Hourly PM2.5 Forecasting for Sustainable Urban Air Quality Management Through Multiresolution Learning and Environmental Context
Year: 2026
Type: article
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.
Source: Sustainability
DOI: 10.3390/su182010531
Authors: Shulei Cheng, Xin Wen, Jiaying Xiao, Wei Fan, Chong Zhou
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7222408516