灌区退水对区域地下水质影响与健康风险评估

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Abstract In order to explore the influence of drainage in irrigated areas on regional groundwater quality and health risks, the groundwater monitoring data of different strata in the western region of Jilin from 2012 to 2014 and 2019~2020 were selected, and the random forest method was used to evaluate the groundwater quality, and the spatio-temporal variation of groundwater quality under the influence of irrigated areas was analyzed. The primary environmental background of the study area and the influence of the irrigation area were comprehensively considered, and the health risk assessment of multiple elements exceeding the standard of groundwater was integrated. The spatio-temporal variation and influencing factors of the superimposed double risks were systematically compared and analyzed. The results show that the chemical types of groundwater in the irrigated area and its surrounding areas are mainly HCO₃^--Na⁺-Ca²⁺ weakly alkaline water, and there is no significant change in the two periods. Groundwater exceeding the standard consists of F, Fe, Mn and "trinitrogen" compounds. Drainage in irrigated area aggravates the change of "trinitrogen" content in surrounding water. There was a 17% increase in category Ⅳ to Ⅴ water in shallow groundwater, and a 12% increase in the number of monitoring sites with health risks exceeding the safety threshold. The health risks of groundwater are caused by the superposition of NO₃^-, NO₂^-, NH₄⁺ in the retreated water of irrigated area and the health risks caused by F and Mn in the native environment. NO₃^-, NO₂^- and NH₄⁺ are the main control factors of health risk in shallow groundwater. Confined water is insensitive to the disturbance of water retreat in irrigated area.

Key words： Western Jilin irrigation discharge groundwater quality random forest health risk assessment

Received: 24 September 2022

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X523

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	Articles by authors
	XU Li-wen
	BIAN Jian-min
	SUN Xiao-qing
	LOU Yu-qi
	SUN Guo-jing

Cite this article:

XU Li-wen,BIAN Jian-min,SUN Xiao-qing等. The influence and health risk assessment of groundwater quality in irrigated area[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(4): 1688-1695.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I4/1688

[1]	高瑞忠,秦子元,张生,等.吉兰泰盐湖盆地地下水Cr6+、As、Hg健康风险评价[J]. 中国环境科学, 2018,38(6):2353-2362. Gao R Z, Qin Z Y, Zhang S, et al. Health risk assessment of Cr6+, As and Hg in groundwater of Jilantai salt lake basin, China[J]. China Environmental Science, 2018,38(6):2353-2362.
[2]	康若熙,谢文理,赵文星,等.从水质变化趋势看常州市武南区域重点河流治理成效[J]. 中国环境科学, 2019,39(10):4186-4193. Kang R X, Xie W L, Zhao W X, et al. Spatiotemporal trends in water quality and the effectiveness of water treatment projects for Wunan District, Changzhou[J]. China Environmental Science, 2019,39(10):4186-4193.
[3]	李婧,唐敏,梁亦欣.2015~2018年河南省辖海河流域水质改善效果评价[J]. 环境工程, 2020,38(5):60-64,190. Li J, Tang M, Liang Y X. Evaluation of water quality improvement effect in Haihe River Basin in Henan Province from 2015 to 2018[J]. Environmental Engineering, 2020,38(5):60-64,190.
[4]	王铁良,苏芳莉,孙迪,等.基于模糊BP神经网络的辽河口湿地水质评价[J]. 西北林学院学报, 2020,35(5):195-200. Wang T L, Su F L, Sun D, et al. Water quality evaluation of Liaohe estuary wetland based on back propagation artificial neural network[J]. Journal of Northwest Forestry University, 2020,35(5):195-200.
[5]	孔刚,王全九,黄强.基于BP神经网络的北京昌平山前平原地下水水质评价[J]. 农业工程学报, 2017,33(S1):150-156,389. Kong G, Wang Q J, Huang Q. Evaluation of groundwater quality in Changping piedmont plain of Beijing based on BP neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(S1):150-156,389.
[6]	黄鹤,梁秀娟,肖霄,等.基于粗糙集的支持向量机地下水质量评价模型[J]. 中国环境科学, 2016,36(2):619-625. Huang H, Liang X J, Xiao Y, et al. Model of groundwater quality assessment with support vector machine based on rough set[J]. China Environmental Science, 2016,36(2):619-625.
[7]	Gu Z Q, Bian J M, Wu J J, et al. Effects of anthropogenic activities on hydrochemical characteristics of ground water of Da'an irrigation area in Western of Jilin Province[J]. Environmental science and pollution research international, 2022,29(20479-20495).
[8]	Wu C, Fang C, Wu X, et al. Health-risk assessment of arsenic and groundwater quality classification using random forest in the Yanchi Region of Northwest China[J]. Exposure and Health, 2020,12(761-774).
[9]	肖燚,郭亚会,李明蔚,等.基于机器学习的地下水水质预测研究[J]. 北京师范大学学报(自然科学版), 2022,58(2):261-268. Xiao Y, Guo Y H, Li M W, et al. Machine learning to predict groundwater quality[J]. Journal of Beijing Normal University (Natural Science), 2022,58(2):261-268.
[10]	杜尚海,古成科,张文静.随机森林理论及其在水文地质领域的研究进展[J]. 中国环境科学, 2022,42(9):4285-4295. Du S H, Gu C K, Zhang W J.A review on the progresses in random forests theory and its applications in hydrogeology[J]. China Environmental Science, 2022,42(9):4285-4295.
[11]	Leo Breiman. Random forests[J]. Machine Learning, 2001,45(5-32).
[12]	闫佰忠,孙剑,安娜.基于随机森林模型的地下水水质评价方法[J]. 水电能源科学, 2019,37(11):66-69. Yan B Z, Sun J, An N. Assessment of groundwater quality based on random forest model[J]. Water Resources and Power, 2019,37(11):66-69.
[13]	吴敏,温小虎,冯起,等.基于随机森林模型的干旱绿洲区张掖盆地地下水水质评价[J]. 中国沙漠, 2018,38(3):657-663. Wu M, Wen X H, Feng Q, et al. Assessment of groundwater quality based on random forest model in arid oasis area[J]. Journal of Desert Research, 2018,38(3):657-663.
[14]	吴娟娟,卞建民,万罕立,等.松嫩平原地下水氮污染健康风险评估[J]. 中国环境科学, 2019,39(8):3493-3500. Wu J J, Bian J M, Wan H L, et al. Health risk assessment of groundwater nitrogen pollution in Songnen Plain[J]. China Environmental Science, 2019,39(8):3493-3500.
[15]	宋晓光,芦岩,梁仕凯,等.张家口坝下地区高氟地下水成因分析与健康风险评价[J]. 地质科技通报, 2022,41(1):240-250,259. Song X G, Lu Y, Liang S K, et al. Analysis of high-fluoride groundwater formation mechanisms and assessment of health risk in Baxia Region, Zhangjiakou[J]. Bulletin of Geological Science and Technology, 2022,41(1):240-250,259.
[16]	邓安琪,董兆敏,高群,等.中国地下水砷健康风险评价[J]. 中国环境科学, 2017,37(9):3556-3565. Deng A Q, Dong Z M, Gao Q, et al. Health risk assessment of arsenic in groundwater across China[J]. China Environmental Science, 2017,37(9):3556-3565.
[17]	赵娟,李育松,卞建民,等.吉林西部地区高砷地下水砷的阈值分析及风险评价[J]. 吉林大学学报(地球科学版), 2013,43(1):251-258. Zhao J, Li Y S, Bian J M, et al. Threshold analysis and health risk assessment of arsenic in groundwater in Western Jilin Province[J]. Journal of Jilin University (Earth Science Edition), 2013,43(1):251-258.
[18]	李西林,孙晓庆,卞建民,等.吉林省通榆县潜水中氟分布规律及健康风险评价[J]. 水电能源科学, 2017,35(6):30-34. Li X L, Sun X Q, Bian J M, et al. Fluorine distribution and health risk assessment of phreatic water in Tongyu County, Jilin Province[J]. Water Resources and Power, 2017,35(6):30-34.
[19]	韩宇.松原灌区农业活动影响下的地下水质量及健康风险评价[D]. 长春:吉林大学, 2017. Han Y. The Evaluation of groundwater quality and health risk under the influence of agricultural activities in Songyuan irrigation area[D]. Changchun:Jilin University, 2017.
[20]	李天宇.大安灌区农业活动影响下的地下水水质评价及健康风险研究[D]. 长春:吉林大学, 2017. Li T Y. The evaluation of groundwater quality and the research on the health risks of the agricultural activities in Da'an irrigation district[D]. Changchun:Jilin University, 2017.
[21]	GB/T 14848-2017 地下水质量标准[S]. GB/T 14848-2017 Groundwater quality standard[S].
[22]	程庆锋.高铁锰氨氮地下水净化工艺优化及菌群结构研究[D]. 哈尔滨:哈尔滨工业大学, 2014. Cheng Q F. Optimization of purifying filter of groundwater containing high concentration of iron, manganese and ammonia and the investigation of microbial community structure[D]. Harbin:Harbin Industrial University, 2014.
[23]	郭常来,曹玉和,蔡贺,等.吉林省松嫩平原氟中毒病区水文地质特征及防氟改水对策[J]. 地质与资源, 2012,21(6):527-530. Guo C L, Cao Y H, Cai H, et al. Hydrogeology and countermeasures to fluorosis in the disease region in Songnen Plain, Jilin Province[J]. Geology and Resources, 2012,21(6):527-530.