Abstract
A hygroscopic growth model suitable for local aerosol characteristics and their temporal variations is necessary for accurate satellite retrieval of ground-level particulate matters (PM). This study develops an empirical method to correct the relative humidity (RH) impact on aerosol extinction coefficient and to further derive PM concentrations from satellite observations. Not relying on detailed information of aerosol chemical and microphysical properties, this method simply uses the in-situ observations of visibility (VIS), RH and PM concentrations to characterize aerosol hygroscopicity, and thus makes the RH correction capable of supporting the satellite PM estimations with large spatial and temporal coverage.In this method, the aerosol average mass extinction efficiency (αext) is used to describe the general hygroscopic growth behaviors of the total aerosol populations. The association between αext and RH is obtained through empirical model fitting, and is then applied to carry out RH correction. Nearly one year of in-situ measurements of VIS, RH and PM10 in Beijing urban area are collected for this study and RH correction is made for each of the months with sufficient data samples. The correlations between aerosol extinction coefficients and PM10 concentrations are significantly improved, with the monthly correlation R2 increasing from 0.26–0.63 to 0.49–0.82, as well as the whole dataset's R2 increasing from 0.36 to 0.68. PM10 concentrations are retrieved through RH correction and validated for each season individually. Good agreements between the retrieved and observed PM10 concentrations are found in all seasons, with R2 ranging from 0.54 in spring to 0.73 in fall, and the mean relative errors ranging from −2.5% in winter to −10.8% in spring. Based on the satellite AOD and the model simulated aerosol profiles, surface PM10 over Beijing area is retrieved through the RH correction. The satellite retrieved PM10 and those observed at ground sites agree well with each other, with R2 = 0.46 and a relative error of 19.3%.
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