PreprintArticleVersion 2Preserved in Portico This version is not peer-reviewed
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
Version 1
: Received: 2 October 2020 / Approved: 5 October 2020 / Online: 5 October 2020 (11:42:48 CEST)
Version 2
: Received: 14 October 2020 / Approved: 15 October 2020 / Online: 15 October 2020 (11:57:19 CEST)
Yu, F.; Mohebbi, A.; Cai, S.; Akbariyeh, S.; Russo, B.J.; Smaglik, E.J. The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis. Environments2020, 7, 102.
Yu, F.; Mohebbi, A.; Cai, S.; Akbariyeh, S.; Russo, B.J.; Smaglik, E.J. The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis. Environments 2020, 7, 102.
Yu, F.; Mohebbi, A.; Cai, S.; Akbariyeh, S.; Russo, B.J.; Smaglik, E.J. The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis. Environments2020, 7, 102.
Yu, F.; Mohebbi, A.; Cai, S.; Akbariyeh, S.; Russo, B.J.; Smaglik, E.J. The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis. Environments 2020, 7, 102.
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 climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were 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). The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150, and precipitation was determined to be the most important independent variable. Coefficient of determination ( ), 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 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can accurately predict the PM2.5 concentration and can be used to forecast future trends.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Amin Mohebbi
Commenter's Conflict of Interests: Author