A Hybrid Neural Network and Cellular Automata Model for spatiotemporal Forecasting of PM10 and PM2.5 in Lima, Peru

Particulate matter (PM) pollution represents a significant public health concern, in Lima, Peru. This issue is further compound by the lack of accurate forecasting tools due to limited monitoring networks. This study addresses this gap by developing and validating a hybrid model combining a Multilayer Perceptron (MLP) neural network and a type of rule-based Cellular Automata (CA) simulation. This model simulates and forecasts the spatiotemporal dispersion of PM10 and PM2.5. Using a decade of historical PM data (2015-2024) from seven monitoring stations and NASA's meteorological data, an optimized MLP was trained to learn the complex, non-linear transition rules from 47 engineered features. The model demonstrated remarkable performance in historical validation (R² > 0.90), outperforming standard baseline models. When fed with weather forecast data, the model can operate as an Early Warning System (EWS), providing a reliable prediction horizon to anticipate the exceedance of Air Quality Standards. The resulting hotspot maps accurately identify high-risk areas, confirming the potential of this hybrid model as a robust, proactive, and quantitative tool for air quality management and public health protection in complex urban environments.