PM2.5 Concentration Prediction in the Beijing–Tianjin–Hebei Region Based on ERA5 Stratified PWV and Atmospheric Pollutants

作者：Jun Shang, Peixuan Zhang, Yong Wang, Yanping Liu, Hongsheng Wang, Suo Li · 发表于：Atmosphere · 年份：2025 · DOI：10.3390/atmos16030269 · 被引用次数：5 · 研究领域：Air Quality Monitoring and Forecasting、Air Quality and Health Impacts、Atmospheric chemistry and aerosols

Accurate prediction of PM2.5 (particle pollution from fine particulate) concentration is crucial for environmental protection and public health. Precipitable water vapor (PWV) in the atmosphere is an important meteorological element with stratification properties, which plays a crucial role in energy transfer, weather dynamics, and PM2.5 generation. However, past studies tend to use total PWV as an input parameter, neglecting the impact of PWV variations in different altitude layers on PM2.5 concentration. To overcome this limitation, this study proposes an innovative approach that employs stratified water vapor data (ERA5-PWV) calculated from the ERA5 reanalysis data instead of the total PWV obtained using the traditional method. This approach provides a more accurate representation of the vertical distribution of atmospheric PWV and enhances the prediction of PM2.5 content. In this study, the stratified ERA5 PWV in the Beijing–Tianjin–Hebei region is integrated with other meteorological elements and atmospheric pollutants, and the FFT-ConvLSTM method, characterized by its spatio-temporal properties, is utilized to predict the PM2.5 concentration by incorporating the spatio-temporal correlation. The FFT-ConvLSTM model is modeled by extracting spatio-temporal features through ConvLSTM, following the identification of the optimal common change period of each element using the FFT technique. This process mitigates the problem of spatio-temporal heterogeneity among elements, thu...