Using Building Heights and Street Configuration to Enhance Intraurban PM10, NOX, and NO2 Land Use Regression Models

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• 作者：Robert Tang ; Marta Blangiardo ; John Gulliver
• 刊名：Environmental Science & Technology
• 出版年：2013
• 出版时间：October 15, 2013
• 年：2013
• 卷：47
• 期：20
• 页码：11643-11650
• 全文大小：301K
• 年卷期：v.47,no.20(October 15, 2013)
• ISSN：1520-5851

文摘

Land use regression (LUR) models have been widely used to provide long-term air pollution exposure assessment in epidemiological studies. However, models have rarely offered variables that account for the dispersion environment close to the source (e.g., street canyons, position and dimensions of buildings, road width). This study used newly available data on building heights and geometry to enhance the representation of land use and the dispersion field in LUR. Models were developed for PM₁₀, NO_X, and NO₂ for 2008鈥?011 for London, U.K. A separate set of models using 鈥渢raditional鈥?land use and traffic indicators (e.g., distance from road, area of housing within circular buffers) were also developed and their performance was compared with 鈥渆nhanced鈥?models. Models were evaluated using leave-one-out (n 鈥?1) (LOOCV) and grouped (n 鈥?25%) cross-validation (GCV). LOOCV R² values were 0.71, 0.50, 0.66 and 0.73, 0.79, 0.78 for traditional and enhanced PM₁₀, NO_X, and NO₂ models, respectively. GCV R² values were 0.71, 0.53, 0.64 and 0.68, 0.77, 0.77 for traditional and enhanced PM₁₀, NO_X, and NO₂ models, respectively. Data on building volume within the area common to a 20 m road buffer within a 25 m circular buffer substantially improved the performance (R² > 13%) of NO_X and NO₂ LUR models.