Isakov Vlad, Arunachalam Saravanan, Batterman Stuart, Bereznicki Sarah, Burke Janet, Dionisio Kathie, Garcia Val, Heist David, Perry Steve, Snyder Michelle, Vette Alan
National Exposure Research Laboratory, United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711 USA.
Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Chapel Hill, NC 27517, USA.
Int J Environ Res Public Health. 2014 Aug 27;11(9):8777-93. doi: 10.3390/ijerph110908777.
A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined "mini-grids" of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.
交通相关空气污染暴露研究中的一个主要挑战是缺乏有关污染物暴露特征的信息。空气质量建模可以提供时空变化的暴露估计值,以研究交通相关空气污染物与不良健康结果之间的关系。采用了一种混合空气质量建模方法来估计交通相关空气污染物的暴露情况,以支持在美国密歇根州底特律市进行的城市空气污染物近路暴露与影响研究(NEXUS)。基于模型的暴露指标与排放和气象的局部变化相关,使用美国气象学会/环境保护局监管模型(AERMOD)和近地表排放研究线源扩散模型(RLINE)扩散模型、来自国家排放清单的本地排放源信息、详细的道路网络位置和交通活动以及底特律市机场的气象数据进行组合估计。使用社区多尺度空气质量(CMAQ)和时空普通克里金(STOK)模型组合估计区域背景贡献。为了捕捉近路污染物梯度,在参与者住所周围设置了精细的模型受体“微型网格”。针对包括每日和高峰时段在内的多个时间段,在每个住所位置预测了一氧化碳、氮氧化物、细颗粒物及其成分(元素碳和有机碳)的暴露指标。与整个研究区域的测量结果相比,评估了暴露指标表征多种环境空气污染物时空变化的能力。