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A Two-Stage Method to Estimate the Contribution of Road Traffic to PM 2.5 Concentrations in Beijing, China

Author

Listed:
  • Xin Fang

    (Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
    These authors contributed equally to this work.)

  • Runkui Li

    (College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    These authors contributed equally to this work.)

  • Qun Xu

    (Department of Epidemiology and Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing 100005, China)

  • Matteo Bottai

    (Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden)

  • Fang Fang

    (Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden)

  • Yang Cao

    (Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
    Clinical Epidemiology and Biostatistics, Faculty of Medicine and Health, Örebro University, Örebro 70281, Sweden)

Abstract

Background : Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM 2.5 ) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM 2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile. Objective : To develop a simplified and indirect method to estimate the contribution of traffic to PM 2.5 concentration in Beijing, China. Methods : Hourly PM 2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM 2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM 2.5 concentration. The geographical trend of PM 2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM 2.5 and non-linear relationship between PM 2.5 and meteorological conditions were assessed using GAMM. Results : The medians of daily PM 2.5 concentrations during 2013–2014 at 35 AQM stations in Beijing ranged from 40 to 92 ?g/m 3 . There was a significant increasing trend of PM 2.5 concentration from north to south. The contributions of road traffic to daily PM 2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations. Conclusions : Traffic emissions account for a substantial share of daily total PM 2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM 2.5 concentrations when there is limited information on vehicle number and types and emission profile.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:1:p:124-:d:62151
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    References listed on IDEAS

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    1. 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.
    2. Wang, Tao & Watson, Jim, 2010. "Scenario analysis of China's emissions pathways in the 21st century for low carbon transition," Energy Policy, Elsevier, vol. 38(7), pages 3537-3546, July.
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    1. Cai-Rong Lou & Hong-Yu Liu & Yu-Feng Li & Yu-Ling Li, 2016. "Socioeconomic Drivers of PM 2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China," IJERPH, MDPI, vol. 13(10), pages 1-19, September.

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