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Assessment and source apportionment of toxic metal in soils using integrated positive matrix factorization and Bayesian maximum entropy: A case study in Z county, southeastern China

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ECOLOGICAL INDICATORS 2022年 145卷

作者： Fei, Xufeng Christakos, George Lou, Zhaohan Xiao, Rui Lv, Xiaonan Ren, Zhouqiao Zhejiang Acad Agr Sci Hangzhou Peoples R China Minist Agr & Rural Affairs Key Lab Informat Traceabil Agr Prod Beijing Peoples R China San Diego State Univ Dept Geog San Diego CA 92182 USA Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

Soil pollution by toxic metals has become an important environmental problem over the last several decades. Because of environmental factor variation, specific spatial patterns of pollution and sources exist. However, commonly used methods rarely take natural spatial heterogeneity into account. positive matrix factorization and Bayesian maximum entropy models combined with specific environmental factors were proposed for quantitative source apportionment to account for spatial heterogeneity. The proposed method was implemented in a region located in southeastern China using dense samples (3627 total samples containing Cd, Hg, As, Pb, Cr, Cu, Zn and Ni data). The results showed that more than one-fifth of soils in the northwest, north-central and southeast of the study region exhibited different degrees of integrated pollution. Cd, Cu and As were the main pollutants, with proportions that exceeded the national standards of 26%, 10% and 7%, respectively. In addition, Cd was the primary element responsible for ecological risk, and As was the greatest hazard to human health. Five main pollution sources were extracted: 72.11% of the toxic metal pollution could be ascribed to anthropogenic sources, and natural sources explained the remaining 27.89%. Traffic emissions (24.31%) consistent with the major road distribution were the main source of Pb and Zn, and atmospheric deposition during the coal combustion (18.04%) distributed across the study area, except for the southeastern mountain areas, was the main source of Hg. Agricultural activities (16.81%) distributed mainly in the north-central regions contributed the most to Cd and Cu, and industrial activities (12.95%) clustered in the northwestern areas contributed the most to As. In addition, natural sources were closely linked to Ni and Cr in the southeastern mountain areas.

关键词： Toxic metal positive matrix factorization Bayesian maximum entropy Spatial heterogeneity Environmental proxies

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Combined use of positive matrix factorization and 13C-15N stable isotopes to trace organic matter-bound potential toxic metals in the urban mangrove sediments

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年第1期904卷

作者： Li, Hanyi Wu, Jiajia Huang, Qian Lin, Lujian Yuan, Bo Wang, Qiang Lu, Haoliang Liu, Jingchun Hong, Hualong Yan, Chonglin Xiamen Univ Key Lab Minist Educ Coastal & Wetland Ecosyst Xiamen 361102 Peoples R China Johannes Gutenberg Univ Mainz Inst Geosci Johann-Joachim Becher Weg 21 D-55128 Mainz Germany Lanzhou Univ Coll Pastoral Agr Sci & Technol State Key Lab Grassland Agroecosyst Lanzhou 730020 Peoples R China

Coastal sediments act as sinks of sediment organic matter (SOM) and metals because of their special land-sea location and depositional properties. However, there are few reports on the correlation between the sources of organic matter (OM) and associated potential toxic metals (PTMs). In this study, we combined C-N stable isotope analysis and positive matrix factorization to identify the matter and metal sources of OM and glomalin-related soil protein (GRSP) in an estuary under several decades of urbanization. The results of the positive matrix factorization (PMF) reveal a correlation between the sources of total sediment metals and the sources of OM-related metals. The sources of both SOM-bound PTMs and GRSP-bound PTMs are significantly related to the sources of total PTMs. OM sources were elucidated through 13C-15 N stable isotopes, and the potential sources of different types of OM differed. In addition, there is a significant correlation between OM-associated PTMs and organic matter sources. Interestingly, the functional groups of SOM were mainly influenced by multiple PTM sources but no OM source, while the functional groups of GRSP were regulated by a single metal source and OM source. This study deepened the understanding of the coupling between PTMs and SOM. The possibility of combined use of positive matrix factorization and 13C-15 N stable isotope tracing of metals as well as the sources of each metal fractions has been evaluated, which will provide new insights for the transportation of PTMs.

关键词： Anthropocene Trace metals positive matrix factorization Land-Sea coordination Urbanization

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Using positive matrix factorization to unmix PAH fingerprints in contaminated sediments

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2023年第8期195卷 1003-1003页

作者： Saba, Tarek Exponent Inc 1 Mill & MainSuite 150 Maynard MA 01754 USA

Some of the challenges to apportioning PAH-related remedy costs at contaminated sediment sites include the lack of source samples, different PAH signatures associated with the same source, historical PAH sources long removed, mixing of urban sediment by boat traffic, and, in turn, PAHs mixing and weathering. Unmixing of PAH fingerprints in sediment sites to PAH source classes (petrogenic, pyrogenic, and runoff) is typically a first step to tracking PAH upland sources and ultimately, responsible parties. This work demonstrates using positive matrix factorization (PMF) as a method to unmix PAH fingerprints to its source classes.A large PAH dataset (over 700 samples) assembled from contaminated urban sediment sites was used as an input to PMF. Using a 3-factor PMF analysis, a petrogenic, pyrogenic, and runoff/weathered PAH end-member fingerprints were identified. Different numerical mixing percentages of the PMF-identified end-member sources were able to replicate the sediment-measured PAH fingerprints, with the percent contribution of each of the end members to each sediment sample calculated. The demonstrated work provides a method to satisfy the unmixing of PAH fingerprints to its source classes, as a step towards apportioning of PAH contamination.

关键词： positive matrix factorization Fingerprints PAHs

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Evaluation of water environment quality in a typical wetland on the Qinghai-Tibet Plateau using positive matrix factorization and self-organizing map

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JOURNAL OF CLEANER PRODUCTION 2024年 479卷

作者： Ming, Di Wang, Lingqing Dai, Lijun Zhang, Qian Liang, Tao Chinese Acad Sci Key Lab Land Surface Pattern & Simulat Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Sino Danish Coll Beijing 100049 Peoples R China

The Qinghai-Tibet Plateau (QTP), the world's largest plateau, harbors rich lake resources but is ecologically fragile. Vulnerable river ecosystems in these areas face significant threats from environmental and anthropogenic pressures. Thus, comprehensive water quality monitoring is essential prior to conservation efforts. This study focuses on the Selin Co International Wetland in Tibet, a model of natural wetland ecosystems. We employed methods including water quality assessment, source apportionment, and clustering analysis to evaluate the water environment. Results from the Water Quality Index (WQI) indicate overall good water quality;however, parameters such as COD, DO, TN, TP, and As require monitoring for potential exceedance. positive matrix factorization (PMF) analysis reveals that the primary sources of substances are identified as natural sources (37.17%), animal disturbance sources (32.93%), and anthropogenic activities (22.31%). The results of the water source analysis using the PMF and the cluster analysis based on the self-organizing map (SOM) indicate that natural and geographical environmental factors are the primary determinants of water quality, with minimal influence from human activities. This research emphasizes the importance of these natural influences, providing a scientific foundation for the protection and management of wetlands in the QTP, which is critical for ensuring regional water resource security.

关键词： Qinghai-Tibet plateau Selin Co international wetland positive matrix factorization Self-organizing map

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Spatial variability of source contributions to nitrate in regional groundwater based on the positive matrix factorization and Bayesian model

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JOURNAL OF HAZARDOUS MATERIALS 2023年 445卷 130569页

作者： Mao, Hairu Wang, Guangcai Liao, Fu Shi, Zheming Zhang, Hongyu Chen, Xianglong Qiao, Zhiyuan Li, Bo Bai, Yunfei China Univ Geosci State Key Lab Biogeol & Environm Geol Beijing 100083 Peoples R China China Univ Geosci MOE Key Lab Groundwater Circulat & Environm Evolut Beijing 100083 Peoples R China China Univ Geosci Sch Water Resources & Environm Beijing 100083 Peoples R China

Groundwater nitrate (NO3-) pollution has attracted widespread attention;however, accurately evaluating the sources of NO3 - and their contribution patterns in regional groundwater is difficult in areas with multiple sources and complex hydrogeological conditions. In this study, 161 groundwater samples were collected from the Poyang Lake Basin for hydrochemical and dual NO3 - isotope analyses to explore the sources of NO3 - and their spatial contribution using the positive matrix factorization (PMF) and Bayesian stable isotope mixing (MixSIAR) models. The results revealed that the enrichment of NO3 - in groundwater was primarily attributed to sewage/ manure (SM), which accounted for more than 50 %. The contributions of nitrogen fertilizer and soil organic nitrogen should also be considered. Groundwater NO3 - sources showed obvious spatial differences in contribu-tions. Regions with large contributions of SM (>90 %) were located in the southeastern part of the study area and downstream of Nanchang, which are areas with relatively high population density. Nitrogen fertilizer and soil organic nitrogen showed concentrated contributions in paddy soil in the lower reaches of the Gan and Rao Rivers, and these accumulations were mainly driven by the soil type, land use type, and topography. This study provides insight into groundwater NO3 - contamination on a regional scale.

关键词： Groundwater nitrate Source apportionment Spatial variability Bayesian mixing model positive matrix factorization

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Application of positive matrix factorization receptor model and elemental analysis for the assessment of sediment contamination and their source apportionment of Deepor Beel, Assam, India

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ECOLOGICAL INDICATORS 2020年 114卷 106291-106291页

作者： Dash, Siddhant Borah, Smitom Swapna Kalamdhad, Ajay S. Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 Assam India

The present study is a first of its kind on the sediment contamination in Deepor Beel, which makes use of source apportionment receptor modelling technique {positive matrix factorization (PMF)} for determining and quantifying the sources' contribution to the pollution of the sediment column of Deepor Beel, Assam. Sediment samples were collected and analysed for seven different heavy metals from 23 sampling locations for a period from October 2017 to February 2019. Polling the entire dataset to a single matrix and carrying out multiple iterations revealed that four factors were optimum and thus, was applied for the simulation of the model. It was observed that the factors 1, 2, 3 and 4 corresponded to the soil parent material, leaching from the Boragaon landfill, discharge of agricultural and domestic wastes, and effluents from the industries and traffic emissions respectively. The sediment samples were further subjected to elemental analysis;X-ray powder diffraction (XRD) followed by Scanning electron microscope - Energy Dispersive X-Ray Spectroscopy (SEM - EDS), to determine the elemental composition and forms of heavy metals present in the sediment columns from various parts of the wetland. Sediment sample collected from the proximity of the landfill site was observed to be affected the most, probably due to leaching effects, especially during the monsoon. The central zone, however, was found to be devoid of any anthropogenic contaminations, while the sediment column near the industrial complex was found to be contaminated to a moderate extent. The study indicates the quantum of sediment contamination in the wetland and the causative parameters responsible, thus proving to be of immense help to the various governmental bodies in the planning and management of resources for sediment remediation of Deepor Beel.

关键词： Heavy metals Sediment contamination Pollution source apportionment positive matrix factorization Elemental analysis Source contribution

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Application of positive matrix factorization to identify potential sources of water quality deterioration of Huaihe River, China

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APPLIED WATER SCIENCE 2019年第3期9卷 63-63页

作者： Jiang, Jiping Khan, Afed U. Shi, Bin Tang, Sijie Khan, Jehanzeb Southern Univ Sci & Technol Sch Environm Sci & Engn State Environm Protect Key Lab Integrated Surface Shenzhen 518055 Peoples R China Univ Engn & Technol Dept Civil Engn Bannu Campus Peshawar 25000 Bannu Pakistan Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Zhejiang Univ Dept Hort Hangzhou 310058 Zhejiang Peoples R China

Identification of nonpoint source (NPS) pollution is essential for effective water management. In this study we used a combined approach of hierarchal cluster analysis (HCA) and positive matrix factorization (PMF) to identify NPS pollution for Huaihe River basin in China. NH3-N, COD, DO and pH were regularly monitored weekly over 2years (2011-2012) from 27 monitoring stations subjected to high anthropogenic and natural changes. As identified by multiple correspondence analyses, the monitoring stations #3, #9 and #21 are located away from the rest of sites. HCA classified all the stations into 4 groups. PMF identified four factors on each group and each season. They were associated with the major causes of Huaihe River water quality deterioration resulted by discharges inputs from urban, agricultural and industrial land uses. Seasonal NPS pollution variation was found, and it is possibly linked with natural processes, for instance hydrological regime. This research work demonstrates the usefulness of PMF model for the identification of NPS pollution in surface waters. Furthermore, our study also shows that urban, agricultural and industrial land uses were the main factors impairing surface water quality, and limiting NPS pollution would be critical for enhancing surface water quality in the study area.

关键词： Cluster analysis Nonpoint source pollution positive matrix factorization Water quality Huaihe River

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A partition computing-based positive matrix factorization (PC-PMF) approach for the source apportionment of agricultural soil heavy metal contents and associated health risks

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JOURNAL OF HAZARDOUS MATERIALS 2020年第0期388卷 121766-000页

作者： Wu, Jin Li, Jiao Teng, Yanguo Chen, Haiyang Wang, Yeyao Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China Chinese Res Inst Environm Sci Beijing 100012 Peoples R China Minist Ecol & Environm Tech Ctr Soil Agr & Rural Ecol & Environm Beijing 100012 Peoples R China Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China China Natl Environm Monitoring Ctr Beijing 100012 Peoples R China

Apportion soil heavy metal sources across large-scale regions is a challenging task. The present study developed a modified receptor model to estimate the contributions of various sources to soil heavy metals and the associated health risks at a large scale. A positive matrix factorization model based on a partition computing approach was employed;the entire study area was divided into several zones for the source apportionment and then calculated together, termed partition computing-PMF (PC-PMF). The agricultural soil in Tianjin, China, was chosen for the case study. The PC-PMF results showed that irrigation, atmospheric deposition and sludge application were the main anthropogenic sources, with contributions of 26.60 %, 19.56 % and 2.86 %, respectively. We subsequently combined PC-PMF with a human health risk assessment model (HHRA) to obtain the human health risk of every source category. The natural background was regarded as a major factor influencing human health in the study area, with contributions of 38.03 % for the noncarcinogenic risk and 28.68 % for the carcinogenic risk. The results indicated that PC-PMF performed better at the source apportionment of soil heavy metals than PMF. This study provides a good example of how the spatial variability can be utilized to reduce the uncertainty in source apportionment.

关键词： Source apportionment Human health risk Agricultural soil heavy metals positive matrix factorization Spatial distribution

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Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using positive matrix factorization (PMF)

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ATMOSPHERE 2021年第5期12卷 614-614页

作者： Faisal, Muhammad Wu, Zening Wang, Huiliang Hussain, Zafar Shen, Chenyang Zhengzhou Univ Coll Water Conservancy Engn Zhengzhou 450001 Peoples R China Zhengzhou Key Lab Water Resource & Environm Zhengzhou 450001 Peoples R China Minist Planning Dev & Special Initiat Water Resources Sect Islamabad 44000 Pakistan

Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (I-geo), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PIN). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. I-geo showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PIN was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PIN indicated the extreme risk of Cd and Hg in the city. positive matrix factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal's presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city.

关键词： road dust heavy metals positive matrix factorization geochemical mapping traditional risk assessment

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PM2.5 Characterization and Source Apportionment Using positive matrix factorization at San Luis Potosi City, Mexico, during the Years 2017-2018

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ATMOSPHERE 2023年第7期14卷 1160-1160页

作者： Barrera, Valter Contreras, Carlos Mugica-Alvarez, Violeta Galindo, Guadalupe Flores, Rogelio Miranda, Javier Univ Autonoma San Luis Potosi Catedrat CONACYT Coordinac Innovac & Aplicac Cienc Ave Sierra Leona 550 San Luis Potosi 78210 Mexico Univ Autonoma San Luis Potosi Lab Nacl Geoproc Informac Fitosanitaria Ave Sierra Leona 550 San Luis Potosi 78210 Mexico Univ Autonoma Metropolitana Azcapotzalco Div Ciencias Bas Ingn Ave San Pablo 180 Mexico City 02200 Mexico Univ Nacl Autonoma Mexico Inst Fis Circuito Invest Cient S-NCiudad Univ Mexico City 04510 Mexico

In growing Mexican cities, there are few studies on air pollution, especially on the topic of characterization for the chemical composition of Particulate Matter (PM). This work presents an X-ray Fluorescence (XRF) analysis and Total Carbon analysis of PM2.5 in a two-year monitoring campaign from 20 May 2017 to 30 July 2018, collecting 96 daily samples in the northeast area of San Luis Potosi city to reconstruct the gravimetric mass and perform a source apportionment study using the positive matrix factorization model (PMF). Concentration differences were due to different yearly seasons. In the year 2017, there was a major influence on heavy metals (V, Cr, Mn, Ni, Cu, Zn, Pb), and for the year 2018, there was a major crustal elements concentration (Na, Al, Si, P). Heavy metal concentrations are higher than any worldwide regulation limit. After applying these methods to the 49 samples collected for the year 2017, the mass reconstruction was nearly 70% of the gravimetric mass. XRF analysis quantified 17 elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) in addition to Total Carbon (Organic Carbon (OC) + Elemental Carbon (EC)). PMF receptor model was applied to identify possible contribution sources and resolved seven physically interpretable factors that contributed to the ambient particulate pollution at the sampling site: Urban Dust (24.2%), Mobile Sources (22.2%), Chemical industry (18.2%), Oil combustion (16.3%), Smelting Industry (12.4%), Fuel Oil + Ceramic Industry (4.4%), and Braking (2.3%). However, the brick kiln's emissions may be present in at least four of the emission sources due to several types of combustible employed.

关键词： particulate matter PM2 5 positive matrix factorization San Luis Potosi Mexico

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