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Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

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Abstract

Fine particulate matter (PM2.5) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m3 (SD = 13.7). PM2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m3. This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Acknowledgments

This study was financially supported by the National Science Foundation of China (No. 41130534). We thank anonymous reviewers and the editor (Prof. Dr. Gerhard Lammel) for their constructive comments.

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Authors and Affiliations

  1. The Key Laboratory for Environmental and Urban Sciences, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China

    Jiansheng Wu, Jiacheng Li, Jian Peng & Chengcheng Dong

  2. College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Ministry of Education, Peking University, Beijing, 100871, China

    Jiansheng Wu & Jian Peng

  3. Department of Urban Planning and Design, University of Hong Kong, Hong Kong, SAR, China

    Weifeng Li

  4. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Guang Xu

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Guang Xu

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  1. Jiansheng Wu
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Correspondence to Jiacheng Li or Weifeng Li.

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Wu, J., Li, J., Peng, J. et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China. Environ Sci Pollut Res 22, 7045–7061 (2015). https://doi.org/10.1007/s11356-014-3893-5

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