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Article

Online Monitoring of Heavy Metals in Groundwater: A Case Study of Dynamic Behavior, Monitoring Optimization and Early Warning Performance

1
State Key Laboratory of Soil Pollution Control and Safety, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2
MEE Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
3
Guangdong Ecological and Environmental Monitoring Center, Guangzhou 518049, China
4
Zhejiang Environment Technology Co., Ltd., Hangzhou 310020, China
*
Authors to whom correspondence should be addressed.
Hydrology 2026, 13(2), 57; https://doi.org/10.3390/hydrology13020057
Submission received: 23 December 2025 / Revised: 20 January 2026 / Accepted: 27 January 2026 / Published: 2 February 2026

Abstract

Groundwater heavy metal contamination (GHMC) has drawn significant attention in China over recent decades due to industrialization. However, effective monitoring and early warning remain global challenges because of the limited understanding of heavy metal behavior in groundwater. This study conducts a detailed comparative analysis of heavy metals and conventional indicators using a long-term, high-frequency online monitoring program. Groundwater online monitoring is an automated system for real-time, continuous collection, and transmission of indicators via sensors and IoT platforms. Conventional indicators refer to the priority parameters used to assess basic water quality, hydrological characteristics and health risks in routine monitoring. Nineteen heavy metals and ten conventional indicators were monitored simultaneously, generating approximately 1.6 million data points over three years. The time series data show that online monitoring effectively captures abnormal changes in heavy metal levels. Abnormal heavy metal fluctuations appear as sharp, isolated spikes lasting at least several hours, while conventional indicators exhibit high-amplitude variations lasting over 30 h—indicating that heavy metal changes are harder to detect in a timely manner. Long-term comparisons also reveal low consistency between heavy metals and conventional indicators, supporting the need for independent heavy metal monitoring. In contrast, strong consistency among heavy metals suggests opportunities to streamline monitoring by selecting representative elements. Monitoring frequency optimization shows that daily measurement is sufficient for heavy metals, which is slightly more frequent than the typical three-day interval for most conventional indicators. Long-term data enable reliable early warnings for both indicator types, with predictions closely matching field observations. However, heavy metal alerts are shorter and less frequent than those for conventional indicators. Integrating both types into a unified early warning system enhances its comprehensiveness, accuracy and timeliness. This study provides a solid scientific foundation for efficient GHMC monitoring and early warning in groundwater in areas under the influence of industrial activities.
Keywords: groundwater; heavy metal contamination; online monitoring; long-term behaviors; monitoring frequency; early warning groundwater; heavy metal contamination; online monitoring; long-term behaviors; monitoring frequency; early warning

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MDPI and ACS Style

Yi, S.; Deng, Y.; Huang, P.; Liu, Y.; Zhang, X.; Shen, Y. Online Monitoring of Heavy Metals in Groundwater: A Case Study of Dynamic Behavior, Monitoring Optimization and Early Warning Performance. Hydrology 2026, 13, 57. https://doi.org/10.3390/hydrology13020057

AMA Style

Yi S, Deng Y, Huang P, Liu Y, Zhang X, Shen Y. Online Monitoring of Heavy Metals in Groundwater: A Case Study of Dynamic Behavior, Monitoring Optimization and Early Warning Performance. Hydrology. 2026; 13(2):57. https://doi.org/10.3390/hydrology13020057

Chicago/Turabian Style

Yi, Shuping, Yi Deng, Pizhu Huang, Yi Liu, Xuerong Zhang, and Yi Shen. 2026. "Online Monitoring of Heavy Metals in Groundwater: A Case Study of Dynamic Behavior, Monitoring Optimization and Early Warning Performance" Hydrology 13, no. 2: 57. https://doi.org/10.3390/hydrology13020057

APA Style

Yi, S., Deng, Y., Huang, P., Liu, Y., Zhang, X., & Shen, Y. (2026). Online Monitoring of Heavy Metals in Groundwater: A Case Study of Dynamic Behavior, Monitoring Optimization and Early Warning Performance. Hydrology, 13(2), 57. https://doi.org/10.3390/hydrology13020057

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