arXiv:2606.24347v1 Announce Type: new Abstract: Accurate short-term PM$_{2.5}$ forecasting is important for public health protection, air-quality early warning, and urban environmental management. However, PM$_{2.5}$ variation is driven by multiple coupled factors, including stable periodic changes induced by human activities and meteorological regularity, station-specific short-term concentration evolution, and meteorology-driven pollutant dispersion among monitoring stations. Existing spatio-temporal forecasting methods may capture station relationships to some extent, but distance-only, cor
The increasing availability of spatio-temporal data and advanced AI techniques like neural networks makes more accurate air quality forecasting possible now.
Improved PM2.5 forecasting is critical for public health interventions, environmental policy, and urban management, directly impacting citizen well-being and economic costs associated with pollution.
This advancement offers more precise short-term PM2.5 predictions by integrating multi-view geological and meteorological data with advanced neural network architectures, moving beyond simpler distance-only models.
- · Public health organizations
- · Environmental regulatory bodies
- · Urban planning departments
- · AI-driven environmental tech companies
- · Regions with poor air quality
- · Less sophisticated forecasting models
More timely and accurate air quality alerts will be issued, allowing for better protective measures.
Improved forecasting could influence industrial emissions regulations and urban development strategies to mitigate pollution sources proactively.
Long-term health outcomes in urban areas might improve due to better air quality management, potentially reducing healthcare burdens.
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Read at arXiv cs.AI