title: Intra-urban spatial variability and uncertainty assessment of PM2.5sources based on carbonaceous species.
abstract: To identify the sources of PM2.5- bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th- 95thpercentiles range) of factor contributions, expressed as reconstructed carbonaceous mass (EC + OC), were estimated from PMF solutions of replicate datasets generated by using a stationary block bootstrap technique. A seven-factor solution was resolved for a set of data pooled across the four sites, as it gave the most interpretable results and had the highest rate of neural network factor matching (76.9%). Identified factors were primarily associated with high plant wax, summertime emission, diesel vehicle emission, fossil fuel combustion, motor vehicle emission, lubricating oil combustion and wood burning. Pearson correlation coefficients (r) and coefficients of divergence (COD) were used to assess spatial variability of factor contributions. The summertime emission factor exhibited the highest spatial correlation (r= 0.74 - 0.88) and lowest CODs (0.32 - 0.38) among all resolved factors; while the three traffic dominated factors (diesel vehicle emission, motor vehicle emission and lubricating oil combustion) showed lower correlations (r= 0.47 - 0.55) and higher CODs (0.41 - 0.53) on average. Average total EC and OC mass were apportioned to each factor and showed a similar distribution across the four sites. Modeling uncertainties were defined as the 5th- 95thpercentile range of the factor contributions derived from valid bootstrap PMF solutions, and were highly correlated with the median factor contribution in each factor (r= 0.77 - 0.98). Source apportionment was also performed on site specific datasets; the results exhibited similar factor profiles and temporal variation in factor contribution as those obtained for the pooled dataset, indicating that the four sites are primarily influenced by similar types of sources. On the other hand, differences were observed in absolute factor contributions between PMF solutions for the pooled versus site-specific datasets, likely due to the large uncertainties in EC and OC factor profiles derived from the site specific datasets with limited numbers of observations.
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
authors.forename: Mingjie
authors.initials: M
authors.lastname: Xie
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
authors.forename: Teresa L
authors.initials: TL
authors.lastname: Coons
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
authors.forename: Joshua G
authors.initials: JG
authors.lastname: Hemann
authors.affiliation: National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Dutton
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
authors.forename: Jana B
authors.initials: JB
authors.lastname: Milford
authors.affiliation: Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Peel
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
authors.forename: Shelly L
authors.initials: SL
authors.lastname: Miller
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, School of Public Health, University of Washington, Seattle, WA 98195, USA.
authors.forename: Sun-Young
authors.initials: SY
authors.lastname: Kim
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, School of Public Health, University of Washington, Seattle, WA 98195, USA.
authors.forename: Sverre
authors.initials: S
authors.lastname: Vedal
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, School of Public Health, University of Washington, Seattle, WA 98195, USA.
authors.forename: Lianne
authors.initials: L
authors.lastname: Sheppard
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Hannigan
keywords: Bootstrap; Positive matrix factorization; Source apportionment; Spatial variability; Uncertainty