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 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 (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 lowest performance in summer with R2 = 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.
Highlights
Nowadays, there is a unanimous scientific consensus that air quality degradation leads to adverse health effects [1]
The model peaked at winter seasons with R2 = 0.984, root mean square error (RMSE) = 0.027, and mean absolute percentage error (MAPE) = 25.311, whereas it had the lowest performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214
These results indicate that the artificial neural network (ANN) model can reasonably predict the PM2.5 mass and can be used to forecast future trends
Summary
There is a unanimous scientific consensus that air quality degradation leads to adverse health effects [1]. One of the factors impacting the air quality index is suspended particulate matter, which is associated with numerous human diseases. The coarser particles are classified as those with diameters smaller than 10 μm (PM10), whereas the finer particles correspond to those with diameters smaller than 2.5 μm (PM2.5). Both classes of particulate matter are highly dependent on climatic factors such as precipitation, relative humidity, wind speed, and air temperature, and are positively correlated with the aerosol optical depth (AOD) [2,3]. AOD, which can be retrieved from remote sensing products, is considered a good proxy for PM2.5 [4]
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