Hourly PM2.5 Forecasting for Sustainable Urban Air Quality Management Through Multiresolution Learning and Environmental Context

Abstract

Reliable hourly forecasts of fine particulate matter (PM2.5) can help cities anticipate deteriorating air quality and prepare measures to limit exposure. A central modeling challenge is to combine rapid pollution fluctuations with meteorological and ecological observations that are updated less frequently. This study develops an adaptive frequency gated network (AFGN) that integrates hourly pollution histories, daily meteorological records, and satellite ecological indicators. Parallel temporal convolutions and recurrent encoders represent concentration changes at multiple resolutions, while attention and signed gating incorporate environmental conditions into the forecast. The framework is evaluated using observations from Beijing, Shanghai, Chengdu, and Wuhan during 2023 and 2024. AFGN achieves lower mean prediction error than the gated recurrent unit (GRU) baseline at 1, 6, and 12 h. At 6 h, mean root mean square error (RMSE) decreases from 13.498 to 12.526 μg/m3, corresponding to an absolute reduction of 0.971 μg/m3 and a relative reduction of 7.20%. These results demonstrate the value of combining multiresolution pollution representations with environmental context for short-range forecasting. The approach provides concentration estimates that could inform the timing of pollution surveillance and exposure advisories as part of sustainable urban air quality management.