A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community. (September 2016)

• Record Type: Journal Article
• Authors: Yu, Chang Ho; Fan, Zhihua; Lioy, Paul J.; Baptista, Ana; Greenberg, Molly; Laumbach, Robert J.
• Is Part Of: Atmospheric environment. Volume 141(2016)
• Page Start: 161
• Page End: 173
• Publication Date: 2016-09
• Subjects: Mobile monitoring -- Traffic-related air pollution -- Spatial and temporal variability -- Parallel measurement -- Traffic proximity -- Road exposure; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2016.06.044
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Air concentrations of traffic-related air pollutants (TRAPs) vary in space and time within urban communities, presenting challenges for estimating human exposure and potential health effects. Conventional stationary monitoring stations/networks cannot effectively capture spatial characteristics. Alternatively, mobile monitoring approaches became popular to measure TRAPs along roadways or roadsides. However, these linear mobile monitoring approaches cannot thoroughly distinguish spatial variability from temporal variations in monitored TRAP concentrations. In this study, we used a novel mobile monitoring approach to simultaneously characterize spatial/temporal variations in roadside concentrations of TRAPs in urban settings. We evaluated the effectiveness of this mobile monitoring approach by performing concurrent measurements along two parallel paths perpendicular to a major roadway and/or along heavily trafficked roads at very narrow scale (one block away each other) within short time period (<30 min) in an urban community. Based on traffic and particulate matter (PM) source information, we selected 4 neighborhoods to study. The sampling activities utilized real-time monitors, including battery-operated PM2.5 monitor (SidePak), condensation particle counter (CPC 3007), black carbon (BC) monitor (Micro-Aethalometer), carbon monoxide (CO) monitor (Langan T15), and portable temperature/humidity data logger (HOBO U12), and a GPS-based tracker (Trackstick). Sampling was conducted for ∼3 h in the morning (7:30–10:30) in 7 separate days in March/April and 6 days in May/June 2012. Two simultaneous samplings were made at 5 spatially-distributed locations on parallel roads, usually distant one block each other, in each neighborhood. The 5-min averaged BC concentrations (AVG ± SD, [range]) were 2.53 ± 2.47 [0.09–16.3] μg/m 3, particle number concentrations (PNC) were 33, 330 ± 23, 451 [2512–159, 130] particles/cm 3, PM2.5 mass concentrations were 8.87 ± 7.65 [0.27–46.5] μg/m 3, and CO concentrations were 1.22 ± 0.60 [0.22–6.29] ppm in the community. The traffic-related air pollutants, BC and PNC, but not PM2.5 or CO, varied spatially depending on proximity to local stationary/mobile sources. Seasonal differences were observed for all four TRAPs, significantly higher in colder months than in warmer months. The coefficients of variation (CVs) in concurrent measurements from two parallel routes were calculated around 0.21 ± 0.17, and variations were attributed by meteorological variation (25%), temporal variability (19%), concentration level (6%), and spatial variability (2%), respectively. Overall study findings suggest this mobile monitoring approach could effectively capture and distinguish spatial/temporal characteristics in TRAP concentrations for communities impacted by heavy motor vehicle traffic and mixed urban air pollution sources. Highlights: A novel mobile monitoring approach captured variation in urban TRAP. The monitoring approach distinguished spatial variation from temporal variation. Spatial distributions are patterned by traffic proximity/surrounding environments. Meteorology and temporal changes explain the variability of parallel measurements. TRAP concentrations were influenced primarily by traffic/meteorological conditions.