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Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model.

Batterman SA, Zhang K, Kononowech R - Environ Health (2010)

Bottom Line: The highest concentrations of both CO and PM(2.5) were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour).The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA. stuartb@umich.edu

ABSTRACT

Background: Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.

Methods: Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.

Results: Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM(2.5) were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.

Conclusions: The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.

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Related in: MedlinePlus

Predicted concentrations across the case study area. A. Annual average CO concentration. B. Annual average PM2.5 concentration; C. Worst-case CO day; D. Best-case CO day. M39 and GR (Grand River) are the modeled roads.
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Figure 8: Predicted concentrations across the case study area. A. Annual average CO concentration. B. Annual average PM2.5 concentration; C. Worst-case CO day; D. Best-case CO day. M39 and GR (Grand River) are the modeled roads.

Mentions: Annual average CO concentrations for the case study area show surprising symmetrical gradients about each road (Figure 8A). Across the modeled domain, CO levels averaged only 0.18 ppm (range from 0.03 to 1.15 ppm), far below the 8-hr CO standard of 9 ppm. For PM2.5, concentrations averaged 0.39 μg m-3 (range from 0.07 to 2.4 μg m-3), also well below the annual standard of 15 μg m-3 (Figure 8B). Even though the same traffic volume and meteorology were used, PM2.5 produces a somewhat different spatial pattern than CO due to differences in the hourly volume of heavy duty trucks that emit most of the PM2.5, as well as differences in truck volume between the two roads. For both pollutants, concentration gradients are very steep. Compared to curbside locations, for example, concentrations decreased by about 50% for receptors 60 m from the road, 75% at 100 m, and 90% at 300 m. These distances vary somewhat, depending on road orientation and other factors.


Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model.

Batterman SA, Zhang K, Kononowech R - Environ Health (2010)

Predicted concentrations across the case study area. A. Annual average CO concentration. B. Annual average PM2.5 concentration; C. Worst-case CO day; D. Best-case CO day. M39 and GR (Grand River) are the modeled roads.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914002&req=5

Figure 8: Predicted concentrations across the case study area. A. Annual average CO concentration. B. Annual average PM2.5 concentration; C. Worst-case CO day; D. Best-case CO day. M39 and GR (Grand River) are the modeled roads.
Mentions: Annual average CO concentrations for the case study area show surprising symmetrical gradients about each road (Figure 8A). Across the modeled domain, CO levels averaged only 0.18 ppm (range from 0.03 to 1.15 ppm), far below the 8-hr CO standard of 9 ppm. For PM2.5, concentrations averaged 0.39 μg m-3 (range from 0.07 to 2.4 μg m-3), also well below the annual standard of 15 μg m-3 (Figure 8B). Even though the same traffic volume and meteorology were used, PM2.5 produces a somewhat different spatial pattern than CO due to differences in the hourly volume of heavy duty trucks that emit most of the PM2.5, as well as differences in truck volume between the two roads. For both pollutants, concentration gradients are very steep. Compared to curbside locations, for example, concentrations decreased by about 50% for receptors 60 m from the road, 75% at 100 m, and 90% at 300 m. These distances vary somewhat, depending on road orientation and other factors.

Bottom Line: The highest concentrations of both CO and PM(2.5) were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour).The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA. stuartb@umich.edu

ABSTRACT

Background: Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.

Methods: Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.

Results: Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM(2.5) were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.

Conclusions: The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.

Show MeSH
Related in: MedlinePlus