Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species

Mingjie Xie; Teresa L Coons; Joshua G Hemann; Steven J Dutton; Jana B Milford; Jennifer L Peel; Shelly L Miller; Sun-Young Kim; Sverre Vedal; Lianne Sheppard; Michael P Hannigan

doi:10.1016/j.atmosenv.2012.06.036

Intra-urban spatial variability and uncertainty assessment of PM_2.5 sources based on carbonaceous species

Atmos Environ (1994). 2012 Dec 1:60:305-315. doi: 10.1016/j.atmosenv.2012.06.036.

Authors

Affiliations

¹ Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, CO 80309, USA.
² National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
³ Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
⁴ Department of Environmental and Occupational Health Sciences, University of Washington, School of Public Health, University of Washington, Seattle, WA 98195, USA.

Abstract

To identify the sources of PM_2.5 - 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 PM_2.5 compositional 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 5^th - 95^th percentiles 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 5^th - 95^th percentile 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.

Keywords: Bootstrap; Positive matrix factorization; Source apportionment; Spatial variability; Uncertainty.

Grants and funding

R01 ES012197/ES/NIEHS NIH HHS/United States