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5. The complex challenges of dealing with bathing waters - Prof. David Kay, Aberystwyth University

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On 17 and 18 June 2020 the EPA held its National Water Event as an online conference.

This year's theme was 'Restoring our waters'.

This years event was free to attend. It was the EPA's largest water event ever, with over 1250 attending.

To everyone who joined us: thanks for attending; thanks for your probing questions; thanks for your passion; thanks for caring about our waters. We can achieve more working together.

Special thanks to all our presenters and the team who worked behind the scenes to make sure this years conference happened.

For science and stories about water quality in Ireland, check out www.catchments.ie

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5. The complex challenges of dealing with bathing waters - Prof. David Kay, Aberystwyth University

  1. 1. Within day variability in faecal Indicator organism (FIO) concentrations at Five “At Risk” beaches in Wales June 2020 Dr Mark Wyer, Prof. David Kay, Dr Carl Stapleton, Dr Cheryl Davies, Prof. Paul Brewer and Dr Bill Perkins
  2. 2. Background: Swansea Bay  Average daily range: 1.4 log10 orders of magnitude  Maximum range: 3.1 log10 orders of magnitude
  3. 3. Background: Water Research Paper
  4. 4. Acclimatize  Opportunity to examine within-day variation in FIO concentrations at “At Risk” bathing waters in Wales  Is the pattern observed at Swansea Bay replicated?
  5. 5. Acclimatize Beaches  Four “At Risk” bathing waters:  Cemaes  New Quay North/ Traeth y Dolau  Traeth Gwyn New Quay  Nolton Haven
  6. 6. Sampling and Analysis  Based on the Swansea study  Half-hourly sampling 08:00 to 20:00 BST  25 samples per day  3 days per week through the 20-week bathing season: 60 sampling days (plus a trial day)  At least 1500 samples per bathing season  Samples analyzed in triplicate for:  Escherichia coli (E. coli)  Intestinal enterococci – presumptive and confirmed  Detection limit: 0.3 cfu/100 ml (i.e. 3 cfu/litre)
  7. 7. Cemaes, Anglesey 2017  Most northerly town in Wales  2016 – the only BWD “Poor” class bathing water in Wales  Strong local community concern in the local economy
  8. 8. DSP results – Intestinal enterococci  Average daily range: 2.0 log10 orders of magnitude  Maximum range: 3.6 log10 orders of magnitude
  9. 9. DSP results – Intestinal enterococci  Orange diamonds = Regulatory compliance sample results  10 of the 17 compliance sample results are at, or below, 10 cfu/100 ml  Is compliance sampling representative of the daily variability?
  10. 10. Spatial results DSP – enterococci
  11. 11. New Quay North, Ceredigion 2018  Busy family holiday destination in Cardigan Bay  Consistent “Sufficient” BWD classification
  12. 12. DSP results – enterococci  Average daily range: 2.3 log10 orders of magnitude  Maximum range: 3.9 log10 orders of magnitude
  13. 13. Spatial results DSP – enterococci
  14. 14. Traeth Gwyn, Ceredigion 2018  Adjacent to Quay West holiday park  Steady decline from “Excellent” to a “Sufficient” BWD classification in 2017
  15. 15. DSP results – enterococci  Average daily range: 1.9 log10 orders of magnitude  Maximum range: 3.4 log10 orders of magnitude
  16. 16. Spatial results DSP – enterococci
  17. 17. Nolton Haven, Pembrokeshire 2019  A popular tourist beach in St Bride’s Bay  Had a “Sufficient” BWD classification to 2017 - “Good” in 2018 (worst “Good” beach in Wales!)
  18. 18. DSP results – enterococci  Average daily range: 2.0 log10 orders of magnitude  Maximum range: 3.9 log10 orders of magnitude
  19. 19. Spatial results DSP – enterococci
  20. 20. Daily range summary – IE Site Mean Minimum Maximum N Swansea Bay 1.43 0.48 3.09 60 Cemaes 1.97 0.93 3.57 61 New Quay North 2.26 0.58 3.93 61 Traeth Gwyn 1.87 0.85 3.39 61 Nolton Haven 2.02 0.88 3.88 62  Units: log10 orders of magnitude  Average daily ranges of IE concentrations for the Acclimatize bathing waters are around two orders of magnitude  Maximum values are > three  The Acclimatize studies confirm the observations at Swansea
  21. 21. Conclusions  The consistent within day variation in FIO concentrations first observed at Swansea Bay has been observed at four more beaches in Wales  This could suggest that such within day variation is a generic pattern at UK beaches  The variation casts doubt on the utility of using compliance measurements of FIO concentrations to characterize water quality for a bathing day  The regulatory utility of compliance sample results should be re-assessed
  22. 22. What might be causing the within-day variations?  Diurnal – solar irradiation driving faecal indicator decay, effluent discharge patterns  Semi-diurnal – flood and ebb tidal cycles, tidal locking of inputs  Antecedent conditions – rainfall, temperature, etc.  Intertidal morphology  Sediment – resuspension, dredging, construction/maintenance  Etc…
  23. 23. New Insights and Predictive Modelling Results Summary
  24. 24. Assumptions in the WHO standards derivation
  25. 25. Assumptions in the WHO standards derivation  20 Exposures per bathing season for the keen bather  95 percentiles of the log10 ‘normal’ distribution were needed by WHO Significant Risk of High levels of GI illness  10% GI risk per exposure >500 IE/100ml 95 %ile Substantial Elevation in pGI  5% GI risk per exposure 200 IE/100ml 95 %ile Above the NOAEL in most studies  1% GI risk per exposure <40 IE/100ml 95 %ile Below the NOAEL in most studies
  26. 26. The Annapolis Protocol
  27. 27. The Annapolis Protocol Norman Lowe DCWW Nick Humphrey DCWW Peter Bird EA
  28. 28. USEPA Experience USEPA issues two reports in 2010 a review (2010a) and modelling (2010b) report. They reported explained variances of 20-40% in US applications of this modelling approach. Is this too low for public health Advice?
  29. 29. Annapolis Why is it Relevant?
  30. 30. Compliance Point Intensive sampling
  31. 31. Compliance outcomes – Days with 07:00 to 19:00 data On average the difference in FIO concentrations is enough to affect the compliance outcome for the 3 periods
  32. 32. Confirmed enterococci – Model 1 Model 1 - Tolerance 0.0001 Dependent (Y): Mean log10 Confirmed enterococci (cfu/100 ml) Step Predictor r2 (adj.) Change in r2 (%) Partial r Sig. Toleranc e 1 UVB Radiation on sampling day (kJ/sq. m) X1 0.440 2 Log10 Brynmill Str. Max. Q in previous 48 Hrs (cub. m) X2 0.589 14.894 0.528 0.000 0.916 3 Max. Tide Height on sampling day (m) X3 0.643 5.455 0.385 0.003 0.934 4 Log10 Afan STW Q in previous 48 Hrs (cub. m) X4 0.686 4.250 -0.368 0.006 0.509 5 Mean Wind Sp. in previous 48 Hrs (m/s) X5 0.742 5.615 -0.441 0.001 0.686 6 Min. Tide Ht. in previous 12 Hrs. (m) X6 0.775 3.329 0.382 0.005 0.081 7 Log10 Clyne R. Gauge Q in previous 24 Hrs (cub. m) X7 0.801 2.606 0.365 0.008 0.351 Y = 10.551 – 0.038X1 + 0.440X2 + 0.522X3 – 2.992X4 – 0.236X5 + 0.366X6 + 0.405X7 ± 0.229
  33. 33. Intestinal enterococci 7 predictors
  34. 34. Cemaes Bay – Water Quality Modelling February 2018
  35. 35. Intestinal enterococci model Type 2/01 60 row daily matrix Predictor Variable Coefficient a (Constant) -0.931 X1 Log10 Afon Wygyr Max. Q on sampling day (m3) 1.296 X2 Log10 Rainfall in previous 24 Hrs.+1 0.551 X3 Mean Wind Sector (16 point) on sampling day (Rad.) 0.090 X4 ETR in previous 12 Hrs. (MJ/m2) -0.037 X5 Mean Air Temp. on sampling day (˚C) -0.205 X6 Mean Air Temp. in previous 24 Hrs. (˚C) 0.197 Adjusted r2 = 76.3%
  36. 36. Intestinal enterococci model Type 2/01 Sign outcome using GM 34 cfu/100 ml threshold: 08:00 – 7.75% Good/92.25% Poor 11:00 – 38.73% Good/61.27% Poor 14:00 – 78.87% Good/21.13% Poor
  37. 37. New Quay North – Model Evaluation
  38. 38. Results - CIE Model Type X1 X2 X3 X4 Tolerance r2 (%) Crit. Misclass. (%) 1 Daily LgRF24Hr MATOnDay MAP24Hr 0.7 57.5 8.20 2 Daily LgRF24Hr MATOnDay 0.9 54.7 8.20 1 Half-Day LgRF24Hr MAT24Hr MAP24Hr MxTSamp 0.0001 44.8 14.75 2 Half-Day LgRF24Hr MAT24Hr MAP36Hr 0.8 42.4 13.93 3 Half-Day LgRF24Hr MAT24Hr 0.9 39.1 17.21 1 2nd Half LgRF24Hr MAP36Hr MxTSamp 0.8 53.4 21.31 2 2nd Half LgRF24Hr MxTSamp 0.9 46.8 22.95  The models have comparatively low levels of explained variance  The “Daily” models have:  the lowest critical misclassification (8.2%)  the highest r2 – 54.7% - 57.5%  Rainfall is the principal predictor
  39. 39. Traeth Gwyn – Model Evaluation May 2019 Dr Mark Wyer & Professor David Kay
  40. 40. Results - CIE Model Type X1 X2 X3 X4 X5 X6 Tolerance r2 (%) Crit. Misclass. (%) 1 Daily LgMxHNQ24Hr LgRF48Hr MxTOnDay 0.3 67.4 6.56 3 Daily LgMxHNQ24Hr LgRF48Hr MAP36Hr 0.5 66.5 6.56 6 Half-Day LgMxHNQ36Hr LgRF24Hr TR12Hr MnTSamp MWS3Hr MAP36Hr 0.5 66.5 6.78 7 Half-Day LgMxHNQ36Hr LgRF24Hr MxTSamp MWS6Hr 0.6 60.8 8.47 8 Half-Day LgMxHNQ36Hr LgRF12Hr 0.7 55.9 8.20 9 Half-Day LgMxHNQ36Hr LgRF12Hr 0..8 54.7 7.38 1 2nd Half LgMxHNQ36Hr LgRF24Hr MWS3Hr MAP48Hr MxTSamp 0.5 79.1 5.93 2 2nd Half LgMxHNQ36Hr LgRF24Hr MWS3Hr MxTSamp 0.6 74.8 7.63  The models have comparatively high levels of explained variance/low misclassification  2nd Half of season model 1 has the highest r2 and lowest critical misclassification
  41. 41. CIE 2nd Half Model 1 Predictors: Afon Halen Max. Q in past 36 hrs, Rainfall in past 24 hrs Mean Wind Speed in past 3 hrs, Mean Atm. Pressure in past 48 hrs, Max. Tide in sampling period Tolerance: 0.5 r2: 79.1% Critical misclassification: 5.93%
  42. 42. Nolton Haven - Preliminary Results
  43. 43. Version 1 Viable Models Model Tol. r2 X1 X2 X3 X4 X5 Misclass. (%) Type 2/4 0.4 0.761 LgRF48Hr MRH12Hr MAPOnDay LgMxFBQ12Hr 3.23 Type 2/5 0.5 0.756 LgRF48Hr MRH12Hr MAPOnDay LgMxFBQOnDay 3.23 Type 2b/4 0.4 0.760 LgRF48Hr MRH12Hr MAPOnDay LgMxFBLA12Hr 3.23 Type 2b/5 0.5 0.756 LgRF48Hr MRH12Hr MAPOnDay LgMxFBLAOnDay 3.23 Type 2d/1 0.3 0.813 LgRF48Hr MAPOnDay LgMxFBQ12Hr UVAOnDay MATOnDay 3.23 Type 2e/1 0.1 0.782 LgRF48Hr MRH12Hr MAPOnDay LgMxFBLA12Hr LgFBLA24Hr 3.23 Type 2e/2 0.3 0.812 LgRF48Hr MAPOnDay LgMxFLA12Hr UVAOnDay MATOnDay 1.61  Model 6: low tolerance, final variable appears counter- intuitive  Models 5 and 7: highest r2 > 0.800
  44. 44. Version 1 Type 2d/1 Time series: mean log10 intestinal enterococci concentration Includes Furze Brook variable as discharge
  45. 45. Some Questions  Is the WHO assumed standard deviation in cIE concentrations now more credible?  Can we predict as per Annapolis reliably?  Does this predict the within-day patterns sufficiently for:  Prediction and discounting (as per Annapolis)?  Real time within-day beach management (as per warnings of adverse tidal conditions by life guards?  Can such routine predictions replace regulatory sampling for considerable periods?

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