Evaluation of water quality in the Chalus River using the statistical analysis and water quality index (WQI)

Document Type : Research/Original/Regular Article

Authors

1 Graduated M.Sc. Student/ Department of Civil Engineering, Faculty of Engineering, Yasouj University, Yasouj, Iran

2 Assistant Professor/ Department of Civil Engineering, Faculty of Engineering, Yasouj University, Yasouj, Iran

3 M.Sc. Student/ Department of Civil Engineering, Faculty of Engineering, Yasouj University, Yasouj, Iran

Abstract

Introduction
River pollution is one of the serious risks to the environment, water systems, and human health. Regular monitoring and protection of river water quality is therefore vital to meet environment, and human needs. The increasing trend to fresh water highlights the management of river water resources as a crucial resource. Meanwhile, population growth, irregular urbanization, industrialization, overuse of chemicals materials in agriculture (such as fertilizers and pesticides), discharge of domestic sewage, and solid waste into watercourses, sand mining in riverbeds, water transfer diversion, has seriously challenged the rivers regimes and river ecosystems.
Materials and Methods
The effect of agricultural activities on water quality in the Chalus River was examined. Sampling was conducted and analyzed in surface water at three points from October 2019 to September 2020. Three surface water samples were collected from each station and water quality variables were analyzed. The following water quality parameters have been analysed in the current study; temperature, pH, conductivity, soluble solids, suspended solids, turbidity, salinity, reduction of oxidation potential, alkali, total hardness. Also, oxygen demand parameters of dissolved oxygen, oxygen saturation, biological oxygen requirement were examined. Meanwhile, the nutrient-related water quality parameters include N-NH4, N-NO2, N-Np3, total phosphate, total phosphorus are also evaluated. The heavy metals, inorganic pollution parameters, and suspended chlorophyll content of biological parameters were the other investigated parameters. Multiple statistical methods were used for the results of analyzing the parameter including principal component analysis (PCA), Pearson correlation Coefficient (PCC), and cluster analysis (CA). In addition, water quality index (WQI) was used for determining quality of surface water, hazard quotient (HQ), and hazard index (HI) for evaluating the public health risk for heavy metals.
Results and Discussion
The water quality of Chalous River has decreased from S1 station to downstream. The water quality index based on public health risk assessment showed that station S1 water could be used as drinking water and did not pose a potential risk to the health of adults and children. However, the water quality at Station S2, and especially Station S3, cannot be used for drinking puprpos, due to improper quality and may poses potential risks to the health of adults and children. The station S1 with an average of 15.62 categorized in the excellent water quality category. Meanwhile, the station S2 and S3 with an average of 25.5, and 49.8 assigned as good water quality status, respectively. The amount of As, Cd, Co, Ni, Pb was very small at the studied stations. The hazard coefficient (HQ) and hazard index (HI) values calculated to determine the risk of heavy metal effects on health were identified as non-carcinogenic. HI values of heavy metals calculated for adults and children were Mn> Cu> Al> Zn> Fe, respectively. The manganese and copper are more involved in non-carcinogenic health risks. Also, the values of HQingestion and HQdermal values and the HI value are less than one.
Conclusion
A combination of point and non-point source pollution have been identified as the main source of water quality deterioration. The water quality parameters of Chalous River in S1 station has not exceeded the permissible level of the national standard of Iran. While, the water quality in S2, and S3 stations has decreased that poses potential risks to the human health and need a pollution prevention action plan. The factors such as municipal wastewater, septic tank and water leakage from horse stables, natural and artificial fertilizers used in agriculture, runoff and rock pebbles in the basin are the causes of pollution.

Keywords


Ashar, Y.K., Susilawati, S., & Agustina, D. (2020). Analisis Kualitas (BOD, COD, DO) Air Sungai Pesanggrahan Desa Rawadenok Kelurahan Rangkepan Jaya Baru Kecamatan Mas Kota Depok. Fakultas Kesehatan Masyarakat UINSU Medan, 24 pages.
Bhatta, B., Shrestha, S., Shrestha, P.K., & Talchabhadel, R. (2019). Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin. Catena, 181, 104082.
Chabuk, A., Al-Madhlom, Q., Al-Maliki, A., Al-Ansari, N., Hussain, H.M., & Laue, J. (2020). Water quality assessment along Tigris River (Iraq) using water quality index (WQI) and GIS software. Arabian Journal of Geosciences, 13(14), 1–23.
Elkiran, G., Nourani, V., & Abba, S.I. (2019). Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577, 123962.
Ewaid, S.H., & Abed, S.A. (2017). Water quality index for Al-Gharraf river, southern Iraq. The Egyptian Journal of Aquatic Research, 43(2), 117–122.
Ewaid, S.H., Abed, S.A., & Kadhum, S.A. (2018). Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis. Environmental Technology & Innovation, 11, 390–398.
Garg, V., Aggarwal, S.P., & Chauhan, P. (2020). Changes in turbidity along Ganga River using Sentinel-2 satellite data during lockdown associated with COVID-19. Geomatics, Natural Hazards and Risk, 11(1), 1175–1195.
Irham, M., Abrar, F., & Kurnianda, V. (2017). Analisis BOD dan COD di perairan estuaria Krueng Cut, Banda Aceh. DEPIK Jurnal Ilmu-Ilmu Perairan, Pesisir Dan Perikanan, 6(3), 199–204.
Jaiswal, M., Hussain, J., Gupta, S.K., Nasr, M., & Nema, A.K. (2019). Comprehensive evaluation of water quality status for entire stretch of Yamuna River, India. Environmental Monitoring and Assessment, 191(4), 208.
Jin, X., Bai, Z., Oenema, O., Winiwarter, W., Velthof, G., Chen, X., & Ma, L. (2020). Spatial planning needed to drastically reduce nitrogen and phosphorus surpluses in China’s Agriculture. Environmental Science & Technology, 54(19), 11894–11904.
Kang, D., So, Y.H., Park, K., Kim, I., & Kim, B.W. (2019). Analyses of TOC efficiency and correlation between DO, BOD, COD and influence factors using long-term observation data in the main stream of Nakdong River. Journal of Environmental Science International, 28(5), 465–474.
Khalili, R., Parvinnia, M., & Motaghi, H. (2020). Evaluation of Bashar River water quality using CCME water quality index. Journal of Environmental Science Studies, 5(3), 2807–2814.
Khalili, R., Parvinnia, M., & Motaghi, H. (2021). The effects of forecasted precipitation amount on probable maximum precipitation and probable maximum flood parameters. Journal of Environmental Science Studies, 5(4), 2982–2989.
Khalili, R., Ghaedi, M., Parvinnia, M., & Sabzehmeidani, M.M. (2021). Simultaneous removal of binary mixture dyes using Mn - Fe layered double hydroxide coated chitosan fibers prepared by wet spinning. Surfaces and Interfaces, 23, 100976.
Khalili, R., Parvinnia, M., & Zali, A. (2020). Water quality assessment of Garmarood River using the national sanitation foundation water quality index (NSFWQI), river pollution index (RPI) and weighted arithmetic water quality index (WAWQI). Environment and Water Engineering, 6(3), 274–284.
Khalili, R., Zali, A., & Motaghi, H. (2021). Evaluating the heavy metals in the water and sediments of Haraz River, using pollution load index (PLI) and geo accumulation index (Igeo). Iranian Journal of Soil and Water Research, 52(4), 933-942 (in Persian).
Khan, R., Saxena, A., & Shukla, S. (2020). Evaluation of heavy metal pollution for River Gomti, in parts of Ganga Alluvial Plain, India. SN Applied Sciences, 2(8), 1–12.
Melesse, A.M., Khosravi, K., Tiefenbacher, J.P., Heddam, S., Kim, S., Mosavi, A., & Pham, B.T. (2020). River water salinity prediction using hybrid machine learning models. Water, 12(10), 2951.
Mukherjee, I., Singh, U.K., Singh, R.P., Kumari, D., Jha, P.K., & Mehta, P. (2020). Characterization of heavy metal pollution in an anthropogenically and geologically influenced semi-arid region of east India and assessment of ecological and human health risks. Science of The Total Environment, 705, 135801.
Nath, S.D., Choudhury, T.R., & Sinha, R.C. (2017). An investigation of pH, TDS and trace elements of water in Buriganga river, Bangladesh. International Journal of Sciences & Applied Research, 4(11), 24-33.
Ou, Y., Xue, Z.G., Li, C., Xu, K., White, J.R., Bentley, S.J., & Zang, Z. (2020). A numerical investigation of salinity variations in the Barataria Estuary, Louisiana in connection with the Mississippi River and restoration activities. Estuarine, Coastal and Shelf Science, 245, 107021.
Prasad, S., Saluja, R., Joshi, V., & Garg, J.K. (2020). Heavy metal pollution in surface water of the Upper Ganga River, India: human health risk assessment. Environmental Monitoring and Assessment, 192(11), 1–15.
Santoso, A.D. (2018). Keragaan Nilai DO, BOD dan COD di Danau Bekas Tambang Batubara Studi Kasus pada Danau Sangatta North PT. KPC di Kalimatan Timur. Jurnal Teknologi Lingkungan, 19(1), 89–96.
Şener, Ş., Şener, E., & Davraz, A. (2017). Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Science of the Total Environment, 584, 131–144.
Suryawan, I. W.K., Prajati, G., Afifah, A.S., & Apritama, M.R. (2020). NH3-N and COD reduction in Endek (Balinese textile) wastewater by activated sludge under different DO condition with ozone pretreatment. Walailak Journal of Science and Technology (WJST), 18(6), 9127.
Swain, S.S., Mishra, A., Sahoo, B., & Chatterjee, C. (2020). Water scarcity-risk assessment in data-scarce river basins under decadal climate change using a hydrological modelling approach. Journal of Hydrology, 590, 125260.
Tokatli, C. (2021). Health risk assessment of toxic metals in surface and groundwater resources of a significant agriculture and industry zone in Turkey. Environmental Earth Sciences, 80(4), 1–14.
Ustaoğlu, F., Tepe, Y., & Taş, B. (2020). Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index. Ecological Indicators, 113, 105815.
Verbrugge, L.N.H., Schipper, A.M., Huijbregts, M.A.J., Van der Velde, G., & Leuven, R.S.E.W. (2012). Sensitivity of native and non-native mollusc species to changing river water temperature and salinity. Biological Invasions, 14(6), 1187–1199.
Wang, Y., Xie, Z., Liu, S., Wang, L., Li, R., Chen, S., Jia, B., Qin, P., & Xie, J. (2020). Effects of anthropogenic disturbances and climate change on riverine dissolved inorganic nitrogen transport. Journal of Advances in Modeling Earth Systems, 12(10), e2020MS002234.
Worrall, F., Kerns, B., Howden, N.J.K., Burt, T.P., & Jarvie, H.P. (2020). The probability of breaching water quality standards–a probabilistic model of river water nitrate concentrations. Journal of Hydrology, 583, 124562.
Wu, H., Yang, W., Yao, R., Zhao, Y., Zhao, Y., Zhang, Y., Yuan, Q., & Lin, A. (2020). Evaluating surface water quality using water quality index in Beiyun River, China. Environmental Science and Pollution Research, 27(28), 35449–35458.
Wu, H., Yang, F., Li, H., Li, Q., Zhang, F., Ba, Y., Cui, L., Sun, L., Lv, T., & Wang, N. (2020). Heavy metal pollution and health risk assessment of agricultural soil near a smelter in an industrial city in China. International Journal of Environmental Health Research, 30(2), 174–186.
Wu, Z., Wang, X., Chen, Y., Cai, Y., & Deng, J. (2018). Assessing river water quality using water quality index in Lake Taihu Basin, China. Science of the Total Environment, 612, 914–922.
Xiao, H., Shahab, A., Xi, B., Chang, Q., You, S., Li, J., Sun, X., Huang, H., Li, X., & Saddique, J. (2020). Heavy metal pollution, ecological risk, spatial distribution, and source identification in sediments of the Lijiang River, China. Environmental Pollution, 269, 116189.
Xu, G., Li, P., Lu, K., Tantai, Z., Zhang, J., Ren, Z., Wang, X., Yu, K., Shi, P., & Cheng, Y. (2019). Seasonal changes in water quality and its main influencing factors in the Dan River basin. Catena, 173, 131–140.
Xu, J., Zheng, L., Xu, L., Liu, B., Liu, J., & Wang, X. (2020). Identification of dissolved metal contamination of major rivers in the southeastern hilly area, China: distribution, source apportionment, and health risk assessment. Environmental Science and Pollution Research, 27(4), 3908-3922.
Zounemat‐Kermani, M., Alizamir, M., Fadaee, M., Sankaran Namboothiri, A., & Shiri, J. (2020). Online sequential extreme learning machine in river water quality (turbidity) prediction: a comparative study on different data mining approaches. Water and Environment Journal, 35, 335-348.