| Summary: | This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (<inline-formula><math display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with <inline-formula><math display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with <inline-formula><math display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.
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