A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States

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• 作者：Bernardo S. Beckerman ; Michael Jerrett ; Marc Serre ; Randall V. Martin ; Seung-Jae Lee ; Aaron van Donkelaar ; Zev Ross ; Jason Su ; Richard T. Burnett
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2013
• 出版时间：July 2, 2013
• 年：2013
• 卷：47
• 期：13
• 页码：7233-7241
• 全文大小：434K
• 年卷期：v.47,no.13(July 2, 2013)
• ISSN：1520-5851

文摘

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 渭m in aerodynamic diameter (PM_2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM_2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM_2.5, land use and traffic indicators. Normalized cross-validated R² values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R² were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM_2.5 at multiple scales over the contiguous U.S.