Transport model-based method for estimating micropollutant removal efficiency in riverbank filtration

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-01-23 DOI:10.1016/j.watres.2025.123194

Norbert Erdélyi, Dóra Gere, Eszter Fekete, Gábor Nyiri, Attila Engloner, Andrea Tóth, Tamás Madarász, Péter Szűcs, Zsuzsanna Ágnes Nagy-Kovács, Tamás Pándics, Márta Vargha

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Abstract

Riverbank filtration is a cost-effective and efficient method for drinking water production, using the natural filtration capacity of the river gravelbed. Removal efficiency for organic micropollutants (OMP) in field studies is generally calculated by comparing the concentrations measured in surface water and in the wells either on the same day or with a shift of fixed time interval, neither of which can account for the variability of surface water quality and travel time in the aquifer. The present study proposes a novel method based on travel time distribution determined by a numerical transport model with a hypothesis that it will provide more reliable estimate for OMP removal. The model was developed for two production sites of Budapest Waterworks, Hungary on Danube River. River water and riverbank filtered well water were sampled regularly for one year (158 samples each) and analysed for 41 OMPs (pesticides, pharmaceutical residues and industrial pollutants). Nineteen pollutants were detected in >50% of the well water samples. Median removal rates were 4-97%, while the concentration of five compounds increased in some wells. Removal rates of telmisartan, tramadol, sulfamethoxazole, 4-methyl-benzotriazole, 5-methyl-benzotriazole and desethyl-terbuthylazine correlated negatively to redox potential (|r|=0.456-0.805). Median travel time increased after high flow events resulting in reduced removal of telmisartan, tramadol, 4-methyl-benzotriazole and desethyl-terbuthylazine (|r|= 0.435-0.661). Removal of diatrizoate, iopamidol, tramadol and benzotriazole increased with distance from the shore (148 vs 395 m) by 25%, 28%, 8%, 16%, respectively. Background groundwater contamination increased pesticide concentration in the wells located in agricultural areas 1.5-5-fold compared to river water. The model-based method gave more consistent results compared to traditional calculations for OMP removal efficiency during the sampling campaign and allowed for estimating the impact of various environmental factors.

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基于输运模型的河岸过滤微污染物去除效率估算方法

河岸过滤是一种经济高效的饮用水生产方法，利用河流砾石的自然过滤能力。在实地研究中，有机微污染物（OMP）的去除效率通常是通过比较地表水和井中测量的浓度在同一天或固定时间间隔的变化来计算的，这两种方法都不能解释地表水质量和在含水层中移动时间的变化。本研究提出了一种基于数值输运模型确定的走时分布的新方法，并假设它将提供更可靠的OMP去除估计。该模型是为匈牙利多瑙河上布达佩斯水厂的两个生产基地开发的。对河水和河岸过滤井水进行了为期一年的定期采样（各158个样本），并分析了41种omp（农药、药物残留和工业污染物）。在50%的井水样本中检测出19种污染物。中位去除率为4-97%，但部分井中5种化合物的浓度有所增加。替米沙坦、曲马多、磺胺甲恶唑、4-甲基-苯并三唑、5-甲基-苯并三唑和去乙基-特丁基嗪的去除率与氧化还原电位呈负相关（|r =0.456 ~ 0.805）。高流量事件导致替米沙坦、曲马多、4-甲基苯并三唑和去乙基特丁基嗪的去除减少，中位旅行时间增加（|r = 0.435-0.661）。离海岸越远（148米对395米），三角盐、iopamidol、曲马多和苯并三唑的去除率分别增加25%、28%、8%和16%。地下水本底污染使农区水井中农药浓度比河水高1.5-5倍。与传统的采样过程中OMP去除效率计算方法相比，基于模型的方法给出了更一致的结果，并允许估计各种环境因素的影响。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.