Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

Application of Multivariate Receptor Models in Identification, Quantification, and Management of Common Pollutant Sources in Various Environmental Sectors

Document Type : Original Article

Authors
F. Hedayatzadeh 1
A. Ildermi 2
N. Hassanzadeh 3
1 Ph.D. Student in Environmental Science, Environmental Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
2 Associate Professor, Watershed Management Department, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran.
3 Assistant Professor, Environmental Science Department, Faculty of Natural Resources and Environment, University of Malayer, Malayer, Iran
10.47176/TDASS.2021.159
Abstract
Identification and allocation of various pollutant sources are crucial tools for effective prevention, control of pollution, and management of different environmental sectors. In this regard, methods used for determining, allocating, and managing pollutant emission sources in the environment can be categorized into source identification and quantitative source allocation methods. Source identification methods have limitations, including the inability to process missing data and detect values below the detection limit commonly observed in environmental data. Additionally, these methods cannot quantitatively determine the contribution of natural and human sources of pollutants. Conversely, the second type of methods is capable of quantitatively identifying and determining the share of pollutant sources, among which receptor models are particularly notable. In terms of source allocation, receptor models are mathematical computational approaches that can identify and quantify the contribution of sources based on the chemical and physical characteristics of pollutants in sources and receptors. Given the practical importance of receptor models in quantifying the contribution of various pollutant sources in the environment, the aim of this study is to introduce and compare different types of receptor models for determining pollutant emission sources in the environment. The study also highlights recent research in this area. Therefore, by presenting these models, the current research can serve as a guide and a valuable resource for the identification and quantitative determination of various pollutant sources in the environment, consequently providing a basis for environmental management and improvement.
Keywords
Environmental Pollutants
Source Allocation
Receptor Models
Pollution Resource Management
  • Zhou, Y. Q., Ma, J. R., Zhang, Y. L., Qin, B. Q., Jeppesen, E., Shi, K., Brookes, J. D., Spencer, R. G. M., Zhu, G. W., & Gao, G. (2017). Improving water quality in China: Environmental investment pays dividends. Water Research, 118, 152–159. https://doi.org/10.1016/j.watres.2017.04.035
  • Benabdelkader, A., Taleb, A., Probst, J. L., Belaidi, N., & Probst, A. (2018). Anthropogenic contribution and influencing factors on metal features in fluvial sediments from a semi-arid Mediterranean river basin (Tafna River, Algeria): A multi-indices approach. Science of the Total Environment, 626, 899–914. https://doi.org/10.1016/j.scitotenv.2018.01.107
  • Lv, J. S., & Wang, Y. M. (2018). Multi-scale analysis of heavy metals sources in soils of Jiangsu Coast, Eastern China. Chemosphere, 212, 964–973. https://doi.org/10.1016/j.chemosphere.2018.08.155
  • 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. https://doi.org/10.1007/s10661-014-4154-2
  • Niraula, R., Kalin, L., Srivastava, P., & Anderson, C. J. (2013). Identifying critical source areas of nonpoint source pollution with SWAT and GWLF. Ecological Modelling, 268, 123–133. https://doi.org/10.1016/j.ecolmodel.2013.08.007
  • Wang, Q., Xie, Z., & Li, F. (2015). Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale. Environmental Pollution, 206, 227–235. https://doi.org/10.1016/j.envpol.2015.06.040
  • Huang, Y., Deng, M., Wu, C., Japenga, J., Li, T., Yang, X., & He, Z. A. (2018). Modified receptor model for source apportionment of heavy metal pollution in soil. Journal of Hazardous Materials, 354, 161–169. https://doi.org/10.1016/j.jhazmat.2018.05.006
  • Franco-Uria, A., Lopez-Mateo, C., Roca, E., & Fernandez-Marcos, M. L. (2009). Source identification of heavy metals in pastureland by multivariate analysis in NW Spain. Journal of Hazardous Materials, 165, 1008–1015. https://doi.org/10.1016/j.jhazmat.2008.10.118
  • Li, X., & Feng, L. (2010). Spatial distribution of hazardous elements in urban topsoils surrounding Xi'an industrial areas (NW, China): Controlling factors and contamination assessments. Journal of Hazardous Materials, 174, 662–669. https://doi.org/10.1016/j.jhazmat.2009.09.102
  • Luo, X. S., Yu, S., & Li, X. D. (2011). Distribution, availability, and sources of trace metals in different particle size fractions of urban soils in Hong Kong: Implications for assessing the risk to human health. Environmental Pollution, 159, 1317–1326. https://doi.org/10.1016/j.envpol.2011.01.013
  • Zhao, L., Xu, Y. F., Hou, H., Shangguan, Y. X., & Li, F. S. (2014). Source identification and health risk assessment of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China. Science of the Total Environment, 468, 654–662. https://doi.org/10.1016/j.scitotenv.2013.08.094
  • Huang, Y., Li, T. Q., Wu, C. X., He, Z. H., Japenga, J., Deng, M. H., & Yang, X. E. (2015). An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils. Journal of Hazardous Materials, 299, 540–549. https://doi.org/10.1016/j.jhazmat.2015.07.041
  • Simeonov, V., Stanimirova, I., & Tsakovski, S. (2001). Multivariate statistical interpretation of coastal sediment monitoring data. Fresenius Journal of Analytical Chemistry, 370, 719–722. https://doi.org/10.1007/s002160100863
  • Sundqvist, K. L., Tysklind, M., Geladi, P., Hopke, P. K., & Wiberg, K. (2010). PCDD/F source apportionment in the Baltic Sea using positive matrix factorization. Environmental Science & Technology, 44, 1690–1697. https://doi.org/10.1021/es9030084
  • Mooibroek, D., Hoogerbrugge, R., & Bloemen, H. J. Th. (2007). Implementation of source apportionment using positive matrix factorization: Application of the Palookaville exercise. National Institute for Public Health and the Environment, RIVM Report 863001006/2007. The Netherlands.
  • 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. https://doi.org/10.1016/j.scitotenv.2016.06.046
  • Li, A., Jang, J. K., & Scheff, P. A. (2003). Application of EPA CMB8.2 model for source apportionment of sediment PAHs in Lake Calumet, Chicago. Environmental Science & Technology, 37(13), 2958–2965. https://doi.org/10.1021/es026309v
  • Paatero, P., & Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2), 111–126. https://doi.org/10.1002/env.3170050203
  • Shi, G. L., Zeng, F., Li, X., Feng, Y. C., Wang, Y. Q., Liu, G. X., & Zhu, T. (2011). Estimated contributions and uncertainties of PCA/MLR-CMB results: Source apportionment for synthetic and ambient datasets. Atmospheric Environment, 45, 2811–2819. https://doi.org/10.1016/j.atmosenv.2011.03.007
  • Sahu, M., Hu, S., Ryan, P. H., LeMasters, G., Grinshpun, S. A., Chow, J. C., & Biswas, P. (2011). Chemical compositions and source identification of PM2.5 aerosols for estimation of a diesel source surrogate. Science of the Total Environment, 409(13), 2642–2651.
  • Guo, H., Wang, T., & Louie, P. K. K. (2004). Source apportionment of ambient non-methane hydrocarbons in Hong Kong: Application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model. Environmental Pollution, 129, 489–498. https://doi.org/10.1016/j.envpol.2003.11.006
  • Gupta, A. K., Karar, K., & Srivastava, A. (2007). Chemical mass balance source apportionment of PM10 and TSP in residential and industrial sites of an urban region of Kolkata, India. Journal of Hazardous Materials, 142, 279–287. https://doi.org/10.1016/j.jhazmat.2006.08.013
  • Pant, P., Yin, J., & Harrison, R. M. (2014). Sensitivity of a chemical mass balance model to different molecular marker traffic source profiles. Atmospheric Environment, 82, 238–249. https://doi.org/10.1016/j.atmosenv.2013.10.005
  • Larsen, R. K., & Baker, J. E. (2003). Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere: A comparison of three methods. Environmental Science & Technology, 37(9), 1873–1881. https://doi.org/10.1021/es0206184
  • Yang, B., Zhou, L., Xue, N., Li, F., Li, Y., Vogt, R. D., et al. (2013). Source apportionment of polycyclic aromatic hydrocarbons in soils of Huanghuai Plain, China: Comparison of three receptor models. Science of the Total Environment, 443, 31–39. https://doi.org/10.1016/j.scitotenv.2012.10.094
  • Sofowote, U. M., McCarry, B. E., & Marvin, C. H. (2008). Source apportionment of PAH in Hamilton Harbour suspended sediments: Comparison of two factor analysis methods. Environmental Science & Technology, 42, 6007–6014. https://doi.org/10.1021/es800219z
  • Callén, M. S., Cruz, M. T., López, J. M., Navarro, M. V., & Mastral, A. M. (2009). Comparison of receptor models for source apportionment of the PM10 in Zaragoza (Spain). Chemosphere, 76(8), 120–129. https://doi.org/10.1016/j.chemosphere.2009.04.015
  • Cesari, D., Amato, F., Pandolfi, M., Alastuey, A., Querol, X., & Contini, D. (2016). An intercomparison of PM10 source apportionment using PCA and PMF receptor models in three European sites. Environmental Science and Pollution Research, 23(15), 15133–15148. https://doi.org/10.1007/s11356-016-6599-z
  • Viana, M., Pandolfi, M., Minguillón, M. C., Querol, X., Alastuey, A., Monfort, E., & Celades, I. (2008). Inter-comparison of receptor models for PM source apportionment: Case study in an industrial area. Atmospheric Environment, 42(16), 3820–3832. https://doi.org/10.1016/j.atmosenv.2007.12.056
  • Chen, H., Teng, Y., Yue, W., & Song, L. (2013). Characterization and source apportionment of water pollution in Jinjiang River, China. Environmental Monitoring and Assessment, 185(11), 9639–9650. https://doi.org/10.1007/s10661-013-3279-z
  • Chen, H. Y., Teng, Y. G., Chen, R. H., Li, J., & Wang, J. S. (2016). Contamination characteristics and source apportionment of trace metals in soils around Miyun Reservoir. Environmental Science and Pollution Research, 23, 15331–15342. https://doi.org/10.1007/s11356-016-6694-1
  • Chen, P., Li, L., & Zhang, H. (2015). Spatio-temporal variations and source apportionment of water pollution in Danjiangkou Reservoir Basin, Central China. Water, 7(6), 2591–2611. https://doi.org/10.3390/w7062591
  • Jiang, Y. X., Chao, S. H., Liu, J. W., Yang, Y., Chen, Y. J., Zhang, A. C., & Cao, H. B. (2017). Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China. Chemosphere, 168, 1658–1668. https://doi.org/10.1016/j.chemosphere.2016.11.088
  • Liang, J., Feng, C. T., Zeng, G. M., Gao, X., Zhong, M. Z., Li, X. D., Li, X., He, X. Y., & Fang, Y. L. (2017). Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China. Environmental Pollution, 225, 681–690. https://doi.org/10.1016/j.envpol.2017.03.057
  • PriyaDarshini, S., Sharma, M., & Singh, D. (2016). Synergy of receptor and dispersion modelling: Quantification of PM10 emissions from road and soil dust not included in the inventory. Atmospheric Pollution Research, 7, 403–411. https://doi.org/10.1016/j.apr.2015.10.015
  • Xue, J. L., Zhi, Y. Y., Yang, L. P., Shi, J. C., Zeng, L. Z., & Wu, L. S. (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. https://doi.org/10.1007/s11356-014-2726-x
  • Zhang, Y., Guo, C. S., Xu, J., Tian, Y. Z., Shi, G. L., & Feng, Y. C. (2012). Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models. Water Research, 46, 3065–3073. https://doi.org/10.1016/j.watres.2012.03.006
  • Rastegari Mehr, M., Keshavarzi, B., Moore, F., Sharifi, R., Lahijanzadeh, A. R., & Kermanic, M. (2017). Distribution, source identification and health risk assessment of soil heavy metals in urban areas of Isfahan province, Iran. Journal of African Earth Sciences, 132, 16–26. https://doi.org/10.1016/j.jafrearsci.2017.04.026
  • 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.
  • Soonthornnonda, P., & Christensen, E. R. (2008). Source apportionment of pollutants and flows of combined sewer wastewater. Water Research, 42, 1989–1998. https://doi.org/10.1016/j.watres.2007.11.034
  • Zheng, T., Ran, Y., & Chen, L. (2014). Polycyclic aromatic hydrocarbons (PAHs) in rural soils of Dongjiang River Basin: Occurrence, source apportionment, and potential human health risk. Journal of Soils and Sediments, 14, 110–120. https://doi.org/10.1007/s11368-013-0753-8
  • Ogundele, L. T., Owoade, O. K., Olise, F. S., & Hopke, K. P. (2016). Source identification and apportionment of PM2.5 and PM2.5–10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models. Environmental Monitoring and Assessment, 188, 574. https://doi.org/10.1007/s10661-016-5585-8
  • Duodu, G. O., Ogogo, K. N., Mummullage, S., Harden, F., Goonetilleke, A., & Ayoko, G. A. (2017). Source apportionment and risk assessment of PAHs in Brisbane River sediment, Australia. Ecological Indicators, 73, 784–799. https://doi.org/10.1016/j.ecolind.2016.10.038
  • Lv, J. (2018). Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. Environmental Pollution, 244, 72–83. https://doi.org/10.1016/j.envpol.2018.09.147
  • 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. https://doi.org/10.1016/j.scitotenv.2019.01.377
  • Gulgundi, M. S., & Shetty, A. (2019). Source apportionment of groundwater pollution using Unmix and positive matrix factorization. Environmental Processes, 6, 457–473. https://doi.org/10.1007/s40710-019-00373-y
  •  Guan, Q., Zhao, R., Pan, N., Wang, F., Yang, Y., & Luo, H. (2019). Source apportionment of heavy metals in farmland soil of Wuwei, China: Comparison of three receptor models. Journal of Cleaner Production, 237, 117792. https://doi.org/10.1016/j.jclepro.2019.117792
  • Jiao, W., Niu, Y., Niu, Y., Li, B., & Zhao, M. (2019). Quantitative identification of anthropogenic trace metal sources in surface river sediments from a hilly agricultural watershed, East China. Environmental Science and Pollution Research International, 26(31), 32266–32275. https://doi.org/10.1007/s11356-019-06504-0
  • Zhang, J., Li, R., Zhang, X., Bai, Y., Cao, P., & Hua, P. (2019). Vehicular contribution of PAHs in size-dependent road dust: A source apportionment by PCA-MLR, PMF, and Unmix receptor models. Science of the Total Environment, 649, 1314–1322. https://doi.org/10.1016/j.scitotenv.2018.08.410
  • Hopke, P. K. (1985). Receptor modeling in environmental chemistry. Wiley.
  • Chow, J. C., & Watson, J. G. (2002). Review of PM2.5 and PM10 apportionment for fossil fuel combustion and other sources by the chemical mass balance receptor model. Energy & Fuels, 16, 222–260. https://doi.org/10.1021/ef0101715
  • S. Environmental Protection Agency. (2014). EPA Positive Matrix Factorization (PMF) 5.0 fundamentals and user guide.
  • Thurston, G. D., & Spengler, J. D. (1985). A quantitative assessment of source contributions to inhalable particulate matter pollution in Metropolitan Boston. Atmospheric Environment, 19, 9–25. https://doi.org/10.1016/0004-6981(85)90132-5
  • Hopke, P. K. (2003). Recent developments in receptor modeling. Journal of Chemometrics, 17, 255–265. https://doi.org/10.1002/cem.796
  • Henry, R. C. (2003). Multivariate receptor modeling by N-dimensional edge detection. Chemometrics and Intelligent Laboratory Systems, 65, 179–189. https://doi.org/10.1016/S0169-7439(02)00108-9
  • Norris, G., Vedantham, R., Duvall, R., & Henry, R. C. (2007). EPA Unmix 6.0 fundamentals & user guide. U.S. Environmental Protection Agency, Office of Research and Development.
  • Zhang, Y., Li, Y., Li, Y., Da, W., Yu, M., & Quan, Q. (2019). Application of multivariate statistical methods in the assessment of water quality in selected locations in Jialing River basin in Guangyuan, China. Water Science and Technology: Water Supply, 19(1), 147–155. https://doi.org/10.2166/ws.2018.058
  • Zhang, J., Li, R., Zhang, X., Bai, Y., Cao, P., & Hua, P. (2019). Vehicular contribution of PAHs in size-dependent road dust: A source apportionment by PCA-MLR, PMF, and Unmix receptor models. Science of the Total Environment, 649, 1314–1322. https://doi.org/10.1016/j.scitotenv.2018.08.410
  • Ogwueleka, T. C. (2014). Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis. Water and Environment Journal, 28(1), 31–37. https://doi.org/10.1111/wej.12004
  • Norris, G., Duvall, R., Brown, S., & Bai, S. (2014). EPA Positive Matrix Factorization (PMF) 5.0 fundamentals and user guide. Prepared for the U.S. Environmental Protection Agency Office of Research and Development, Washington, DC.
  • Yang, B., Zhou, L., Xue, N., Li, F., Li, Y., Vogt, R. D., et al. (2013). Source apportionment of polycyclic aromatic hydrocarbons in soils of Huanghuai Plain, China: Comparison of three receptor models. Science of the Total Environment, 443, 31–39. https://doi.org/10.1016/j.scitotenv.2012.10.094
  • Paatero, P., Hopke, P. K., Hoppenstock, J., & Eberly, S. I. (2003). Advanced factor analysis of spatial distributions of PM2.5 in the eastern United States. Environmental Science & Technology, 37, 2460–2476. https://doi.org/10.1021/es0261978
  • Lee, D. H., Kim, J. H., Mendoza, J. A., Chang, H. L., & Joo, H. K. (2016). Characterization and source identification of pollutants in runoff from a mixed land use watershed using ordination analyses. Environmental Science and Pollution Research, 23(10), 9774–9790. https://doi.org/10.1007/s11356-016-6155-x
  • Deng, J., Zhang, Y., Qiu, Y., Zhang, H., Du, W., Xu, L., et al. (2018). Source apportionment of PM2.5 at the Lin'an regional background site in China with three receptor models. Atmospheric Research, 202, 23–32. https://doi.org/10.1016/j.atmosres.2017.11.017
  • Viana, M., Pandolfi, M., Minguillón, M. C., Querol, X., Alastuey, A., Monfort, E., & Celades, I. (2008). Inter-comparison of receptor models for PM source apportionment: Case study in an industrial area. Atmospheric Environment, 42(16), 3820–3832. https://doi.org/10.1016/j.atmosenv.2007.12.056
  • Jain, S., Sharma, S. K., Choudhary, N., Masiwal, R., Saxena, M., Sharma, A., Mandal, T. K., Gupta, A., Gupta, N. C., & Sharma, C. (2017). Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India. Environmental Science and Pollution Research, 24(7), 14637–14656. https://doi.org/10.1007/s11356-017-8925-5
Transactions on Data Analysis in Social Science
Volume 3, Issue 3
Summer 2021
Pages 159-168

  • Receive Date 05 April 2021
  • Revise Date 02 July 2021
  • Accept Date 07 September 2021