Artificial intelligence has become a useful tool in numerous domains, including environmental science. This review explores the application of machine learning and deep learning, as AI technologies, applied in calculating and modelling water quality indexes and water quality classification. WQIs are used to assess the overall status of water bodies and compliance with environmental regulations. AI offers a compelling alternative, with the potential to enhance accuracy, reduce time, and provide insights into complex environmental data. The challenges of deploying AI, such as data availability, model transparency, and system integration, are also discussed.
Water Quality Monitoring and Sustainable Development
Water quality monitoring is an important responsibility of each state to make sure that the population has access to safe water and the needs are met without creating pressures on the water resources. UN Sustainable Development aims to ensure the availability and sustainable management of water and sanitation for all. It focuses on providing safe and affordable drinking water, access to proper sanitation facilities, and promoting good hygiene practices. The goal also emphasizes the importance of protecting and restoring water ecosystems to maintain water quality and encourages efficient and sustainable management of water resources.
Water management is very complex and includes monitoring water quality and quantity parameters as well as biodiversity and aquatic life, identifying pollution sources and pollutants removal, sanitation, flood protection, resource allocation, etc.
Sources of Pollution and Monitoring Practices
The main sources of water pollution are discharges from urban agglomerations, leakage, and runoff from agriculture and industrial activities. Water monitoring includes a large number of hydrological, physical, chemical, and biological parameters, some of which are measured on-site and others by sample analysis in the laboratory. Most countries have monitoring programs that specify sampling locations, parameters to be determined, and frequency of sampling that are associated with efforts and costs for labour, reagents, equipment, etc.
Each country or region has its own quality standards that define limit values for parameters and classification systems to evaluate the state of a water body and its adequacy for different uses. The classification of the water bodies follows the One Out–All Out principle, which means that the status is given by the ranking of the worst parameter. Other countries have developed classification systems based on water quality indexes (WQIs), which are dimensionless numbers that aggregate the values of several selected indicators.
There are different WQIs, but the process of calculating the WQI usually includes the following steps:
-Selection of relevant water quality parameters.
– Assigning a weight to each parameter.
– Calculation of sub-indexes.
– Aggregation of sub-indexes into the WQI.
According to the value of the WQI, the water is then categorized into quality classes, depending on the calculation method and water uses.
The WQI method is laborious and has several limitations that may lead to inaccurate results, but for many years it has been a good instrument to assess the overall water quality and its long-term trends.
Artificial intelligence (AI)
refers to the simulation of human intelligence processes by machines, particularly computer systems, which include learning, reasoning, self-correction, perception, and interaction. AI can analyse in a short period of time huge amounts of data, identify patterns or anomalies, calculate indicators, provide visual representations of data, etc., which can support the assessment of water quality, as well as the identification of pollution sources and remediation measures.
The recent development of AI tools for assessing water quality has the potential to bring significant improvements. It may reduce monitoring efforts and costs and increase the accuracy of WQI prediction and water quality classification (WQC) in several ways, some of which are mentioned below:
– Advanced analysis of monitoring data, followed by calculation of WQI.
– Complex modelling of WQI values may allow the prediction of WQI.
– AI algorithms can assign datasets into quality classes based on raw monitoring data without the need of calculating WQIs.
In addition, the combination of AI and remote sensing may be able in the future to replace traditional monitoring methods with satellite data and real-time, on-site sensor data (IoT—Internet of Things), reducing the cost and efforts of water quality monitoring.
AI breakthrough: detecting mesoplastics for the first time
Unlike traditional cleanup efforts, River Watchers not only documents litter presence but also provides granular insight into its type, volume, and location. VITO’s AI model can recognize large objects such as PET bottles and cans but also detects mesoplastics—particles between 5 and 25 mm, like cigarette butts, plastic pellets, and foil fragments. This marks a significant innovation, as mesoplastics have long been difficult to monitor and remove, despite their key role in spreading microplastics.
This is the first citizen science initiative worldwide to apply AI for detecting mesoplastics along riverbanks.
Case Study
River Watchers turns every smartphone user into a litter detective in new citizen science project
Today, VITO and River Cleanup are launching the River Watchers project. This innovative citizen science initiative brings together citizens and artificial intelligence (AI) in the fight against pollution along riverbanks. The premise is simple: anyone with a smartphone can participate. By photographing trash on a walk and uploading the images, each citizen helps map river pollution. Thanks to AI, even small and hard-to-detect waste is now being made visible for the first time. With the insights gathered, governments and organizations can take more targeted action (Arne Van Overloop, remote sensing AI expert at VITO & Thomas de Groote, founder of River Cleanup).
Conclusion
Every day, around 10 million kilograms of plastic end up in rivers globally, breaking down into microplastics that eventually enter our food chain. River Watchers tackles this challenge by combining citizen engagement with advanced technology. Participants document waste with their smartphones, and AI processes the results. The project has two core goals: increasing knowledge about litter and boosting environmental awareness among citizens.
To explore the latest innovations in water and energy technologies and discover a wide range of products and solutions from around the world, you can visit the virtual exhibition AQUA ENERGY EXPO, which features leading companies in water treatment, desalination, and sustainable energy, through the following link: https://aquaenergyexpo.com/
Reference
1-Citizens track river litter with AI
https://vito.be/en/news/citizens-track-river-litter-ai-innovative-vito-and-river-cleanup-project
2-Thousands of citizen photos train AI to monitor river pollution
