Abstract
Over the past few decades, rivers have become severely polluted as a result of receiving vast quantities of domestic and industrial wastewater. The identification of the major factors that influence water quality is crucial to understand the interactions of anthropogenic and natural factors and develop river restoration projects. In this study, the QUAL2Kw water quality model was used to quantitatively evaluate the most critical factors for water quality at two sites with different meteorological conditions and urban scales. The genetic algorithm (GA) was used to optimize the parameters in the model. The Monte Carlo simulation (MCS) method was used to assess the model uncertainty and sensitivity in all reaches for five water quality outputs (temperature, CBOD, DO, TP, and TN) in two seasons. The K-means clustering method associated with the sensitivity results was used to identify the major factors influencing the water quality in all reaches from the input data and the model parameters. The results showed that CBOD, TN, and TP were most sensitive to headwater and tributary quality. DO tended to be affected by more natural reactions than the other water quality indicators. In the cold and dry seasons and the more urbanized areas, river pollution was more severe, and the impact of natural reactions was reduced. The simulation results revealed the reliability of QUAL2Kw in modeling the quantity and quality of all river reaches. The method applied in this study is beneficial for the improvement and management of the water environment.
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References
Alberto WD, Del Pilar DM, Valeria AM et al (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia River basin (Cordoba-Argentina). Water Res 35(12):2881–2894. https://doi.org/10.1016/s0043-1354(00)00592-3
Allam A, Tawfik A, Yoshimura C, Fleifle A (2016) Simulation-based optimization framework for reuse of agricultural drainage water in irrigation. J Environ Manag 172:82–96. https://doi.org/10.1016/j.jenvman.2016.02.022
Ambrose RB, Wool TA, Barnwell TO, Jr. (2009) Development of water quality modeling in the United States. Environ Eng Res 14(4):200–211
Belgiorno V, Naddeo V, Scannapieco D et al (2013) Ecological status of rivers in preserved areas: effects of meteorological parameters. Ecol Eng 53:173–182. https://doi.org/10.1016/j.ecoleng.2012.12.039
Bryan EH (1972) Quality of stormwater drainage from urban land. Jawra J Am Water Resour Assoc 8(3):578–588
Cao B, Li C, Liu Y, Zhao Y, Sha J, Wang Y (2015) Estimation of contribution ratios of pollutant sources to a specific section based on an enhanced water quality model. Environ Sci Pollut Res 22(10):7569–7581. https://doi.org/10.1007/s11356-015-4266-4
Cho JH, Ha SR (2010) Parameter optimization of the QUAL2K model for a multiple-reach river using an influence coefficient algorithm. Sci Total Environ 408(8):1985–1991. https://doi.org/10.1016/j.scitotenv.2010.01.025
Eckstein J, Riedmueller S, Reed S (2000) YASAI: yet another simulation add-in for Excel. Version 2.0 user guide. From http://www.yasai.rutgers.edu/yasai-guide-20.html
Federigi I, Verani M, Carducci A (2017) Sources of bathing water pollution in northern Tuscany (Italy): effects of meteorological variables. Mar Pollut Bull 114(2):843–848. https://doi.org/10.1016/j.marpolbul.2016.11.017
Geist J (2011) Integrative freshwater ecology and biodiversity conservation. Ecol Indic 11(6):1507–1516. https://doi.org/10.1016/j.ecolind.2011.04.002
Gholizadeh MH, Melesse A, 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. Sci Total Environ 566:1552–1567. https://doi.org/10.1016/j.scitotenv.2016.06.046
Goktas RK, Aksoy A (2007) Calibration and verification of QUAL2E using genetic algorithm optimization. J Water Resour Plan Manag 133(2):126–136. https://doi.org/10.1061/(asce)0733-9496(2007)133:2(126)
Grizzetti B, Pistocchi A, Liquete C, Udias A, Bouraoui F, van de Bund W (2017) Human pressures and ecological status of European rivers. Sci Rep 7:1–11. https://doi.org/10.1038/s41598-017-00324-3
Holguin-Gonzalez JE, Boets P, Alvarado A et al (2013) Integrating hydraulic, physicochemical and ecological models to assess the effectiveness of water quality management strategies for the River Cuenca in Ecuador. Ecol Model 254:1–14. https://doi.org/10.1016/j.ecolmode1.2013.01.011
Huang F, Wang X, Lou L et al (2010) Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Res 44(5):1562–1572. https://doi.org/10.1016/j.watres.2009.11.003
Jackson RB, Carpenter SR, Dahm CN et al (2001) Water in a changing world. Ecol Appl 11(4):1027–1045. https://doi.org/10.2307/3061010
Johnson AC, Acreman MC, Dunbar MJ, Feist SW, Giacomello AM, Gozlan RE, Hinsley SA, Ibbotson AT, Jarvie HP, Jones JI, Longshaw M, Maberly SC, Marsh TJ, Neal C, Newman JR, Nunn MA, Pickup RW, Reynard NS, Sullivan CA, Sumpter JP, Williams RJ (2009) The British river of the future: how climate change and human activity might affect two contrasting river ecosystems in England. Sci Total Environ 407(17):4787–4798. https://doi.org/10.1016/j.scitotenv.2009.05.018
Jung KY, Lee K-L, Im TH et al (2016) Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environ Technol Innov 5:67–82. https://doi.org/10.1016/j.eti.2015.12.001
Kannel PR, Lee S, Kanel SR, Lee YS, Ahn KH (2007) Application of QUAL2Kw for water quality modeling and dissolved oxygen control in the river Bagmati. Environ Monit Assess 125(1–3):201–217. https://doi.org/10.1007/s10661-006-9255-0
Kannel PR, Kanel SR, Lee S, Lee YS, Gan TY (2011) A review of public domain water quality models for simulating dissolved oxygen in rivers and streams. Environ Model Assess 16(2):183–204. https://doi.org/10.1007/s10666-010-9235-1
Karami J, Alimohammadi A, Modabberi S (2012) Analysis of the Spatio-temporal patterns of water pollution and source contribution using the MODIS sensor products and multivariate statistical techniques. IEEE J-STARS 5(4):1243–1255. https://doi.org/10.1109/jstars.2012.2187273
Kuppusamy MR, Giridhar VV (2006) Factor analysis of water quality characteristics including trace metal speciation in the coastal environmental system of Chennai Ennore. Environ Int 32(2):174–179. https://doi.org/10.1016/j.envint.2005.08.008
Mavukkandy MO, Karmakar S, Harikumar PS (2014) Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India). Environ Sci Pollut Res 21(17):10045–10066
McMillan HK, Westerberg IK, Krueger T (2018) Hydrological data uncertainty and its implications. Wiley Interdiscip Rev-Water 5(6). https://doi.org/10.1002/wat2.1319
Mei K, Liao L, Zhu Y et al (2014) Evaluation of spatial-temporal variations and trends in surface water quality across a rural-suburban-urban interface. Environ Sci Pollut Res 21(13):8036–8051
Meybeck M (2003) Global analysis of river systems: from Earth system controls to Anthropocene syndromes. Philos Trans R Soc Lond B: Biol Sci 358(1440):1935–1955
Ministry of ecological environment of the People’s Republic of China (2002) Environmental quality standards for surface water. from http://kjs.mee.gov.cn/hjbhbz/bzwb/shjbh/shjzlbz/200206/t20020601_66497.shtml
Mustapha A, Zaharin A et al (2013) River water quality assessment using environmentric techniques: case;study of Jakara River basin. Environ Sci Pollut Res 20(8):5630–5644
Oki T, Kanae S (2006) Global hydrological cycles and world water resources. Science 313(5790):1068–1072
Ouyang Y, Nkedi-Kizza P, Wu QT et al (2006) Assessment of seasonal variations in surface water quality. Water Res 40(20):3800–3810. https://doi.org/10.1016/j.watres.2006.08.030
Park SS, Lee YS (2002) A water quality modeling study of the Nakdong River, Korea. Ecol Model 152(1):65–75. https://doi.org/10.1016/s0304-3800(01)00489-6
Parmar DL, Keshari AK (2012) Sensitivity analysis of water quality for Delhi stretch of the River Yamuna, India. Environ Monit Assess 184(3):1487–1508. https://doi.org/10.1007/s10661-011-2055-1
Paul MJ, Meyer JL (2001) Streams in the urban landscape. Annu Rev Ecol Syst 32:333–365. https://doi.org/10.1146/annurev.ecolsys.32.081501.114040
Pelletier GJ (2009) YASAI.xla: a modified version of an open-source add-in for Excel to provide additional functions for Monte Carlo simulation, from http://www.ecy.wa.gov/programs/eap/models.html
Pelletier GJ, Chapra S (2005) A modeling framework for Simulating River and stream water quality. QUAL2Kw Theory Documentation, from http://www.ecy.wa.gov/programs/eap/models/
Pelletier GJ, Chapra SC, Tao H (2006) QUAL2Kw - a framework for modeling water quality in streams and rivers using a genetic algorithm for calibration. Environ Model Softw 21(3):419–425. https://doi.org/10.1016/j.envsoft.2005.07.002
Putri MSA, Lou CH, Syai'in M et al (2018) Long-Term River water quality trends and pollution source apportionment in Taiwan. Water 10(10). https://doi.org/10.3390/w10101394
Schneiderman EM, Steenhuis TS, Thongs DJ et al (2007) Incorporating variable source area hydrology into a curve-number-based watershed model. Hydrol Process 21(25):3420–3430. https://doi.org/10.1002/hyp.6556
Sha J, Li Z, Swaney DP, Hong B, Wang W, Wang Y (2014) Application of a Bayesian watershed model linking multivariate statistical analysis to support watershed-scale nitrogen management in China. Water Resour Manag 28(11):3681–3695. https://doi.org/10.1007/s11269-014-0696-x
Sharma D, Kansal A (2013) Assessment of river quality models: a review. Rev Environ Sci Biotechnol 12(3):285–311. https://doi.org/10.1007/s11157-012-9285-8
Singh KP, Malik A, 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 538(1–2):355–374. https://doi.org/10.1016/j.aca.2005.02.006
Su SL, Li D, Zhang Q et al (2011) Temporal trend and source apportionment of water pollution in different functional zones of Qiantang River, China. Water Res 45(4):1781–1795. https://doi.org/10.1016/j.watres.2010.11.030
Tao H (2008) Calibration, sensitivity and uncertainty analysis in surface water quality modeling. Dissertations & Theses - Gradworks
Wang X, Wang Q, Wu C, Liang T, Zheng D, Wei X (2012) A method coupled with remote sensing data to evaluate non-point source pollution in the Xin'anjiang catchment of China. Sci Total Environ 430:132–143. https://doi.org/10.1016/j.scitotenv.2012.04.052
Wen YR, Schoups G, van de Giesen N (2017) Organic pollution of rivers: combined threats of urbanization, livestock farming and global climate change. Sci Rep 7. https://doi.org/10.1038/srep43289
Winter JG, Duthie HC (2000) Export coefficient modeling to assess phosphorus loading in an urban watershed. Jawra J Am Water Resour Assoc 36(5):1053–1061
Xia XH, Zhou JS, Yang ZF (2002) Nitrogen contamination in the Yellow River basin of China. J Environ Qual 31(3):917–925. https://doi.org/10.2134/jeq2002.0917
Ye H, Guo S, Li F, Li G (2013) Water quality evaluation in Tidal River reaches of Liaohe River Estuary, China using a revised QUAL2K model. Chin Geogr Sci 23(3):301–311. https://doi.org/10.1007/s11769-013-0586-9
Zhai X, Zhang Y, Wang X et al (2014) Non-point source pollution modelling using soil and water assessment tool and its parameter sensitivity analysis in Xin’anjiang catchment, China. Hydrol Process 28(4):1627–1640. https://doi.org/10.1002/hyp.9688
Zhang Z, Chen Y, Wang P et al (2014) River discharge, land use change, and surface water quality in the Xiangjiang River, China. Hydrol Process 28(13):4130–4140. https://doi.org/10.1002/hyp.9938
Zhang C, Shen Y, Liu F, Meng L (2016) Changes in reference evapotranspiration over an agricultural region in the Qinghai-Tibetan plateau, China. Theor Appl Climatol 123(1–2):107–115. https://doi.org/10.1007/s00704-014-1335-4
Zhao J, Fu G, Lei K et al (2011) Multivariate analysis of surface water quality in the three gorges area of China and implications for water management. J Environ Sci 23(9):1460–1471. https://doi.org/10.1016/s1001-0742(10)60599-2
Zhou F, Huang GH, Guo H et al (2007) Spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong. Water Res 41(15):3429–3439. https://doi.org/10.1016/j.watres.2007.04.022
Acknowledgments
The Environmental Protection Bureau and Environment Monitoring Station of Huangshan City and Qinghai Province are acknowledged for providing data for the Xinanjiang watershed and Huangshui watershed.
Funding
This work was supported by the Water Environment Institute, Chinese Academy for Environmental Planning (2016A008) and Environment Monitoring Station of Huangshan City (HZCG2017Z159).
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Kang, G., Qiu, Y., Wang, Q. et al. Exploration of the critical factors influencing the water quality in two contrasting climatic regions. Environ Sci Pollut Res 27, 12601–12612 (2020). https://doi.org/10.1007/s11356-020-07786-5
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DOI: https://doi.org/10.1007/s11356-020-07786-5