TY  - Data
T1  - Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)
A1  - Zhang Hongliang
DO  - 10.5281/zenodo.10073944
PY  - 2024
DA  - 2024-08-29
PB  - National Cryosphere Desert Data Center
AB  - This dataset includes the research areas of PM, feature importance, spatial and temporal patterns 2.5 and PM10, as well as uncertainty in estimating annual mortality rates.In this work, a simple structured, efficient, and robust model based on LightGBM was developed to fuse multi-source data and estimate India's long-term (1980-2022) historical daily ground PM concentration (LongPMIn). The LightGBM model showed good accuracy with R2 values of 0.77, 0.70, and 0.66 in out of sample, out of field, and out of year cross validation (CV) tests, respectively. The performance gap between PMs is small, and 2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 micrograms m-3) indicate a low risk of overfitting. Has strong generalization ability, can publicly access, long-term, high-quality daily PM2.5 and PM10, and then reconstruct the product (10 kilometers, 1980-2022). This indicates that India has experienced severe PM pollution in the Indian Ganges Plain (IGP), especially during winter. Since 2000, PM concentrations have significantly increased in most regions. The turning point occurred in 2018, when the Indian government launched the National Clean Air Program, resulting in a decrease in PM2.5 concentrations in most areas. Severe PM2.5 pollution has led to a continuous increase in attributed premature mortality rates, rising from 0.73 (95% confidence interval (CI) [0.65, 0.80]) in 2000 to 1.22 (95% CI [1.03, 1.41]) in 2019, particularly in IGP where attributed mortality rates increased from 360000 to 600000. LongPMIn has the potential to support various applications in air quality management, public health initiatives, and climate change response.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/59772e67-7fcb-42aa-a688-120b203fee15
ER  -