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Recent PM2.5 air quality improvements in India benefited from meteorological variation

Nature Sustainability volume 7, pages 983–993 (2024)Cite this article

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Abstract

Improving air quality amid rapid industrialization and population growth is a huge challenge for India. To tackle this challenge, the Indian government implemented the National Clean Air Programme (NCAP) to reduce ambient concentrations of particulate matter with diameters less than 2.5 μm (PM2.5) and 10 μm (PM10) in hundreds of non-attainment cities that failed to meet the national ambient air quality standards. Here we evaluate the efficacy of the NCAP using data from the national air quality monitoring network combined with regional model simulations. Our results show an 8.8% yr−1 decrease in annual PM2.5 pollution in the six non-attainment cities with continuous air pollution monitoring since 2017. Four of these six cities achieved over 20% reductions in PM2.5 pollution by 2022 relative to 2017, thereby meeting the NCAP target. However, we find that ∼30% of the annual PM2.5 air quality improvements, and approximately half of the reductions during the heavily polluted winter months, can be attributed to favourable meteorological conditions that are unlikely to persist as the climate warms. Meanwhile, in 2022, annual PM2.5 levels in 44 out of 57 non-attainment cities with continuous monitors still failed to meet air quality standards. This work highlights the need for substantial additional mitigation measures beyond current NCAP policies to improve air quality in India.

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Fig. 1: Availability of continuous PM monitoring data in Indian cities.
Fig. 2: Observed surface PM2.5 air quality improvements during 2017–2022.
Fig. 3: Changes in anthropogenic emissions of primary PM2.5 and key precursors since 2010.
Fig. 4: Meteorological contributions to recent PM2.5 air quality improvements.
Fig. 5: Comparison of daily PM2.5 and meteorological variables between winter 2017 and 2021.

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Data availability

Surface PM2.5 and other air pollution data from the CAAQM network are available at https://airquality.cpcb.gov.in/ccr/#/caaqm-dashboard-all/caaqm-landing. Surface PM2.5 data from the US AirNow network are available at https://www.airnow.gov/international/us-embassies-and-consulates/. Manual monitoring data for PM2.5 and other air pollution data are available at https://cpcb.nic.in/manual-monitoring/. The CEDS emission database is available via GitHub at https://github.com/JGCRI/CEDS/. The EDGAR emission database is available at https://edgar.jrc.ec.europa.eu/dataset_ap61. The ECLIPSE emission database is available at https://iiasa.ac.at/models-tools-data/global-emission-fields-of-air-pollutants-and-ghgs. Satellite observations of SO2 and NO2 concentrations from OMI are available at https://giovanni.gsfc.nasa.gov/giovanni/ and from TROPOMI at https://www.temis.nl/airpollution/no2.php. Satellite observations of NH3 concentrations are available at https://iasi.aeris-data.fr/nh3/. Meteorology data from ERA5 are available at https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset and from the NCEI at https://www.ncei.noaa.gov/. WRF-Chem outputs and processed air quality data generated in this study are publicly available via the Princeton archive at https://doi.org/10.34770/xtje-mj26.

Code availability

Source code for the WRF-Chem model utilized in this study is available at https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html#WRF-Chem. All custom codes are direct implementation of standard methods and techniques as described in detail in the Methods.

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Acknowledgements

We acknowledge project support from the M.S. Chadha Center for Global India and the Center for Policy Research on Energy and the Environment in the School of Public and International Affairs at Princeton University. K.M.R.H. is supported by a NERC Independent Research Fellowship (MITRE; grant number NE/W007924/1). We acknowledge the Central and State Pollution Control Boards for making surface PM2.5 pollution measurements available through the CAAQM and NAMP monitoring network. We thank D. Sharma and C. Nguyen for helping to collect the CAAQM surface air quality data for 2015–2022. We thank S. Smith for instructions on using the CEDS global emissions inventory, and C. Venkataraman and T. Ganguly for instructions on using the India national emissions inventories. We also thank F. Paulot, V. Naik, L.W. Horowitz and M. Lin for their suggestions early in this study. We thank M. Nambiar, R. Gupta, E. T. Downie, D. Chug, R. Chandra and W. Dong for constructive feedback on the manuscript.

Author information

Authors and Affiliations

  1. Center for Policy Research on Energy and Environment, School of Public and International Affairs, Princeton University, Princeton, NJ, USA

    Yuanyu Xie, Mi Zhou & Denise L. Mauzerall

  2. Department of Meteorology, University of Reading, Reading, UK

    Kieran M. R. Hunt

  3. National Centre for Atmospheric Sciences, University of Reading, Reading, UK

    Kieran M. R. Hunt

  4. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    Denise L. Mauzerall

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  1. Yuanyu Xie
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  2. Mi Zhou
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Contributions

Y.X. and D.L.M conceptualized the study. Y.X. retrieved and constructed the dataset and performed the analysis. M.Z. contributed to data processing, WRF-Chem model simulations and model evaluations. K.M.R.H. analysed the western disturbances. Y.X. and D.L.M. integrated the results and wrote the manuscript. All authors contributed to the interpretation of the findings, provided revisions to the manuscript and approved the final manuscript.

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Correspondence to Yuanyu Xie or Denise L. Mauzerall.

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Extended data

Extended Data Fig. 1 Continuous PM monitoring stations in India.

(a) Location of continuous PM monitoring stations from CAAQM (black) and the US AirNow (green) networks; (b) Comparison of daily mean PM2.5 concentrations measured during 2017–2022 at five US AirNow monitoring sites and all CAAQM sites located within 5 km radius of the US AirNow sites. The correlation r2, normalized mean bias (NMB) and number (N) of PM2.5 measurements are shown.

Extended Data Fig. 2 Continuous PM monitoring for each season.

Changes in the total number of NCAP non-attainment cities that had continuous PM monitoring from the CAAQM and US AirNow networks (bars, left axis) and number of total surface PM monitoring stations the CAAQM and US AirNow networks (lines, right axis) during 2017–2022 for (a) spring (MAM), (b) summer (JJA), (c) fall (SON) and (d) winter (DJF).

Extended Data Fig. 3 Continuous PM10 monitoring stations in India.

(a) Location of the 131 non-attainment cities (dots) and other cities with PM10 monitoring (open blue circles) on the topographic map (in meters) over India. Blue indicates where continuous PM10 monitoring is available from the CAAQM/US AirNow networks for at least one year during 2017–2022; black indicates no continuous PM10 monitoring is available from the CAAQM/US AirNow networks during 2017–2022; (b) Time series of annual mean PM10 concentrations in 2017–2022 averaged in non-attainment cities with consecutive PM10 data starting from each year during 2017–2021 (right axis); the left axis represent the ratio relative to 2017, the NCAP baseline; the number of cities with available consecutive PM10 observations up to 2022 (numbers in parenthesis) are shown in different shades of grey; larger dots represent greater number of cities included for averaging; error bars denotes ±one standard error of means across available cities (n=13,17,27,36 for 2018–2021 as reported in parenthesis).

Extended Data Fig. 4 Annual and seasonal PM2.5 pollution trends in Indian cities.

Time series of (a) annual and (b-e) seasonal (MAM-spring, JJA-summer, SON-fall, DJF-winter) mean PM2.5 concentrations in 2017–2022 averaged in cities with consecutive PM2.5 data starting from 2017 (black, number of cities reported in parenthesis), and for cities with consecutive data starting from 2018–2021 (different shades of grey; number of cities reported at the bottom); seasonal trends for non-attainment (all) cities is shown in black (orange); the left axis represent the ratio relative to 2017, the NCAP baseline; data starting from 2018–2021 are scaled to match with the ratio relative to 2017; larger dots represent greater number of cities included for averaging; error bars denotes ±one standard error of means across available cities (n=7,33,53,74,99 reported in panel a; n=15(17 for all cities),35,37,45,53 reported in panel b; n=11(12 for all cities),29,32,43,52 reported in panel c; n=13(14 for all cities),32,41,46,54 reported in panel d; n=28(36 for all cities),36,42,47,555 reported in panel e).

Extended Data Fig. 5 Observed decreases in surface PM2.5 during 2017–2022 for each season.

Seasonal mean PM2.5 measured at CAAQM and U.S. AirNow continuous monitoring sites during 2017–2022 for (a) March–May (MAM), (b) June–August (JJA), (c) September–November (SON), and (d) December–February (DJF). Note that DJF for 2017 represents December 2017 to February 2018. The larger dots with black circles represent PM2.5 concentrations at non-attainment cities that are available for six consecutive years. The smaller dots without black circles represent PM2.5levels for cities without six consecutive years of data.

Extended Data Fig. 6 Changes in anthropogenic emissions over India from three global emission inventories.

(a-c) Timeseries of anthropogenic emissions of SO2, NOx, NH3, OC, BC, PM2.5, PM10 and NMVOC during 2000–2020 relative to 2017 from CEDS (v2021-04-21, left), EDGAR (v6.1, middle) and ECLIPSE (v6b, right). Data from ECLIPSE during 2019–2020 are projections. (d) comparison of annual total emissions in 2017 over India (Tg yr−1) from the three global emission inventories.

Extended Data Fig. 7 Changes in SO2, NOx and NH3 during 2017–2022 over India.

(a) Satellite observed total column SO2 from OMI in India for 2017, 2022 and the difference between 2022 and 2017. (b-c) Same as (a) but for total column NO2 from TROPOMI (b) and OMI (c) for 2018, 2022 and the difference between 2022 and 2018. (d) same as (a) but for total column NH3 from IASI for 2017, 2022 and the difference between 2022 and 2017. Circles are surface observations of SO2 (a), NOx (b-c) and NH3 (d) for 2018, 2022 and the difference between 2022 and 2018.

Extended Data Fig. 8 Correlations between surface PM2.5 and meteorological variables.

Correlation coefficient r between detrended daily surface PM2.5 and meteorological variables for daily, 3-day, 5-day and 7-day averages in December–February during 2017–2022. From left to right: surface temperature (T2m), precipitation (Precip), relative humidity (RH), boundary layer height (BLH), surface pressure (Pressure), surface wind speed (WS-10m), temperature inversion between 925hPa and 2m (INV925-2m), temperature inversion between 850hPa and 2m (INV850-2m), 850hPa wind speed (WS-850), 500hPa wind speed (WS-500). Dots indicate statistical significance at 95 percentile confidence intervals.

Extended Data Fig. 9 Changes in meteorology in the winter of 2017 and 2021.

Differences in inversion (a,b), precipitation (c,d, contour), wind speed at 10 meters (e,f) and transect of geopotential height and vertical-meridional circulation anomalies averaged between 73E–88E (g,h) in the winter of 2017 (left) and 2021 (right) relative to 2000–2022 mean. Tracking of western disturbance with average vorticity greater than 9e−5 m/s over northern India are each shown in c,d with different colors and shapes. Black shading in g,h indicates the surface topography along the transects.

Extended Data Fig. 10 Sensitivity simulations with changing emissions for the winter of 2017 and 2021.

(a) Differences in the simulated surface PM2.5 concentrations (in percent) in response to changes in emission alone (EMIS), meteorology alone (MET), and to changes in both emission and meteorology (EMIS+MET) relative to the baseline simulation with emissions and meteorology from 2017. Difference for emission perturbation simulations for 2021 (orange and dark blue) is compared to simulation with emissions from 2017 and meteorology from 2021. (b) same as (a) but for meteorological variables. The observed changes (OBS) in PM2.5 and meteorological variables are shown as gray bars. Bars represent changes averaged from the 28 non-attainment cities shown in Fig. 4c, d, circles represent changes averaged from all 131 non-attainment cities.

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Xie, Y., Zhou, M., Hunt, K.M.R. et al. Recent PM2.5 air quality improvements in India benefited from meteorological variation. Nat Sustain 7, 983–993 (2024). https://doi.org/10.1038/s41893-024-01366-y

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