research paper on air india

After decades of decline, Air India is betting billions on a comeback

CEO Campbell Wilson says journey to restoring Air India’s reputation is ‘well under way’.

Air India was once so renowned for its service that Singapore’s founding statesman Lee Kuan Yew used the airline as a blueprint for launching the city-state’s own flag carrier in the early 1970s.

In recent decades, India’s national airline came to be seen as a cautionary tale of decline as it racked up billions of dollars in losses and battled a reputation for tardiness and poor service.

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When the Tata Group bought the company in October 2021, returning control to the wealthy Tata family after decades of state ownership, CEO Natarajan Chandrasekaran laid out a clear objective: “To build a world-class airline”.

Tasked with leading this mission is Air India CEO Campbell Wilson, who was recruited from Singapore’s low-cost airline Scoot in 2022 to turn around the carrier, founded in 1932 by French-Indian aviator entrepreneur JRD Tata.

“Standards have slipped considerably over the years,” Wilson told Al Jazeera in an exclusive interview.

But Wilson is adamant that “the journey to restoring Air India to its former glory is well under way” under a five-year turnaround plan unveiled last year.

The Tata Group has spent tens of billions in transforming the company in recent years, investing in 470 new aircraft, cabin modernisation, a brand refresh and customer service changes.

After the “capital-intensive” five-year plan is completed, Air India hopes to capitalise on the huge growth potential of the Indian aviation market, the world’s third-largest with some 145 million domestic passengers annually.

The Tata Group’s initial priority has been its ageing fleet, the upkeep of which has been neglected for decades.

Shortly after the ownership change, Air India added 36 leased aircraft – 11 Boeing 777s and 25 Airbus A320s – which allowed the airline to launch six new international routes and increase frequency across a further 24.

Air India’s largest investment came with its announcement of plans to buy 470 new Airbus and Boeing aircraft at a cost of $70bn, including 140 A320neos, 70 A321neos, and 190 of the 737 MAX.

Wilson said the acquisitions will enable Air India to “operate the most advanced and fuel-efficient fleet within five years”.

The airline also plans to spend $400m to retrofit its existing fleet by refurbishing cabin interiors.

Wilson said the retrofit will initially focus on the airline’s narrow-body A320neo and A321neo aircraft, after which 40 legacy wide-body Boeing 777s and 787s will receive a “complete makeover with all new interiors”.

Other changes to improve onboard service include introducing premium economy seats on selected long-haul flights and new food menus.

Henry H Harteveldt, the president of Atmosphere Research Group, said the Tata Group’s investments may help to build a foundation for Air India to succeed, but the changes will not matter much if the airline does not manage to be reliable and punctual.

Above all else, Air India should strive to be seen as “the on-time machine”, Harteveldt told Al Jazeera.

“If a service isn’t considered reliable, customers won’t have the confidence to book with that airline,” he said.

Damaged relations

The Tata Group’s biggest challenge of all may be addressing Air India’s damaged relationship with its customers.

Apart from recurring issues with reliability and punctuality, the airline’s image has been tarnished by high-profile controversies involving its customer service, such as an incident in February in which an 80-year-old passenger collapsed after being forced to walk 1.5km (2.4 miles) from the plane to the immigration counter due to a shortage of wheelchairs.

John Gradek, an expert in aviation management at McGill University in Montreal, Canada, said that Air India’s fleet renewal efforts will fail to revive the airline’s fortunes unless it can establish a “new customer service mindset among its customer-facing staff”, a task that has proven difficult “for many airlines looking to grow their international brand”.

More than two years after the Tata Group’s takeover of Air India, the airline’s turnaround plan has completed its first phase.

Its achievements so far include a $200m investment in new IT to boost reliability and the recruitment of more than 3,800 new employees across several areas to support growth.

Harteveldt said the investment in IT was especially welcome as Air India has been “tech-starved for a long time because of the Indian government’s inability or unwillingness to invest in the airline adequately”.

For the Tata Group, the progression has continued into 2024 with the scheduling of new international routes with the newly delivered Airbus A350-900 between Delhi and Dubai.

Additional changes, such as consolidating carrier Vistara into Air India, are expected to occupy Tata’s focus for the remainder of 2024, in keeping with Chandrasekaran’s view of consolidation as “an important milestone in the journey to make Air India a truly world-class airline”.

There are also internal issues with its subsidiary Air India Express, which has both domestic and international flights. Since Tuesday, it has cancelled at least 90 flights as more than 100 crew members have called in sick at the last minute, essentially a strike action reportedly over pay and related matters.

Cancellations across the Indian budget carrier represent owner Tata Group’s second setback in as many months, as Vistara was forced to adjust its schedule with flight cancellations amid a pilot shortage only in April.

Harteveldt said the “devil is in the details when it comes to airline mergers”.

If the airline teams can “be transparent, even humble”, operational faults can be smoothed out during the integration’s initial months, he said.

After being in the government’s hands for more than half a century, Air India’s recovery is expected to take time, Harteveldt said, but there is “no reason in the world why, with the right investments and focus, Air India can’t successfully distinguish itself from other Indian-based airlines”.

Wilson said Air India’s long-term goal is to grow its market share to 30 percent both domestically and internationally by 2027.

The goal, he said, is to create an airline that is “bold, confident, and vibrant, but also warm and deeply rooted to its rich history, traditions, and warm Indian hospitality”.

Still, Wilson said he is under no illusions that the turnaround will happen overnight.

“It’s a marathon, not a sprint,” he said.

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An empirical study towards air pollution control in Agra, India: a case study

• Published: 27 November 2020
• Volume 2 , article number  2090 , ( 2020 )

Cite this article

• Ranjit Kumar   ORCID: orcid.org/0000-0002-8858-7453 1 ,
• Pratima Gupta 1 &
• Ashok Jangid 2  

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Air pollution affects many people in developed and developing countries worldwide. It is costing around 2% and 5% of GDP (gross domestic product) in developed and developing countries, respectively. The air qualities have been deteriorating day by day and now the situation has become worst. An increase in air pollution will worsen the environment and human health status. Hence, there is an urgent need for air pollution control. Air pollution can be controlled by reducing the emission of air pollutants from industrial and domestic sources through source emission control decrease in fuel combustion, modifying technology, and growing plants. This is the ripe time to take some bold steps in controlling the menace of air pollution. The scientific bodies, NGOs, researchers, and policymakers have made several efforts but pollution levels are increasing everywhere. The UNEP (The United Nations Environment Program), IPCC (The Intergovernmental Panel on Climate Change), etc., have prepared several reports and organize every year COPs (Conference of Parties) meeting but all have gone in vain as the main culprit feedback has not been taken into the consideration. So, a sincere effort is required to fight the effects of climate change. This paper presents a case study of the Agra region over the Indo-Gangetic basin. The air quality of different sites in Agra has been evaluated, and a survey has been conducted to get the public opinion on air pollution, causes, impacts, and solutions. 75% of respondents were aware of the poor air quality of Agra, however, the percentage of respondent who was unaware is not small. It is a matter of concern. 85% of respondents think that poor education and unawareness are the major cause of air pollution. They suggested education, value-based education to improve air quality. Based on these outcomes, it has been concluded that nature-based solutions for air pollution control can be achieved by making people environmentally conscious through value-based education.

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1 Introduction

Air pollution, global warming, and climate change have become a big issue all over the world as it affects the entire ecosystem and human health. Air pollution causes climate crisis and biodiversity crises and posed threats to the survival of human beings [ 1 , 2 , 3 ]. It is mainly due to an increase in emissions of polluting gases and particles in the atmosphere. Gases and particles get emitted in the atmosphere from natural and anthropogenic activities viz. residential cooking, vehicles, industries, construction, and deforestation [ 4 , 5 , 6 , 7 ]. About half of the urban population being monitored is exposed to air pollution that is at least 2.5 times higher than the levels with WHO air quality guidelines. Worldwide, the most widely monitored air pollutants are PM, NO 2 , SO 2 , CO, and O 3 . They are also called criteria pollutants as they are the most common indicators of air quality. In India, CPCB (Central Pollution Control Board) classifies cities as critically polluted if the levels of criteria pollutants are more than 1.5 times the standards. New Delhi, Beijing, Agra, Shanghai, and Mumbai are the most polluted cities in the world [ 7 , 8 ]. The PM 2.5 levels of these cities have risen exponentially. It has also crossed the National and International standards (World Health Organization, US Environmental Protection Agency (USEPA), European Protection Air Quality (EUPAQ), China, National Ambient Air Quality Standards (NAAQS), India) (Fig.  1 ).

Source : WHO 2018

The level of PM 2.5 over 20 most polluted cities in the world (standard value of WHO (10 µg m −3 ); USEPA (12 µg m −3 ); EUPAQ (15 µg m −3 ); China (35 µg m −3 ); India (40 µg m −3 )).

Various governmental organizations and international bodies have proved that air pollution is a major risk to the environment, quality of life, and health of the population [ 9 ]. Every year, approximately 6.5 million people die prematurely from illness caused by outdoor air pollution worldwide and premature deaths will rise 90% by 2040 in developing countries [ 7 ]. In the past decades, a considerable magnitude of air pollution pulls up the number of people suffering from respiratory disease and many times leading to deaths or serious health hazards [ 8 , 9 ]. Air quality standards are set by most countries to protect the public health of their citizens [ 10 , 11 , 12 ]. In 2018, the WHO (World Health Organization) compiled a list of top 500 cities by PM 2.5 annual mean concentration across the world. Kanpur had the worst air quality in the world in that list, surprisingly nine other cities of India figured among the top 20 cities of the world viz. Faridabad, Gaya, Varanasi, Patna, Delhi, Lucknow, Agra, Gurgaon, and Muzaffarpur (Fig.  1 ). As per WHO global ambient air quality update 2018, only 3% of cities in low- and middle-income countries with more than 100,000 inhabitants and 51% in high-income countries out of 4300 cities of 108 countries included in the WHO database complies with WHO air quality guidelines [ 8 ].

The air pollution problem was formally recognized in the 1972 United Nations Declaration on the Human Environment and The United Nations Framework Convention on Climate Change (UNFCCC) Conference (COP) 2011, held in Durban, South Africa. COP 2011 had representatives from 194 countries coming together with their progress plans, to finance environment-saving effort through Green Climate Fund. These types of concrete efforts need to be taken continuously and frequently as no single effort can be a panacea to this old sore. A problem of this scale, where causes and its effects are much diverse, it becomes very important to start once again from the basics and basics lies in education and awareness [ 13 , 14 ]. Various studies have been focused on the risk assessment, perception of social factors, awareness among people about air pollution [ 15 , 16 , 17 , 18 , 19 , 20 ]. Improving awareness about sustainability involves issues like the impact of anthropogenic activities on human health, earth system, control of greenhouse gases, energy consumption patterns, pollution, and transport [ 21 ]. Living sustainability depends on a duty to seek harmony with other people and with nature. Recently, in the year 2015, UNFCCC (United Nations Climate Change Framework Convention), COP 21 was held in Paris, where several countries globally debated and came to a conclusion to set a limit of average global temperature increase to 1.5 °C. It directs countries to cut their carbon emission excessively. It can be materialized if people are directly involved in this step along with the industries. The aim of holding the increase in global average temperature below 2 ˚C or 1.5 ˚C would be a herculean task to achieve with current trends of trajectories of global greenhouse gas emissions. The existing trends are only pointing towards more deteriorating conditions as the emission graph is continuously tracked its known trend [ 21 , 22 , 23 , 24 , 25 ]. Postponement of implementation of much-needed changes will only hamper the revival chances through mitigation measures. It is very important to assess, re-plan, re-strategies, and re-prioritize our needs and paths to achieve development keeping the climatic conditions in the loop [ 26 , 27 , 28 ]. In this context, public opinion may be very important and eye-opening. In India, Agra is one of the most polluted cities and at the same time, it is a world-famous tourist destination. Hence, a survey-based study was conducted over the Agra region in India to get a glimpse of thought perceptions of the common public towards regional and global problems like air pollution, its causes, impacts, and solutions.

2 Material and methods

2.1 site characteristics.

Agra, situated at 27.17° N, 78.01° E over the Indo-Gangetic basin is presently among the most polluted cities in the country. The population of Agra is 15,85,704 as per the census of 2011 and the 30th most populated district in Uttar Pradesh in India. The urban agglomeration of Agra has a population of 1,760,285. Males constitute 53% of the population and females 47%. Agra is famous worldwide as it hosts the ‘Taj Mahal’, which is one of the seven wonders of the world, and other historical monuments. The poorly maintained roads, traffic congestion, wood and cow dung cakes burning for cooking, coal, petrol, and diesel combustion, and vehicular emissions in Agra have resulted in the city having a high pollution level. Agra is one of the most polluted cities in India. Therefore, it was an apt choice to do such type of study.

In this study, the data have been collected in the form of a survey. A questionnaire was developed (Fig.  2 ), served to the local community, and information was collected from various departments, colleges, and schools of Agra as well as local people. The response can be a real representation of the opinion of the common people towards such global challenges of air pollution. This also enables us to develop an understanding regarding people’s limited knowledge about environmental issues and shortcomings of the existing education system. The survey reported the educational approaches used in air quality research communities, both nationally and internationally. There are many factors which make an impact on individuals and community related to environmental concern, and there are various ways that people can learn about environmental issues and how to address them. Out of the total persons who took part in the survey, 20% of the respondents were females and 80% were male (Fig.  3 ). 50% were living in urban areas, 40% were from rural areas, whereas 10% were from suburban areas. 6% of the survey takers were in the age group of above 40 years, 56% were in the age group of 10–20 years, whereas 38% were in the age group of 21–40 years. The questionnaire survey has been designed in a very simple manner which is easily understandable for the local community in Agra. The questionnaire of survey data was collected by the researcher by offline mode. Before the statistical analysis, screening of survey data has been done and the responses were cross-checked.

Questionnaire of survey

Percentages of the respondent a area, b gender and c age ratio

2.3 Statistical analysis

Statistical analysis was employed on the datasets using SPSS. The nonparametric method was used for the determination of the statistical significance of the collected survey data. The t test has been used to assess the relationship between two categories of variables amongst gender, area, and age. The significant level was set at p  < 0.05.

3 Result and discussion

3.1 key findings of the survey.

The feedback based on the survey conducted in Agra is very much fact-finding and presented in Figs.  4 and 5 . There were 20 questions which have been asked to the respondent in the questionnaire regarding various issues related to air pollution, and their response statistics were plotted. The feedback of the respondent has been categorized into three major categories; gender, area, and age-wise. The views on the ways societies come to know about air pollution problems may reveal values, beliefs, and varieties of “truths” underpinning how problems are identified and approached. 91% of the total respondents (72% male and 19% female) (Figs.  4 (1) and 5 (1)) believe that air quality has deteriorated in the last few years. In (Figs.  4 (2) and 5 (2)), 95% of total respondents in which 75% male and 20% female accepted that air pollution harms humanity. Air pollution is a serious problem, 75% of the total respondents (65% male and 10% female) know and understand the extent to which it can damage their life but 22% of respondents were in the opinion that air pollution will affect little while 3% are ignorant, which is a very large number (Figs.  4 (3) and 5 (3)). This is worrisome. They know the health hazards of air pollution and 73% of respondents blame air pollution for irritation in the eyes, nose, and throat, breathlessness is marked by 22%, and 5% think that depression may be also due to air pollution (Figs.  4 (4) and 5 (4)). 45% of the total respondents (35% male and 10% female) stated that the pollution level in Agra is cleaner than other big cities and the other 55% say it is worse (Figs.  4 (5) and 5 (5)). Cooking fuel is found to be one of the major causes of air pollution as 93% of respondents were dependent on LPG (liquefied petroleum gas) as a mode of cooking while the rest of the respondent relies on kerosene, coal, and wood (Figs.  4 (6) and 5 (6)). People were in a clear opinion that polluting companies should be fined heavily to cut down air pollution even it may put some jobs at risk. They believe that citizens should now take the initiative in their hands for clean air and a safe environment and ask authorities to take actions judiciously. 92% of the total respondents (73% male and 19% male) were of the view that the ultimate solution to this glaring situation of global warming and climate change can be tackled by making common masses aware of causes, consequences, and culprits of air pollution (Figs.  4 (8) and 5 (8)). Environmental education may be an important means and 95% of respondents in which 84% male and 11% female feel it may improve public understanding of environmental issues (Figs.  4 (9) and 5 (9)). 91% respondents know the air quality index (AQI) (Figs.  4 (10) and 5 (10)). The excess use of energy causes air pollution, 84% of the total respondents (66% male and 18% female) are aware that air pollution is due to excessive use of energy, while 16%, which is not a small number, are unaware (Figs.  4 (11) and 5 (11)). The biggest source of knowledge regarding the air pollution and environment is print media, although digital media have surpassed all records of visual advertisement as 73% say newspapers are their main source on environmental issues whereas only 26% say it is a television (Figs.  4 (12) and 5 (12)). In the response to the question of the moral responsibility to minimize air pollution, 98% of the total respondents (81% male and 17% female) feel that air pollution control is our moral responsibility and believe that the negligence of our moral responsibility is doing more harm to the environment than any other thing (Figs.  4 (13) and 5 (13)). 98% of respondents in which 82% male and 14% female were in view that bringing certain changes in daily habits of life may minimize the energy consumption (Figs.  4 (14) and 5 (14)). But the positives that can be taken from this survey is that they are ready to make changes in their lifestyles and want to contribute to the fight against the menace of air pollution. Taking moral responsibility is important to overcome this problem as 91% of the respondents (73% male and 18% female) feel that it is the selfish interest of some people that are hampering the cause (Figs.  4 (15) and 5 (15)). 98% of respondents in which 82% male and 16% female stated that ignorance of moral responsibility for self-interest or may be due to unconsciousness towards the human values (Figs.  4 (16) and 5 (16)) may be proven to be dangerous. Various studies have suggested that interest, beliefs, values, and the basis of knowledge power are linked with culture, nature, and economy [ 11 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. 98% of respondents have shown their willingness to take some steps for clean air and bring some changes in attitude regarding environmental issues. 95% believe consciousness and human values will go a long way (Figs.  4 (17) and 5 (17)). The socio-ecological approaches to environmental education emerged as ways for dealing with conflicting multilayered demands concerning the environment. The research finding of this survey was very similar to the earlier studies for socio-ecological perspectives on environmental education [ 11 , 12 , 20 ]. The characteristics of the survey were outlined for providing the foundation of socio-ecological approaches. The socio-ecological approaches illustrate how environmental problems are addressed, inquired, analysed, interpreted, and what conclusions are drawn regarding the solution of air pollution [ 36 ]. 98% of respondents in which 78% male and 18% female are of the view that student life is the time to lay the foundation of intellectual and conscious citizens (Figs.  4 (18) and 5 (18)). 96% of them in one voice say that value-based education from elementary level can inculcate the habit of energy efficiency as well as consciousness towards the environment. They also believe that this will produce future intellectuals and very informed citizens (Figs.  4 (19) and 5 (19)). 97% of people in which 80% male and 17% female say that value-based education should be added to the curriculum starting from elementary level to research courses because our existence can only be supplemented on the pillars laid by the educating system (Figs.  4 (20) and 5 (20)). The statistical analysis was performed on the dataset, and the t test for all variables has been found statistically non-significant at p  < 0.05. The t test with the participants from different gender, age group, area-wise, and questionnaire is presented in Table 1 . The categories of gender, area, and age have been observed that the p value ( T  <  =  t ) was less than the absolute value (critical value). Respondent was positively found to be statistically significant with gender especially in males ( p  = 0.28). It may be due to the fact that the male respondent is more exposed outdoor than female ( p  = 4.0 × 10 –7 ). In the area-wise, the respondent from a rural area ( p  = 0.036) has shown the statistical significance than urban ( p  = 4.3 × 10 –6 ) and suburban (1.4 × 10 –8 ) area. The p value of different age group viz. below 20 years, 21–40 years, and above 40 years was less than the absolute value. The age group below 20 years ( p  = 0.01) was positively significant than other age groups. It may be due to the fact that the youngster is more sensitive to air pollution than older. Therefore, the results of the age group of 21–40 and above 40 years were not statistically significant.

A statistical response of total respondent to questions of the survey

Response to the questions of questionnaire

In the study, it is quite evident that the participation of females is limited. It may be due to their illiteracy and socio-economic status and understanding of air pollution and other environmental issues. There is an urgent need to enhance their understanding of such a common public problem of air pollution. The government should come up with a different innovative scheme and value-based education to address these issues. Value-based education means to be a starting point of the real-life solution where people are directly involved. They also expressed their fear in the feedback remark that they are sitting on a pile of explosives and in a situation of now or never. It will be too late if some drastic steps are not taken immediately. But it is also a fact that this survey was conducted in educational institutions and nearby areas where people are more active and are educated. But still, many are unaware of several implications, causes, and remedy of the menace of air pollution. So, a condition in core civil areas may be more disturbing.

3.2 Perception of value-based education

Education is an act of acquiring or imparting a particular set of knowledge or skills through the process of learning or teaching, especially in schools or colleges. But the most important demand for time is to change and that change should also be reflected in the education system. The necessity to add values of education has risen. The addition of values does not kill the essence of the traditional educational methodology but complements it. Value-based education integrates ethics, moral duties, cultural importance which develops the trainer as well as the learner to be a better human being. It is just a way of conceptualizing existing education practices with the purpose of good for all, at the core of its heart. It initiates a positive relationship with everything living or non-living around. It teaches to give respect to others whether living or non-living. The changes happening around the world are badly affecting the environment and there is a need for value-based education to educate people and come up with new ideas/techniques. These scenarios can be coupled with various areas, i.e. value-based education, socio-economic, climatic, cultural, and behavioural impact. The perspective needs to be discussed and a plan needs to be unraveled that how to implement these scenarios step by step to provide a complete framework of implementing value-based education in true spirit. Environmental problems need to be addressed through value-based education as it provides an opportunity to educate and train the people who are or will be making or breaking the environment further. This type of education should start from the elementary level to the advanced level and then to a very advanced level. The crucial and most difficult step of the method is the implementation of plans as it demands changes in the way of life morally and the integrity of the administration to implement. Many times it demands changes in existing laws into a more stringent one, despite drawing flak from some parts of the society.

3.3 Control of air pollution: value-based education

Air pollution can be controlled through nature-based or technology-based solutions. Technology-based solutions control one type of pollution but also add other kinds of pollutants to our environment. The outcome or findings of the present survey provoked the nature-based solution of air pollution, global warming, and climate change. The result of the survey conducted clearly shows that people have migrated from sustainable development to unsustainable development mainly due to their ignorance towards the environment and selfishness. People accept their negligence and unawareness. The survey also points out that people due to their selfish ends sometimes do not care about the environment. The survey also had a suggestion-based question in which it was asked to suggest ways to control the menace of air pollution. Respondents suggested that environmental education be imparted from the elementary level and organizations such as educational institutions, the social, or religious body should be a frontrunner to bring changes in habit of inhabitants. They also suggested that the government should frame stricter laws and ensure its implementation. Reduction in the use of private vehicles and control in the consumption of energy were pointed out. A lot of people came out with the idea of the extensive plantation. Almost everyone suggested practical awareness among citizens in the form of workshops/conferences/seminars on causes, impacts, and solutions to air pollution. The new generations with the proper guidance of experienced are ready to embark on the journey to integrate value in the education system and also to work together for sustainable development. People are ready to improve their understanding of society and the common public on climate and future socio-economic scenarios. These deeply rooted values can be imbibed in students through value-based education. Some steps to make them aware have already been taken, but a lot needs to be done.

Based on the response of respondents, four-step strategies have been proposed viz. measurement and assessment, set standards, reduction of emission, and implementation (Fig.  5 ) to combat the menace of air pollution. Measurement and assessment of air quality are required and it involves the general public, local communities, schools, university, industry, non-governmental organization, community groups, and NGOs. There is a need to set the standard through consensus amongst stakeholders, policymaker/decision-makers, industrialists, media, and the common public. There should be separate emissions standards for different sources. The reduction of emission at the source can be achieved by the betterment of technology through modification and replacement of existing equipment with more advanced ones. Hence, the reduction in dependencies on fossil fuels and the identification and use of clean and alternate energy sources are needed. Implementation and execution are of the most important as the whole model relies on the success of these steps. Hence, the direct involvement of the government body and judiciary is required. These four-way efforts should be made in parallel. This is like 1-2-3-4 formulae to formulate the problems of air pollution. Individuals (adults and children) may change their behaviour when their values, beliefs, and environmental understanding improve which can be achieved by becoming conscious of the environment. The new process of bidirectional perception is only a step towards bringing the separate tasks under one single umbrella (Fig.  6 ). There should be effective in dealing with the exchange of information, data collection, knowledge, interpretation, implementation, and further actions and co-ordination between organizations. Value-based education will be the central idea of the bidirectional perception process, as it will develop a sense of working together in teams, bringing about a change in the thinking of present and generations to come. All this will go to improve the knowledge of air pollution, climate change, and mitigation. The contribution of the common public is the only way of success. But to develop a sense of belongingness with the environment, they must know about air pollution and its vulnerabilities. It should be backed by the proper amalgamation of scientific advancements and inculcation values and ethics in an individual which can be achieved through value-based education and being environmentally conscious. Its implementation should be time-bound and impacts must be monitored. Any control technologies can never be full-proof for such a diverse and complex issue of air pollution and climate change, but when efforts are made honestly and implemented properly, surprising results come. This mechanism may be very effective, cost–benefit, and sustainable.

Bidirectional perception of the process of value-based education

3.4 Policy implications

The findings of this survey suggested the awareness of air pollution and the implementation of air quality control measures. Value-based education can be a powerful tool to make people aware of such relations and get rid of ignorance. Therefore, value-based education has to play an important role. It builds character which is beneficial for the growth of both the individual and the societies. Value-based education also seeks to integrate the principle, values, and practices of sustainable development into all aspects of life, to address the social, cultural, and environmental problems we face in the twenty-first century. Environmental literacy for adults means developing and participating in social practices likely to change the way our societies think about and act upon ecological issues. Nature-based solutions and sustainable consumption and production is the need time and needed to be included in government policies.

3.5 Limitations of the work and potential future work

The present study focused on the public perception of the status of air quality in the Agra region over north-central India. Air pollution has become a big issue nowadays all over the world. The main sufferer of air pollution is human being and the main culprit is also human beings while the main cause of air pollution is indiscriminate exploitation of natural resources and excessive consumption of energy by human being. All the efforts made so far to control air pollution were technology-based in spite of knowing the fact that human being is the main culprit. In this study, the common public has been involved and opinions were invited in the form of questionnaires regarding causes, impacts, and solutions of air pollution. Respondents feedback were torch-bearing for environmentalists, educationists, academicians, government body, and stakeholders for the plan. Input obtained from the respondents of the Agra region only but it can be considered as a pilot work and can be representative of a regional perception on such a global problem. This is a limitation of this work. In the future, such type of a comprehensive study is required by involving the public of all segments which may help to design a proper plan for pollution reduction and a healthy environment.

4 Conclusion

A survey-based on questionnaires has been conducted to know the views of the public and explore the role of education in control of air pollution over the Agra region in north-central India. This study reveals that the public is aware of deteriorating air quality, but they are not making efforts to fight them just due to ignorance and their selfishness. Based on the feedback of respondents, following conclusions and suggestions have been derived:

A change in habit and lifestyle is required.

Ban on the industry which pollutes air even at the cost of employment is needed.

Value-based education is needed to combat the menace of air pollution.

The ultimate solution to the air pollution problem is making the people aware of the causes, consequences, culprits, and control of energy consumption through education.

A four-step non-destructive and holistic method has been suggested for air pollution control which can be achieved through value-based education and being environmentally conscious.

Nature-based methods should be adopted for control of pollution as technology-based solutions are specific.

An organization like educational institutions, indigenous groups, and religious bodies should be involved as they will act as a catalyst for bringing change in habit of the common public and future generation.

Data availability

All the generated and analyzed data are included in this article.

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Acknowledgements

We wish to thank all the participants of the survey for their voluntarily support. We wish to thank Prof. Sahab Dass, Head, Department of Chemistry for providing necessary facilities and kind encouragement. The help and support from Mr. Hazur Saran is greatly appreciated.

The financial assistance from ISRO-GBP project is gratefully acknowledged.

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Ranjit Kumar & Pratima Gupta

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R.K. has planned the work and designed the manuscript. P.G. has done experimental works and wrote the paper. A.J. has made figures. R.K. has planned the work and designed the manuscript. All three authors have reviewed the paper a number of times.

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Kumar, R., Gupta, P. & Jangid, A. An empirical study towards air pollution control in Agra, India: a case study. SN Appl. Sci. 2 , 2090 (2020). https://doi.org/10.1007/s42452-020-03826-4

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Analysis of Air Pollution Data in India between 2015 and 2019

1 Center for Policy Research on Energy and Environment, School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA 2 Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA

  Copyright  The Author(s). This is an open access article distributed under the terms of the  Creative Commons Attribution License (CC BY 4.0) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Sharma, D., Mauzerall, D. (2022). Analysis of Air Pollution Data in India between 2015 and 2019. Aerosol Air Qual. Res. 22, 210204. https://doi.org/10.4209/aaqr.210204

• Analysis of PM 10 , PM 2.5 , SO 2 , NO 2 and O 3 measurements across India from 2015–2019.
• First comprehensive analysis of Indian government and US Air-Now data.
• More national ambient air quality standard exceedances in north than south India.
• Provides baseline for evaluation of mitigation measures and atmospheric models.

India suffers from among the worst air pollution in the world. In response, a large government effort to increase air quality monitoring is underway. We present the first comprehensive analysis of government air quality observations from 2015–2019 for PM 10 , PM 2.5 , SO 2 , NO 2 and O 3 from the Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring (CAAQM) network and the manual National Air Quality Monitoring Program (NAMP), as well as PM 2.5 from the US Air-Now network. We address inconsistencies and data gaps in datasets using a rigorous procedure to ensure data representativeness. We find particulate pollution dominates the pollution mix across India with virtually all sites in northern India (divided at 23.5°N) exceeding the annual average PM 10 and PM 2.5 residential national ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India exceeding the PM 10 standard by 50% and the PM 2.5 standard by 40%. Annual average SO 2 , NO 2 and MDA8 O 3 generally meet the residential NAAQS across India. Northern India has (~10%–130%) higher concentrations of all pollutants than southern India, with only SO 2 having similar concentrations. Although inter-annual variability exists, we found no significant trend of these pollutants over the five-year period. In the five cities with Air-Now PM 2.5 measurements - Delhi, Kolkata, Mumbai, Hyderabad and Chennai, there is reasonable agreement with CPCB data. The PM 2.5 CPCB CAAQM data compares well with satellite derived annual surface PM 2.5 concentrations (Hammer et al. , 2020), with the exception of the western desert region prior to 2018 when surface measurements exceeded satellite retrievals. Our reanalyzed dataset is useful for evaluation of Indian air quality from satellite data, atmospheric models, and low-cost sensors. Our dataset also provides a baseline to evaluate the future success of National Clean Air Programme as well as aids in assessment of existing and future air pollution mitigation policies.

Keywords: Air pollution, India, surface observations, CPCB, continuous and manual data, US AirNow

1 INTRODUCTION

Concerns over poor air quality in India have increased over the past few years with increasing evidence of the adverse impacts on health (Balakrishnan   et al. , 2014; Chowdhury and Dey, 2016; Balakrishnan   et al. , 2019), agricultural yields (Avnery   et al. , 2011, 2013; Ghude   et al. , 2014; Gao   et al. , 2020) and the economy (Pandey   et al. , 2021). Rapid growth and industrialization in India have resulted in some of the most polluted air in the world. Projections forecast further decreases in air quality and a 24% increase in PM 2.5   associated premature mortalities by 2050 relative to 2015 (GBD MAPS Working Group, 2018; Brauer   et al. , 2019). According to recent estimates based on the Global Exposure Mortality Model (GEMM), total premature mortality due to ambient PM 2.5   exposure in India increased approximately 47% between 2000 and 2015 (Chowdhury   et al. , 2020). Surface O 3   concentrations are also likely to increase with growing industrial emissions and increasing temperatures due to climate change resulting in additional stress on agricultural yields and public health (Avnery   et al. , 2011; Silva   et al. , 2017).

India has a national ambient surface monitoring network that started in 1987 and has become more extensive over time with a substantial increase in the number and spatial extent of continuous and manual monitoring stations between 2015 and 2019. At present, the Central Pollution Control Board (CPCB), along with the State Pollution Control Boards (SPCBs), run the most extensive monitoring network in the country under the National Air Quality Monitoring Program (NAMP). As of 2019, NAMP cooperatively operated (with CPCB and SPCBs) over 750 manual monitoring stations (compared with 20 in 1987 when monitoring first began and 450 in 2015 when our analysis starts) which publicly archive annual average concentrations of PM 10 , PM 2.5 , SO 2   and NO 2   ( https://cpcb.nic.in/namp-data/ ). As of 2019, over 220 Continuous Ambient Air Quality Monitoring (CAAQM) stations operated (compared with less than 50 stations in 2015 when our analysis starts). CPCB archives publicly available, real time data, every 15 minutes, from over 220 stations across India of an extensive list of criteria and non-criteria air pollutants and meteorological variables ( https://app.cpcbccr.com/ccr/ ). Stations vary in the air pollutant species and meteorological data they collect. The manual monitors provide better spatial coverage than the continuous monitors but provide data on fewer air pollutants at much lower temporal resolution (annual average values versus every 15 minutes). However, both sets of monitoring stations sample exclusively urban areas despite the fact that rural areas have significant emissions from households and agricultural waste burning (Balakrishnan   et al. , 2014; Venkatraman   et al. , 2018). Pant   et al.   (2019) and the Supplementary Information (SI) (Section 1) describe other Indian monitoring networks which are less extensive and are not publicly available. India has fewer monitoring stations than most south and east Asian countries, with ~1 monitor/6.8 million persons (Apte and Pant 2019; Brauer   et al. , 2019; Martin   et al. , 2019). Despite recent increases in urban monitoring stations across India, vast regions do not have monitors and except for satellite data for a few species, little information is available on surface concentrations of air pollutants in non-urban locations in India.

Recently, extreme levels of fine particulate air pollution in India, combined with a growing appreciation of the adverse impacts of elevated air pollution on health, led the Indian government to launch the National Clean Air Program (NCAP) in 2019 (Ministry of Environment, Forests and Climate Change NCAP, 2019). NCAP targets a reduction of 20–30% in PM 10   and PM 2.5   concentrations by 2024 relative to 2017 levels. One focus of NCAP is augmentation of the national monitoring network for which substantial financial support was announced in the 2020 Union Budget.

Despite a growing monitoring network and the need for analysis, prior to our work, no study holistically analyzed existing government surface air pollutant monitoring data across India. Most research studies analyzing ground monitoring data have focused on Delhi and the surrounding National Capital Region (NCR) (Guttikunda and Gurjar, 2012; Sahu and Kota, 2017; Sharma   et al. , 2018; Chowdhury   et al. , 2019; Guttikunda   et al. , 2019; Wang and Chen, 2019; Hama   et al. , 2020), and other major cities (Gurjar   et al. , 2016; Sreekanth   et al. , 2018, Yang   et al. , 2018; Chen   et al. , 2020). In addition, some studies also used ground observations to bias correct satellite measurements for India (Pande   et al. , 2018; Chowdhury   et al. , 2019; Navinya   et al. , 2020). However, a need remains for a comprehensive analysis of all surface data collected by manual NAMP and continuous CAAQM monitoring networks between 2015–2019 over which period monitoring increased substantially.

Here we provide the first national analysis of all available surface measurements of key criteria pollutants (PM 10 , PM 2.5 , SO 2 , NO 2   and O 3 ) across India between 2015–2019. We use publicly available data from the NAMP manual and CAAQM real-time stations which have different spatial distributions and temporal resolutions. Collating spatio-temporal distributions of pollutant concentrations on inter-annual, annual, seasonal and monthly timescales, we present an overview of the variability in air pollution levels across the country and separately analyze pollution levels in northern (north of 23°N) and southern India. We conduct case studies of five cities in India in which U.S. State Department PM 2.5   monitors (Air-Now network) are present and, using additional data collected by CAAQM monitors, compare pollution status between these cities. We also compare analyzed annual average PM 2.5   from the CAAQM network with the satellite derived surface PM 2.5   (Hammer   et al. , 2020) and find good agreement between the two datasets. Our analysis will provide a valuable baseline to evaluate the future success of the NCAP in meeting its air pollution mitigation targets.

2 METHODOLOGY

  2.1 criteria pollutant data.

We analyze all open-source data available from the manual (NAMP) and continuous (CAAQM) networks, as well as from the US Embassy and consulates Air-Now network from 2015–2019 for five criteria pollutants—PM 10 , PM 2.5 , SO 2 , NO 2   and O 3 .

Datasets from 2015-2018 were acquired for NAMP and were acquired from 2015–2019 for CPCB-CAAQM and Air-Now networks directly from the following sources:

• NAMP   manual monitoring network ( https://cpcb.nic.in/namp-data/ ): Annual average and annual maximum and minimum concentrations were obtained from a total of 730 manual stations. Higher resolution temporal measurements are not publicly reported by NAMP. We analyze data from 2015–2018 as datasets for 2019 were unavailable when our analysis was completed in December 2020.
• CAAQM   continuous monitoring network from the Central Control Room for Air Quality Management website ( https://app.cpcbccr.com/ccr/ ): One-hour averages were calculated from reported 15 minute average concentrations. Neither the continuous nor manual monitoring stations include geolocations. To obtain the latitude/longitude coordinates of each station, we used the monitoring station name and geolocated them using Google maps.
• S. State Department Air-Now network   ( https://www.airnow.gov/ ): One-hour average PM 2.5   concentrations were obtained for monitors located in Delhi, Mumbai, Hyderabad, Kolkata and Chennai.

  2.2 Data Quality Control

We directly utilize the data available from the NAMP and Air-Now networks, but process the data we use from the CAAQM network to ensure representative monthly, seasonal, and annual average air pollutant concentrations using the following method:

• Missing data is removed. Values in excess of the reported range (see Table S1) are assumed to be errors and are removed. Values of 999.99 for PM 10   and PM 5   are retained as they may represent concentrations above the upper detection limit of the instrument. The U.S. Air-Now network data in New Delhi report 1-hour average PM 2.5   concentrations between 1300 and 1486 µg m – 3   during Diwali for each year. As CAAQM does not report values in excess of 999.99 µg m – 3   for PM 2.5   our annual means based on CAAQM will likely be biased low in some locations. In sequences of 24 or more consecutive identical hourly values, only the first value out of the sequence is retained. Data were processed following the QA/QC procedure described below. The percentage of data removed due to this processing is provided in Tables S2(a) and S2(b).
• Diurnal mean values are calculated for criteria pollutants PM 10 , PM 5 , SO 2 , NO 2   and O 3   for each 12-hour day-night interval (between 6 am–6 pm and 6 pm–6 am (next day)), using a minimum of one hourly observation for each 12-hour period. Daily means are calculated only for days that have a daytime or nighttime mean value. For O 3 , daily mean (MDA8) values are calculated as the maximum of 8-hour moving averages over a 24-hour period using at least 6 hourly observations. For all pollutants, monthly mean values are calculated for months that have at least 8 daily mean values (at least 25% of observations). To obtain annual average concentrations, we calculate quarterly means and require at least one monthly mean value as input to each quarterly mean concentration. At least two quarterly mean values are used for calculating annual average concentrations. This procedure is followed to ensure representativeness of data in diurnal, daily, monthly, seasonal, annual and interannual timeseries.   Fig. 1   shows a flow chart describing the methodology for generating each step of the time-series.

  3 RESULTS

  3.1 strengths and weaknesses of available air quality datasets.

Until the start of 2018 the Indian monitoring network had limited extent. Very few stations have operated continuously from 2015 to the present. The number of stations in the continuous monitoring network has increased dramatically since 2017 ( Fig. 2 ) making it far more feasible now to evaluate air quality across India than in the past. However, spatial coverage is still limited with unequal distribution of monitors. All monitors are in cities, with a concentration in the largest cities, and none are in rural areas.   Fig. 3   shows the percentage of valid hourly observations, compared with total hours annually, from each CAAQM station between 2015 and 2019. Although the current data is sufficient to provide an overview of air quality across much of India, it is currently challenging to use air quality datasets to conduct long term trend analysis due to their limited spatial and temporal coverage.

  3.2 Spatial Distribution of Air Pollutants from 2015–2019

Figs. 4   and   5   show annual average concentrations of five criteria pollutants (PM 10 , PM 2.5 , SO 2 , NO 2   and O 3 ) at continuous and manual monitoring stations across India, from 2015 to 2019. The general distribution pattern of air pollution, showing higher pollution levels in northern than southern India, is captured in both the manual and continuous monitoring station data.

The number of continuous and manual monitoring stations have both increased substantially between 2015 and 2019 with 15 (147) CAAQM stations meeting our criteria for PM 10 , 33 (181) for PM 2.5 , 31 (163) for SO 2 , 34 (175) for NO 2   and 32 (168) for O 3   and in 2015 (2019) (see Figs. 4 and 5 for details of other years and manual stations). Of the total, nearly 60% of the CAAQM continuous monitoring stations are in northern India with 20% of the total stations in Delhi in 2019. Despite being a high pollution zone with nearly 15% of the Indian population ( http://up.gov.in/upstateglance.aspx ), the Indo Gangetic Plain has only 13% (9%) of total continuous (manual) monitoring stations. NAMP manual monitoring stations are more widely distributed than continuous monitors across India, with more monitors in the south and thus provide more representative spatial distributions of pollutants. However, they only provide annual average pollutant concentrations and thus cannot be used to analyze seasonal variations.

Elevated concentrations of PM 10   and PM 2.5   were recorded by both CAAQM and NAMP manual monitors across northern Indian states in all years, with particularly high concentrations across the Indo-Gangetic Plain (IGP). Ground observations of SO 2   are generally low across the country with high concentrations found at a few urban and industrial locations. This has been corroborated by previous studies (Guttikunda and Calori, 2013). The role of alkaline dust in scavenging SO 2   in India likely reduces ambient concentrations (Kulshrestha   et al. , 2003). In contrast, annual average NO 2   and MDA8 O 3   concentrations are highly variable depending on location with higher O 3   concentrations often seen in the IGP region.

  3.3 Annual Variation in Pollutant Concentrations in Northern and Southern India

The spatial distribution of pollutants is affected by meteorology, geography, topography, population density, location specific emission sources including industries, vehicular density, resuspended dust from poor land use management etc. In northern India (north of 23.5°N), higher population density and higher associated activities in industry, transport, power generation, seasonal crop residue burning, and more frequent dust storms contribute to higher particulate loads than in southern India (Sharma and Dixit, 2016; Cusworth   et al. , 2018). We observed significant differences between northern and southern India in the spatio-temporal patterns of PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3 .

Fig. 6   shows annual average concentrations (µg m – 3 ) of PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3   respectively, for northern and southern India (divided at 23.5°N) from CAAQM stations. The number of stations used to calculate annual average values is shown in Fig. 4 for each species. Annual average concentrations of PM 10 , PM 2.5 , and NO 2   are higher in northern India, whereas SO 2   and MDA8O 3   are similar in the north and the south. Annual average concentrations from CAAQM continuous and NAMP manual monitoring stations, combined (S1 a), and only manual monitoring Stations (S1 b) are plotted separately in Fig. S1. We found inter-annual variability but no significant annual trend in the timeseries of these pollutants. Annual average concentrations over the five year period in northern (and southern) India were: 197 ± 84 µg m – 3   (93 ± 30 µg m – 3 ) for PM 10 , 109 ± 29 µg m – 3   (47 ± 16 µg m – 3 ) for PM 2.5 , 12 ± 7 µg m – 3   (12 ± 10 µg m – 3 ) SO 2 , 35 ± 21 µg m – 3   (27 ± 16 µg m – 3   ) for NO 2   and 73 ± 29 µg m – 3   (66 ± 31 µg m – 3 ) for MDA8 O 3 . In the five-year period, annual NAAQS were met at approximately 3% of all CAAQM stations measuring PM 10 , 13% of PM 2.5 , 70% of NO 2   and 98% of SO 2   (Table S3). MDA8 O 3   standard of 100 µg m – 3   (to be met 98% of the time within a year) was met at 77% of all CAAQM stations between 2015–2019, inclusive. Particulate matter dominates the pollution mix with national average annual mean concentrations exceeding the NAAQ standard for all analyzed years and in northern India more than double the allowed concentration.   Fig. 7   shows annual average concentrations of these pollutants from CAAQM stations that meet our analysis criteria and are available each year from 2015 through 2019. The change in annual concentrations relative to the annual average concentrations in 2015–2017 at the stations operational throughout this period is shown in Fig. S2 in order to provide a comparison useful for evaluating the success of the NCAP.

  3.5 Seasonal and Monthly Patterns of Air Pollutants

Seasonal concentrations of air pollutants in India are heavily influenced by meteorology and location. Influence of meteorology on spatio-temporal distributions of pollutants across India is described in Section S3. Fig. S3 shows the mean seasonal distribution of boundary layer height, surface pressure, precipitation, and omega/vertical and horizontal wind velocity. We calculate seasonal and monthly concentrations of PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3   between 2015–2019 for northern and southern India in each season ( Fig. 8 ) and month ( Fig. 9 ) and show seasonal spatial distributions of these pollutants across India (Fig. S4). We analyze seasonal composites computed as averages for the spring or pre-monsoon period, March–April–May (MAM), the monsoon period, June–July–August (JJA), the autumn or post monsoon period, September–October–November (SON) and winter, December–January–February (DJF). In all seasons, substantially higher concentrations are observed for PM 10   and PM 2.5 , in northern India with concentrations of NO 2 , SO 2   and MDA8 O 3   only slightly more elevated in northern than southern India. The DJF average concentrations are highest for PM 10 , PM 2.5   and NO 2   in northern (southern) India: 270 ± 51 (137 ± 11) µg m –3 , 170 ± 26 (69 ± 2) µg m –3 , 47 ± 2 (35 ± 7) µg m –3 , respectively. Seasonal average concentrations of SO 2   peak in MAM in northern India (15 ± 3 µg m –3 ) and in DJF in southern India (16 ± 4 µg m –3 ), with highest concentrations in winter across the country. For DA8 O 3 , highest seasonal concentrations occur in MAM (DJF) in the north 71.8 ± 28 µg m –3   and south (84 ± 8 µg m –3 ).

Monthly variations in pollution are also a function of regional circulation patterns. The summer monsoon facilitates dilution of pollution via strong south-westerly winds from the Arabian Sea and wet scavenging of anthropogenic pollution (Zhu   et al. , 2012). Wet deposition removes PM 10 , PM 2.5   and water soluble SO 2   (Chin, 2012) leading to substantially lower ambient concentrations of these pollutants in JJA across India. Minimum concentrations of all pollutants occur in August.

Outside the monsoon, weak regional circulation and large scale high pressure systems result in accumulation of pollutants near the surface which is most pronounced in winter. Highest monthly concentrations are seen in November–January, inclusive, for PM 10 , PM 2.5 , SO 2   and NO 2 . For, MDA8O 3 , highest monthly concentrations are recorded in May (January) for northern (southern) India. Precursor emissions, surface temperature and solar insolation modulate a complex chemistry that drives the ozone cycle (Lu   et al. , 2018).

  3.6 Case studies of Delhi, Kolkata, Mumbai, Hyderabad and Chennai

Delhi, Kolkata, Mumbai, Hyderabad and Chennai are the five cities in India in which the U.S. State Department Air-Now network real time monitoring stations record PM 2.5   concentrations at the US embassy and consulates. In these five cities, we compare daily and monthly mean PM 2.5   measurements from the Air-Now and CAAQM networks.   Fig. 10   shows scatterplots between daily mean PM 2.5   from the Air-Now monitor located in each of the five cities with all CPCB CAAQM monitors in those cities for 2015–2019, inclusive. We find a good correlation between the daily average PM 2.5   concentrations from the two networks at all the cities (r > 0.8), except Chennai (r~0.47) where CPCB concentrations are biased higher than the Air-Now concentrations. On highly polluted days in Delhi, the Air-Now monitors report higher PM 2.5   concentrations than the CPCB monitors in part because Air-Now monitors are able to report hourly concentrations above 1000 µg m –3   while the CPCB monitors cannot.

We examine how concentrations of PM 10 , PM 2.5 , SO 2 , NO 2   and O 3   vary between cities in which Air-Now monitors exist from 2015–2019 (see Fig. 11).   Fig. 11   compares the monthly average concentrations of PM 2.5   between the two networks, examines the variation in concentrations over time for other species measured only by CPCB, and compares observed concentrations with the annual NAAQS for residential areas. Annual average concentrations from the stations combined in each city that meet our criteria is shown in Fig. S5 and a timeseries for each pollutant at each station is shown in Fig. S6. From CAAQM and Air-Now networks, we find Delhi has the highest daily, monthly mean and annual average concentrations of PM 10   and PM 2.5 , followed by Kolkata and Mumbai (Figs. 10, 11; Fig. S5).

For Delhi, between 2015 and 2019, annual average concentrations of PM 2.5   from the CAAQM station closest to the U.S. embassy (RK Puram, Delhi) greatly exceeded the residential NAAQS for PM 2.5   of 40 µg m –3   and ranged from 101 to 119 µg m –3   with the Air-Now station ranging from 95 to 124 µg m –3 . Chennai has the lowest monthly and annual average concentrations of PM 2.5 . The US state department annual average PM 2.5 values overall are consistent with the CAAQM stations and show a similar trend across cities. All five cities failed to meet the annual average CPCB PM 10   standard of 60 µg m –3   in all years.

Monthly and annual average SO 2   concentrations are far below the annual standard of 50 µg m –3   at all locations throughout the year in these five cities with Delhi reporting the highest annual average concentrations among the five cities followed by Mumbai. Starting in 2018 both Delhi and Mumbai had SO 2   concentrations lower than prior years.

Monthly average NO 2   concentrations are highest in Delhi in all years and starting in 2017, decrease from a peak over 100 µg m –3   in 2017 to a peak of 52 µg m –3   in 2019. Kolkata and Hyderabad also have relatively high concentrations of NO 2   with annual average concentrations exceeding the residential NAAQS of 40 µg m –3   starting in 2018.

Monthly MDA8 O 3   concentrations across all five cities are similar, particularly after 2018 and are generally falling below the residential 8-hour average NAAQS of 100 µg m 3 . Similar monthly tropospheric ozone concentrations in these cities, despite different levels of particulate matter, NO 2   and meteorology, make it a topic for further investigation.

  4 DISCUSSION

  4.1 growing dataset and existing gaps.

Prior to 2015 surface air quality monitoring data was available from only a few stations in India. Over the period we analyzed, 2015–2019, the number of monitoring stations across India increased dramatically. Our compilation and rigorous quality control of these data provide, for the first time, a comprehensive dataset of criteria pollutants that can be used to evaluate air pollutant concentrations simulated by atmospheric chemical transport models, satellite retrievals and reanalysis. Our dataset also provides a baseline for the NCAP. Previous studies have used ground observations from selected locations without transparently addressing existing data gaps and are not clear in their evaluation and quality assurance of surface observations. Here, we have carefully evaluated the archived data for completeness and accuracy, discarding values in excess of instrumental range, and requiring representative temporal coverage for each averaging period at each monitor. For example, for inclusion in our analysis a monitor measuring a species we analyze must report daily averages at least one hour per 12-hour daytime or night-time period, eight days for each monthly average, and one month per quarter and atleast two quarters for each annual average (see Tables S2(a), S2(b) and S3). However, spatial coverage remains spotty with monitoring stations predominantly located in large cities; smaller cities and rural locations lack coverage. Further expansion of the monitoring networks to facilitate an improved understanding of spatial distributions of pollutants across urban/rural India and to evaluate future trends in pollutant concentrations is needed. Very few stations provide valid observations continuously from 2015 onwards limiting our ability to analyze past trends in air quality. However, trend analyses starting in 2018 will be valuable and possible in the future.

  4.2 Differences in Air Quality Observations

We compare monthly, seasonal and annual mean concentrations of air pollutants we analyze with other studies that have analyzed surface measurements of the same pollutants, cities and time periods across India (Table S5). We find that the range of concentrations of criteria pollutants reported in our analysis of CPCB data are similar to the values presented in research studies using ground observations during the same period (Kota   et al. , 2018; Sreekanth   et al. , 2018; Guttikunda   et al. , 2019; Mahesh   et al. , 2019; Ravinder   et al. , 2019; Jain   et al. , 2020; Tyagi   et al. , 2020; Jat   et al. , 2021). However, as shown in Table S5, in case studies covering extreme events and studies in bigger cities and more polluted regions, like Delhi and the IGP, differences exist between the CPCB concentrations we calculate and those reported in the literature from surface monitoring stations, models and satellite data (Kota   et al. , 2018; Tyagi   et al. , 2019; Jat   et al. , 2021).

In   Fig. 12 , we compare the spatial patterns of annual average surface PM 2.5   concentrations derived from satellite data with measurements from the CPCB continuous network. The surface satellite concentrations were obtained by combining data from Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging Spectroradiometer), MAIAC (Multi Angle Implementation of Satellite Correction) and SeaWiFS (Sea Viewing Wide Field of View Sensor) satellite products and using the GEOS-Chem model to obtain gridded surface PM 2.5   concentrations at 0.05° × 0.05° (Hammer   et al. , 2020). The product we use is V4.GL.03 available at   https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.GL.03 . Reasonable agreement is seen between the annual mean surface concentrations of PM 2.5   derived from the satellite data and from the CPCB CAAQM observations from 2015-2019. Agreement is particularly good over the IGP and in central and southern India. However, along the western desert region (near Thar desert in Rajasthan), satellite concentrations of surface PM 2.5   (~40–50 µg m –3 ) were substantially lower than concentrations obtained from the CPCB CAAQM monitors (~80–100 µg m –3 ) for 2015–2017. In 2018 and 2019 the correspondence between the two datasets improved with most annual mean PM 2.5   concentrations in the western desert region generally between ~40 and 60 µg m –3 .

  5 CONCLUSIONS

This study provides the first comprehensive analysis of all existing government monitoring data available for PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3   using the continuous (CAAQM) and manual (NAMP) monitoring networks in India as well as the data from the US State Department Air-Now network, between 2015 and 2019 (2018 for NAMP). Our analysis shows that the Indian data record, in terms of number of monitoring stations, observations and quality of data, has improved significantly over this period. Despite the effort to augment surface monitoring infrastructure, gaps remain in spatial and temporal coverage and additional monitoring stations in small cities and rural areas are needed. Monitoring stations located in bigger cities (e.g., five Air-Now cities) have better data quality, from more widely distributed stations within the city, than is available for smaller cities. Pollution hotspots are occasionally found in smaller cities where monitoring stations are sparse. No stations have yet been placed in rural areas and are needed there in order to better characterize air quality and pollution sources across India (e.g., the effect of agricultural waste burning on air quality).

We find that fine particulate pollution dominates the pollution mix across India with virtually all sites in northern India (north of 23.5°N) exceeding the annual average PM 10   and PM 2.5   national residential ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India (south of 23.5°N) exceeding the PM 10   standard by 50% and PM 2.5   standard by 40%. Comparison of PM 2.5   surface observations from the CPCB continuous monitoring network with surface satellite concentrations finds good agreement across India, particularly for 2017 and 2018. Prior to 2017 CAAQM concentrations were substantially higher than indicated by the satellite data over the western desert region. Annual average SO 2 , NO 2   and MDA8 O 3   generally meet the residential NAAQS across India. We find that northern India has (~10%–130%) higher average concentrations of all pollutants than southern India, except for SO 2   where the concentrations are similar. Although inter-annual variability exists, no significant trend of these pollutants was observed over the five-year period except for a small decrease over time in PM 10   and PM 2.5   in winter, which is more pronounced in the stations in northern and central India.

Our analysis of surface measurements is valuable for evaluating air pollutant concentrations simulated in atmospheric chemistry models. We found good agreement between the annual average CAAQM PM 2.5   we analyzed and satellite derived surface PM 2.5   from Hammer   et al.   (2020). Our data set can also be used to evaluate satellite retrievals of NO 2   and O 3   as well as seasonal variability in PM 2.5   concentrations. Finally, India is targeting a reduction of 20–30% in particulate pollution under NCAP by 2024 relative to 2017. Our analysis from 2015–2019 at different spatial and temporal scales of surface pollution provides a baseline to evaluate the future success of the programme as well as aids in the assessment of existing and future air pollution mitigation policies.

  ADDITIONAL INFORMATION

  data access.

The raw data from the continuous CPCB monitors used in our analyses along with the code for data quality control and the calculation of various temporal averages is available at   https://doi.org/10.34770/60j3-yp02

  ACKNOWLEDGEMENTS

We thank Mi Zhou for early assistance in data processing and two anonymous reviewers for helpful suggestions to improve our manuscript. Funding for D.S. was provided by a Science, Technology and Environmental Policy fellowship at the Center for Policy Research on Energy and Environment at Princeton University.

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