Effective management of air pollution in Isfahan metropolis requires robust, operational forecasting tools. Aiming to develop an efficient and cost-effective model for the simultaneous prediction of gaseous (CO, NO₂, SO₂) and non-gaseous (UVAI) pollutants, this study leveraged the capabilities of Artificial Neural Networks (ANN). The primary innovation of this research lies in its strategic and hybrid approach to input variable selection. In addition to traditional environmental variables (such as NDVI and distance from point sources), the model incorporates critical anthropogenic and urban variables, specifically population density and building density. This integration of data aims to account for the dynamic effects of local wind patterns induced by the city's physical fabric within the prediction model. The dataset comprised 2400 data points covering spatial and temporal variables. After normalization, the data were split into training (70 %) and test (30 %) sets. Model optimization parameters included 20,000 epochs, 10 hidden-layer neurons, and a weight-decay penalty (0.01) to mitigate overfitting. The model's quantitative performance was excellent. The coefficient of determination (R2) for UVAI prediction was 0.999, indicating exceptional accuracy. Additionally, R2 values were calculated at 0.98 for NO₂, 0.94 for SO₂, and approximately 0.90 for CO. These results, coupled with near-zero values for Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), confirm the model's high capability for the early and reliable prediction of pollutants. Variable Importance in Projection (VIP) analysis revealed that population density and building density were the most significant predictors of urban pollution, highlighting the critical importance of controlling urban development to improve air quality. However, limitations regarding temporal data and the inherent non-dynamic nature of ANNs in fully modeling atmospheric phenomena remain fundamental constraints. Consequently, future research proposals focus on expanding temporal datasets (specifically including multi-year data for long-term validation) and transitioning towards hybrid models, such as CNN-LSTM. These approaches aim to process time-series dynamics better and maximize model accuracy across various atmospheric scenarios. Ultimately, the proposed ANN model provides a robust decision-support tool for short-term air quality management in Isfahan. © 2025 Elsevier B.V.
