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Comparison of positive matrix factorization and Multilinear Engine for the source apportionment of particulate pollutants

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2003年第1期66卷 15-28页

作者： Ramadan, Z Eickhout, B Song, XH Buydens, LMC Hopke, PK Clarkson Univ Dept Chem Engn Potsdam NY 13699 USA Univ Nijmegen Analyt Chem Lab Nijmegen Netherlands

New approaches to solving the factor analysis (FA) problem have recently been developed by recognizing that factor analysis is fundamentally a least-squares (LS) problem. This approach is called positive matrix factorization (PMF). Two programs have been written to implement different algorithms for solving the problem. These programs are PMF2 and Multilinear Engine (ME-2). The two programs use different algorithms to obtain the least-squares solution and the constraints are imposed in different ways. Elemental composition data for particle samples collected in Phoenix, AZ from June 1996 through June 1998, were used to compare the source apportionment of these two programs. The ME-2 results presented in this paper are compared with the previously published PMF2 results. The identification of the eight PMF sources returned one questionable source: wood burning and some peculiar mass contributions. The extra features of ME-2 made it possible to also investigate the sources responsible for the fine particles. The mixed-way approach indicated the existence of incinerators in the Phoenix area. Like PMF, ME-2 identified high source contributions for biomass burning, motor vehicles (with higher contribution in winter), coal-fired power plants (secondary particles with higher contributions in summer), soil, and nonferrous smelting process. Sea salt and heavy-duty diesel were identified by the ME two-way analysis, but they disappeared in the three-way analysis of the dual fine particle sequential sampler (DFPSS) and DICHOT data. Instead, an obvious incinerator source was identified again. Thus, PMF and ME-2 identified the same major sources responsible for the PM2.5 in Phoenix, but some of the sources identified by PMF2 appear to be uncertain. The three-way analysis provided additional information about possible sources, but also returned unexplainable sources. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： positive matrix factorization Multilinear Engine particulate pollutants

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Source apportionment of PM10 at a small industrial area using positive matrix factorization

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ATMOSPHERIC RESEARCH 2010年第1期95卷 88-100页

作者： Lim, Jong-Myoung Lee, Jin-Hong Moon, Jong-Hwa Chung, Yong-Sam Kim, Ki-Hyun Chungnam Natl Univ Dept Environm Engn Taejon 305764 South Korea Korea Atom Energy Res Inst Dept Nucl Basic Sci Taejon 305353 South Korea Sejong Univ Dept Earth & Environm Sci Seoul 143747 South Korea

In this study, PM10-bound concentrations of 28 trace metals and 3 ionic components were measured from samples collected at Daejeon Industrial Complexes I and II, Korea from April 2000 to December 2002. positive matrix factorization (PMF) and conditional probability function (CPF) were applied to these PM data sets to identify the diverse sources in the industrial area. A total of nine source types were identified to be important which include: secondary aerosol, cement/construction, soil dust, road dust, vehicle exhaust, incineration/Pb-related industry, metal smelting, fossil fuel combustion, and field burning. Results of our study suggest that there are competing relationships between anthropogenic and natural source processes in this industrial area. (C) 2009 Elsevier B.V. All rights reserved.

关键词： PM10 Small industrial area positive matrix factorization Conditional probability function

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ANALYSIS OF DAILY PRECIPITATION DATA BY positive matrix factorization

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ENVIRONMETRICS 1994年第2期5卷 127-144页

作者： JUNTTO, S PAATERO, P 1Finnish Meteorological Institute Air Quality Department Sahaajankatu 22 E FIN-00810 Helsinki Finland2University of Helsinki Department of Physics Siltavuorenpenger 20 D FIN-00170 Helsinki Finland

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.

关键词： FACTOR ANALYSIS PRECIPITATION positive matrix factorization SOURCE APPORTIONMENT EMEP DATA

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Using positive matrix factorization to investigate microbial dehalogenation of chlorinated benzenes in groundwater at a historically contaminated site

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CHEMOSPHERE 2018年 211卷 515-523页

作者： Capozzi, Staci L. Rodenburg, Lisa A. Krumins, Valdis Fennell, Donna E. Mack, E. Erin Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA Rutgers State Univ Dept Environm Sci New Brunswick NJ USA EI DuPont Nemours & Co Corp Remediat Grp Wilmington DE 19805 USA

Chlorinated benzenes are common groundwater contaminants in the United States, so demonstrating whether they undergo degradation in the subsurface is important in determining the best remedy for this contamination. The purpose of this work was to use a new data mining approach to investigate chlorinated benzene degradation pathways in the subsurface. positive matrix factorization (PMF) was used to analyze long-term measurements of chlorinated benzene concentrations in groundwater at a contaminated site in New Jersey. A dataset containing 597 groundwater samples and 5 chlorinated benzenes and benzene collected from 144 wells over 20 years was investigated using PMF2 software. Despite the heterogeneity of this dataset, PMF analysis revealed patterns indicative of microbial dechlorination in the groundwater and provided insight about where dechlorination is occurring, to what extent, and under which geochemical conditions. PMF resolved a factor indicative of a source of 1,2,4-trichlorobenzene and 1,2-dichlorobenzene and two factors representing stages of dechlorination, one more advanced than the other. The PMF results indicated that virtually all of the 1,2-dichlorobenzene at the site arises from its use onsite, not from the dechlorination of trichlorobenzenes. Factors were further interpreted using ancillary data such as geochemical indicators and field parameters also measured in the samples. Analysis suggested that the partial and advanced dechlorination signals occur under different subsurface physical conditions. The results provided field validation of the current understanding of anaerobic dechlorination of chlorinated benzenes in the subsurface developed from laboratory studies. PMF is thereby shown to be a useful tool for investigating chlorinated benzene dechlorination. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Chlorinated benzenes Data mining positive matrix factorization Groundwater

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Groundwater Quality Characterization of North Brahmaputra Basin using positive matrix factorization

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES 2021年第2期91卷 393-404页

作者： Chaturvedi, Richa Das, Bodhaditya Banerjee, Saumen Bhattacharjee, Chira R. Def Res & Dev Org Dept Chem Def Res Lab DRL Tezpur 784001 Assam India Assam Univ Dept Chem Silchar 788011 Assam India

This study applies positive matrix factorization (PMF) to 140 groundwater samples collected from four different Public Health Centers in North Brahmaputra basin, Assam, India. The aim of this technique is to identify and quantify the pollution sources (natural and anthropogenic) that affect the water quality. Multivariate statistical analysis, especially factor analysis, is successful in interpreting the water quality data, but it has some limitations: It does not consider analytical uncertainty and factor loadings may be negative which do not give a clear representation of the data. Therefore, we applied PMF to groundwater data and compared the results with those obtained from factor analysis. The major findings from the study are as follows: The first and the second factors show that the natural means are the main source of pollution where Cl, SO4, Ca, Mg, TA and TH were the main contributors from erosion and weathering of rocks. The Pb and NO(3)from the third and the fourth factor, respectively, are the major sources of contamination from anthropogenic activities such as the use of fertilizers. The fifth factor results in Fe, As, Mn and Cr, suggesting that both natural and anthropogenic processes are the main pollution contributors. PMF exhibits a more realistic representation of data and helps us to better understand the major sources of contamination and the variation in groundwater quality data. Hence, it can be successfully used for the characterization of groundwater chemistry.

关键词： Multivariate analysis Groundwater quality positive matrix factorization Physico-chemical parameters North Brahmaputra basin

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Source Apportionment of Groundwater Pollution using Unmix and positive matrix factorization

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ENVIRONMENTAL PROCESSES-AN INTERNATIONAL JOURNAL 2019年第2期6卷 457-473页

作者： Gulgundi, Mohammad Shahid Shetty, Amba Presidency Univ Dept Civil Engn Bengaluru 560064 Karnataka India

Receptor models are used to understand the attributes of groundwater contaminants by recognizing their sources and evaluating the contribution from each source to receptor concentrations. Two receptor models, Unmix and positive matrix factorization (PMF), were applied to the data obtained from 41 sampling points on 20 parameters, in order to identify and apportion the pollution sources to groundwater quality in Peenya region of Bengaluru. Overall six and seven sources were identified by Unmix and PMF models, respectively. Most groundwater quality variables were found to be influenced primarily by pollution from chromium electroplating, sewage, geology of the area and lead acid battery manufacturing units located in the study area. The models could recognize significant sources adding to groundwater quality in the region with most of them being anthropogenic due to the presence of industrial activity. It was observed that both models gave good outcomes with regard to their capacity to repeat measured concentrations with fundamentally the same slopes in a large portion of the cases, yet with the PMF model demonstrating the best correlation and the nearest slope to unit. Receptor models are regularly applied to distinguish source contributions. The dissimilarities among the results of various models are essential to better interpret source apportionment.

关键词： Groundwater quality Source apportionment Receptor model Unmix positive matrix factorization

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Source apportionment of pollution in groundwater source area using factor analysis and positive matrix factorization methods

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HUMAN AND ECOLOGICAL RISK ASSESSMENT 2017年第6期23卷 1417-1436页

作者： Guo, Xueru Zuo, Rui Shan, Dan Cao, Yang Wang, Jinsheng Teng, Yanguo Fu, Qing Zheng, Binghui Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Beijing Normal Univ Engn Res Ctr Groundwater Pollut Control & Remedia Minist Educ China Beijing Peoples R China Sino Japan Friendship Ctr Environm Protect Beijing Peoples R China Chinese Res Inst Environm Sci Beijing Peoples R China

By means of factor analysis (FA) and positive matrix factorization (PMF) methods, groundwater pollution sources were identified in the Jinji groundwater source area, which is beside the Yellow River and is the only urban water supply source for the city of Wuzhong in Northwestern China. The sources of groundwater were quantified based on 16 samples of shallow groundwater from the source area. The source apportionment with the PMF model identified three dominant groundwater pollution sources. These were anthropogenic activities of agricultural and industrial pollution with a pollution contribution of 53.0%, water-rock interaction of 24.6%, and evaporation and concentration of 22.4%. The source apportionment with the FA model identified four sources which were evaporation and concentration, with the largest contribution (42.6%), followed by anthropogenic activities (29.2%), mineral dissolution and industrial pollution (22.4%), and natural effects (5.8%). Specific attention should be paid to these natural (fluoride, TH, etc.) and anthropogenic sources (NH4+, NO2-, turbidity, total bacterial count, etc.), and pertinent measures should be taken to control local groundwater pollution. The most significant trait of the PMF is its scientific interpretation and physical explanation of the results, depending on non-negative restriction of the pollution source profiles and contributions.

关键词： source apportionment groundwater factor analysis positive matrix factorization water source area

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Source identification of coarse particles in the Desert Southwest, USA using positive matrix factorization

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ATMOSPHERIC POLLUTION RESEARCH 2017年第5期8卷 873-884页

作者： Clements, Andrea L. Fraser, Matthew P. Upadhyay, Nabin Herckes, Pierre Sundblom, Michael Lantz, Jeffrey Solomon, Paul A. Rice Univ Dept Civil & Environm Engn 6100 Main StMS-519 Houston TX 77005 USA Arizona State Univ Sch Sustainable Engn & Built Environm POB 873005 Tempe AZ 85287 USA Arizona State Univ Sch Mol Sci POB 871604 Tempe AZ 85287 USA Pinal Cty Air Qual Control Dist 31 N Pinal St F Florence AZ 85132 USA US EPA Off Air & Radiat 4220 S Maryland PkwyBld C Las Vegas NV 89119 USA US EPA Off Res & Dev 944 E Harmon AveRm 235 Las Vegas NV 89119 USA Colorado State Univ Dept Atmospher Sci Ft Collins CO 80523 USA ConocoPhillips Global Water Sustainabil Ctr Qatar Sci & Technol Pk Doha Qatar

The Desert Southwest Coarse Particulate Matter Study was undertaken to further our understanding of the spatial and temporal variability and sources of fine and coarse particulate matter (PM) in rural, arid, desert environments. Sampling was conducted between February 2009 and February 2010 in Pinal County, AZ near the town of Casa Grande where PM concentrations routinely exceed the U.S. National Ambient Air Quality Standards (NAAQS) for both PM10 and PM2.5. In this desert region, exceedances of the PM10 NAAQS are dominated by high coarse particle concentrations, a common occurrence in this region of the United States. This work expands on previously published measurements of PM mass and chemistry by examining the sources of fine and coarse particles and the relative contribution of each to ambient PM mass concentrations using the positive matrix factorization receptor model (Clements et al., 2014). Coarse particles within the region were apportioned to nine sources including primary biological aerosol particles (PBAPs - 25%), crustal material (20%), re-entrained road dust (11%), feedlot (11% at the site closest to a cattle feedlot), secondary particles (10%), boron-rich crustal material (9%), and transported soil (6%), with minor contributions from ammonium nitrate, and salt (considered to be NaCl). Fine particles within the region were apportioned to six sources including motor vehicles (37%), road dust (29%), lead-rich (10%), with minor contributions from brake wear, crustal material, and salt. These results can help guide local air pollution improvement strategies designed to reduce levels of PM to below the NAAQS. (C) 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Desert aerosols Coarse particles positive matrix factorization Crustal material Feedlot Road dust Primary biological aerosol particles

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Application of a self-organizing map and positive matrix factorization to investigate the spatial distributions and sources of polycyclic aromatic hydrocarbons in soils from Xiangfen County, northern China

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ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2017年 141卷 98-106页

作者： Tao, Shi-yang Zhong, Bu-qing Lin, Yan Ma, Jin Zhou, Yongzhang Hou, Hong Zhao, Long Sun, Zaijin Qin, Xiaopeng Shi, Huading Sun Yat Sen Univ Ctr Earth Environm & Resources Sch Earth Sci & Geol Engn Guangzhou 510275 Guangdong Peoples R China Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China Univ Calif Los Angeles Dept Environm Hlth Sci Jonathan & Karin Fielding Sch Publ Hlth Los Angeles CA 90095 USA

The concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs) were measured in 128 surface soil samples from Xiangfen County, northern China. The total mass concentration of these PAHs ranged from 52 to 10,524 ng/g, with a mean of 723 ng/g. Four-ring PAHs contributed almost 50% of the total PAH burden. A self organizing map and positive matrix factorization were applied to investigate the spatial distribution and source apportionment of PAHs. Three emission sources of PAHs were identified, namely, coking ovens (21.9%), coal/biomass combustion (60.1%), and anthracene oil (18.0%). High concentrations of low-molecular-weight PAHs were particularly apparent in the coking plant zone in the region around Gucheng Town. High-molecular-weight PAHs mainly originated from coal/biomass combustion around Gucheng Town, Xincheng Town, and Taosi Town. PAHs in the soil of Xiangfen County are unlikely to pose a significant cancer risk for the population.

关键词： Polycyclic aromatic hydrocarbons Self-organizing map positive matrix factorization Coal Anthracene oil Soil

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A full algorithm to compute the constrained positive matrix factorization and its application in unsupervised unmixing of hyperspectral imagery

A full algorithm to compute the constrained positive matrix ...

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Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII

作者： Masalmah, Yahya M. Velez-Reyes, Miguel Univ Turabo Dept Elect & Comp Engn POB 3030 Gurabo PR 00778 USA Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)9780819471574

This paper presents a full algorithm to compute the solution for the unsupervised unmixing problem based on the positive matrix factorization. The algorithm estimates the number of endmembers as the rank of the matrix. The algorithm has an initialization stage using the SVD subset selection algorithm. Testing and validation with real and simulated data show, the effectiveness of the method. Application of the approach to environmental remote sensing is shown.

关键词： hyperspectral imagery spectral unmixing abundance estimation endmember determination iterative methods initialization positive matrix factorization

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