research paper on pollution in india

Evolution of India's PM 2.5 pollution between 1998 and 2020 using global reanalysis fields coupled with satellite observations and fuel consumption patterns †

First published on 27th September 2022

General practice is to rely on ambient monitoring data for reporting and regulatory applications in air quality management. Based on data collated for 2021, Delhi ranked the most polluted capital city, and another 62 Indian cities are in the top 100 most polluted cities list (https://www.iqair.com). This path limits the scrutiny and evaluation only to the cities with a monitoring station, neglecting a large section of non-urban areas. In this paper, we present a summary of evolution of PM 2.5 pollution in India between 1998 and 2020, using reanalysed ground-level PM 2.5 concentrations estimated by combining satellite AOD retrievals with chemical transport model results from a GEOS-Chem-CEDS system, and subsequently calibrated to on-ground observations. Between 1998 and 2020, India's annual average PM 2.5 values steadily increased across the country, Delhi remained the most polluted state in all the years, and total population complying with the annual ambient standard of 40 μg m −3 dropped from 60.5% to 28.4%. According to the GBD-MAPS program, 81% of PM 2.5 pollution in India is sourced to fuel (coal, petrol, diesel, gas, biomass, and waste) combustion that supports daily activities in the fields of personal transport, freight transport, electricity generation, industrial manufacturing, cooking, heating, construction, road dust resuspension, and waste burning. We overlayed the pollution trend with fuel consumption and activity patterns to further explain this evolution. While the 2 month COVID-19 lockdown period in 2020 provides evidence to argue that the only way to achieve “clean air” is by (a) cutting emissions at all the sources and (b) cutting emissions regionally, some hard decisions are required to enable and sustain larger reductions across sectors to reach not only the national ambient standard, but also the WHO guideline of 5 μg m −3 .

1 Introduction

As of February 2022, there are 340 continuous ambient monitoring stations in India covering 174 cities. Of these, 148 cities operate only 1 station, which can only be used for guidance and cannot be used as a representative sample to study trends or base policy discussions on. However, this is 400% improvement on September 2017 numbers, when only 74 stations were operational in 43 cities. Delhi is the most spatially represented city with 40 stations, followed by Mumbai (21) as the only cities with more than 20 operational stations. This count is still less than the recommended number of at least 77 and 65 stations in Delhi and Mumbai, respectively, according to thumb rules established by the Central Pollution Control Board (CPCB). 2 India's monitor density of 0.25 per million population is not a representative sample for regulatory and research grade pollution analysis. 3,4 This density factor is the lowest among the big countries – China (1.2), the USA (3.4), Japan (0.5), Brazil (1.8) and most European countries (2–3). While the limited monitoring data are useful in building trends, studying compliance, and raising public awareness, a large portion of the country is neglected by focusing only on urban areas with monitors. 5

On the other hand, we have the atmospheric modelling community, combining a larger pool of data from multiple resources including satellite retrievals and bottom-up emission inventories coupled with chemical transport models, helping us build patterns in emissions, pollution, and activity data, all in the hope of plugging the gaps in ambient and emission monitoring data. 6–12 This path is data intensive and computationally challenging, and requires substantial personnel training to move forward from planning to execution. The applications of these systems have multiplied since the availability of satellite databases from NASA and ESA's open access portals and through analytical software like Google Earth Engine, which allows the user to analyse the data without downloading. Ref. 7 presented a version of community emissions data system (CEDS), which collates existing best-available global and regional emission inventories in a model-ready format. Besides the widely used global inventories like EDGAR 13,14 and GAINS-ECLIPSE, 15 the new system also includes regional inventories from China, 16 East-Asia, 17 India, 18 Nepal, 19,20 and Africa, 21 making the inventories relevant for regional chemical transport modelling applications. Ref. 10 and 11 present an inverse modelling technique using TROPOMI's NO 2 columnar observations to estimate emission loads for a city airshed and large point sources like coal-fired power plants. For known locations, this method can be used to crosscheck past inventories or even authenticate the reported stack emission rates. Ref. 12 developed algorithms to estimate PM 2.5 concentrations at 1 km resolution using a combination of MODIS-AOD, MERRA2 reanalysis AOD, and ground measurements for 20 years. While there is a lot of technical and personnel capacity building to take shape on ambient and emissions monitoring, these global databases provide a large backdrop to understand the growing air pollution problem in India, allowing us to identify the missing hotspots and authenticate the pollution loads at the source level.

While the kind of information gathered from monitoring and modelling exercises is different in shapes and sizes, both are integral pillars of an air quality management campaign. In this paper, we present a summary of evolution of PM 2.5 pollution in India between 1998 and 2020 using global reanalysis fields, coupled with discussion on trends in fuel consumption and activity patterns.

All the reanalysis and source contribution data are available at 0.01° resolution (∼1 km). These data were aggregated to 640 districts and 36 states and union territories, as per the 2011 census. The states include Telangana from the bifurcation of Andhra Pradesh in 2014. The state of Jammu & Kashmir (as per the 2011 census) includes the new union territories – Jammu & Kashmir and Ladakh. For convenience, data for years 1998, 2000, 2005, 2010, 2015, and 2020 only are presented in the figures and tables. All the extracted data by state and by district for all years are included in the ESI. † .

Annual gridded population data between 2000 and 2019 are obtained from the Oakridge National Labs LANDSCAN program. 23 Several resources for sectoral energy and emission discussion were accessed from their respective annual reports and open-access databases presented in the ESI. †

3 Observations and discussion

3.1 modelled annual averages.

Gridded concentrations at 0.1° (∼10 km) are overlapped with gridded population at the same resolution, to estimate the fractions of population exposed to various pollution bins ( Fig. 2 ). In these 23 years, total population complying with the annual ambient standard (green-shade bars) dropped from 60.5% to 28.4%, with most of this change coming from non-urban areas in the IGP. The biggest change in %exposed is on the lower concentration side – 5–20 μg m −3 , which is synonymous to background concentrations. In 2020, only a small portion of India's population lived in areas complying with the World Health Organization (WHO)'s new guideline of 5 μg m −3 . In the same period, the population exposed to poor, very poor, and severe AQI levels (red-shade bars) increased from 0.0% to 17.8%, which is an indication of urban areas spreading their emission footprints. Traditionally, these increases are observed over cities and now visible over rural areas. According to the 2011 census, 2774 rural settlements were reclassified as urban settlements, pushing the total to 3894. This number is expected to cross 6000 in the new census. There are at least 51 cities with more than 1 million inhabitants and the urban population is expected to grow from 30% to 50% by 2050.

At the state level, Delhi remained the most polluted state for all the years (1998–2020). A summary of state average concentrations and ranks among the 36 states and union territories is presented in Table 2 (36 being the least polluted and 1 being the most polluted). The most polluted states are from the landlocked IGP – Punjab, Haryana, Delhi, Uttar Pradesh, Bihar, and West Bengal, together hosting 400 + million people. Telangana and Andhra Pradesh moved 6 spots up on the most polluted list from 24 to 18 and from 30 to 24, respectively. Maharashtra with 18 non-attainment cities under the NCAP (the most) moved from 19 to 15. Puducherry (coastal, union territory) is the most improved and moved from 22 to 29. Overall, annual average PM 2.5 concentrations worsened in all the states. Between 1998 and 2020, Jharkhand, Maharashtra, Odisha, and West Bengal's averages increased 80–90% – these are also the states which experienced an increase in the capacity and number of coal-fired thermal power plants. 27,28 The most polluted state, Delhi, increased 40% from 80 μg m −3 to 111 μg m −3 . An improvement between 2010 and 2020 is primarily due to the introduction of newer fuel and vehicle standards, promotion of piped liquified natural gas (LPG), expansion of LPG connections, and completion of Western and Eastern freight corridors. On average, PM 2.5 pollution levels in Northern, IGP, Western, Northeast, Central-East, and Southern India and Union Territories increased 28%, 53%, 32%, 46%, 79%, 64%, and 42% respectively.

A summary of PM 2.5 averages for districts covering the capital or large cities is presented in Table 3 . Between 1998 and 2020, pollution levels increased in all the cities. Average PM 2.5 pollution levels in Northern, IGP, Western, Northeast, Central-East, and Southern India and Union Territories increased 25%, 47%, 35%, 55%, 70%, 60%, and 65% respectively. In 2020, only the cities from Southern, Northeast, and North India averaged under (or near) the national standard of 40 μg m −3 and the most polluted cities are in IGP and Central-East regions, with some cities averaging above 100 μg m −3 . This reflects population density linked to the domestic and commercial demand for amenities and transport, higher density of industrial corridors including power plants, and land-locked geography with limited cross-ventilation in the Northern Plains. Also, these results are based on a global GEOS-chem modelling system with a spatial resolution of 0.5° (∼50 km). While the model is shown to capture the range of pollution as a comparison of modelled and monitored averages, 6,8 we cannot expect the model to represent the spatial variations within an urban extent, which will be higher.

3.2 Modelled seasonal averages

Like annual averages ( Fig. 1 ), seasonal averages also gradually increased across India. The highest concentrations are observed in DJF, followed by SON, MAM and JJA. All the seasons have a base emissions rate linked to all the known sectors – passenger and freight movement, heavy and light industries, residential cooking and lighting, road and construction dust, and waste burning. The overall increase in the season averages between 2000 and 2020 is a consequence of consistent increase in activity and fuel consumption levels in all the sectors.

Besides these everyday emissions, there are unique seasonal features either driving the emissions up or reducing the pollution down. Surface temperatures during the winter months over North India is expected to drop under 10 °C driving the need for space heating, which is often met by burning wood, coal, cow dung, and in some cases also waste, thus driving the emission load in the region. During this period, the mixing layer heights are also the lowest, thus more than doubling the impact of rising emissions. 30,31 The fall months also experience a substantial increase in the emissions via post-harvest agricultural residue burning for 2–4 weeks in Oct–Nov and onset of western disturbance resulting in slow movement of pollution over the IGP, increased residence for the chemical reactions and formation of secondary particulates. 32,33 Burning of agricultural residue and forest fires are observed across India over various months, but their impact is most tracked and analysed in Oct–Nov over Punjab, Haryana, and Delhi. The spring months experience sporadic dust storms from the West. The monsoon months are marked with substantial rains across the sub-continent, scavenging most of the particulates in the air. Besides worsening pollution levels over the IGP, a gradual increase in the power plant emissions and resulting PM 2.5 pollution over Central India, especially the Korba region of Chhattisgarh, is noteworthy. 27 In 2020, MAM includes adjustments for emission reductions observed during the lockdown periods of COVID-19's first wave, 34–36 which is reflected in the change in the colour shades, unlike other seasons which continued to show an increase in the seasonal averages.

3.3 Modelled source contributions

A summary of source contributions averaged by state is presented in Table 4 . Residential cooking and heating using coal and biomass is the largest contributor in all the states, ranging between 19.0% in Chhattisgarh to 40.0% in Sikkim, followed by light and heavy industries and electricity generation. The household cooking and heating activities are also the only source of pollution linked to indoor health impacts. 9 In rural areas, a combination of coal, wood, cow dung, crop residue, and shrubs is a major source of residential fuel. While the rate of consumption of LPG and electricity is increasing in urban areas, the reconnection and refilling rates are still low in rural areas. 38 The removal of subsidies for low-income groups in 2020 also led to a decline in the rural refilling rates.

Coal-fired thermal power plants in Central-Eastern states exhibited contributions of 14.6–21.5% annually – most among the regions. Nearly 50% of the installed coal-based power generation capacity is operating in this region and delay in the implementation of emission standards at existing and new coal-fired power plants will increase this share in the coming years. 27,39

While the transport sector (road and shipping) as a national average is only 7.4%, the same analysis conducted at a finer resolution around an urban airshed is likely to produce a different transport-centric result. Variations in PM 2.5 concentrations within an urban extent for most of the cities in Table 4 are presented as part of the air pollution knowledge assessments (APnA) program which included emissions and chemical transport modelling at 0.01° (∼1 km) resolution. 40,41 From urban scale simulations, PM 2.5 pollution linked to vehicle exhaust and on-road resuspended dust ranged from 25–50%, with hotspots along the major road corridors and at local industrial estates including brick kilns. A summary of source apportionment results for 50 airsheds in India is included in the ESI † and discussed in detail here. 1 This is one of the main bottlenecks for extending global simulations for urban-centric analysis, where the influential area for creating a representative picture are limited and often smaller than the model grid size (for example, 0.5° in the case of GEOS-chem, which is approximately 50 km and most of the cities in India extend only up to 30–40 km). Ref. 12 extended the reanalysis fields from MERRA2 using a regression model combined with satellite derived AOD and ground measurements to build a higher resolution (1 km) PM 2.5 concentration database for India. While the model can capture similar spatial and temporal patterns at the regional scale, it also fails to capture variability within an urban extent, because of the input (MERRA2) resolution.

On-road dust resuspension, other anthropogenic dust sources and wind-blown dust account for 15.8% as a national average, which is consistent with the physical nature of PM 2.5 . The same fraction will be higher in the PM 10 concentrations (not included in this paper) with most of the dust size falling in the coarse-fraction (PM 2.5 –PM 10 ) category. The population in Gujarat and Rajasthan is exposed to this source fraction the most (22.3% and 19.2% respectively) consistent with the arid and dry nature of the region, hosting a large portion of the Thar Desert. Dust storms are most common during the months of April and May, further increasing its share. 42

Biomass (including post-harvest agricultural waste) burning is a major source pollution in Oct–Nov in the North-Western states, with reported highs of 50–80% peak contributions on days of severe to emergency category AQI pollution levels. 44,45 While this is the most discussed source in the media during the beginning of Delhi's winter peaks, on an annual basis this category accounts for only 3.0% of its average PM 2.5 levels. Higher annual shares of up to 12.8% are observed in the Northeast, which includes open forest fires peaking in May.

3.4 How sectors evolved

Between 1995 and 2020, transport sector improved its standards 6 times, with the sulphur content reducing from 1% to 10 ppm. The Bharat-VI vehicle standards were introduced in 2020 and fuel standards were introduced in 2018 in Delhi and nationwide in 2020. While the standards improved periodically, most of these gains were lost to rapid increase in the number of vehicles on roads and their usage. 46–48 The total number of registered vehicles in India touched 295 million in 2019. Of this 220 (75%), 38 (13%), 2 (0.7%), 14 (5%), and 20 (7%) million were 2-wheelers (2 Ws), 4-wheelers (4 Ws – including jeeps and sport utility vehicles), buses (intra- and inter-city), goods (light- and heavy-vehicles), and other non-road vehicles, respectively. Between 2011 and 2019, the total number of vehicles doubled and between 2000 and 2019, it increased 6 times ( Fig. 5a ). During these periods, passenger (2 Ws and 4 Ws) and commercial (buses and freight) vehicles had similar growth rates. The passenger vehicle usage also increased, 12.3 times to 22.6 billion passengers per km and freight movement increased 5.8 times to 2.7 billion tonnes per km negating most of the gains from fuel and vehicle standards introduced prior to Bharat VI ( Fig. 5b ).

The total fuel consumption in India is recorded at 26.3, 30.0, 83.2, 2.4, 8.0, and 42.8 million tonnes in 2020 for LPG, petrol, diesel, kerosene, aviation turbine fuel (ATF), and others, respectively. Compared to 2000, this is 3.8, 4.5, 2.1, 0.2, 3.6, and 1.1 times, respectively ( Fig. 5c ). Only fuel to show a decline in consumption in kerosene, mostly used for lighting and for some cooking in rural areas. Delhi was the first city to declare kerosene free in 2014, after its imports were banned in 2003. LPG is mostly consumed in the residential sector and some in the transport sector where petrol or diesel to gas conversion kits were available. A bump in the use of LPG is due to the start of Pradhan Mantri's Ujjwala Yojana (PMUY) in 2016, which added 90 million new connections across India in January 2022. Petrol is mostly consumed in the passenger vehicle sector. With an increase in the number of vehicles and their usage, total fuel consumption also increased over the years. Diesel is ∼30% of the total fuel consumed in 2020 and is used among all sectors – some passenger vehicles, all the heavy-duty vehicles including buses, non-road vehicles like tractors, generator sets at residential, commercial, and industrial locations, and agricultural pump sets. There is some decline in the use of diesel generator sets in cities, with more access to uninterrupted power supply from the grids and an addition of 50 GW of renewables in 2022. 49 The other fuels category includes naphtha, fuel oil, lubricants, waxes, and refinery oil, mostly consumed in various industrial processes.

The consumption of petroleum coke (petcoke) increased dramatically in 2011–12 after the introduction of INR 400 coal tax per ton ( Fig. 5d ). Petcoke is one of the dirtiest fractions of crude processing with up to 6% of sulphur content and mostly used as a replacement of coal in the iron and steel and cement industries. 50 In 2020, the total consumption of petcoke was recorded at 21.7 million tonnes, peaking at 25.6 million tonnes in 2018. In 2017, there were notions by the Environment Pollution (Prevention & Control) Authority (EPCA) to ban the import of petcoke, limit its use in the non-cement sector, and declare petcoke as a “regular fuel”.

The total electricity consumed in India is 1227 TWh in 2020–21 between industry (41%), agriculture (18%), domestic (26%), commercial (8%), railways (2%), and others (6%), which is on average 4.0 times the consumption levels in 2000–01 ( Fig. 5e ). Between 2000 and 2020, the average per-capita consumption doubled from 600 to 1200 kWh. Coal, diesel, and gas are responsible for 75% of 388 GW of installed power generation capacity with an average of 20% transmission losses. The total coal consumption in India was 910 million tonnes in 2019–20, which is on average 2.6 times the consumption levels in 2000–01 ( Fig. 5f ). Approximately 65% is utilized at power plants, followed by heavy industries like iron and steel, cement, and fertilizers, and some in the domestic sector for cooking and heating. Around 70% of the coal is mined in India and most of the imported coal is consumed in heavy industries to benefit from its low ash content and higher calorific value. Environmental regulations for coal-fired thermal power plants were amended in 2015, strengthening PM emission standards and introducing standards for SO 2 , NO x , and mercury. 28 However, the implementation of these regulations is delayed for various reasons – the lack of preparedness from the operators due to covid-19 lockdowns in 2020–21. The share of SO 2 and NO x emissions from the power plants is the largest, which contribute to PM 2.5 concentrations via chemical transformation in the form of sulphates and nitrates over long distances. The full implementation of the new regulations is expected to reduce power plant contributions to total PM 2.5 concentrations from current and planned units by up to 80%. 27

4 Conclusions

On average, every lockdown period witnessed at least 25% drop in PM pollution across India, most (as high as 70%) coming from cities. PM pollution is affected by all the known sources and all the regulations discussed above led to these drops. 35,52 A climatological analysis of the satellite retrieval based AOD estimated a drop of 50% in the PM 2.5 concentrations at the start of the lockdowns and slowing catching up to the decadal averages at the end of the 4 th lockdown. 53

The 2-month period provides evidence to argue that the only way to achieve “clean air” is by (a) cutting emissions at all the sources and (b) cutting emissions regionally and not just in cities as envisioned under the NCAP. While some programs like the promotion of electric vehicles for personal and public transport, increasing the number of LPG connections and access to refills in non-urban areas, and building of renewable-based power generation plants are helping to reduce the pollution burden in small doses, some hard decisions are required to enable and sustain larger reductions across sectors to reach both the national ambient standard of 40 μg m −3 and the WHO guideline of 5 μg m −3 for PM 2.5 pollution in India.

Author contributions

Conflicts of interest, acknowledgements.

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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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Air pollution health research priorities for India: Perspectives of the Indo-U.S. Communities of Researchers

Terry gordon.

a Department of Environmental Medicine, New York University School of Medicine, Tuxedo, NY, 10987, United States of America

Kalpana Balakrishnan

b Department of Environmental Health Engineering, Faculty of Public Health, Sri Ramachandra University, Porur, Chennai, 600116, India

c Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

Sanjay Rajagopalan

d Department of Internal Medicine, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States of America

Jonathan Thornburg

e RTI International, Research Triangle Park, NC, United States of America

George Thurston

Anurag agrawal.

f CSIR Institute of Genomics and Integrative Biology, Delhi University, New Delhi, India

Gwen Collman

g Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, United States of America

Randeep Guleria

h All India Institute of Medical Sciences, New Delhi, India

Sneha Limaye

i Chest Research Foundation, Pune, India

Sundeep Salvi

Vasu kilaru.

j Office of Research and Development, U.S. E.P.A., Research Triangle Park, NC 27711, United States of America

Srikanth Nadadur

1. basis for document on air pollution research gaps in india.

This white paper represents the culmination of over 2 years of efforts and bilateral dialog between scientists at Indian governmental agencies, U.S. federal agencies, and academic institutions in India and the U.S. to develop strategies to mitigate air pollution-related health effects and to promote collaborative research initiatives to accelerate a scientific knowledge base that may help accomplish this goal. A series of virtual meetings initiated by the National Institute of Environmental Health Sciences (NIEHS) led to the formation of a Communities of Researchers (CoRs) organized around three themes of health research, exposure assessment, and training. Virtual meetings and visits of U.S. scientists at laboratories in India were held over 18 months. The CoRs were successful in identifying gaps in research in the three theme areas and providing research recommendations that were discussed at length at a joint Indo-US Workshop to “Explore Bilateral Research Opportunities to Address Air Quality and Health Issues” in New Delhi, India on November 8–10, 2016. This workshop was jointly sponsored by NIEHS and the Indo-US Science and Technology Forum (IUSSTF) with additional support provided by the Centers for Disease Control (CDC) and the Research Triangle Institute (RTI) and US Embassy, New Delhi, India. The information gathered through all these efforts, including the bilateral workshop, provided the direct basis of this document. The workshop participants also thoroughly reviewed the recent Indian Ministry of Health and Family Welfare report ( MHFW (Ministry of Health and Family Welfare), 2015 ), that provided a comprehensive account on the status of air quality-related health issues in India, and targeted actions aimed at providing the largest exposure reductions (instead of traditional approaches to air quality management) to address the substantial national health burden that can be attributed to both ambient and household air pollution in India ( Sagar et al., 2016 ).

A core group of eight scientists representing each CoRs, through multiple conference calls and exchange of information, developed a set of charge questions for broader discussion at the workshop in New Delhi with the participation of about 100 scientists in the areas of health, exposure and education. The questions were based on the previously identified gaps in knowledge to facilitate a focused discussion around the defined themes of exposure assessment and air pollution health research. Each breakout group consisted of approximately 40 scientists that separately and together deliberated on the major questions. An additional goal was to identify potential opportunities for collaboration and exchange of expertise between the U.S. and India. These collaborations would integrate exposure and health outcomes analyses to demonstrate the health burden due to high levels of air pollution in the Indian population at large. Discussions were also aimed at developing a focused set of research priorities with shared expertise that may be jointly supported by the U.S. and India and identifying critical needs in training and capacity building with advanced technical expertise in air pollution exposure assessment, modeling, and population health research.

2. Background

According to the global air pollution observatory maintained by World Health Organization ( http://www.who.int/gho/phe/outdoor_air_pollution/en/ ), 13 of the world's 20 cities with the highest annual levels of particulate matter < 2.5 μm in diameter (PM 2.5 ) are in India, with its capital city New Delhi leading cities within India. Given the challenges in regulation, increasing economic activity, and industrialization across the country, a progressive worsening of ambient air pollution (AAP) in these Indian cities is nearly certain. To add to this burden of AAP, nearly 76% of rural households are dependent on solid biomass as cooking fuels and thus experience household air pollution (HOAP) exposures that greatly exceed World Health Organization (WHO) Air Quality Guideline (AQG) levels ( Balakrishnan et al., 2013 ).

The report on Global Burden of Disease (GBD) estimates 2 million premature deaths annually in India due to AAP and HOAP exposure ( GBD (Global Burden of Disease), 2016 ). This places air pollution near or at the top of the list of all known risk factors for ill health in the country, above high blood pressure, smoking, child and maternal malnutrition, and risk factors for diabetes. However, given the current demographics in India skew towards a more youthful population, there may be a masking of the cumulative cardio-pulmonary effects of air pollution exposures, that will only be observable as latent effect decades later. If PM 2.5 levels were to remain constant at current levels, it would suggest that the per-capita mortality attributable to PM 2.5 in India would increase by 21% in the year 2030, aided and abetted by a dramatic increase in the age > 50 population. To even keep PM 2.5 -attributable mortality rates (deaths per 100,000 people per year) constant, the average PM 2.5 levels would need to be reduced by 20–30% over the next 15 years, particularly to reduce the increases in PM 2.5 -attributable mortality in the elderly. Thus, significant improvements in indoor and outdoor air quality are required to produce a significant reduction in PM 2.5 -attributable mortality in India ( Apte et al., 2015 ).

The strategic direction for air quality improvement in India is hampered by the lack of adequate inventories on emissions and uncertainty in the pollution mixture in ambient air ( Garaga et al., 2018 ). It is likely that both factors differ from what is observed in developed economies of the West. Source emissions can differ significantly in composition, but only limited research in India has addressed the role of PM 2.5 source and composition on adverse health effects. Initial results suggest that emissions from fossil fuel combustion are of greater health consequence, per μg/m 3 of PM 2.5 , than from biomass or windblown sources ( Thurston and Balmes, 2017 ). However, emissions from indoor cooking using biomass contribute to approximately one-quarter of the mass of ambient PM 2.5 pollution in the country, suggesting India-specific solutions are needed to additionally address these sources ( Chafe et al., 2014 ). In India, exposure to ambient and household air pollution forms a continuum, owing to the significant penetration of AAP indoors and the substantial contribution of HOAP to outdoor levels. Thus, indoor and outdoor sources cannot be considered in isolation or examined in a compartmentalized manner, but rather as a continuum ( Balakrishnan et al., 2014 ) with a common impact on health outcomes and common approaches to measure, quantify, and control diverse sources that contribute to this problem.

The risk of exposure to air pollution occurs in both rural and urban populations, however, the routine monitoring of air quality, in India and many countries across the globe, is nearly exclusively confined to urban centers ( Garaga et al., 2018 ). This makes the task of understanding the nature and distribution of nationwide population exposures much more difficult. Another important aspect relates to the spectrum of exposures. In India, exposure to locally strong sources such as biomass cooking, trash burning, street food carts, and small industries contribute to large spatial gradients in exposures that are poorly captured by outdoor ambient levels measured at central sites ( Pant et al., 2016 ). Given the high levels of ambient PM 2.5 in metropolitan areas, where the contributions of HOAP may be dissimilar compared to rural settings, derived exposure-response relationships will be different and need to be addressed adequately in estimating health impacts at the national level (i.e., the PM 2.5 mixtures are likely to vary significantly between urban and rural populations, and one can also expect the exposure-response relationships to also vary between these two populations). Further considerations in India include additional exposures to air toxics and heavy metals, which are highly prevalent in urban industrial clusters. Several toxic pollutants are co-emitted along with PM and contribute to adverse health risks ( Pant et al., 2016 ; Guo et al., 2017 ). A comprehensive environmental health assessment should consider factors such as toxicity, emissions volume, potential population exposed, exposure pathways, and health outcomes.

3. Air pollution sources, concentration, and exposure scenarios in India

This section provides a background summary on AAP and HOAP exposures relative to the major sources and emissions in India. Understanding of the unique exposure profiles to air pollution is critical to better understanding of the magnitude of health effects attributable to air pollution and provision of recommendations.

3.1. Ambient air pollution (AAP)

The rapid growth in the industrial, power, and transportation sectors nationally, combined with growth in urbanization, both planned and unplanned, have contributed to the rapid increase in AAP levels in India. Together, the substantial growth in the number of automobiles and coal-based power production are expected to significantly contribute to the worsening of air quality in the next decade in India. For more than half of the cities included in the National Air Quality Monitoring Program (NAMP), two critical measures, PM 2.5 and PM 10 (daily and annual levels), routinely exceed Interim Target-1 levels (75 and 150 μg/m 3 for daily and 35 and 70 μg/m 3 annually, respectively), as designated by the WHO.

The assessment of the contribution of emission sources to air pollution is inadequate in India, and limited data are available for sulfur dioxide (SO 2 ), nitrogen oxides (NOx), and carbon monoxide (CO). Although a decline in the levels of SO 2 has been observed in many cities, these levels are still unhealthy. While the monitoring of ambient air pollution has increased, at least in major cities, there are substantial gaps in monitoring in large parts of the country, especially in rural locations ( http://www.cpcb.nic.in/RealTimeAirQualityData.php ). Given the critical need to provide a national map of air pollution, remote sensing methodologies are increasingly relied upon and felt to be critical to provide exposure-health effects data and to develop source apportionment information vital to assessment of intervention strategies. Indeed, satellite modeling of aerosol optical depth (AOD) has demonstrated a stark picture of AAP in both urban and rural areas. The Indo-Gangetic Plain registered critical levels of PM 2.5 (mean annual PM 2.5 > 50 μg/m 3 ) attributable to multiple ambient sources, the use of biomass and coal for household cooking and heating needs, and the burning of agricultural residue ( Dey et al., 2012 ).

3.2. Household air pollution (HOAP)

Solid cook fuel emissions result primarily from incomplete combustion. The traditional stoves are extensively used in rural Indian households and typically operate under inefficient conditions of combustion and emit hundreds of different chemical substances, during the burning of solid fuels. Whereas the national air pollution monitoring program in India provides routine air pollution concentrations for many urban centers, the currently available data on HOAP exposures are largely known from scientific publications from individual research studies. Over 200 studies have characterized HOAP exposures in solid fuel-using households of developing countries and a majority of these studies are primarily based in India. The exposure assessment methodology has included questionnaire-based assessments, actual long-term field-based measurements of HOAP, and personal exposures for women, men, and children. A compilation of the quantitative HOAP results, including those in India, is available in the WHO Global Household Air Pollution Database ( Balakrishnan et al., 2014 ).

HOAP exposures are well known to be heterogeneous in composition and have multiple determinants such as fuel/stove type, kitchen area ventilation, and quantity of fuel, age, gender, and time spent near the cooking area. These factors influence spatial and temporal patterns of exposures between as well as within households. Regardless of the variability in results across studies, however, the data provide evidence of extreme exposures in communities using solid cook-fuel to exceed, by several fold, WHO-AQGs.

More recently, due to lack of direct studies quantifying the potential mortality effects of HOAP, modeling approaches such as Integrated-Exposure-Response Curves ( Burnett et al., 2014 ) were utilized to link population-level estimates of HOAP exposure with health outcomes, analysis. This model still needs verification in real-world HOAP-exposed populations, as the HOAP composition and levels of exposure in developing countries is different from developed world HOAP and AAP. The global exposure estimate for solid fuel users in the 2010 Global Burden of Disease assessment ( Lim et al., 2012 ; Smith et al., 2014 ) was based upon an India exposure model ( Balakrishnan et al., 2013 ). This model utilized PM 2.5 measurements obtained in rural households and cooking-related household data available in the National Family Health Survey. These models, developed for solid fuel-using households, determined that PM 2.5 concentrations were up to 337 μg/m 3 ( Balakrishnan et al., 2013 ), and exceed the current WHO-AQG IT-1 of 35 μg/m 3 ( WHO, 2006 ) and the Indian standard of 40 μg/m 3 ( Central Pollution Control Board, 2009 ).

3.3. Sources of air pollution in India

Air pollution sources in urban and rural environments include stationary, mobile, and region-specific emissions. The most commonly identified region-specific emission sources in India are motor vehicles, manufacturing, electricity generation, construction, road dust, agricultural waste burning, combustion of oil, coal, and biomass in the households, and marine/sea salt. The Indo-Gangetic Plain, for instance, has a large number of brick kilns, and is also home to a large population still using biomass and/or coal for combustion needs. The states in the east of India, such as Bihar, West Bengal, Jharkhand, Orissa, and Chhattisgarh, harbor large coal mines and power plants in conjunction with extensive biomass use, making these areas among the most polluted parts of the country.

Vehicular emissions represent the most rapidly growing source of air pollution in cities and urban neighborhoods. Thirty cities have at least 30% of the households which own a 2 wheel motor vehicle, and 19 major cities have at least 10% households with a 4 wheel car or utility vehicle. The transport sector has seen an exponential increase in highway freight and the use of diesel. Using GIS-based methods, Jerrett and colleagues have determined that nearly 55% of the population (~7.8 million people) in Delhi resides within 500 m of roads, and is, therefore, at increased risk from traffic pollution ( Jerrett et al., 2010 ). Exposure assessment of commuter exposures in Delhi ( Apte et al., 2011 ) has demonstrated that the in-vehicle PM 2.5 concentrations are at least 1.5 times higher than corresponding ambient PM 2.5 concentrations.

In addition to vehicular sources in India, there are several other unique sources that contribute to a substantial proportion of AAP. These include trash burning, unregulated use of personal diesel generator sets, brick kilns and local industries, and power plants. Local dust sources such as areas with little green cover, construction sites, and resuspension of road dust, are major contributors to PM levels. The air quality in cities is often heightened by meteorology with substantial variations in temperature, humidity, and rainfall between winter and summer. The use of biomass, primarily for heating, is thought to be responsible for as much as 30% of PM pollution in winter and, together with weather factors such as “inversions” and the burning of agricultural waste, may significantly elevate winter levels, particularly in large cities such as Delhi. In 2010, the portion of outdoor combustion-derived PM 2.5 pollution attributable to indoor solid fuel-cooking was estimated to be around 26% in India ( Chafe et al., 2014 ). These indoor sources are a substantial contribution to outdoor air pollution and, thus, are particularly important from a health perspective as the indoor-generated air pollutants can silently and disproportionately contribute to ambient air pollution in rural/semi-urban areas.

4. Air-quality monitoring in India

The air monitoring network in India is rapidly expanding. AAP information is collected primarily by the Government of India's National Air Quality Monitoring Program (NAMP), administered by the Central Pollution Control Board (CPCB), which is part of the Ministry of Environment, Forests and Climate Change, Government of India. The primary responsibility for conducting air quality monitoring is shared by the CPCB (national level) and the State Pollution Control Boards (state level), as well as the Pollution Control Committees in Union territories. The National Environmental Engineering Research Institute (NEERI) and a few academic research institutes also carry out pollution monitoring but on a limited scale. Delhi and Pune have citywide monitoring networks, administered by the Ministry of Earth Sciences, which are outside the NAMP monitoring network ( Beig et al., 2013 ). Measurements of criteria air pollutants have also been reported in many individual studies. The CPCB revised National Ambient Air Quality Standards in 2009 to include 12 pollutants (SO 2 , NO 2 , PM 10 , PM 2.5 , ozone, lead, arsenic, nickel, CO, NH 3 , benzene, and benzo-a-pyrene (BaP)) with regular monitoring of SO 2 , NO 2 , and PM 10 by the NAMP, while ozone is monitored in a few mega cities. Other pollutants, such as air toxics (benzene, toluene, and xylene - BTX), BaP, arsenic, and nickel are being monitored on a more limited scale but the capacities are being expanded. Although ozone is listed as a regulated pollutant, its monitoring is extremely limited in a few cities and is currently not reported under NAMP.

Although there is a wish to have a more concerted effort to monitor air pollutants across India (about 248 cities in India as of May 2015), there is a time lag in data reporting and the specific monitoring capacity for PM 2.5 is very limited. The MOEF/CPCB have recently developed an 11 city network which provides air quality index (AQI) and Bulletins, based upon ozone, PM 2.5 , PM 10 , NO 2 , among others, in near real-time. To address the paucity of ambient air quality data in rural areas, hybrid models that utilize satellite data in conjunction with emissions inventories have been used to derive estimates of global ground-level PM 2.5 concentrations ( van Donkelaar et al., 2010 ; Brauer et al., 2012 ). These first-level estimates constitute the primary exposure metrics for the GBD assessment for AAP. These studies have led to further understanding of the regional distribution of air pollution in India and the finding that PM 2.5 concentrations in the populated rural areas of the Indo Gangetic Basin (IGB) are higher than the PM 2.5 concentrations in many urban centers located within the Indian peninsula. The satellite extraction data are available at only 0.1° resolution (~10 km), so there is uncertainty associated with these derivative measurements. Inclusion of in-situ measurements and an analysis of spatio-temporal variability in relation to WHO-AQG levels have been evaluated as refinements to this method ( Dey et al., 2012 ).

4.1. Personal monitoring

Air pollution concentrations measured at fixed locations can differ substantially from those derived from personal exposure assessments made in the same microenvironments ( Breslin et al., 1967 ; Rodes et al., 1991 ; Pant et al., 2016 ). Personal exposures are dependent on many influencing factors, especially the time-weighted proximity to emissions from both distant and localized sources. Fixed-location monitors do not incorporate the element of proximity, nor do they account for time the person is near the source, thereby leading to potential exposure misclassification ( Rodes and Thornburg, 2012 ). Exposure misclassification can bias the relative risk estimates measured in epidemiological studies ( Flegal et al., 1986 ) or the (pre-) clinical associations of physiological changes resulting from air pollution exposure ( Brook et al., 2011 ).

The continued evolution of more powerful and lower cost microelectronics in conjunction with new manufacturing practices such as 3D printing has enabled the development of low-burden, low-cost air pollution sensors. A plethora of new air pollution sensors designed specifically as wearable systems for personal exposure monitoring are now available for use in research and citizen scientist studies. These new systems have a lower participant burden compared to older personal exposure systems ( Williams et al., 2009 ), and the weight and noise reductions translate to higher study protocol compliance among participants thereby increasing the quality of the exposure data ( Lawless et al., 2012 ). Low burden personal exposure monitors also enable exposure monitoring of children as young as 3 years old that have greater susceptibility to the health impacts of air pollution ( Chartier, 2016 ). Commercially available sensors from North American, European, and Chinese manufacturers are available in India. The reliability and data quality varies greatly between individual sensors so a user must carefully review independently generated performance data or collect their own data ( Williams et al., 2014a ; Williams et al., 2014b ; http://www.aqmd.gov/aq-spec ). Also, in many cases, the low cost sensors have a limited range of linear response and therefore calibration may be necessary. Since 2016, > 20 India-based companies have started producing low-cost air quality sensors; however, the wide-scale availability and quality of these sensors are unknown.

Personal exposure monitoring has an important role in future Indian air pollution health research because exposure misclassification can be minimized. Exposure characterization studies using wearable sensors can identify the sources of AAP or HOAP that comprise a population's exposure to PM, NO 2 , O 3 , and other gases ( RTI International, 2013 ). Given the high AAP and HOAP concentrations in areas like the Indo-Gangetic Plain, exposure characterization studies will provide preliminary data to guide policy interventions to reduce AAP or HOAP. Exposure characterization data will also inform the design of studies to link air pollutants to specific acute and chronic disease exacerbations ( Dutmer et al., 2015 ). Eventually, large scale epidemiological health affects studies that use a combination of personal exposure monitoring, satellite measurements, and traditional ambient air quality measurement networks will provide quantitative data for assessing the health impacts of air pollution in India where the dynamic economic, cultural, and climatological conditions may hinder traditional approaches.

4.2. Air quality standards in India

The Indian Central Pollution Control Board (2009) revised the annual average PM 10 to 60 μg/m 3 which is lower than the Interim Target 1 (IT-1) guideline value of 70 μg/m 3 recommended by the WHO (2006) . The annual average PM 10 levels reported by CPCB (> 90 μg/m 3 ) is routinely higher than the recommended guideline values across most locations in India. The CPCB recommended annual average value for PM 2.5 (40 μg/m 3 ) is higher than WHO recommended value (35 μg/m 3 ) and similar to PM 10 , the annual average PM 2.5 levels are higher than the guideline values in most locations throughout India. The situation is similar for NO 2 and SO 2 , with the annual levels for these pollutants exceeding, by a substantial margin, the 24-h WHO-AQG levels.

It is of critical importance to realize, however, that the above standards are based on the study of air pollution mixtures from countries in Europe and North America, which, especially in the case of PM attributable to AAP and household sources, may not be directly transferrable to locations in much of India. It will be necessary, therefore, to evaluate the source-specific components of PM air pollution in India, for comparison with that in other parts of the world, as well as to the conduct of local studies of the health-air pollution relationship in India. Especially important will be the collection and analysis of PM 2.5 samples at a variety of locations across the country, and statistical analyses of the health effects relationships in urban centers and rural areas, as a function of composition and source. This will be an ideal opportunity for initiating a collaborative study by the US and Indian scientists

5. Air pollution and epidemiology in India

The vast majority of epidemiological studies reporting health effects associations with AAP in India use data from urban centers, and mostly report on prevalence of respiratory morbidity. A systematic review of the literature on the health effects of ambient air pollution in Asia published by the HEI (Health Effects Institute) (2011) identified 43 studies carried out between 1980 and 2008, that reported adverse health effects of air pollution in India. These studies, largely concentrated in the cities of Delhi and Mumbai, reported that the prevalence of diminished lung function, acute and chronic respiratory symptoms such as cough and wheeze, and asthma in children and adults was increased in areas with elevated levels of air pollution.

There are limited time-series analyses that have reported increases in acute respiratory illness ( Bladen, 1983 ), all-cause mortality ( Cropper et al., 1997 ), and emergency visit for cardio-respiratory conditions ( Pande et al., 2002 ). More recent time-series studies report increased rates of natural all-cause mortality with short-term (daily) exposure to PM 10 in Chennai, Ludhiana, and Delhi ( Balakrishnan et al., 2011 ; Kumar et al., 2010 ; Rajarathnam et al., 2011 ). Studies using similar methods have also been reported for other cities and time periods ( Dholakia et al., 2014 ; Maji et al., 2017 ). The estimates of changes in daily rates of mortality associated with short-term PM exposure observed in these studies are similar to those reported in multi-city studies conducted in China, South Korea, Japan, Europe, and North America ( Wong et al., 2008 ). In addition, a growing body of literature reports that acute health effects are associated with episodic extreme air pollution events such as crop burning ( Awasthi et al., 2010 ; Pande et al., 2002 ), use of fireworks during Diwali ( Parkhi et al., 2016 ), and in critically polluted areas within large cities ( Kumar et al., 2007 ; Siddique et al., 2011 ).

Using the 2015 Global Burden of Disease (GBD) assessment, approximately 1.09 million premature deaths and 29.6 million disability-adjusted life-years (DALYs) were attributable to HOAP resulting from solid cooking fuels in India, whereas 977,000 premature deaths and 27.3 million DALYs were attributable to AAP in India from PM 2.5 exposures. Among the 60+ risk factors examined in the 2015 GBD assessment, the combined burden from AAP and HOAP exceeded the burden from any other risk factor in the list, including individual risks from smoking, diet, or high blood pressure.

Due to the unavailability of India-specific data from epidemiologic studies (multi-city time series or long term cohort studies) that can provide estimates for the effect of long-term exposure on cardiovascular mortality and morbidity and impact on life expectancy, the results of studies conducted in North America and Western Europe have been used to estimate disease burden in India ( Burnett et al., 2014 ; MHFW (Ministry of Health and Family Welfare), 2015 ). The similarity between risk estimates for effects of short-term exposure on daily mortality for India and global estimates is however noteworthy, especially given the differences in concentration ranges, source mixtures, demographics, and underlying disease rates, and supports the idea that the temporary use of international studies to estimate Indian disease burden is a good first order approximation. In addition, a limited number of population studies carried out in India corroborate the broader global evidence for the higher incidence of chronic non-communicable respiratory and cardiovascular diseases in India ( Basu et al., 2001 ; Dutta et al., 2012 ; Lahiri et al., 2000 ; Ray et al., 2006 ; Roy et al., 2001 ; Roychoudhury et al., 2012 ).

Unlike studies of AAP that have characterized exposure to air pollution in terms of estimated levels of PM and other pollutants, most epidemiologic studies of HOAPs have used qualitative indicators to characterize exposure, such as the use of solid vs. clean cook-fuels, involvement in cooking, or proximity to stove. As reviewed by Smith et al. (2014) , several Indian studies are currently included in systematic reviews/meta-analyses used by the GBD efforts to estimate HOAP-related risks for COPD.

As discussed above, the use of Integrated Exposure-Response functions (IERs) with AAP, second hand smoking, and active smoking have allowed the development of HOAP risk estimates for ischemic heart disease and other respiratory diseases reported in the GDB assessment. The HOAP exposure model used in the GBD assessments is based upon measurements and modeling results from India, with estimated daily average PM 2.5 exposures of 285, 337, and 204 μg/m 3 for children, women, and men, respectively ( Balakrishnan et al., 2013 ; Smith et al., 2014 ). The current IERs, however, need to be improved by the development of data on the potential for acute and chronic health effects at high levels of air pollution commonly encountered in India. For example, a recent analysis of PM 2.5 and mortality in China found that, in less urban parts of the nation (where biomass is a major source), there was a leveling of health effects with increases in PM 2.5 ( Chen et al., 2017 ). However, this “leveling off” of AAP-associated health impacts was not seen in the more urbanized Eastern part of the nation, where fossil fuel emissions are more dominant, perhaps suggesting that high levels of PM 2.5 from biomass burning are of lesser human health impact per μg/m 3 than elevations in fossil fuel combustion-derived air pollution ( Thurston and Balmes, 2017 ).

Recently launched epidemiologic cohort studies are making efforts to estimate the effects of long-term exposure to ambient and household air pollution on a range of maternal (birth weight), child (acute respiratory infections), and adult (chronic respiratory symptoms and lung function) health outcomes in populations residing in both urban and rural locations ( Balakrishnan et al., 2015 ). The exposure estimates from these studies are being applied in other long-term cohort studies examining cardiovascular risk factors such as high blood pressure, brachial artery hyper-reactivity, and carotid intima-media thickness ( Thanikachalam et al., 2015 ). Recent developing-world cohort studies of the mortality impacts of biomass burning exposure, while limited, suggest that mortality effects from HOAP are, unlike AAP, primarily due to respiratory effects and not cardiovascular ( Alam et al., 2012 ; Mitter et al., 2016 ), but more direct assessments are needed in India to assess their applicability.

Major gaps remain in our understanding as to the relationship between air pollution exposures and health in India, including: 1) the relationship between PM 2.5 mass concentrations and both morbidity and mortality (as opposed to PM 10 , which includes coarse particles); 2) the extent to which these PM 2.5 -health relationships vary across the nation; and 3) how variations in the source and composition of PM impact the toxicity of the PM over space and time. By evaluating these aspects of the PM 2.5 -health relationship, control measures can be devised that better optimize the public health benefits of future air pollution mitigation measures. This information will also be potentially useful in making public health-based decisions regarding control strategies for climate mitigation measures, allowing and optimization of the clean air health co-benefits of CO 2 reduction plans.

6. Recommendations

The breakout group discussion on the charge questions ( Tables 1 and ​ and2) 2 ) and a more expanded discussion around identification of gaps in knowledge, resources, expertise, and technology, as well as how these gaps can be filled with a collaborative research effort among U.S. and Indian scientists, led to a set of Health Effects and Exposure Assessment research priorities. The research and policy priorities identified in these deliberations were grouped into short-, medium-, and long-term goals aimed at providing scientific bases for developing prevention and intervention strategies that may aid in reducing the air pollution health burden in India. The overarching goal of the conference was for scientists from India and the U.S. to jointly identify and prioritize bilateral research collaborations to study the health consequences of India's air pollution and its future reductions. In general, the short-term priorities were based upon research designs that encompassed readily available data sets or that were needed to answer urgent public health concerns. A group of training recommendations was also developed to address long-term capacity building.

Charge questions on air pollution health research issues in India.

Charge questions on air pollution exposure assessment in India.

A. Health effects assessment recommendations

There was widespread agreement of the critical need for India-specific data on air pollution health effects and dissemination of these data to the public, medical community, and policy makers.

A1. Short-term research priorities in health

• Initiate retrospective analyses of available health and exposure data with a focus on developing exposure-response relationships. This could include expanding the limited base of time-series studies ( Balakrishnan et al., 2011 ; Kumar et al., 2010 ; Rajarathnam et al., 2011 ) to multiple cities or an extended time-series study in a single city based on data availability/quality. Multiple existing cohort studies were enumerated by the group, thus opening up the possibility of adding retrospective exposure data to on-going cohorts.
• Initiate retrospective analyses of available health and exposure data during recent smog episodes in National Capital area (2015, 2016) such as hospital/clinic visits, hospital admission, prescription of drugs for asthma, pulmonary infection.
• There was an overwhelming recognition for the need to focus on high profile “charismatic” health outcomes that would have a strong impact on public perceptions and policy makers, potentially including studies involving children (e.g., asthma), birth outcome studies (e.g., birth weight), cardiovascular events (e.g., acute coronary syndrome and heart failure), and cognitive outcomes (e.g., IQ). These studies can be carried out on select major cities and national and state capitals.

A2. Medium/long-term research priorities in health effects

• There are opportunities to utilize a number of intervention studies that could be initiated based on the proposed policy/regulatory decisions on either a medium- or long-term basis.

Upcoming fuel conversions (e.g., to natural gas: see Yinon and Thurston (2017) ).

Upcoming auto diesel engine Euro VI/reduced sulfur diesel fuel.

Moveable/mobile studies.

Examination of the effect of odd/even car data or similar such policy changes across India.

Cost effectiveness of different room filtration systems. Efficacy of different particle masks and indoor filtration systems or combination of these approaches to reduce personal exposure and health effects.

• Development of a data infrastructure to aggregate health effects data would help further air pollution health research. The electronic consolidation of emergency department (ED) visits and hospitalization records can provide a wealth of information in documenting and tracking the potential health effects from air pollution. In the U.S., syndromic surveillance databases, primarily designed for identifying and tracking disease out-breaks, have been used to document a wide range of public health concerns including the potential cardiovascular effects of acute exposure to air pollution.

B. Exposure monitoring and estimation recommendations

B1. short term research priorities in exposure.

• There is an urgent need to build a comprehensive nationwide air pollution monitoring network that provides reliable and real time air pollution information on criteria pollutants, including composition of fine particulate matter mass. This should focus on integrating current networks and expand access to rural areas given the significant differences in composition and exposure between urban and rural populations.
• There is a need to develop effective communication strategies to inform the public about air pollution data (e.g., from real-time monitors) via an index such as the air quality index (AQI). These may include posting AQI via mobile applications, as well as at bus/train stations and airports, accompanied by relevant health warnings and appropriate response measures for different sectors of the society (e.g., school kids, industries, old people, etc.).
• Meteorological data from the Indian Meteorological Department should be made available online and preferably with air pollution data to enhance impact and understanding among public. Adding temperature (which acts as a stressor) data along with the AQI would draw the attention of the public and make them aware of the AQI metric.
• Small-scale industries are numerous and can represent major local sources of air pollution (e.g., brick kilns, diesel generator sets, etc.). Therefore, it is recommended that emissions from these sources are measured and/or estimated periodically and tracked either through periodic fence-line monitoring or through ambient monitors in industrial zones that are representative of these source emissions impacts.
• Metadata and data quality metrics (uncertainty, minimum detection limits, flow rates, instrument maintenance, etc.).
• Retrospective monitoring data for PM 2.5 and PM 10 measurements are available back to 2010 and 2002, respectively.
• Satellite data for PM and gas concentrations have been collected since 2000.
• Exposure modeling efforts to predict historic exposure concentrations for urban centers and rural areas across India.
• Autonomous body to develop and implement standardized protocols for monitoring periodic calibration and verification of instruments across government agencies, national laboratories and academic institutions including developing consistent procedures for onsite calibrations.

B2. Medium/long term research priorities in exposure

• State-of-the-art estimation and modeling methodologies should be developed. Also, published modeling studies have focused on urban megacenters in Northern India and more regional and localized modeling studies are necessary to sufficiently evaluate smaller cities and rural areas (e.g., Garaga et al., 2018 ).
• Region or locality-specific indigenous emission factors are required to be developed and utilized for all source categories to ensure representative emission estimates. (e.g., monitoring emissions from major local sources such as biomass burning and diesel generator sets).
• All emission profiles should be made available through an open data platform.
• Access to technologies for chemical speciation data on emissions to identify emission signatures in more extended urban areas as well as rural areas. These efforts should integrate satellite data and low-cost sensors for unmonitored areas, in order to guide the siting of additional air quality monitors in rural and urban areas.

Comprehensive monitoring “Supersites”, including chemical speciation (e.g., Solomon et al., 2008 ).

Calibration of air monitors to maximize data quality.

Testing and validation of low-cost sensors being developed in India.

• Guidance for calibration, data QA/QC.

C. Communication and policy recommendations

• There is a need to develop effective communication strategies to inform the public about air pollution data (e.g., from real-time monitors) via an index such as the air quality index (AQI). These may include posting AQI via mobile applications, as well as at bus/train stations and airports, accompanied by relevant health warnings and appropriate response measures for different sectors of the society (e.g., school children, industries, old people, etc.).
• Establish collaborative partnership with media to promote dissemination of reliable, accurate, and balanced news and data are valuable members of this framework to promote widespread public awareness.
• Integrate citizen science with community health warnings to local areas, especially during high air pollution episodes.
• The research agencies such as ICMR and CSIR under the Ministry of Health and Family Welfare, and the Prime Minister's Office should promote collaborative research with ongoing air pollution exposure assessment of epidemiological studies funded by National Institute of Health, USA, European Union and NGOs.

D. Training needs recommendations

The breakout group identified a near absence of environmental public health education opportunities in India and how U.S.-India collaborative efforts can aid in addressing this critical need. One of the short term priorities was to develop a mechanism for developing curricula/field training in environmental public health, initially focused on air pollution epidemiology and toxicology. This can be initiated with short-term professional training courses through development of exchange visitors program as well as via visiting faculty from the U.S. offering hands-on courses across geographical regions of India. The groups also identified training needs for young investigators, graduate students, and established faculty refocusing their research in environmental public health, including:

• Study design (questionnaire-based surveys, daily catalogs of activities)
• Monitoring [collection of environmental and biological samples]
• Integration of exposure and health data.
• short term priority to identify existing expertise across government agencies, national labs, and universities and provide resources and access to support air pollution health research in areas such as targeted/untargeted metabolomics, genetic/ epigenetic, clinical diagnostic markers.
• analytical capabilities for chemical speciation of environmental samples
• Creation of national/regional databases and repositories for exposure and biological data. While many studies of exposure have been conducted in India, the data are not always easily accessible for further research or meta-analysis. Training in the availability, transparency, and documentation of the data behind existing research would further research in the area of air pollution and health.
• Exposure monitoring and modeling. Optimal utilization of existing exposure and health data requires the training of individuals in biostatistics and epidemiology with an emphasis on exposure assessment data modeling.

7. Conclusions

The goals of the two-year bi-lateral dialog between researchers in India and the U.S. provided a fruitful exchange of information and mutual understanding that resulted in taking stock of the state of research of air pollution health research in India. These interactions resulted in identifying the research gaps, needs and potential opportunities for sharing of expertise, technology, and experience from the U.S. Through these interactions over the two years by a small group of scientists, a set of charge questions was developed for a focused discussion by a larger community of scientists that worked in isolation at the two-day workshop held in New Delhi, India. The goals of this workshop were to deliberate on the charge questions and develop consensus on recommendations to promote active collaboration, access to expertise, required training to bring the state-of-the-art environmental health expertise (e.g., environmental epidemiology, exposure assessment, source characterization, and chemical speciation). In addition, opportunities for short-term training by an exchange of visitors across the U.S. and India were recognized. It's our hope that the recommendations developed here will: 1) facilitate joint activities among experts from India and the U.S.; and 2) promote potential opportunities for U.S. and Indian research funding agencies to collaboratively develop research and training programs to address the research needs previously identified ( MHFW (Ministry of Health and Family Welfare), 2015 ).

Acknowledgements

The workshop that contributed to face to face interaction of the members of Indo US Communities of Researchers held in New Delhi (in November 2016) was supported by generous funding by the Indo US Science and Technology Forum to Drs. Gordon and Salvi (Indo-US Workshop on Research Opportunities for Air Pollution and Health Issues/WS-10–2016) and conference grant to Dr. Gordon (U13 ES027717–01) by the National Institute of Environmental Health Sciences, NIH. The authors also acknowledge support from the staff of Economics, Environment, Science and Technology division, US Embassy New Delhi with coordination, and planning of the workshop. Additional financial support was provided by Research Triangle International, Research Triangle Park, NC and Center for Disease Control, Atlanta, GA.

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River pollution in India: exploring regulatory and remedial paths

• Published: 03 March 2024

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• Rajneesh Kumar 1 ,
• Manish Kumar Goyal 1 ,
• Rao Y. Surampalli 2 &
• Tian C. Zhang 3  

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India’s rivers are essential for providing fresh water for human sustenance, but their water quality has deteriorated due to pollution from industrial waste, domestic sewage, agricultural runoff, and more. Parameters such as physical, chemical, biological, and intended use criteria are used to assess water quality. Water quality monitoring is crucial for preserving freshwater resources and human health, and integrating IoT and AI has revolutionized environmental monitoring. However, India faces challenges in accessing remote locations for monitoring and a lack of manpower. To address these challenges, real-time monitoring systems and remote sensing technologies should be implemented, and wastewater treatment facilities should be upgraded. Public awareness campaigns on responsible consumption and waste disposal practices are also needed. Strengthening and enforcing regulatory measures to limit pollutant discharge and addressing deforestation and improper land use practices are essential. A comprehensive law on environmental protection is necessary for meaningful enforcement and responsibility within society. By addressing these challenges and adopting innovative solutions, India can safeguard its water resources and ensure a sustainable future.

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Acknowledgements

The authors express their sincere gratitude to the Central Pollution Control Board for providing the water quality data used in this research. We would also like to extend our thanks to the authors of the cited articles in this review paper, whose work has been a source of great insight and knowledge. Their contributions have significantly enriched the content and understanding of our research.

This study was funded by the Department of Science and Technology, Government of India [DST/PRC/CPR/IITIndore (G)] for the project entitled “Technological Innovation and Intellectual Property”.

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Kumar, R., Goyal, M.K., Surampalli, R.Y. et al. River pollution in India: exploring regulatory and remedial paths. Clean Techn Environ Policy (2024). https://doi.org/10.1007/s10098-024-02763-9

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• Published: 21 February 2022

Physicochemical and biological analysis of river Yamuna at Palla station from 2009 to 2019

• Pankaj Joshi 1 ,
• Akshansha Chauhan 2 ,
• Piyush Dua 3 ,
• Sudheer Malik 4 &
• Yuei-An Liou 2 , 5  

Scientific Reports volume  12 , Article number:  2870 ( 2022 ) Cite this article

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Yamuna is one of the main tributaries of the river Ganga and passes through Delhi, the national capital of India. In the last few years, it is considered one of the most polluted rivers of India. We carried out the analysis for the physiochemical and biological conditions of the river Yamuna based on measurements acquired at Palla station, Delhi during 2009–19. For our analysis, we considered various physicochemical and biological parameters (Dissolved Oxygen (DO) Saturation, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Alkalinity, Total Dissolved Solids (TDS), and Total Coliform. The water stats of river Yamuna at Palla station were matched with Water Standards of India, United Nations Economic Commission for Europe (UNECE), and World Health Organization (WHO). Maximum changes are observed in DO saturation and total coliform, while BOD and COD values are also seen higher than the upper limits. Total alkalinity rarely meets the minimum standards. TDS is found to be satisfactory as per the standard limit. The river quality falls under Class D or E (IS2296), Class III or IV (UNECE), and fails to fulfill WHO standards for water. After spending more than 130 million USD for the establishment of a large number of effluent treatment plants, sewage treatment plants, and common effluent treatment plants, increasing discharges of untreated sewage, partially treated industrial effluents and reduced discharge of freshwater from Hathnikund are causing deterioration in water quality and no major improvements are seen in water quality of river Yamuna.

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Introduction

Water is the main need of human life. The majority of ancient civilizations were developed on the bank of major rivers across the world. Rivers fulfill the major demands of the freshwater supply from drinking to agriculture. In the northern parts of India, the Yamuna River basin is ranked the second largest basin after the Ganga River. It is the second-largest tributary of the river Ganga (the longest river of India) with a total catchment area of 345,848 km 2 and it originates at Yamunotri Glacier, Uttrakhand, India at a height of 6387 m 1 . It covers a total distance of 1376 km through four major states of India: Uttarakhand, Haryana, Delhi, and Uttar Pradesh, and finally confluences with river Ganga at Triveni Sangam, Prayagraj, Uttar Pradesh. Although it does not flow through Himachal Pradesh but receives water via the river Tons (which originates in Himachal Pradesh). The other tributaries of the river Yamuna are Chambal, Sindh, Betwa, and Ken 2 , 3 . The water abstracted from the river is mostly used for irrigation (about 94%), while 4% for domestic water supply and the remaining 2% for industrial and other uses 4 .

During the last few decades, Yamuna has been considered one of the most polluted rivers of India. Discharge from industries, partially or untreated sewage, and agricultural waste are the main sources for the river Yamuna degradation 5 , 6 , 7 . Almost 85% of the total pollution in the river Yamuna is due to domestic sources mainly from urban cities Sonipat, Panipat, Delhi, Ghaziabad, Mathura, Agra, Etawah, and Prayagraj. Industrial zones at various places like Yamunanagar, Panipat, Ghaziabad, Delhi, Noida, Faridabad, and Baghpat which are in the upper Yamuna basin (Fig.  1 ), comprise industries like Oil refineries, distilleries, pulp, pharmaceutical, chemical, electroplating, weaving, and sugar, and contribute to the degradation of Yamuna water quality significantly 8 , 9 . According to Kumar et al. 10 , Delhi leads the list of cities with 79% pollution load in river Yamuna followed by Agra and Mathura with a contribution of 9% and 4%, respectively, whereas a pollution load of 2% by Sonipat and Baghpat. The annual mixing of sewage from domestic and industrial sources in the Yamuna River basin is about 9.63 km 3 , 11 . In the last few decades, a sudden rise in the built-up and cropland areas is observed in the Yamuna River basin (Fig.  2 ). Kumar et al. 10 suggested a rise of 100% in the urbanization in Haryana and Rajasthan states and significant fall is observed in wetland, grassland, water bodies and forest areas of Yamuna River basin. The green revolution in India helped rise in the productivity of various crops, but the major water supply to the crop depends on the groundwater. The DO level of water in Delhi stretch shows a sudden fall due to high carbon level so that most of the time the river can not sustain fishery.

( Source : HSPCB).

River Yamuna in Haryana.

( Source : WRIS).

Upper river Yamuna basin area.

Parween et al. 12 showed the positive rise in the potassium and nitrate that affected the Yamuna River basin. Domestic waste consists of mainly organic matter and micro-organisms along with detergents, grease and total salts mixed in river Yamuna through various drainages in National Capital region. Lokhande and Tare 3 have shown rise in the flow rate of Yamuna during non-monsoon months due to the sewage water. Industrial effluents are the main source of heavy metal pollution like Cd, As, Cr, Fe and Zn with other inorganic and organic wastes adding to pollutant inventory 10 . According to National Capital Region Planning Board (NCRPB) report, sewage generation in Haryana was 374 MLD in 2001, and 599 MLD in 2011, whereas the sewage treatment capacity was 164 MLD in 2001 and 199 MLD in 2011. State monitoring committee appointed by National Green Tribunal (NGT) 2019 suggested that Haryana discharged 1140 MLD of untreated or partially treated sewage per day into river Yamuna, also 1268 industrial units discharged 138.75 MLD partially treated and another 827 units discharged 48.319 MLD of treated effluents per day in Yamuna River 13 .

Central Pollution Control Board (CPCB) is responsible for controlling the various sources of pollution in India and also monitoring water quality of the rivers with the State Pollution Control Board (SPCB) 4 , 5 , 14 , 15 . CPCB started national water quality monitoring in 1978 under Global Environmental Monitoring System (GEMS), followed by the Monitoring of Indian National Aquatic Resources (MINARS) in 1984, and helped reduce river pollution via National Water Quality Monitoring Programme (NWMP) 16 . Central Water Commission (CWE) is monitoring the water quality of all the major river basins in India through 519 water quality sites and 33 water sampling stations. According to CWC, the water quality of river Yamuna is monitored at 18 different stations of which 12 are manual and 6 are telemetry stations, starting from Naugaon (N-30.78, E-78.13) at Uttarakhand as the first river point station to the last river point station Pratappur (N-25.37, E-81.67) Uttar Pradesh.

Due to degradation in the water quality of the Yamuna River, Yamuna Action Plan (YAP-I) was launched in 1993 by the Ministry of Environment and Forests (MoEF), India to rejuvenate the Yamuna River especially in the Delhi segment having maximum pollution load. Haryana and Uttar Pradesh were also included along with Delhi in YAP-II in 2003. YAP-III, with an estimated cost of Rs.1656 crore, was launched in 2018 as an integrated component of the Namami Gange Mission 17 . The river Yamuna is analyzed monthly, seasonally, and yearly for its physical, chemical, and biological properties in previous research for various states and cities influenced by it or small stretches of river 18 , 19 , 20 , 21 . A sufficient amount of literature is available for the water quality of Yamuna and most of the analysis focused on the Delhi and stations lie after the Delhi’s. Kumar et al. 10 discussed the variability of various water quality parameters from 1999 to 2005 to investigate the relationship between environmental parameters and pollution sources. Kaur et al. 22 discussed the impact of industrial development and land use/land cover changes over the Yamuna water quality in Panipat, which is located between the Hathnikund and Baghpat stretch. The sampling was carried out during February, July, October, and December, 2018 to observe the variations in the quality of water and the impact of pollutants on the river Yamuna. Patel et al. 23 carried out the water quality analysis over the Yamuna River using satellite and remote sensing during the lockdown period and compared it with the water quality parameter before the lockdown. The analysis was carried out from January 2020 to April 2021. The impact of lockdown on the water quality was discussed assuming that the industrial waste and the other pollutants sources were reduced during the time of lockdown. Paliwal et al. 24 carried out the modeling analysis of the Yamuna water quality for the Delhi stretch. They emphasize the inflow of untreated water and effluents from various drains in the Yamuna in the Delhi stretch using QUAL2E-UNCAS before 2007. They also highlighted the need for common treatment plants and a rise in freshwater supply. Krishan et al. 25 conducted the groundwater study near the bank of the Yamuna in the Agra and Mathura districts. The study area is located downstream of Delhi and affected by severe falls in water quality. The change in the water quality at Agra and Mathura was discussed and the assessment of the treated water after the filtration was discussed. Kaur et al. 26 investigated the Yamuna water quality at the Delhi stretch and one site located near the Yamunotri. The analyses were implemented during March and October for 2017 and 2018. They have discussed the impact of the inflow of pollutants from the Delhi and NCR regions in the Yamuna. Jaiswal et al. 21 carried out the multivariate study of the Yamuna river water to study the river water quality across the whole stretch. The samples were collected from July to October and November to June for 2013 and 2014. The analysis suggested that the water quality of Palla was suitable for drinking during the study period. We found that these analyses were mostly carried out for a short period in recent years. Some long-term analyses were carried out before 2007. So, there is a dire need for long-term analysis in recent years. In recent times, Lokhande and Tare 3 performed the first long-term analysis of various water quality parameters of the river Yamuna and discussed the trends of various parameters. Due to classified data, Lokhande and Tare 3 were not able to quantify the monthly variations of various water parameters. Hence, these analyses, lack the long-term variability discussion and quantitative changes. In the current study, we conducted monthly and annual mean analysis of various physical and biological parameters, including Biological Oxygen Demand (BOD), Chemical Oxygen demand (COD), Dissolved Oxygen (DO) Saturation, Total Alkalinity, Total Dissolved Solids (TDS), Total Coliform and average rainfall from 2009 to 2019 at Palla station located at northwestern Delhi. We have shown the quantitative change in the physiochemical and biological parameters of the river Yamuna at the Palla station, which is mostly affected by the pollutants load of the watershed of Haryana. We compared the results with the water quality guidelines of national and international standards given in Table 1 to figure out the changes in water quality of river Yamuna at Palla station in the last 11 years. The current water quality at Palla is not suitable for drinking and sometimes not good for agricultural purposes due to the high influx of water pollutants.

The river Yamuna enters National Capital Territory (NCT) at approximately 1.5 km before village Palla, which is 23 km upstream of the Wazirabad barrage. Palla station (N-28.82 and E-77.22) is a manual type station with zero gauges at 206 m. Before entering NCT at Palla, the river Yamuna traveled about 393 km from its source and about 220 km from the Hatnikund barrage. According to Haryana State Pollution Control Board (HSPCB), the numbers of industries in Yamunanagar, Kernal, Panipat, and Sonipat are 142, 9, 346, and 503, generating effluent of 16,420.90, 26.00, 65,696.97, and 15,668.50 KLD, respectively, up to August 2019. As in Fig.  3 , several drains of Haryana state including 3 major drains at Dhanaura escape, Main Drain No.2, and Drain No. 8 also fallout in river Yamuna before reaching Palla 28 (Figs.  3 , 11 ).

Location of Palla station. The base image is provided by ESRI and projection is done using ArcGIS Pro.

Data retrieval

Water quality data of Yamuna River at Palla station was obtained from Water Resources and Information System (WRIS), India, which is a centralized platform, acting as a database related to all water resources at the national or state level. It was initiated by CWC along with the Ministry of Water Resources, Ministry of Jal Shakti, and Indian Space Research Organization (ISRO) in 2008 to provide a single-window solution to all water resources data and information in a standardized national GIS framework 29 . Depending upon the availability of monthly average data, the parameters BOD, COD, DO saturation, total alkalinity, TDS, and total coliform at Palla station were analyzed during the period from January 2009 to December 2019. The rainfall data was procured from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and available on the website http://www.soda-pro.com/web-services/meteo-data/merra . The spatial resolution of data is 0.50° × 0.625° and the time step ranges from 1 min to 1 month 30 , 31 . We also included the water discharge data in the current study of three locations Hathnikund Barrage, Baghpat, and Delhi Railway Bridge (DRB) stations. The freshwater in river Yamuna reached to the Palla station is controlled at the Hathnikund Barrage. Baghpat station is located just before the Palla station (no data is available at Palla station) and DRB is located after the Palla station. These stations are chosen based on the availability of data and the locations to show the water discharge in the Yamuna. The water discharge data of this location is taken from the Central Pollution Control Board of India ( https://yamuna-revival.nic.in/wp-content/uploads/2020/07/Final-Report-of-YMC-29.06.2020.pdf ).

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The paper did not involve any human participants.

Result and discussion

Analysis of various parameters related to the water quality of river Yamuna at Palla station was carried out.

The annual mean variation in rainfall at the Palla region (Fig.  4 a) suggested that the highest annual mean rainfall (95.8 mm) occurred during the year 2010. After 2010, the yearly average rainfall continuously declined every year till 2014 with a mean value of 17.9 mm. Although from 2014 to 2018, a rise in average rainfall was observed each year and during 2018, the average rainfall was calculated as 53.1 mm, but the linear trend illustrated a sharp decline in average rainfall between 2009 and 2019 as shown in Fig.  4 a.

( a ) Yearly mean rainfall at Palla station and adjacent region from 2009 to 2019. The red line shows a linear trend of rainfall. ( b ) Rainfall monthly distribution at Palla station from 2009 to 2019. ( c ) Monthly mean variation of rainfall from 2009 to 2019.

The box plot in Fig.  4 b shows the rainfall data of each month during 2009–2019. The distribution suggested that the monthly mean rainfall variations were highest during August along with July and September. May was an almost driest month along with April and March as the rainfall was minimum during these months. The highest values of average rainfall for August, September, and July were 515.28, 233.45, 207.99 mm respectively, whereas these months’ minimum average rainfalls were 101.74, 61.95, and 72.76 mm, respectively, during 2009 and 2019. The median values of rainfall during March, April, October, and November were below 10 mm and, during January, February, June, and December, the median values were between 10 and 20 mm, while the median values for July, August, and September were above 60 mm.

The monthly mean variation of rainfall (2009–19) further elaborates the stats of rainfall in the Palla region. From 2009 to 2019 rainfall data showed various ups and downs in monthly rainfall (Fig.  4 c). In India, the onset of monsoon is observed during the start of June each year in the coastal part of India, which lasts approximately for a period of four months (June to September) each year. The northern parts of India receive major rainfall during this period, which is known as the monsoon season. During June, the average rainfall remained lower than the decadal mean during 2014–2019 except for 2017. During July, we observed the same deficit of rainfall during 2014–2019 except in 2018. The major deficit in rainfall was observed for August from 2012 to 2018 whereas, during August 2019, the rainfall remained higher than the decadal mean value. During September, the rainfall deficit was observed from 2012 to 2016. Hence, during 2012–2017, we observed a significant fall in the monsoon rainfall. The winter rainfall is also observed during January and February in northern parts of India. We also observed a significant fall in the winter rainfall for January and February since 2014. Yamuna River before Palla station receives runoff water from cities like Sonipat, Panipat, Karnal, and Yamunanagar. The rainfall statistics of Haryana analyzed by India Meteorological Department (IMD) between 1989 and 2018 showed annual rainfalls of 1053.5, 578.8, 573.9, and 495.3 mm at Yamunanagar, Sonipat, Karnal, and Panipat districts, respectively 32 . During the monsoon season, sudden changes in physicochemical and biological parameters were observed by various researchers. The addition of monsoon runoff in rivers dilutes the industrial effluent and sewage, causing the decline in parameters like BOD, COD, alkalinity, pH, and conductivity 33 , 34 , 35 , 36 . The catchment area of Yamuna is the smallest in Delhi. The size of the catchment area of the river is an important factor for the dilution of anthropogenic waste in river water. The small catchment area increases soil components in the river and makes difficult the dilution process of anthropogenic waste 37 . These conditions can further affect the river water quality. Hence, we further analyzed the variations in the various physical and biological parameters for each month from 2009 to 2019.

Dissolved oxygen (DO) saturation

Dissolved Oxygen (DO) saturation is a vibrant parameter for aquatic system health determination. The pollutants like sewage, soil, agricultural runoff, and other organic pollutants can reduce the DO saturation of water 38 and low DO saturation can impact the life of major aquatic organisms. The water having DO < 5% of saturation lies in an extremely severe pollution region; DO between 5 and 10% of saturation lies in severe pollution; DO in a range of 10–70% represents moderate pollution, whereas DO above 70% indicates slightly or no pollution condition. Heavy pollution load due to untreated sewage and industrial effluents are the main causes of decreasing DO concentration 39 . Value of DO saturation is also affected by the change in water salinity (chlorine), temperature, and air pressure.

Increasing pollution in the Yamuna River caused a decrease in DO saturation concentration along with an increase in temperature and salinity of water 3 . Figure  5 shows the yearly variation of DO saturation, and monthly distribution and mean variation of DO saturation at Palla station from 2009 to 2019. In recent years, the annual mean value of DO saturation during 2017–19 was found to be 4.81%. From 2009 to 2011, the mean DO saturation was found to be 69.49% with a maximum value (81.58%) in 2010. From 2012 to 2015, the yearly mean DO saturation was 86.48%. During this period, a small decrease was seen between 2013 and 2015. In recent years, the DO saturation reached critically low values to the average value before 2015 as demonstrated in Fig.  5 a. These conditions indicate the rise in pollutants in the river Yamuna.

( a ) Yearly variation of DO saturation from 2009 to 2019. The red line shows a linear trend of DO saturation. ( b ) Monthly distribution of DO saturation at Palla station from 2009 to 2019. ( c ) Monthly mean variation of DO (%) saturation from 2009 to 2019.

The monthly distribution of DO saturation during 2009–2019 is shown in Fig.  5 b with boxes ranging from 25 to 75%. During May, the maximum median value was 88%, whereas in January minimum median value was about 25%. Similarly, the maximum monthly mean value was 62.7% during May, while the minimum mean value was 34.1% during January. Similarly, maximum DO saturation of 118% was perceived for May, while a minimum of 2.4% was observed for December. The monthly mean distribution suggested that the mean DO saturation plunged between 50 and 60% with a slight growth in trend from January to December. We further showed the monthly mean values of DO saturation in Fig.  5 c. From 2009 to 2015, the values suggested no major changes with monthly values well above 50, but just after 2017, a sudden fall was observed in the DO saturation each month. We observed 10 times fall in DO saturation just after 2017. During 2009–14, the DO saturation of Yamuna River at Palla station was Class I category of international standards for surface water of UNECE, but during 2016–19 its quality degraded to Class IV due to regular fall in the DO values of the river water. The lack of fresh water and rising carbon concentration have affected the DO concentration significantly.

Biological oxygen demand (BOD)

Biological Oxygen Demand (BOD) is one of the methods to assess the quality of water by calculating the oxygen requirement for decomposition of its organic matter. The yearly variations in BOD at Palla station during 2009–19 are shown in Fig.  6 a. The yearly mean BODs in 2009 and 2010 were estimated to be around 6 mg/l, while, during 2013 and 2012, the least values of BOD (1.4 and 2.7 mg/l, respectively) were found. The year 2015 showed the highest yearly mean BOD value of around 12 mg/l followed by years 2014 and 2016 with a value of about 9.5 mg/l. The decline in yearly mean BOD was observed from 2015 to 2019. In particular, a BOD of 3.5 mg/l was perceived for years 2018 and 2019. The monthly variation of BOD during 2009–19 was shown in Fig.  6 b, where the interquartile range for each month was between 25 and 75 percentiles. The first quartile for all months lay below the range between 0 and 5 mg/l, with outliers for months May, July, October, and December with BOD above 20 mg/l. January and February had the highest median value of BOD at 6.7 mg/l, while September had the least median value of 2.1 mg/l.

( a ) Temporal variation of yearly mean BOD from 2009 to 2019. The red line shows a linear trend of BOD. ( b ) Monthly distribution of BOD at Palla station from 2009 to 2019. ( c ) Monthly mean variation of BOD from 2009 to 2019.

The median value for April, June, July, October, and December lay between 3 and 5 mg/l. The monthly mean BOD during 2009–19 represented that BOD was maximum in December (8.9 mg/l) followed by the second highest mean value of 8 mg/l in May. The minimum mean value of 2.7 mg/l was found in August, while in April, September, October, and November, the mean BOD lay between 3 and 6 mg/l. Mean BOD from January to March and in July was in the range of 6–7.5 mg/l.

The fluctuation in BOD was observed during 2009–19 in Fig.  6 c. In January, February, and March, a decrease in BOD occurred during 2009–13, while in May, June, and July increase in BOD was seen during 2013–16. In August, September, and October, BOD mostly fell below 5 mg/l throughout the study period. The highest BOD values were measured in December 2015, October 2014, and July 2016 (41.6, 26.5, and 24.8 mg/l, respectively). The decreasing trend was observed for January, February, and July, with a rising trend for March, April, May, and November. From 2014 to 2016, the BOD values were found to be several times higher than the acceptable limits. In March and April, BOD values were less than 4, and else BOD was higher than 4 even in monsoon months. In years 2018–19, BOD is observed to be mostly ≤ 4 mg/l. However, the overall BOD values suggest that the water quality of Yamuna mostly lay beyond the C category (BIS) as the BOD values are mostly > 4 mg/l. To attain river quality standard it has to be ≤ 3 mg/l.

Chemical oxygen demand (COD)

Chemical Oxygen Demand (COD) determines the amount of oxygen required for the oxidation of organic matter present in water. We have shown the changes in COD at Palla station in Fig.  7 . Yearly mean COD varied indistinctly during 2009–19 even though the linear trend has moved upward with increasing year as shown in Fig.  7 a. The yearly mean COD increased from 15.9 to 30.8 mg/l during 2010–2012 and from 11.5 to 44.3 mg/l during 2013- 2015. Mean COD for the years 2018 and 2019 was 15.1 and 20.4 mg/l, respectively. The highest yearly mean COD was 44.33 mg/l for the year 2015 followed by the year 2012 with 30.8 mg/l, while the least mean COD value was observed in 2013 as 11.5 mg/l. A sharp decline in yearly mean COD was noted during the years 2012–13 and 2015–16. The monthly data of each month during 11 years period is shown in Fig.  7 b. The width between the first and third quartiles for January, April, and May indicated maximum variation in COD values. The September quartile indicated less variation in COD as compared with the other months. The CODs for January and May had the highest median values of 27 and 24.5 mg/l, respectively, whereas April had the least median value of 8 mg/l. The median CODs for March, August, September, October, and November lay in the range of 11–16 mg/l. We found that the monthly mean CODs of May and November had maximum and minimum values of 33.9 and 14.2 mg/l, respectively.

( a ) Yearly variation of COD from 2009 to 2019. The red line shows a linear trend of COD. ( b ) Monthly distribution of COD saturation at Palla station from 2009 to 2019. ( c ) Monthly mean variation of COD from 2009 to 2019.

The trend of COD followed a decreasing path moving from January to December. The monthly mean CODs of January, February, and May were found to be higher than 30 mg/l whereas for April, June, and October they lay between 20 and 30 mg/l, and for the remaining months mean COD was found to be lesser than 20 mg/l. A decline in the mean COD was observed from May to August, with a sharp increase in mean COD from March to May.

Figure  7 c shows changing COD for each month as the year preceding. From 2009 to 2012, the COD values for January, February, March, and April were higher than 30 mg/l, whereas, since 2017, the COD values were seen well below 30 mg/l during these months. In May, an exceptional high COD of 125 mg/l was observed and for the same month, a continuous increase in COD was noted from the years 2013 to 2016. Also, the rise in COD was seen for June, August, October, and December during 2012–15. Excluding May, February, April, and September had maximum CODs of 85, 78, and 67 mg/l, respectively. During 2009–19, January and June to November showed a growing linear trend, whereas the rest of the months had a declining trend. Compared with the international standards, the annual and monthly mean CODs exceeded the WHO guideline and were classified as Classes III-V of UNECE standards. Also, the values were found well above the threshold value. During the years 2009, 2012, and 2013, the monsoon period showed COD value in Class II, while, during the overall monsoon period, water quality lay in Class III. During the year 2015, Yamuna, in terms of COD, was in the worst condition as it lied in Class V. Monthly variation COD for 2018–19 rarely plunged under WHO standards and represented to be in Classes III and IV.

Total alkalinity

Total alkalinity is mostly due to calcium carbonate (CaCO 3 ) and also important for sustaining aquatic life. The yearly mean variation of total alkalinity during 2009–19 is shown in Fig.  8 a. Total alkalinity was found to be the highest during 2015 followed by 2011 with values of 187.37 and 164.6 mg/l, respectively, while, for the rest of the year, the annual means were in the range of 110–150 mg/l. The linear trend for the yearly mean for the entire period remained constant. With these values of total alkalinity, the quality of river water lay in category II (UNECE 1994).

( a ) Yearly variation of total alkalinity from 2009 to 2019. The red line shows a linear trend of total alkalinity. ( b ) Monthly variation of total alkalinity from 2009 to 2019. ( c ) Monthly mean variation of total alkalinity from 2009 to 2019.

We have shown the monthly distribution of total alkalinity in Fig.  8 b with the third quartile of each month lying below the mark of 200 mg/l. The median values dropped from January to July and then raised from July to December. The monthly mean total alkalinities in February and January showed the first and second-highest median values of 159.8 and 155.5 mg/l, respectively, while July and June showed the lowest median values of 86.5 and 88.9 mg/l, respectively. Median values during May, August, and September lay in the range of 90–100 mg/l, while for March, October, November, and December they were between 100 and 150 mg/l. The linear trend for monthly mean total alkalinity also followed the same pattern as the yearly mean. The total alkalinity declined from January to May from 159.4 to 105.5 mg/l and, then from August to December, it raised from 93.8 to 170.7 mg/l. August and December showed the lowest and highest values of total alkalinity, respectively.

The monthly variation in total alkalinity for each year during 2009–19 is shown in Fig.  8 c. June (2015), November (2011), and December (2015) were the months with the highest total alkalinities of 723.3, 569.9, and 428.1 mg/l, respectively. During 2009–19, total alkalinities for February, March, May, July to September remained within 200 mg/l. The lowest total alkalinity of around 55 mg/l was noticed for June (2010) and July (2012). Although the linear trend for monthly total alkalinity showed almost the same slope except for January, July, and November. A wave pattern in total alkalinity for August, September, and October was noticed with an increasing trend during 2009–19. In 2019, the total alkalinity was below 150 mg/l throughout the year 2019. For maintaining WHO and BIS standards, the minimum total alkalinity must be 200 mg/l, while it was not possible to fulfill the standards for both monthly and yearly aspects in the study areas of concern. There were only a few months when total alkalinity was found to be higher than 200. Comparing with UNECE standard, 11 out of 12 monthly mean alkalinities lay in Class II and the remaining one month in Class III category. March 2018 was the last month since 2018 when Yamuna's total alkalinity was well above-mentioned the water standards.

Total dissolved solids (TDS)

Total Dissolved Solids (TDS) define the presence of inorganic compounds along with organic matter in small concentrations originated by naturally, household, and industrial sources. The data was available from 2013 onwards. The yearly mean TDS was found to be highest in 2015 (447 mg/l) followed by 2017 and 2018 with 421 mg/l (Fig.  9 a). The lowest yearly mean TDS was observed as 256 mg/l in 2010, while the recent value of 272 mg/l was observed in 2019. Although the linear trend for yearly TDS indicated the rise in overall TDS. A maximum drop in yearly TDS was observed with a fall of 36% during 2018–19.

( a ) Yearly variation of TDS from 2013 to 2019. The Red line shows a linear trend of TDS. ( b ) Monthly variation of TDS from 2013 to 2019. ( c ) Monthly mean variation of TDS from 2013 to 2019.

The 25 to 75 percentiles of interquartile range of all twelve months for TDS are shown in Fig.  9 b. Although March had the maximum width of interquartile range, the maximum median value of TDS was 678 mg/l for January. Except for August and October, the median TDS values for the rest of the months lay in the range of 200–400 mg/l. August had the lowest median TDS of 162 mg/l and October had 404 mg/l. A sharp decline in the trend of monthly mean TDS was observed during 2013–19, but mean TDS fluctuated throughout the year. Similarly, with median TDS, the monthly mean TDS for January and August had maximum-minimum mean values of 628.3 and 177.57 mg/l, respectively. Except for August, the monthly mean values of TDS were above the mark of 200 mg/l. From April to July, they lay in the range of 300–400 mg/l, while in February, March, and December, they ranged between 400 and 500 mg/l.

In February and March, TDS had a higher magnitude of rising, while it appeared to be almost constant in August as shown in Fig.  9 c. In January, TDS was measured as 732 mg/l in 2014 and dropped to its lowest point of 310 mg/l in the next year, but then it reached 998 mg/l in 2017. The TDS values during June were well below the mark of 300 mg/l from 2009 to 2019, but during June 2015, the monthly mean TDS was found to be 1333 mg/l. This was the maximum value of TDS during the whole study period. For the same year, the second-highest TDS of 1067 mg/l was also observed in December. TDS remained mostly below 300 mg/l for August and September (mostly during monsoon months) with the lowest TDS of 128 mg/l in August 2017. In 2019, the TDS value mostly ranged between 200 and 300 mg/l. Yearly and monthly mean values of TDS were observed almost under WHO standards and in the Class A category of Indian standards. The TDS value was found below 500 mg/l during each monsoon season. During winter, summer, and post-monsoon months, the TDS of river water never exceeded the Class C category as per BIS standard and during January, the monthly mean average remained higher in comparison to other months.

Total coliform

Human and animal discharges are the main source of fecal coliform bacteria whose excessive presence in water degrades the water quality. During 2009–19, there was an exponential rise in total coliform as shown in Fig.  10 a. The yearly mean in 2009 was 177 MPN/100 ml, which in the decade reached 139,200 MPN/100 ml in 2019. The difference of yearly mean for two periods of 2009–13 and 2016–19 was more than 100 times the value at its starting period. The year 2018 was observed with the highest yearly mean of 490,818 MPN/100 ml, which was reduced in 2019, with the lowest total coliform count of 136.7 MPN/100 ml in 2010. Note that the years 2014–15 were excluded from comparison for this case due to less availability of data for the whole year.

( a ) Yearly variation of total coliform from 2009 to 2019. Redline shows an exponential trend of total coliform. ( b ) Monthly variation of total coliform from 2009 to 2019. ( c ) Monthly mean variation of total coliform from 2009 to 2019.

Figure  10 b shows a large monthly variation in total coliform in each month during 2009–19. The median of most of the months was below 500 MPN/100 ml, whereas median values were 1050, 620, and 16,775 MPN/100 ml for September, October, and November, respectively. The monthly mean for January, February, March, April, June, and October was above 100,000 MPN/100 ml. Maximum and minimum monthly means were observed for October (133,797 MPN/100 ml) and May (12,663 MPN/100 ml), respectively.

Figure  10 c indicates the exponential rise in the trend of total coliform in each month since 2009. Total coliform counts during 2009–14 were well below the 500 MPN/100 ml mark, while some months also showed counts near 800 MPN/100 ml. However, from 2016 onwards, the counts crossed a sustainable mark of 5000 MPN/100 ml for every month of each year. From 2009 to 2013, the total coliform counts fell mostly in Class B but exceeded all limits of Indian water quality standards to a great extent during 2016–19. The monthly mean was not even close to the maximum total coliform limit of 5000 MPN/100 ml, which made water quality in Class D and E categories. WHO standards nullify the presence of fecal coliform in water, whereas the Yamuna River was found to be in its alarming situation for this particular parameter.

Water discharge

The freshwater supply and inflow of wastewater in the river may affect the water quality. To uncover the influence, we analyzed the water discharge in the river Yamuna from 2013 to 2018 (the data is available for this period only). In Fig.  11 , we show the major locations of water abstraction and confluence in the Yamuna river starting from Yamunotri to Faridabad stretch. Hathnikund barrage was constructed to regulate the Yamuna water supply to Haryana, Uttar Pradesh, and Delhi for agricultural and domestic purposes and it was also decided by the Government to maintain 10 cumes of water in the Yamuna downstream to maintain the aqua life in river. In Fig.  12 , we show the water discharge data of Hathnikund, Baghpat, and DRB. From 2013 to 2018, a significant fall in the water discharge is observed especially at Hathnikund, which allows the upstream water to reach Palla. The mean discharge in the river was found to be 123.7 cumes during the whole study period and the minimum was found much lower than 10 cumes (as suggested by the Government of India). Also, a significant fall is observed in recent years. The mean water discharge values at Baghpat are found to be 225 cumes and sometimes they reached far below than 5 cumes.

( Source : https://yamuna-revival.nic.in/wp-content/uploads/2020/07/Final-Report-of-YMC-29.06.2020.pdf ).

Points of water abstraction and additions in Yamuna river.

( a ) Temporal variation of water discharge at Hathnikund, Baghpat, and Delhi Railway Bridge (DRB) stations during Jan 2013 to May 2018. ( b ) The distribution of monthly mean water discharge during Jan 2013 to May 2018 at Baghpat. ( c ) Monthly mean variation of discharge from 2013 to 2018.

At DRB, the mean discharge value is found to be 107 cumes. During monsoon months, the water discharge at Baghpat is higher than that at the Hathnikund so that the watershed of the Yamuna also helps in the rise of the water discharge due to rainfall and also major drains confluence in the Yamuna. We can see that during August, the water discharge is the highest followed by July and September. During January, May, and December, the water discharge values reached far below the prescribed limit, and hence sometimes during these seasons, the Yamuna almost dried up and only the sewage and the industrial wastes water flow during these months (Fig.  12 c). After 2015, a significant fall in water discharge is observed during the summer and winter months. With the abstraction of fresh water at Hathnikund barrage and inflow of the drains, the water quality parameters are affected by a large extent in river Yamuna. Therefore, for further analysis of the impact of water discharge and rainfall, we also analyzed the relationship of monthly mean discharge, rainfall, DO saturation, BOD, and COD as shown by the polar plots (Figs. 13 , 14 ).

The relationship between the water discharge, Rainfall, BOD, and COD.

The monthly relationship between the water discharge, Rainfall and DO saturation.

Variability of DO, BOD, and COD

Water quality can be affected by various factors. Hence, we investigated the variability of the DO, BOD, and COD with water discharge and rainfall. In Fig.  13 , we plot the relationship of monthly mean water discharge at Baghpat, rainfall, BOD, and COD. The rainfall mostly affected the discharge values, but, sometimes, with low rainfall high discharge is observed. COD is mostly higher during the whole study period whereas BOD values have shown a fall in recent years. The impact of rainfall and high discharge is visible and low BOD and COD are observed. When the discharge is more than 525 cumes, the COD and BOD values are lower, and also more than 30 mm of rainfall is observed at that time. Further, we investigated the monthly relationship of the Discharge, rainfall, and DO saturation. Further analysis (Fig.  14 ) clearly shows that most rainfall occurred from June to September and caused a rise in the water discharge. During this time, the DO saturation values are more than 50%. The low DO saturation values are mostly observed during the time of low discharge and low rainfall (< 10%). We also found that during July to September with discharge between 200 to 400 cumes and rainfall more than 40 mm, the DO saturation remains lower than 10%. Further, Drain No. 6 having a catchment area of Samalkha Ganur and Sonipat, carries around 49 MLD of sewage in the year 2017 and rises to 210 MLD in the year 2018. Drain No. 8 crosses Drain No. 6 at Akbarpur, Sonipat, and meets river Yamuna just upstream of Palla village. There is a huge increase in the flow of Drain No. 8 between 2017 and 2018 from 196 to 2590 MLD. Drain No. 6 lined separately flow inside Drain No. 8 for 10 KM. Effluents from both drains mix with each other during the rainy season and due to accidental breach ( https://sandrp.in/2015/04/13/blow-by-blow-how-pollution-kills-the-yamuna-river-a-field-trip-report/ ). Similarly, sewage flow of Drain No. 2, which meets river Yamuna 100 km upstream of Delhi is increased from 62 to 2092 MLD between 2017 and 2018 by Central Pollution Control Board of India. As per Haryana State Pollution Control Board (HSPCB), the capacities of Sewage treatment plants (STP) for Drains No. 2, 6, and 8 were 72, 104.5, 125.3 MLD, respectively, till 2018. Also, the capacities of the common effluent treatment plant (CETP) for Drains No. 2, 6, and 8 were 21, 33.2, and 10 MLD, respectively. Since the total capacity for treating wastewater was far beyond sewage generation during 2017–18 and hence the untreated water mixed with the river water. This rise in wastewater generation from industrial and urban areas has caused a drastic decrease in DO saturation and an increase in Total Coliform. As per CPCB 2018 report ( https://yamuna-revival.nic.in/wp-content/uploads/2020/07/Final-Report-of-YMC-29.06.2020.pdf ), 7-day average discharge of the 10-year return period (7Q10) does not meet the habitat requirements of the indicator fish species. These conditions show the impact of rapid urbanization and industrialization along the river bank with high carbon concentration. With the recent development in industrial regions, change in land use/land cover and rapid urbanization in the Haryana, the watershed of Yamuna suffered a lot and hence the water quality of the river. One of the major causes of the sudden fall in the DO saturation in recent years is the fall in the freshwater discharge at the Hathnikund and also the fall in the rainfall in recent years. Also, the deteriorating water quality of Yamuna is a major concern for the Government and mostly this is affected in the stretch between Hathnikund and Palla due to rise in inflow of untreated drains water supply. In recent years, the National Green Tribunal (NGT) of India also requested the states Government to take necessary actions to combat present situation of river Yamuna by installation of more STEPs, ETPs and maintaining the treatment capacity of present treatment plants and channelizing the sewage network to reach treatment plants properly. However, due to lack of adequate fresh water supply and mixing of untreated sewage through regulated and unregulated drains, the quality of the Yamuna river lies in critical conditions.

Although National Capital Territory (NCT) is held responsible for most polluting river Yamuna, the study reveals that the quality of the river it receives is not admirable. The study of physiochemical and biological parameters shows variation in its monthly and yearly values during 2009–19. The effect of monsoon season can be easily seen on parameters like BOD, COD, total alkalinity, TDS and total coliform as their values declined, while DO saturation % showed a significant rise. DO saturation declined by more than 85% during this period. The BOD values improved during the last two years (2018–2019), but were still slightly higher than the permissible limit, while the COD value always remained quite higher than the permissible limits. In 2015, the worst condition was observed in terms of BOD and COD. Total alkalinity also remained low and below the prescribed standards, but TDS is the only parameter whose value was mostly in desired limits throughout the period. An exponential rise was observed in the total coliform count, which was 100–1000 times the maximum limit of IS:2296. Increasing discharge of partially treated industrial effluent and untreated sewage into the Yamuna in the past decade is considered to be the primary cause of the deterioration of water quality. Even after completion of YAP phases I and II, and ongoing phase III, the river still falls in the category of Class D or E under BIS specification, Class III or IV of UNECE standards, and does not fulfill the WHO guideline for water quality at Palla station.

Data availability

All the data used in the present study is freely available in the public domain and the web addresses are discussed in the manuscript, however, we will provide data to all the interested scientists.

Abbreviations

Bureau of Indian Standards

Biological oxygen demand

Common effluent treatment plants

Chemical oxygen demand

Central Pollution Control Board

Central Water Commission

Dissolved oxygen

Effluent treatment plant

Global Environmental Monitoring System

Haryana State Pollution Control Board

India Meteorological Department

Indian Space Research Organization

Kiloliters per day

Modern-Era Retrospective analysis for Research and Applications, Version 2

Monitoring of Indian National Aquatic Resources

Millions of litres per day

Ministry of Environment and Forests

Most probable number

National Capital Region Planning Board

National Capital Territory

National Green Tribunal

National Water Quality Monitoring Programme

State Pollution Control Board

Total dissolved solid

United Nations Economic Commission for Europe

World Health Organization

Water Resources and Information System

Yamuna Action Plan

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Acknowledgements

We would like to thank the National Water Informatics Centre (NWIC), a unit of the Ministry of Jal Shakti for providing updated data on water resources through a ‘Single Window’ source. We also thank research center O.I.E of Mines Paris Tech and ARMINES for providing meteorological data. The data used in the current study were freely available and their links are mentioned in their respective places. We express a great sense of gratitude towards the Central Pollution Control Board of India and other agencies for making data available.

This research was financially supported by the Ministry of Science and Technology (MOST) of Taiwan under the codes MOST 109-2923-E-008-004-MY2 and MOST 110-2111-M-008-008.

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Pankaj Joshi

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Akshansha Chauhan & Yuei-An Liou

Department of Engineering, University of Technology and Applied Sciences, Suhar, 311, Sultanate of Oman

Department of Chemistry, SRM IST, Delhi-NCR Campus, Ghaziabad, India

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Conceptualization—A.C. and Y.-A.L., Data analysis: P.J. and A.C., Methodology: P.J., S.M., Y.-A.L., Writing—original draft by P.J., A.C., S.M., P.D.; Review and editing—A.C., P.D., and Y.-A.L.

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Joshi, P., Chauhan, A., Dua, P. et al. Physicochemical and biological analysis of river Yamuna at Palla station from 2009 to 2019. Sci Rep 12 , 2870 (2022). https://doi.org/10.1038/s41598-022-06900-6

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Received : 08 December 2021

Accepted : 07 February 2022

Published : 21 February 2022

DOI : https://doi.org/10.1038/s41598-022-06900-6

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