First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact
Abstract Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studi...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-43862-3 |