Dataset Information

Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year.

ABSTRACT: Accurate quantification of exposures to traffic-related air pollution in near-highway neighborhoods is challenging due to the high degree of spatial and temporal variation of pollutant levels. The objective of this study was to measure air pollutant levels in a near-highway urban area over a wide range of traffic and meteorological conditions using a mobile monitoring platform. The study was performed in a 2.3-km(2) area in Somerville, Massachusetts (USA), near Interstate I-93, a highway that carries 150,000 vehicles per day. The mobile platform was equipped with rapid-response instruments and was driven repeatedly along a 15.4-km route on 55 days between September 2009 and August 2010. Monitoring was performed in 4-6-hour shifts in the morning, afternoon and evening on both weekdays and weekends in winter, spring, summer and fall. Measurements were made of particle number concentration (PNC; 4-3,000 nm), particle size distribution, fine particle mass (PM(2.5)), particle-bound polycyclic aromatic hydrocarbons (pPAH), black carbon (BC), carbon monoxide (CO), and nitrogen oxides (NO and NO(x)). The highest pollutant concentrations were measured within 0-50 m of I-93 with distance-decay gradients varying depending on traffic and meteorology. The most pronounced variations were observed for PNC. Annual median PNC 0-50 m from I-93 was two-fold higher compared to the background area (>1 km from I-93). In general, PNC levels were highest in winter and lowest in summer and fall, higher on weekdays and Saturdays compared to Sundays, and higher during morning rush hour compared to later in the day. Similar spatial and temporal trends were observed for NO, CO and BC, but not for PM(2.5). Spatial variations in PNC distance-decay gradients were non-uniform largely due to contributions from local street traffic. Hour-to-hour, day-to-day and season-to-season variations in PNC were of the same magnitude as spatial variations. Datasets containing fine-scale temporal and spatial variation of air pollution levels near highways may help to inform exposure assessment efforts.

SUBMITTER: Padro-Martinez LT

PROVIDER: S-EPMC3491988 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year.

Padró-Martínez Luz T LT Patton Allison P AP Trull Jeffrey B JB Zamore Wig W Brugge Doug D Durant John L JL

Atmospheric environment (Oxford, England : 1994) 20121201

Accurate quantification of exposures to traffic-related air pollution in near-highway neighborhoods is challenging due to the high degree of spatial and temporal variation of pollutant levels. The objective of this study was to measure air pollutant levels in a near-highway urban area over a wide range of traffic and meteorological conditions using a mobile monitoring platform. The study was performed in a 2.3-km(2) area in Somerville, Massachusetts (USA), near Interstate I-93, a highway that ca ...[more]

PMID: 23144586

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Project description:Relatively few studies have characterized differences in intra- and inter-neighborhood traffic-related air pollutant (TRAP) concentrations and distance-decay gradients in along an urban highway for the purposes of exposure assessment. The goal of this work was to determine the extent to which intra- and inter-neighborhood differences in TRAP concentrations can be explained by traffic and meteorology in three pairs of neighborhoods along Interstate 93 (I-93) in the metropolitan Boston area (USA). We measured distance-decay gradients of seven TRAPs (PNC, pPAH, NO, NOX, BC, CO, PM2.5) in near-highway (<400 m) and background areas (>1 km) in Somerville, Dorchester/South Boston, Chinatown and Malden to determine whether (1) spatial patterns in concentrations and inter-pollutant correlations differ between neighborhoods, and (2) variation within and between neighborhoods can be explained by traffic and meteorology. The neighborhoods ranged in area from 0.5 to 2.3 km2. Mobile monitoring was performed over the course of one year in each pair of neighborhoods (one pair of neighborhoods per year in three successive years; 35-47 days of monitoring in each neighborhood). Pollutant levels generally increased with highway proximity, consistent with I-93 being a major source of TRAP; however, the slope and extent of the distance-decay gradients varied by neighborhood as well as by pollutant, season and time of day. Correlations among pollutants differed between neighborhoods (e.g., ? = 0.35-0.80 between PNC and NOX and ? = 0.11-0.60 between PNC and BC) and were generally lower in Dorchester/South Boston than in the other neighborhoods. We found that the generalizability of near-road gradients and near-highway/urban background contrasts was limited for near-highway neighborhoods in a metropolitan area with substantial local street traffic. Our findings illustrate the importance of measuring gradients of multiple pollutants under different ambient conditions in individual near-highway neighborhoods for health studies involving inter-neighborhood comparisons.

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.

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Dataset Information

Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year.

Publications

Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year.

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