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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

Comparison of CALINE model predictions (shown as solid lines) and reduced model predictions (shown as points) showing effects of A) wind angle; B) wind speed; and C) traffic volume for four distances (30, 60, 120, 240 m) from the road. All plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel D plots concentrations from the two models for all conditions showing 1:1 line and 15% error intervals.
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Figure 4: Comparison of CALINE model predictions (shown as solid lines) and reduced model predictions (shown as points) showing effects of A) wind angle; B) wind speed; and C) traffic volume for four distances (30, 60, 120, 240 m) from the road. All plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel D plots concentrations from the two models for all conditions showing 1:1 line and 15% error intervals.

Mentions: CALINE4 predictions strongly depend on wind angle, and the highest concentrations outside the mixing zone are produced by a wind angle of ~10° as measured from the road centerline; the highest concentrations on the roadway occur for winds parallel to the road [11]. (Results shown later in Figure 4A.)


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

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

Comparison of CALINE model predictions (shown as solid lines) and reduced model predictions (shown as points) showing effects of A) wind angle; B) wind speed; and C) traffic volume for four distances (30, 60, 120, 240 m) from the road. All plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel D plots concentrations from the two models for all conditions showing 1:1 line and 15% error intervals.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Comparison of CALINE model predictions (shown as solid lines) and reduced model predictions (shown as points) showing effects of A) wind angle; B) wind speed; and C) traffic volume for four distances (30, 60, 120, 240 m) from the road. All plots use nominal conditions (wind speed = 4 m s-1, VPH = 10,000; emission rate = 300 g mi-1; wind angle = 180°). Panel D plots concentrations from the two models for all conditions showing 1:1 line and 15% error intervals.
Mentions: CALINE4 predictions strongly depend on wind angle, and the highest concentrations outside the mixing zone are produced by a wind angle of ~10° as measured from the road centerline; the highest concentrations on the roadway occur for winds parallel to the road [11]. (Results shown later in Figure 4A.)

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