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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]