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

ABSTRACT: 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 O₃ 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 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 PM_2.5 and O₃ in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (

SUBMITTER: Xing J 

PROVIDER: S-EPMC7787966 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature