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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 CO concentrations showing sensitivity to: A) atmospheric stability category; B) wind speed; C) mixing height; and traffic volume. Unless otherwise modified, plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel A uses a wind speed of 2 m s-1. Panel C uses a constant link emission rate.
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Figure 3: Predicted CO concentrations showing sensitivity to: A) atmospheric stability category; B) wind speed; C) mixing height; and traffic volume. Unless otherwise modified, plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel A uses a wind speed of 2 m s-1. Panel C uses a constant link emission rate.

Mentions: Figure 3 depicts results of the sensitivity analysis, in which individual parameters were varied from the nominal condition. For stability category (SC), the largest effect is seen at long distances from the roadway (Figure 3A). For example, in comparison to concentrations predicted under SC F (most stable giving the highest concentrations, concentrations under SC A (most unstable) were 8% lower at a distance of 50 m, 23% lower at 100 m, 31% at 150 m, and 47% lower at 300 m. Changing the SC from A to F is the most extreme comparison possible, and in many areas, these two SCs are uncommon (occurring less than 5% of the time). Predictions for the more common SCs were similar, e.g., changes from F to B ranged were within 1 to 12% for the same comparisons just discussed. We also note that the greatest differences occurred at relatively large distances when roadway impacts are not likely to be large. For these reasons, we conclude that SC has only moderate influence on CALINE4 predictions. This conclusion differs from point source modeling in which stability category is one of the most sensitive parameters. Line source models for vehicles are much less sensitive since the amount of initial mixing induced by mechanical and thermal turbulence is relatively large, which reduces the importance of ambient stability near the roadway [11]. Additionally, crosswind (horizontal) dispersion parameters have small effects in line source models, and both sources and receptors are near ground level and thus always "in" the plume. Greater sensitivity to SC can result in some cases, e.g., low traffic volumes when vehicle-induced turbulence is less significant [11].


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

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

Predicted CO concentrations showing sensitivity to: A) atmospheric stability category; B) wind speed; C) mixing height; and traffic volume. Unless otherwise modified, plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel A uses a wind speed of 2 m s-1. Panel C uses a constant link emission rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Predicted CO concentrations showing sensitivity to: A) atmospheric stability category; B) wind speed; C) mixing height; and traffic volume. Unless otherwise modified, plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel A uses a wind speed of 2 m s-1. Panel C uses a constant link emission rate.
Mentions: Figure 3 depicts results of the sensitivity analysis, in which individual parameters were varied from the nominal condition. For stability category (SC), the largest effect is seen at long distances from the roadway (Figure 3A). For example, in comparison to concentrations predicted under SC F (most stable giving the highest concentrations, concentrations under SC A (most unstable) were 8% lower at a distance of 50 m, 23% lower at 100 m, 31% at 150 m, and 47% lower at 300 m. Changing the SC from A to F is the most extreme comparison possible, and in many areas, these two SCs are uncommon (occurring less than 5% of the time). Predictions for the more common SCs were similar, e.g., changes from F to B ranged were within 1 to 12% for the same comparisons just discussed. We also note that the greatest differences occurred at relatively large distances when roadway impacts are not likely to be large. For these reasons, we conclude that SC has only moderate influence on CALINE4 predictions. This conclusion differs from point source modeling in which stability category is one of the most sensitive parameters. Line source models for vehicles are much less sensitive since the amount of initial mixing induced by mechanical and thermal turbulence is relatively large, which reduces the importance of ambient stability near the roadway [11]. Additionally, crosswind (horizontal) dispersion parameters have small effects in line source models, and both sources and receptors are near ground level and thus always "in" the plume. Greater sensitivity to SC can result in some cases, e.g., low traffic volumes when vehicle-induced turbulence is less significant [11].

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