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

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 h, 1km × 1 km 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 features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels.