TY - JOUR
T1 - The influence of seasonal meteorology on vehicle exhaust pm2.5 in the state of california
T2 - A hybrid approach based on artificial neural network and spatial analysis
AU - Yu, Fan
AU - Mohebbi, Amin
AU - Cai, Shiqing
AU - Akbariyeh, Simin
AU - Russo, Brendan J.
AU - Smaglik, Edward J.
N1 - Funding Information:
The grant for this study was awarded by the United States Department of Transportation (USDOT) Pacific Southwest Region 9 University Transportation Center (UTC) to Northern Arizona University as a sub awardee.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Aerosol optical depth
KW - Artificial neural network
KW - MOVES
KW - Spatial analysis
KW - Vehicle exhaust PM2.5
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U2 - 10.3390/environments7110102
DO - 10.3390/environments7110102
M3 - Article
AN - SCOPUS:85098715635
SN - 2076-3298
VL - 7
SP - 1
EP - 19
JO - Environments - MDPI
JF - Environments - MDPI
IS - 11
M1 - 102
ER -