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Atmosphere 2017, 8(1), 1; doi:10.3390/atmos8010001

Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales

1
National Geographic Conditions Monitoring Research Center, Chinese Academy of Surveying and Mapping, Beijing 100830, China
2
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
3
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China
4
Department of Geography, University of Utah, Salt Lake, UT 84112, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Robert Talbot
Received: 28 November 2016 / Revised: 9 December 2016 / Accepted: 20 December 2016 / Published: 23 December 2016
(This article belongs to the Special Issue Urban Air Pollution)
View Full-Text   |   Download PDF [3244 KB, uploaded 23 December 2016]   |  

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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 distribution. View Full-Text
Keywords: PM2.5; LUR; air pollution; spatial scale; GIS PM2.5; LUR; air pollution; spatial scale; GIS
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Zhai, L.; Zou, B.; Fang, X.; Luo, Y.; Wan, N.; Li, S. Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales. Atmosphere 2017, 8, 1.

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