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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

Wind direction and speed plots for 2006 (all hours) and morning and afternoon rush hour periods.
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Figure 7: Wind direction and speed plots for 2006 (all hours) and morning and afternoon rush hour periods.

Mentions: We briefly focus on the 2006 meteorological data. There were 688 hours of calms (when predictions were not attempted). Most days had few if any hours of calms (291 days had ≤3 hours of calms). The hourly wind speed averaged 4.29 m s-1 and ranged from 1.3 to 16.5 m s-1 (calms excluded). Wind direction and speed "roses" that show the probability and speed of winds in 16 sectors (each subtending 22.5°) are presented in Figure 7 for three cases: (a) all hours of the year; (b) the morning (7 - 9 am) rush hour period; and (c) the evening (4 - 6 pm) rush hour period. In the morning, moderate SW, SSW and WSW winds dominate (Figure 7b), while in the afternoon, winds shift to the WNW and are stronger; occasionally, moderate SSE and SE winds occur (Figure 7c). This diurnal variation is not represented by the annual patterns (Figure 7a). Other trends emerge when examining the lowest wind speeds that can produce the highest concentrations. As shown in Additional file 1: figures S6 - S8, which contrast winds in the morning rush hour periods on the basis of speed, the lightest winds (≤2.5 m s-1) arise primarily from the NNE and S sectors, directions not apparent in the annual analyses. Strong seasonal patterns are shown in Additional file 1: figures S9 - S12, e.g., winter is dominated by SW and WNW winds, spring with WNW winds, and summer with SW and light NNE. As discussed below, such seasonal and diurnal patterns can greatly influence concentration predictions.


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

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

Wind direction and speed plots for 2006 (all hours) and morning and afternoon rush hour periods.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Wind direction and speed plots for 2006 (all hours) and morning and afternoon rush hour periods.
Mentions: We briefly focus on the 2006 meteorological data. There were 688 hours of calms (when predictions were not attempted). Most days had few if any hours of calms (291 days had ≤3 hours of calms). The hourly wind speed averaged 4.29 m s-1 and ranged from 1.3 to 16.5 m s-1 (calms excluded). Wind direction and speed "roses" that show the probability and speed of winds in 16 sectors (each subtending 22.5°) are presented in Figure 7 for three cases: (a) all hours of the year; (b) the morning (7 - 9 am) rush hour period; and (c) the evening (4 - 6 pm) rush hour period. In the morning, moderate SW, SSW and WSW winds dominate (Figure 7b), while in the afternoon, winds shift to the WNW and are stronger; occasionally, moderate SSE and SE winds occur (Figure 7c). This diurnal variation is not represented by the annual patterns (Figure 7a). Other trends emerge when examining the lowest wind speeds that can produce the highest concentrations. As shown in Additional file 1: figures S6 - S8, which contrast winds in the morning rush hour periods on the basis of speed, the lightest winds (≤2.5 m s-1) arise primarily from the NNE and S sectors, directions not apparent in the annual analyses. Strong seasonal patterns are shown in Additional file 1: figures S9 - S12, e.g., winter is dominated by SW and WNW winds, spring with WNW winds, and summer with SW and light NNE. As discussed below, such seasonal and diurnal patterns can greatly influence concentration predictions.

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