Spatiotemporal and probability variations of surface PM2.5 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 PM2.5 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 PM2.5. Annual and seasonal PM2.5 concentrations have decreased over most areas in China during the 6-year period. We decomposed the observed day-to-day variation of PM2.5 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 PM2.5. We found that the nationwide decline in seasonal mean PM2.5 concentrations mainly reflected decreased occurrences of extremely high PM2.5 concentrations, which was strongly driven by the interannual variation of meteorology. These changes in the annual means and probability distributions of PM2.5 since December 2013 has led to significant decline of the estimated mortality risks associated with long-term and short-term PM2.5-exposures. Regions that were less polluted saw the largest relative benefit per unit decrease in PM2.5 concentration, due to the steepness of the exposure-response curve at the low-concentration end. Our integrated methodology effectively diagnosed the drivers of PM2.5 variability and the associated health risks and can be used as part of the decision tool for PM2.5 pollution management over China.

Characterizing the aging of biomass burning organic aerosol by use of mixing ratios: a meta-analysis of four regions.

Characteristic organic aerosol (OA) emission ratios (ERs) and normalized excess mixing ratios (NEMRs) for biomass burning (BB) events have been calculated from ambient measurements recorded during four field campaigns. Normalized OA mass concentrations measured using Aerodyne Research Inc. quadrupole aerosol mass spectrometers (Q-AMS) reveal a systematic variation in average values between different geographical regions. For each region, a consistent, characteristic ratio is seemingly established when measurements are collated from plumes of all ages and origins. However, there is evidence of strong regional and local-scale variability between separate measurement periods throughout the tropical, subtropical, and boreal environments studied. ERs close to source typically exceed NEMRs in the far-field, despite apparent compositional change and increasing oxidation with age. The absence of any significant downwind mass enhancement suggests no regional net source of secondary organic aerosol (SOA) from atmospheric aging of BB sources, in contrast with the substantial levels of net SOA formation associated with urban sources. A consistent trend of moderately reduced ΔOA/ΔCO ratios with aging indicates a small net loss of OA, likely as a result of the evaporation of organic material from initial fire emissions. Variability in ERs close to source is shown to substantially exceed the magnitude of any changes between fresh and aged OA, emphasizing the importance of fuel and combustion conditions in determining OA loadings from biomass burning.