By Jialan Deal and Elizabeth Stifel
The Seine played a starring role in the 2024 Summer Olympics and Paralympics serving as the site of the opening ceremony as well as hosting events such as the triathlon and marathon swimming. However, the spotlight on the Seine has also highlighted an ongoing issue: urban water pollution. Paris spent $1.5 billion cleaning the Seine, but tests by World Triathlon showed fluctuating levels of fecal bacteria such as enterococci and E. coli, resulting in repeated delays and cancellations of water events to protect the health of athletes. Poor water quality caught attention in Paris for disrupting a sporting event, but in other places, it is a daily, life-threatening challenge.
“Proper diagnosis is imperative to plan and prioritize investments for pollution control and health risk mitigation,” says International Water Management Institute (IWMI) senior researcher and CGIAR One Health expert Javier Mateo-Sagasta. He emphasizes the importance of targeted strategies, especially in low- and middle-income countries, where the impact of water pollution on health is most severe. By reducing contamination from sources like livestock and aquaculture, initiatives like the CGIAR Initiative on One Health aim to curb the spread of harmful pathogens, protecting vulnerable communities and improving overall water safety.
Assessing the issue
Monitoring and modelling water pollution is essential for effective assessment and planning. Water quality monitoring tracks the levels of certain pathogens, like E. coli and salmonella, and their antibiotic-resistant strains in surface waters. This helps everyone from public health inspectors and researchers to community members to understand where and when these pathogens are present, gather data to improve models that predict how these contaminants move and change in watersheds and assess health risks from activities like drinking, bathing, and irrigation.
However, significant data gaps severely limit these efforts, especially in developing countries. According to recent reports published by UN-Water and the UN Environment Programme, the poorest half of the world contributes less than 3% of global water quality data points, providing only 4,500 lake quality measurements out of nearly 250,000. While existing data already show freshwater quality declining since 2017, the lack of data in many regions suggests the situation could be even worse where information is sparse. If these gaps persist, by 2030, more than half of the global population will live in countries without sufficient water quality data to guide management decisions for issues like droughts, floods, wastewater effluents and agricultural runoff.
While the testing of the Seine was conducted by Paris Olympic officials, communities can also take part in this crucial step. In Ethiopia, One Health trained the Addis Ababa Water and Sewerage Authority to identify waterborne microbial hazards quickly and affordably. This training enabled IWMI researchers to publish findings revealing that 20% of the fecal bacteria had antibiotic-resistant traits, and that these hazards were highest in the dry season when river water is used for irrigation.
IWMI has also been working on incorporating community members into monitoring processes through citizen science and the release of miniSASS, an AI-powered app which allows communities to assess the health of rivers by photographing water sources. The AI identifies pollution indicators, collecting critical data on the extent and impact of wastewater contamination, empowering local communities with environmental monitoring skills.
Modeling water pollution
With data from water monitoring, we can build models that represent real-world water systems, helping us understand cause-and-effect relationships of the problems we see. These models allow us to predict future scenarios, test the effectiveness of proposed pollution control measures, and assess the impact of climate changes, such as reduced precipitation and runoff, on pollutant concentrations. This helps in developing effective strategies to manage water quality issues.
“Our research uses water quality modeling to simulate scenarios and evaluate measures to reduce the spread of antibiotic-resistant bacteria and genes,” notes Mateo-Sagasta. “We are seeking to have models that track transmission routes, predict the movement of resistant bacteria, and assess their behavior influenced by water flows, hydrological processes, and microbial interactions. A strong modeling framework helps create accurate models that guide policy decisions and management strategies to curb antibiotic resistance in water systems.”
These models can also evaluate the effectiveness and cost of remedial actions. While Paris invested billions in upgrading infrastructure and building new facilities, many developing communities are only beginning to tackle water pollution and need affordable solutions. IWMI’s technical guide on microplastic pollution advises decision-makers, in consultation with local stakeholders, to choose the most cost-effective and sustainable solutions rather than opting for single, costly measures. Comprehensive data and detailed models are crucial for making informed decisions.
Although Paris’ efforts are commendable, the challenges they faced in cleaning the Seine illustrate that developing cleaner, safer urban water systems is a lengthy and intricate process even for a country with many resources. In developing countries, properly diagnosing water pollution issues is even more essential as it may pose a direct threat to human health. This highlights the need for a mix of stronger policies, better modeling tools, and more investment in water quality data from poorer regions to support informed decision-making and ensure equitable water management worldwide.