Land Use Regression Modeling of PM_2.5 Concentrations at Optimized Spatial Scales

Author： Zhai Liang Zou Bin Fang Xin Luo Yanqing Wan Neng Li Shuang

Publisher： MDPI

E-ISSN： 2073-4433|8|1|1-1

ISSN： 2073-4433

Source： Atmosphere, Vol.8, Iss.1, 2016-12, pp. : 1-1

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Abstract

Though land use regression (LUR) models have been widely utilized to simulate air pollution distribution, unclear spatial scale effects of contributing characteristic variables usually make results study-specific. In this study, LUR models for PM_2.5 in Houston Metropolitan Area, US were developed under scales of 100 m, 300 m, 500 m, 800 m, and 1000–5000 m with intervals of 500 m by employing the idea of statistically optimized analysis. Results show that the annual average PM_2.5 concentration in Houston was significantly influenced by area ratios of open space urban and medium intensity urban at a 100 m scale, as well as of high intensity urban at a 500 m scale, whose correlation coefficients valued −0.64, 0.72, and 0.56, respectively. The fitting degree of LUR model at the optimized spatial scale (adj. R² = 0.78) is obviously better than those at any other unified spatial scales (adj. R² ranging from 0.19 to 0.65). Differences of PM_2.5 concentrations produced by LUR models with best-, moderate-, weakest fitting degree, as well as ordinary kriging were evident, while the LUR model achieved the best cross-validation accuracy at the optimized spatial scale. Results suggested that statistical based optimized spatial scales of characteristic variables might possibly ensure the performance of LUR models in mapping PM_2.5 distribution.