Abstract
Rivers are a vital component of both urban and rural aquatic ecosystems, and the rapidly increasing pollution in rivers severely threatens the security of these ecosystems. In this study, the surface water quality in the Maozhou River basin, Guangdong Province, China, was assessed from 2018 to 2020 using multivariate statistical techniques and the Integrated Water Quality Index (IWQI). In addition, spatial trends in surface water quality were studied using a geographic information system. The results indicated that the water quality in 82.17% of the studied section met the Class V standard for surface water quality, with IWQI values ranging from 12.157 to 3.650. The surface water quality was clustered into eight groups and further divided into unsuitable (low quality) and suitable (acceptable quality) according to standard surface water quality thresholds (China in Environmental quality standards for surface water, 2002). Four groups (G1, G2, G3, and G4) were classified as unsuitable, because they fell short of the Class V standard for surface water quality. In these groups, industrial sewage, endogenous pollution, domestic sewage, and rainfall runoff, were the primary sources of pollution, and the main background pollutants for the water quality target of the functional zones were COD, NH3-N, TP, and LAS. The other four groups were classified as suitable. In these groups, endogenous pollution and rainfall runoff were the primary sources of pollution, and the main background pollutants for the water quality target of the functional zones were NH3-N and TP. Among them, NH3-N and LAS were recognized as responsive and sensitive to the surface water quality and spatio-temporal variability. Owing to pollution treatment and management measures undertaken by the Chinese government, the black-odorous water in the Maozhou River basin has disappeared, and the water quality in the Maozhou River basin has been maintained at the “medium and good” level. However, the surface water quality in the estuary region and the southwest tributary in the basin requires further improvement. This calls for further efforts to improve surface water quality and to properly deal with various sources of pollution in the watershed. This combined method has proved to be effective for surface water quality evaluation and management at river or basin scales. The results of this work are expected to provide a scientific foundation for aquatic ecosystem management and planning.
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References
Abbasi T, Abbasi SA (2012) Water quality indices. Elsevier, Amsterdam
Adimalla N, Qian H (2019) Hydrogeochemistry and fluoride contamination in the hard rock terrain of central Telangana, India: analyses of its spatial distribution and health risk. SN Appl Sci 1:202. https://doi.org/10.1007/s42452-019-0219-08
Ana EMC, José ARL, Diego AMC, José TV, Josue DLB, Janete MR (2019) Identification of the hydrogeochemical processes and assessment of groundwater quality, using multivariate statistical approaches and water quality index in a wastewater irrigated region. Water 11:1702. https://doi.org/10.3390/w11081702
Babiker IS, Mohamed MA, Hiyama T (2007) Assessing groundwater quality using GIS. Water Resour Manage 21:699–715. https://doi.org/10.1007/s1169-006-9059-6
Cai WB, Han BL, Lu F, Xian CF, Yang ZY (2020) Comprehensive evaluation of the eco-environment in the four global bay areas. Acta Ecol Sin 40:8392–8402. https://doi.org/10.5846/stxb202004301062
Chen L, He Q, Liu K, Li J, Jing C (2019) Downscaling of GRACE-derived groundwater storage based on the random forest model. Remote Sens. https://doi.org/10.3390/rs11242979
Chen YP, Yan H, Yao YJ, Zeng CL, Gao P, Zhuang LY, Fan LY, Ye DQ (2020) Relationships of ozone formation sensitivity with precursors emissions, meteorology and land use types, in Guangdong-Hong Kong-Macao Greater Bay Area. China Environ Sci 94:1–13
Chin YL, Mohd HA, Sarva MP, Aminatul HBY, Baba M (2012) Delineation of temporal variability and governing factors influencing the spatial variability of shallow groundwater chemistry in a tropical sedimentary island. J Hydrol. https://doi.org/10.1016/j.jhydrol.2012.02.015
China GB3838-2002 (2002) Environmental quality standards for surface water [S]
China HJ494-2009 (2009) Water quality guidance on sampling techniques [S]
Dassi L (2011) Investigation by multivariate analysis of groundwater composition in a multilayer aquifer system from North Africa: a multi-tracer approach. Appl Geochem 26:1386–1398. https://doi.org/10.1016/j.apgeochem.2011.05.012
Ding FF, Liu YY, Wang L, Liu HF, Ji CY, Zhang L, Wu D (2020) Analysis of the palladium response relationship of a receiving water body under multiple scenario changes in rainfall-runoff pollution. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-12597-3
Edegbene AO, Arimoro FO, Odume ON (2019) Developing and applying a macroinvertebrate-based multimetric index for urban rivers in the Niger Delta, Nigeria. Ecol Evol 9:12869–12885. https://doi.org/10.1002/ece3.5769
Eilander D, Annor FO, Lannini L, Giesen NVD (2014) Remotely sensed monitoring of small reservoir dynamics: a bayesian approach. Remote Sens 6:1191–1210. https://doi.org/10.3390/rs6021191
Emenike CP, Tenebe IT, Jarvis P (2018) Fluoride contamination in groundwater sources in southwestern Nigeria: assessment using multivariate statistical approach and human health risk. Ecotox Environ Safe 156:391–402. https://doi.org/10.1016/j.ecoenv.2018.03.022
Fang XQ, Fan Q, Li HW, Liao ZH, Xie JL, Fan SJ (2018) Multi-scale correlations between air quality and meteorology in the Guangdong–Hong Kong–Macau Greater Bay Area of China during 2015–2017. Atmos Environ 191:463–477. https://doi.org/10.1016/j.atmosenv.2018.08.018
Fragaa I, Charters FJ, Oullivan AD, Cochrane TA (2016) A novel modelling framework to prioritize estimation of non-point source pollution parameters for quantifying pollutant origin and discharge in urban catchments. J Environ Manag 167:75–84
Gao YY, Qian H, Ren WH, Wang HK, Liu FX, Yang FX (2020) Hydrogeochemical characterization and quality assessment of groundwater based on integrated-weight water quality index in a concentrated urban area. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.121006
Grunsky EC, Drew LJ, Sutphin DM (2009) Process recognition in multi-element soil and stream-sediment geochemical data. Appl Geochem 24:1602–1616. https://doi.org/10.1016/j.apgeochem.2009.04.024
Hamlat A, Guidoum A (2018) Assessment of groundwater quality in the Seraidi region of Northwestern Algeria using water quality index (WQI). Appl Water Sci 8:220. https://doi.org/10.1007/s13201-018-0863-y
Harvey ET, Kratzer S, Philipson P (2015) Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters. Remote Sens Environ 158:417–430. https://doi.org/10.1016/j.rse.2014.11.017
Howladar MF, Al Numanbakth MA, Faruque MO (2017) An application of water quality index (WQI) and multivariate statistics to evaluate the water quality around Maddhapara Granite Mining Industrial Area, Dinajpur. Bangladesh Environ Syst Res 6:1–18. https://doi.org/10.1186/s40068-017-0090-9
Islam AT, Shen S, Haque MA, Bodrud-Doza M, Maw K, Habib MA (2017) Assessing groundwater quality and its sustainability in Joypurhat district of Bangladesh using GIS and multivariate statistical approaches. Environ Dev Sustain 20:1935–1959. https://doi.org/10.1007/s10668-017-9971-3
Jiang ZS, Li B, Wu JC, Tang T, Li H. Xu H (2019) Ecological effects of water environment management project on the Maozhou River Basin. J Hydroecol. https://kns.cnki.net/kcms/detail/42.1785.X.20200924.1209.002.html
Lambraski N, Antonakos A, Panagopoulos G (2004) The use of multicomponent statistical analysis in hydrogeological environmental research. Water Res 38:1862–1872. https://doi.org/10.1016/j.watres.2004.01.009
Li BD, Liu C, Liu XB, Wang SY (2021) Water quality response characteristics and cause analysis of thermal stratification of large reservoirs. J China Inst Water Resour Hydropow Res 19:1–9. https://doi.org/10.13244/j.cnki.jiwhr.20200113
Liang ZH, Chen XH, Luo H, Wu Q, Li MG (2020) Spatio-temporal distribution characteristics of water quality in Shenzhen Bay and pollution source analysis. Water Resour Protect. https://doi.org/10.3880/j.issn.1004-6933.2020.04.015
Lin T, Xu PP, Qian H (2017) Assessment of water quality and analysis of pollution source in Ningxia section of the Yellow River. Environ Chem 36:1388–1396
Liu YS, Tang YY, Zhong GS, Zeng H (2019) A comparison study on heavy metal/metalloid stabilization in Maozhou River sediment by five types of amendments. J Soils Sediments 19:3922–3933. https://doi.org/10.1007/s11368-019-02310-w
Lou SH, Tang YD, Tao M, Ren KJ, Shao YH, Zhen WS (2020) Methods and practice of comprehensive improvement of Maozhou river water environment in Shenzhen, China. Water Wastew. https://doi.org/10.19853/j.zgjsps.1000-4602.2020.10.001
Lv JJ, Yang JY, Liao WF, Jiang JH, Chen CH, Zhao J, Yang S (2014) Study on the water quality and environmental conditions of the formation of black-odorous water. J Cent China Norm Univ (natural Sciences). https://doi.org/10.19603/j.cnki.1000-1190.2014.05.017
Madan KJ, Ankit SMAJ (2020) Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Res. https://doi.org/10.1016/j.watres.2020.115867
Mao QD, Xia D, Wen QP, Shi JZ (2019) A Revised Method of surface water quality evaluation based on background values and its application to samples collected in Heilongjiang Province, China. Water 11:1057. https://doi.org/10.3390/w11051057
Mcgregor GB, Fabbro LD (2000) Dominance of cylindrospermopsis raciborskii (Nostocales, Cyanobacteria) in Queensland tropical and subtropical reservoirs: implications for monitoring and management. Lakes Reserv Res Manag 5:195–205. https://doi.org/10.1046/j.1440-1770.2000.00115x
Naveedullah N, Hashmi MZ, Yu CN, Shen CF, Muhammad N, Shen H, Chen YX (2016) Water quality characterization of the Siling reservoir (Zhejiang, China) using water quality index. Clean: Soil, Air, Water 44:553–562. https://doi.org/10.1002/clen.201400126
Nong XZ, Shao DG, Zhong H, Liang JK (2020) Evaluation of water quality in the South-to-North Water Diversion Project of China using the water quality index (WQI) method. Water Res. https://doi.org/10.1016/j.watres.2020.115781
Peyman N, Sany SBT, Tajfard M, Hashim R, Rezayi M, Karlen DJ (2017) The status and characteristics of eutrophication in tropical coastal water. Environ Sci Process Impacts 19:1086–1103. https://doi.org/10.1039/c7em00200a
Ramos MAG, de Oliveira ESB, Piao ACS, Leite DAND, de Angelis DD (2016) Water quality index (WQI) of Jaguari and Atibaia rivers in the region of Paulinia, Sao paulo. Brazil Environ Monit Assess. https://doi.org/10.1007/S10661-016-5261-z
Sany SBT, Monazami G, Rezayi M, Tajfard M, Borgheipour H (2019) Application of water quality indices for evaluating water quality and anthropogenic impact assessment. Int J Environ Sci Technol 16:3001–3012. https://doi.org/10.1007/s13762-018-1894-5
Shi W, Xia J, Zhang X (2016) Influences of anthropogenic activities and topography on water quality in the highly regulated Huai River basin, China. Environ Sci Pollut Res 23:21460–21474. https://doi.org/10.1007/s11356-016-7368-8
Shi XY, Wang Y, Jiao JJ, Zhong JL, Wen HG, Dong R (2018) Assessing major factors affecting shallow groundwater geochemical evolution in a highly urbanized coastal area of Shenzhen City, China. J Geochem Explor 184:17–27. https://doi.org/10.1016/j.gexplo.2017.10.003
Shi GJ, Guo LY, Lin T, Zhang YY (2020) Variation characteristics and future changes of precipitation in Shenzhen. J Yangtze River Sci Res Inst 37:28–33. https://doi.org/10.11988/ckyyb.20190088
Shyu GS, Cheng BY, Chiang CT, Yao PH, Chang TK (2011) Applying factor analysis combined with Kriging and information entropy theory for mapping and evaluating the stability of groundwater quality variation in Taiwan. Int J Environ Res Pub Health 8:1084–1109. https://doi.org/10.3390/ijerph8041084
Sun R, Luo X, Tang B, Li Z, Wang T, Tao L, Mai B (2016) Persistent halogenated compounds in fish from rivers in the Pearl River Delta, South China: geographical pattern and implications for anthropogenic effects on the environment. Environ Res 146:371–378. https://doi.org/10.1016/j.envres.2016.01.021
Varol M (2019) Arsenic and trace metals in a large reservoir: seasonal and spatial variations, source identification and risk assessment for both residential and recreational users. Chemosphere 228:1–8. https://doi.org/10.1016/j.chemosphere.2019.04.126
Varol M, Gokot B, Bekleyen A, Sen B (2012) Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey. CATENA 92:11–21. https://doi.org/10.1016/j.catena.2011.11.013
Vorosmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn SE, Sullivan CA, Liermann CR, Davies PM (2010) Global threats to human water security and river biodiversity. Nature 467:555–561. https://doi.org/10.1038/nature09440
Weng H, Kou J, Shao Q (2020) Evaluation of urban comprehensive carrying capacity in the Guangdong–Hong Kong–Macao Greater Bay Area based on regional collaboration. Environ Sci Pollut Res 27:20025–20036. https://doi.org/10.1007/s11356-020-08517-6
Wu JG, Huang JH, Han XG, Gao XM, He FL, Jiang MX, Jiang ZG, Primack RB, Shen ZH (2004) The three Gorges dam: an ecological perspective. Front Ecol Environ 2:241–248. https://doi.org/10.1890/1540-9295(2004)002[0241:TTGDAE]2.0.CO;2
Wu J, Zhou H, He S, Zhang Y (2019) Comprehensive understanding of groundwater quality for domestic and agricultural purposes in terms of health risks in a coal mine area of the Ordos basin, north of the Chinese Loess Plateau. Environ Earth Sci 78:1–17. https://doi.org/10.1007/s12665-019-8471-1
Xiao J, Wang LQ, Deng L, Jin ZD (2019) Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2018.09.322
Xu ZX (2005) Comprehensive water quality identification index for environmental quality assessment of surface water. J Tongji Univ (natural Science) 33:483–488
Xu PP, Zhang QY, Qian H, Li MN, Hou K (2019a) Characterization of geothermal water in the piedmont region of Qinling Mountains and Lantian-Bahe Group in Guanzhong Basin, China. Environ Earth Sci 78:1–7. https://doi.org/10.1007/s12665-019-8418-6
Xu PP, Feng WW, Qian H, Zhang QY (2019b) Hydrogeochemical characterization and irrigation quality assessment of shallow groundwater in the Central-Western Guanzhong Basin, China. Int J Environ Res Public Health 16:18. https://doi.org/10.3390/ijerph16091492
Xu PP, Li MN, Qian H, Zhang QY, Liu FX, Hou K (2019c) Hydrochemistry and geothermometry of geothermal water in the central Guanzhong Basin, China: a case study in Xi’an. Environ Earth Sci. https://doi.org/10.1007/s12665-019-8099-1
Xu PP, Zhang QY, Qian H, Li MN, Yang FX (2021) An investigation into the relationship between saturated permeability and microstructure of remolded loess: a case study from Chinese Loess Plateau. Geoderma. https://doi.org/10.1016/J.GEODERMA.2020.114774
Yuan HZ, Yin HB, Yang Z et al (2020) Diffusion kinetic process of heavy metals in lacustrine sediment assessed under different redox conditions by DGT and DIFS model. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.140418
Zhang XD, Qian H, Chen J, Qiao L (2014) Assessment of groundwater chemistry and status in a heavily used semi-arid region with multivariate statistical analysis. Water 6:2212–2232. https://doi.org/10.3390/w6082212
Zhang QY, Xu PP, Qian H (2019) Assessment of groundwater quality and human health risk (HHR) evaluation of nitrate in the Central-Western Guanzhong Basin, China. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph16214246
Zhang YS, Wu L, Ren HZ, Deng LC, Zhang PC (2020c) Retrieval of water quality parameters from hyperspectral images using hybrid bayesian probabilistic neural network. Remote Sens 12:1567. https://doi.org/10.3390/rs12101567
Zhang QY, Xu PP, Qian H (2020b) Groundwater Quality Assessment Using Improved Water Quality Index (WQI) and Human Health Risk (HHR) evaluation in a semi-arid region of Northwest China. Expo Health. https://doi.org/10.1007/s12403-020-00345-w
Zhang QY, Xu PP, Qian H, Yang FX (2020a) Hydrogeochemistry and fluoride contamination in Jiaokou Irrigation District, Central China: assessment based on multivariate statistical approach and human health risk. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.140460
Zheng G (2010) Investigation and assessment on heavy metals pollution of agricultural soil in Guanzhong Irrigation District. Chin J Soil Sci 41:473–478. https://doi.org/10.19336/j.cnki.trtb.2010.02.044
Zhou H, Shi P, Wang JA, Yu D, Gao L (2010) Rapid urbanization and implications for river ecological services restoration: case study in Shenzhen, China. J Urban Plan Dev 137:121–132. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000051
Acknowledgements
The authors extend particular thanks to the Shenzhen municipal Ecology and Environment Bureau for providing water quality data. This study was supported by the project of Scientific Research Project of Science and Technology Innovation Fund from CCCC First Highway Consultants Co., Ltd (KCJJ2018-10, Key Technologies of Comprehensive Regulation of Water Environment in Highly Built Urban Areas).
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This article is part of a Topical Collection in Environmental Earth Sciences on Groundwater quality and contamination and the application of GIS, guest edited by Narsimha Adimalla and Hui Qian.
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Lin, T., Yu, H., Wang, Q. et al. Surface water quality assessment based on the Integrated Water Quality Index in the Maozhou River basin, Guangdong, China. Environ Earth Sci 80, 368 (2021). https://doi.org/10.1007/s12665-021-09670-y
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DOI: https://doi.org/10.1007/s12665-021-09670-y