Aerosol type plays a critical role in the relationship between aerosol optical depth (AOD) and particulate matter (PM) mass concentration. Here, we present a mathematical formulation of how PM2.5 is related to AOD; when simplified to a linear equation, it reveals a functional dependence of the slope on aerosol type, hygroscopic growth, and boundary layer height, while the influence of the vertical aerosol profile is embedded in the intercept. We further employed a daily averaged AERONET measurement training dataset to develop a Normalized Gradient Aerosol Index (NGAI) for classifying sub-aerosol-types: mineral dust (MD), urban-industrial pollution (U/I) and biomass burning (BB). By distinguishing the aerosol subtypes beforehand, the derived AOD-PM2.5 linear regressions were significantly improved, demonstrating that NGAI can exploit the difference in aerosol hygroscopicity and improve the surface dry PM2.5 estimations. In addition, the hygroscopic growth factor, f(RH), can be estimated based on the slope (β1) of the AOD-PM2.5 expression.