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Philip K Hopke - One of the best experts on this subject based on the ideXlab platform.
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Effect of adding variables on rotational ambiguity in Positive Matrix factorization solutions
Chemometrics and Intelligent Laboratory Systems, 2017Co-Authors: Fereshteh Emami, Philip K HopkeAbstract:Abstract A major problem that limit the use of the model-free analysis methods, like Positive Matrix factorization (PMF), for the resolution of multivariate data is that usually, there is rotational ambiguity in the results. Implementing adequate constraints may reduce such ambiguities. In some special cases, non-negativity constraints may serve as a sufficient condition to uniquely resolve profiles. In this study, effect of adding new variables on the extent of rotational ambiguity is investigated, and profiles with lower rotation have been obtained on the basis of data set structure. The displacement (DISP) method of error estimation (EE) for the factor elements incorporated into the US Environmental Protection Agency's version of Positive Matrix factorization (PMF) has been applied to capture the uncertainty of PMF analyses due to rotational ambiguity. To explore these considerations, results are presented for synthetic including submicron particle size distributions (12–470 nm) measured between January 2008 and December 2010 at the New York State Department of Environmental Conservation (NYS DEC) site in Rochester, NY. Generally, added informative variables can reduce the ambiguity in the profiles. The results of the PMF analysis showed the effect of adding new variables to feasible solutions led to significantly decreased (up to 44%) DISP intervals. More robust results with lower rotational uncertainty became possible and the results were more easily interpreted. This approach can be applied to any other field that utilize quantitative factor analysis methods.
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Positive Matrix Factorization of 47 Years of Particle Measurements in Finnish Arctic
Aerosol and Air Quality Research, 2015Co-Authors: James R. Laing, Philip K Hopke, Eleanor F. Hopke, Liaquat Husain, Vincent A. Dutkiewicz, Jussi Paatero, Yrjö ViisanenAbstract:Forty seven years of weekly total suspended particle filters collected at Kevo, Finland from October 1964 through 2010 by the Finnish Meteorological Institute were analyzed for near-total trace elements, soluble trace elements, black carbon, major ions and methane sulfonic acid (MSA). The chemical composition dataset was analyzed by Positive Matrix Factorization using EPA PMF5. The entire dataset (1964-2010) was modeled as well as three separate time periods, 1964-1978, 1979-1990, and 1991-2010. The dataset was split in 1979 due to a change from Whatman 42 cellulose filters to a glass fiber filters, and in 1990 due to drops in concentrations related to the economic collapse of the Soviet Union. Two factors representing non-ferrous metal smelters were found for all time periods. One factor was dominated by Cu and the other by Ni and Co. Each of the time periods contained a factor describing stationary fuel combustion with high percentages of V, BC, and nss-SO4=; a ferrous metal factor dominated by Fe and some Mn; a biogenic sulfate factor; a factor containing the majority of Mo and W; and a factor dominated by Sn. The 1979-1990, 1991-2010, and 1964-2010 results contained a factor for As and Re, and a factor with the majority of Mn and Cd, which were not observed in 1964-1978. The 1964-1978 time period results contain three unique factors, a factor dominated by Ag, a factor dominated by Au, and a sea salt factor characterized by a high percentage of Na and Mg. The 1964-2010 period contains an Ag and Au factor as well. Ag and Au both have high concentrations in the late 1960s that decrease dramatically starting in the early 1970s. The increased uncertainty due to the high blanks in the glass fiber filters may account for the inability to determine a sea salt factor in the later time periods.
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Source apportionment of the ambient PM2.5 across St. Louis using constrained Positive Matrix factorization
Atmospheric Environment, 2012Co-Authors: Fulvio Amato, Philip K HopkeAbstract:In most cases, receptor models are applied to data from a single monitoring site even if there are multiple sampling locations in a given urban area. When it can be reasonably expected that the sites are affected by the same set of sources, it is possible to use the spatial variability of the source contributions to enhance the source apportionment. With the framework of Positive Matrix factorization, it is possible to enhance the results through an effective use of multiple site data. There have been several previous studies of the sources of ambient PM2.5 in St Louis, MO based on data from the US EPA chemical speciation network and the St Louis-Midwest Supersite. However, these different analyses identified different sets of sources including the omission of known major emission sources. A re-examination of the previous studies was undertaken using knowledge of the existing sources based on independent data and the resulting profiles were used to constrain the solution. These new solutions provide more realistic results in which the source impacts of all of the major sources could be assessed at each site. © 2011 Elsevier Ltd.
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PCDD/F Source Apportionment in the Baltic Sea Using Positive Matrix Factorization
Environmental science & technology, 2010Co-Authors: Kristina L. Sundqvist, Philip K Hopke, Mats Tysklind, Paul Geladi, Karin WibergAbstract:Positive Matrix Factorization (PMF) was used to identify and apportion candidate sources of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) in samples of offshore and coastal surface sediments from the Baltic Sea. Atmospheric deposition was the dominant source in offshore and pristine areas, in agreement with previous studies. Earlier chlorophenol use and a source suggested origins from pulp and paper production and related industries were identified as important coastal sources. A previously presumed major source, chlorine bleaching of pulp, was of only minor importance for modern Baltic surface sediments. The coastal source impacts were mostly local or regional, but pattern variations in offshore samples indicate that coastal sources may have some importance for offshore areas. Differences between sub-basins also indicated that local and regional air emissions from incineration or other high-temperature processes are more important in the southern Baltic Sea compared to those in northerly areas. These regional differences demonstrated the importance of including offshore sediments from the Bothnian Bay, Gulf of Finland, and other areas of the Baltic Sea in future studies to better identify the major PCDD/F sources to the Baltic Sea.
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pcdd f source apportionment in the baltic sea using Positive Matrix factorization
Environmental Science & Technology, 2010Co-Authors: Kristina Sundqvist, Philip K Hopke, Mats Tysklind, Paul Geladi, Karin WibergAbstract:Positive Matrix Factorization (PMF) was used to identify and apportion candidate sources of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) in samples of offshore and coastal surface sediments from the Baltic Sea. Atmospheric deposition was the dominant source in offshore and pristine areas, in agreement with previous studies. Earlier chlorophenol use and a source suggested origins from pulp and paper production and related industries were identified as important coastal sources. A previously presumed major source, chlorine bleaching of pulp, was of only minor importance for modern Baltic surface sediments. The coastal source impacts were mostly local or regional, but pattern variations in offshore samples indicate that coastal sources may have some importance for offshore areas. Differences between sub-basins also indicated that local and regional air emissions from incineration or other high-temperature processes are more important in the southern Baltic Sea compared to those in northerly areas. These regional differences demonstrated the importance of including offshore sediments from the Bothnian Bay, Gulf of Finland, and other areas of the Baltic Sea in future studies to better identify the major PCDD/F sources to the Baltic Sea.
Kiyoung Lee - One of the best experts on this subject based on the ideXlab platform.
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Identification of the sources of PM10 in a subway tunnel using Positive Matrix factorization
Journal of the Air & Waste Management Association (1995), 2014Co-Authors: Duckshin Park, Taejeong Lee, Do-yeon Hwang, Wonseok Jung, Yongil Lee, Ki-chul Cho, Dong-sool Kim, Kiyoung LeeAbstract:The level of particulate matter of less than 10 μm diameter (PM10) at subway platforms can be significantly reduced by installing a platform screen-door system. However, both workers and passengers might be exposed to higher PM10 levels while the cars are within the tunnel because it is a more confined environment. This study determined the PM10 levels in a subway tunnel, and identified the sources of PM10 using elemental analysis and receptor modeling. Forty-four PM10 samples were collected in the tunnel between the Gireum and Mia stations on Line 4 in metropolitan Seoul and analyzed using inductively coupled plasma–atomic emission spectrometry and ion chromatography. The major PM10 sources were identified using Positive Matrix factorization (PMF). The average PM10 concentration in the tunnels was 200.8 ± 22.0 μg/m3. Elemental analysis indicated that the PM10 consisted of 40.4% inorganic species, 9.1% anions, 4.9% cations, and 45.6% other materials. Iron was the most abundant element, with an average con...
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Source identification of PM10 pollution in subway passenger cabins using Positive Matrix factorization
Atmospheric Environment, 2012Co-Authors: Duckshin Park, Younghun Yoon, Eun-young Park, Kiyoung LeeAbstract:Abstract Monitoring the air quality in subway passenger cabins is important because of the large number of passengers and potentially high levels of air pollution. This report characterized PM10 levels in subway cabins in Seoul, Korea, and identified PM10 sources using elemental analysis and receptor modeling. PM10 levels in subway cabins were continuously measured using a light scattering monitor during rush and non-rush hours. A total of 41 measurements were taken during rush and non-rush hours, and the measurements were repeated in all four seasons. Filter samples were also collected for elemental composition analysis. Major PM10 sources were identified using Positive Matrix factorization (PMF). The in-cabin PM10 concentrations were the highest in the winter at 152.8 μg m−3 during rush hours and 90.2 μg m−3 during non-rush hours. While PM10 levels were higher during rush hours than during non-rush hours in three seasons (excluding summer), these levels were not associated with number of passenger. Elemental analysis showed that the PM10 was composed of 52.5% inorganic elements, 10.2% anions, and 37.3% other. Fe was the most abundant element and significantly correlated (p
Jana B. Milford - One of the best experts on this subject based on the ideXlab platform.
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Positive Matrix Factorization of PM2.5: Comparison and Implications of Using Different Speciation Data Sets
Environmental science & technology, 2012Co-Authors: Mingjie Xie, James J. Schauer, Michael P. Hannigan, Steven J. Dutton, Jana B. Milford, Joshua G. Hemann, Shelly L. Miller, Jennifer L. Peel, Sverre VedalAbstract:To evaluate the utility and consistency of different speciation data sets in source apportionment of PM2.5, Positive Matrix factorization (PMF) coupled with a bootstrap technique for uncertainty assessment was applied to four different 1-year data sets composed of bulk species, bulk species and water-soluble elements (WSE), bulk species and organic molecular markers (OMM), and all species. The five factors resolved by using only the bulk species best reproduced the observed concentrations of PM2.5 components. Combining WSE with bulk species as PMF inputs also produced five factors. Three of them were linked to soil, road dust, and processed dust, and together contributed 26.0% of reconstructed PM2.5 mass. A 7-factor PMF solution was identified using speciated OMM and bulk species. The EC/sterane and summertime/selective aliphatic factors had the highest contributions to EC (39.0%) and OC (53.8%), respectively. The nine factors resolved by including all species as input data are consistent with those from ...
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Use of synthetic data to evaluate Positive Matrix factorization as a source apportionment tool for PM2.5 exposure data.
Environmental science & technology, 2006Co-Authors: Gregory Brinkman, Michael P. Hannigan, Gary Vance, Jana B. MilfordAbstract:Positive Matrix factorization (PMF) was applied to synthetic datasets that simulate personal exposures to airborne PM2.5 from 12 sources. Three different filter analysis scenarios using different analytical chemistry techniques were considered. The full suite scenario quantified elemental carbon, organic carbon, inorganic ions, trace elements, and trace organic species including carboxylic acids and organic compounds with −OH functionality. A second scenario excluded trace elements and a third assumed that derivatization steps to quantify polar organic compounds were not performed. Similar errors in source apportionment were seen with all three scenarios. In most cases, PMF failed to separate out factors corresponding to road dust and vegetative debris, two sources that made relatively uniform contributions to the synthetic exposures. Factors representing wood smoke, natural gas combustion, and meat cooking sources were difficult to identify due to a lack of unique tracers with concentrations reliably abo...
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Source apportionment of exposure to toxic volatile organic compounds using Positive Matrix factorization
Journal of Exposure Science & Environmental Epidemiology, 2001Co-Authors: Melissa J Anderson, Shelly L. Miller, Jana B. MilfordAbstract:Data from the Total Exposure Assessment Methodology studies, conducted from 1980 to 1987 in New Jersey (NJ) and California (CA), and the 1990 California Indoor Exposure study were analyzed using Positive Matrix factorization, a receptor-oriented source apportionment model. Personal exposure and outdoor concentrations of 14 and 17 toxic volatile organic compounds (VOCs) were studied from the NJ and CA data, respectively. Analyzing both the personal exposure and outdoor concentrations made it possible to compare toxic VOCs in outdoor air and exposure resulting from personal activities. Regression analyses of the measured concentrations versus the factor scores were performed to determine the relative contribution of each factor to total exposure concentrations. Activity patterns of the NJ and CA participants were examined to determine whether reported exposures to specific sources correspond to higher estimated contributions from the factor identified with that source. For a subset of VOCs, a preliminary analysis to determine irritancy-based contributions of factors to exposures was carried out. Major source types of toxic VOCs in both NJ and CA appear to be aromatic sources resembling automobile exhaust, gasoline vapor, or environmental tobacco smoke for personal exposures, and automobile exhaust or gasoline vapors for outdoor concentrations.
Pentti Paatero - One of the best experts on this subject based on the ideXlab platform.
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application of Positive Matrix factorization in source apportionment of particulate pollutants in hong kong
Atmospheric Environment, 1999Co-Authors: Eddie Lee, Chak K Chan, Pentti PaateroAbstract:Abstract An advanced algorithm called Positive Matrix factorization (PMF) in receptor modeling was used to identify the sources of respirable suspended particulates (RSP) in Hong Kong. The compositional data obtained from the Hong Kong Environmental Protection Department from 1992 to 1994 were analyzed. The species analyzed in this study are Al, Ca, Mg, Pb, Na+, V, Cl−, NH4+, SO42−, Br−, Mn, Fe, Ni, Zn, Cd, K+, Ba, Cu, and As. Unlike the conventional receptor modeling algorithm, factor analysis PMF only generates non-negative source profiles. To eliminate sulfate from such factors where it is not physically plausible, special penalty terms were included in the model so that sulfate concentrations could be selectively decreased in specified factors. A 9-factor model containing non-zero sulfate concentrations in three factors gives the most satisfactory source profiles. Ammonium sulfate, chloride depleted marine aerosols and crustal aerosols are the three non-zero sulfate sources. Other factors are marine aerosols, non-ferrous smelters, particulate copper, fuel oil burning, vehicular emission and bromide/road dust. The last two sources can be combined as a single source of vehicle/road dust. The compositional profiles of these factors were also developed. The mass profiles obtained can be improved by further refinement of distribution of sulfate in the sources.
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Positive Matrix factorization applied to a curve resolution problem
Journal of Chemometrics, 1998Co-Authors: Yulong Xie, Philip K Hopke, Pentti PaateroAbstract:Positive Matrix factorization (PMF) is a least squares approach to factor analysis which was originally developed for environmental data analysis and has been applied to several problems in resolving sources of environmental pollutants. PMF has been used as both a two-way and three-way data analysis tool. In this investigation, three-way data arrays were used to explore the ability of PMF in curve resolution. Pulsed gradient spin echo (PGSE) nuclear magnetic resonance (NMR) data were measured for spectral mixtures where the concentrations of the compounds decay exponentially. Three-way data arrays were constructed by packing different parts of the data from single experiments and were analyzed with three-way PMF to obtain the NMR spectra, decay profiles and the self-diffusion coefficients of constituents. Copyright © 1998 John Wiley & Sons, Ltd.
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source identification of bulk wet deposition in finland by Positive Matrix factorization
Atmospheric Environment, 1995Co-Authors: Pia Anttila, Pentti Paatero, Unto Tapper, Olli JarvinenAbstract:Abstract A new variant of factor analysis (Positive Matrix factorization, PMF) is applied to a Finnish data set (18 years, 15 locations) of monthly bulk wet deposition concentrations of strong acids, SO4, NO3, NH4, total nitrogen (Ntot), total phosphorus (Ptot), Ca, K, Mg, Na, Cl, and total organic carbon (TOC). PMF produces strictly nonnegative factors, optimally based on error estimates of data values, with almost no rotational ambiguity. The application of PMF to environmental data is outlined: handling of outliers and missing values, determination of error estimates, interpretation of results. The results are displayed in different ways: (1) seasonal profiles of factors; (2) factor compositions by absolute value; (3) factor compositions scaled by their importance in explaining the variation of data. For most compounds 90–95% of the total weighted variation is explained by four factors. Each of the 15 data matrices is analysed with four factors. Different types of factors are characterized by the following five key elements: strong acids (H+), nitrogen compounds (N), Cl, TOC and P. Likely main sources for factors are discussed. A high degree of neutralization is observed at all inland stations. Only at four stations the acidity-related substances—SO4 and NO3—are mainly explained by the H-factor. The neutralization caused by the Estonian oil-shale industry is detected at one station. The N-factor is the major anthropogenic factor associating acidic anions SO4 and NO3 together with NH4. Some features of the factors H and N seem to be connected with degradation processes during the collection period of one month. The marine source creates a well-defined Cl-factor at five stations. The annual cycle of the TOC-factor and its association with Ca and K could be connected to airborne particulate matter, such as soil dust. The seasonal behaviour and elemental concentrations of the P-factor suggest a biological origin: pollen, spores, plant debris. The anion-cation balance is shown for all factors and it is mostly good.
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Analysis of daily precipitation data by Positive Matrix factorization
Environmetrics, 1994Co-Authors: Sirkka Juntto, Pentti PaateroAbstract:A new factor analysis method called Positive Matrix factorization (PMF) has been applied to daily precipitation data from four Finnish EMEP stations. The aim of the analysis was to investigate the structure of the data matrices in order to find the apparent source profiles from which the precipitation samples are constituted. A brief description of PMF is given. PMF utilizes the known uncertainty of data values. The standard deviations were derived from the results of double sampling at one station during one year. A goodness-of-fit function Q was calculated for factor solutions with 1–8 factors. The shape of the residuals was useful in deciding the number of factors. The strongest factor found was that of sea-salt. The most dominant ions in the factor were sodium, chloride and magnesium. At the coastal stations the ratio Cl/Na of the mean concentrations in the factor was near the ratio found in sea water but at the inland stations the ratio was smaller. For most ions more than 90 per cent of the weighted variation was explained. The worst explained was potassium (at worst c. 60 per cent) which is possibly due to contamination problems in the laboratory. In most factors of different factorizations the anions and cations were fairly well balanced.
B Bonsang - One of the best experts on this subject based on the ideXlab platform.
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volatile organic compounds sources in paris in spring 2007 part ii source apportionment using Positive Matrix factorisation
Environmental Chemistry, 2011Co-Authors: Cecile Gaimoz, Stephane Sauvage, Valerie Gros, Frank Herrmann, Jonathan Williams, Nadine Locoge, Olivier Perrussel, B BonsangAbstract:Environmental context Volatile organic compounds are key compounds in atmospheric chemistry as precursors of ozone and secondary organic aerosols. To determine their impact at a megacity scale, a first important step is to characterise their sources. We present an estimate of volatile organic compound sources in Paris based on a combination of measurements and model results. The data suggest that the current emission inventory strongly overestimates the volatile organic compounds emitted from solvent industries, and thus needs to be corrected. Abstract A Positive Matrix factorisation model has been used for the determination of volatile organic compound (VOC) source contributions in Paris during an intensive campaign (May–June 2007). The major sources were traffic-related emissions (vehicle exhaust, 22% of the total mixing ratio of the measured VOCs, and fuel evaporation, 17%), with the remaining emissions from remote industrial sources (35%), natural gas and background (13%), local sources (7%), biogenic and fuel evaporation (5%) and wood-burning (2%). It was noted that the remote industrial contribution was highly dependent on the air-mass origin. During the period of oceanic influences (when only local and regional pollution was observed), this source made a relatively low contribution (<15%), whereas the source contribution linked to traffic was high (54%). During the period of continental influences (when additional continental pollution was observed), remote industrial sources played a dominant role, contributing up to 50% of measured VOCs. Finally, the Positive Matrix factorisation results obtained during the oceanic air mass-influenced period were compared with the local emission inventory. This comparison suggests that the VOC emission from solvent industries might be overestimated in the inventory, consistent with findings in other European cities.
