Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran; Institute of Environment, University of Tabriz, Tabriz, East Azerbaijan, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran; Medical Geology and Environmental Research Center, University of Tabriz, Tabriz. 5166616471, Iran.
J Environ Manage. 2023 Apr 15;332:117287. doi: 10.1016/j.jenvman.2023.117287. Epub 2023 Jan 28.
This paper investigates aggregated risks in aquifers, where risk exposures may originate from different contaminants e.g. nitrate-N (NO-N), arsenic (As), boron (B), fluoride (F), and aluminium (Al). The main goal is to develop a new concept for the total risk problem under sparse data as an efficient planning tool for management through the following methodology: (i) mapping aquifer vulnerability by DRASTIC and SPECTR frameworks; (ii) mapping risk indices to anthropogenic and geogenic contaminants by unsupervised methods; (iii) improving the anthropogenic and geogenic risks by a multi-level modelling strategy at three levels: Level 1 includes Artificial Neural Networks (ANN) and Support Vector Machines (SVM) models, Level 2 combines the outputs of Level 1 by unsupervised Entropy Model Averaging (EMA), and Level 3 integrates the risk maps of various contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium) modelled at Level 2. The methodology offers new data layers to transform vulnerability indices into risk indices and thereby integrates risks by a heuristic scheme but without any learning as no measured values are available for the integrated risk. The results reveal that the risk indexing methodology is fit-for-purpose. According to the integrated risk map, there are hotspots at the study area and exposed to a number of contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium).
本文研究了含水层中的综合风险，其中风险暴露可能来自不同的污染物，如硝酸盐-N（NO-N）、砷（As）、硼（B）、氟（F）和铝（Al）。主要目标是开发一种新的概念来解决数据稀疏条件下的总风险问题，作为一种通过以下方法进行管理的有效规划工具：（i）通过 DRASTIC 和 SPECTR 框架对含水层脆弱性进行制图；（ii）通过无监督方法对人为和地质污染物的风险指数进行制图；（iii）通过三级多层次建模策略来改进人为和地质风险：一级包括人工神经网络（ANN）和支持向量机（SVM）模型，二级通过无监督熵模型平均（EMA）结合一级的输出，三级整合在二级建模的各种污染物（硝酸盐-N、砷、硼、氟和铝）的风险图。该方法提供了新的数据层，将脆弱性指数转化为风险指数，从而通过启发式方案整合风险，但没有任何学习，因为没有可用于综合风险的实测值。结果表明，风险索引方法是适用的。根据综合风险图，研究区域存在热点，且暴露于多种污染物（硝酸盐-N、砷、硼、氟和铝）。
