Preprints
https://doi.org/10.5194/essd-2024-34
https://doi.org/10.5194/essd-2024-34
06 Feb 2024
 | 06 Feb 2024
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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

Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang

Abstract. Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM datasets 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 multi-source data and estimate long-term (1980–2022) historical daily ground PM datasets 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 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 open-accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). It shows that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations significantly increased (p<0.05) in most regions since 2000 (0.34 μg m-3 year-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Program, PM2.5 concentrations declined in most regions (- 0.78 μg m-3 year-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % 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 to 0.60 million. The LongPMInd datasets have the potential to support multi-applications of air quality management, public health, and climate change. The daily and monthly PM2.5 and PM10 datasets are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-34', Anonymous Referee #1, 06 Mar 2024
    • AC1: 'Reply on RC1', Hongliang Zhang, 24 May 2024
  • RC2: 'Comment on essd-2024-34', Anonymous Referee #2, 28 Apr 2024
    • AC2: 'Reply on RC2', Hongliang Zhang, 24 May 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-34', Anonymous Referee #1, 06 Mar 2024
    • AC1: 'Reply on RC1', Hongliang Zhang, 24 May 2024
  • RC2: 'Comment on essd-2024-34', Anonymous Referee #2, 28 Apr 2024
    • AC2: 'Reply on RC2', Hongliang Zhang, 24 May 2024
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang

Data sets

LongPMInd: long-term (1980-2022) daily ground particulate matter datasets in India Shuai Wang, Sri Harsha Kota, and Hongliang Zhang https://zenodo.org/records/10073944

Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang

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Short summary
Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
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