Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure, and related health impacts, especially in China, which experiences both PM2.5 and O3 pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants and thus cannot support simultaneous assimilation for both PM2.5 and O3. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM2.5 and O3 in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NOx, sulfur dioxide = SO2, ammonia = NH3, VOC, and primary PM2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O3 and PM2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results in small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions
however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO2 in January, April, and October, and SO2 in April, but overestimate NH3 in April, and VOC in January and October. Primary PM2.5 emissions were also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH3 observations to improve the RSM-assimilation performance and emission inventories.