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

Fan Yu, Amin Mohebbi, Shiqing Cai, Simin Akbariyeh, Brendan J. Russo, Edward J. Smaglik

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number102
Pages (from-to)1-19
Number of pages19
JournalEnvironments - MDPI
Volume7
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Aerosol optical depth
  • Artificial neural network
  • MOVES
  • Spatial analysis
  • Vehicle exhaust PM2.5

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)

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