Contribution for pollution sources and their assessment in urban and industrial sites of Ergene River Basin, Turkey

M. Celen1, H. N. Oruc1, A. Adiller2,3, G. Yıldız Töre4, G. Onkal Engin2
1Gebze Technical University, Institute of Earth and Marine Sciences, Gebze, Turkey
2Yildiz Technical University, Civil Engineering Faculty, Environmental Engineering Department, Istanbul, Turkey
3Uskudar University, Vocational School of Health Services, Department of Environmental Health, Istanbul, Turkey
4Tekirdağ Namık KemalUniversity, Çorlu Engineering Faculty, Environmental Engineering Department, Tekirdag, Turkey

Tóm tắt

In this paper, principal component analysis (PCA) and positive matrix factorization (PMF) multivariate statistical techniques were applied to evaluate the spatiotemporal variation in 13 conventional water quality parameters (TOC, TN, NO2−, NO3−, TP, SO4−2, Cl−, TSS, color, pH, temperature, DO, EC) at eight monitoring stations for a duration of a year (2016–2017) in the upstream parts of the Ergene basin (NW Turkey). The eight monitoring stations were divided into two groups (five sites for Gr-A and three sites for Gr-B) considering pollution levels of the parameters and point/non-point sources determined by field observations. The principal component analysis defined four and three latent factors explaining 87% and 89% of the total variance in Gr-A and Gr-B datasets, respectively. Component numbers defined in PCA were manually assigned to the positive matrix factorization model. PCA was seen to be an important index for defining the number of factors causing high uncertainty for PMF. The factors derived from the PMF model revealed that the dominant pollutant sources for Gr-A sites are textile and leather industry discharges, agricultural activities, domestic discharges and seasonal factors. Gr-B sites are defined as domestic discharges, agricultural fertilizers and industrial discharges. Therefore, PMF analysis for conventional water quality parameters is a consistent statistical technique for the identification of complex pollution sources.

Tài liệu tham khảo

A.P.H.A. (1995) Standard methods for the examination of water and waste water. American Public Health Association, Washington, DC A.P.H.A. (1998) Standard methods for examination of water and wastewater. American Public Health Association, Washington, DC, USA Adams VD (1990) Water and wastewater examination manual, 1st edn. Routledge, Boca Raton USA. https://doi.org/10.1201/9780203734131 Albek E (1999) Identification of the different sources of chlorides in streams by regression analysis using chloride-discharge relationships. Water Environ Res. https://doi.org/10.2175/106143096X122384 Al-Dabbous AN, Kumar P (2015) Source apportionment of airborne nanoparticles in a middle eastern city using positive matrix factorization. Environ Sci Process Impacts. https://doi.org/10.1039/C5EM00027K Alves R, Machado C, Beda C, Fregonesi B, Nadal M, Sierra J, Munoz S (2018) Water quality assessment of the pardo river basin, Brazil: a multivariate approach using limnological parameters, metal concentrations and indicator bacteria. Arch Environ Contam Toxicol. https://doi.org/10.1007/s00244-017-0493-7 Berhe AB (2020) Evaluation of groundwater and surface water quality suitability for drinking and agricultural purposes in Kombolcha town area, eastern Amhara region. Ethiopia Appl Water Sci. https://doi.org/10.1007/s13201-020-01210-6 Bhat SA, Meraj G, Yaseen S, Pandit AK (2014) Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Sukhnag Stream: An Inflow Stream of Lake Wular (Ramsar Site), Kashmir Himalaya. J Ecosyst 2014:1–19. https://doi.org/10.1155/2014/898054 Bilgin A (2015) An assessment of water quality in the Coruh Basin (Turkey) using multivariate statistical techniques. Braz J Biol. https://doi.org/10.1007/s10661-015-4904-9 EEA (2019) Retrieved from Copernicus Land Monitoring Services: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 Emadian MS, Sefiloglu O, Balcioglu I, Tezel U (2021) Identification of core micropollutants of Ergene River and their categorization based on spatiotemporal distribution S. Sci Total Environ J. https://doi.org/10.1016/j.scitotenv.2020.143656 EPA (2019) EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. https://www.epa.gov/sites/production/files/201502/documents/pmf_5.0_user_guide.pdf Fran S, Catherine L, Wendy N, Newham M, Polson C (2019) Chapter 11—urbanization: hydrology, water quality, and influences on ecosystem health. In: Approaches to water sensitive urban design, pp 229–248 Gholizadeh MH, Melesse AM, Reddi L (2016) Analysis of spatiotemporal trends of water quality parameters using cluster analysis in South Florida. World Environmental and Water Resources Congress 2016. American Society of Civil Engineers, Reston, VA, 519–528 Güneş EH, Güneş Y, Talınlı I (2008) Toxicity evaluation of industrial and land base sources in a river basin. Desalination. https://doi.org/10.1016/j.desal.2007.02.116 Helness H, Damman S, Sivertsen E, Ugarelli R (2019) Principal component analysis for decision support in integrated water management. Water Sci Technol Water Supply. https://doi.org/10.2166/ws.2019.106 Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol. https://doi.org/10.1037/h0071325 Huang F, Wang X, Lou L, Zhou Z, Wu J (2010) Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Res. https://doi.org/10.1016/j.watres.2009.11.003 Kaiser HF (1974) An index of factorial simplicity. Psychometrika. https://doi.org/10.1007/BF02291575 Kemker C (2014) Turbidity, total suspended solids and water clarity. Fundamentals of Environmental Measurements. http://www.fondriest.com/environmental-measurements/parameters/water-quality/turbidity-total-suspended-solids-water-clarity/ Lee DH, Kim JH, Mendoza JA, Chang HL, Joo HK (2016) Characterization and source identification of pollutants in runoff from a mixed land use watershed using ordination analyses. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-016-6155-x Li A, Jang JK, Scheff PA (2003) Application of EPA CMB8.2 model for source apportionment of sediment PAHs in Lake Calumet, Chicago. Environ Sci Technol. https://doi.org/10.1021/es026309v Li H, Hopke PK, Liu X, Du X, Li F (2015) Application of positivematrix factorization to source apportionment of surface water quality of the Daliao River basin, Northeast China. Environmental Monitor Assess. https://doi.org/10.1007/s10661-014-4154-2 Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of ground- water quality in a blackfoot disease area in Taiwan. Sci Total Environment. https://doi.org/10.1016/S0048-9697(02)00683-6 Ma X, Wang L, Yang H, Li N, Gong C (2020) Spatiotemporal analysis of water quality using multivariate statistical techniques and the water quality identification index for the Qinhuai River Basin, East China. Impact Climate Change Hum Activit Aquatic Environ 1:2. https://doi.org/10.3390/w12102764 Madramootoo CA, Johnston WR, Willardson LS (1997) Management of agricultural drainage water quality. Food and Agriculture Organization of the United Nations. https://www.fao.org/3/w7224e/w7224e00.htm. Accessed 17 Mar 2021 Manousakas M, Diapouli E, Papaefthymiou H, Migliori A, Karydas AG, Padilla-Alvarez R, Bogovac M (2015) Source apportionment by PMF on elemental concentrations obtained by PIXE analysis of PM10 samples collected at the vicinity of lignite power plants andmines in Megalopolis, Greece. Methods Phys Res. https://doi.org/10.1016/j.nimb.2015.02.037 Medeiros GA, Tresmondi AC, Queiroz BP, Fengler HF, Rosa HA, Fialho JM, Lopes RS (2017) Water quality, pollutant loads, and multivariate analysis of the effects of sewage discharges into urban streams of Southeast. Energy Ecol Environment. https://doi.org/10.1007/s40974-017-0062-y Men C, Liu RM, Wang QR, Guo LJ, Miao YX, Shen ZY (2019) Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system. Sci Total Environment. https://doi.org/10.1016/j.scitotenv.2018.10.212 Njuguna MS, Onyango AJ, Githaiga BK, Gituru WR, Xue Y (2020) Application of multivariate statistical analysis and water quality index in health risk assessment by domestic use of river water. Case study of Tana River in Kenya. Process Saf Environ Protect 149:158. https://doi.org/10.1016/j.psep.2019.11.006 Olsen RL, Chappell RW, Loftis JC (2012) Water quality sample collection, data treatment and results presentation for principal component analysis-literature review and Illinois River watershed case study. Water Res. https://doi.org/10.1016/j.watres.2012.03.028 OSİB (2016) Project of the effects of climate change on water resources. Ankara: Ministry of Water Management General Directorate of Water Affairs and Forestry of the Republic of Turkey Paatero P, Hopke PK (2009) Rotational tools for factor analytic models. Chemometr Intell Lab Syst. https://doi.org/10.1002/cem.1197 Paatero P (1997) Least squares formulation of robust non-negative factor analysis. Chemom Intell Lab Syst. https://doi.org/10.1016/S0169-7439(96)00044-5 Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics. https://doi.org/10.1002/env.3170050203 Parworth C, Fast J, Mei F, Shippent T, Sivaraman C, Tilp A, Zhang Q (2015) Long-term measurements of submicrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol Chemical Speciation Monitor (ACSM). Atmos Environ. https://doi.org/10.1016/j.atmosenv.2015.01.060 Praus P (2007) Urban water quality evaluation using multivariate analysis. Acta Montanistica Slovaca 12:150–158 Qadir A, Malik RN, Husain SZ (2007) Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan. Environ Monitor Assess. https://doi.org/10.1007/s10661-007-9846-4 Reghunath R, Murthy TR, Raghavan BR (2002) The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res. https://doi.org/10.1016/S0043-1354(01)00490-0 Salim AB, Gowhar M, Sayar Y, Ashok KP (2014) Statistical assessment of water quality parameters for pollution source identification in Sukhnag stream: an inflow stream of Lake Wular (Ramsar Site), Kashmir Himalaya. J Ecosyst. https://doi.org/10.1155/2014/898054 Salim I, Sajjad UR, Paule-Mercado C, Memon AS, 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. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.01.377 Seaw N, Theapanondh S (2015) Source apportionment analysis of airborne VOCs using positive matrix factorization in industrial and urban areas in Thailand. Atmos Pollut. https://doi.org/10.5094/APR.2015.073 Sergeant CJ, Starkey EN, Bartz KK, Wilson MH, Mueter FJ (2016) A practitioner’s guide for exploring water quality patterns using principal components analysis and Procrusteres. Environ Monitor Asses. https://doi.org/10.1007/s10661-016-5253-z Shi GL, Xu J, Peng X, Tian Y, Wang W, Han B (2016) Using a new WALSPMF model to quantify the source contributions to PM2.5 at a harbour site in China. Atmos Environ. https://doi.org/10.1016/j.atmosenv.2015.11.046 Shi GL, Zeng F, Li X, Feng YC, Wang YQ, Liu GX, Zhu T (2011) Estimated contributions and uncertainties of PCA/MLR-CMB results: source apportionment for synthetic. Atmos Environ. https://doi.org/10.1016/j.atmosenv.2011.03.007 Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Modell Softw. https://doi.org/10.1016/j.envsoft.2006.02.001 Singh KP, 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 Res. https://doi.org/10.1016/j.watres.2004.06.011 Singh KP, Malik A, Mohan D, Sinha S (2005) Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques–a case study. Anal Chim Acta. https://doi.org/10.1016/j.aca.2005.02.006 Solanki VR, Hussain M, Raja SS (2010) Water quality assessment of Lake Pandu Bodhan, Andhra Pradesh State, India. Environ Monitor Assess 163:411–419. https://doi.org/10.1007/s10661-009-0844-6 Sundaray SK, Nayak BB, Lin S, Bhatta D (2011) Geochemical speciation and risk assessment of heavy metals in the river estuarine sediments-a case study: Mahanadi basin, India. J Hazard Mater. https://doi.org/10.1016/j.jhazmat.2010.12.081 Tokatlı C (2020) Water quality assessment of Ergene River basin using multivariate statistical analysis. J Limnol Freshw Fish Res. https://doi.org/10.17216/limnofish.524036 Varol M, Şen B (2008) Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey. Environ Monitor Assess. https://doi.org/10.1007/s10661-008-0650-6 Vega M (1998) Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. https://doi.org/10.1016/S0043-1354(98)00138-9 Wang QH, Dong YX, Zheng W, Zhou GH (2007) Soil geochemical baseline values and environmental background values in Zhejiang, China (in Chinese). Geol Bull China 26:590–597 Wu ML, Wang YS, Sun C-C, Wang H, Dong J-D, Yin JP (2010) Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea. Mar Pollut Bull J 60:852–860 Wunderlin AD, Diaz MD, Ame MV, Pesce FS, Hued AC, Bistoni MD (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River basin (Córdoba-Argentina). Water Res. https://doi.org/10.1016/j.marpolbul.2010.01.007 Xia F, Zhang C, Liyin Q, Qiujin S, Randy AD, Minghua Z (2020) A comprehensive analysis and source apportionment of metals in riverine sediments of a rural-urban watershed. J Hazard Mater. https://doi.org/10.1016/j.jhazmat.2019.121230 Xue Y, Song J, Zhang Y, Feihe K, Wen M (2016) Nitrate pollution and preliminary source identification of surface water in a semi-arid river basin, using isotopic and hydrochemical approaches. Water. https://doi.org/10.3390/w8080328 Yang Y, Li T, Zhang T, Yu Q (2020) Time dimension analysis; Comparison of Nanjing local driving cycles in 2009 and 2017. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2019.101949 Yang Y-H, Zhou F, Guo H-C, Sheng H, Liu H, Dao X, He C-J (2010) Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods. Environ Monit Assess. https://doi.org/10.1007/s10661-009-1242-9 Yıldız Töre G, Insel G, Ubay Cokgor E, Ferlier E, Kabdaşlı N, Orhon D (2011) Pollution profile and biodegradation characteristics of fur-suede processing effluents. Environ Technol. https://doi.org/10.1080/09593330.2010.529465 Zhang GL, Bai JH, Xiao R, Zhao QQ, Jia J, Cui BS, Liu XH (2017) Heavy metal fractions and ecological risk assessment in sediments from urban, rural and reclamation-affected rivers of the Pearl River Estuary. Chemosphere. https://doi.org/10.1016/j.chemosphere.2017.05.155 Zhang Q, Zeng G, Li J, Fang Y, Yuan Q, Wang Y, Ye F (2009) Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China. Environ Monitor Assess. https://doi.org/10.1007/s10661-008-0301-y Zhao KL, Fu WJ, Qiu QZ, Ye ZQ, Li YF, Tunney H, Dou CY (2019) Spatial patterns of potentially hazardous metals in paddy soils in a typical electrical waste dismantling area and their pollution characteristics. Geoderma. https://doi.org/10.1016/j.geoderma.2018.10.004