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Fine particulate matter and polycyclic aromatic hydrocarbon concentration patterns in Roxbury Massachusetts: a community-based GIS analysis.

机译:马萨诸塞州罗克斯伯里的细颗粒物和多环芳烃浓度模式:基于社区的GIS分析。

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摘要

Given an elevated prevalence of respiratory disease and density of pollution sources, residents of Roxbury, Massachusetts, have been interested in better understanding their exposures to air pollution. To determine whether local transportation sources contribute significantly to exposures, we conducted a community-based pilot investigation to measure concentrations of fine particulate matter (particulate matter < 2.5 microm; PM(2.5)) and particle-bound polycyclic aromatic hydrocarbons (PAHs) in Roxbury in the summer of 1999. Community members carried portable monitors on the streets in a 1-mile radius around a large bus terminal to create a geographic information system (GIS) map of concentrations and gathered data on site characteristics that could predict ambient concentrations. Both PM(2.5) and PAH concentrations were greater during morning rush hours and on weekdays. In linear mixed-effects regressions controlling for temporal autocorrelation, PAH concentrations were significantly higher with closer proximity to the bus terminal (p < 0.05), and both pollutants were elevated, but not statistically significantly so, on bus routes. Regressions on a subset of measurements for which detailed site characteristics were gathered showed higher concentrations of both pollutants on roads reported to have heavy bus traffic. Although a more comprehensive monitoring protocol would be needed to develop robust predictive functions for air pollution, our study demonstrates that pollution patterns in an urban area can be characterized with limited monitoring equipment and that university-community partnerships can yield relevant exposure information.
机译:由于呼吸道疾病的流行和污染源的密度增加,马萨诸塞州罗克斯伯里的居民对更好地了解他们暴露于空气污染的兴趣很浓。为了确定当地的运输来源是否对暴露造成显着影响,我们进行了一项基于社区的试点研究,以测量Roxbury中细颗粒物(颗粒物<2.5微米; PM(2.5))和颗粒结合的多环芳烃(PAHs)的浓度在1999年夏天,社区成员在大型巴士总站周围半径1英里的街道上携带便携式监视器,以创建浓度的地理信息系统(GIS)地图,并收集可预测环境浓度的现场特征数据。在高峰期和工作日中,PM(2.5)和PAH的浓度均较高。在控制时间自相关的线性混合效应回归中,PAH浓度随着靠近公交车终点站而显着升高(p <0.05),并且两种污染物在公交车路线上均升高,但在统计学上没有显着升高。在收集了详细站点特征的部分测量结果上的回归表明,在报告的公交车流量大的道路上,两种污染物的浓度都较高。尽管需要更全面的监视协议来开发强大的空气污染预测功能,但我们的研究表明,可以使用有限的监视设备来表征市区的污染模式,并且大学与社区的伙伴关系可以提供相关的暴露信息。

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