Distribution, ecological fate, and risks of steroid estrogens in environmental matrices

文献类型： 外文期刊

第一作者： Odinga, Emmanuel Stephen

作者： Odinga, Emmanuel Stephen;Zhou, Xian;Waigi, Michael Gatheru;Ling, Wanting;Mbao, Evance Omondi;Ali, Qurban;Shiraku, Margaret L.

作者机构：

关键词： Steroid estrogens; Bibliometric analysis; Distribution; Ecological fate; Risks

期刊名称：CHEMOSPHERE （ 影响因子：8.943； 五年影响因子：8.52 ）

ISSN： 0045-6535

年卷期： 2022 年 308 卷

页码：

收录情况： SCI

摘要： Over the past two decades, steroidal estrogens (SEs) such as 17 alpha-ethylestradiol (EE2), 17 beta-estradiol (E2),17 alpha-estradiol (17 alpha-E2), estriol (E3) and estrone (E1) have elicited worldwide attention due to their potentially harmful effects on human health and aquatic organisms even at low concentration ng/L. Natural steroidal es-trogens exhibit greater endocrine disruption potency due to their high binding effect on nuclear estrogen re-ceptors (ER). However, less has been explored regarding their associated environmental risks and fate. A comprehensive bibliometric study of the current research status of SEs was conducted using the Web of Science to assess the development trends and current knowledge of SEs in the last two decades, from 2001 to 2021 October. The number of publications has tremendously increased from 2003 to 2021. We summarized the contamination status and the associated ecological risks of SEs in different environmental compartments. The results revealed that SEs are ubiquitous in surface waters and natural SEs are most studied. We further carried out an in-depth evaluation and synthesis of major research hotspots and the dominant SEs in the matrices were E1, 17 beta-E2, 17 alpha-E2, E3 and EE2. Nonetheless, investigations of SEs in soils, groundwater, and sediments remain scarce. This study elucidates SEs distribution, toxicological risks, ecological fate and mitigation measures, which will be beneficial for future monitoring, management, and risk assessment. Further studies are recommended to assess the toxicological risks of different SEs in complex environmental matrices to pursue a more precise and holistic quantitative estimation of estrogenic risk.

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