“Is it safe to swim today?”

The answer is usually cautiously given. Current water quality monitoring models are based on fortnightly sampling so can easily miss significant polluting events. Hao Wang and his team at Deltares are developing a new and more accurate early warning system to predict bathing water quality.

Why this new model is so important

Bacteria are an increasing problem in bathing waters. As climate change causes a rise in heavy rainfall frequency, pollutants are escaping from sewerage to surface water. Pollution events, such as agricultural run-off and sewer overflow, result in pathogens and nutrients entering coastal waters where people bathe – and in some cases, they remain there.

Taking bathing water samples fortnightly is arguably better than nothing; however, this standard practice easily misses these one-off events. As Hao Wang from Deltares says, “Water quality can change very quickly after heavy rain, sewer overflows or strong sunlight”.

To help fill this knowledge gap, computer models based on bacterial behaviour are used to predict water quality. The new model gives a more accurate insight by taking into account two more factors – sunlight and sediment. This is coupled with high-resolution hydrological and hydrodynamic models.

Read the latest publications:

Research highlights

1. Sunlight makes a difference in clear waters, where UV light reaches and therefore reduces E.coli. However, UV is not as effective in darker or vegetation-rich waters.
2. The composition of sediment also affects E.coli bacteria. For example, bacteria easily detach from sand but will attach more firmly to clay. Attached bacteria remain in the water for longer.
3. By developing a model that includes sunlight and sediment in its predictions, agencies such as local authorities can more accurately answer the question, “Is it safe to swim?”

Methodology

Working in Katwijk in the Netherlands, the research team is:

1. Combining existing models
   This gives a detailed picture of an aquatic continuum in the Netherlands along with major pollution sources.
2. Adding BlueAdapt climate scenarios
   The next stage is to bring in prediction scenarios. This helps users estimate actual and future pollution and the impact this could have on water quality.
3. Expanding the models
   The effects of UV on pathogens and the local sediment are added to the model, giving a more accurate prediction of bacterial behaviour.

Results

This project provides an excellent new tool for practical health risk prevention. As well as anticipating risks and issuing water quality warnings, it can also be used to assess long-term climate impacts. This advanced model will support local authorities, consultancy companies and other researchers – and is great news for open-water swimmers and other recreational sea users.

So is it safe to swim? The answer in the future will be a categorical “yes” or “no”.

Summary and next steps

1. Climate change is increasing the chance of pathogen existence in coastal waters.
2. Research into the effects of UV and sediment type on bacteria enable us to predict more accurately the lifecycle of the bacteria in a specific area.
3. By bringing UV and sediment into prediction models, pollution risks can be more accurately forecast.
4. Agencies (such as local authorities) can issue timely warnings to coastal water users such as swimmers.

Although developed using E.coli, the model can be adapted for other faecal bacteria and even viruses.

Longer-term, these new models can be used to predict the impact of climate change by applying climate scenarios. What will the effect of rising sea temperatures be on the pathogen risk?