Methods for estimating uncertainty in PMF solutions : Examples with ambient air and water quality data and guidance on reporting PMF results

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 randomerrors and rotational ambiguity. To demonstrate the utility of the EEmethods, results are presented for three data sets: (1) speciated PM2.5 data froma chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species inwater quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high- resolution aerosolmass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results fromsensitivity 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 toomany factors. DISP diagnosticswere 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. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Primary Sources of Polycyclic Aromatic Hydrocarbons to Streambed Sediment in Great Lakes Tributaries Using Multiple Lines of Evidence

Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread and potentially toxic contaminants in Great Lakes (USA/Canada) tributaries. The sources of PAHs are numerous and diverse, and identifying the primary source(s) can be difficult. The present study used multiple lines of evidence to determine the likely sources of PAHs to surficial streambed sediments at 71 locations across 26 Great Lakes Basin watersheds. Profile correlations, principal component analysis, positive matrix factorization source-receptor modeling, and mass fractions analysis were used to identify potential PAH sources, and land-use analysis was used to relate streambed sediment PAH concentrations to different land uses. Based on the common conclusion of these analyses, coal-tar-sealed pavement was the most likely source of PAHs to the majority of the locations sampled. The potential PAH-related toxicity of streambed sediments to aquatic organisms was assessed by comparison of concentrations with sediment quality guidelines. The sum concentration of 16 US Environmental Protection Agency priority pollutant PAHs was 7.4-196 000 mu g/kg, and the median was 2600 mu g/kg. The threshold effect concentration was exceeded at 62% of sampling locations, and the probable effect concentration or the equilibrium partitioning sediment benchmark was exceeded at 41% of sampling locations. These results have important implications for watershed managers tasked with protecting and remediating aquatic habitats in the Great Lakes Basin.Environ Toxicol Chem2020;00:1-17. (c) 2020 The Authors.Environmental Toxicology and Chemistrypublished by Wiley Periodicals LLC on behalf of SETAC.Peer reviewe

A novel approach for simple statistical analysis of high-resolution mass spectra

Recent advancements in atmospheric mass spectrometry provide huge amounts of new information but at the same time present considerable challenges for the data analysts. High-resolution (HR) peak identification and separation can be effort- and time-consuming yet still tricky and inaccurate due to the complexity of overlapping peaks, especially at larger mass-to-charge ratios. This study presents a simple and novel method, mass spectral binning combined with positive matrix factorization (binPMF), to address these problems. Different from unit mass resolution (UMR) analysis or HR peak fitting, which represent the routine data analysis approaches for mass spectrometry datasets, binPMF divides the mass spectra into small bins and takes advantage of the positive matrix factorization's (PMF) strength in separating different sources or processes based on different temporal patterns. In this study, we applied the novel approach to both ambient and synthetic datasets to evaluate its performance. It not only succeeded in separating overlapping ions but was found to be sensitive to subtle variations as well. Being fast and reliable, binPMF has no requirement for a priori peak information and can save much time and effort from conventional HR peak fitting, while still utilizing nearly the full potential of HR mass spectra. In addition, we identify several future improvements and applications for binPMF and believe it will become a powerful approach in the data analysis of mass spectra.Peer reviewe

Insights into atmospheric oxidation processes by performing factor analyses on subranges of mass spectra

Our understanding of atmospheric oxidation chemistry has improved significantly in recent years, greatly facilitated by developments in mass spectrometry. The generated mass spectra typically contain vast amounts of information on atmospheric sources and processes, but the identification and quantification of these is hampered by the wealth of data to analyze. The implementation of factor analysis techniques have greatly facilitated this analysis, yet many atmospheric processes still remain poorly understood. Here, we present new insights into highly oxygenated products from monoterpene oxidation, measured by chemical ionization mass spectrometry, at a boreal forest site in Finland in autumn 2016. Our primary focus was on the formation of accretion products, i.e., dimers. We identified the formation of daytime dimers, with a diurnal peak at noontime, despite high nitric oxide (NO) concentrations typically expected to inhibit dimer formation. These dimers may play an important role in new particle formation events that are often observed in the forest. In addition, dimers identified as combined products of NO3 and O3 oxidation of monoterpenes were also found to be a large source of low-volatility vapors at night. This highlights the complexity of atmospheric oxidation chemistry and the need for future laboratory studies on multi-oxidant systems. These two processes could not have been separated without the new analysis approach deployed in our study, where we applied binned positive matrix factorization (binPMF) on subranges of the mass spectra rather than the traditional approach where the entire mass spectrum is included for PMF analysis. In addition to the main findings listed above, several other benefits compared to traditional methods were found.Peer reviewe

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat