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Variability in Observation-based Onroad Emission Constraints from a Near-road Environment.

ABSTRACT: This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NO_x) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NO_x ratios (ΔCO:ΔNO_x) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NO_x ratios are used to infer NO_x emission adjustments for a priori emissions estimates from EPA's MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNO_x from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNO_x but not cross-road gradient ΔCO:ΔNO_x. The inferred NO_x emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NO_x emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNO_x estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

SUBMITTER: Simon H

PROVIDER: S-EPMC7821344 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:Airborne black carbon from urban traffic is a climate forcing agent and has been associated with health risks to near-road populations. In this paper, we describe a case study of black carbon concentration and compositional variability at and near a traffic-laden multi-lane highway in Cincinnati, Ohio, using an onsite aethalometer and filter-based NIOSH Method 5040 measurements; the former measured 1-min average black carbon concentrations and the latter determined the levels of organic and elemental carbon (OC and EC) averaged over an approximately 2-h time interval. The results show significant wind and temperature effects on black carbon concentration and composition in a way more complex than predicted by Gaussian dispersion models. Under oblique low winds, namely ux [= u × sin(g=q)]~ (0,-0.5 m s-1), which mostly occurred during morning hours, black carbon concentrations per unit traffic flow were highest and had large variation. The variability did not always follow Gaussian dispersion but was characteristic of a uniform distribution at a near-road distance. Under all other wind conditions, the near-road black carbon variation met Gaussian dispersion characteristics. Significant differences in roadside dispersion are observed between OC and EC fractions, between PM2.5 and PM10-2.5, and between the morning period and rest of the day. In a general case, the overall black carbon variability at the multi-lane highway can be stated as bimodal consisting of Gaussian dispersion and non-Gaussian uniform distribution. Transition between the two types depends on wind velocity and wind angle to the traffic flow. In the order of decreasing importance, the microclimatic controlling factors over the black carbon variability are: 1) wind velocity and the angle with traffic; 2) diurnal temperature variations due to thermal buoyancy; and 3) downwind Gaussian dispersion. Combinations of these factors may have created various traffic-microclimate interactions that have significant impact on near-road black carbon transport.

Project description:Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

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Variability in Observation-based Onroad Emission Constraints from a Near-road Environment.

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