Retrieval of Black Carbon Aerosol Surface Concentration Using Integrated MODIS and AERONET Data

Abstract. Black Carbon (BC) is a carbonaceous aerosol that strongly absorbs solar radiation. The high emissions of these highly absorbent particles exacerbate regional air quality and pose significant threats to global climate, both in the short and long term. Therefore, accurately quantifying the spatial distribution of BC is crucial for improving regional air quality and mitigating the climate change impacts driven by human activities. In this study, we developed a novel algorithm for retrieving BC surface concentration using MODIS and AERONET data. The algorithm first determined the seasonal background aerosol model using the K-means clustering method, based on AERONET V3 daily products. It then employed the Maxwell–Garnett effective medium approximation model to calculate the complex refractive index of the internally mixed aerosols and used the 6SV2.1 radiative transfer code to establish lookup tables for optimal BC fraction and column concentration estimation. Subsequently, the column concentration data were converted to surface concentration using a conversion coefficient derived from MERRA-2. Finally, the retrieved MODIS BC surface concentration was validated with in-situ Aethalometer measurements. The validation showed a correlation coefficient (R) of 0.727, a root mean square error (RMSE) of 0.353, a mean absolute error (MAE) of 0.211, and a linear fit function of y = 0.718x + 0.015. These statistical parameters outperform those obtained from MERRA-2 BC data, demonstrating the superior performance of the proposed algorithm in this study area.