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Changes in factor profiles deriving from photochemical losses of volatile organic compounds:Insight from daytime and nighttime positive matrix factorization ana

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Journal of Environmental Sciences 2025年第5期151卷 627-639页

作者： Baoshuang Liu Tao Yang Sicong Kang Fuquan Wang Haixu Zhang Man Xu Wei Wang Jinrui Bai Shaojie Song Qili Dai Yinchang Feng Philip K.Hopke State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control&Tianjin Key Laboratory of Urban Transport Emission Research College of Environmental Science and EngineeringNankai UniversityTianjin 300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300350China Beijing Make Environment Science&Technology Co. Ltd.Beijing 100083China Shijiazhuang Environmental Prediction Center Shijiazhuang 050022China Department of Public Health Sciences University of Rochester School of Medicine and DentistryRochesterNY 14642USA Institute for a Sustainable Environment Clarkson UniversityPotsdamNY 13699USA

Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved *** speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct *** mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,*** and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite *** concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly *** reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical *** losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high *** the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its *** losses of highly active VOC species substantially reduced their concentrations in apportioned factor *** study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.

关键词： Volatile organic compounds Dispersion normalization Photochemical loss Factor profile positive matrix factorization

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Integration of positive matrix factorization and water quality models for pollution source identification and water quality enhancement in rivers

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APPLIED WATER SCIENCE 2025年第3期15卷 1-22页

作者： Kim, Semin Jeonbuk Natl Univ Dept Civil Engn Jeonju 54896 Jeonbuk Do South Korea

Identifying the primary pollution source poses a challenge in river watersheds characterized by diverse land-cover types and mixed pollution sources. We addressed this challenge by focusing on the major tributaries influencing the water quality of the Mankyung River's mainstream, successfully identifying the primary pollution source. Additionally, it identified the limiting nutrient for algal growth in the Mankyung River, proposing an alternative strategy to enhance water quality and mitigate algal growth. positive matrix factorization (PMF) was employed to discern pollution sources in major tributaries, namely Jeonju-cheon and Iksan-cheon, impacting mainstream water quality. For Jeonju-cheon, pollution from urban and agricultural areas, including wastewater treatment plants, emerged as the primary source. For Iksan-cheon, pollution from urban and agricultural areas predominated. The nitrogen-to-phosphorus ratio and correlation analysis revealed that total phosphorus is the limiting factor for algal growth. Furthermore, scenarios to improve water quality and reduce algal growth were developed, and the Environmental Fluid Dynamic Code (EFDC) was used in the simulation, while the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was used in water quality assessment. The findings demonstrated improved water quality and decreased algal blooms in the downstream Mankyung River region. This research provides a foundation for applying PMF, the EFDC, and the WQI in tracking pollution sources and enhancing water quality in rivers.

关键词： positive matrix factorization Pollution source tracking Water quality Environmental fluid dynamics code Mankyung River

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Source Contribution Analysis of Polycyclic Aromatic Hydrocarbons in PM2.5 at Three Japanese Cities Using positive matrix factorization with Organic Tracers

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ATMOSPHERE 2025年第2期16卷 175-175页

作者： Ikemori, Fumikazu Murakami, Yuki Takabayashi, Megumi Nishimura, Rie Hiramatsu, Mami Ueda, Maku Yoshino, Ayako Chatani, Satoru Sato, Kei Sugata, Seiji Nagoya City Inst Environm Sci Nagoya 4570841 Japan Nara Prefecture Landscape & Environm Ctr Sakurai 6330062 Japan Waterworks Bur Nara Prefecture Nara 6308113 Japan Res Inst Environm Agr & Fisheries Habikino 5830862 Japan Dept Environm Agr Forestry & Fisheries Osaka 5598555 Japan Natl Inst Environm Studies Tsukuba 3058506 Japan

To estimate the sources of polycyclic aromatic hydrocarbons (PAHs) and their contributions to the total PAH emissions, more than 40 PAHs and organic tracers in PM2.5 collected in 3 cities of Japan were measured and a positive matrix factorization (PMF) model analysis was performed. During the warm season, high PAH concentrations were detected in Nagoya, which is located in the port area. Total PAHs were classified into five sources: biomass combustion, semivolatile PAHs, and ship and industrial emissions, which were major, and road traffic and plastic combustion, which were minor. Analysis of the ship and industrial emissions revealed that the concentrations of these sources in the severe PAH pollution event in Nagoya exceeded 15 ng/m3 and the significant contribution exceeded 80%. In addition, PAHs indicating a risk of carcinogenicity, such as 1 to 2B by IARC and 1 to B2 by U.S. EPA, had the highest contribution to this factor among the five factors such as biomass burning and ship and industrial emissions. Our results suggest that sources of high PAH emissions exist in the port areas of Nagoya. The PMF analysis performed in this study, using combustion organics as indicators, is expected to aid other countries and regions in identifying the sources of PAHs for their effective control.

关键词： polycyclic aromatic hydrocarbons organic markers PASHs positive matrix factorization source contribution GC-MS/MS

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Nutritional profiling of Oncorhynchus mykiss from Çamlıgoze Dam, Türkiye: Health risk assessment through Monte Carlo simulation and elemental source attribution using positive matrix factorization

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 198卷

作者： Yazman, Mehmet Metin Ustaoglu, Fikret Yuksel, Bayram Giresun Univ Dept Food Proc TR-28600 Giresun Turkiye Giresun Univ Dept Biol Gure Campus TR-28200 Giresun Turkiye Giresun Univ Dept Property Protect & Secur TR-28600 Giresun Turkiye

& Ccedil;aml & imath;goze Dam in Sivas, T & uuml;rkiye, is a significant aquaculture site producing rainbow trout (Oncorhynchus mykiss) for export to over 70 countries. This study evaluated the accumulation of potentially toxic elements (PTEs) in fish, identified pollution sources, and assessed health risks. Using positive matrix factorization (PMF), Monte Carlo Simulation (MCS), and Sankey diagrams, we determined that all PTEs, including inorganic arsenic (iAs), mercury, and lead, were below regulatory safety thresholds. The hazard index (HI) for non-carcinogenic risk was below 1, indicating no immediate health threat. The cancer risk (CR) for iAs was 8.36E-06, which falls within the acceptable range defined by the USEPA (1E-06-1E-04), suggesting negligible cancer risk from fish consumption. PMF identified geogenic sources for aluminum and chromium, while agricultural runoff was the primary contributor to arsenic and manganese. No significant contamination from mining was observed. Nutritionally, the fish were rich in phosphorus, magnesium, and zinc. These results support the safety and dietary value of & Ccedil;aml & imath;goze Dam trout, while emphasizing the importance of ongoing environmental monitoring and mitigation strategies to protect aquaculture sustainability.

关键词： positive matrix factorization Monte Carlo simulation Rainbow trout ( Oncorhynchus mykiss) Potentially toxic elements Nutritional profiling

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Temporal dynamics and source characteristics of fine particulate matter using positive matrix factorization (PMF)

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Atmospheric Pollution Research 2025年第8期16卷

作者： Kumar, Vikas Sahu, Manoranjan Biswal, Basudev Prakash, Jai Choudhary, Shruti Raliya, Ramesh Chadha, Tandeep S. Fang, Jiaxi Biswas, Pratim Centre for Climate Studies Indian Institute of Technology Bombay Mumbai 400076 India Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai 400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai 400076 India Department of Civil Engineering Indian Institute of Technology Bombay Mumbai 400076 India Department of Atmospheric Science Central University of Rajasthan Rajasthan 305801 India Department of Chemical Environmental and Materials Engineering University of Miami Coral Gables United States Aerosol and Air Quality Research Laboratory Department of Energy Environmental and Chemical Engineering Washington University in St. Louis St Louis 63130 MO United States Applied Particle Technology Inc St Louis MO United States

The temporal granularity of data in receptor models plays a key role in identifying emission patterns and episodic pollution events, which are essential for robust source apportionment. Low-time resolution measurements may overlook short-term variations, leading to an incomplete representation of pollution sources. This study investigated the source characteristics and contributions of PM_2.5 across different time resolutions (1-h, 2-h, 4-h, 8-h, 12-h, and 24-h) and seasons, using multi-time-season-resolved chemical composition data collected at Major Dhyan Chand National Stadium in Delhi from May 2019 to February 2020. positive matrix factorization (PMF) identified eight consistent factors across all time resolutions and seasons: two solid fuel combustion sources (SFC1 and SFC2), an S-rich source, traffic (exhaust and non-exhaust), dust, and three anthropogenic industrial/combustion plume events (Cl-Br, Pb-Se, and Cu-Cd). The comparison of source profiles and contributions at different time resolutions revealed that SFC1 exhibited the highest temporal variability across all seasons, followed by Cl-Br, traffic, Pb-Se, and Cu-Cd, while S-rich and dust factors remained relatively stable. The variation in source profiles over time, influenced by species mixing, posed challenges for source identification. A sensitivity analysis using the coefficient of divergence (CoD) showed that heterogeneity in source profiles increased with coarser time resolution, indicating the need for high-resolution data to capture dynamic source variations. Given these findings, a rolling-PMF approach with high-resolution data could improve real-time source apportionment by updating source profiles at regular intervals to reflect ever-changing emissions sources better. This study highlights the importance of high-resolution data in achieving accurate and temporally resolved source apportionment, essential for air quality management and policy development. © 2025 Turkish National Comm

关键词： PM2.5 positive matrix factorization Receptor models Source apportionment Time resolution

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positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

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TALANTA 2019年 192卷 32-39页

作者： Li, Xue Huang, Dandan Zeng, Jiafa Chan, Chak Keung Zhou, Zhen Jinan Univ Inst Mass Spectrometry & Atmospher Environm 601 Huangpu Ave West Guangzhou 510632 Guangdong Peoples R China Jinan Univ Atmospher Pollut Online Source Anal Engn Res Ctr Guangzhou 510632 Guangdong Peoples R China City Univ Hong Kong Sch Energy & Environm Hong Kong Hong Kong Peoples R China State Environm Protect Key Lab Cause & Prevent Ur Shanghai 200233 Peoples R China

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath;however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (similar to 30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers;(ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MSbased breath analysis.

关键词： Breath analysis positive matrix factorization Mass spectrometry Exhalome Volatile organic compounds

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positive matrix factorization of a 32-month series of daily PM_2.5 speciation data with incorporation of temperature stratification

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ATMOSPHERIC ENVIRONMENT 2013年 65卷 11-20页

作者： Xie, Mingjie Piedrahita, Ricardo Dutton, Steven J. Milford, Jana B. Hemann, Joshua G. Peel, Jennifer L. Miller, Shelly L. Kim, Sun-Young Vedal, Sverre Sheppard, Lianne Hannigan, Michael P. Univ Colorado Ctr Engn Dept Mech Engn Coll Engn & Appl Sci Boulder CO 80309 USA US EPA Natl Ctr Environm Assessment Res Triangle Pk NC 27711 USA Colorado State Univ Dept Environm & Radiol Hlth Sci Ft Collins CO 80523 USA Univ Washington Sch Publ Hlth Dept Environm & Occupat Hlth Sci Seattle WA 98195 USA

This study presents source apportionment results for PM2.5 from applying positive matrix factorization (PMF) to a 32-month series of daily PM2.5 compositional data from Denver, CO, including concentrations of sulfate, nitrate, bulk elemental carbon (EC) and organic carbon (OC), and 51 organic molecular markers (OMMs). An optimum 8-factor solution was determined primarily based on the interpretability of the PMF results and rate of matching factors from bootstrapped PMF solutions with those from the base case solution. These eight factors were identified as inorganic ion, n-alkane, EC/sterane, light n-alkane/polycyclic aromatic hydrocarbon (PAH), medium alkane/alkanoic acid, PAH, winter/methoxyphenol and summer/odd n-alkane. The inorganic ion factor dominated the reconstructed PM2.5 mass (sulfate + nitrate + EC + OC) in cold periods (daily average temperature <10 degrees C;43.7% of reconstructed PM2.5 mass) whereas the summer/odd n-alkane factor dominated in hot periods (>20 degrees C;53.1%). The two factors had comparable relative contributions of 26.5% and 27.1% in warm periods with temperatures between 10 degrees C and 20 degrees C. Each of the seven factors resolved in a previous study (Dutton et al., 2010b) using a 1-year data set from the same location matches one factor from the current work based on comparing factor profiles. Six out of the seven matched pairs of factors are linked to similar source classes as suggested by the strong correlations between factor contributions (r = 0.89-0.98). Temperature-stratified source apportionment was conducted for three subsets of the data in the current study, corresponding to the cold, warm and hot periods mentioned above. The cold period (7-factor) solution exhibited a similar distribution of reconstructed PM2.5 mass as the full data set solution. The factor contributions of the warm period (7-factor) solution were well correlated with those from the full data set solution (r = 0.76-0.99). However, the reconstructed P

关键词： Source apportionment positive matrix factorization Organic molecular markers Temperature-stratified Reconstructed PM2.5 mass

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Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization

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ATMOSPHERIC ENVIRONMENT 2004年第20期38卷 3349-3362页

作者： Kim, E Hopke, PK Edgerton, ES Clarkson Univ Dept Chem Engn Potsdam NY 13699 USA Clarkson Univ Dept Civil & Environm Engn Potsdam NY 13699 USA Atmospher Res & Anal Inc Durham NC 27707 USA

Daily integrated PM2.5 (particulate matter less than or equal to 2.5 mum in aerodynamic diameter) composition data including eight individual carbon fractions collected at the Jefferson Street monitoring site in Atlanta were analyzed with positive matrix factorization (PMF). Particulate carbon was analyzed using the thermal optical reflectance method that divides carbon into four organic carbon (OC), pyrolized organic carbon (OP), and three elemental carbon (EC) fractions. A total of 529 samples and 28 variables were measured between August 1998 and August 2000. PMF identified 11 sources in this study: sulfate-rich secondary aerosol I (50%), on-road diesel emissions (11%), nitrate-rich secondary aerosol (9%), wood smoke (7%), gasoline vehicle (6%), sulfate-rich secondary aerosol II (6%), metal processing (3%), airborne soil (3%), railroad traffic (3%), cement kiln/carbon-rich (2%), and bus maintenance facility/highway traffic (2%). Differences from previous studies using only the traditional OC and EC data (J. Air Waste Manag. Assoc. 53(2003a)731;Atmos Environ. (2003b)) include four traffic-related combustion sources (gasoline vehicle, on-road diesel, railroad, and bus maintenance facility) containing carbon fractions whose abundances were different between the various sources. This study indicates that the temperature resolved fractional carbon data can be utilized to enhance source apportionment study, especially with respect to the separation of diesel emissions from gasoline vehicle sources. Conditional probability functions using surface wind data and identified source contributions aid the identifications of local point sources. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： thermal optical method carbon fraction positive matrix factorization source apportionment conditional probability function

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Characterization of non-methane volatile organic compounds sources in Houston during 2001 using positive matrix factorization

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ATMOSPHERIC ENVIRONMENT 2005年第32期39卷 5934-5946页

作者： Kim, E Brown, SG Hafner, HR Hopke, PK Clarkson Univ Dept Chem Engn Potsdam NY 13699 USA Clarkson Univ Dept Civil & Environm Engn Potsdam NY 13699 USA Sonoma Technol Inc Petaluma CA 94954 USA Clarkson Univ Ctr Air Resource Engn & Sci Potsdam NY 13699 USA

Non-methane volatile organic compounds (NMOC) that were most likely to contribute to ozone formation in the Houston, Texas area were measured by Texas Commission on Environmental Quality using AutoSystem Gas Chromatograph. Hourly, integrated NMOC measured between 9 pm and 6 am at the three monitoring sites (Deer Park, Haden Rd., and Clinton Dr.) in Houston were analyzed with positive matrix factorization (PMF). Only NMOC data collected during the nighttime was utilized to minimize the influence of photochemistry, motor vehicles, and biogenic sources. A total of 401-883 samples and 38 to 40 NMOC species measured between July 2001 and October 2001 were analyzed. PMF identified seven sources at Deer Park (flare emissions, industrial source, petrochemical source, natural gas/propane, refinery, isoprene source, and motor vehicle), Haden Rd. (flare emissions, petrochemical source, industrial source, solvent, natural gas/propane, refinery, and motor vehicle), and Clinton Dr. (flare emissions, two industrial sources, refinery, natural gas/propane, motor vehicle, and isoprene source). Five similar source types were found among three sites although the sources were differently located. Conditional probability function analysis using surface wind data and identified source contributions aided the identifications of local point sources by providing indications of likely directions for the sources. The results agreed well with the locations of known local NMOC sources. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： non-methane volatile organic compounds positive matrix factorization source apportionment conditional probability function

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The application of positive matrix factorization (PMF) to eco-efficiency analysis

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2012年第1期98卷 11-14页

作者： Wu, Jiaying Wu, Zhijun Hollaender, Robert Univ Leipzig Fac Econ & Management Sci D-04109 Leipzig Germany Leibniz Inst Tropospher Res D-04318 Leipzig Germany

A new method for weighting and aggregating eco-efficiency indicators is of the utmost importance, if researchers in the field are to provide simplified and physically meaningful information to policy makers. To date, there is still considerable debate over which weighting and aggregating methods to use in this context. We apply a new variant of factor analysis, positive matrix factorization (PMF), to a simple eco-efficiency analysis case study. PMF constrains its solutions to be non-negative, providing two important advantages over traditional factor analysis (FA) or principal component analysis (PCA): the rotational ambiguity of the solution space is reduced, and all the results are guaranteed to be physically meaningful. We suggest that PMF is better choice than either FA or PCA for eco-efficiency indicators, especially when dealing with complex social-economic and environmental data. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： positive matrix factorization Eco-efficiency Non-negative constraints

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