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Abstract: This study presents source apportionment results for PM2.5 from applying positive matrix factorization (PMF) to a 32-month series of daily PM2.5 compositional data from Denver, CO, including concentrations of sulfate, nitrate, bulk elemental carbon (EC) and organic carbon (OC), and 51 organic molecular markers (OMMs). An optimum 8-factor solution was determined primarily based on the interpretability of the PMF results and rate of matching factors from bootstrapped PMF solutions with those from the base case solution. These eight factors were identified as inorganic ion, n-alkane, EC/sterane, light n-alkane/polycyclic aromatic hydrocarbon (PAH), medium alkane/alkanoic acid, PAH, winter/methoxyphenol and summer/odd n-alkane. The inorganic ion factor dominated the reconstructed PM2.5 mass (sulfate + nitrate + EC + OC) in cold periods (daily average temperature <10 °C; 43.7% of reconstructed PM2.5 mass) whereas the summer/odd n-alkane factor dominated in hot periods (>20 °C; 53.1%). The two factors had comparable relative contributions of 26.5% and 27.1% in warm periods with temperatures between 10 °C and 20 °C. Each of the seven factors resolved in a previous study (Dutton et al., 2010b) using a 1-year data set from the same location matches one factor from the current work based on comparing factor profiles. Six out of the seven matched pairs of factors are linked to similar source classes as suggested by the strong correlations between factor contributions (r = 0.89–0.98). Temperature-stratified source apportionment was conducted for three subsets of the data in the current study, corresponding to the cold, warm and hot periods mentioned above. The cold period (7-factor) solution exhibited a similar distribution of reconstructed PM2.5 mass as the full data set solution. The factor contributions of the warm period (7-factor) solution were well correlated with those from the full data set solution (r = 0.76–0.99). However, the reconstructed PM2.5 mass was distributed more to inorganic ion, n-alkane and medium alkane/alkanoic acid factors in the warm period solution than in the full data set solution. For the hot period (6-factor) solution, PM2.5 mass distribution was quite different from that of the full data set solution, as illustrated by regression slopes as low as 0.2 and as high as 4.8 of each matched pair of factors across the two solutions. [Copyright &y& Elsevier]

Abstract: The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003–2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Bi-weekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio–ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites. [Copyright &y& Elsevier]

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 PM2.5 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 5th – 95th 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 5th – 95th 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. [Copyright &y& Elsevier]