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Unveiling nitrate origins in semiarid aquifers: A comparative analysis of Bayesian isotope mixing models using nitrate and boron isotopes and a positive matrix factorization model

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JOURNAL OF HYDROLOGY 2024年 639卷

作者： Torres-Martinez, Juan Antonio Mahlknecht, Jurgen Mora, Abrahan Kaown, Dugin Koh, Dong-Chan Mayer, Bernhard Tetzlaffg, Dorthe Tecnol Monterrey Escuela Ingn & Ciencias Campus MonterreyEugenio Garza Sada 2501 Monterrey 64849 NL Mexico Tecnol Monterrey Escuela Ingn & Ciencias Campus Pueblatlixcayotl 5718 Puebla De Zaragoza 72453 Puebla Mexico Seoul Natl Univ Sch Earth & Environm Sci 1 Gwanak-ro Seoul 08826 South Korea Korea Inst Geosci & Mineral Resources Daejeon 34132 South Korea Univ Sci & Technol Daejeon 34113 South Korea Univ Calgary Dept Earth Energy & Environm 2500 Univ Drive NW Calgary AB T2N 1N4 Canada Leibniz Inst Freshwater Ecol & Inland Fisheries Muggelseedamm 310 D-12587 Berlin Germany Humboldt Univ Dept Geog Rudower Chaussee 16 D-12489 Berlin Germany

Nitrate contamination of groundwater is a pressing global concern, affecting over 80 million people worldwide, with agricultural activities being the primary contributor to nitrogen inputs into aquifers. The primary objective of this study was to identify the predominant sources of nitrate pollution and biogeochemical transformations in the semiarid region of the Meoqui-Delicias aquifer, Mexico. In this region, the uncontrolled use of chemical fertilizers and manure leads to excessive nutrient input, which accelerates the deterioration of groundwater quality. This study introduces an innovative dual-model approach (delta N-15(NO3) vs. delta B-11) to compare the Bayesian Isotope Mixing Model (BIMM) with positive matrix factorization (PMF) for nuanced source apportionment. This approach enhanced the differentiation between manure and sewage as nitrate sources. Results from the delta N-15(NO3) vs. delta B-11 model revealed that manure (52.4%) was the most significant source of nitrate pollution in the aquifer, followed by soil (37.4%), chemical fertilizers (5.5%), and sewage (4.7%). The PMF model corroborated this finding and indicated that 60.2% of the NO3--N pollution in the aquifer was attributed to manure and sewage, confirming the superior performance of the proposed BIMM in source differentiation. Our findings enhance the understanding of nitrate dynamics in semi-arid regions and can serve as a scientific evidence base for targeted interventions in nutrient management in the study area and elsewhere.

关键词： MixSIAR Model Nitrate isotope composition Boron isotope ratios Groundwater pollution positive matrix factorization

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The application of positive matrix factorization in the analysis, characterisation and detection of contaminated soils

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CHEMOSPHERE 2007年第7期69卷 1055-1063页

作者： Vaccaro, S. Sobiecka, E. Contini, S. Locoro, G. Free, G. Gawlik, B. M. Joint Res Ctr Inst Environm & Sustainabil European Commiss I-21020 Ispra Italy

Multivariate factor analytical techniques are widely used for the approximation, in terms of a linear combination of factors, of multivariate experimental data. The chemical composition of soil samples are multivariate in nature and provide datasets suitable for the application of these statistical techniques. Recent developments of multivariate factor analytical techniques have led to the approach of positive matrix factorization (PMF), a weighted least squares fit of a data matrix in which the weights are determined depending on the error estimates of each individual data value. This approach relies on more physically significant assumptions than methods like Principal Components Analysis which is frequently used in the analysis of soil datasets. In this paper we apply PMF to characterise the pollutant source in a set of geographically referenced soil samples taken within a 200 m radius of a site characterised by a high concentration of heavy metals. Each sample has been analysed for major and minor elements (using wavelength-dispersive X-ray fluorescence spectrometry), carbon, hydrogen and nitrogen (using a CHN elemental analyzer) and mercury (using cold-vapour atomic absorption spectrometry). Analysis of the soils using PMF resulted in a successful partitioning of variances into sources related to background soil geochemistry, organic influences and those associated with the contamination. Combining these results with a geostatistical approach successfully demarcated the main source of the combined organic and heavy metal contamination. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： factor analysis positive matrix factorization WD-XRF CV-AAS elemental analysis CHN soil contamination

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Groundwater and surface water quality characterization through positive matrix factorization combined with GIS approach

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WATER RESEARCH 2019年 159卷 122-134页

作者： Zanotti, C. Rotiroti, M. Fumagalli, L. Stefania, G. A. Canonaco, F. Stefenelli, G. Prevot, A. S. H. Leoni, B. Bonomi, T. Univ Milano Bicocca Dept Earth & Environm Sci Piazza Sci 1 I-20126 Milan Italy Paul Scherrer Inst Lab Atmospher Chem CH-5232 Villigen Switzerland

This study aims at testing the effectiveness of positive matrix factorization in characterizing ground-water and surface water quality, in terms of identifying main hydrochemical features and processes (natural and anthropogenic) that govern them. This method is applied in a hydro-system featured by a strong interrelation between groundwater and surface water and highly impacted by agricultural activities. Therefore, a holistic approach considering groundwater together with the surface water bodies, consisting in lake, several rivers and springs, was used. Multivariate statistical analysis, in particular Factor Analysis, has been proved to be effective in elaborating and interpreting water quality data highlighting the information carried within them, but it presents some limitations: it does not consider data uncertainty and it groups variables which are correlated positively and negatively. Moreover, in some cases the resulting factors are not clearly interpretable, describing each one various overlapping features/processes. Here, positive matrix factorization is applied to groundwater and surface water quality data, and the results are compared to those obtained through a Factor Analysis in terms of both factor profiles and their spatial distribution through a GIS approach. Results of isotopes analysis are used to validate PMF output and support interpretation. positive matrix factorization allows to consider data uncertainty and the solution respects two positivity constraints, based on the concept of chemical mass balance, which leads to a more environmentally interpretable solution. Results show that positive matrix factorization identifies five different factors reflecting main features and natural and anthropogenic processes affecting the study area: 1) surface water used for irrigation, 2) groundwater subjected to reducing processes at advanced stages, 3) groundwater subjected to reducing processes at early stages, 4) groundwater residence time and 5) the e

关键词： Multivariate statistical analysis Water quality positive matrix factorization Factor analysis Oglio river

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Identification of spatial patterns and sources of heavy metals in greenhouse soils using geostatistical and positive matrix factorization (PMF) methods

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LAND DEGRADATION & DEVELOPMENT 2021年第18期32卷 5412-5426页

作者： Meng, Min Yang, Linsheng Yu, Jiangping Wei, Binggan Li, Hairong Cao, Zhiqiang Chen, Qing Zhang, Guoyin Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Land Surface Pattern & Simulat Beijing Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing Peoples R China China Agr Univ Coll Resources & Environm Sci Beijing Peoples R China Inst Agr Resources & Environm Hebei Acad Agr & Forestry Sci Shijiazhuang Hebei Peoples R China

Greenhouse vegetable products are important dietary sources. Heavy metals in greenhouse soils are associated with food safety and public health. Therefore, heavy metals in greenhouse soils are of significant concern. This study used positive matrix factorization (PMF) and geostatistical analyses to assess possible sources and spatial distribution patterns of heavy metals in 248 greenhouse soil samples obtained from southern Hebei Province, China. The results showed that the mean arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) concentrations in the greenhouse soil samples were 13.5, 0.16, 74.3, 31.4, 0.03, 26.0, 156, and 98.0 mg/kg, respectively. Approximately 97.18%, 32.26%, 25.81%, 22.98%, 8.06%, 6.45%, 2.02%, and 0.40% of sample sites were contaminated with Cd, Cu, Hg, Zn, Cr, As, Pb, and Ni, respectively. The PMF indicated that Cd and Pb was mainly influenced by metal smelting activity;Cu and Zn accumulation in greenhouse soils was mainly from manure and chemical fertilizer application, while Ni, Cr, and As were strongly derived from natural sources. The spatial distribution patterns varied among metals, with Ni and Pb mainly controlled by natural factors, whereas Cd, Cu, Cr, and Zn were moderately to strongly influenced by fertilizer application. However, Pb is also affected by atmospheric deposition caused by industrial emissions. The spatial distribution of As is controlled by both natural and anthropogenic factors.

关键词： greenhouse soil heavy metals positive matrix factorization source spatial variation

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Source apportionment of PM_2.5 using positive matrix factorization (PMF) at a rural site in Korea

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2018年 214卷 325-334页

作者： Kim, Sunhye Kim, Tae-Young Yi, Seung-Muk Heo, Jongbae Seoul Natl Univ Grad Sch Publ Hlth Dept Environm Hlth Seoul South Korea Seoul Natl Univ Grad Sch Publ Hlth Inst Hlth & Environm Seoul South Korea Seoul Natl Univ Grad Sch Publ Hlth Ctr Hlth Environm Educ & Res Seoul South Korea

The sources of different pollutants contributing to ambient fine particles (PM2.5) on Daebu Island, Korea, were estimated. Twenty four hour integrated filter samples were collected from May 21-November 1, 2016, and analyzed for organic carbon, elemental carbon, ions, and trace elements. positive matrix factorization was conducted on the PM2.5 chemical speciation data from the samples to define the pathways and sources of PM2.5 at the sampling site. A total of 80 samples and 24 chemical species were used to run the model and a total of nine sources were identified: secondary sulfate (29.0%), mobile (22.0%), secondary nitrate (13.2%), oil combustion (10.1%), coal combustion (9.4%), aged sea salt (7.9%), soil (5.6%), non-ferrous smelting (1.7%), and industrial activity (1.1%). Conditional probability and potential source contribution functions were then used to determine whether these sources were local or came from pollutants transported over long-range distances. The anthropogenic sources came from local emissions and originated from both industrialized and metropolitan areas, whereas the secondary inorganic aerosols were strongly influenced by the long-range transport of air pollutants from Shandong and Jiangsu provinces in China. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Fine particulate matter Source apportionment positive matrix factorization Conditional probability function Potential source contribution function

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Feature extraction for identification of drug and explosive concealed by body packing based on positive matrix factorization

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MEASUREMENT 2014年第1期47卷 193-199页

作者： Jiang, Yongcheng Liu, Ping Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Anhui Peoples R China Hefei Univ Technol Sch Elect Sci & Appl Phys Hefei 230009 Anhui Peoples R China

In the analysis towards the energy dispersive X-ray diffraction (EDXRD) spectra of drug and explosive concealed by body packing, positive matrix factorization (PMF) was introduced to extract features from EDXRD spectra of samples in a set of drugs and explosive concealed in the anthropomorphic phantom, because PMF prevents the negative factors from occurring, avoids contradicting physical reality, and makes factors more easily interpretable. In order to compare with the features extracted by PMF, Principal Component Analysis (PCA) and robust PCA were investigated. Then, K-nearest neighbour (KNN) and support vector machine (SVM) were introduced to classify the samples according to the features extracted by PMF, PCA and robust PCA. It is shown that the recognition rates obtained by PMF are highest (above 99.5%) and insensitive to classifiers. This work demonstrates that PMF is effective in feature extraction for identification of drug and explosive concealed by body packing. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： positive matrix factorization Feature extraction Body packing Energy dispersive X-ray diffraction

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Source apportionment and source-to-sink transport of major and trace elements in coastal sediments: Combining positive matrix factorization and sediment trend analysis

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年第Part1期651卷 344-356页

作者： Li, Tao Sun, Guihua Yang, Chupeng Liang, Kai Ma, Shengzhong Huang, Lei Luo, Weidong China Geol Survey Guangzhou Marine Geol Survey Guangzhou 510760 Guangdong Peoples R China

Major and trace elemental concentrations in coastal marine sediments were incorporated into positive matrix factorization (PMF) to identify potential sources and source contributions. Transport pathways of fine-grained sediments and sediment-bound elements were inferred from sediment trend analysis (STA). The spatial distribution patterns of 21 elements (Co, Cu, Ni, Sr, Zn, V, Ba, Sc, Ga, Pb, Cr, Zr, SiO2, Al2O3, Fe2O3, MgO, CaO, K2O, MnO, TiO2, and P2O5) coupled with sediment grain sizes were investigated. The natural and anthropogenic sources of the elements were distinguished by their medium enrichment factors (EFs). Seven sources were recognized by PMF: weathering products, anthropologic emissions, sand, older sediment, biogenic carbonates, products of siliceous organisms, and mine exploitation. Some land-derived elements, including weathering products, anthropogenic-related elements, and mining-related elements, had a significant positive correlation with sediment silt, clay, and organic carbon contents. The spatial patterns of the land-derived elements' concentrations and source contributions were consistent with the sediment transport pathways inferred from the STA. This result revealed that the delivery of the land-derived elements was determined by marine current flows and the associated sediment transport processes. Conversely, elements originating from marine sources, such as sand and older sediment, and from the biological activities of calcareous and siliceous organisms showed little response to sediment transport and deposition processes. Our study links the outputs of statistically oriented approaches (e.g., PMF) to a process-based understanding of elemental transport in marine environments. (c) 2018 Elsevier B.V. All rights reserved.

关键词： positive matrix factorization Sediment trend analysis Source apportionment Major and trace elements Taiwan Strait

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PM₁₀ metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone

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ATMOSPHERIC RESEARCH 2010年第4期96卷 612-625页

作者： Alleman, Laurent Y. Lamaison, Laure Perdrix, Esperanza Robache, Antoine Galloo, Jean-Claude Ecole Mines Douai Dept Chim & Environm F-59508 Douai France Univ Lille Nord France F-59000 Lille France

The elemental composition data of ambient aerosols collected upon selected wind sectors in the highly industrialised harbour of Dunkirk (France) were interpreted using pollution roses, elemental ratios, Enrichment Factors (EF), Principal Component Analysis (PCA) and positive matrix factorization (PMF) receptor model. The objective was to identify the possible sources of PM10 aerosols, their respective chemical tracers and to determine their relative contribution at the sampling site. PM10 particles samples were collected from June 2003 to March 2005 in order to analyse up to 35 elements (Ag, Al, As, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Eu, Fe, K, La, Mg, Mn, Mo, Na, Ni, Pb, Rb, S, Sb, Sc, Si, Sm, Sr, Th, Ti, U, V, Zn and Zr) using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES) and ICP-Mass Spectrometry (MS). A significant effort has been made on estimating the total uncertainty of each result by regularly analysing blanks, quality controls and SRM NIST standards. Based on this procedure, a selected set of 24 "robust" elements was compared to the 35-element matrix in order to evaluate the sturdiness of our PMF statistical treatment. Eight source factors were resolved by PCA for all the wind sectors explaining 90% of the total data variance. The PMF results confirmed that eight physically interpretable factors contributed to the ambient particulate pollution at the sampling site: crustal dust (11%), marine aerosols (12%), petrochemistry activities (9.2%), metallurgical sintering plant (8.6%), metallurgical coke plant (12.6%), ferromanganese plant (6.6%), road transport (15%) and a less clearly interpretable profile probably associated to dust resuspension (13%). These weighted contributions against wind direction frequencies demonstrate that industrial sources are the most important contributors to this site (37%) followed by the natural sources (detrital and marine sources) (23%) and the road transport (15%). (C) 2010 Elsevier B.V. All rights rese

关键词： Trace element PM10 positive matrix factorization Source-receptor model ICP-MS

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Assessing the long-range transport of PAH to a sub-Arctic site using positive matrix factorization and potential source contribution function

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ATMOSPHERIC ENVIRONMENT 2011年第4期45卷 967-976页

作者： Sofowote, Uwayemi M. Hung, Hayley Rastogi, Ankit K. Westgate, John N. Deluca, Patrick F. Su, Yushan McCarry, Brian E. McMaster Univ Dept Chem & Chem Biol Hamilton ON L8S 4M1 Canada Environm Canada Air Qual Res Div Toronto ON M3H 5T4 Canada Univ Toronto Scarborough Dept Phys & Environm Sci Toronto ON M1C 1A4 Canada McMaster Univ Ctr Spatial Anal Sch Geog & Earth Sci Hamilton ON L8S 4K1 Canada

Gas-phase and particle-phase atmospheric samples collected in a sparsely populated sub-Arctic environment in the Yukon Territory, Canada were analyzed for a wide range of organic pollutants including polycyclic aromatic hydrocarbons (PAH). Receptor modeling using positive matrix factorization (PMF) was applied to a PAH data set from samples collected between August 2007 and December 2008 to afford four factors. These factors were designated as fossil fuel combustion emissions, particle-phase wood combustion emissions, gas-phase wood combustion emissions, and unburned petroleum/petrogenic emissions. The multiple linear regression-derived average contributions of these factors to the total PAH concentrations were 14% for fossil fuel combustion, 6% for particle-phase wood combustion emissions, 46% for gas-phase wood combustion emissions and 34% for petrogenic emissions. When the total PAH concentrations (defined as the sum of twenty-two PAH) and the PMF-modeled PAH concentrations set were compared, the correlation was excellent (R-2 = 0.97). Ten-day back trajectories starting at four different heights were used in a potential source contribution function analysis (PSCF) to assess the potential source regions of these PAH factors. Mapping the computed PSCF values for the four PMF factors revealed different source regions in the northern hemisphere for each PMF factor. Atmospheric transport of PAH occurred from both relatively short and long distances with both continental (North American) and trans-oceanic (Asian) sources contributing significantly to the total PAH. This study provides evidence of the transport of fossil fuel and wood combustion emissions from Asia, continental North America and northern Europe to sub-Arctic Canada (and by extension to the Canadian Arctic) primarily during cooler (fall-winter) months. This study demonstrates for the first time that the combined PMF-PSCF methodology can be used to identify geographically-disperse PAH source contributors

关键词： Polycyclic aromatic hydrocarbons positive matrix factorization Potential source contribution function Long-range transport Back trajectories Arctic haze

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The analysis of time-resolved optical waveguide absorption spectroscopy based on positive matrix factorization

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JOURNAL OF COLLOID AND INTERFACE SCIENCE 2013年 403卷 134-141页

作者： Liu, Ping Li, Zhu Li, Bo Shi, Guolong Li, Minqiang Yu, Daoyang Liu, Jinhuai Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Chinese Acad Sci Inst Intelligent Machines Res Ctr Biomimet Funct Mat & Sensing Devices Hefei 230031 Peoples R China

Time-resolved optical waveguide absorption spectroscopy (OWAS) makes use of an evanescent field to detect the polarized absorption spectra of sub-monomolecular adlayers. This technique is suitable for the investigation of kinetics at the solid/liquid interface of dyes, pigments, fluorescent molecules, quantum dots, metallic nanoparticles, and proteins with chromophores. In this work, we demonstrate the application of positive matrix factorization (PMF) to analyze time-resolved OWAS for the first time. Meanwhile, PCA is researched to compare with PMF. The absorption/desorption kinetics of Rhodamine 6G (R6G) onto a hydrophilic glass surface and the dynamic process of Meisenheimer complex between Cysteine and TNT are selected as samples to verify experimental system and analytical methods. The results are shown that time-resolved OWAS can well record the absorption/desorption of R6G onto a hydrophilic glass surface and the dynamic formation process of Meisenheimer complexes. The feature of OWAS extracted by PMF is dynamic and consistent with the results analyzed by the traditional function of time/wavelength-absorbance. Moreover, PMF prevents the negative factors from occurring, avoids contradicting physical reality, and makes factors more easily interpretable. Therefore, we believe that PMF will provide a valuable analysis route to allow processing of increasingly large and complex data sets. (C) 2013 Elsevier Inc. All rights reserved.

关键词： positive matrix factorization Optical waveguide absorption spectroscopy In situ monitoring Feature extraction

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