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Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants.

ABSTRACT: Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NO_x, CO and PM_2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NO_x, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM_2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

SUBMITTER: Milando CW

PROVIDER: S-EPMC8064696 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into "low", "medium" or "high" traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments.

Project description:BACKGROUND:Communities need to efficiently estimate the burden from specific pollutants and identify those most at risk to make timely informed policy decisions. We developed a risk-based model to estimate the burden of black carbon (BC) and nitrogen dioxide (NO2) on coronary heart disease (CHD) across environmental justice (EJ) and non-EJ populations in Allegheny County, PA. METHODS:Exposure estimates in census tracts were modeled via land use regression and analyzed in relation to US Census data. Tracts were ranked into quartiles of exposure (Q1-Q4). A risk-based model for estimating the CHD burden attributed to BC and NO2 was developed using county health statistics, census tract level exposure estimates, and quantitative effect estimates available in the literature. RESULTS:For both pollutants, the relative occurrence of EJ tracts (> 20% poverty and/or > 30% non-white minority) in Q2 - Q4 compared to Q1 progressively increased and reached a maximum in Q4. EJ tracts were 4 to 25 times more likely to be in the highest quartile of exposure compared to the lowest quartile for BC and NO2, respectively. Pollutant-specific risk values (mean [95% CI]) for CHD mortality were higher in EJ tracts (5.49 × 10- 5 [5.05 × 10- 5 - 5.92 × 10- 5]; 5.72 × 10- 5 [5.44 × 10- 5 - 6.01 × 10- 5] for BC and NO2, respectively) compared to non-EJ tracts (3.94 × 10- 5 [3.66 × 10- 5 - 4.23 × 10- 5]; 3.49 × 10- 5 [3.27 × 10- 5 - 3.70 × 10- 5] for BC and NO2, respectively). While EJ tracts represented 28% of the county population, they accounted for about 40% of the CHD mortality attributed to each pollutant. EJ tracts are disproportionately skewed toward areas of high exposure and EJ residents bear a greater risk for air pollution-related disease compared to other county residents. CONCLUSIONS:We have combined a risk-based model with spatially resolved long-term exposure estimates to predict CHD burden from air pollution at the census tract level. It provides quantitative estimates of effects that can be used to assess possible health disparities, track temporal changes, and inform timely local community policy decisions. Such an approach can be further expanded to include other pollutants and adverse health endpoints.

Project description:Inhaled particles and gases can harm health by promoting chronic inflammation in the body. Few studies have investigated the relationship between outdoor air pollution and inflammation by race and ethnicity, socioeconomic status, and lifestyle risk factors. We examined associations of particulate matter (PM) and other markers of traffic-related air pollution with circulating levels of C-reactive protein (CRP), a biomarker of systemic inflammation. CRP was measured from blood samples obtained in 1994-2016 from 7,860 California residents participating in the Multiethnic Cohort (MEC) Study. Exposure to PM (aerodynamic diameter ≤2.5 μm [PM2.5], ≤10 μm [PM10], and between 2.5 and 10 μm [PM10-2.5]), nitrogen oxides (NOx, including nitrogen dioxide [NO2]), carbon monoxide (CO), ground-level ozone (O3), and benzene averaged over one or twelve months before blood draw were estimated based on participants' addresses. Percent change in geometric mean CRP levels and 95% confidence intervals (CI) per standard concentration increase of each pollutant were estimated using multivariable generalized linear regression. Among 4,305 females (55%) and 3,555 males (45%) (mean age 68.1 [SD 7.5] years at blood draw), CRP levels increased with 12-month exposure to PM10 (11.0%, 95% CI: 4.2%, 18.2% per 10 μg/m3), PM10-2.5 (12.4%, 95% CI: 1.4%, 24.5% per 10 μg/m3), NOx (10.4%, 95% CI: 2.2%, 19.2% per 50 ppb), and benzene (2.9%, 95% CI: 1.1%, 4.6% per 1 ppb). In subgroup analyses, these associations were observed in Latino participants, those who lived in low socioeconomic neighborhoods, overweight or obese participants, and never or former smokers. No consistent patterns were found for 1-month pollutant exposures. This investigation identified associations of primarily traffic-related air pollutants, including PM, NOx, and benzene, with CRP in a multiethnic population. The diversity of the MEC across demographic, socioeconomic, and lifestyle factors allowed us to explore the generalizability of the effects of air pollution on inflammation across subgroups.

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.

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Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants.

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