The Influence of Seasonal Climate Variability on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach based on Artificial Neural Network and Spatial Analysis

Abstract

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 based on Aerosol Optical Depth (AOD) and the climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data was generated using Motor Vehicle Emission Simulator (MOVES). The measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA's Moderate Resolution Spectroradiometer (MODIS), respectively. The data was resampled to a singular seasonal format and was scaled down over grids of 10 by 10 to 150 by 150. Coefficient of determination (R2), 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 R2 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the least performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model developed is capable of accurately predicting the PM2.5 concentration and can be used to forecast future trends.