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使用1-硝基芘和其他硝基芳烃的柴油尾气浓度分布的邻里尺度空间模型。

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1-Nitropyrene and Other Nitroarenes.

作者信息

Schulte Jill K, Fox Julie R, Oron Assaf P, Larson Timothy V, Simpson Christopher D, Paulsen Michael, Beaudet Nancy, Kaufman Joel D, Magzamen Sheryl

机构信息

University of Washington , Box 357234, Seattle, Washington 98195-7234, United States.

Seattle Children's Research Institute , P.O. Box 5371, Seattle, Washington 98145-5005, United States.

出版信息

Environ Sci Technol. 2015 Nov 17;49(22):13422-30. doi: 10.1021/acs.est.5b03639. Epub 2015 Nov 6.

DOI:10.1021/acs.est.5b03639
PMID:26501773
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5026850/
Abstract

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitropyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R(2) of 0.87 and cross-validated R(2) of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods.

摘要

随着越来越多的证据表明接触柴油废气对人类健康构成独特风险,对柴油废气污染物精细尺度模型的需求也在增加。我们对几种硝化多环芳烃(NPAHs)的空间分布进行了建模,以确定华盛顿州西雅图市两个社区柴油废气污染的精细尺度梯度。我们的建模方法融合了土地利用回归、气象扩散建模以及来自固定和移动平台的污染物监测。我们将这些建模技术应用于邻域尺度下1-硝基芘(1-NP)的浓度,1-硝基芘是一种高度特异性的柴油废气标志物。我们还开发了二次有机气溶胶中存在的另外两种硝基芳烃的模型:2-硝基芘和2-硝基荧蒽。1-NP的夏季预测因子,包括到铁路的距离、卡车排放和移动黑碳测量,比其他NPAHs的预测因子对柴油来源具有更高的特异性。冬季采样结果未能得出稳定的模型,可能是由于在湍流天气条件下污染物的区域混合。夏季1-NP模型的R(2)为0.87,交叉验证的R(2)为0.73。高密度采样和混合建模的综合方法成功地在非常精细的尺度上预测了柴油废气污染,并确定了城市社区内NPAH浓度的明显梯度。

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