First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact

Jing Wei (), Zhanqing Li (), Alexei Lyapustin, Jun Wang, Oleg Dubovik, Joel Schwartz, Lin Sun, Chi Li, Song Liu and Tong Zhu
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Jing Wei: University of Maryland
Zhanqing Li: University of Maryland
Alexei Lyapustin: NASA Goddard Space Flight Center
Jun Wang: The University of Iowa
Oleg Dubovik: Université de Lille, CNRS
Joel Schwartz: Harvard TH Chan School of Public Health
Lin Sun: Shandong University of Science and Technology
Chi Li: Washington University in St. Louis
Song Liu: Southern University of Science and Technology
Tong Zhu: Peking University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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 studies. We find that 96%, 82%, and 53% of Earth’s populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

Date: 2023
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DOI: 10.1038/s41467-023-43862-3

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