Assessing the impact of reverse osmosis plant operations on water quality index improvement through machine learning approaches and health risk assessment

被引:0
作者
Abbasi, Fariba [1 ]
Kazemi, Azadeh [2 ]
Badeenezhad, Ahmad [3 ]
Moazamfard, Mostafa [4 ]
Armand, Raham [5 ]
Mohammadpour, Amin [6 ]
机构
[1] Bushehr Univ Med Sci, Persian Gulf Biomed Sci Res Inst, Syst Environm Hlth & Energy Res Ctr, Bushehr, Iran
[2] Arak Univ, Fac Agr & Environm, Dept Environm Sci & Engn, Arak 38156879, Iran
[3] Behbahan Univ Med Sci, Dept Environm Hlth Engn, Behbahan, Iran
[4] Behbahan Univ Med Sci, Dept Operating Room, Behbahan, Iran
[5] Behbahan Univ Med Sci, Behbahan, Iran
[6] Jahrom Univ Med Sci, Res Ctr Social Determinants Hlth, Jahrom, Iran
关键词
Water quality; Groundwater; Reverse osmosis; Machine learning; Health risk assessment; Monte Carlo simulation; GROUNDWATER; FLUORIDE; NITRATE; IRAN;
D O I
10.1016/j.rineng.2025.104363
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Reverse osmosis (RO) is used to improve drinking water quality, but knowledge of outlet quality and system performance is required. This study evaluated RO performance in treating groundwater in southern Iran, focusing on the water quality index (WQI) and health risks. The parameters in the inlet flow that were above the standard level in some samples include total hardness (TH), sulfate (SO4), chloride (Cl), total dissolved solids (TDS), electrical conductivity (EC), and Turbidity. But in the outlet Flow, the parameters exceeding the standard level were TH, SO4, Cl, and TDS. The highest mean removal efficiencies in the RO system were for free chlorine residual (FCR) (98.43 %) and SO4 (82.89 %). The WQI of the inlet, classified as good in 97.67 % of the total samples, improved to excellent in 95.35 % of the samples at the outlet after treatment by the RO system. Machine learning results revealed that the random forest (RF) model was the most accurate in predicting the WQI, with TDS and EC as the key influencing factors. The non-carcinogenic risk from fluoride (F) and nitrate (NO3) in children group exceeded the permissible limit in approximately 4.6 % and 6.9 % of inlet water samples, respectively. The 95th percentile hazard index (HI) for children was 2.32 for inlet water and 1.10 for outlet water, while for adults, it was 1.08 and 0.52, respectively. The F level and ingestion rate (IR) were the most effective parameters on HI. These findings highlight the need for RO-purified water and emphasize regular monitoring of treatment plants.
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页数:13
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共 83 条
  • [21] Kontos Y.N., Kassandros T., Perifanos K., Karampasis M., Katsifarakis K.L., Karatzas K., Machine learning for groundwater pollution source identification and monitoring network optimization, Neural Comput. Appl., 34, pp. 19515-19545, (2022)
  • [22] Dodig A., Ricci E., Kvascev G., Stojkovic M., A novel machine learning-based framework for the water quality parameters prediction using hybrid long short-term memory and locally weighted scatterplot smoothing methods, J. Hydroinform., 26, pp. 1059-1079, (2024)
  • [23] Khan I., Umar R., Machine learning-driven optimization of water quality index: a synergistic ENTROPY-CRITIC approach using spatio-temporal data, Earth Syst. Environ., 8, pp. 1453-1475, (2024)
  • [24] Sharma A., Sharma R., Rana R., Kalia A., Water quality prediction using machine learning models, E3S Web Conf., 596, pp. 35307-35334, (2024)
  • [25] Uddin M.G., Nash S., Rahman A., Olbert A.I., Performance analysis of the water quality index model for predicting water state using machine learning techniques, Process. Saf. Environ. Protect., 169, pp. 808-828, (2023)
  • [26] Abbas F., Cai Z., Shoaib M., Iqbal J., Ismail M., Arifullah A., Alrefaei A.F., Albeshr M.F., Machine learning models for water quality prediction: a comprehensive analysis and uncertainty assessment in Mirpurkhas, Sindh, Pakistan, Water (Switzerland), 16, (2024)
  • [27] Pourkhabbaz H.R., Mohammadyari F., Aghdar H., Tavakoly M., Planning approach to land use change modeling using satellite images several times Behbahan City, Town Country Plan., 7, pp. 187-207, (2015)
  • [28] Shafiei N., Mehrpak R., Investigating the trend of vegetation changes in the Behbahan City, GIS Remote Sens., 4, pp. 1-7, (2022)
  • [29] Gupta N., Pandey P., Hussain J., Effect of physicochemical and biological parameters on the quality of river water of Narmada, Madhya Pradesh, India, Water Sci., 31, pp. 11-23, (2017)
  • [30] Shaibur M.R., Howlader M., Ahmmed I., Sarwar S., Hussam A., Water quality index and health risk assessment for heavy metals in groundwater of Kashiani and Kotalipara upazila, Gopalganj, Bangladesh, Appl. Water Sci., 14, pp. 1-18, (2024)