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 that experiences both PM(2.5) and O(3) pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM(2.5) and O(3). 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 PM(2.5) and O(3) in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NO(x), SO(2), NH(3), VOC and primary PM(2.5)) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O(3) and PM(2.5) were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results 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 NO(2) in January, April and October, and SO(2) in April, but overestimate NH(3) in April and VOC in January and October. Primary PM(2.5) emissions are also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH(3) observations to improve the RSM-assimilation performance and emission inventories.