Limits...
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.

Show MeSH

Related in: MedlinePlus

Scatterplot of predicted versus observed 24-hour CO concentrations at the Allen Park monitoring site in 2004. Solid line shows 1:1 line; dashed lines show factor of two boundary.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2914002&req=5

Figure 5: Scatterplot of predicted versus observed 24-hour CO concentrations at the Allen Park monitoring site in 2004. Solid line shows 1:1 line; dashed lines show factor of two boundary.

Mentions: Figure 5 shows predicted and observed 24-hr CO concentrations at the Allen Park monitoring site. Considering the annual average, the model under-predicted the average measured concentration (0.33 ppm) by 33%. Considering 24-hr concentrations, 57% of predictions fell within a factor of two of observations, and the correlation coefficient was 0.33 between observed and predicted concentrations (n = 263). Model performance improved somewhat by restricting the analysis to April through October (thus omitting the colder months when CO emissions are more variable, e.g., strongly dependent on engine temperature). During this period, the average under-prediction was 25%, 59% of the 24-hr data was within a factor of two, and the correlation coefficient was 0.39 (n = 134).


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

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

Scatterplot of predicted versus observed 24-hour CO concentrations at the Allen Park monitoring site in 2004. Solid line shows 1:1 line; dashed lines show factor of two boundary.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Scatterplot of predicted versus observed 24-hour CO concentrations at the Allen Park monitoring site in 2004. Solid line shows 1:1 line; dashed lines show factor of two boundary.
Mentions: Figure 5 shows predicted and observed 24-hr CO concentrations at the Allen Park monitoring site. Considering the annual average, the model under-predicted the average measured concentration (0.33 ppm) by 33%. Considering 24-hr concentrations, 57% of predictions fell within a factor of two of observations, and the correlation coefficient was 0.33 between observed and predicted concentrations (n = 263). Model performance improved somewhat by restricting the analysis to April through October (thus omitting the colder months when CO emissions are more variable, e.g., strongly dependent on engine temperature). During this period, the average under-prediction was 25%, 59% of the 24-hr data was within a factor of two, and the correlation coefficient was 0.39 (n = 134).

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