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Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

Author

Listed:
  • Ping Zhang

    (School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China)

  • Bo Hong

    (College of Landscape Architecture and Arts, Northwest A & F University, Yangling 712100, China)

  • Liang He

    (Xi'an Environmental Monitoring Station, Xi'an 710054, China)

  • Fei Cheng

    (Forestry College, Guangxi University, Nanning 530004, China)

  • Peng Zhao

    (College of Life Sciences, Northwest University, Xi'an 710069, China)

  • Cailiang Wei

    (School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China)

  • Yunhui Liu

    (College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China)

Abstract

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO 2 , and NO 2 , while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

Suggested Citation

  • Ping Zhang & Bo Hong & Liang He & Fei Cheng & Peng Zhao & Cailiang Wei & Yunhui Liu, 2015. "Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural N," IJERPH, MDPI, vol. 12(10), pages 1-25, September.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:10:p:12171-12195:d:56520
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    References listed on IDEAS

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    Cited by:

    1. Ekaterini Hadjisolomou & Konstantinos Stefanidis & George Papatheodorou & Evanthia Papastergiadou, 2016. "Assessing the Contribution of the Environmental Parameters to Eutrophication with the Use of the “PaD” and “PaD2” Methods in a Hypereutrophic Lake," IJERPH, MDPI, vol. 13(8), pages 1-14, July.
    2. Xin Fang & Runkui Li & Qun Xu & Matteo Bottai & Fang Fang & Yang Cao, 2016. "A Two-Stage Method to Estimate the Contribution of Road Traffic to PM 2.5 Concentrations in Beijing, China," IJERPH, MDPI, vol. 13(1), pages 1-19, January.

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