A hybrid model for enhanced forecasting of PM2.5 spatiotemporal concentrations with high resolution and accuracy

作者：Xiaoxiao Feng, Xiaole Zhang, Stephan Henne, Yibo Zhao, Jie Liu, Tse-Lun Chen, Jing Wang · 发表于：Environmental Pollution · 年份：2024 · DOI：10.1016/j.envpol.2024.124263 · 被引用次数：29 · 研究领域：Air Quality Monitoring and Forecasting、Air Quality and Health Impacts、Atmospheric chemistry and aerosols

Forecasting concentrations of PM2.5 is important due to its known impacts on public health and environment. However, PM2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM2.5 with 1 hr, 1km×1km spatiotemporal resolution. The LSTM system forecast time-dependent PM2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM2.5 concentrations at different frequencies, capturing temporal variability of PM2.5 at various time scales. The RF model predicted distributions of PM2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input ...