Distribution prediction of natural low-quality groundwater in the plains of Henan Province based on machine learning

doi:10.13745/j.esf.sf.2025.10.33

Abstract

Abstract:

As a crucial drinking water source for over two billion people worldwide, groundwater quality is intrinsically linked to human health and ecosystem integrity. Geogenic groundwater contamination (GGC), characterized by excessive levels of arsenic (As), fluoride (F), and iodine (I), originates from natural geological processes. The distribution of GGC, influenced by geological structures, hydrogeochemistry, and anthropogenic activities, exhibits regional patterns with local complexities. Research into its formation mechanisms and control strategies is therefore critical for ensuring water security. Using Henan Province as a case study, this research employed methods including Gibbs diagrams to analyze groundwater hydrochemical characteristics and their controlling factors, thereby identifying the origins of GGC. The correlation between GGC and the distribution of endemic diseases was investigated. Furthermore, machine learning models were introduced to achieve accurate spatial prediction of GGC. Subsequently, health risk control zones were proposed. The results indicate that: (1) GGC in the study area is concentrated in the Eastern Henan Plain and the regions along the Yellow River, with contamination levels in phreatic water being significantly higher than those in confined water; (2) Weakly alkaline and reducing environments represent key hydrogeochemical conditions for GGC formation, where rock weathering and dissolution combined with intense evaporation govern the enrichment of characteristic ions; (3) A spatial correlation exists between GGC distribution and endemic disease areas; (4) Arsenic, fluoride, and iodine in groundwater all exhibit significant spatial aggregation. Notably, the high-high (HH) clusters, indicating areas with co-occurrence of high arsenic, fluoride, and iodine, show strong agreement with the high-risk zones predicted by the machine learning models. Based on these findings, scientifically delineating protection zones in key regions such as Puyang, Xinxiang, Zhoukou, Kaifeng, and Shangqiu cities holds significant practical importance for ensuring local residents’ drinking water safety.

Key words: arsenic, fluorine and iodine, groundwater chemistry, health risk, endemic disease, Henan Province

CLC Number:

P641.2

YU Furong, LI Rui, LI Zhiping, WU Lin, LIU Zhongpei. Distribution prediction of natural low-quality groundwater in the plains of Henan Province based on machine learning[J]. Earth Science Frontiers, 2026, 33(1): 63-79.

Figures/Tables 16

References 52

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参数类别	具体参数	数据类型	单位/描述
气候气象条件	年平均气温	数值型	℃
	年降水量	数值型	mm/a
	潜在蒸发量	数值型	mm/a
	干燥指数(AI)	数值型	无单位
地表沉积条件	表层岩性类型	类别型	黏土、砂砾等
	土壤渗透系数	数值型	cm/s
	表层有机质含量	数值型	%
	地表盐渍化程度	数值型	无单位
地下水理化特征	pH值	数值型	无单位
	主要阳离子	数值型	mg/L
	主要阴离子	数值型	mg/L
	微量元素	数值型	μg/L
水文地质条件	地下水埋深	数值型	m
水文地质条件	含水层水力传导系数	数值型	m/d
气候气象条件	年平均气温	数值型	℃
	年降水量	数值型	mm/a
	潜在蒸发量	数值型	mm/a

参数类别	具体参数	数据类型	单位/描述
气候气象条件	年平均气温	数值型	℃
	年降水量	数值型	mm/a
	潜在蒸发量	数值型	mm/a
	干燥指数(AI)	数值型	无单位
地表沉积条件	表层岩性类型	类别型	黏土、砂砾等
	土壤渗透系数	数值型	cm/s
	表层有机质含量	数值型	%
	地表盐渍化程度	数值型	无单位
地下水理化特征	pH值	数值型	无单位
	主要阳离子	数值型	mg/L
	主要阴离子	数值型	mg/L
	微量元素	数值型	μg/L
水文地质条件	地下水埋深	数值型	m
水文地质条件	含水层水力传导系数	数值型	m/d
气候气象条件	年平均气温	数值型	℃
	年降水量	数值型	mm/a
	潜在蒸发量	数值型	mm/a

组分	各赋值类别的标准限值/(mg·L^-1)
组分	0	1	2
砷(As)	≤0.01	0.01~0.05	≥0.05
氟(F)	≤1.0	1.0~2.0	≥2.0
碘(I)	≤0.1	0.1~0.50	≥0.50

组分	各赋值类别的标准限值/(mg·L^-1)
组分	0	1	2
砷(As)	≤0.01	0.01~0.05	≥0.05
氟(F)	≤1.0	1.0~2.0	≥2.0
碘(I)	≤0.1	0.1~0.50	≥0.50

层位	年份	原生劣质组分	最小值	最大值	平均值	标准差	变异系数	原生劣质地下水分布率
潜水	2020	As	0	0.12	0.01	0.01	2.27	14.49%
		F	0.01	3.3	0.72	0.59	0.83	21.98%
		I	0.01	0.8	0.07	0.1	1.36	12.08%
	2021	As	0	0.14	0.01	0.01	2.38	16.91%
		F	0.05	3.4	0.72	0.61	0.85	25.85%
		I	0.05	0.9	0.07	0.09	1.39	7.25%
	2022	As	0	0.16	0.01	0.01	2.81	13.04%
		F	0.05	3.3	0.68	0.58	0.85	22.46%
		I	0.03	1	0.07	0.1	1.4	8.21%
承压水	2020	As	0	0.09	0	0.01	2.55	2.86%
		F	0.05	2.2	0.72	0.52	0.72	32.86%
		I	0.01	1	0.08	0.15	1.77	15.71%
	2021	As	0	0.04	0	0.01	2	4.29%
		F	0.05	2.72	0.75	0.61	0.82	35.71%
		I	0.02	0.9	0.06	0.1	1.61	8.57%
	2022	As	0	0.05	0.01	0.01	2.22	1.43%
		F	0.06	2.56	0.76	0.61	0.8	30.00%
		I	0.05	0.9	0.09	0.15	1.63	10.00%