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 PM_2.5 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-PM_2.5 linear regressions were significantly improved, demonstrating that NGAI can exploit the difference in aerosol hygroscopicity and improve the surface dry PM_2.5 estimations. In addition, the hygroscopic growth factor, f(RH), can be estimated based on the slope (β₁) of the AOD-PM_2.5 expression.