Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3565-2024
https://doi.org/10.5194/essd-16-3565-2024
Data description paper
 | 
08 Aug 2024
Data description paper |  | 08 Aug 2024

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

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

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Cited articles

Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N.-B., Tan, Z., and Han, D.: LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion, Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, 2022. 
Bai, K., Li, K., Shao, L., Li, X., Liu, C., Li, Z., Ma, M., Han, D., Sun, Y., Zheng, Z., Li, R., Chang, N.-B., and Guo, J.: LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics, Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, 2024. 
Bali, K., Dey, S., and Ganguly, D.: Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data, Atmos. Environ., 248, 118180, https://doi.org/10.1016/j.atmosenv.2020.118180, 2021. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
Brauer, M., Guttikunda, S. K., Nishadh, K. A., Dey, S., Tripathi, S. N., Weagle, C., and Martin, R. V.: Examination of monitoring approaches for ambient air pollution: A case study for India, Atmos. Environ., 216, 116940, https://doi.org/10.1016/j.atmosenv.2019.116940, 2019. 
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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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