Low-wind and other microclimatic factors in near-road black carbon variability: A case study and assessment implications.
ABSTRACT: 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:The objective of this research is to learn how the near-road gradient, in which NO2 and NOX (NO + NO2) concentrations are elevated, varies with changes in meteorological and traffic variables. Measurements of NO2 and NOX were obtained east of I-15 in Las Vegas and fit to functions whose slopes (dCNO2 /dx and dCNOX /dx, respectively) characterize the size of the near-road zone where NO2 and NOX concentrations from mobile sources on the highway are elevated. These metrics were used to learn about the near-road gradient by modeling dCNO2 /dx and dCNOX /dx as functions of meteorological variables (e.g., wind direction, wind speed), traffic (vehicle count), NOX concentration upwind of the road, and O3 concentration at two fixed-site ambient monitors. Generalized additive models (GAM) were used to model dCNO2 /dx and dCNOX /dx versus the independent variables because they allowed for nonlinearity of the variables being compared. When data from all wind directions were included in the analysis, variability in O3 concentration comprised the largest proportion of variability in dCNO2 /dx, followed by variability in wind direction. In a second analysis constrained to winds from the west, variability in O3 concentration remained the largest contributor to variability in dCNO2 /dx, but the relative contribution of variability in wind speed to variability in dCNO2 /dx increased relative to its contribution for the all-wind analysis. When data from all wind directions were analyzed, variability in wind direction was by far the largest contributor to variability in dCNOX /dx, with smaller contributions from hour of day and upwind NOX concentration. When only winds from the west were analyzed, variability in upwind NOX concentration, wind speed, hour of day, and traffic count all were associated with variability in dCNOX /dx. Increases in O3 concentration were associated with increased magnitude near-road dCNO2 /dx, possibly shrinking the zone of elevated concentrations occurring near roads. Wind direction parallel to the highway was also related to an increased magnitude of both dCNO2 /dx and dCNOX /dx, again likely shrinking the zone of elevated concentrations occurring near roads. Wind direction perpendicular to the road decreased the magnitude of dCNO2 /dx and dCNOX /dx and likely contributed to growth of the zone of elevated concentrations occurring near roads. Thus, variability in near-road concentrations is influenced by local meteorology and ambient O3 concentration.
Project description:Highway-building environments are prevalent in metropolitan areas. This paper presents our findings in investigating pollutant transport in a highway-building environment by combing field measurement and numerical simulations. We employ and improve the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model to simulate the spatial variations of black carbon (BC) concentrations near highway I-87 and an urban school in the South Bronx, New York. The results of CTAG simulations are evaluated against and agree adequately with the measurements of wind speed, wind directions, and BC concentrations. Our analysis suggests that the BC concentration at the measurement point of the urban school could decrease by 43-54% if roadside buildings were absent. Furthermore, we characterize two generalized conditions in a highway-building environment, i.e., highway-building canyon and highway viaduct-building. The former refers to the canyon between solid highway embankment and roadside buildings, where the spatial profiles of BC depend on the equivalent canyon aspect ratio and flow recirculation. The latter refers to the area between a highway viaduct (i.e., elevated highway with open space underneath) and roadside buildings, where strong flow recirculation is absent and the spatial profiles of BC are determined by the relative heights of the highway and buildings. The two configurations may occur at different locations or in the same location with different wind directions when highway geometry is complex. Our study demonstrates the importance of incorporating highway-building interaction into the assessment of human exposure to near-road air pollution. It also calls for active roles of building and highway designs in mitigating near-road exposure of urban population.
Project description:Elevated air pollution levels adjacent to major highways are an ongoing topic of public health concern worldwide. Black carbon (BC), a component of particulate matter (PM) emitted by diesel and gasoline vehicles, was measured continuously via a filter-based light absorption technique over ~ 16 months at four different stations positioned on a perpendicular trajectory to a major highway in Las Vegas, NV. During downwind conditions (winds from the west), BC at 20 m from the highway was 32 and 60% higher than concentrations at 100 and 300 m from the roadway, respectively. Overall highest roadside (20-m site) BC concentrations were observed during the time period of 4 a.m.-8 a.m. under low-speed variable winds (3.02 ?g/m3) or downwind conditions (2.84 ?g/m3). The 20-m site BC concentrations under downwind conditions are 85% higher on weekday periods compared to weekends during the time period of 4 a.m.-8 a.m. Whereas total traffic volume was higher on weekdays versus weekends and differed by approximately 3% on weekdays versus weekends, similarly, the detected heavy-duty fraction was higher on weekdays versus weekends and differed by approximately 21% on weekdays versus weekend. Low wind speeds predominated during early morning hours, leading to higher BC concentrations during early morning hours despite the maximum traffic volume occurring later in the day. No noticeable impact from the airport or nearby arterial roadways was observed, with the 300-m site remaining the lowest of the four-site network when winds were from the east. Multivariate linear regression analysis revealed that heavy-duty traffic volume, light-duty traffic volume, wind speed, weekday versus weekend, surface friction velocity, ambient temperature, and the background BC concentration were significant predictors of roadside BC concentrations. Comparison of BC and PM2.5 downwind concentration gradients indicates that the BC component contributes substantially to the PM2.5 increase in roadside environments. These results suggest that BC is an important indicator to assess the contribution of primary traffic emissions to near-road PM2.5 concentrations, providing opportunities to evaluate the feasibility and effectiveness of mitigation strategies.
Project description:Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196?m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23?m and reached 82?m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100?m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.
Project description:Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ?100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.
Project description:Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available.
Project description:Ultrafine particles (UFP; aerodynamic diameter < 0.1 micrometers) are a ubiquitous exposure in the urban environment and are elevated near highways. Most epidemiological studies of UFP health effects use central site monitoring data, which may misclassify exposure. Our aims were to: (1) examine the relationship between distant and proximate monitoring sites and their ability to predict hourly UFP concentration measured at residences in an urban community with a major interstate highway and; (2) determine if meteorology and proximity to traffic improve explanatory power. Short-term (1 - 3 weeks) residential monitoring of UFP concentration was conducted at 18 homes. Long-term monitoring was conducted at two near-highway monitoring sites and a central site. We created models of outdoor residential UFP concentration based on concentrations at the near-highway site, at the central site, at both sites together and without fixed sites. UFP concentration at residential sites was more highly correlated with those at a near-highway site than a central site. In regression models of each site alone, a 10% increase in UFP concentration at a near-highway site was associated with a 6% (95% CI: 6%, 7%) increase at residences while a 10% increase in UFP concentration at the central site was associated with a 3% (95% CI: 2%, 3%) increase at residences. A model including both sites showed minimal change in the magnitude of the association between the near-highway site and the residences, but the estimated association with UFP concentration at the central site was substantially attenuated. These associations remained after adjustment for other significant predictors of residential UFP concentration, including distance from highway, wind speed, wind direction, highway traffic volume and precipitation. The use of a central site as an estimate of personal exposure for populations near local emissions of traffic-related air pollutants may result in exposure misclassification.
Project description:Sick building syndrome (SBS) includes general, mucosal and skin symptoms. It is typically associated with an individual's place of work or residence. The aim of this study was to explore the effect of traffic exposure on SBS symptoms in Beijing, China.From January to May, 2011, recruitment occurred at kindergartens in 11 districts in Beijing. Self-administered questionnaires were distributed by teachers to legal guardians of children and then returned to teachers. The questionnaire asked them to recall the presence of 12 SBS symptoms from the previous three months. Living near a highway or main road (within 200 meters) was used as a proxy for traffic exposure. Multivariable logistic regression was used to test the association between traffic exposure and a higher number of SBS symptoms, controlling for key covariates.There were 5487 valid questionnaires (65.0% response rate). Univariate analysis showed that living near a main road or highway (OR = 1.40), female gender (OR = 1.44), and environmental tobacco smoking (ETS) (OR = 1.13) were significant risk factors for general symptoms. Grandparent's generation (OR = 0.32) and home ownership (owner vs. renter) (OR = 0.89) were significant protective factors. The adjusted odds ratio (aOR) for the association between living close to a highway and general symptoms remained significant in the multivariable model (aOR = 1.39; 95% CI = 1.21: 1.59). ORs and aORs were similar for mucosal and skin symptoms.This study found traffic exposure to be significantly associated with SBS symptoms. This finding is consistent with current literature that indicates an association between adverse health effects and living near highway or main road.
Project description:Estimating ultrafine particle number concentrations (PNC) near highways for exposure assessment in chronic health studies requires models capable of capturing PNC spatial and temporal variations over the course of a full year. The objectives of this work were to describe the relationship between near-highway PNC and potential predictors, and to build and validate hourly log-linear regression models. PNC was measured near Interstate 93 (I-93) in Somerville, MA using a mobile monitoring platform driven for 234 h on 43 days between August 2009 and September 2010. Compared to urban background, PNC levels were consistently elevated within 100-200 m of I-93, with gradients impacted by meteorological and traffic conditions. Temporal and spatial variables including wind speed and direction, temperature, highway traffic, and distance to I-93 and major roads contributed significantly to the full regression model. Cross-validated model R(2) values ranged from 0.38 to 0.47, with higher values achieved (0.43 to 0.53) when short-duration PNC spikes were removed. The model predicts highest PNC near major roads and on cold days with low wind speeds. The model allows estimation of hourly ambient PNC at 20-m resolution in a near-highway neighborhood.
Project description:BACKGROUND: Understanding the ecological consequences of roads and developing ways to mitigate their negative effects has become an important goal for many conservation biologists. Most mitigation measures are based on road mortality and barrier effects data. However, studying fine-scale individual spatial responses in roaded landscapes may help develop more cohesive road planning strategies for wildlife conservation. METHODOLOGY/PRINCIPAL FINDINGS: We investigated how individuals respond in their spatial behavior toward a highway and its traffic intensity by radio-tracking two common species particularly vulnerable to road mortality (barn owl Tyto alba and stone marten Martes foina). We addressed the following questions: 1) how highways affected home-range location and size in the immediate vicinity of these structures, 2) which road-related features influenced habitat selection, 3) what was the role of different road-related features on movement properties, and 4) which characteristics were associated with crossing events and road-kills. The main findings were: 1) if there was available habitat, barn owls and stone martens may not avoid highways and may even include highways within their home-ranges; 2) both species avoided using areas near the highway when traffic was high, but tended to move toward the highway when streams were in close proximity and where verges offered suitable habitat; and 3) barn owls tended to cross above-grade highway sections while stone martens tended to avoid crossing at leveled highway sections. CONCLUSIONS: Mortality may be the main road-mediated mechanism that affects barn owl and stone marten populations. Fine-scale movements strongly indicated that a decrease in road mortality risk can be realized by reducing sources of attraction, and by increasing road permeability through measures that promote safe crossings.