title: Applying land use regression model to estimate spatial variation of PM₂.₅ in Beijing, China.
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/m(3) (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/m(3). 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.
authors.affiliation: The Key Laboratory for Environmental and Urban Sciences, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China.
authors.forename: Jiansheng
authors.initials: J
authors.lastname: Wu
authors.forename: Jiacheng
authors.initials: J
authors.lastname: Li
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
authors.forename: Weifeng
authors.initials: W
authors.lastname: Li
authors.forename: Guang
authors.initials: G
authors.lastname: Xu
authors.forename: Chengcheng
authors.initials: C
authors.lastname: Dong
mesh_terms: D000392:Air Movements; D000393:Air Pollutants / Q000737:chemistry*; D002681:China; D002940:Circadian Rhythm; D004784:Environmental Monitoring / Q000379:methods*; D006802:Human Activities; D008962:Models, Theoretical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis; D012621:Seasons
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter