Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

Application of Positive Matrix Factorization (PMF) and Multivariate Statistical Techniques in Identifying and Managing Sources of Heavy Metal Pollutants in Sediments

Document Type : Original Article

Authors
F. Hedayatzadeh 1
N. Hassanzadeh 2
N. Bahramifar 3
A. Ildoromi4 A. Ildoromi4 2
1 Ph.D. Student in Environmental Science, Environmental Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
2 Associate Professor, Watershed Management Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
3 Assistant Professor, Environmental Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
10.47176/TDASS.2021.136
Abstract
Understanding and identifying various sources of water pollution and the processes affecting them are essential for achieving a comprehensive description of the quality of essential water resources. To this end, implementing a suitable network for monitoring water quality is crucial. Therefore, this study aims to examine the effectiveness of the Positive Matrix Factorization (PMF) model compared to multivariate statistical techniques. Additionally, the combined application of the PMF model with these methods in determining the contribution and management of heavy metal pollutants in aquatic sediments is investigated. Multivariate statistical analysis methods have proven effective in preparing and interpreting data on the quality of aquatic environments and determining the information available in them. However, they have some limitations. Thus, in this research, the application of Positive Matrix Factorization for sediment quality data, especially concerning heavy metals, is compared with multivariate statistical methods. The study also evaluates the status and extent of using this model in various environmental studies worldwide in recent years. Positive Matrix Factorization allows considering uncertain data and provides a positive constraint, leading to an environmentally interpretable result. The results of examining the applications of the PMF model in determining the contribution of various pollutants, including heavy metals, in different environmental sectors over the past two decades, indicate a significant increase in its usage in recent years compared to the past. Recent study results suggest that while Positive Matrix Factorization leads to a stronger understanding of the sources of pollution in the studied system compared to multivariate statistical methods, the combined application of the PMF model with other multivariate statistical methods for determining pollutant sources results in a more accurate and comprehensive analysis of pollutants, including heavy metals.
Keywords
Heavy metal pollutants
Source apportionment
Ecosystem management
Positive Matrix Factorization (PMF)
Multivariate statistical techniques
  • Ogoyi, D. O., Mwita, C. J., Nguu, E. K., & Shiundu, P. M. (2011). Determination of heavy metal content in water, sediment and microalgae from Lake Victoria, East Africa. The Open Environmental Engineering Journal, 4, 156–161.
  • Li, X., Wang, Y., Li, B., Feng, C., Chen, Y., & Shen, Z. (2013). Distribution and speciation of heavy metals in surface sediments from the Yangtze estuary and coastal areas. Environmental Earth Sciences, 69, 1537–1547.
  • Chen, Y., Liu, R., Sun, C., Zhang, P., Feng, C., & Shen, Z. (2012). Spatial and temporal variations in nitrogen and phosphorous nutrients in the Yangtze River Estuary. Marine Pollution Bulletin, 64, 2083–2089.
  • Liu, R. M., Chen, Y. X., Sun, C. C., Zhang, P. P., Wang, J. W., Yu, W. W., & Shen, Z. Y. (2014). Uncertainty analysis of total phosphorus spatial–temporal variations in the Yangtze River Estuary using different interpolation methods. Marine Pollution Bulletin, 86, 68–75.
  • Battarbee, R. W., Turner, S., Yang, H., Rose, N. L., Smyntek, P. M., Reimer, P. J., Oldfield, F., Jones, V. J., Flower, R. J., Roe, K., Shilland, E., & Blaauw, M. (2015). Air pollutant contamination and acidification of surface waters in the North York Moors, UK: Multiproxy evidence from the sediments of a moorland pool. The Holocene, 25, 226–237.
  • Syakti, A. D., Demelas, C., Hidayati, N. V., Rakasiwi, G., Vassalo, L., Kumar, N., Prudent, P., & Doumenq, P. (2015). Heavy metal concentrations in natural and human-impacted sediments of Segara Anakan Lagoon, Indonesia. Environmental Monitoring and Assessment, 187, 4079.
  • Wang, J. W., Liu, R. M., Wang, H. T., Yu, W. W., Xu, F., & Shen, Z. Y. (2015). Identification and apportionment of hazardous elements in the sediments in the Yangtze River estuary. Environmental Science and Pollution Research, 22, 20215–20225.
  • Ding, Z., & Hu, X. (2013). Assessment of As and metallic element contamination of riverine sediments from Yangze River in Nanjing reach, China. Fresenius Environmental Bulletin, 22, 824–831.
  • Duan, X., Li, Y., Li, X., Li, M., & Zhang, D. (2013). Distributions and sources of polychlorinated biphenyls in the coastal East China Sea sediments. Science of the Total Environment, 463, 894–903.
  • Gonzalez-Macias, C., Sanchez-Reyna, G., Salazar-Coria, L., & Schifter, I. (2014). Application of the positive matrix factorization approach to identify heavy metal sources in sediments: A case study on the Mexican Pacific Coast. Environmental Monitoring and Assessment, 186, 307–324.
  • Yuan, X., Zhang, L., Li, J., Wang, C., & Ji, J. (2014). Sediment properties and heavy metal pollution assessment in the river, estuary and lake environments of a fluvial plain, China. Catena, 119, 52–60.
  • Bhat, S. A., Meraj, G., Yaseen, S., & Pandit, A. K. (2014). Statistical assessment of water quality parameters for pollution source identification in Sukhnag stream: An inflow stream of Lake Wular (Ramsar site), Kashmir Himalaya. Journal of Ecosystems, 1–19.
  • Duan, W., He, B., Nover, D., Yang, G., Chen, W., Meng, H., Zou, S., & Liu, C. (2016). Water quality assessment and pollution source identification of the eastern Poyang Lake Basin using multivariate statistical methods. Sustainability, 8(2), 133.
  • Juahir, H., Zain, S. M., Yusoff, M. K., Hanidza, T. I. T., Armi, A. S. M., Toriman, M. E., & Mokhtar, M. (2011). Spatial water quality assessment of Langat River basin (Malaysia) using environmetric techniques. Environmental Monitoring and Assessment, 173, 625–641.
  • Kumar, A. S., Reddy, A. M., Srinivas, L., & Reddy, P. M. (2014). Assessment of surface water quality in Hyderabad Lakes by using multivariate statistical techniques, Hyderabad-India. Environmental Pollution, 4(2), 14–23.
  • Muangthong, S., & Shrestha, S. (2015). Assessment of surface water quality using multivariate statistical techniques: Case study of the Nampong River and Songkhram River, Thailand. Environmental Monitoring and Assessment, 187(9), 548.
  • Mustapha, A., & Aris, A. Z. (2012). Spatial aspects of surface water quality in the Jakara Basin, Nigeria using chemometric analysis. Journal of Environmental Science and Health, Part A, 47, 1455–1465.
  • Xiao, M., Bao, F., Wang, S., & Cui, F. (2016). Water quality assessment of the Huaihe River segment of Bengbu (China) using multivariate statistical techniques. Water Resources, 43(1), 166–176.
  • Lin, Y., Chang-Chien, G., Chiang, P., Chen, W., & Lin, Y. (2013). Multivariate analysis of heavy metal contaminations in seawater and sediments from a heavily industrialized harbor in Southern Taiwan. Marine Pollution Bulletin, 76, 266–275.
  • Qu, M. K., Li, W. D., Zhang, C. R., Wang, S. Q., Yang, Y., & He, L. Y. (2013). Source apportionment of heavy metals in soils using multivariate statistics and geostatistics. Pedosphere, 23(4), 437–444.
  • Comero, S., Locoro, G., Free, G., Vaccaro, S., De Capitani, L., & Gawlik, B. M. (2011). Characterisation of Alpine lake sediments using multivariate statistical techniques. Chemometrics and Intelligent Laboratory Systems, 107, 24–30.
  • Rahman, S. A., Hamzah, M. S., Wood, A. K., Elias, M. S., Salim, N. A. A., & Sanuri, E. (2011). Sources apportionment of fine and coarse aerosol in Klang Valley, Kuala Lumpur using positive matrix factorization. Atmospheric Pollution Research, 2, 197–206.
  • Malandrino, M., Di Martino, M., Giacomino, A., Geobaldo, F., Berto, S., Grosa, M. M., & Abollino, O. (2013). Temporal trends of elements in Turin (Italy) atmospheric particulate matter from 1976 to 2001. Chemosphere, 90, 2578–2588.
  • Tian, Y., Shi, G., Han, S., Zhang, Y., Feng, Y., Liu, G., Gao, L., Wu, J., & Zhu, T. (2013). Vertical characteristics of levels and potential sources of water-soluble ions in PM10 in a Chinese megacity. Science of the Total Environment, 447, 1–9.
  • Xu, J., Peng, X., Guo, C. S., Xu, J., Lin, H. X., Shi, G. L., & Tysklind, M. (2016). Sediment PAH source apportionment in the Liaohe River using the ME2 approach: A comparison to the PMF model. Science of the Total Environment, 553, 164–171.
  • Xue, J., Zhi, Y., Yang, L., Shi, J., Zeng, L., & Wu, L. (2014). Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China). Environmental Science and Pollution Research, 21, 7698–7707.
  • Panda, U. C., Rath, P., Bramha, S., & Sahu, K. C. (2010). Application of factor analysis in geochemical speciation of heavy metals in the sediments of a lake system-Chilika (India): A case study. Journal of Coastal Research, 26, 860–868.
  • Huang, S., Tu, J., Jin, Y., Hua, M., Wu, H., Xu, W., Yang, Y., Wang, H., Su, Y., & Cai, L. (2018). Contamination assessment and source identification of heavy metals in river sediments in Nantong, Eastern China. International Journal of Environmental Research, 12, 373–389.
  • Zhang, X., Wei, S., Sun, Q., Wadood, S. A., & Guo, B. (2018). Source identification and spatial distribution of arsenic and heavy metals in agricultural soil around Hunan industrial estate by positive matrix factorization model, principal components analysis, and geostatistical analysis. Ecotoxicology and Environmental Safety, 159, 354–362.
  • Dong, B., Zhang, R., Gan, Y., Cai, L., Freidenreich, A., Wang, K., Guo, T., & Wang, H. (2019). Multiple methods for the identification of heavy metal sources in cropland soils from a resource-based region. Science of the Total Environment, 651, 3127–3138.
  • Boroumandi, M., Khamehchiyan, M., Nikoudel, M. R., & Mohammadzadeh, M. (2019). Evaluation of soil pollution sources using multivariate analysis combined with geostatistical methods in Zanjan Basin, Iran. Geopersia, 9(2), 293–304.
  • Zhang, M., Wang, X., Liu, C., Lu, J., Qin, Y., Mo, Y., Xiao, P., & Liu, Y. (2020). Identification of the heavy metal pollution sources in the rhizosphere soil of farmland irrigated by the Yellow River using PMF analysis combined with multiple analysis methods: Using Zhongwei city, Ningxia, as an example. Environmental Science and Pollution Research, 27(14), 16203–16214.
  • Chai, L., Li, H., Yang, Z., Min, X., Liao, Q., Liu, Y., Men, S., Yan, Y., & Xu, J. (2017). Heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan, China: Distribution, contamination, and ecological risk assessment. Environmental Science and Pollution Research, 24, 874–885.
  • Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., & Fernandez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research, 34, 807–816.
  • Papaioannou, A., Mavridou, A., Hadjichristodoulou, C., Papastergiou, P., Pappa, O., Dovriki, E., & Rigas, I. (2009). Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public health. Environmental Monitoring and Assessment, 170, 87–97.
  • Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.
  • Hutcheson, G., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. London, UK: Sage.
  • Todeschini, R. (1998). Introduzione alla chemiometria. Napoli, Italy: Edises.
  • Chen, P., Li, L., & Zhang, H. (2015). Spatio-temporal variations and source apportionment of water pollution in Danjiangkou Reservoir Basin. Central China Water, 7, 2591–2611.
  • Li, Y., Xu, L., & Li, S. (2009). Water quality analysis of the Songhua River Basin using multivariate techniques. Journal of Water Resource Protection, 1, 110
  • Huang, W. J., Chen, W. Y., Chuang, Y. H., Lin, Y. H., & Chen, H. W. (2014). Biological toxicity of groundwater in a seashore area: Causal analysis and its spatial pollutant pattern. Chemosphere, 100, 8–15.
  • Paatero, P., & Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5, 111–126.
  • Reff, A., Eberly, S. I., & Bhave, P. V. (2007). Receptor modeling of ambient particulate matter data using positive matrix factorization: Review of existing methods. Journal of the Air & Waste Management Association, 57, 146–154.
  • (2012). Integrated Risk Information System (IRIS). National Center for Environmental Assessment, Office of Research and Development, Washington, DC.
  • Ali, M. M., Ali, M. L., Islam, M. S., & Rahman, M. Z. (2016). Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh. Environmental Nanotechnology, Monitoring & Management, 5, 27–35.
  • Banerjee, S., Kumar, A., Maiti, S. K., & Chowdhury, A. (2016). Seasonal variation in heavy metal contaminations in water and sediments of Jamshedpur stretch of Subarnarekha River, India. Environmental Earth Sciences, 75, 265.
  • Bilgin, A., & Konanç, M. U. (2016). Evaluation of surface water quality and heavy metal pollution of Coruh River Basin (Turkey) by multivariate statistical methods. Environmental Earth Sciences, 75, 1029.
  • Khound, N. J., & Bhattacharyya, K. G. (2017). Multivariate statistical evaluation of heavy metals in the surface water sources of Jia Bharali River Basin, North Brahmaputra Plain, India. Applied Water Science, 7, 2577–2586.
  • Kuang, C., Li, Q., Zhang, Y., Wang, H., & Liu, Z. (2016). Assessment of heavy metal contamination in water body and riverbed sediments of the Yanghe River in the Bohai Sea, China. Environmental Earth Sciences, 75, 1105.
  • Wang, J., Liu, G., Liu, H., & Lam, P. K. (2017). Multivariate statistical evaluation of dissolved trace elements and a water quality assessment in the middle reaches of Huaihe River, Anhui, China. Science of the Total Environment, 583, 421–431.
  • Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., & Fernandez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research, 34, 807–816.
  • Singh, K. P., Malik, A., Mohan, D., & Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—A case study. Water Research, 38(18), 3980–3992.
  • Huang, J. L., Ho, M. H., & Du, P. F. (2011). Assessment of temporal and spatial variation of coastal water quality and source identification along Macau Peninsula. Stochastic Environmental Research and Risk Assessment, 25(3), 353–361.
  • Zhou, F., Huang, G. H., Guo, H., Zhang, W., & Hao, Z. (2007). Spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong. Water Research, 41(15), 3429–3439.
  • Carrer, S., & Leardi, R. (2006). Characterizing the pollution produced by an industrial area—Chemometric methods applied to the Lagoon of Venice. Science of the Total Environment, 370, 99–116.
  • Hoinaski, L., Franco, D., Stuetz, R., Sivret, E., & de Melo Lisboa, H. (2013). Investigation of PM10 sources in Santa Catarina, Brazil through graphical interpretation analysis combined with receptor modelling. Environmental Technology, 34, 2453–2463.
  • Henry, R. C., & Christensen, E. R. (2010). Selecting an appropriate multivariate source apportionment model result. Environmental Science & Technology, 44(7), 2474–2481.
  • Hopke, P. K. (2010). The application of receptor modeling to air quality data. Pollution Atmosphérique.
  • Anttila, P., Paatero, P., Tapper, U., & Järvinen, O. (1995). Source identification of bulk wet deposition in Finland by positive matrix factorization. Atmospheric Environment, 29, 1705–1718.
  • Li, H., Hopke, P. K., Liu, X., Du, X., & Li, F. (2015). Application of positive matrix factorization to source apportionment of surface water quality of the Daliao River basin, northeast China. Environmental Monitoring and Assessment, 187, 1–12.
  • Soonthornnonda, P., & Christensen, E. R. (2008). Source apportionment of pollutants and flows of combined sewer wastewater. Water Research, 42, 1989–1998.
  • Fortner, E., Onasch, T., Canagaratna, M., Williams, L. R., Lee, T., Jayne, J., & Worsnop, D. (2018). Examining the chemical composition of black carbon particles from biomass burning with SAMS. Journal of Aerosol Science, 120, 12–21.
  • Mohr, C., DeCarlo, P. F., Heringa, M. F., Chirico, R., Slowik, J. G., Richter, R., Reche, C., Alastuey, A., Querol, X., Seco, R., Peñuelas, J., Jiménez, J. L., Crippa, M., Zimmermann, R., Baltensperger, U., & Prévôt, A. S. H. (2012). Identification and quantification of organic aerosol from cooking and other sources in Barcelona using aerosol mass spectrometer data. Atmospheric Chemistry and Physics, 12, 1649–1665.
  • Kuang, B. Y., Lin, P., Huang, X. H. H., & Yu, J. Z. (2015). Sources of humic-like substances in the Pearl River Delta, China: Positive matrix factorization analysis of PM2.5 major components and source markers. Atmospheric Chemistry and Physics, 15, 1995–2008.
  • Sowlat, M. H., Hasheminassab, S., & Sioutas, C. (2016). Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF). Atmospheric Chemistry and Physics, 16, 4849–4866.
  • Visser, S., Slowik, J. G., Furger, M., Zotter, P., Bukowiecki, N., Canonaco, F., Flechsig, U., Appel, K., Green, D. C., Tremper, A. H., Young, D. E., Williams, P. I., Allan, J. D., Coe, H., Williams, L. R., Mohr, C., Xu, L., Ng, N. L., Nemitz, E., Barlow, J. F., Halios, C. H., Fleming, Z. L., Baltensperger, U., & Prévôt, A. S. H. (2015). Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter. Atmospheric Chemistry and Physics, 15, 11291–11309.
  • Yan, C., Nie, W., Äijälä, M., Rissanen, M. P., Canagaratna, M. R., Massoli, P., Junninen, H., Jokinen, T., Sarnela, N., Häme, S. A. K., Schobesberger, S., Canonaco, F., Yao, L., Prévôt, A. S. H., Petäjä, T., Kulmala, M., Sipilä, M., Worsnop, D. R., & Ehn, M. (2016). Source characterization of highly oxidized multifunctional compounds in a boreal forest environment using positive matrix factorization. Atmospheric Chemistry and Physics, 16, 12715–12731.
  • Comero, S., Servida, D., & De Capitani, L., & Gawlik, B. M. (2012). Geochemical characterization of an abandoned mine site: A combined positive matrix factorization and GIS approach compared with principal component analysis. Journal of Geochemical Exploration, 118, 30–37.
  • González-Macías, C., Sánchez-Reyna, G., Salazar-Coria, L., & Schifter, I. (2014). Application of the positive matrix factorization approach to identify heavy metal sources in sediments: A case study on the Mexican Pacific Coast. Environmental Monitoring and Assessment, 186, 307–324.
  • Li, H., Hopke, P. K., Liu, X., Du, X., & Li, F. (2015). Application of positive matrix factorization to source apportionment of surface water quality of the Daliao River basin, northeast China. Environmental Monitoring and Assessment, 187, 80.
  • Haji Gholizadeh, M., Melesse, A. M., & Reddi, L. (2016). Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida. Science of the Total Environment, 566–567, 1552–1567.
  • Zanotti, C., Rotiroti, M., Fumagalli, L., Stefania, G. A., Canonaco, F., Stefenelli, G., Prévôt, A. S. H., Leoni, B., & Bonomi, T. (2019). Groundwater and surface water quality characterization through positive matrix factorization combined with GIS approach. Water Research, 159, 122–134.
  • Jiang, J. U., Khan, A., Shi, B., Tang, S., & Khan, J. (2019). Application of positive matrix factorization to identify potential sources of water quality deterioration of Huaihe River, China. Applied Water Science, 9, 63.
  • Salim, I., Sajjad, R. U., Paule-Mercado, M. C., Memon, S. A., Lee, B. Y., Sukhbaatar, C., & Lee, C. H. (2019). Comparison of two receptor models PCA-MLR and PMF for source identification and apportionment of pollution carried by runoff from catchment and sub-watershed areas with mixed land cover in South Korea. Science of the Total Environment, 663, 764–775.
  • Sun, X., Wang, H., Guo, Z., Lu, P., Song, F., Liu, L., Liu, J., Rose, N. L., & Wang, F. (2020). Positive matrix factorization on source apportionment for typical pollutants in different environmental media: A review. Environmental Science: Processes & Impacts, 22(3), 239–255.
Transactions on Data Analysis in Social Science
Volume 3, Issue 3
Summer 2021
Pages 136-149

  • Receive Date 17 May 2021
  • Revise Date 20 July 2021
  • Accept Date 30 August 2021