2,076 research outputs found
Fine particulate matter and polycyclic aromatic hydrocarbon concentration patterns in Roxbury, Massachusetts: a community-based GIS analysis.
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
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Modeling Spatial Patterns of Traffic-Related Air Pollutants in Complex Urban Terrain
Background: The relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in traffic-related pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion. Objective: We developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time. Methods: In this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [ in aerodynamic diameter ], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long- and short-term temporal trends, and multiple sources. Results: We observed spatial heterogeneity of both UFP and concentrations. A variety of statistical methods consistently found a 15–20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For , temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways. Conclusions: The combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients
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