Impact case study database

CREH at Aberystwyth University influenced the revision of the WHO ‘Guidelines for Safe Recreational Water Environments’. Kay was on the expert drafting group and Fewtrell managed the consultation process and first draft of the expert report agreed in October 2020 [5.1]. Kay and Fewtrell also advised on the revision of the EU Bathing Water Directive (BWD), BWD 2006/7/EU, which sets standards for EU bathing waters and was implemented in the 2015 bathing water season across more than 22,000 European beaches [5.2; 5.3; 5.4]. This benefited significantly the EU coastal tourism sector, which generates EUR183,000,000,000 of gross value added per annum [5.5]. The Lead Scientist directing the WHO Bathing Water Guidelines stated in 2018, that CREH are:

key members of the WHO technical advisory group on water quality. In this capacity, WHO has drawn on CREH experts to provide an updated review of evidence to inform revision of both EU BWD and WHO guidelines and also to provide rapid technical advice to Member States as needed. [5.2]

Kay was also recommended by the WHO as a technical adviser to Blue Flag International, the Foundation for Environmental Education (FEE) which administers the world-wide Blue Flag Awards (BFA) for bathing waters. In this capacity Kay responded to multiple technical questions for FEE-BFA [5.6]. A similar WHO recommendation led to Kay acting as the technical adviser on bathing water quality for relevant events at the 2016 Rio Olympics [5.2]. As the technical adviser Kay analysed, and reported the microbial water quality for recreational water events completed in nearshore waters off the beaches surrounding Rio to the organisers of the Rio Olympics and the WHO.

Improving implementation of Environmental Policy: increasing compliance of recreational waters through predictive modelling and public notification of adverse water quality events

Scientists from CREH oversaw the implementation of real-time water quality predictive modelling and water management advisory notification approach in Swansea Bay (between 2013 and 2020) and Cemaes Bay, Anglesey, (between 2018 and 2020) [5.7; 5.8; 5.9]. Both of these sites were considered by local communities and experts as ‘at risk’ of failing EU standards and of de-designation as bathing beaches. GPB100,000,000 of water company investment over many years had not improved Swansea Bay's 'at-risk' classification, and Cemaes Bay failed EU water quality tests in 2016 and 2017. Following the implementation of CREH’s water quality modelling, prediction and notification system, both Swansea Bay and Cemaes Bay passed EU water quality standards and the latter achieved a ‘Good’ EU classification in 2020. This also protected and enhanced public health as bathers were told in real-time when recreational waters were safe to use, allowing them ’informed choice’ as recommended in WHO Guidelines published in 2003 and 2020 (draft), and EU regulations published in 2006 and 2018.

The CREH team worked with Anglesey County Council’s task group and Environmental Health Team to support water quality management at Cemaes Bay from 2017. As the Isle of Anglesey County Chief Executive noted in 2018:

I would like to record my appreciation for your lead and the work you have undertaken with Dr Mark Wyer and your team during 2017, that led to the development of the Bathing Water Prediction Model, which was used successfully during this year’s bathing water season.

Your involvement with the task group, and continued commitment to provide representation and input in this matter has been invaluable to the Council, and the assistance and support provided to the members of staff from the Environmental Health team when issues have arisen has been greatly appreciated by them. [5.8]

CREH’s predictive modelling was also adopted by the Environment Agency (EA) and other regulatory agencies to model bathing water microbial dynamics and associated health risk. In 2016, the Environment Agency (EA) Science Lead in this area explained the importance of Aberystwyth University’s predictive modelling on UK regulatory pollution forecasts:

I regularly highlight the Smart Coasts Project report to colleagues working in other regulatory Agencies and the policy community … to shape their understanding of bathing water dynamics and what can be done to model them in an effective way. [5.9]

The EA has used CREH’s multiple regression approach for water quality prediction since 2014 to underpin its Pollution Risk Forecasting (PRF) system at more than 150 bathing waters across the UK [5.9], supporting the Bathing Water Directives short-term pollution provision and warning bathers when water quality standards may not be met, and thereby protecting a bathing site’s classification:

In 2015, using the PRF system, over 23,000 bathing water quality forecasts were made of which approximately 2,400 were warnings. From this, 65 bathing waters had their classification protected … including 23 sites that would have a deterioration of class... [5.9]

For the WHO Lead Scientist in this area:

A particularly novel aspect of WHO Guidelines (2003) and the EU Bathing Water Directive (2006) is the option for regulators to implement real-time prediction of water quality. This is of particular public health benefit because it provides regulators with the option to deliver 'informed choice' to potential bathing water users considering visiting a public bathing water. Development in this area draw on the pioneering work of CREH as reported in the peer reviewed literature (Water Research 2018), the Smart Coasts and Acclimatize project and operational models in daily use in Swansea and Cemaes Bays led by CREH. [5.2]

CREH’s work safeguarded public health and the economies of communities based on tourism [5.10] which ‘contributes £4 billion pounds to the UK economy’ [5.11], as bathers are now told in real-time when recreational waters are safe to use, allowing them ’informed choice’ as recommended in WHO Guidelines published in 2003 and 2020. Previous models, applied world-wide, use the bathing day as the unit of prediction which incorrectly assumes within-day uniformity in bacterial concentration. AU's predictive modelling produces more accurate water quality predictions. This approach also informed the modelling adopted by the Environment Agency (EA) and other regulatory agencies to model bathing water microbial dynamics and associated health risk [5.9].