Spatiotemporal and probability variations of surface PM 2.5 over China between 2013 and 2019 and the associated changes in health risks: An integrative observation and model analysis.

We used statistical methods and the GEOS-Chem model to interpret the observed spatiotemporal and probability variations of surface PM _2.5 concentrations in China from December 2013 to November 2019, as well as to assess the drivers for the variations and the implications for health risks associated with long-term and short-term exposure to PM _2.5 . Annual and seasonal PM _2.5 concentrations have decreased over most areas in China during the 6-year period. We decomposed the observed day-to-day variation of PM _2.5 concentrations in eastern Chinese cities and found that it showed two distinct major spatial modes, which fluctuated in strength seasonally. The first mode, characterized by most of Eastern China being in the same phase, was mainly associated with the regional ventilation of pollutants. The second mode showed a dipolar pattern between the Beijing-Tianjin-Hebei area and the Yangtze River Delta area and was more prominent in summer. Using model simulations, we showed that this dipole mode was chemically driven by the secondary formation of sulfate in summer. We further used a gamma distribution to succinctly interpret the changes in the probability distributions of PM _2.5 . We found that the nationwide decline in seasonal mean PM _2.5 concentrations mainly reflected decreased occurrences of extremely high PM _2.5 concentrations, which was strongly driven by the interannual variation of meteorology. These changes in the annual means and probability distributions of PM _2.5 since December 2013 has led to significant decline of the estimated mortality risks associated with long-term and short-term PM _2.5 -exposures. Regions that were less polluted saw the largest relative benefit per unit decrease in PM _2.5 concentration, due to the steepness of the exposure-response curve at the low-concentration end. Our integrated methodology effectively diagnosed the drivers of PM _2.5 variability and the associated health risks and can be used as part of the decision tool for PM _2.5 pollution management over China.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)