The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), ...displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003–2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high-resolution aerosol mass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results from sensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying too many factors. DISP diagnostics were consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors.
•We provide examples and interpretation of new error estimation methods in EPA PMF.•Multiple error estimation methods improve understanding of rotational uncertainty.•Multiple error estimation methods provide range of uncertainty in factor profiles.
We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in ...tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 (Abhd2), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2. The Abhd2KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids.
Multiple source apportionment approaches were employed to investigate PAH sources which contribute to small craft harbor (SCH) sediments in Nova Scotia (NS), Canada. A total of 580 sediment samples ...were analyzed using PAH diagnostic ratios, Unmix Optimum receptor modeling, and by assessment of the composition of the PAH profile. PAH diagnostic ratios suggest PAHs are primarily of pyrogenic (thermal) origin, while UnmixO modeling identifies four individual sources which best describe surficial sediments and suggests contributions from both pyrogenic and petrogenic origins. These include coal combustion, automobile exhaust, and biomass incineration. PAH profile assessment determined an overwhelming contribution of high molecular weight PAHs, which exhibited a strong correlation with total PAH concentrations.
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•Sediment was assessed for source apportionment of polycyclic aromatic hydrocarbons•Multiple lines of evidence investigated PAH sources from NS harbor sediments•US EPA Unmix Optimum modeling was applied to reveal potential PAH sources•PAHs produced from pyrogenic processes appear as the dominating source•Results suggest terrestrial activities greatly contribute PAHs to sediment
The physicochemical properties of coarse-mode, iron-containing particles and their temporal and spatial distributions are poorly understood. Single-particle analysis combining X-ray elemental mapping ...and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively collected particles was used to investigate the physicochemical properties of iron-containing particles in Cleveland, OH, in summer 2008 (Aug–Sept), summer 2009 (July–Aug), and winter 2010 (Feb–March). The most abundant classes of iron-containing particles were iron oxide fly ash, mineral dust, NaCl-containing agglomerates (likely from road salt), and Ca–S containing agglomerates (likely from slag, a byproduct of steel production, or gypsum in road salt). The mass concentrations of anthropogenic fly ash particles were highest in the Flats region (downtown) and decreased with distance away from this region. The concentrations of fly ash in the Flats region were consistent with interannual changes in steel production. These particles were observed to be highly spherical in the Flats region, but less so after transport away from downtown. This change in morphology may be attributed to atmospheric processing. Overall, this work demonstrates that the method of passive collection with single-particle analysis by electron microscopy is a powerful tool to study spatial and temporal gradients in components of coarse particles. These gradients may correlate with human health effects associated with exposure to coarse-mode particulate matter.
Unmix Optimum (UnmixO) was developed to analyze data, such as sediment PAH data, that were resistant to existing methods of multivariate analysis. Using a geometrical approach, UnmixO uses multiple ...advanced nonlinear optimization algorithms to find potential sources that obey non-negativity constraints while optimally fitting the data. UnmixO does not require specific knowledge of the uncertainties in the data and will work better for smaller data sets than other multivariate models. UnmixO was able to identify polycyclic aromatic hydrocarbon (PAH) contaminant sources contributing to sediment samples based on sample composition data with good diagnostic values. Results were compared to published EPA Chemical Mass Balance (CMB) sediment results from Lady Bird Lake (LBL) Austin, TX and 40 lakes (40LKS) across the U.S. A Chi-sum approach determined which UnmixO source profile best matched profiles used in CMB sediment studies; two coal tar (CT) sealcoat sources and a mixed combustion source contributed to the sediment PAHs. These results were consistent with CMB results for the LBL and 40LKS studies that estimated CT sealcoats contribute over 80% of PAHs to urban lakes. UnmixO results also showed that CT sealant's contribution to sediments decreased after the City of Austin ban in 2006.
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•Unmix Optimum (UnmixO) receptor model has been developed along with new diagnostic plots.•UnmixO and a chi-square approach with measured profiles found that coal tar (CT) sealant contributes to lake sediments.•PAHs in U.S. east and central region lake samples were primarily from CT sealants (over 80%).
High time-resolution aerosol sampling was conducted for one month during July–August 2007 in Dearborn, MI, a non-attainment area for fine particulate matter (PM2.5) National Ambient Air Quality ...Standards (NAAQS). Measurements of more than 30 PM2.5 species were made using a suite of semi-continuous sampling and monitoring instruments. Dynamic variations in the sub-hourly concentrations of source ‘marker’ elements were observed when discrete plumes from local sources impacted the sampling site. Hourly averaged PM2.5 composition data for 639 samples were used to identify and apportion PM2.5 emission sources using the multivariate receptor modeling techniques EPA Positive Matrix Factorization (PMF) v4.2 and EPA Unmix v6.0. Source contribution estimates from PMF and Unmix were then evaluated using the Sustained Wind Instance Method (SWIM), which identified plausible source origins. Ten sources were identified by both PMF and Unmix: (1) secondary sulfate, (2) secondary nitrate characterized by a significant diurnal trend, (3) iron and steel production, (4) a potassium-rich factor attributable to iron/steel slag waste processing, (5) a cadmium-rich factor attributable to incineration, (6) an oil refinery characterized by La/Ce>1 specific to south wind, (7) oil combustion, (8) coal combustion, (9) motor vehicles, and (10) road dust enriched with organic carbon. While both models apportioned secondary sulfate, oil refinery, and oil combustion PM2.5 masses closely, the mobile and industrial source apportionments differed. Analyses were also carried out to help infer time-of-day variations in the contributions of local sources.
► PM2.5 sources were investigated in Dearborn, MI, a U.S. EPA non-attainment area for NAAQS PM2.5 mass in the summer of 2007. ► Hourly PM2.5 chemical composition data were collected using SEAS-III and used in PMF v4.2 and Unmix v6.0 receptor modeling. ► Sources found from earlier long term 24-h integrated FRM sampling were readily observed from just four weeks of hourly data. ► SWIM analysis found hazardous air pollution elements showed dynamic concentration variations and were from local sources.
As described in this paper, nonparametric wind regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible source regions of ...pollutants as defined by wind direction sectors. It is described in detail with an example of its application to SO2 data from East St. Louis, IL. The model uses nonparametric kernel smoothing methods to apportion the observed average concentration of a pollutant to sectors defined by ranges of wind direction and speed. Formulas are given for the uncertainty of all of the important components of the model, and these are found to give nearly the same uncertainties as blocked bootstrap estimates of uncertainty. The model was applied to data for the first quarter (January, February, and March) of 2003, 2004, and 2005. The results for East St. Louis show that almost 50% of the average SO2 concentration can be apportioned to two 30° wide wind sectors containing a zinc smelter and a brewery; a nearby steel mill did not appear to have a significant impact on SO2 during this period.
Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with ...increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009–2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.
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