Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed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)

How to cite: Yu, F.; Mohebbi, A.; Cai, S.; Akbariyeh, S.; Russo, B.; Smaglik, E. 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. Preprints 2020, 2020100075 (doi: 10.20944/preprints202010.0075.v2). Yu, F.; Mohebbi, A.; Cai, S.; Akbariyeh, S.; Russo, B.; Smaglik, E. 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. Preprints 2020, 2020100075 (doi: 10.20944/preprints202010.0075.v2).

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.

Subject Areas

Vehicle Exhaust PM2.5, MOVES, Artificial Neural Network, Spatial Analysis, Aerosol Optical Depth

Comments (1)

Comment 1
Received: 15 October 2020
Commenter: Amin Mohebbi
Commenter's Conflict of Interests: Author
Comment: PLease see the attached reviewer response. 
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