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. 2021 Dec:243:103894.
doi: 10.1016/j.jconhyd.2021.103894. Epub 2021 Sep 25.

Factors controlling iodine enrichment in a coastal plain aquifer in the North Jiangsu Yishusi Plain, China

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Factors controlling iodine enrichment in a coastal plain aquifer in the North Jiangsu Yishusi Plain, China

Wenhao Wei et al. J Contam Hydrol. 2021 Dec.

Abstract

Iodine is an essential micronutrient in the human diet and an appropriate human iodine intake level is important for population health. Excessive iodine intake is often associated with high iodine groundwater which serves as an important drinking water source in many regions. This study aims to identify the source and key hydrogeochemical processes for iodine accumulation and mobility in the groundwaters of the Northern Jiangsu Yishusi Plain. Combined hydrogeochemical and statistical analyses, specifically random forest modeling and factor analysis, were used to explore the mechanisms affecting the spatial distribution of iodine. The concentration of iodine in the investigated groundwaters was found to vary widely and to range between 4.8 and 4750 μg/L, with 48.9% of the total samples (674) exceeding the threshold value of 100 μg/L for toxic exposure, as defined by the Chinese high‑iodine standard guideline. High iodine concentrations are shown to mainly occur in the marine plain and the shallow aquifer associated with the floodplains of the Old Yellow River. The marine or lagoons-facies sediments were identified as the most plausible iodine source. In addition, mixing of groundwater with paleo-seawater might also have played a role in the coastal area. In contrast, the flood sediments of the Old Yellow River are shown to be an unlikely source. However, they serve as a cover layer that favored the development of reducing hydrogeochemical conditions that can trigger iodine mobilization via the reductive dissolution of iron oxides and the degradation of organic matter. Slow groundwater flow rates also appear to favor iodine release from sediments.

Keywords: Groundwater; Hydrogeochemistry; Iodine; Machine learning; Random forest model.

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