Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method

摘要

The spatiotemporal concentration of multiple pollutants is crucial information for pollution control strategies to safeguard public health.Despite considerable efforts,however,significant uncertainty remains.In this study,a three-dimensional variational model is coupled with a data assimilation (DA) system to analyze the spatiotemporal variation of PM_(2.5) for the whole of China.Monthly simulations of six sensitivity scenarios in different seasons,including different assimilation cycles,are carried out to assess the impact of the assimilation frequency on the PM_(2.5) simulations and the model simulation accuracy afforded by DA.The results show that the coupled system provides more reliable initial fields to substantially improve the model performance for PM_(2.5),PM_(10),and O_3.Higher assimilation frequency improves the simulation in all geographic areas.Two statistical indicators-the root mean square error and the correlation coefficient of PM_(2.5) mass concentrations in the analysis field-are improved by 12.19 μg/m~3 (33%) and 0.21 (48%),respectively.Although the 24-h assimilation cycle considerably improves the model,assimilation at a 6-h cycle raises the performance for PM_(2.5) to the performance goal level.The analysis shows that assimilating at a 24-h cycle diminishes over time,whereas the positive impact of the 6-h cycle persists.One pivotal finding is that the assimilation of PM_(2.5) in the outermost domain results in a substantial improvement in PM_(2.5) prediction for the innermost domain,which is a potential alternative method to the existing domain-wide data fusion algorithm.The effect of assimilation varies among topographies,a finding that provides essential support for further model development.