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Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan.

ABSTRACT: The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies, e.g., to estimate health impacts.

SUBMITTER: Milando CW

PROVIDER: S-EPMC5889051 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan.

Milando Chad W CW Batterman Stuart A SA

Atmospheric environment (Oxford, England : 1994) 20180321

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines ...[more]

PMID: 29632433

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Project description:BackgroundNear-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.MethodsProcess-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.ResultsPredicted 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.ConclusionsThe 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.

Project description:Background:Air pollution has become a major problem around the world and is increasingly an issue in Togo due to increased vehicular traffic. Gaseous pollutants are released by engines and are very harmful to human health and the environment. The fuels used on the major road in Togo, the N2, are adulterated with unknown contents and are of poor quality. Many of the vehicles come from neighboring countries, such as Benin, Ghana and Nigeria. Objectives:The present study aims to evaluate the pollution rate in Togo through the estimation of the concentrations of sulfur dioxide (SO2), nitrogen oxides (NOx), and particular matter (PM) on the international road, the National Road N2, in Lomé, compared to the World Health Organization's (WHO) standard limit. Methods:The simulations of pollutant concentration were performed using the Industrial Source Complex Short Term Version 3 model, which is included in the United States Environmental Protection Agency Regulatory Model (USEPA) AERMOD View software. The meteorological averages data were obtained from the local station near the National Road N2 in Togo in 2018. Hourly averages were calculated according to the European Monitoring Evaluation Programme/European Environmental Agency air pollutant emission inventory guidebook 2016 and were processed using AERMET View and a terrain pre-processor, AERMAP. For the model, the sources of pollution were the vehicles traveling on the road segment. The source was a line volume with 20 m of width and 2 m of height. The estimation methodology covered exhaust emissions of NOx, SO2 and PM contained in the fuel. Results:The simulations provided average hourly, daily and annual concentrations of the different pollutants: 71.91 ?g/m3, 42.41 ?g/m3,11.23 ?g/m3 for SO2; 16.78 ?g/m3, 9.89 ?g/m3, 2.46 ?g/m3 for NOx and below the detection limit, 0.62 ?g/m3, 0.15 ?g/m3 for PM, respectively. These results indicate that on the National Road N2 in Togo, the concentrations of SO2 were high compared to those of NOx and PM. The daily average concentration of SO2 was twice the permissible limits set by the WHO. Conclusions:Emissions obtained from the AERMOD for NOx and PM were less than the permissible limits set by the WHO, while the rate of SO2 was twice the permissible limit. The fuels used on this road were very rich in sulfur. The sulfur level in fuels must be monitored by stakeholders in Togo. Competing Interests:The authors declare no competing financial interests.

Project description:The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

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Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan.

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Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan.

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