Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)

Abstract

Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM concentrations are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient-Boosting Machine (LightGBM) was developed to fuse multisource data and estimate long-term (1980–2022) historical daily ground PM concentrations in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation (CV) test R2 values of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 µg m−3) indicate low overfitting risks. With great generalization ability, the openly accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). This showed that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations have significantly increased (p<0.05) in most regions since 2000 (0.34 µgm-3yr-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Programme, and PM2.5 concentrations declined in most regions (−0.78 µgm-3yr-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % confidence interval (CI) [0.65, 0.80]) million in 2000 to 1.22 (95 % CI [1.03, 1.41]) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 million to 0.60 million. LongPMInd has the potential to support multiple applications of air quality management, public health initiatives, and efforts to address climate change. The daily and monthly PM2.5 and PM10 concentrations are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).