Effect of agricultural soil wind erosion on urban PM2.5 concentrations simulated by WRF-Chem and WEPS: A case study in Kaifeng, China. (May 2023) - Zhang, Haopeng; Song, Hongquan; Wang, Xiaowei; Wang, Yaobin; Min, Ruiqi; Qi, Minghui; Ru, Xutong; Bai, Tianqi; Xue, Hua
Abstract: Dust emission induced by agricultural soil wind erosion is one of the main sources of atmospheric particulate matter (PM) in dryland areas. However, most current air quality models do not consider this emission source, resulting in large uncertainties in PM simulations. Here we estimated the agricultural PM2.5 (particulate matter with an aerodynamic diameter <2.5 μm) emission around Kaifeng, a prefecture-level city in central China, using the Wind Erosion Prediction System (WEPS), with the MEIC (Multi-resolution Emission Inventory for China) as an anthropogenic emission source. We then plugged these estimates into the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an air pollution episode in Kaifeng, China. Results showed that the addition of agricultural soil PM2.5 emissions significantly improved the ability of WRF-Chem to accurately simulate PM2.5 concentrations. The PM2.5 concentration mean bias and correlation coefficient of not considered and considered agricultural dust emission were −72.35 μg m −‍3 and 3.31 μg m −‍3 and 0.3 and 0.58, respectively. The PM2.5 emitted by the agricultural soil wind erosion contributed around 37.79% of the PM2.5 in the Kaifeng municipal district during this pollution episode. This study confirmed that the dust emission caused by agricultural soil wind erosion can significantly impact urban PM2.5 concentrations which surrounded by large areas of farmland, and also indicated that coupling dust emissions from farmland with anthropogenic air pollutant emissions can improve the accuracy of air quality models. Graphical abstract: Image 1 Highlights: PM2.5 emission induced by agricultural soil wind erosion was estimated using WEPS. PM2.5 emission from agricultural soil erosion was coupled in the WRF-Chem model. WRF-Chem considered agricultural dust can significantly improve the PM2.5 accuracy. Agricultural dust contributed 37.79% of PM2.5 in Kaifeng during this episode.