Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. - Abed Bashar Doost; Mohammad Saadi Mesgari (Nīvār)
Air pollution poses significant risks to human health and the environment, which makes it necessary to create effective strategies for air quality management. This study presents an approach for air quality management in Tehran using the Convolutional Neural Network (CNN) algorithm. The proposed method provides the possibility of spatial modeling and preparation of risk maps of two important air pollutants, namely particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10). To develop this air pollution model, the data available in the database containing the annual average of two pollutants from 2012 to 2022 were used. In this model, various parameters affecting air pollution including altitude, humidity, distance to industrial areas, normalized difference index of plants (NDVI), population density, precipitation, distance to the street, temperature, traffic volume, wind direction, and wind speed are considered. Taken and spatial modeling of two pollutants using CNN has been done. The evaluation of the model was done using different evaluation criteria, and the findings showed that the R-squared (R2) values in this model for PM2.5 and PM10 pollutants are 0.889 and 0.972, respectively. The accuracy of the risk map was evaluated using relative operating characteristic (ROC) for two pollutants, and the findings showed that the CNN model has an acceptable accuracy in producing the pollution risk map. In general, risk maps provide useful information about geographic areas with high pollution risks and help in decision-making and targeted pollution reduction efforts.