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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >A Method for Estimating Urban Background Concentrations in Support of Hybrid Air Pollution Modeling for Environmental Health Studies
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A Method for Estimating Urban Background Concentrations in Support of Hybrid Air Pollution Modeling for Environmental Health Studies

机译：支持环境健康研究的混合空气污染模型估算城市背景浓度的方法

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摘要

Exposure studies rely on detailed characterization of air quality, either from sparsely located routine ambient monitors or from central monitoring sites that may lack spatial representativeness. Alternatively, some studies use models of various complexities to characterize local-scale air quality, but often with poor representation of background concentrations. A hybrid approach that addresses this drawback combines a regional-scale model to provide background concentrations and a local-scale model to assess impacts of local sources. However, this approach may double-count sources in the study regions. To address these limitations, we carefully define the background concentration as the concentration that would be measured if local sources were not present, and to estimate these background concentrations we developed a novel technique that combines space-time ordinary kriging (STOK) of observations with outputs from a detailed chemistry-transport model with local sources zeroed out. We applied this technique to support an exposure study in Detroit, Michigan, for several pollutants (including NOx and PM2.5), and evaluated the estimated hybrid concentrations (calculated by combining the background estimates that addresses this issue of double counting with local-scale dispersion model estimates) using observations. Our results demonstrate the strength of this approach specifically by eliminating the problem of double-counting reported in previous hybrid modeling approaches leading to improved estimates of background concentrations, and further highlight the relative importance of NOx vs. PM2.5 in their relative contributions to total concentrations. While a key limitation of this approach is the requirement for another detailed model simulation to avoid double-counting, STOK improves the overall characterization of background concentrations at very fine spatial scales.
机译：暴露研究依赖于空气质量的详细特征，这些特征来自稀疏的常规环境监测仪或可能缺乏空间代表性的中央监测站。或者，一些研究使用各种复杂度的模型来表征当地规模的空气质量，但通常背景浓度表示不佳。解决此缺点的一种混合方法将区域尺度模型提供背景浓度，并结合局部尺度模型评估本地资源的影响。但是，这种方法可能会使研究区域中的来源重复计算。为了解决这些局限性，我们仔细地将背景浓度定义为在不存在本地来源的情况下将要测量的浓度，并且为了估算这些背景浓度，我们开发了一种将时空普通克里金法（STOK）与观测结果相结合的新技术来自详细的化学运输模型，其中局部来源归零。我们应用这项技术来支持在密歇根州底特律针对几种污染物（包括NOx和PM2.5）进行暴露研究，并评估了估计的混合浓度（通过结合解决这一背景本地估计的重复计算的背景估计来计算）色散模型估算值）。我们的结果通过消除以前的混合建模方法中报告的重复计数问题，从而改善了背景浓度估计值，从而证明了该方法的优势，并进一步强调了NOx和PM2.5在总排放量中的相对贡献浓度。尽管此方法的主要局限性是需要进行另一个详细的模型仿真以避免重复计算，但STOK可以在非常精细的空间尺度上改善背景浓度的总体特征。

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