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5. The complex challenges of dealing with bathing waters - Prof. David Kay, Aberystwyth University
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. Background: Swansea Bay
Average daily range: 1.4 log10 orders of magnitude
Maximum range: 3.1 log10 orders of magnitude
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. Acclimatize Beaches
Four “At Risk”
bathing waters:
Cemaes
New Quay North/
Traeth y Dolau
Traeth Gwyn
New Quay
Nolton Haven
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. 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. DSP results – Intestinal enterococci
Average daily range: 2.0 log10 orders of magnitude
Maximum range: 3.6 log10 orders of magnitude
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?
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. DSP results – enterococci
Average daily range: 2.0 log10 orders of magnitude
Maximum range: 3.9 log10 orders of magnitude
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. 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. 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…
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
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?
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. 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
34. Cemaes Bay – Water Quality
Modelling
February 2018
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. 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
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. Traeth Gwyn – Model Evaluation
May 2019 Dr Mark Wyer & Professor David Kay
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. 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%
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. Version 1 Type 2d/1
Time series: mean log10 intestinal enterococci concentration
Includes Furze Brook variable as discharge
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?