Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations. (15th February 2020)

• Record Type: Journal Article
• Authors: Cai, Jing; Ge, Yihui; Li, Huichu; Yang, Changyuan; Liu, Cong; Meng, Xia; Wang, Weidong; Niu, Can; Kan, Lena; Schikowski, Tamara; Yan, Beizhan; Chillrud, Steven N.; Kan, Haidong; Jin, Li
• Is Part Of: Atmospheric environment. Volume 223(2020)
• Publication Date: 2020-02-15
• Subjects: Land use regression model -- Air pollution -- Spatial variation -- Exposure assessment; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2020.117267
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Background: Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data. Objective: Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM2.5 ), black carbon (BC) and nitrogen dioxide (NO2 ) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study. Method: Two one-week integrated samples were collected at 30 PM2.5 (BC) sites and 45 NO2 sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model. Results: LUR explained 65% of the spatial variability in PM2.5, 78% in BC and 73% in NO2 . Mean (±Standard Deviation) of predicted PM2.5, BC and NO2 exposure levels were 48.3 (±6.3) μg/m 3, 7.5 (±1.4) μg/m 3 and 27.3 (±8.2) μg/m 3, respectively. Weak spatial corrections (Pearson r = 0.05–0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM2.5, BC and NO2 levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO2 levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100–700m) were found for BC and NO2 . Conclusion: We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM2.5, NO2 and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM2.5, NO2 and BC concentrations. Graphical abstract: Image 1 Highlights: Lack of spatially resolved air pollution data limits LUR model application in China. We are one of the few building LUR models upon specific-monitoring network in China. PM2.5, BC and NO2 models explain a large fraction of concentration variability. We add experience on air pollution exposure assessment for population-based studies.