Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department Water Resources and Drinking Water, 8600 Dübendorf, Switzerland.
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department Water Resources and Drinking Water, 8600 Dübendorf, Switzerland.
Sci Total Environ. 2022 Aug 10;833:155131. doi: 10.1016/j.scitotenv.2022.155131. Epub 2022 Apr 8.
Naturally occurring, geogenic manganese (Mn) and iron (Fe) are frequently found dissolved in groundwater at concentrations that make the water difficult to use (deposits, unpleasant taste) or, in the case of Mn, a potential health hazard. Over 6000 groundwater measurements of Mn and Fe in Southeast Asia and Bangladesh were assembled and statistically examined with other physicochemical parameters. The machine learning methods random forest and generalized boosted regression modeling were used with spatially continuous environmental parameters (climate, geology, soil, topography) to model and map the probability of groundwater Mn > 400 μg/L and Fe > 0.3 mg/L for Southeast Asia and Bangladesh. The modeling indicated that drier climatic conditions are associated with a tendency of elevated Mn concentrations, whereas high Fe concentrations tend to be found in a more humid climate with elevated levels of soil organic carbon. The spatial distribution of Mn > 400 μg/L and Fe > 0.3 mg/L was compared and contrasted with that of the critical geogenic contaminant arsenic (As), confirming that high Fe concentrations are often associated with high As concentrations, whereas areas of high concentrations of Mn and As are frequently found adjacent to each other. The probability maps draw attention to areas prone to elevated concentrations of geogenic Mn and Fe in groundwater and can help direct efforts to mitigate their negative effects. The greatest Mn hazard is found in densely populated northwest Bangladesh and the Mekong, Red and Ma River Deltas of Cambodia and Vietnam. Widespread elevated Fe concentrations and their associated negative effects on water infrastructure pose challenges to water supply. The Mn and Fe prediction maps demonstrate the value of machine learning for the geospatial prediction modeling and mapping of groundwater contaminants as well as the potential for further constituents to be targeted by this novel approach.
自然存在的地质成因锰 (Mn) 和铁 (Fe) 经常溶解在地下水中，其浓度使得水难以使用（沉淀、口感不佳），或者在 Mn 的情况下，成为潜在的健康危害。收集并统计了东南亚和孟加拉国超过 6000 个地下水 Mn 和 Fe 测量值，并与其他物理化学参数一起进行了统计分析。使用随机森林和广义增强回归建模等机器学习方法，结合空间连续的环境参数（气候、地质、土壤、地形），对地下水 Mn>400μg/L 和 Fe>0.3mg/L 的概率进行建模和制图。建模表明，干燥的气候条件与 Mn 浓度升高的趋势有关，而高 Fe 浓度则倾向于出现在土壤有机碳含量较高、湿度较大的气候条件下。对 Mn>400μg/L 和 Fe>0.3mg/L 的空间分布进行了比较和对比，并与关键的地质成因污染物砷 (As) 的空间分布进行了对比，证实高 Fe 浓度通常与高 As 浓度相关，而 Mn 和 As 浓度较高的区域通常相邻。概率图提请注意地下水中地质成因 Mn 和 Fe 浓度升高的地区，并有助于指导减轻其负面影响的工作。孟加拉国人口稠密的西北部和柬埔寨以及越南的湄公河、红河和马江三角洲地区存在最大的 Mn 危害。广泛存在的高 Fe 浓度及其对水基础设施的负面影响给供水带来了挑战。Mn 和 Fe 预测图展示了机器学习在地下水污染物的地理空间预测建模和制图中的价值，以及该新方法可能进一步针对其他成分的潜力。
