting of Pollution Under Control Certificate, lane discipline, vehicle maintenance etc.
  • National Air Quality index (AQI) was launched by the Prime Minister in April, 2015 starting with 14 cities and now extended to 34 cities.
  • A Graded Response Action Plan for control of air pollution in Delhi and NCR region has been notified. This plan specifies actions required for controlling particulate matter (PM emissions from various sources and prevent PM10 and PM2.5 levels to go beyond ‘moderate’ national Air Quality Index (AQI) category. The measures are cumulative. Emergency and Severe levels include cumulatively all other measures listed in the lower levels of AQI including Very Poor, Poor and Moderate. Actions listed in the Poor to Moderate category need to be implemented though out the year.
  • Central Pollution Control Board (CPCB) has issued a comprehensive set of directions under section 18 (1) (b) of Air (Prevention and Control of Pollution) Act, 1986 for implementation of 42 measures to mitigate air pollution in major cities including Delhi and NCR comprising of action points to counter air pollution in major cities which include control and mitigation measures related to vehicular emissions, re-suspension of road dust and other fugitive emissions, bio-mass/municipal solid waste burning, industrial pollution, construction and demolition activities, and other general steps.
  • In order to involve people in the effort, Government had launched a campaign called ‘Harit Diwali and Swasth Diwali’ during September 2017 involving over 2000 schools in Delhi and over two lakh schools in the country. The Government had also organized a Mini Marathon for ‘Swachh Hawa for Swachh and Swasth Bharat’ on 15th October 2017 at India Gate in which nearly 15,000 school children had participated.
  • Regular co-ordination meetings are held in the Ministry at official and ministerial level with Delhi and other State Governments to avoid the emergency situation. In this regard several meetings have been held this year under the chairmanship of Hon’ble Minister for Environment Forest and Climate Change and Secretary (EF&CC) involving Environment Minister of the States and Senior State Functionaries including Chief Ministers, Ministers, Chief Secretaries and Additional Chief Secretaries.
  • CPCB had taken a number of Proactive steps to help improve ground implementation .40 CPCB teams deployed for ground feedback on air polluting activities in Delhi –September 01, 2017 onwards Field visit to four pollution hotspots (Anand Vihar, ITO, Punjabi Bagh and DTU) and suggested interventions July 2017 .On the spot reporting to DPCC, and weekly summary reports to Delhi Govt.
  • During air pollution emergency period from 7.11.2017 to 14.11.2017 and measures like ban on construction, sprinkling of water, ban on entry of truck etc. which are there under GRAP were implemented.
  • A High Level Task Force (HLTF) headed by Principal Secretary to PM has been constituted by the government for management of air pollution in Delhi and NCR. First meeting of Task Force was held on 4th December 2017. On the basis of direction of the Task Force, Sub-Committee of High Level Task Force for Prevention of Stubble Burning in Haryana, Uttar Pradesh and Punjab has been constituted and report has been submitted for consideration by the HLTF. The Task Force has proposed a draft Air Action Plan on Abatement of Air Pollution in the Delhi National Capital Region in which time bound activities have been outlined. This has been put in the public domain for suggestions/comments from citizens and experts for possible refinements.

The air quality has improved since the episodic high pollution incidence of November 07- 14, 2017. Further, overall the improvement has been observed this year in terms of less numbers of ‘severe’, ‘very poor’ and poor days and more number of good, satisfactory and moderate AQI days in Delhi as compared to last year. It may be noted that the number of good, moderate and satisfactory AQI days in 2017 were 151 compared to 109 days in 2016. Similarly, the number of poor, very poor and severe AQI days have shown a drop in the current year as compared to last year: 181 in 2017 against 214 in 2016.

Noise Pollution:

As a follow-up of Section 5.2.8 (IV) of National Environmental Policy (NEP)- 2006, ambient noise has been included as a regular parameter for monitoring in specified urban areas. Protocol for National Ambient Noise Monitoring Network Programme has been prepared and circulated to State Pollution Control Boards. Central Pollution Control Board in association with State Pollution Control Boards has established Real Time National Ambient Noise Monitoring Network in 07 metropolitan cities and installed 70 no. of Noise Monitoring System in Mumbai, Delhi, Kolkata, Chennai, Bangalore, Lucknow and Hyderabad (Ten stations in each).

The average level of noise pollution in respect of seven metro cities of the country during last three years is provided in Table. The analysis of data indicates fluctuating trend in the noise levels. During day time, Lucknow recorded the maximum sound level followed by Kolkata, Delhi and Mumbai. Similarly, during night time maximum sound level observed at Chennai followed by Lucknow, Kolkata and Mumbai.The steps taken to reduce noise pollution inter alia include advisories for noise monitoring on the occasion of Diwali; prohibition of the use of fireworks between 10.00 p.m. and 06.00 a.m.; publicity regarding the ill effects of fire­ crackers, sensitization of students through course curriculum besides general awareness building of public at large to avoid bursting of fire-crackers; and issuance of directions under Section 5 of Environment (Protection) Act, 1986 and under section 18 (1) (b) of Air (Prevention and Control of Pollution) Act, 1981. The noise emission standards related to equipment(s) are the Environment (Protection) Rules, 1986.

Scheme of Assistance for Abatement of Pollution:

The scheme of Assistance for Abatement of Pollution was conceptualized in 1992 during the 7th Five-Year Plan with the objective inter alia to strengthen the CPCB and SPCBs/PCCs for enforcing statutory provisions for pollution abatement. The scheme is a part of a centrally sponsored umbrella scheme of ‘Pollution Abatement’. The scheme of assistance for pollution abatement comprise of sub-components are Grants-in-Aid-General; Grants for creation of Capital Assets; Environment Health Cell (EHC) & Trade and Environment (PL) including North Eastern Region Grants-in-Aid-General and North Eastern Region Grants for creation of Capital Assets. The scheme had an allocation of Rs 45 crore in the XI Five Year Plan and Rs. 60 Crore in the XII FYP. The Scheme provides 100 % grant to SPCBs/PCCs, Governmental organizations.

Under this Scheme the Grants are provided to the State Pollution Control Boards/UT Pollution Control Committees, Environment Departments of States/UTs, Central/State Research Institutes, and other government agencies/organizations with the aim of strengthening their technical capabilities to achieve the objectives of the Policy Statement. Assistance is also provided to North Eastern Pollution Control Boards & Pollution Control Committees as salary support for the technical staff. In addition, support is also extended for undertaking projects for Abatement of Pollution.

During this year (2017-18), an allocation of Rs.5.20 crore (including Rs. 1.00 Crore for NE Region) in the BE was made for providing financial assistance to the on-going/new projects. The assistance has been extended to two State Pollution Control Boards/ Pollution Control Committees and one institutes for Environmental Health in the current financial year. A Grant-in-Aid to was made to Centre for Science and Environment for conducting training programme for environmental regulators during this year.

Scheme of Common Effluent Treatment Plants (CETPs):

  • The concept of the Common Effluent Treatment Plants (CETPs) arose in order to make a co-operative movement for pollution control. The main objective of the CETPs is to reduce the treatment cost to be borne by an individual member unit to a minimum while protecting the environment to a maximum. Wastewater treatment and water conservation are the prime objectives of the CETP. The concept of CETPs was envisaged to treat the effluent emanating from the clusters of compatible small – scale industries. It was also envisaged that burden of various Government authorities working for controlling pollution and monitoring of water pollution could be reduced once the CETPs are implemented and commissioned.

A Centrally Sponsored Scheme (CSS) had been undertaken by the Government for enabling Small Scale Industries (SSI) to set up new and upgrade the existing Common Effluent Treatment Plants to cover all the States in the country. The CSS of CETPs had been revised by the Ministry since 2012 with the following salient features:

  • The Central subsidy has been enhanced from 25%to 50%of the project cost.
  • All the three levels of treatment, primary, secondary and tertiary are to be covered for assistance. Progressive technologies like Zero Liquid Discharge will also be considered for assistance, subject to a ceiling.
  • The management of the CETP is to be entrusted to a Special Purpose Vehicle registered under an appropriate statute.
  • Performance guarantee at full design load is to be ensured upfront.
  • However, after the evaluation of the Plan Scheme of MoEF&CC in 2016-17, It was decided to discontinue CETP Scheme after funding support to the existing on-going projects.
  • During this year (2017-18), an allocation of Rs.14.00 Crore in the BE was made for providing financial assistance to the on­going CETP projects at Ludhiana Palsana & Pali.

Control of Pollution- Development of Environmental Standards : The Ministry of Environment, Forest and Climate Change (MoEF&CC) formulates and notifies standards for emission or discharge of environmental pollutants viz. Air pollutants, water pollutants and noise limits, from industries, operations or processes with an aim to protect and improve the quality of the environment and abate environmental pollution. The standards are framed in consultation with the concerned stakeholders. The process is based on the best practices and techno-economic viability. The notification of standards also involves formulation of load based standards i.e. emission/ discharge limits of pollutants per unit of product obtained/ processes performed to encourage resource utilization efficiency and conservation aspects.

The standards for any industrial process / operation recommended by Central Pollution Control Board (CPCB) are subjected to stakeholder consultation including general public. The comments are compiled and technically examined by CPCB and change, if any, incorporated. The modified standards are placed before the “Expert Committee (EC)’ of MoEF&CC for approval. The EC of MoEF&CC comprises of representatives from industry associations, subject experts, and concerned Ministries of the industrial sectors, besides the officials of MoEF&CC and CPCB. The EC recommended standards for approval and legal vetting are published in Gazette of India. During the year, Standards in respect of following category of Industries have been notified.

Sewage Treatment Plants (STPPs) Effluent discharge Standards Gazette Notification G.S.R. 1265(E) dated 13/10/2017: The issue has gained significant because of the stress of water bodies which are getting increasing polluted and may have severe repercussion in maintain the quality of environment in the country. There is not specific standard related to Sewage Treatment Plants (STPs) currently and effluent standard are governed by general standard including marine discharge of environment pollutant, which do not lay down any norm with respect to fecal coliform. In the absent of such standard, the treated water may not meet the required norms with respect to drinking water or bathing. The Ministry has notified environment standard for STPs for effluent discharge standard (applicable to all mode of disposal) vide No.G.S.R. 1265(E) dated 13/10/2017. Before finalization of the aforesaid notification the Ministry has taken detail consultation with lined Ministries /Departments i.e. Ministry of Housing and Urban Affairs, National Mission for Clean Ganga, Central Public Health and Environmental Engineering Organisation (CPHEEO), Central Pollution Control Board (CPCB) and different stakeholders.

In the notified standard, the permitted pH range of treated effluent is 6.5 to 9.0. Bio­Chemical Demand (BOD) is ’20’ and ’30’ and Total Suspended solids (TSS) is <50 and <100 in Metro cities all State Capitals except in the state of Arunachal Pradesh, Assam, Manipur, Sikkim, Himachal Pradesh, Uttarakhand, Jammu and Kashmir, and Union territory of Andaman and Nicobar Islands, Dadar and Nagar Haveli, Daman and Diu, Lakshadweep and areas/regions other than these states respectively. Fecal coliform (FC) standard is milliliter). These Standards shall apply to all STPs to be commissioned on or after the 1st June, 2019 and the old/Existing STPs shall achieve these standards within a period of five years from the date of publication of this notification in the Official Gazette. In case of discharge of treated effluent into sea, it shall be through proper marine outfall and the existing shore discharge shall be converted to marine outfall, and in case where the marine outfall provides a minimum initial dilution of 150 times at the point of discharge and a minimum dilution of 1500 times at a point 100 meters away from discharge point, then the existing norms shall apply as specified in the general discharge standards. Reuse/Recycling of treated effluent shall be encouraged and in case where part of the treated effluent is reused and recycled involving possibility of human contact, standards as specified above shall apply. Central Pollution Control Board/State Pollution Control Boards/Pollution Control Committees may issue more stringent norms taking account to local condition under section 5 of the Environmental (Protection) Act, 1986.

Fertilizer Industry Environmental Standards Gazette Notification G.S.R. 1607 (E) dated 29/12/2017:

The MoEF&CC has notified revised environmental standards for effluent and emissions for Fertilizer Industries vide No. G.S.R. 1607(E) dated 29/12/2017. Before finalization of the aforesaid notification the Ministry has taken detail consultation with lined Ministries /Departments i.e. Ministry of Chemicals and Fertilizers, Central Pollution Control Board (CPCB) and different stakeholders. The Ministry of Chemicals and Fertilizers has suggested more stringent norms in compared to proposed draft notification by MoEF&CC with respect to free ammonical nitrogen, Cyanide (CN), particulate matter and total Fluoride as Fluorine etc. In the notification effluent standards for Fertilizer Industry covers mainly for i) Straight Nitrogenous Fertilizer Plant/Ammonia (Urea Plant), Calcium Ammonium Nitrate and Ammonium Nitrate Fertilizers ii) Straight Phosphatic Fertilizer Plant. iii) Complex Fertilizer Plant and / or NP/NPK (N-Nitrogen, P-Phosphorus and K­Potassium and for Emission standards for i) Straight Nitrogenous Le a) Ammonia Plant-Reformer and b) Urea Plant – Prilling Tower ii) Ammonium Nitrate/ Calcium Ammonium Nitrate/NPK plant, iii) Phosphatic Fertilizer Plants i.e. Phosphoric Acid Plants/ Rock grinding and Acidulation SSP Plants and iv) Nitric Acid Plant.

The Standards in respect of 18 other categories of industries such as Man Made Fibre Industry; Pulp and Paper Industry; Paint Industry; Brick Kiln Industry; Automobile Service Station, Bus Depot and Workshop; Fermentation Industry; Coffee Processing Industry; Iron and Steel Industry; Tannery Industry; Diesel Locomotive, Airport Noise Standards, Emission Standards for Boilers using industries for SO2 and NOx, Emission Standards for Lime Kiln Industry, Glass Industry, Ceramic Industry, Foundry Industry, Reheating Furnace for SO2 and NOx and Standards for Kerosene are under

Recognition of Environmental Laboratories under Environment (Protection) Act, 1986: The successful implementation of environmental protection programmes essentially requires identifying and quantifying the pollution sources and pollutants, conducting baseline survey, laying down standards and build-up monitoring systems. Environmental laboratory requires to be provided with all necessary instruments and equipment’s and also expertise and capability of its staff for monitoring all parameters including water, air, noise, hazardous waste, soil, sludge etc. to meet these requirements. Under the provisions of Section 12 of Environment (Protection) Act, 1986 ,the Central Government recognizes Environmental Laboratories to carry out the functions entrusted to an environmental laboratory and under Section 13 of E (P) Act, 1986 the Central Government appoints Government Analyst(s) for carrying-out analysis of samples under E(P)Act, 1986 The Ministry has been recognizing of Environmental Laboratories and Government Analyst(s) under E (P) Act, 1986 with the aim of increasing facilities for analysis of environmental samples. The Guidelines for establishment and recognition of the laboratories have been revised and procedures streamlined in 2008 with emphasis on quality assurance and quality control. These revised guidelines are available on the website of the Ministry (www.moef.nic.in). In order to recognize the laboratory, laboratory submitting their application to the Ministry for consideration. These applications for recognition of laboratory are considered by an Expert Committee. Six (06) Private and One (01) Govt. Laboratories have been recognised and Nineteen (19) private laboratories have been recommended for recognition under E (P) Act, 1986 during the year.

Environmental Health:

Ministry has been implementing a programme on environmental health. An Apex Committee and Working Group have been re-constituted for screening /evaluation of project proposals on environmental health. Four (4) projects have been extended financial assistance to carry out studies of impact of pollution on human health.

Taj Protection Mission:

In pursuance of the Hon’ble Supreme Court’s Order, projects for environmental protection of World Heritage Site of Taj Mahal were initiated and funded by the Ministry. The Planning Commission approved Rs.600 crore on a 50:50 cost sharing basis with the State Government to implement various schemes in the Taj Trapezium Zone for environmental protection of the Taj Mahal. In the first phase during the IX Five Year Plan, 10 Projects were approved by the Government and implemented by the State Government of Uttar Pradesh.

  • At present, only a token of Rs. One Lakh is available under the scheme.
  • The U.P Govt. was requested to submit fresh proposals to seek provision of more funds during the XII FYP from the Planning Commission. However, till date no comprehensive proposal has been received from the Government of UP.
  • The TTZ Authority has been extended up to 30.12.2018 to monitor progress of the implementation of various schemes for protection of the Taj Mahal and programmes for protection and improvement of the environment in the TTZ area.

Central Pollution Control Board:

The Central Pollution Control Board (CPCB) performs functions as laid down under The Water (Prevention & Control of Pollution) Act, 1974, and The Air (Prevention and Control of Pollution) Act, 1981.

The Central Pollution Control Board has been playing a vital role in abatement and control of pollution in the country by generating environmental quality data, providing scientific information, formulating national policies and programmes, training and promoting awareness.

Name of the Scheme/Programme:

Coordinating activities of State Pollution Control Boards/Pollution Control Committees for prevention & control of pollution;

  • Development of industry specific national minimal effluent and emission standards and industry specific environmental guidelines and documents Implementation of CREP Compliance of Standards for major polluting industrial sectors
  • Action plans for improvement of environment in critically polluted areas/clusters and monitoring their implementation
  • Action plans for monitoring air quality in polluted cities .
  • National water quality monitoring and publishing annual water quality reports;
  • National ambient air quality monitoring and publishing annual air quality reports;
  • National Ambient Noise Monitoring and publishing annual noise monitoring report.
  • Carrying out and sponsoring research activities relevant to environmental protection;
  • Publishing material relevant to environment protection.

Central Pollution Control Board (CPCB) is focusing on strengthening of ambient air quality monitoring network for assessment of air quality at national, regional and local level. NAMP stations operated through State pollution control Boards needs further strengthening to monitor all notified parameters for ambient air, besides emphasis is being given for establishment of Continuous Ambient Air Quality Monitoring Stations (CAAQM) in all major cities.

The manual water quality monitoring network is being expanded further, realizing the need for establishment of a network of real time water quality monitoring stations on river Ganga to ensure that the water quality is maintained.

Efforts are being made for strengthening of the compliance mechanism, so that no untreated industrial effluent is discharged into the environment. Installation of online effluent and emission monitoring in polluting industry and data connectivity with SPCB/CPCB is a step towards self-monitoring and transparency.

Efforts are for improving the performance of existing sewage treatment plants (STPs) and adopting non-conventional technologies that are in synergy with the conventional methods for improving the water quality of river Ganga and its tributaries.

Initiatives are being taken for water conservation in Industries trough process modification and adoption of state of art technology. Zero liquid discharge concepts shall be applied wherever possible to conserve the water and protect the environment. Problem of Municipal Solid Waste and domestic sewage would be given utmost attention.

National Water Quality Monitoring Programme:

 Central Pollution Control Board (CPCB) in association with State Pollution Control Boards and Pollution Control Committees (SPCBs & PCCs) has established a water quality monitoring network. The network presently comprises of 3000 stations in 29 states and 6 union Territories. 2101 locations are monitored on monthly basis whereas 893 locations on half yearly basis and 6 locations on yearly basis. Time series data of water quality was analyzed periodically and

identified the issue of indiscriminate sewage discharge in 302 polluted stretches of rivers. Polluted river stretches throughout the country have been identified and concerned SPCBs have been requested for taking measures for restoration of water quality through identification of sources of pollution and interventions through treatment of municipal as well as industrial effluents.

Interstate River Boundary Monitoring: Water Quality Monitoring of Rivers at the Interstate Borders is carried out at 86 locations on 42 rivers on quarterly basis though few river locations are monitored once in a year. A detailed report on “Status of Water Quality of Rivers at Interstate Borders” already published underseries IRBM/01/2015 and also posted on website of CPCB.

Real Time Water Quality Monitoring System (RTWQMS) On River Ganga and Yamuna: 44 Real Time Water Quality Monitoring Stations (RTWQMS) have been established on river Ganga to assess the water quality.02 RTWQMS have been installed on river Yamuna viz.Wazirabad and Okhla in Delhi to assess water quality of river Yamuna.

CPCB’s Activities on Ganga Rejuvenation:

Activities executed under NGRBA Project are summarized as follows:

  • Compliance verification of Grossly Polluting Industries.
  • Performance evaluation of Sewage Treatment Plants.
  • Intensive water quality monitoring in polluted stretches
  • Periodic pollution assessment of major drains falling into River Ganga.
  • Groundwater monitoring in adjacent districts of River Ganga.
  • Installation of Real Time Water Quality Monitoring Stations (RTWQMS).

Development of Standards for Treated Effluent of Sewage Treatment Plants : Central Pollution Control Board (CPCB) carried out study on status of Municipal wastewater generation and treatment capacity in Metropolitan cities, Class I cities and Class II towns of India and published a document (CUPS/61/2005-06). CPCB reported during 2010-2011 that out of 38254 MLD of sewage generated by class I cities and class II towns, only 11787 MLD has been treated and thereby leaving huge gap between sewage generation and sewage treatment. CPCB, reassessed sewage generation and treatment capacity for Urban Population of India for the year 2015. The sewage generation estimated to be 62000 MLD approximately and sewage treatment capacity developed so far is only 23277 MLD from 816 STPs.

There are no specific standards for discharge of treated sewage into streams. So far, General Standards for Discharge of Environmental Pollutants into inland surface, public Sewers, land for irrigation, marine coastal areas under Schedule-VI of The Environment (Protection) Rules, 1986 have been used for design of STPs and assessment of performance of STPs. General Standards does not account for coliform standards.

State Pollution Control Boards and Pollution Control Committee are also using General Standards for Discharge of Environmental Pollutants for granting consent to Sewage Treatment Plants and there are no specific standards for effluent of sewage treatment plants.

Standards for effluent of Sewage Treatment Plants are framed with respect to physio­chemical and bacteriological parameters and notified vide Notification dated 13th October 2017,

National Ambient Air Quality Monitoring Programme:

CPCB is executing a nation-wide National Air Quality Monitoring Programme (NAMP). The ambient air quality monitoring network has 691 operating stations covering 303 cities/towns in 29 States and 6 Union Territories.

Growth of Continuous Ambient Air Quality Monitoring Station (CAAQMS) & Air Quality index :

CPCB, SPCBs and PCCs are monitoring ambient air quality of different cities and publish real-time data in public domain for taking corrective measures in time. Presently about 90 Continuous Ambient Air Quality Monitoring stations (CAAQMS) are operating in the country.In the beginning of the year, CPCB network had data connected from 58 stations in 35 cities spread over 13 States. National Air Quality Index, which combines the effect of all air quality parameters and generates a singl number has been developed by CPCB. The National AQl communicates air quality in terms of one number and one color for general public. Air Quality Index (AQl), inaugurated by the Hon’ble Prime Minister of India, is being continuously published on a web portal of CPCB, updated on hourly basis. The AQI software fetches the ambient air qualitv data from the CAAQM stadons and publishes the values of AQI for each parameter at each station without human interference. This application has become very popular and has created awareness in the field of environment. Media has also started reporting the air quality in the country on day-to-day basis, especially in Delhi city. At present, this network has been expanded to include 90 stations located in 53 cities of 16 States.

AQI Bulletin containing the data for each city is published every day at 4:00 pm for further easy understanding of the citizens. The entire process of generating AQI values, publishing every hour, preparation of bulletin and uploading it on CPCB website are automated.

Mobile APP ‘SAMEER’ for AQI display and Public Complaints:

An APP ‘SAMEER’ is developed and available for Android and iOS devices, to display of AQI at city and station level, AQI Bulletin. A Public Forum is available at the APP, which helps the public in submitting suggestions or complaints related to air pollution issues along with photos in support of complaint. It also facilitates public to lodge their complaints regarding Air Pollution which automatically collects the locations and forward it to the respective agency foR redressal.

Air Quality Monitoring Network in Delhi and NCR:

Delhi is currently having 10 manual monitoring stations and 38 CAAQMS (6 CPCB, 8 IMD and 24 DPCC). The existing monitoring network in other states under NCR has 30 monitoring stations. There are 21 manual stations (2 Haryana, 10 Uttar Pradesh and 9 Rajasthan) and 9 CAAQM (4 Haryana, 3 Uttar Pradesh and 2 Rajasthan).

In the monitoring network expansion plan in NCR submitted to the Hon’ble Supreme Court 21 more CAAQM stations was proposed; whereas 28 (22 in Haryana and 6 in Uttar Pradesh) more manual monitoring stations are to be added soon. On completion of proposed network on ambient air quality monitoring in the region, a total of 117 monitoring stations in Delhi – NCR would be in place: 68 CAAQMS for online line real time data disseminations and 49 manual stations for trend analyses (total 117).

Special air quality monitoring during Deepawali 2016 and 2107:

With a view to study the impact of Deepawali festival CPCB conducted monitoring at selected location. Fireworks always add particulates and other criteria pollutants like SO2 and NO2 to air. As the ingredients in firecrackers have different elements and metals, these are instantaneously added to ambient air in the form of particulate (particularly in PM2.5) during Deepawali festivals. CPCB has performed detail analysis of metals elements in PM2.5.

With the reduction of fire cracking activities due to Hon’ble Court’s direction for banning on sale, this year Deepawali was marked with less dust pollution. PM2.5 was reduced by 39% compared to 2016 Deepawali day. The reduction of PM2.5 was related to less fire cracking activities is further justified as the major signature elements were also found considerably reduced this year. Sulphur got reduced by 20%, Potassium by 30%, Ca, Cu, Zn, Sb by about 35-40%, Fe and Ba by 50% and Al andC12by10%.

Pollution in Delhi during 2016 and 2017: It was observed in Delhi, the transitional phase towards winter is always critical due to lower mixing height, higher humidity on dry season, fall of ambient air temperature coupled with lower temperature difference between maximum and minimum, calm to low wind speed etc. The continuity of episode days in 2017 was more or less same as compared to 2016, however the meteorological conditions were much more critical in 2017, as compared to 2016. This maybe seen the following table:

Comparison of Air Pollution Episode days in Delhi

National Ambient Noise Monitoring Network:

CPCB in association with State Pollution Control Boards has laid down National Ambient Noise Monitoring Network covering 07 metropolitan cities i.e. in Mumbai, Delhi, Kolkata, Chennai, Bangalore, Lucknow and Hyderabad and installed 70 Noise Monitoring System (10 stations in each city).

Real-Time Emission & Effluent Monitoring Systems:

With the advancements made in technology of pollution monitoring, automation in instrumentation/ equipment, CPCB has planned to bring data in CPCB server through online measurements from industrial units for emissions and effluents discharged into the environment. This activity is started with highly polluting industries in 17 Categories of Industries and Grossly Polluting Industries located on the banks of River Ganga.

Presently 2266 industries under 17 Categories of industries and 744 GPI Industries have installed Emission Monitoring Systems and Effluent Monitoring Systems and data is being transmitted continuously to CPCB and various SPCBs. The online data is being scrutinized and alerts are generated for respective industrial Unit Heads, officials looking after the specific sector/ category of industries at CPCB and SPCBs/PCCs. These alerts act as useful and timely information to act immediately to stop the identified pollution source within shortest possible time.

E-Track for Industries:

India E-Track Industries is an online portal and MIS System for GPI and 17 categories industries. In this portal there is provision to enter GPI and 17 categories industries data/information in numbers. There is also provision for update compliance and connectivity status of GPI and 17 categories Industries through MS Excel file.

Progress/Achievements of Various Activities:

Assessment of Pollution:

  • Operation and maintenance of 691 manual Ambient Air Quality Monitoring Stations (AAQMS) covering 303 cities/towns in 29 States and 6 Union Territories.
  • CPCB has developed a network of real time data from CAAQM stations being operated by CPCB, SPCBs and PCCs. This data is provided to all stake holders and being published in public domain for taking corrective measures in time. In the beginning of the year 2015, CPCB network has data connected from 27 stations in 10 cities spread in 06 states, has been expanded to total 40 stations located in 22 cities of 11 states.
  • Operation of 3000 Water Quality Monitoring Stations (WQMS) at various aquatic resources. Time series data of water quality was analysed and identified the issue of sewage disposal in 302 river polluted stretches.
  • 70 National Ambient Noise Monitoring Network (NANMN) stations have been installed spreading over 10 cities and data is being disseminated.

Industrial Pollution Control:

Development of Environmental Standards: The Ministry of Environment, Forest & Climate Change (MoEF&CC) formulates and notifies standards for emission for discharge of environmental pollutants viz. Air pollutants, water pollutants and noise limits, from industries, operations or processes with an aim to protect and improve the quality of the environment and abate environmental pollution. The standards are framed in consultation with all concerned stakeholders for the benefit of environment. The process is based on the best practices and techno ­economic viability. The notification of standards also involves formulation of load based standards i.e., emission/discharge limits of pollutants per unit of product obtained/ processes performed to encourage resource utilization efficiency and conservation aspects.

MoEF&CC has notified regulation on Lead Contents in House hold & Decorative Paints. The limit for lead has been fixed 90 ppm. As per Rule 7 of this notification, CPCB has developed the compliance and testing procedure in association with Central Power Research Institute (CPRI) and placed at CPCB website.

Image of NEAS

Copyright © 2021 - All Rights Reserved - Official Website of Ministry of Environment, Forest and Climate Change, Government of India Note: Content on this website is published and managed by Ministry of Environment, Forest and Climate Change For any query regarding this website, Please contact the Web Information Manager: Mr.Neelesh Kumar Sah, Designation:Joint Secretary, Email ID:sahnk[at]cag[dot]gov[dot]in, Contact no:+91-011-20819220.

Image of W3-css

Visitor : 2833068

Modified: 07-11-2023

Book cover

Introduction to Development Engineering pp 161–182 Cite as

Monitoring Industrial Pollution in India

  • Anant Sudarshan 7  
  • Open Access
  • First Online: 09 September 2022

8669 Accesses

1 Citations

Many developing countries are attempting to prevent a rapid deterioration of air quality while still encouraging economic growth. In settings where state capacity is severely limited, enhancing the effectiveness of regulators is critical to success. Previous work has documented how Indian environmental regulators are constrained by having poor information on the pollution emitted by manufacturing plants, due to high monitoring costs, corruption, or staff constraints. This case study discusses a pilot project in the Indian state of Gujarat, designed to evaluate the benefits of Continuous Emissions Monitoring Systems (CEMS) – technology used to remotely monitor pollution emitted by industrial plants in real time. We show how the institutional context in which CEMS was deployed, which included an inflexible legal and regulatory framework and collusion between industry and labs to falsify data, cannot be divorced from an assessment of the performance of the technology solution. The eventual benefits of CEMS in the status quo regulatory framework proved limited. Nevertheless, the technology also provided an opportunity to change the rules of the game, allowing Gujarat to experiment with India’s first emissions trading scheme.

  • Industrial air pollution
  • Particulate matter
  • Continuous emission monitoring systems
  • Emissions trading scheme

Download chapter PDF

1 The Development Challenge

As India has developed, so has her demand for energy. This demand has largely been met with abundant, inexpensive, and highly polluting fossil fuels. These choices have had fundamental environmental consequences, imposing costs that significantly threaten the country’s economic prospects. Perhaps the most significant of these is the dangerously rapid deterioration in the quality of India’s air, with satellite data suggesting an increase of over 70% in the concentration of particulate matter 2.5 (PM 2.5) between 1998 and 2016.

Air pollution now poses one of the most severe public health challenges to the country (Balakrishnan et al., 2019 ). Study after study has pointed to the general health risks of air pollution, in terms of life expectancy for example, and additional new research suggests that the effects of poor air quality may even extend to reduced crop yields, lower labor productivity, and decreased cognitive skills (Chang et al., 2019 ; Bharadwaj et al., 2017 ; Burney & Ramanathan, 2014 ). In other words, the apparent trade-off between environmental protection and economic growth is something of a Hobson’s choice—the Indian growth story cannot continue without cleaning up the air.

Notwithstanding the poor quality of its environment, India has a fairly strong and wide-ranging set of environmental laws. Nevertheless, these have not been sufficient to effectively control industrial air pollution. Empirical evidence from the highly industrialized Indian states of Maharashtra and Gujarat highlight the degree to which factories have been found to be violating pollution norms. In the cities of Surat and Ahmedabad in Gujarat, for instance, Duflo et al. ( 2013 ) collected data from hundreds of industrial plants and found that about 35% were polluting above the legally-prescribed limits. In Maharashtra, Greenstone et al. ( 2018 ) digitized over 13,200 regulatory pollution tests, spanning the period from September 2012 to February 2018, and found over half to reveal exceedances in the regulatory limits.

One reason why Indian manufacturing emissions pose a particularly thorny challenge to understaffed environmental regulators is that it is difficult to monitor a large number of highly polluting but relatively small factories. Footnote 1 In the state of Maharashtra, for instance, data collected between 2017 and 2020 showed that even when considering only the largest plants, the frequency of pollution testing remained well below once per year, on average.

The development challenge therefore has two parts. First, how can we reduce industrial air pollution in an expansive and rapidly developing country like India? And second, how can we improve the quality of air pollution data available to regulators, and can these types of improvements help reduce air pollution?

One promising solution to this challenge is the use of Continuous Emissions Monitoring Systems (CEMS). These instruments are installed in the smokestacks of factories where they continuously measure the concentration (or mass) of the air pollution that is being emitted. The data collected are then transmitted in real time to a remotely located computer server, vastly improving the quality of information available to the environmental regulators.

This case study discusses a project that aimed to evaluate the impacts of CEMS in a large-scale randomized control trial (RCT) conducted in partnership with the Gujarat Pollution Control Board (GPCB), the environmental regulator in the state of Gujarat in India. The goal of the project was to see whether the “big data” generated through CEMS could spur improved regulatory actions, thus lowering air pollution. While CEMS is not a new technology per se, its deployment in the Indian setting was novel. As we learned from our experience, technology acts through the interaction of human beings and hardware. As such, its effectiveness cannot be divorced from the incentives and capabilities of the people using it. Consequently, a central theme running through this chapter is the importance of evaluating a technology within the specific institutional and economic context in which it will be used.

The remainder of this chapter is structured as follows. In Sect. 2 , we provide background information on the setting for the project, including how it was conceptualized and implemented, as well as findings from related research. In Sect. 3 , we describe the CEMS technology in more detail and how the pilot implementation was designed to enable rigorous measurement of its benefits. In Sect. 4 , we provide some novel results on the impacts of CEMS on industrial air pollution in India. In addition, we discuss what it means for a technology intervention to “work” or to be “successful” in a developing country context. Section 5 concludes.

There is no single cause for the problem of excessive industrial air pollution in India. As is mentioned above, the performance of India’s existing regulatory framework has been far from perfect. Low institutional capacity and expertise, high transaction costs in taking legal action, corruption, high compliance costs for industry, and poor data have all contributed to this problem in varying degrees.

The starting point for this project is the ground-breaking RCT by Duflo et al. ( 2013 , 2018 ), which studied the role of information and monitoring in regulating industrial air pollution in Gujarat. When Duflo et al. began this work in 2010, air pollution levels were rising across industrial cities in Gujarat. When third-party laboratories were used to audit the emissions of these factories, however, there appeared to be widespread compliance with environmental standards. This was a puzzling result. How could these test results be reconciled with the common sight of black air rising out of hundreds of small chimneys in and around Gujarati cities?

To answer this question, Duflo et al. ran a 2-year RCT in partnership with the Gujarat Pollution Control Board, the state environmental regulator. First, the study shed light on how the regulator was crippled by a persistent culture of data falsification. It documented collusion between the industrial plants and the auditors that were supposed to report on their performance. Next, the study evaluated an intervention designed to resolve this problem of collusion by severing the conflicts of interest, namely, by shifting the auditor hiring and payment decisions away from the individual plants and towards the government regulator.

Although the researchers were able to provide a partial solution to the monitoring problem (by making the audit reports more accurate), manual testing remained an infrequent and expensive method of gathering data. The regulators would still be unaware of how much the industries were emitting on a regular basis. In addition, the status quo emissions audits could not say anything about what the factories were doing when they were not being actively tested. In consequence, they were perhaps best viewed as simply an assessment of how industrial plants performed when on their best behavior. Overall, Duflo et al. ( 2013 , 2018 ) suggest that high-quality, continuous information might be necessary to know which plants were polluting the most and that these monitoring improvements could potentially reduce emissions.

2.1 How Was the Project Location Chosen?

Large field experiments involving partnerships between researchers and the government are often opportunistic, and a function of the initial interests of individual politicians and bureaucrats. In this case, India’s Minister of Environment, Forest, and Climate Change felt that CEMS might not only solve the information problem but could also be a first step towards implementing market-based methods of environmental regulation. The project described here was thus the first attempt in India to carry out a systematic field evaluation of the effectiveness of CEMS. It also became the precursor to India’s first cap-and-trade market for industrial particulate pollution, which was launched in Surat in 2019.

The location of the CEMS pilot itself shines some light on the myriad forces that determine how and where a policy innovation is first implemented. The project initially involved the state governments of Gujarat, Maharashtra, and Tamil Nadu, three of the largest industrial states in India. However, requiring industrial plants to install CEMS proved to be politically and administratively difficult for a number of reasons. For example, the technology was untested, and several implementation challenges could be foreseen. For risk-averse regulators, there was a potential downside to being the “first mover” on a project that might not end well. In addition, the installation of CEMS devices would impose a significant cost on polluters—in the order of several thousand dollars—and this would surely result in pushback and reluctance on the part of the industrial factories.

Furthermore, up to that point, there was no precedent to using CEMS data as the basis for prosecuting industrial polluters. Indian environmental law is built upon criminal penalties. The basis upon which plants can be found to be in violation of pollution limits is enshrined in the laws. New methods of monitoring cannot be easily used as the legal basis for regulatory actions without evidence of their reliability and accuracy. In sum, requiring plants to install equipment that had not been granted legal status posed challenges that would understandably create industry resistance.

Eventually, the government of Gujarat was able to prevail upon the industry association of the city of Surat to support (or at least not oppose) the use of CEMS devices. Surat is a hub for the textile industry and is the site of a dense manufacturing cluster of hundreds of relatively small-scale plants that typically burn solid fuels.

One potential reason for their agreement is that the government informed them that CEMS would later be used to introduce emissions markets. This acted as a powerful incentive for the manufacturing plants. It changed the impression surrounding the equipment installation mandate from a pure negative (e.g., regulation would not change, while monitoring would increase and expensive equipment would have to be purchased) to a potential opportunity. Market-based regulation had been repeatedly recommended in India as a potential solution to the inflexibility, uncertainty, and the high costs associated with status-quo regulation.

2.2 Identifying the Experimental Sample

Having identified Surat as a project site, a decision needed to be made on how to select the initial sample of plants that would be transitioned to the new technology. This filtering process was driven by the potential of a plant to harm people through air pollution, as well as by the limitations of the technology itself. As a starting point, the sample of eligible plants was first restricted to factories located within 30 km of the city center, in order to focus attention to the plants that were located in the most densely populated areas. For a sense of scale, the metropolitan population of Surat was about 6.5 million, with a density of over 4000 people per square kilometer.

Conditional on being located within this radius, a factory needed to be burning solid or liquid fossil fuels and needed to have a stack (i.e., chimney) large enough to accommodate a CEMS device. This led to a sample of 373 plants. Most of these plants were operating in the textile sector (over 94%) and were burning coal (37%) or lignite (27%). Over the course of the multiyear project, 42 plants closed down due to an economic downturn in Gujarat, which correspondingly reduced the pilot sample size.

In the case of the other two states, Maharashtra and Tamil Nadu, there was no suitable set of plants that could be transitioned to CEMS, despite multiple years of efforts on our part. Thus, the geographic scope of our overall CEMS research effort shrank from three states to one.

The point of recounting this history is to underscore that the location of technology pilots, as well as the population involved, emerge through multiple layers of deliberate or accidental selection. This means that it is worth paying attention to the external validity concerns for any given technology deployment. In this case, the impacts of rolling out CEMS in Gujarat may not apply in Maharashtra or Tamil Nadu. For instance, if the plants that are successfully able to resist CEMS also happen to be politically well-connected and more difficult to regulate, then what we learn about the impacts of CEMS on our pilot population would not apply to other populations.

2.3 How Does CEMS Work?

Continuous Emissions Monitoring Systems (CEMS) for particulate emissions consist of a network of hardware devices and software programs that link monitored industrial plants to the environmental regulator in a manner that allows emissions data to be securely transmitted at regular intervals. The CEMS hardware components required at each industry site consist of the following:

Particulate matter (PM) CEMS analyzer and flow meters to measure the mass of pollutants emitted.

Data logger unit for saving records on-site, in case of Internet failure.

Data acquisition system (DAS), normally consisting of an on-site computer and a server at the site of the regulator.

Software to visualize and analyze CEMS emissions data.

The CEMS analyzer for particulates is a device that relates the physical properties of emissions from a factory chimney to the concentration (or mass flowrate) of suspended particles in the air. For example, optical devices measure the attenuation in the intensity of a laser beam sent through smoke. An alternative technology exploits the so called “triboelectric” effect and relies on measuring the electric charge induced by the movement of particles near a probe.

These approaches to measurement are “indirect” since they measure a property of the gas that is used as a proxy for the presence of solid particles. As a consequence, these devices must first be calibrated against manual readings that directly measure the weight of particles in a specified volume of exhaust. In other words, an electrical signal generated by the analyzer must be mapped to a value of particulate emissions. Typically, this mapping is obtained by fitting a linear model relating a set of manual measurements taken at different levels of boiler loads (typically nine readings) to the corresponding measures of current produced by the analyzer. This delivers an equation y  =  a  +  bx where x is a current reading in amperes, y is an estimate of the concentration of pollutants, and a and b are parameters estimated during an initial calibration process.

3 Innovate, Evaluate, Adapt

There is a well-known management cliche that, “if you can’t measure it, you can’t manage it.” There is some truth to this when it comes to regulating industrial emissions. When the government is unaware of how plants are behaving most of the time, they have little ability to target regulatory actions, and may have no legal basis to penalize otherwise polluting factories who manage to pass the occasionally scheduled tests. Furthermore, from the point of view of human health—the ultimate motivation for environmental regulation in the first place—what matters is not how much pollution a plant emits at any given point in time but the cumulative mass released into the ambient air over a period of time. A highly polluting plant operating for only a few days in the year might fare the worst on a one-off manual test. But it may in fact be a lot less harmful than a “cleaner” plant that is operating 24 h a day, 365 days a year.

All of this suggests that if regulators are to do their job properly, they need information that identifies which plants are polluting. Unfortunately, the standard method for measuring factory air pollution, which is commonplace around the world including in India, involves a painstakingly manual process. To measure the concentration of suspended particulate emissions, a team of engineers must extract a sample of air from the smokestack of a factory while carefully following a prescribed protocol. This air sample must then be transferred to a laboratory where solid particles are dried to remove moisture and then weighed. The mass of these collected particles is then converted into a concentration measure after “normalizing” the volume of air extracted by standardizing it to a particular temperature and pressure. The whole procedure is labor- and time-intensive. The CEMS technology improves matters by transmitting emissions data in real time, directly from the chimney of a plant to the regulator. In comparison to the manual, point-in-time, method of measurement, the CEMS technology offers a potentially dramatic improvement.

There are a number of possible benefits from CEMS. Improving the information available to environmental regulators could allow existing rules to work better, for example, by allowing the government to target inspections and use calibrated penalties on the worst offending plants. CEMS could also open up the possibility of new forms of regulation that are made possible by higher quality and higher frequency data. For instance, emissions trading schemes or pollution taxes can be introduced only when there is an accurate count of the quantity being traded or taxed. These policy tools may have important advantages over existing “command and control” approaches to regulation but are feasible only if there is high-quality of data. Thus, on the surface at least, the CEMS technology would seem to provide everything needed to resolve the challenge of monitoring plants and, in so doing, to reduce the pollutants they release.

3.1 Setting Up an Evaluation

The goal of our pilot was to understand the impact of mandating CEMS on plant and regulator behavior. On the side of the plants, the primary outcomes of interest were pollution and reporting quality. On the side of the regulator, we were interested in whether the government interacted differently with these plants, enforcing more penalties, for instance. We were able to do this by integrating an RCT into the implementation of the pilot project.

The evaluation initially began with a small group of installations serving as a technical dry run. The purpose was to field-test the CEMS installation and calibration protocol stipulated in the Government of India’s Central Pollution Control Board (CPCB) specifications. We selected 11 industrial plants (which we refer to collectively as “Phase I”) from a cluster of factories in Surat. Next, four different vendors supplied these eleven plants with CEMS devices, consisting of nine DC triboelectric-based devices (measuring particulate matter (PM) mass flow), and two electrodynamic-based devices (measuring PM mass concentration). The 11 industries were chosen to represent the diversity of the full sample and to cover a range of boiler sizes and types of installed air pollution control devices (e.g., numbers of cyclons, bag filters, etc.).

The field tests were important because they showed that the error associated with a CEMS measurement taken at any point in time disappears as multiple observations are added together. These sorts of tests provided confirmation that even if a device noisily measured instantaneous levels of emissions, it could still provide valuable information to determine long-run aggregate or average levels of emissions.

For the remainder of the sample, we used an RCT design to rigorously quantify the impact of CEMS. The simplest possible design would have involved dividing plants into a treatment group and a control group and then mandating that factories in the former group install CEMS, while factories in the latter group are regulated and monitored as usual. But this design would have required an enforced pause in the rollout of CEMS, after the treatment group plants had installed their devices and begun sending the regulator real-time data.

In our context, an enforced pause was not feasible for several reasons. First, installing CEMS devices was a time-consuming affair because the devices were not readily available in a still nascent market. Saturating the treatment group of factories alone took several months due to delays in purchase orders being fulfilled, delays in calibrating devices, and data connectivity problems that appeared at scale (and were not encountered during implementation for the limited number of Phase I industries.) Losing many more months of time in order to enforce a pause was therefore unacceptable to the government. Also, it would have probably resulted in the small number of CEMS vendors operating in the market to exit due to lack of business, or declining to deploy the necessary staff to the field for required after-sales services.

Second, as we have mentioned, it was not easy to ensure that the industries in Surat would comply with the new requirements to install hardware. It was believed that the likelihood of smoothly saturating the entire population of industrial plants would be significantly reduced if there was a long period of time during which half of the plants in the sample were held off from placing new equipment orders.

Finally, as our work progressed, it became increasingly likely that the GPCB would want to launch an emissions trading scheme in Surat. Thus, the effort to set up these devices had the dual goals of understanding the impact of improved data on environmental outcomes, and eventually measuring the impact of a market-based approach, like cap-and-trade, to reducing industrial air pollution. All of these considerations led both the government and the research team to want to avoid long delays in implementation.

To meet these constraints and allow for a rigorous evaluation design, the remaining plants were randomly assigned into three groups (Phases II, III, and IV). Each group was mandated to install a CEMS and begin sending data to the regulator in a sequential manner. In this type of staggered, phased-in research design, the plants that were mandated to install CEMS in the later phases could serve as a comparison group for the plants that had installed CEMS earlier. For example, once Phase II installations had begun sending data, and while Phase III plants were installing and calibrating their devices, the Phase IV plants would still be continuing business as usual. Thus, the outcomes for Phase II and Phase IV plants could be compared against one another in order to estimate the causal effects of CEMS. This was possible due to random assignment to each group.

3.2 Implementation Challenges in the Field

On paper, the experimental design was fairly straightforward. In practice, implementation was anything but simple. Figure 7.1 provides a timeline for the pilot as a whole. The pilot began in early 2014 and ran until late 2018. From an experimental research standpoint, the period between the second baseline survey (or midline survey, carried out in 2016) and the endline survey (carried out in 2018) constitutes the effective duration of the treatment. This is just a fraction of the overall duration of the project. The 2-year period between the first baseline survey and the second baseline survey represents a period of time in which a mature technology—that had been readily deployed in large plants in developed countries—was effectively unusable in Surat.

figure 1

Timeline of CEMS rollout and data collection

Notes : Each survey was 3 months long

Why was this the case? The primary challenge was the interaction between the technology and the institutions and systems around it. The most notable unexpected challenges appeared in the process of installation and calibration of the CEMS devices, which I now detail below.

3.2.1 Problems Encountered While Installing CEMS

The first step in setting up a functioning CEMS network is for plants to equip themselves with the necessary hardware. This proved to be a rocky experience, in part because no well-functioning market in CEMS devices was in place when the pilot started. This was not surprising, given that there was no demand for CEMS before the equipment mandate. This posed several challenges to pilot implementation.

The most important of these was the near complete absence of after-sales service and trained personnel. Initially, the goal of the equipment vendors was to make a one-time sale, under the assumption that the pilot was likely to be an isolated, one-off experiment. Investing in trained staff to be based in Surat likely made little business sense for many of these companies. High-quality service contracts were likewise unavailable. The consequence was a host of equipment problems that could not be properly addressed and industry dissatisfaction with the technology, creating a fragile ecosystem prone to breakdowns. Only after several months of persuasion and pressure by the government on the manufacturers and vendors of CEMS devices did the situation improve.

A second area which proved to have several teething problems was the implementation of a robust data acquisition system to transfer emissions information from industrial sites to the state regulator. Two aspects of the prevailing ecosystem made this hard. First, different CEMS vendors used varying, proprietary data storage formats, making it difficult to create a common platform that might be able to read and transfer data. Second, industries in Surat frequently had poor Internet connections, old computers, no dedicated IT staff, and faced occasional power outages that would force a complete reset of the system. These issues are commonplace in developing countries but are not necessarily the focus of manufacturers of CEMS technology. Creating IT and data storage systems that were robust to these weaknesses proved to be challenging and time-consuming.

Finally, since the equipment mandate merely required industrial polluters to install a CEMS device, many industries chose the lowest price and lowest quality option. This type of equipment choice enabled plants to initially meet the regulatory requirement without incurring a large upfront payment but heightened the costs associated with the lack of after-sales service. Thus, several months after initial installation, some of these plants found their devices to be dysfunctional and new purchases became necessary, which set back the overall timeline for the experiment.

3.2.2 Problems Encountered While Calibrating CEMS

CEMS devices also need to be calibrated before being used. If the initial calibration parameters are misreported, the subsequent measurement of emissions will be biased. The calibration step requires plants to work with equipment vendors and environmental auditors to make manual measurements and then correlate these with the electrical output from the CEMS analyzers. The calibration requirement is crucial because it represents an important mechanism through which data can be falsified, in a similar way to the corruption noted in Duflo et al. ( 2013 ).

Calibration will not cause problems in a laboratory environment. However, it is exactly the sort of weakness that can render a technology unreliable in the field. Importantly, the calibration process requires information from outside the CEMS itself, since CEMS must be used in conjunction with data from a different method of measurement. This means that the issues that can affect the manual data collection process can also affect CEMS. In sum, we cannot evaluate CEMS without considering the underlying economic incentives of the calibration agents and the institutions that influence them.

Early on in the project, there were several instances of CEMS transmitting unreliable data, whether deliberately falsified or not. Figure 7.2 provides examples of data that can be identified as unreliable through visual inspection or a series of automated data checks. Mitigating these problems in the field was not easy and required a combination of measures including: regular data validation; statistical tests on the emissions time series; double-blind calibration, where the lab involved in testing manual samples is separated from those collecting samples; encryption of transmitted data; and regular spot-checks following calibration. All of these measures needed to be developed from the ground up, significantly reducing the degree to which these devices could be treated as a “plug-and-play” solution to the information problem.

figure 2

Examples of potentially invalid CEMS data

Notes : Suspicious data transmitted by four CEMS devices during the month of December 2019. Clockwise from top left. (1) Zero values indicate device disconnection in a Type-1 device; (2) flatline values can result either from voltage top-censoring or a probe which has not been cleaned; (3) abnormally low variance suggests wrongly calibrated device or voltage amplification settings; (4) sudden decreases in overall emission levels are suspicious, and sudden changes in variance suggest tampering of voltage amplification settings

3.2.3 Linking Data to Regulatory Practice

The installation of a CEMS analyzer in a factory chimney is unlikely to be useful in isolation. It is not uncommon for governments to accumulate large quantities of data without using them to guide policy. In order for the CEMS technology to achieve its potential, the environmental regulator needs to determine how the resulting data will be analyzed and used. Defining data use protocols in advance would seem to be an essential factor contributing to the success of this technology.

The GPCB chose to institute—but did not perfectly implement—a detailed Action Matrix linking the data they received to regulatory action. Plants were penalized if they did not transmit data regularly and if their pollution levels exceeded the specified thresholds. Table 7.1 summarizes the key features of this protocol.

4 Did CEMS Work?

In a narrow sense, we might regard a technology as “working” if it delivers in the field a performance that is very close to the design specifications determined in a lab or via controlled engineering trials. This definition is not very useful from the perspective of a policymaker. Thus, a slightly broader definition is to define CEMS as “working” if it delivers useful data. The extent to which CEMS data is useful depends on both its information content and its potential value to regulators.

Yet even this broader definition may also be inadequate. Even if CEMS delivered valuable information, it might not result in lower levels of pollution. Changes in the behavior of industrial plants are likely to occur only if the regulator uses the new information to change the incentives for plants to pollute. Institutional weaknesses, rent-seeking, political patronage, and other implementation failures could lead to regulators ignoring to act upon these improved sources of information. In this section, I report on some of the descriptive and experimental results from our deployment, in order to assess the technical performance, usefulness, and impact of CEMS in Gujarat.

4.1 Assessing the Technical Performance of CEMS

Suppose we define a working CEMS device as an instrument that reliably delivers a signal that accurately represents plant emissions. An important insight from our experiment was that the CEMS devices did not automatically work until they were coupled with regulatory measures that appropriately aligned the incentives of plants and vendors with those of the regulator. As discussed, the mandate to install CEMS devices was followed by a host of implementation challenges. Some of these reflected difficulties that are widespread in developing countries, including poor infrastructure or the absence of trained staff in factories. Others, such as the paucity of after-sales service, arose out of market weaknesses that were a consequence of mandating purchases in a very nascent market. Still others arose from the opportunity for data falsification created by the calibration requirement. Although improper calibration does not reflect an engineering failure, the ease with which regulatory devices can be gamed is quite fundamental to an assessment of the technology’s effectiveness.

However, although the new monitoring system was not technically perfect at the outset, it improved steadily and significantly until it reached a point where a meaningful experimental evaluation could be conducted. Some of this improvement occurred after the environmental regulator introduced the Action Matrix , shown in Table 7.1 . This is because this protocol created the first real set of consequences for plants that did not maintain their devices, failed calibration audits, or did not transmit data regularly. Statistical checks were later developed to automatically identify plants whose data suggested the possibility of miscalibration or tampering, further improving matters. Figure 7.3 shows that after the final calibration, a strong relationship existed between CEMS readings and concurrently measured manual measurements.

figure 3

Accuracy of CEMS data

Notes : Correlation between CEMS reading and PM sample. The figure shows data the data from calibration of CEMS devices. The figure shows the normalized values of the PM samples and CEMS readings per industry, and the red line shows the fitted values

Figure 7.4 shows the history of CEMS uptime rates for 66 industrial plants, with the regulatory measures taken by the Gujarat Pollution Control Board marked with vertical lines. The performance of CEMS appears to improve with the introduction of each measure. Eventually, a high level of data availability is achieved. This evidence has important implications for how a new technology is introduced and used. It seems intuitive that new regulation should be introduced only after the underlying technology is working very well. However, our experience suggests that the presence or absence of regulation cannot be disconnected from the performance of the enabling technology. In other words, in order to achieve an equilibrium involving a well-functioning CEMS network, it may be necessary to introduce regulation that commits to using the data generated by CEMS, even before all of the technical problems are resolved.

figure 4

Average CEMS data availability at 66 industrial plants

Notes : Average percentage data availability from CEMS devices over time where periods of no transmission from a plant are set as zero. The introduction of the action matrix in February 2018 was followed by a significant improvement in performance. The introduction of an emissions trading scheme in July 2019 produced similar results

4.2 Assessing the Usefulness of CEMS

CEMS are intended to augment an existing testing protocol in which the main problem is that information is only collected at a single point in time. The case for CEMS is based in part on the notion that continuous data is more useful as a measure of environmental performance than a single snapshot.

To investigate this, we examined periods of time in which we could compare readings from CEMS devices to manual samples collected by the GPCB. Recall that these manual samples formed the basis of regulation in the status quo. There are two potential sources of bias. First, there is the falsification of data identified in Duflo et al. ( 2013 ). Second, there is possible bias stemming from the fact that plants may behave differently when they are being tested to when they are operating normally.

Figure 7.5 shows a comparison between manual and continuous data for a plant in Surat. For this plant, it is striking how the period of time in which the manual sampling takes place is entirely unrepresentative of pollution at all other times. This suggests that relying on manual tests alone could result in significantly underestimating how much a plant is contributing to air pollution. Furthermore, the variation in emissions levels over time suggests that even if there were no systematic bias associated with pollution performance during testing periods, it is unlikely that extrapolations based on a single observation could reasonably approximate the true levels of pollution released by the plant.

figure 5

CEMS data transmitted before, during, and after an in-person emission inspection

Notes : This plant appears to be strategically reducing emissions while being tested

4.3 Estimating the Impact of CEMS on Pollution Emissions

The third measure of success relates to whether or not CEMS reduced pollution emissions, which is the ultimate goal of the policy intervention. According to this measure, we found limited evidence that the technology worked.

The direct effect of requiring plants to install CEMS on subsequent pollution levels can be estimated due to the RCT design we implemented. To rigorously quantify the effects of CEMS, we need to not only measure what happened to factories after installing these devices but also identify a counterfactual describing what outcomes would have occurred absent CEMS. The phased rollout of the pilot, combined with random assignment to different groups, meant that at the start of the experiment, the plants that were asked to install CEMS early on (Phase 2) were on average identical to those that were asked to install CEMS later on (Phase 4). For this reason, any differences in pollution emissions that are observed between the two groups after CEMS data is sent to the regulator can be attributed to the installation of CEMS. All other factors unrelated to the technology mandate would affect both groups equally, and thus would not bias any comparisons between the two groups.

Table 7.2 presents a comparison of baseline characteristics of treatment and control plants in the study sample. The randomization check shows that plant characteristics are balanced across treatment groups. Out of all of the baseline measures reported, there are just two variables that show a statistically significant difference between treatment groups, at the 10% level (these are the cost of recent modifications and the number of inspections performed by the GPCB in 2014). In Panel A we examine various costs associated with purchasing or using air pollution control equipment. The variable costs from operations and maintenance associated with using this equipment are significant, roughly 40% of capital costs on average. This becomes important because the presence of equipment is easily observed and enforceable when granting permissions for plants to begin manufacturing. Accordingly, all these factories in our sample do possess pollution control devices (Panel B), with cyclones being the most common. The high levels of pollution that we observe are nevertheless influenced by the costs of operating equipment. Panel C reports statistics on inputs and revenue from these plants and boiler and thermopack capacities (which influence fuel use and pollution). Panel D shows that pollution levels at baseline are remarkably high with mean levels of pollution concentration above 300 mg/Nm 3 . We also measure levels of regulatory engagement in terms of the number of inspections conducted and legal actions required in the year of the survey.

Table 7.3 reports the treatment effects of CEMS at endline, based on estimating the following regression equation:

Here y i 1 is the outcome variable for plant i measured during the endline survey, y i 0 is the baseline value, and T is a dummy indicating membership of the treatment group. We use this model to examine changes in three outcomes: PM readings from a manual sample taken at the plant; the logged value of the plant test result; and a binary measure of compliance, indicating whether the test resulted in a PM concentration reading below 150 mg/Nm 3 . This value is the fixed regulatory limit for the concentration of particulate emissions from an industry smokestack. Note that since the treatment itself was the installation of CEMS, there was no continuous data available for control plants.

In Column 4 of Table 7.3 , we report changes in a fourth outcome variable, the results of Ringelmann tests that we carried out at periodic intervals throughout the experiment. A Ringelmann test is a simple visual measure of the color of smoke in the chimney stack, which is a crude proxy for the level of particulates in emissions. These tests were repeated between the baseline and endline, providing a panel dataset. To estimate the impacts of the treatment on this outcome, we estimate a similar regression model to the one above, but with additional weather controls and with multiple observations for each plant over time, rather than values captured only at baseline and endline.

Across all specifications, we find no strong evidence that plants in the treatment group polluted less than their counterparts in the control group. Between baseline and endline, there was an overall reduction in both the level and variance of the manual spot measurements of pollution. The average PM concentration at endline was 185 mg/Nm 3 with a standard deviation of 211. At baseline, the average concentration was 338 mg/Nm 3 with a standard deviation of 374. However, this reduction occurred in both factories with and without CEMS making it difficult to attribute the change to the new technology. This finding is a cautionary tale underscoring why the use of a treatment and control group is so important. Had we only looked at pollution changes in factories with CEMS, we might have noticed they became cleaner and then credited this to the technological intervention. As it happens, unless plants in later phases pre-emptively cut pollution in anticipation of a future mandate (possible, but unverifiable), it may be the case that better monitoring alone is not sufficient to solve the development challenge at hand.

Why was factory pollution unresponsive to the installation of CEMS? One answer might lie in how the data was actually used, or not used, by the regulator. Recall Table 7.1 , which describes the protocol that the GPCB stated would determine how it acted upon the continuous pollution data it received. The protocol envisages actions of steadily increasing severity, rising to a legal notice. However, upon examining the history of regulatory interactions at the end of the pilot, we found that the regulators’ bark was worse than its bite. Although the first few actions in the schedule (e.g., SMS messages, in-person meetings, and letters) were executed, there was no significant difference between the treatment and control groups in the probability that a plant received a legal notice or even an in-person pollution test. In other words, when it came to taking actions that involved more than “cheap talk,” the regulator blinked.

Interestingly, this lack of follow-through action in response to high CEMS readings was not perfectly mirrored in the other data source available to the regulator, namely, the results of traditional manual tests. This difference is clearly visible in Fig. 7.6 . The conditional probability of plants receiving a legal notice significantly increases when their last manual test reveals very high readings, but no such pattern exists when examining CEMS data. In other words, although the continuous monitoring systems generate useful, even superior, information, the regulator chose not to use it for targeting enforcement. If the plants understood that this would be the case, it is unlikely that CEMS would have had an observable effect on behavior.

figure 6

Probability of legal action

Notes : Conditional probability of legal action by the Gujarat Pollution Control Board as a function of pollution measured by CEMS instruments and manual samples expressed as multiples of regulatory standard. Dots represent mean values in bins and bars are 95% confidence intervals

5 Conclusion

The experiences recounted in this case study underscore the complexity involved in translating technology from a developed country to a developing country context. The Surat experience with CEMS demonstrates how technical solutions to development problems cannot be divorced from the institutional context in which they are used. It is often impossible to identify the effect of these interactions either in the lab or through iterative design processes in the field, because they are only apparent in applications at scale.

In the present example, the technical pilots that preceded the RCT provided an example of small-scale field trials based on which several technical tweaks were made. Although this step was necessary for implementation, it was far from sufficient. Other challenges such as calibration problems or staff capacity constraints became apparent only when several hundred devices were deployed at once.

Development engineering requires a blend of the social sciences and engineering. The Surat CEMS deployment was evaluated using an RCT design, drawing upon the expertise of a team of economists. However, without continuous technical innovations designed in situ, there would have been no intervention to test. The eventual results were mixed, placing the spotlight on additional questions, some of which have to do with the design of regulatory institutions and the legal framework underlying environmental regulation in India.

In 2019, the Gujarat Pollution Control Board launched its Emissions Trading Scheme (ETS) across a cluster of industrial plants in Surat. The Surat ETS is the first cap-and-trade market in particulate emissions across in the world. The data generated by the Continuous Emissions Monitoring Systems that were set up as a part of the project we have described here went on to form the data foundation for the ETS.

In the past, India’s environmental regulations have been criticized for being blunt and inflexible, proving both costly for industry and difficult for the government to implement and enforce. Market-based instruments, such as emissions trading schemes, provide an alternative that could meet the dual challenge of economic growth and environmental safety. Thus, the Surat ETS represents a paradigm shift in Indian regulation, stemming in part from the recognition that no matter how well these instruments worked, if they were used within an archaic regulatory framework, pollution was unlikely to be reduced. In a sense, the most important impact of these devices may have been to open up the option of using modern market-based regulation in India, something that would have been completely impossible without a transformation in how pollution from industry was monitored.

6 Discussion Questions

As a development engineer, what criteria would you apply before declaring a technological solution a success? What types of disciplinary skills would a research team need to possess to give them the best chance of meeting these targets? What are the differences between the criteria for success that an R&D lab, a venture capitalist in a startup, or a policymaker might apply when evaluating a technology solution to a development problem?

The adoption of technology designed to solve development problems often requires government support through tax-incentives, regulatory mandates, or subsidies. On the one hand, if such policy assistance is to be entertained, then rigorous evidence on effectiveness seems essential. On the other hand, it may be difficult for any technology to improve without learning-by-doing and the opportunity to iterate and improve given results in the field. Choose a technology other than CEMS (perhaps from another chapter in the book) and discuss how you would approach this balancing act? As policymakers, what strategies would move us most quickly towards effective, cheap, reliable technology solutions, and what do you think a phrase like “data-driven policy-making” should mean in this context?

Can you think of ways to make it hard for plants to falsify data coming from a CEMS device, holding the technology constant? Is there a new rule the government might impose or additional data that could be used to cross-check the validity of data? How would you judge the cost-effectiveness and feasibility of these solutions in a developing country context?

In India, 33% of manufacturing output comes from small-scale plants, located in about 6,000 clusters scattered across the country (Shah et al., 2015 ).

Balakrishnan, K., et al. (2019). The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The Global Burden of Disease Study 2017. Lancet Planet Health, 3 , e26–e39. https://doi.org/10.1016/S2542-5196(18)30261-4

Article   Google Scholar  

Bharadwaj, P., Gibson, M., Zivin, J. G., & Neilson, C. (2017). Gray matters: Fetal pollution exposure and human capital formation. Journal of the Association of Environmental and Resource Economists, 4 (2), 505–542. https://doi.org/10.1086/691591

Burney, J., & Ramanathan, V. (2014). Recent climate and air pollution impacts on Indian agriculture. Proceedings of the National Academy of Sciences of the United States of America, 111 (46), 16319–16324. https://doi.org/10.1073/pnas.1317275111

Article   CAS   Google Scholar  

Chang, T. Y., Zivin, J. G., Gross, T., & Neidell, M. (2019). The effect of pollution on worker productivity: Evidence from call Center Workers in China. American Economic Journal: Applied Economics, 11 (1), 151–172. https://doi.org/10.1257/app.20160436

Duflo, E., Greenstone, M., Pande, R., & Ryan, N. (2013). Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from India. The Quarterly Journal of Economics, 128 (4), 1499–1545. https://doi.org/10.1093/qje/qjt024

Duflo, E., Greenstone, M., Pande, R., & Ryan, N. (2018). The value of regulatory discretion: Estimates from environmental inspections in India. Econometrica, 86 (6), 2123–2160. https://doi.org/10.3982/ECTA12876

Greenstone, M., Harish, S., Pande, R., & Sudarshan, A. (2018). The solvable challenge of air pollution in India. Proceedings of the India Policy Forum, 14 , 1–40.

Google Scholar  

Shah, R., Gao, Z., & Mittal, H. (2015). Innovation, entrepreneurship, and the economy in the US, China, and India: Historical perspectives and future trends . Elsevier. https://doi.org/10.1016/C2014-0-01381-0

Book   Google Scholar  

Download references

Author information

Authors and affiliations.

Energy Policy Institute at The University of Chicago, University of Chicago, Chicago, IL, USA

Anant Sudarshan

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anant Sudarshan .

Editor information

Editors and affiliations.

Center for Effective Global Action, University of California, Berkeley, Berkeley, CA, USA

Temina Madon

Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, USA

Ashok J. Gadgil

Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA

Richard Anderson

Department of Economics, University of Zurich, Zurich, Switzerland

Lorenzo Casaburi

Chief Research and Evaluation Officer, The Pharo Foundation, Nairobi, Kenya

Kenneth Lee

Department of Economics, University of California, Davis, Davis, CA, USA

Arman Rezaee

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2023 The Author(s)

About this chapter

Cite this chapter.

Sudarshan, A. (2023). Monitoring Industrial Pollution in India. In: Madon, T., Gadgil, A.J., Anderson, R., Casaburi, L., Lee, K., Rezaee, A. (eds) Introduction to Development Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-86065-3_7

Download citation

DOI : https://doi.org/10.1007/978-3-030-86065-3_7

Published : 09 September 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-86064-6

Online ISBN : 978-3-030-86065-3

eBook Packages : Earth and Environmental Science Earth and Environmental Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research
  • Ground Reports
  • 50-Word Edit
  • National Interest
  • Campus Voice
  • Security Code
  • Off The Cuff
  • Democracy Wall
  • Around Town
  • PastForward
  • In Pictures
  • Last Laughs
  • ThePrint Essential

Logo

Tamil Nadu Finance Minister Thangam Thennarasu presents the State Budget 2024–25 during the Budget Session of the State Assembly, in Chennai, 19 February, 2024 | Photo: ANI Photo

Chennai:  With a seven-point “grand Tamil dream”, along with 22 permanent flood prevention and mitigation projects and development initiatives for North Chennai, the Tamil Nadu Budget 2024-25 presented Monday by State Finance Minister Thangam Thennarasu estimated a total revenue expenditure of Rs 3,48,289 crore.

In his maiden budget, Thennarasu, who replaced Palanivel Thiaga Rajan (PTR) last year, revealed the estimated revenue deficit of the state to be over Rs 49,000 crore.

“In aggregate, the revenue deficit in Revised Estimates 2023-24 is estimated to increase to Rs 44,907 crore compared to Rs 37,540 crore in Budget Estimates. Excluding loss funding to TANGEDCO, the revenue deficit is estimated to be Rs 27,790 crore in Revised Estimates 2023-24, as compared to Rs 36,017 crore in Budget Estimates,” said Thennarasu.

The fiscal deficit in Revised Estimates (RE) 2023-24 is estimated to marginally increase to Rs 94,060 crore, as against Rs 92,075 crore in Budget Estimates (BE). The fiscal deficit as a percentage of gross state domestic product (GSDP) has increased from 3.25 percent in BE to 3.45 percent in RE 2023-24, owing to a downward revision in GSDP estimates.

Also read: ‘Important to preserve diversity’ — why DMK is pressing for state autonomy ahead of Lok Sabha polls

North Chennai development & flood mitigation

The industrial area of North Chennai — at the centre of media attention in December 2023 post the Chennai floods, oil spill, ammonia gas leak etc — has been allotted Rs 1,000 crore under the “Vada Chennai Valarchi Thittam” (North Chennai development scheme).

The project includes construction of new tenements at Water Basin Road through Tamil Nadu Urban Habitat Development Board (TNUHDB) at an estimated cost of Rs 75 crore; super-speciality block in Children’s Hospital, Egmore, at a cost of Rs 53 crore; two new blocks in RSRM hospital, Royapuram, at a cost of Rs 96 crore; 3 new floors in Government Peripheral Hospital, Periyar Nagar, at a cost of Rs 55 crore; a new industrial training institute (ITI) at a cost of Rs 11 crore; restoration of Retteri, Villivakkam and Padi lakes at a cost of Rs 30 crore; and modernisation and computerisation of 10 schools at a cost of Rs 45 crore.

A new project to improve the sewerage and drinking water infrastructure, and to reduce water pollution in North Chennai will be initiated at a cost of Rs 946 crore.

Stating that the Union government has not released any disaster relief to Tamil Nadu from the National Disaster Response Fund (NDRF), Thennarasu said that in the current budget a sum of Rs 8,398 crore has been allocated for the Water Resources Department. Of the allotted amount, close to Rs 350 crore will be spent on the 22 permanent flood prevention and mitigation works in Chennai and surrounding districts that were significantly affected by Cyclone Michaung.

Thennarasu noted, “In order to restore the damages caused by the unprecedented rainfall and consequent flooding in the southern districts, works for permanent flood control are being undertaken at a cost of Rs 280 crore.”

Also read: Why AIADMK’s support for Modi govt’s ‘One Nation, One Election’ policy is conditional

The 7 objectives

“Virtues are the bedrock of a government,” said Thennarasu. “This Government, which is functioning on these ideals under the leadership of our honourable Chief Minister, has a grand Tamil dream. Akin to the colours of a rainbow, this dream has seven major objectives — social justice, welfare of the marginalised, transforming young Tamils as global achievers, knowledge-based economy, equality focused on the welfare of women, Sustainable green future, and Tamil language and culture.”

Tamil Nadu Finance Minister Thangam Thennarasu holding a briefcase containing the documents of the State Budget 2024–25, arrives with State Chief Minister MK Stalin to present it in the Budget Session of the State Assembly | Photo: ANI

He announced a new scheme, ‘Kalaignarin Kanavu Illam’, which will be implemented in the coming year, at a cost of Rs 3,500 crore. Under this, 8 lakh concrete houses will be constructed in rural areas of the state to make Tamil Nadu “hut-free” by 2030. In the first phase, 1 lakh new houses will be built at a unit cost of Rs 3.50 lakh per house in the coming year.

He also stated that “to spread the euphonious notes of Tamil language across the world, an allocation of Rs 2 crore will be made in the coming year.”

Not just Tamil, but with a view to documenting and preserving the Saurashtra and Baduga languages spoken in Tamil Nadu, along with the linguistic resources and phonetic forms of various tribes such as Todar, Kothar, Solagar, Kani and Narikuravar from an ethnographic perspective, the state government will allocate Rs 2 crore for the benefit of future generations.

To help boys from poor and marginalised backgrounds studying in government schools realise their dreams of higher education and transform them into achievers, a grand scheme, “Tamizh Pudhalvan”, will be implemented in the coming financial year.

Archeological department 

Tamil Nadu Budget has also allocated Rs 25.65 crore for the archaeology department.

Noting that Tamil Nadu is the only state in the country which has been consistently providing significant funding for archaeological excavations, Thennarasu allocated Rs 17 crore for the construction of an open-air museum in Keeladi.

He also allocated Rs 5 crore for excavation in eight locations in Tamil Nadu — including Keeladi in Sivaganga, Vembakottai in Virudhunagar, Porpanaikottai in Pudukkottai, Keelnamandi in Tiruvannamalai, Thirumalpuram in Tenkasi, Konkalnagar in Tirupur, Marungur in Cuddalore and Chennanur in Krishnagiri — and Rs 3 crore for determining the genetic antiquity, migration patterns and diet, among other things, of ancient Tamils.

Rs 65 lakh was allocated to conduct a pilot study for a deep-sea excavation in the coastal regions of Korkai and Alagankulam, the ancient ports of the Pandyan dynasty.

(Edited by Zinnia Ray Chaudhuri)

Also read: TN governor cuts down policy address to 3 mins. ‘Requests to show respect to national anthem ignored’

Subscribe to our channels on YouTube , Telegram & WhatsApp

Support Our Journalism

India needs fair, non-hyphenated and questioning journalism, packed with on-ground reporting. ThePrint – with exceptional reporters, columnists and editors – is doing just that.

Sustaining this needs support from wonderful readers like you.

Whether you live in India or overseas, you can take a paid subscription by clicking here .

  • state budget

LEAVE A REPLY Cancel

Save my name, email, and website in this browser for the next time I comment.

Most Popular

Who are dallewal & pandher, leaders of farmers’ agitation, and why skm isn’t with them, india can’t afford to shut its doors to myanmar unrest. it’s a threat to indian security, ‘even foreign countries know ‘aayega toh modi hi’ — ahead of ls polls, pm confident of bjp’s return.

close

Required fields are marked *

Copyright © 2024 Printline Media Pvt. Ltd. All rights reserved.

  • Terms of Use
  • Privacy Policy

Help | Advanced Search

Computer Science > Computer Vision and Pattern Recognition

Title: scalable methods for brick kiln detection and compliance monitoring from satellite imagery: a deployment case study in india.

Abstract: Air pollution kills 7 million people annually. Brick manufacturing industry is the second largest consumer of coal contributing to 8%-14% of air pollution in Indo-Gangetic plain (highly populated tract of land in the Indian subcontinent). As brick kilns are an unorganized sector and present in large numbers, detecting policy violations such as distance from habitat is non-trivial. Air quality and other domain experts rely on manual human annotation to maintain brick kiln inventory. Previous work used computer vision based machine learning methods to detect brick kilns from satellite imagery but they are limited to certain geographies and labeling the data is laborious. In this paper, we propose a framework to deploy a scalable brick kiln detection system for large countries such as India and identify 7477 new brick kilns from 28 districts in 5 states in the Indo-Gangetic plain. We then showcase efficient ways to check policy violations such as high spatial density of kilns and abnormal increase over time in a region. We show that 90% of brick kilns in Delhi-NCR violate a density-based policy. Our framework can be directly adopted by the governments across the world to automate the policy regulations around brick kilns.

Submission history

Access paper:.

  • Download PDF
  • HTML (experimental)
  • Other Formats

license icon

References & Citations

  • Google Scholar
  • Semantic Scholar

BibTeX formatted citation

BibSonomy logo

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

  • Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

CNBC TV18

  • Personal Finance
  • New Election Exchange
  • New CNBC-TV18 Edge
  • New SME Champion Awards
  • New Latest News
  • Live Market Live

CNBC-TV18 Specials

  • Young Turks
  • Mind Matters
  • Climate Clock
  • Marquee Nights
  • Future Female Forward
  • 11:11 Newsletter

CNBC-TV18 Binge

  • Global Markets
  • Cryptocurrency

Terms and Conditions

  • Terms of Use
  • Privacy Policy

No completion or occupancy certificate for projects violating dust pollution control norms: CAQM

Municipal bodies, urban local bodies and all related departments in delhi-ncr have also been advised to ensure that building plan sanctions, tender notices, contract documents and agreements, etc, include the rules and safeguards for compliance towards the effective mitigation of dust pollution, as per pti..

Profile image

By PTI   Feb 21, 2024 12:23:06 AM IST (Published)

No completion or occupancy certificate for projects violating dust pollution control norms: CAQM

Construction firm NCC eyeing fresh orders under Jal Jeevan Mission in FY25

Hudco aims to grow its asset base to ₹1.5 lakh crore by fy26, a brief look at telangana's ₹2.76 lakh crore budget, finance minister sitharaman says pli scheme has created 7 lakh jobs with ₹1.07 lakh crore investment, share market live.

Support 110 years of independent journalism.

  • Spotlight on Policy
  • Sustainability

Decarbonising the grid without large-scale public investment is impossible

Research shows that using public borrowing to fund renewable energy projects is cheaper than relying on the private sector.

By Chris Hayes and Melanie Brusseler

pollution project of india

Several months of rumours, internal briefings and mixed messaging culminated in the Labour Party officially abandoning its pledge to invest £28bn a year in its green prosperity plan, down-scaling its spending plans to less than £15bn over the course of a full parliament. Great British Energy, a central part of this plan, will remain with an initial capitalisation of £8.3bn, presumably financed by gilt issuance. While the larger climb-down is motivated by the party’s stated commitment to fiscal credibility, it has retained its target to reach clean energy by 2030. The two stances are incompatible; the situation demands more public investment, not less, and GB Energy must lead the way.

In an effort to appear hospitable to business, Rachel Reeves has repeatedly stressed her wish to “de-risk” private investment not just in British business but also for our infrastructure, “unlocking” private capital to fill the void left by previous governments. The cruel irony is that our new era of high interest rates, which has given the shadow Treasury team fiscal cold feet, is itself a symptom of precisely the conditions that make private capital less, rather than more, likely to take up the mantle – at least where renewable energy investment is concerned. The reasons for this can broadly be put into three categories: cost, certainty and coherence.

Cost is the simplest. The interest-hiking cycle frightening fiscal policymakers has applied to everyone, not just to governments. Governments, at least in the rich world, can still borrow more cheaply than private companies. If anything, the spread in yields between corporate and sovereign bonds rises during periods of financial instability. Over the past decade, the spread of UK BBB-rated corporate bonds (the credit rating invariably assigned to new offshore wind projects) over gilts has been in the region of 1.5-2.5 percentage points.

This is not a marginal concern. Renewable energy – with its profile of high upfront investment costs followed by extremely low operating costs – is acutely sensitive to the cost of capital, with debt usually comprising up to 80 per cent of the financing mix. The International Energy Agency estimates that a 2 percentage point increase in the cost of capital inflated a solar or wind project’s “levelised cost of electricity” (the average unit electricity cost over the lifetime of an asset) by a staggering 20 per cent. In so far as it is avoidable, this is upward redistribution from billpayers to the financial sector. Combine this with the freedom from the need to pay dividends beyond making equity holders whole and you have a radically cheaper energy proposition to the public. Given that electricity is, in economist Isabella Weber’s words , a systemically significant price, the need to keep it low and stable is a matter of macroeconomic urgency. Indeed, failure to do so over the last two years is the main reason interest rates are currently as prohibitively high as they are.

[Read more: What do by-election victories mean for Labour’s policy agenda? ]

The Saturday Read

Morning call, events and offers, the green transition.

  • Administration / Office
  • Arts and Culture
  • Board Member
  • Business / Corporate Services
  • Client / Customer Services
  • Communications
  • Construction, Works, Engineering
  • Education, Curriculum and Teaching
  • Environment, Conservation and NRM
  • Facility / Grounds Management and Maintenance
  • Finance Management
  • Health - Medical and Nursing Management
  • HR, Training and Organisational Development
  • Information and Communications Technology
  • Information Services, Statistics, Records, Archives
  • Infrastructure Management - Transport, Utilities
  • Legal Officers and Practitioners
  • Librarians and Library Management
  • OH&S, Risk Management
  • Operations Management
  • Planning, Policy, Strategy
  • Printing, Design, Publishing, Web
  • Projects, Programs and Advisors
  • Property, Assets and Fleet Management
  • Public Relations and Media
  • Purchasing and Procurement
  • Quality Management
  • Science and Technical Research and Development
  • Security and Law Enforcement
  • Service Delivery
  • Sport and Recreation
  • Travel, Accommodation, Tourism
  • Wellbeing, Community / Social Services

Certainty is what investors crave as a precondition for overcoming what Keynes called the “liquidity preference” and sinking their capital into long-term projects. Hence the UK’s often celebrated “contracts for difference” scheme, which addresses the volatility pervasive to spot markets in wholesale electricity – especially where renewables are concerned – and fixes generators’ prices, providing some certainty to the price side of the profitability equation. Until recently, this regime had considerable success mobilising investment in a nascent offshore wind industry; only Denmark has more offshore wind capacity per capita. But as geopolitical, ecological and macro-financial turbulence has become the order of the day, the relative stability once taken more or less for granted on the cost side has been replaced by supply-chain snarls, raw input inflation and, of course, the much tighter financing conditions that prompted Labour to seek salvation from the private sector.

By the time these large capital-intensive projects are operational, the risks attending these costs have been resolved, yet the investment decision itself is made based on price-fixing agreements secured long beforehand. Either investors abandon their plans – as with the calamitous failure of the last such auction round – or demand a risk premium. In other words, private renewable investment is secured by fossilising yesterday’s uncertainties into today’s prices (for 15 years) – uncertainties that are immaterial to the one cast-iron certainty from society’s perspective, namely that such investment must take place one way or another if we are to confront the climate crisis .

Finally, as the architects of the future systems explicitly acknowledge, the components of our energy system must be understood in terms of their contribution to the larger coherent system. And yet the vertical disintegration and horizontal fragmentation characterising our current privatised model increasingly places system-level need in tension with project-level expected profitability. Despite the obvious surplus created by the larger system, which is not in any question, investment is determined by the ability of its isolated components to capture a sufficient share of that surplus under prevailing market and regulatory conditions. In other words, the investment pipeline is gummed up by intra-system distributional squabbles (among and between generators, retailers, the grid, etc) that ought to be subsumed on to a larger balance sheet. Instead they are invariably overcome by enlisting the unwitting consumer via higher bills.

An alternative is possible. As a new report by Common Wealth argues at length, energy decarbonisation can be achieved cheaply, effectively and robustly if politicians have the courage to undertake the scale of public borrowing necessary and turn GB Energy into the leading developer of revenue-generating energy assets, rather than a minor bit-player. It is an illusion to expect the private sector to do the government’s work without extracting a higher price. The best way to de-risk investment is simply for states to invest themselves.

Read the full report on public power generation from the Common Wealth think tank .

Content from our partners

How to tackle the UK's plastic pollution problem – with Coca-Cola

How to tackle the UK’s plastic pollution problem – with Coca-Cola

The hard truth about soft skills

The hard truth about soft skills

Why we need a national employment service

Why we need a national employment service

A Decade of the Lobbying Act: The need for new regulations

A Decade of the Lobbying Act: The need for new regulations

How the UK-India trade deal threatens affordable medicines

How the UK-India trade deal threatens affordable medicines

  • OH&S, Risk Management

IMAGES

  1. Drinking sewage in Varanasi

    pollution project of india

  2. Water pollution in India

    pollution project of india

  3. Air pollution causes 12 lakh deaths in India annually; Delhi most

    pollution project of india

  4. Delhi Govt Decides To Set Up Smog Towers To Tackle Air Pollution: Here

    pollution project of india

  5. India: Poverty Is Key Source Of Global Pollution • Earth.com

    pollution project of india

  6. Water Pollution Status In India

    pollution project of india

COMMENTS

  1. Pollution in India

    Key insights Average PM2.5 concentration in India Most polluted countries based on PM2.5 concentration globally 2022 India population exposed to hazardous air pollution levels Detailed...

  2. Inside India's Gargantuan Mission to Clean the Ganges River

    The Ganges itself has become a dumping ground for countless pollutants: toxic pesticides, industrial waste, plastic, and, more than anything, billions upon billions of liters of human effluent....

  3. Catalyzing Clean Air in India

    Catalyzing Clean Air in India MULTIMEDIA VIDEO Beyond Boundaries - Understanding Airsheds and PM2.5 A 5 minute animation film that introduces the concept of airshed management of air pollution in India and helps understand how PM2.5 is formed, how it travels and how it impacts health. STORY HIGHLIGHTS

  4. How India's air pollution is being turned into floor tiles

    How India's air pollution is being turned into floor tiles. Smog is a leading cause of ill health around the world, but one Indian inventor is hoping to make it easier to breathe by scrubbing soot ...

  5. Air quality and climate policy integration in India

    Air pollution has emerged as one of India's gravest social and environmental problems in recent years. At the same time, the country is experiencing signs of a warming climate with potentially devastating effects in the long term. ... IEA projects that strong growth in economic output could more than double industry energy demand by 2040 ...

  6. Climate change: What emission cuts has India promised?

    Rameshwar Prasad Gupta, India's environment secretary, said last year that the country's emissions would peak between 2040 and 2045 and then decrease. India currently has the capacity to generate ...

  7. Climate Targets, Air Quality, Mega Projects: India's 10 Biggest

    1. India set ambitious targets to combat climate change In one of the most ambitious targets set by a developing country to combat climate change, Prime Minister Narendra Modi announced at the...

  8. Air pollution: Delhi's smog problem is rooted in India's water crisis

    The answer lies in water, writes climate expert Mridula Ramesh. India loses an estimated $95bn (£70bn) to air pollution every year. From mid-March to mid-October, when Delhi's air quality varies ...

  9. India's States Take Action Against Regional Air Pollution

    FEATURE STORY December 1, 2021 India's States Take Action Against Regional Air Pollution STORY HIGHLIGHTS Air quality management in India is a regional issue. Fortunately, there is now greater political will among India's states for tackling air pollution.

  10. An environmental justice analysis of air pollution in India

    An environmental justice analysis of air pollution in India Priyanka N. deSouza, Ekta Chaudhary, Sagnik Dey, Soohyeon Ko, Jeremy Németh, Sarath Guttikunda, Sourangsu Chowdhury, Patrick Kinney,...

  11. A silent killer is choking India's capital. For millions, there's no

    Delhi is often ranked among the world's most polluted cities, and air pollution there reached "hazardous" levels in early November, according to India's National Air Quality Index (AQI), which...

  12. Air pollution in India

    Dust & Construction contribute about 59% to the air pollution in India, which is followed by Waste Burning. Crafting activities are mostly in the urban areas while Waste Burning is in the rural areas (agriculture). Air pollution in India is a serious environmental issue. [1] Of the 30 most polluted cities in the world, 21 were in India in 2019.

  13. India

    India has launched an ambitious National Clean Air Program to reduce particulate matter pollution by 30% by 2024. Indian Institute of Technology Kanpur has collaborated with the Department of Environment, Forest & Climate Change [and others], supported by Clean Air Fund, to enable real time measures to mitigate and plug pollution sources.

  14. Air pollution in India: Status and Challenges

    Central Pollution Control Board (CPCB) has established National Ambient Air Quality Standards (NAAQS) for some of the most common air pollutants which are mainly particulate matter (PM), carbon monoxide (CO), ground-level ozone, nitrogen dioxide (NOx), sulfur dioxide (SO 2) and lead. These are known as "criteria" air pollutants (EPA, 2015).

  15. Developing Strategies for Control of Air Pollution in India and its

    As part of India's efforts to curb air pollution, the project provides technical assistance to MoEF&CC on the National Clean Air Action Programme. Air pollution is a major challenge faced globally, with detrimental environmental, health and economic impacts.

  16. New Delhi air pollution: Why can't India's capital clean up its toxic

    We have science and the finance, but we lack a reduction-based approach," said Sunil Dahiya, from the Centre for Research on Energy and Clean Air (CREA) in New Delhi. In comparison to Beijing ...

  17. 5 Biggest Environmental Issues in India in 2024

    If you liked reading about some of the biggest environmental issues in India, you might also like: 14 Biggest Environmental Problems of 2024. Top environmental issues in India: 1. Air Pollution; 2. Water Pollution; 3. Food and water shortages; 4. Waste management; 5.

  18. Clean Air Project India

    The CAP India project aims to support NCAP by demonstrating viable approaches for cities to address air pollution. NCAP was launched in January 2019 with a goal to meet the prescribed annual average ambient air quality standards across the country.

  19. Lancet study: Pollution killed 2.3 million Indians in 2019

    Pollution led to more than 2.3 million premature deaths in India in 2019, according to a new Lancet study. Nearly 1.6 million deaths were due to air pollution alone, and more than 500,000 were ...

  20. Clean Air Research Initiative (CARI)

    Air Pollution in India is a serious issue with the major sources being fuelwood and biomass burning, fuel adulteration, vehicle emission and traffic congestion. ... In view of these facts, this project will understand the cause, source, type and effects of fire in a dumpsite/landfill and to develop an integrated approach for its proper control. 3.

  21. Pollution of the Ganges

    The irrigation potential of the project alone is 300 million acres with water supply throughout the year. It is a gigantic multi-purpose project where cleaning of many major rivers of India by providing adequate base flows and minimum environmental flows (not Ganges river alone) from the water pollution is one of its purposes.

  22. Pollution

    Air quality regulation and actions for abatement of air pollution is undertaken under various provisions of Air (Prevention and Control of Pollution) Act, 1981 and Environment (Protection) Act, 1985 which prescribes the mechanism and authorities for handling the issue. The major impact is highlighted with reference to health of people.

  23. Monitoring Industrial Pollution in India

    The project described here was thus the first attempt in India to carry out a systematic field evaluation of the effectiveness of CEMS. It also became the precursor to India's first cap-and-trade market for industrial particulate pollution, which was launched in Surat in 2019.

  24. Focus on North Chennai and a 7-point 'grand dream' in Tamil ...

    A new project to improve the sewerage and drinking water infrastructure, and to reduce water pollution in North Chennai will be initiated at a cost of Rs 946 crore. Stating that the Union government has not released any disaster relief to Tamil Nadu from the National Disaster Response Fund (NDRF), Thennarasu said that in the current budget a ...

  25. [2402.13796] Scalable Methods for Brick Kiln Detection and Compliance

    Air pollution kills 7 million people annually. Brick manufacturing industry is the second largest consumer of coal contributing to 8%-14% of air pollution in Indo-Gangetic plain (highly populated tract of land in the Indian subcontinent). As brick kilns are an unorganized sector and present in large numbers, detecting policy violations such as distance from habitat is non-trivial. Air quality ...

  26. No completion or occupancy certificate for projects ...

    The central panel for controlling air pollution in Delhi-NCR on Tuesday directed agencies concerned not to issue completion and occupancy certificates to project proponents who violate dust pollution control norms. Inspections of construction and demolition sites indicate a significant need to improve levels of compliance and implementation of ...

  27. Decarbonising the grid without large-scale public investment is

    Several months of rumours, internal briefings and mixed messaging culminated in the Labour Party officially abandoning its pledge to invest £28bn a year in its green prosperity plan, down-scaling its spending plans to less than £15bn over the course of a full parliament. Great British Energy, a central part of this plan, will remain with an initial capitalisation of £8.3bn, presumably ...

  28. Page couldn't load • Instagram

    humansofmanipaljaipur on February 19, 2024: "It was during the 90s when not many people were aware of the environmental issues, I remember tal..."