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 (${\mathrm{R}}^2$), 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 ${\mathrm{R}}^2$ = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with ${\mathrm{R}}^2$ = 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.

中文翻译：

---

加利福尼亚州季节性气象对车辆尾气PM2.5的影响：基于人工神经网络和空间分析的混合方法

本研究旨在开发一种基于反向传播人工神经网络（ANN）和空间分析技术的混合方法，以使用气溶胶光学深度（AOD）从加利福尼亚州的汽车尾气排放量预测大小为2.5 µm（PM2.5）的颗粒物以及几个气象指标（相对湿度，温度，降水和风速）。PM2.5数据是使用机动车排放模拟器（MOVES）生成的。分别从加利福尼亚灌溉管理信息系统（CIMIS）和美国宇航局的中分辨率分光辐射计（MODIS）获得了测量的气象变量和AOD。数据被重新采样为季节性格式，并在10×10到150×150的网格上缩小比例。${\mathrm{[R}}^2$），平均绝对百分比误差（MAPE）和均方根误差（RMSE）用于评估ANN预测模型的质量。该模型在冬季达到顶峰${\mathrm{[R}}^2$ = 0.984，RMSE = 0.027和MAPE = 25.311，而夏季的性能最低， ${\mathrm{[R}}^2$= 0.920，RMSE = 0.057，MAPE = 65.214。这些结果表明，ANN模型可以合理地预测PM2.5的质量，并可以用来预测未来的趋势。