Spatial patterning in PM2.5 constituents under an inversion-focused sampling design across an urban area of complex terrain.
Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during "frequent inversion" hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling.
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Brett J
BJ
Tunno
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Rebecca
R
Dalton
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Drew R
DR
Michanowicz
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Jessie L C
JL
Shmool
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Ellen
E
Kinnee
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Sheila
S
Tripathy
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Leah
L
Cambal
University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, USA.
Jane E
JE
Clougherty
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D001336:Automobiles; D004784:Environmental Monitoring / Q000379:methods*; D005163:Factor Analysis, Statistical; D040362:Geographic Information Systems; D006801:Humans; D019216:Metals, Heavy / Q000032:analysis*; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D010414:Pennsylvania; D012621:Seasons; D062206:Spatial Analysis; D014505:Urban Population
D000393:Air Pollutants; D019216:Metals, Heavy; D052638:Particulate Matter