Urban drainage systems contribute to over half of water-related ecological risks in China, yet conventional management still relies on static chemical snapshots that fail to capture how living ecosystems respond to complex contaminant mixtures and dynamic environmental conditions.
In a new perspective published in Water & Ecology , a research team led by Aijie Wang at Harbin Institute of Technology details how artificial intelligence (AI) and biological big data can together revolutionize urban water safety.
“The future of water management is not just measuring chemicals—it is about the understanding of how living systems respond to those chemicals in real time,” says Wang. “We need to treat urban waterways as dynamic ecosystems, not passive pipelines.”
The perspective introduces three complementary layers of biological information for dynamic diagnosis. First, high-throughput imaging and computer vision track behavioral and morphological changes in indicator organisms such as zooplankton and protozoa in near real time. Systems like ROMIDAS™ have already generated more than 15 terabytes of continuous microscopic image data, enabling AI-driven feature extraction and risk assessment.
Second, metabolic biomarkers—including chlorophyll and algal toxins—captured through remote sensing, in situ spectroscopy, and autonomous underwater robots can reveal physiological stress and subsurface bloom precursors before surface expansion.
Third, functional gene biomarkers provide mechanistic signals linking contaminant transformation to ecological health effects, offering early warnings before visible damage occurs.
Building on these biological data layers, the authors propose the Integrated Chemical–Biological Environmental Risk Assessment framework, designed for the small-sample, high-dimensional datasets common in real-world environmental monitoring. “By combining interpretable machine learning, stability testing, and cross-model agreement, the framework prioritizes potential risk factors that remain consistent across analyses, helping turn complex environmental data into actionable priorities for management,” says Wang.
The authors also outlined a five-module intelligent management platform: monitoring-network optimization, ecological risk diagnosis, pollution source tracing and early warning, plant–network–river coordinated operation, and sponge city stormwater source control. “Together, these modules form a closed-loop workflow from real-time sensing to adaptive management,” explains Wang. “Real-world examples show what this could look like in practice: anomaly warnings for the Maozhou River could be issued within approximately 30 minutes, while a separate multi-agent AI control study reported 25.4% lower overflow volume and 18.0% lower river pollutant concentrations.”
“This is about shifting from reactive cleanup to predictive resilience,” adds senior and co-corresponding author Bin Liang. “By integrating biological intelligence with AI, we can create urban water systems that diagnose their own health and suggest interventions before crises emerge.”
The researchers acknowledged that deployment still faces hurdles, including sensor reliability, data security, and infrastructure costs. “We recommend a phased implementation strategy starting with existing monitoring stations and scaling progressively toward autonomous intelligent systems,” says Liang.
Contact Authors:
Aijie Wang
-State Key Laboratory of Urban-rural Water Resources and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
-State Key Laboratory of Urban-rural Water Resources and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
waj0578@hit.edu.cn
Bin Liang
liangbin1214@hit.edu.cn
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Water & Ecology
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.