title: Spatial variation in diesel-related elemental and organic PM2.5components during workweek hours across a downtown core.
abstract: Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km2), cross-stratifying to disentangle source impacts (i.e., truck density, bus route frequency, total traffic density). For buses, outbound and inbound trips per week were summed by route and a kernel density was calculated across sites. Programmable monitors collected fine particulate matter (PM2.5) samples specific to workweek hours (Monday-Friday, 7 am-7 pm), summer and winter 2013. Integrated filters were analyzed for black carbon (BC), elemental carbon (EC), organic carbon (OC), elemental constituents, and diesel-related organic compounds [i.e., polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes]. To our knowledge, no studies have collected this suite of pollutants with such high sampling density, with the ability to capture spatial patterns during specific hours of interest. We hypothesized that we would find substantial spatial variation for each pollutant and significant associations with key sources (e.g. diesel and gasoline vehicles), with higher concentrations near the center of this small downtown core. Using a forward stepwise approach, we developed seasonal land use regression (LUR) models for PM2.5, BC, total EC, OC, PAHs, hopanes, steranes, aluminum (Al), calcium (Ca), and iron (Fe). Within this small domain, greater concentration differences were observed in most pollutants across sites, on average, than between seasons. Higher PM2.5and BC concentrations were found in the downtown core compared to the boundaries. PAHs, hopanes, and steranes displayed different spatial patterning across the study area by constituent. Most LUR models suggested a strong influence of bus-related emissions on pollution gradients. Buses were more dominant predictors compared to truck and vehicular traffic for several pollutants. Overall, we found substantial variation in diesel-related concentrations in a very small downtown area, which varied across elemental and organic components.
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States. Electronic address: Bjt25@pitt.edu.
authors.forename: Brett J
authors.initials: BJ
authors.lastname: Tunno
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Jessie L C
authors.initials: JLC
authors.lastname: Shmool
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Drew R
authors.initials: DR
authors.lastname: Michanowicz
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Sheila
authors.initials: S
authors.lastname: Tripathy
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Lauren G
authors.initials: LG
authors.lastname: Chubb
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Ellen
authors.initials: E
authors.lastname: Kinnee
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Leah
authors.initials: L
authors.lastname: Cambal
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Courtney
authors.initials: C
authors.lastname: Roper
authors.affiliation: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA, United States.
authors.forename: Jane E
authors.initials: JE
authors.lastname: Clougherty
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D002244:Carbon / Q000032:analysis; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D040362:Geographic Information Systems; D008670:Metals / Q000032:analysis; D018986:Motor Vehicles; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D010414:Pennsylvania; D011084:Polycyclic Aromatic Hydrocarbons / Q000032:analysis; D012621:Seasons; D013997:Time Factors; D014507:Urbanization; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D008670:Metals; D052638:Particulate Matter; D011084:Polycyclic Aromatic Hydrocarbons; D001335:Vehicle Emissions; D002244:Carbon
keywords: Air pollution monitoring; Black carbon (BC); Elemental constituents; Fine particulate matter (PM(2.5)); Geographic information systems (GIS); Land use regression (LUR); Organic compounds