Yanyu Li , Qizhong Wu , Huaqiong Cheng , Yiming Sun , Jieying He , Jie Li
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引用次数: 0
Abstract
Soil fugitive dust (SFD) emission is a vital to environmental supervision in Beijing-Tianjin-Hebei and its Surrounding Areas (BTHSA). However, SFD emission inventory is updated slowly and has great uncertainty for air quality models. In this study, the Google Earth Engine (GEE) cloud platform is used for image acquisition, data preprocessing, and index calculation to rapidly produce bare soil maps, and a dynamic method of developing SFD emission inventory via bare soil maps is developed. The results showed that the BTHSA is susceptible to wind erosion and that the total bare soil area reached 1.05 × 105 km2, and the SFD PM2.5 emission was 1.2 × 105 tons in 2020 according to the wind erosion model. SFD PM2.5 emission is higher in plains areas than in mountainous areas in the BTHSA. The Community Multiscale Air Quality (CMAQ) modeling system is used to validate the SFD emissions with ground-based observational data. SFD emission generates greatly increase PM2.5 in simulations and significantly alleviates 57.9% of the negative biases in PM2.5 in the BTHSA. Identifying the spatiotemporal characteristics of SFD emissions is crucial for controlling air pollution in cities.
期刊介绍:
Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.