Batterman Stuart, Ganguly Rajiv, Harbin Paul
Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Civil Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.
Int J Environ Res Public Health. 2015 Apr 1;12(4):3646-66. doi: 10.3390/ijerph120403646.
Vehicle traffic is one of the most significant emission sources of air pollutants in urban areas. While the influence of mobile source emissions is felt throughout an urban area, concentrations from mobile emissions can be highest near major roadways. At present, information regarding the spatial and temporal patterns and the share of pollution attributable to traffic-related air pollutants is limited, in part due to concentrations that fall sharply with distance from roadways, as well as the few monitoring sites available in cities. This study uses a newly developed dispersion model (RLINE) and a spatially and temporally resolved emissions inventory to predict hourly PM2.5 and NOx concentrations across Detroit (MI, USA) at very high spatial resolution. Results for annual averages and high pollution days show contrasting patterns, the need for spatially resolved analyses, and the limitations of surrogate metrics like proximity or distance to roads. Data requirements, computational and modeling issues are discussed. High resolution pollutant data enable the identification of pollutant "hotspots", "project-level" analyses of transportation options, development of exposure measures for epidemiology studies, delineation of vulnerable and susceptible populations, policy analyses examining risks and benefits of mitigation options, and the development of sustainability indicators integrating environmental, social, economic and health information.
车辆交通是城市地区空气污染物的最重要排放源之一。虽然移动源排放的影响在整个城市地区都能感受到，但移动排放的浓度在主要道路附近可能最高。目前，关于与交通相关的空气污染物的时空模式以及污染份额的信息有限，部分原因是浓度随着与道路距离的增加而急剧下降，以及城市中可用的监测站点较少。本研究使用新开发的扩散模型（RLINE）和时空分辨排放清单，以非常高的空间分辨率预测美国密歇根州底特律市每小时的PM2.5和NOx浓度。年平均值和高污染日的结果显示出不同的模式、对空间分辨分析的需求以及诸如与道路的接近程度或距离等替代指标的局限性。讨论了数据要求、计算和建模问题。高分辨率污染物数据有助于识别污染物“热点”、对交通选择进行“项目层面”分析、制定流行病学研究的暴露测量方法、划定脆弱和易感人群、进行政策分析以检查缓解方案的风险和益处，以及制定整合环境、社会、经济和健康信息的可持续性指标。
