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Despite much effort made in studying human health associated with fine particulate matter (PM 2.5), our knowledge about PM 2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM 2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM 2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM 2.5 at individual stations (SC-PM 2.5). Then we merged SC-PM 2.5 with the spatial pattern of GEOS-Chem modeled PM 2.5 to obtain a gridded PM 2.5 dataset (GC-PM 2.5). We validated the GC-PM 2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM 2.5 measurements. The spatial patterns of temporally averaged PM 2.5 mass concentrations are consistent between GC-PM 2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM 2.5 data reproduce the day-to-day variation of observed PM 2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM 2.5 variations and their health impacts since 1980. • We integrate visibility data and GEOS-Chem simulations to estimate PM 2.5 concentrations in 2014 over North China. • Visibility converted PM 2.5 are spatiotemporally consistent with PM 2.5 measurements. • Our method provides a novel, plausible way to retrieve long-term variation of PM 2.5. [ABSTRACT FROM AUTHOR]

Atmospheric transport of fine particulate matter (PM 2.5 ), the leading environmental risk factor for public health, is estimated to exert substantial transboundary effects at present. During the past several decades, human-produced pollutant emissions have undergone drastic and regionally distinctive changes, yet it remains unclear about the resulting global transboundary health impacts. Here we show that between 1950 and 2014, global anthropogenic PM 2.5 has led to 185.7 million premature deaths cumulatively, including about 14% from transboundary pollution. Among four country groups at different affluence levels, on a basis of per capita contribution to transboundary mortality, a richer region tends to exert severer cumulative health externality, with the poorest bearing the worst net externality after contrasting import and export of pollution mortality. The temporal changes in transboundary mortality and cross-regional inequality are substantial. Effort to reduce PM 2.5 -related transboundary mortality should seek international collaborative strategies that account for historical responsibility and inequality., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest., (Copyright © 2021 Science China Press. Published by Elsevier B.V. All rights reserved.)