Direct and Inverse Reduced-Form Models for Reciprocal Calculation of BC Emissions and Atmospheric Concentrations

Atmospheric black carbon (BC) concentrations are governed by both emissions and meteorological conditions. Distinguishing these effects enables quantification of the effectiveness of emission mitigation actions by excluding meteorological effects. Here, we develop reduced-form models in both direct (RFDMs) and inverse (RFIMs) modes to estimate ambient BC concentrations. The models were developed based on outputs from multiyear simulations under three conditional scenarios with realistic or fixed emissions and meteorological conditions. We established a set of probabilistic functions (PFs) to quantify the meteorological influences. A significant two-way linear relationship between multiyear annual emissions and mean ambient BC concentrations was revealed at the grid cell scale. The correlation between them was more significant at grid cells with high emission densities. The concentrations and emissions at a given grid cell are also significantly correlated with emissions and concentrations of the surrounding areas, respectively, although to a lesser extent. These dependences are anisotropic depending on the prevailing winds and source regions. The meteorologically induced variation at the monthly scale was significantly higher than that at the annual scale. Of the major meteorological parameters, wind vectors, temperature, and relative humidity were found to most significantly affect variation in ambient BC concentrations.