Bayesian Analysis of a Reduced-Form Air Quality Model

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• 作者：Kristen M. Foley ; Brian J. Reich ; Sergey L. Napelenok
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2012
• 出版时间：July 17, 2012
• 年：2012
• 卷：46
• 期：14
• 页码：7604-7611
• 全文大小：340K
• 年卷期：v.46,no.14(July 17, 2012)
• ISSN：1520-5851

文摘

Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level ozone concentrations. A Bayesian hierarchical model is used to combine air quality model output and monitoring data in order to characterize the impact of emissions reductions while accounting for different degrees of uncertainty in the modeled emissions inputs. The probabilistic model predictions are weighted based on population density in order to better quantify the societal benefits/disbenefits of four hypothetical emission reduction scenarios in which domain-wide NO_x emissions from various sectors are reduced individually and then simultaneously. Cross validation analysis shows the statistical model performs well compared to observed ozone levels. Accounting for the variability and uncertainty in the emissions and atmospheric systems being modeled is shown to impact how emission reduction scenarios would be ranked, compared to standard methodology.