Risk factors for norovirus contamination of oyster production...

www.cefas.defra.gov.uk

Risk factors for norovirus contamination of oyster

production areas in England and Wales

Carlos J. A. Campos

Cefas report to Defra under contract FC003A – Advice and Evidence on

Shellfisheries

November 2015

ii

Risk factors for norovirus in oysters

© Crown copyright [2015]

You may re-use this information (not including logos) free of charge in any format or medium, under the

terms of the Open Government Licence. To view this licence, visit www.nationalarchives.gov.uk/doc/open-

government-licence/ or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU,

or e-mail: [email protected]

This document/publication is also available on Defra website at:

[http://www.cefas.defra.gov.uk/our-science/animal-health-and-food-safety/food-safety/shellfisheries-water-

quality.aspx]

Consultation was carried out on 24/04/2014 to the following organisations: Anglian Water, Environment

Agency, Northern Ireland Water, Shellfish Association of Great Britain, Scottish Government, Seafish,

Southern Water, South West Water, Welsh Assembly Government, Welsh Water and Wessex Water. The

report was revised according to comments received.

Revision history

Working draft submitted to Defra 25 March 2014

Working draft post-stakeholder consultation 5 December 2014

Final draft post peer review consultation 16 November 2015

Report compiled by: Carlos J. A. Campos

Report approved by: Simon Kershaw (Contract Manager)

David Lees (Project Sponsor)

Version: 1.3

http://www.nationalarchives.gov.uk/doc/open-government-licence/

http://www.nationalarchives.gov.uk/doc/open-government-licence/

mailto:[email protected]

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Contents

Executive summary ............................................................................................................. 1

Main conclusions ................................................................................................................. 4

1. Introduction ................................................................................................................... 5

1.1 Aim of the study.......................................................................................................... 6

2. Data sources and methods ........................................................................................... 7

2.1 Microbiological data .................................................................................................... 7

2.2 Risk factors ................................................................................................................ 7

2.3 Statistical methods ..................................................................................................... 9

3. Results ........................................................................................................................ 11

3.1 Factors determining microbiological contamination of oysters ................................. 11

3.2 Linear regression models to determine norovirus contamination based on significant

risk factors ...................................................................................................................... 18

3.3 Effect of discharges from combined sewer overflows on norovirus contamination of

oysters ............................................................................................................................ 22

3.4 Case study for NoV management using risk predictors at one site .......................... 24

4. Discussion .................................................................................................................. 26

5. Conclusions ................................................................................................................ 32

6. Acknowledgements ..................................................................................................... 33

7. References ................................................................................................................. 33

8. Appendices ................................................................................................................. 38

Executive summary

Background. Human noroviruses (NoV) cause the majority of sporadic cases and

outbreaks of shellfish-related gastroenteritis in the UK (Lee and Younger, 2002; J. Harris,

PHE, pers. comm.). It is generally accepted that the prevalence and distribution of these

viruses in shellfish is influenced by the degree of NoV infection in the community and the

occurrence of sewage pollution events in the environment. However, to date

comprehensive assessments of the factors influencing the transmission of these viruses in

commercial shellfisheries have not been performed. This constrains the development of

measures to control the risk of NoV infection.

Objective and methods. We investigated the relationships between a selection of

catchment hydrometric, climatic, physical and demographic factors and levels of

microbiological contaminants (NoV [genogroups I and II] and Escherichia coli) quantified in

oysters from 31 sampling sites in England and Wales (E&W) from May 2009 to April 2011.

The NoV dataset analysed has been previously reported by Lowther et al. (2012). The

selected risk factor categories were: river flows, rainfall, base flow index, water

temperature, mean annual human population in the catchment, population density in the

catchment, sewage treatment level, consented dry weather flow of sewage discharges,

catchment area, fluvial distance from the sampling point to the sewage treatment works

(STW) outfall and tidal range.

We measured the strength of linear associations between the microbiological levels and

these risk factors using Pearson product-moment correlation. We developed linear

regression models from bivariate plots to discern if risk factors could be used by regulators

and members of the shellfish industry to predict NoV in shellfish. We evaluated the

significance of the number of intermittent sewage discharges on mean levels of NoV in

oysters from 10 sampling sites. Finally, we used data on water temperature, river flows,

NoV and E. coli from one of the sampling site to discuss options to better manage the risk

of NoV contamination of shellfish.

Results. Regarding environmental variables, we found a consistent inverse relationship

between seawater temperature and NoV content of oysters. On an individual sample

basis, significantly higher quantities of the virus were detected in oysters from colder

waters (<5°C) than those in oysters from warmer waters (>10°C). Furthermore, many

sampling sites demonstrated a significant winter-summer difference in NoV levels which

was strongly inversely correlated with water temperature. This correlation was not

observed for E. coli concentrations detected in oysters. Regarding hydrometric variables,

we observed a significant, but only weakly predictive, correlation between rainfall and/or

river flows and the concentrations of E. coli in oysters. The most consistent of the

hydrometric factors by season for E. coli in oysters was antecedent rainfall within a 7 day

window. However, overall, this correlation was not observed for NoV irrespective of the

NoV genotype, season or the window of antecedent rainfall or river flow used in the

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analysis. Indeed, during the winter, an inverse relationship of NoV with hydrometric factors

was found indicating possible dilution effects. Further examination of sites showing strong

winter-summer variation in NoV levels marginally improved the correlation between NoV

and river flow within an antecedent period of 7 days. However, over all sites, the

correlation was only very weakly predictive.

Regarding human faecal pollution sources, we detected moderate positive correlations

between the levels of NoV found in oysters and a variety of proxies for faecal pollution.

Correlations were found with the following risk factors:

Consented dry weather flow of sewage discharged into the catchment.

Catchment area.

Number of continuous discharges to the estuary.

Mean annual human population in the catchment.

Number of intermittent discharges in the catchment.

Weaker positive correlations were also observed with the consented dry weather flow of

sewage discharged directly to the shellfish water, the number of continuous discharges in

the catchment and the number of trade discharges in the catchment. Some discrepancy

was found in the correlation results between NoV levels and the demographic proxies. For

example, positive correlations were observed for mean annual human population but not

for population density.

Geometric mean levels of NoV in oysters were higher when mean annual human

population exceeded 80,000, catchment area was larger than 32,000 hectares (320 km2),

when the most significant sewage source exceeded 2,000m3/day and there were more

than two continuous sources of sewage pollution impacting the fisheries.

In a sub-set of sites for which combined sewer overflow (CSO) spill data were available,

we detected higher levels of NoV in oysters from sites impacted by a high (>10 per year)

number of intermittent discharges. However, NoV was still detected in sites with <10

intermittent discharges per year. Further studies are required to confirm the relationships

between CSO discharges and the degree of NoV contamination; the relative importance of

continuous versus intermittent discharges and the proportion of separate/combined

sewerage systems in the shellfish water catchments.

Conclusions. We concluded that, in the catchments studied, water temperature and

potential or actual sources of human faecal pollution were the main factors influencing the

risk of NoV contamination in oysters. The importance of controlling the number of CSO

discharges impacting estuaries was emphasised by the correlation seen between NoV

contamination and the number of discharges and by the observation that the majority of

sites were impacted by >10 discharges per year. We also observed that, in the catchments

studied, the hydrological processes driving the abundance of E. coli in shellfish do not

operate in a similar way to those driving the abundance of NoV. This result is probably

linked to the role of diffuse pollution from agricultural sources in contributing to E. coli, but

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not NoV contamination. This study focuses on a group of human-derived pathogens that

are abundant in sewage-related sources. However, the risks from zoonotic pathogens

should not be discounted. Other groups of microorganisms such as FRNA bacteriophages

have been found to better represent the behaviour and persistence of NoV in shellfish than

faecal indicator bacteria (Doré et al. 2000; Flannery et al. 2009).

The contribution of demography to NoV risk was found to be linked to the total catchment

population and not to the degree of urban development or area occupied by the

population. This probably reflects greater concentrations of viruses shed in more populous

catchments. It also suggests that a relatively dispersed pool of infected individuals may be

sufficient to contaminate the receiving waters and that NoV risk may not be particularly

well attenuated by distance.

We discuss options to manage the risk of NoV contamination in oysters from a study site

where peak levels of NoV contamination were generally associated with periods of peak

river flow discharges and low water temperatures. The case study demonstrates that NoV

risk management at this site could potentially be improved through predictive modelling

using environmental monitoring data. Shellfish producers could consider using water

temperature and river flow monitoring data to proactively manage the risk of NoV

contamination in catchments where these factors predict risk. However, the inter-

relationship of NoV risk factors is site-specific and therefore requires site-specific

characterisation studies.

The correlation of NoV with sources of human faecal pollution, and the frequency of CSO

discharges, emphasises the general need for either targeted pollution abatement

strategies in contaminated fisheries to reduce the contribution of continuous and

intermittent discharges to NoV contamination and/or the development of site-specific risk

modelling to permit proactive management during periods of high risk (e.g. when impacted

by CSO discharges).

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Main conclusions

Water temperature, consented dry weather flow of sewage discharges, catchment

area, number of continuous and intermittent discharges, mean annual human

population in the catchment and river flows are significant risk factors for NoV

contamination in shellfish waters.

Median levels of NoV in oysters grown in colder waters (<5°C) are ten times higher

than those grown in warmer waters (>10°C).

Elevated mean concentrations of NoV in shellfish were found in catchments with more

than 32,000 hectares (320 km2), 80,000 people, 50 intermittent sewage discharges

and two large (dry weather flow>2,000m3/day). The significance of these thresholds to

risk management could be investigated further using site-specific information on the

seasonality of human populations, NoV epidemiology and operational performance of

sewerage systems.

The strength and significance of the risk factors varies between NoV and the statutory

indicator of faecal pollution (E. coli). Therefore, a distinct set of measures is required to

manage the risk of NoV contamination of shellfisheries.

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1. Introduction

Consumption of bivalve shellfish grown in faecally polluted waters is widely recognised to

pose a risk of infection with human noroviruses (NoV). Sporadic cases and outbreaks of

NoV gastroenteritis often occur following consumption of raw oysters harvested from

waters impacted by sewage contamination. In contrast, species that although

contaminated are cooked prior to consumption (e.g. mussels, cockles, clams) present a

lower risk of infection.

The prevalence and distribution of NoV in naturally contaminated shellfish has been

reported to vary according to the microbiological quality of the growing waters (EFSA,

2012). Using a standardised quantitative real-time reverse transcription (RT)-PCR method,

Lowther et al. (2012) quantified NoV concentrations in samples of oysters collected from

39 production areas across the UK and found high percentage of positive samples (76%),

although 52% of these samples had low NoV content (<100 detectable genome copies per

gram digestive tissues. Currently, there is no threshold infectivity limit for NoV detected by

RT-PCR and therefore these results provide an indirect measure of risk. However,

published data on shellfish-related outbreaks indicate that NoV concentrations in oysters

linked to cases of human illness vary from less than 100 copies to more than 10,000

copies/g (EFSA, 2012).

The most effective public health measures to control human NoV infection from oyster

consumption are to avoid contamination by either preventing human faecal contamination

in production areas or restricting commercial harvesting from faecally contaminated areas

(EFSA, 2012). This may include removal of sources of human faecal pollution impacting

harvesting areas and/or avoidance of harvesting during periods of high risk of

contamination. Targeting such measures requires a good understanding of the risk factors

leading to NoV contamination so that risk management measures can be improved.

In England and Wales (E&W), a significant proportion of coastal land adjacent to areas

used for shellfish production is urban residential. In urbanised catchments, there is a high

density of point-sources of human pollution which are likely to be the primary mechanism

for introduction of NoV into coastal waters. Factors related to the volume, timing and

quality of sewage pollution entering such waters have been suggested to explain the

abundance and distribution of NoV in commercial shellfisheries (Flannery et al. 2012;

Campos et al. 2013). Other factors suggested to play a role in the epidemiology and

prevalence of the virus include climatic factors (Miossec et al. 2000; Lopman et al. 2009;

Bruggink and Marshall, 2010; Ahmed et al. 2013), hydrology (Doré et al. 2007), population

immunity status (Lopman et al. 2009) and viral evolution (Lopman et al. 2009).

Although the circulation and fate of NoV within production areas (Miossec et al. 2000;

Gentry et al. 2009) and the impact of sewage pollution events on NoV contamination (Ueki

et al. 2005) have been investigated in a limited number of case studies, a comprehensive

assessment of the relationships between different possible risk factors (e.g. sources of

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sewage pollution, hydrological, hydrodynamic and climatic factors) and NoV contamination

levels in shellfish has not yet been undertaken.

There are two monitoring programmes concerned with the quality of shellfish in the UK.

These are related to the European food hygiene regulations (primarily Regulation (EC) No

854/2004) which sets out specific requirements for the microbiological monitoring and

classification of shellfish production areas and Directive 2000/60/EC (Water Framework

Directive; WDF) which establishes a framework for the protection and achievement of

‘good status’ of inland surface waters (rivers and lakes), transitional waters (estuaries),

coastal waters and other waterbodies. In the past, the Shellfish Waters Directive (SWD)

(2006/113/EC) (subsumed into the WFD since 2013) was an important driver for

improvements in the sewerage infrastructure in E&W1. This investment has prioritised the

installation of UV disinfection at STW, reduction of the frequency and volume of

intermittent sewage discharges from storm overflows, and installation of storm spill

monitoring, as the primary mechanisms for the improvement of shellfish waters.

An important characteristic of these statutory controls is the use of faecal indicator

organisms such as Escherichia coli or faecal coliforms enumerated in the shellfish or

growing waters. A viral standard does not currently exist in either European legislation

although the European Food Safety Authority recommends establishing a NoV standard

for oysters as an additional control to improve risk management of production areas

(EFSA, 2012). Evidence now suggests that current controls may not be sufficient to protect

consumers from NoV (Doré et al. 2010; Westrell et al. 2010). In the UK, the majority of

shellfish-related outbreaks of NoV illness are associated with oysters, typically harvested

from class B areas (E. coli<4,600 MPN/100g in 90% of samples) and purified by means of

depuration in approved plants (Cefas, 2011).

1.1 Aim of the study

We investigated the relationships between levels of microbiological parameters (NoV and

E. coli) monitored in oysters and a selection of potential risk factors including pollution

source, hydrometric, climatic, and demographic factors for 31 harvesting sites in E&W.

This study aims to improve the understanding of the factors driving NoV contamination in

shellfish waters. This understanding should assist prioritisation of sewerage infrastructure

investment and improved risk management.

1 The WFD does not contain a specific microbiological standard for shellfish protected areas. However, it

does require that the introduction of legislative principles do not lead to any deterioration in water quality. Government policy in E&W is to continue, under the WFD, an equivalent level of protection for shellfish waters as that afforded by the SWD. Under the Statement of Obligations that apply to water companies over the Price Review period of 2015-2020, Defra has expressed that actions will continue to be taken to endeavour to meet the equivalent of the guideline (G) standard of the now repealed SWD. However, the G standard will be based on E. coli rather than faecal coliforms to align with the standards of the European Food Hygiene Regulations (Defra, 2012).

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2. Data sources and methods

2.1 Microbiological data

We used an existing database of levels of NoV (genogroups I and II; reported as genome

copies/g) and E. coli (reported as most probable number/100g) quantified in native oysters

(Ostrea edulis) from 11 sampling points within 11 production areas and in Pacific oysters

(Crassostrea gigas) from 20 sampling points within 13 production areas around the coast

of E&W from May 2009 to April 2011.

The database also contains water temperature measurements taken by local enforcement

authorities (LEA)2 at the time of sampling. All production areas were commercially

harvested and classified under Regulation (EC) No 854/2004. The classifications of

sampled sites were class A (1 site), class B (28 sites) and class C (2 sites). Oyster

samples were collected on a monthly basis from designated official control sampling points

from May 2009 to April 2011. All samples were obtained directly from production areas

and prior to any further commercial processing (e.g. depuration) which may have been

performed prior to placing the oysters on the market.

Levels of NoV and E. coli were quantified using quantitative real-time reverse transcription

(RT)-PCR (ISO/TS 15216-1: 2013) and most probable number (ISO/TS 16649-2: 2001)

accredited methods, respectively. Sampling was conducted on a monthly basis throughout

the study period at each site. A total of 669 valid sample results were obtained from the 31

sampling sites. The dataset has been reported previously (Lowther, 2011; Lowther et al.

2012). Access to the database was kindly provided by the Food Standards Agency.

2.2 Risk factors

The potential risk factor categories were chosen after conducting a review of the relevant

literature (Campos and Lees, 2014). The risk factor categories and the data sources used

for their parameterisation are listed below:

Human population in the catchment. Census 2011 total human population and

population density by lower super output area. Data source: Office for National

Statistics.

Catchment area (hectares). Data source: Environment Agency.

2 Local enforcement authorities are responsible for collecting shellfish samples from designated harvesting

areas and sending these to the relevant local testing laboratory for analysis under the microbiological monitoring/classification programme in England and Wales.

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Total urbanised area (hectares) in the catchment. Data source: Department for

Communities and Local Government.

Sewage discharges impacting shellfish beds. Database of consented water company

and private sewage discharges to controlled waters. The database contains

geographically-referenced information on the location of classified shellfish production

areas, location of sampling points and location where effluent discharges enter the

environment, effluent type and amount that can be discharged. The following

parameters were categorised from the risk maps: number of continuous discharges to

the estuary, number of intermittent discharges to the estuary, number of trade

discharges to the estuary, dry weather flow (m3/day) of continuous discharges to the

estuary, total number of continuous discharges in the catchment, total number of

intermittent discharges in the catchment, total number of trade discharges in the

catchment, dry weather flow (m3/day) of continuous discharges in the catchment,

fluvial distance (km) from the sampling point to the nearest continuous discharge. The

fluvial distance from the sampling point to the nearest intermittent discharge was not

considered as a parameter for analysis because the impact of intermittent discharges

on shellfish waters is assessed for agglomerations of discharges as recommended by

the Environment Agency consenting policy. Data sources: Environment Agency

national discharge permit database and Cefas Shellfish Hygiene System.

Seawater temperature at time of sampling as per protocol for the collection of shellfish

samples under the official microbiological classification monitoring programme (Cefas,

2013).

Rainfall in the catchment. Total daily rainfall (mm) on the day of sampling and total

cumulative rainfall 7 days prior to day of sampling. Data source: Environment Agency.

Flows in the most significant watercourse in the catchment. Mean flows (m3/s) on the

day of sampling and total cumulative flows 7 days prior to day of sampling. Data

source: NERC/CEH National River Flow Archive.

Tidal range in the shellfish growing water. Classified as microtidal (<2m), mesotidal (2–

4m), macrotidal (4–6m) and hypertidal (>6m) and mean high water springs (metres).

Data source: nautical charts issued by Imray Laurie Norie & Wilson Ltd. and UK

Hydrographic Office.

Base flow index in the catchment. Data source: NERC-CEH National River Flow

Archive.

Frequency and duration of sewage discharges from sewer overflows. Data source:

Environment Agency. We considered including information on the frequency and

duration of sewage discharges from storm overflows impacting the 31 sampling sites.

We were not able to obtain complete sewage spill data for all significant discharges

impacting the 31 sampling sites. However, we were able to conduct a study of this

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impact for a limited number of study sites for which data are available. The results of

this analysis are given in section 3.3.

We also evaluated several other potential risk factor categories but were unable to acquire

sufficient quality or coverage of data to permit analysis. The risk factor categories not

incorporated into the analysis are:

Population equivalent (PE) for STW. Consideration was given to using PE for the

municipal continuous discharges in the study catchments as a proxy for the proportion

of catchment population served by the treatment works. Analysis of the national

discharge permit database evidenced that these data were not available for all STW

investigated. Therefore, in the context of this assessment, it was not possible to

identify to proportion of population connected to the mains and that served by septic

tanks.

NoV hospital outbreaks. We considered including information on the number of NoV

outbreaks in hospitals in the database. Numbers of NoV cases (comprising both

patients and staff combined) reported to the hospital outbreak reporting scheme for the

duration of the study were kindly provided by Public Health England (PHE). These

data were identified by administrative region and by week. However, no information

could be provided on the location of the hospitals. Further correspondence with PHE

indicated that the agency does not have permission to disclose hospital names

because this is considered sensitive information. In addition, data recording is

voluntary and there are differences in the numbers reported that reflect different

reporting practices. Therefore, we were not able to combine these data with the

catchment risk factors and NoV results.

Sediment type. The work proposal had identified that it would be informative to

investigate the relationships between sediment types and the NoV content in oysters.

We considered using the catchment hydro-geology information available from NERC-

CEH National River Flow Archive (NRFA). We found that NRFA catchment boundaries

are not coincident with those used in our geographic information systems and

concluded that it would not be possible to use NRFA information within the resources

available for this study.

2.3 Statistical methods

Statistical analyses were undertaken using Minitab 16 statistical software. Oyster samples

that returned not detected results for a particular genogroup were assigned a

concentration of 20 copies/g for that genogroup (i.e. half the limit of detection [LoD] of 40

copies/g). Samples giving positive results below the limit of quantification (LoQ; 100

copies/g) were assigned a concentration of 50 copies/g. Where total counts of NoV

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(GI+GII) were used, positive samples with results <LoQ for both genogroups were

assigned a concentration of 100 copies/g. We included these results in the <100 copies/g

bracket where such brackets were used. For E. coli, sample results <20 MPN/100g were

assigned a concentration of 10 MPN/100g.

We identified four metrics for analysis as follows:

Relationships between levels of NoV/E. coli and environmental parameters (rainfall,

river flows, water temperature) using:

o All data and data for individual seasons (spring, summer, autumn, winter) from 31

sites;

o Data from sites exhibiting significant winter-summer differences in NoV

contamination; and

Relationships between average levels of NoV/E. coli and demographic, hydrodynamic

and pollution source risk factors using all data for 31 sites.

We investigated the seasonal variation of NoV results in oysters using one-way ANOVA

followed by Tukey’s method for comparisons of confidence intervals for all pairwise

differences. For this analysis, individual sample results were classified according to

season considering spring: March–May; summer: June–August; autumn: September–

November; and winter: December–February.

We investigated the relationships between microbiological parameters (NoV and E. coli) in

oysters and the risk factors using Pearson’s correlation coefficient on log10-transformed

data pairs. This coefficient is a statistical measure of the strength of the relationship

between pairs of data and is denoted by r. Positive r values denote positive linear

correlation; negative r values denote negative linear correlation; and a value of 0 denotes

no linear correlation. Statistical significance of all tests was evaluated at the 5% and 1%

significance levels.

We used the Akaike Information Criterion (AIC) to determine how closely the estimated

data fit the measured data and therefore as an indication of the most parsimonious or

robust correlation model. The AIC was computed from the residual sum of squares of all

significant models (SS), sample sizes (n) and the number of model parameters (k)

according to the formula: AIC=n*ln(SS/n)+2*k. The model with the lower AIC was identified

as the one showing better the correspondence between the estimated and measured data.

Therefore, the most robust correlations are those with the highest Pearson correlation

coefficients and lowest AIC.

Linear (first order) regression models were computed to investigate the associations

between the levels of microbiological contaminants and risk factors. The level of explained

11


variance of the models was assessed using the coefficient of determination (R2, expressed

as %), adjusted for degrees of freedom. Student’s t-test was used to examine the

significance of differences for bivariate comparisons between geometric mean

concentrations of NoV.

In this study, no distinction was made between microbiological results from native oyster

and Pacific oyster samples since these species have been shown to accumulate E. coli

(Younger and Reese, 2013) and NoV (Lowther, 2011) to the same extent in E&W.

3. Results

3.1 Factors determining microbiological contamination of oysters

3.1.1 Water temperature

Seawater temperature was available for 28 of the 31 sites studied. Temperatures across

all sites ranged from -1ºC to 21.5ºC. The lowest average temperature (8.4°C) was

recorded in site 31 and the highest average temperature (13.6°C) was recorded in site 21.

The results of correlation analyses between water temperature and levels of

microbiological contaminants in oysters are shown in Tables 1 and 2. Considering all of

the environmental variables tested, water temperature was the most significant factor

associated with NoV contamination in oysters. This is evidenced by the highest correlation

coefficient and lowest AIC.

Table 1 Results of Pearson correlation (r) analysis between microbiological parameters and hydrometric risk factors for 31 sites.

All samples

AIC (E. coli)

Norovirus (log10) AIC (GI+GII)

Risk factor (log10) E. coli (log10)

GI GII GI+GII

Water temperature (n=532) -0.039 - -0.413* -0.516* -0.515* -383.9

Rainfall (day of sampling) (n=466) 0.142** -29.1 0.089 0.019 0.037 -

Rainfall (7 days) (n=466) 0.279* -15.8 0.006 0.014 0.013 -

River flow (day of sampling) (n=386) 0.261* 66.3 -0.056 -0.032 -0.041 -

River flow (7 days) (n=345) 0.118** 59.9 0.075 0.160* 0.139* 40.5

Spring

Norovirus (log10)

E. coli (log10)

GI GII GI+GII

Water temperature (n=130) -0.124 - -0.271* -0.441* -0.416* -98

Rainfall (day of sampling) (n=115) 0.108 - -0.038 0.062 0.038 -

Rainfall (7 days) (n=115) -0.031 - -0.069 0.043 0.013 -

River flow (day of sampling) (n=95) 0.081 - -0.052 0.097 0.041 -

River flow (7 days) (n=95) 0.098 - -0.044 0.104 0.049 -

Summer

Norovirus (log10)

E. coli (log10)

GI GII GI+GII

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Water temperature (n=134) -0.135 - -0.171 -0.027 -0.146 -

Rainfall (day of sampling) (n=122) 0.219 - 0.118 0.178 0.174 -

River flow (day of sampling) (n=96) 0.234** 17.2 0.149 0.217** 0.211** -24.1

Rainfall (7 days) (n=116) 0.234** -23.0 0.107 0.117 0.141 -

River flow (7 days) (n=98) 0.245** 33.2 0.084 -0.022 0.054 -

Autumn

Norovirus (log10)

E. coli (log10)

GI GII GI+GII

Water temperature (n=129) 0.124

- -0.089

-0.221**

-0.182**

-132.0

Rainfall (day of sampling) (n=112) 0.000

- 0.198

0.198

0.164

-

Rainfall (7 days) (n=100) 0.350*

-11.3 0.123

0.099

0.113

-

River flow (day of sampling) (n=91) 0.250** 22.7 -0.015 -0.057 -0.050 -

River flow (7 days) (n=91) 0.232** 19.8 -0.011 -0.038 -0.038 -

Winter

Norovirus (log10)

E. coli (log10)

GI GII GI+GII

Water temperature (72) -0.015 - -0.197** -0.155 -0.180** -43.2

Rainfall (day of sampling) (62) 0.071 - 0.044 -0.085 -0.061 -

Rainfall (7 days) (62) 0.338* -23.2 -0.255* -0.182 -0.217** -20.1

River flow (day of sampling) (50) -0.221** -2.3 -0.313* -0.124 -0.221** -4.5

River flow (7 days) (50) -0.250** -6.8 -0.313* -0.145 -0.237** -9.3

n = number of samples. * Statistically significant (p<0.01). ** Statistically significant (p<0.05). G-genogroup. Very weak correlation: r = 0.00–0.19; weak correlation: r = 0.20–0.39; moderate correlation: r = 0.40–0.59; strong correlation: r = 0.60–0.79; very strong correlation: r = 0.80–1.00. (Evans, 1996). Spring: March–May; summer: June–August; autumn: September–November; winter: December–February. AIC – Akaike Information Criterion.

For all samples, moderate negative correlations were obtained between water

temperatures and NoV levels. The strength of the correlations was similar for GI, GII and

GI+GII. However, this consistency was not observed when the dataset was split into

seasonal sub-datasets, i.e. negative correlations were obtained in autumn, winter and

spring but not summer (Table 1). In sampling sites with significant summer-winter variation

in NoV levels, this risk factor demonstrated a strong correlation with NoV levels (GII,

GI+GII) (Table 2).

Table 2 Results of Pearson correlation (r) analysis between microbiological parameters

and hydrometric risk factors for 15 harvesting sites with significant winter-summer variation

in NoV levels.

All samples

Norovirus

Risk factor E. coli AIC GI GII GI+GII AIC

13


(E. coli) (GI+GII)

Water temperature (n=252) -0.035 - -0.551* -0.666* -0.655* -227.4

Rainfall (day of sampling) (n=188) 0.199** -35.9 -0.093 -0.223**

-0.190** -69.1

Rainfall (7 days) (n=188) 0.368* -53.4 0.011 -0.043 -0.021 -

River flow (day of sampling) (n=188) 0.159** -33.4 0.050 0.194* 0.130 -

River flow (7 days) (n=188) 0.147** -32.8 0.021 0.233* 0.175** -68.4

n = number of samples. * Statistically significant (p<0.01). ** Statistically significant (p<0.05). G-genogroup. Very weak correlation: r = 0.00–0.19; weak correlation: r = 0.20–0.39; moderate correlation: r = 0.40–0.59; strong correlation: r = 0.60–0.79; very strong correlation: r = 0.80–1.00. (Evans. 1996) AIC – Akaike Information Criterion.

14


301031 301031 301031

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Log10 water temperature (oC)

Log10 n

oro

viru

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GI+

GII)

(copie

s/g

)

2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

25 26 27 28 29 30

31

Figure 1 Linear regressions showing the relationships between levels of total norovirus (GI+GII) in oysters and water temperature at each study site. Linear models are listed in the Appendix II. Water temperature data not available for sites 5, 10 and 20.

15


The linear (first order) regression models of log10-transformed NoV in oysters versus log10-

transformed seawater temperatures show that levels of NoV generally decrease with

increasing temperature at the sampling sites (Figure 1). The coefficients of determination

(R2) for sites 2, 3, 4, 6, 13, 17, 22, 23 and 30 exceed 50% indicating that a significant

proportion of the variance in log10 of total NoV levels is explained by the variance in

seawater temperatures. The distribution of data points close to the regression line is

evident for some of the most contaminated sites (2, 4, 6, 7, 17) and also some of the least

contaminated sites (8, 11).

We grouped NoV results into six seawater temperature ranges at the time of sampling

(Figure 2) and detected significantly higher (one-way ANOVA; p=0.000; Appendix III)

levels of NoV in oyster samples collected in colder waters (<5°C) than those collected in

warmer waters (>10°C). Outlier results not conforming to this pattern were observed at

higher temperatures. These results were detected in sites number 2, 3, 4, 7, 13, 14, 15,

16, 18, 23, 24 and 29. None of the outlier results were detected in the top 10 sites with the

lowest geometric mean level of NoV contamination.

>2015-19.910-14.95-9.90-4.9<0

100000

10000

1000

100

10

Temperature range (oC)

Log10 n

oro

virus (

GI+

GII)

(copie

s/g

)

Figure 2 Box-and-whisker plots of levels of total norovirus (GI+GII) in oyster tissues for six

ranges of seawater temperature at the time of sampling.

3.1.2 Rainfall and river flows

Rainfall data were available for all catchments draining to the 31 oyster production areas.

River flow data were available for 17 sites impacted by freshwater inputs. Rainfall ranged

from 0.1mm to 82mm whereas river flows ranged from 0.004m3/s to 64.1m3/s.

16


The results of correlation analyses between the hydrometric parameters and

microbiological contaminants in oysters are shown in Tables 1 and 2. Both hydrometric

parameters were positively correlated to E. coli in oysters when all sample results were

considered. However, these correlations were very weak or weak indicating that other

factors, or a range of factors, also contribute to the variability of E. coli contamination seen

in the sampling sites. The strongest correlation was obtained with river flows on the day of

sampling.

In the summer, autumn and winter, the most significant factors associated with E. coli

contamination were river flows on the day of sampling, river flows with a time window of 7

days and rainfall also with a time window of 7 days. No significant correlations were

obtained between E. coli and the hydrometric parameters. Although E. coli contamination

was influenced by hydrometric parameters, this correlation was not, in general, observed

for NoV. The only hydrometric parameter demonstrating any correlation in the overall

dataset was a weak positive correlation between NoV (GII, GI+GII) levels in oysters and

river flows with a time window of 7 days. At a seasonal level, there was a weak correlation

between river flows on the day of sampling and NoV (GII, GI+GII) levels in the summer.

Similarly to that observed with E. coli, no significant correlations were obtained between

the hydrometric parameters and NoV levels in oysters during the spring. Interestingly, we

detected weak but consistent negative correlations between NoV levels and the

hydrometric parameters in winter samples. The significant risk factors were total

cumulative rainfall 7 days prior to sampling, river flows on the day of sampling and total

cumulative flows 7 days prior to sampling. The correlation between hydrometric factors

and microbiological parameters was analysed for 15 sites with significant differences in

NoV contamination between summer and winter (Table 2). As expected, we detected very

weak positive correlations between E. coli in oysters and rainfall on the day of sampling,

river flows also on the day of sampling and river flows with a time window of 7 days. Weak

positive correlations were also found between E. coli and rainfall with a time window of 7

days. Overall, the strength of these correlations was similar to that detected when all

sample results were used. Rainfall on the day of sampling and river flows with a time

window of 7 days were found to be significant risk factors for NoV (GII, GI+GII)

contamination in oysters. However, the strength of these positive correlations was weak

(GII) or very weak (GI+GII).

3.1.3 Demographic and pollution source risk factors

Table 3 shows the results of correlation analyses between the geometric mean values of

the microbiological parameters in oysters and the faecal pollution related risk factors

examined. Overall, no correlation was detected with any of these risk factors and the E.

coli content of oysters. In contrast, positive correlations were observed between a number

of these risk factors and NoV concentration in oysters.

17


Table 3 Results of Pearson correlation (r) analysis between microbiological parameters and environmental risk factors for the study sites. Geometric mean

E. coli (log10) Geometric mean of norovirus

(log10)

Risk factor (log10) GI GII GI+GII AIC (GI+GII)

Size of continuous discharges in the catchment (n=30) -0.154 0.572* 0.494* 0.520* 2.8

Total catchment area (n=31) -0.099 0.463* 0.423** 0.436** -31.6

Number of continuous discharges to the estuary (n=25) -0.222 0.374** 0.448** 0.427** -33.4

Mean annual human population in the catchment (31) -0.012 0.454* 0.398** 0.422** -23.4

Number of intermittent discharges in the catchment (n=31) -0.115 0.413** 0.417** 0.420** -30.9

Size of continuous discharges to the estuary (n=28) -0.087 0.433** 0.350 0.382** 3.2

Number of continuous discharges in the catchment (n=28) -0.179 0.358** 0.361** 0.364** -21.2

Number of trade discharges in the catchment (not on public sewer) (n=31) -0.173 0.376** 0.353 0.360** -19.6

Population density in the catchment (n=31) -0.014 0.308 0.316 0.322 -

Urban area in the catchment (n=31) -0.230 0.256 0.311 0.290 -

Number of intermittent discharges to the estuary (n=31) -0.161 0.101 0.104 0.094 -

Number of trade discharges to the estuary (n=31) 0.020 0.074 0.101 0.082 -

Fluvial distance from monitoring point to nearest continuous discharge (n=31) -0.022 -0.130 -0.004 -0.042 -

Base flow index (n=31) -0.089 -0.095 0.047 -0.002 -

Tidal range (n=31) -0.119 -0.122 -0.125 -0.126 -

Mean high water springs (n=31) 0.144 0.196 0.034 0.084 -

n = number of samples.* Statistically significant (p<0.01). ** Statistically significant (p<0.05). G-genogroup. n = 31 in all cases. Very weak correlation: r = 0.00–0.19; weak correlation: r = 0.20–0.39; moderate correlation: r = 0.40–0.59; strong correlation: r = 0.60–0.79; very strong correlation: r = 0.80–1.00. (Evans, 1996). AIC – Akaike Information Criterion.

18


The most significant risk factors (moderate Pearson’s correlation coefficients) were

detected with the consented dry weather flow of sewage discharged from the catchment,

catchment area, number of continuous discharges to the shellfish waters, mean annual

human population in the catchment and number of intermittent discharges in the

catchment. A second group of risk factors was found to be less significant (weak

Pearson’s correlation coefficients). This group included the following risk factors: size of

continuous discharges directly impacting the shellfish waters, number of continuous

discharges in the catchment and number of trade discharges in the catchment.

The sampling sites were grouped according to the levels of sewage treatment (primary,

secondary, tertiary) in the nearest continuous STW discharge from the sampling point. No

significant differences were detected in geometric mean NoV levels in oysters from sites

impacted by sewage effluents subject to different levels of treatment.

We detected discrepant results concerning the relationships between NoV contamination

in oysters and the group of selected demographic risk factors, i.e. mean annual human

population was found to be a moderately significant risk factor for NoV whereas population

density and the extent of urban area in the catchment were not.

The three proxies for sewage dispersion and dilution in the shellfish waters (fluvial

distance from monitoring point to the continuous discharge, tidal range and mean high

water springs) were not found to be significant risk factors for E. coli nor NoV

contamination in oysters.

3.2 Linear regression models to determine norovirus contamination based on

significant risk factors

Figure 3 shows the linear regression model used to visualise relationship between mean

total NoV at the site and mean annual human population in the study catchments. Levels

of NoV tend to increase as human population also increases. The model showed good fit

to the data (p=0.018). However, the coefficient of determination (R2) indicated that human

population accounted for only 15% of the variance in total NoV levels. The uncertainty of

the model is graphically displayed by the predicted 95% confidence interval.

A visual inspection of the models reveals that the data are spread about the regression

line, implying relatively poor fit. In particular, a large variation of NoV results (174–2,243

copies/g) was detected over a relatively small variation in human population (200,000–

213,130). The four mean NoV levels in oysters exceeding 500 copies/g were detected in a

single catchment with about 76,900 people and three catchments with 213,000–220,000

people. No significant differences were found in NoV levels between catchments with less

than 100,000 people and those with >100,000 people.

19


100000010000010000

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Resident human population in the catchment

Geom

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R-Sq 17.8%

R-Sq(adj) 15.0%

Regression

95% CI

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Figure 3 Relationships between levels of total norovirus (GI+GII) in oyster tissues and mean annual human population for the study catchments. Linear model: log10geometric mean NoV=0.9648+0.2627*log10human population (p=0.018). Each sampling site is identified by number.

Similar further analysis by linear regression was performed to examine the relationships

between NoV levels and the other significant faecal pollution related risk factors identified

in Table 3. Figure 4 shows the relationship with catchment area, Figure 5 with sizes of

continuous discharges and Figure 6 with number of continuous discharges. Essentially,

these plots show the same characteristics as those for human population in the catchment

in Figure 3 (i.e. relatively low predictive value, with a significant proportion of data points

outside of the 95% confidence interval). However, the catchment area (Figure 4) did show

a good fit to the data (p=0.014).

Regarding the number of continuous discharges, Figure 6 shows that most sites with low

mean levels of NoV (<200 copies/g) were in areas impacted by only a few (<2) discharges.

The AIC in Table 3 indicates that this is the best fitting model of the three models selected

for linear regression analyses. The removal of the two outlier data pairs (sampling sites 4

and 11) from the dataset increased the coefficient of determination (R2) of the models from

14.3% to 41.8%.

20


10000010000

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Catchment area (hectares)

Geom

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GII)

(copie

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R-Sq(adj) 16.2%

Regression

95% CI

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1617

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Figure 4 Relationships between levels of total norovirus (GI+GII) in oysters and catchment area. Linear model: log10geometric mean NoV=0.7503+0.3304*log10catchment area (p=0.014). Each sampling site is identified by number.

100000100001000100

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R-Sq 14.6%

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Regression

95% CI

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Figure 5 Relationships between levels of total norovirus (GI+GII) in oysters and size (dry weather flow) of the main continuous discharge to the estuary. Linear model: log10geometric mean NoV=1.809+0.1411*log10DWF (p<0.05). Each sampling site is identified by number.

21


16121086421

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Number of continuous sewage discharges

Geom

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GII)

(copie

s/g

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R-Sq(adj) 15.2%

Regression

95% CI

10

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2809

1617

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01

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30

Figure 6 Relationships between levels of total norovirus (GI+GII) in oysters and number of

continuous discharges to the estuary. Linear model: log10geometric mean

NoV=2.094+0.4028*log10number of continuous discharges (p<0.05). Each sampling site is

identified by number.

10010

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Number of intermittent dicharges

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Regression

95% CI

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Figure 7 Relationships between levels of total norovirus (GI+GII) in oysters and number of

intermittent discharges in the catchment. Linear model: log10geometric mean

NoV=1.758+0.3120*log10 number of discharges (p<0.019). Each sampling site is identified

by number.

22


Student t-tests were used to examine the significance of differences for bivariate

comparisons between geometric mean concentrations of total NoV in relation to five

individual risk factors. These analyses indicated significant elevations in geometric mean

concentrations of NoV in shellfish when mean human population in the catchments

exceeds 80,000, when the catchment area exceeds 32,000 hectares, when the consented

dry weather flow of sewage discharged directly into the receiving water exceeds

2,000m3/day, when there are more than 50 intermittent discharges in the catchment or

there are more than 2 continuous discharges impacting the receiving water (Table 4).

Table 4 Results of t-tests comparing geometric mean levels of NoV for selected thresholds

in five significant risk factors.

Risk factor n Geometric mean of norovirus (GI+GII)

t value p

Mean annual human population<80,000 286 120 -6.21 <0.001

Mean annual human population>80,000 376 257

Catchment area<32,000 hectares 317 127 -6.48 <0.001

Catchment area>32,000 hectares 345 261

Consented dry weather flow<2,000m3/day 245 122 -5.97 <0.001

Consented dry weather flow>2,000m3/day 417 237

Number of intermittent discharges in the catchment<50 324 122 -7.2 <0.001

Number of intermittent discharges in the catchment>50 338 276

Number of continuous discharges<2 365 121 -8.00 <0.001

Number of continuous discharges>2 296 312

G-genogroup.

3.3 Effect of discharges from combined sewer overflows on norovirus

contamination of oysters

Combined sewer overflow (CSO) discharge data were available for 10 sampling sites.

Figure 8 shows the linear regression model of the relationship between the geometric

mean levels of NoV in oysters from these sites with the number of discharges impacting

the shellfishery.

Overall, oysters with higher mean levels of NoV were associated with sites impacted by

higher number of intermittent discharges. The coefficient of determination (R2) indicated

that the number of discharges accounted for 62% of the variance in log10 of total NoV

levels in these sampling sites. The linear model showed good fit to the data (p=0.005).

However, differences in mean levels of NoV in oysters between these sampling sites were

less than 1log10. Six of the 10 sampling sites were impacted by >10 discharges per year.

The average NoV levels at these sites ranged from 73 copies/g to 569 copies/g.

23


It is important to note that this assessment is for a low number of sites (10) and based on

limited information on the frequency of intermittent sewage discharges. However, the data

does indicate an important potential correlation, and one worthy of investigation.

10010

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Average number of sewage spills

Geom

etr

ic m

ean n

oro

viru

s (

GI+

GII)

(copie

s/g

)

R-Sq 66.1%

R-Sq(adj) 61.9%

Regression

95% CI

3128

2726

2117

16

15

14

13

Figure 8 Linear regression showing the relationship between geometric mean levels of

NoV in oysters and average number of intermittent sewage discharges in ten sampling

sites for the period April 2010–March 2011. Linear model: geometric mean NoV

(copies/g)=1.688+0.4904*log10average number of discharges. The number of discharges

has been identified using the 12/24h block counting method developed by the Environment

Agency. NB. Average number of discharges calculated as the total number of recorded

discharges divided by the number of discharges for which event duration monitoring data

are available. Site 13–one discharge monitored (total number of impacting intermittent

discharges (ID)=18); site 14–three discharges monitored (total number of impacting

IDs=14); site 15 – one discharge monitored (total number of IDs=62); site 16–two

discharges monitored (total number of IDs=38); site 17–one discharge monitored (total

number of IDs=4); site 21–eight discharged monitored (total number of IDs=50); sites 26,

27, 28–five discharges monitored (total number of IDs=11); site 31–one discharge

monitored (total number of IDs=8).

Further work is required to confirm if these results are representative of typical conditions

concerning the impact of intermittent sewage discharges on NoV contamination of

shellfish. Site-specific information on the frequency and magnitude (volume) of discharges

from all storm overflows impacting a sampling site would be required to undertake such an

assessment. Such data should be available towards the end of the 2015–2020 water

24


company investment programme (AMP6), when event duration monitors with data

recorded by telemetry will be installed in all intermittent discharges impacting shellfish

waters in E&W.

3.4 Case study for NoV management using risk predictors at one site

From the results shown above, it is clear that, overall, the sources and abundance of

human faecal pollution correlate with the likelihood of NoV contamination in oysters.

However, it is also apparent that additional site-specific factors (e.g. water temperature,

river flows, CSO discharges) can further influence the extent of impact of this

contamination on the shellfishery. In this case study, we examined significant NoV risk

factors relevant to this native oyster harvesting site to understand whether site-specific

NoV risk predictors could be developed. In this harvesting area, the sampling location is

approximately 3km from the most significant STW outfall. Figures 5 and 8 indicate that this

site (site 13) is impacted by moderately high volumes of continuous sewage discharges

and a high number of CSO discharges. On the basis of E. coli monitoring, this harvesting

site would be class C under Regulation (EC) No 854/2004.

This catchment is drained by rivers with a base-flow dominated regime with prolonged

periods of zero flow (i.e. ephemeral flow regime) and surface water runoff substantially

reduced by groundwater abstraction. During summer dry-weather conditions, the

proportion of microbiological contamination from urban point sources is considerably

higher than that from catchment diffuse sources. Similar studies could be conducted for

shellfish water catchments with different hydrological and morphological characteristics.

Figure 8 further shows the variation of NoV and E. coli in oysters at this site in relation to

the environmental risk factors of seawater temperature (at the time of sampling) and total

daily flows in the river discharging into the harvesting site. Figure 8 shows that peak levels

of NoV contamination at this site were generally associated with periods of peak river flow

discharges and also with low water temperatures. Low NoV levels (<100 copies/g) were

normally associated with periods of no river flow discharge. In the second year of

monitoring, the variation of E. coli results was not associated with that of NoV.

Categorisation of NoV results by “risk classes” indicates that the oyster harvest season (1

November–30 April) during the period of this study corresponded to periods of high (500–

1,000 NoV copies/g) or very high (>1,000 NoV copies/g) risk of NoV contamination.

Conversely, the closed season at this site (1 May–31 October) represents comparatively

lower risk as indicated by five NoV results lower than 100 copies/g. Within the harvest

period, the highest levels of NoV contamination appeared to be correlated with peak river

flows.

25


Figure 8 An example representation of levels of total norovirus and E. coli in oyster

tissues, flows in the main freshwater input to the fishery and water temperature from 1 May

2009 to 30 April 2011 at site number 13. NB. Norovirus risk classes (very low risk: <100

copies/g; low risk: 100–200 copies/g; medium risk: 200–500 copies/g; high risk: 500–1000

copies/g; very high risk: >1000 copies/g) and oyster season shown as red line on graph.

26


4. Discussion

Although it is clear that NoV (GI and GII) contamination arises exclusively through human

faecal pollution, the mechanism and characteristics of virus transmission in the

environment are not well understood. This constrains the application of measures to

control the risk of shellfish-related gastroenteritis (Hall, 2012; Lopman et al. 2012). This

study makes a contribution to improve this understanding by exploring large-scale

relationships between the levels of NoV and E. coli in commercially harvested oysters and

a selection of potential risk factors. The concentrations of NoV used in this study were

quantified using an accredited PCR method. A limitation of this method is that it may

potentially detect both infectious and non-infectious virus particles and surrogate studies

with FRNA bacteriophage have suggested the potential for PCR to overestimate infectious

viral particles in UV disinfected sewage effluent samples when compared with CSO

discharges (Flannery et al. 2013). However, the European Food Safety Authority (EFSA)

has recommended that the PCR method for NoV be regarded as providing an indirect

measure of public health risk (EFSA, 2012). In practical terms, PCR is the only method

demonstrated to be sufficiently sensitive for detection of the low levels of virus in

environmental samples and a cell culture system for this pathogen has not yet been

developed. New techniques involving the use of intercalating dyes combined with PCR

(Coudray-Meunier et al. 2013) or three-dimensional tissue culture of human intestinal cells

(Straub et al. 2013) have been explored to discriminate between infectious and non-

infectious viruses but further developments are needed before these methods can be

applied to catchment-level investigations (Girones et al. 2010).

The risk factors selected were those likely to best represent human faecal pollution inputs

to the study sites, the factors associated with loadings and dispersion of sewage

contamination and the seawater temperature which is likely to influence virus survival and

uptake by molluscs. In practice, these risk factors are likely to be interdependent and

rather site-specific. However, we attempted in this analysis to draw conclusions on the risk

factors or their proxies which, overall, better describe the likelihood of NoV contamination

in oyster fisheries.

Probably the most significant factor impacting NoV seasonality is the winter dominated

occurrence of the virus in the general population (Public Health England, 2013). This

undoubtedly drives NoV loadings arriving at STW and hence the higher NoV prevalence in

sewage during winter months and the consequential winter seasonality of shellfish-related

gastroenteritis outbreaks in temperate climates (Rippey, 1994; Lowther et al. 2008; Rajko-

Nenow et al. 2012). The significant correlations between NoV in oysters and water

temperatures are consistent with the timing of peak loads in the general population and

with data showing that the virus remains intact (and presumably viable) in the environment

for several months at low temperature (Kukkula et al. 1999; Richards et al. 2012) and even

under freezing conditions (Richards et al. 2012). The biological mechanisms driving this

seasonality are insufficiently known. Although the immunity status of the human population

and changes in the frequency of gene variants are likely to be important driving forces, the

biological status of the shellfish and the resistance of NoV to environmental stressors may

27


also play a role. Emergence of new variants has been linked to increased NoV activity

early in the season of peak prevalence (Kroneman et al. 2006). Furthermore, different

genogroups may exploit different environmental conditions at different times. It is possible

that periods of reduced NoV removal efficiency occur in biological sewage treatments. This

has been previously demonstrated for faecal indicator bacteria (Kay et al. 2008).

Furthermore, it has been pointed out that GI strains may be more stable in marine

environments and more resistant to sewage treatments than GII strains (da Silva et al.

2007; Nenonen et al. 2008, 2009). Molecular characterisation and genotyping of NoV in

wastewater, oysters and stool samples carried out in Ireland indicated the occurrence of

multiple genotypes in environmental samples with predominance of the GII.4 variant in

wastewater and oysters. These results were consistent with the prevalence of GII strains

in the community during the study period (Rajko-Nenow et al. 2013). A linked factor is

probably the slow removal of virus from contaminated bivalves at low seawater

temperatures (Doré et al. 1998, 2010) which influences likely depuration rates during

short-term environmental temperature fluctuations. In the UK, average levels of NoV in

Pacific oysters (Crassostrea gigas) in October–March can be as much as 17 times higher

than those during the remainder of the year (Lowther et al. 2008). Evidence from in vitro

and in vivo studies demonstrates that oysters selectively accumulate NoV strains through

specific binding to carbohydrate ligands; NoV GI strains are more actively and efficiently

concentrated than GII strains and this is consistent with high proportion of GI strains

associated with shellfish-related outbreaks (Le Guyader et al. 2012).

The differences observed in correlation coefficients between NoV and water temperatures

in autumn, winter and spring indicate that developing linear models separately for each

season would reflect the small ranges of NoV levels and water temperatures for particular

seasons. This could lead to misleading inferences on the relationship between

temperature and NoV contamination in shellfish.

It is difficult to predict the implications of variations in water temperature on NoV

contamination of shellfish waters in widely diverse geographical areas. Detection and

quantification of NoV was carried out in 24 designated bathing waters in 9 European

countries as part of the Virobathe Project (Wyn-Jones et al. 2011). NoV was detected and

quantified using nested RT-PCR in samples of fresh and marine water collected during the

2006 bathing season. Most sites were known to be impacted by sewage pollution.

Although viruses were found less often in sites where sewage input was anticipated to be

lower, there was substantial variability in virus occurrence between sites around Europe.

Almost all of the NoV GI positive samples were detected in sites on the Mediterranean

coast where water temperatures are usually higher. The most comprehensive database of

NoV levels in shellfish has been reported in the EFSA opinion (2012). This database is

restricted to western European countries (UK, France, Ireland) and characterisation of

NoV contamination in other parts of Europe characterised by warmer climates is currently

lacking.

Seawater temperature monitoring, in combination with sanitary profiling of shellfish waters,

is therefore a possible approach to management of NoV risk as previously reported

28


(Lowther et al. 2012). While water temperature monitoring may exist in real-time for some

harvesting sites, they are not available for every estuary. Installation of sensors for

recording of water temperature data combined with microbiological monitoring and

hydrographic dye tracing studies to evaluate the physical dispersion and dilution of

sewage plumes could assist the development of models for forecasting NoV contamination

in shellfish. These field measurements could be complemented by information from

synthetic aperture radar (DiGiacomo et al. 2004) or ocean colour (Nezlin and DiGiacomo,

2005) satellite imagery as observational tools for large scale sewage pollution events on

the coast.

Whilst seawater temperature is a good predictor of the temporal degree of NoV

contamination within a site, it clearly cannot, on its own, predict the overall degree of

contamination occurring. For this, we evaluated various proxies of human faecal pollution

inputs into the study sites. No correlation was detected between any of the sixteen human

faecal pollution related risk factors studied and the E. coli content of oysters. It is important

to note that the levels of E. coli contamination would be influenced by the existence of

disinfected sewage discharges and/or fluxes of bacteria from agricultural land. However,

the study was not designed to investigate the individual contribution of these sources. In

contrast to the E. coli results, there was a clear and significant correlation between several

of the human faecal pollution proxies and the overall NoV contamination levels seen at the

study sites. The most significant risk factors were, by decreasing order of importance:

The total combined consented dry weather flow to the STW in the catchment.

The catchment area.

The number of continuous discharges to the estuary.

The mean human population in the catchment.

The number of intermittent discharges in the catchment.

The total combined consented dry weather flow to STW impacting the estuaries.

The number of continuous discharges in the catchment.

The number of trade discharges in the catchment.

Some sites (e.g. sites 4 and 11) are apparent model outliers. It would be informative to

investigate these sites in more detail to improve understanding of the site-specific

characteristics influencing the levels of explained variance in the models.

The population density in the catchment, the urban area in the catchment, the number of

intermittent discharges to the estuary, the number of trade discharges to estuary, the

distance of the sampling point to the nearest continuous discharges, the base flow index,

and the state and range of the tide, were not significant factors in this analysis. These

results clearly indicate a correlation in the sampling sites between human sewerage-

related sources of microbiological pollution, or related demographic proxies, and NoV in

oysters. The impact of sewage sources can be described on the basis of the number of

sources or volume discharged to the receiving waters. Further site-specific studies are

being conducted to better characterise the risk of contamination associated with different

types of discharge/sewage treatment levels and the fate of NoV in the impacted fishery.

29


The lack of correlations between NoV levels and urban area/population density in the

catchments indicates that the environmental transmission of the virus is not markedly

influenced by the degree of urban development (residential area) in the catchment. Rather,

NoV contamination is dependent on local climatic patterns (Lopman et al. 2009) and the

epidemiological profile of the population which may vary between communities of different

sizes (Fernández et al. 2012). This finding, combined with the high infectivity and

environmental resistance of the virus (Hall et al. 2011), may help explain the occasional

occurrence of cases of shellfish-related NoV gastroenteritis in rural areas.

Previous studies have found higher prevalence of NoV GII than GI in crude sewage

(Henshilwood, 2002; Lowther, 2011) and in commercially harvested oysters (Lowther et al.

2012). Significant seasonal differences in the prevalence of these genogroups have also

been detected in sewage with higher levels of GII detected in the winter (Nordgren et al.

2009; Lowther, 2011). Although GII was the only genogroup positively associated with

cumulative river flows on an individual sample result basis, we did not detect substantial

differences in the strength of the relationships between genogroups in correlation analyses

of average NoV levels. These results suggest that both genogroups have similar

mechanisms of environmental transmission and therefore the cumulative total of

genogroups can be used to reflect the contribution of environmental contamination in NoV

risk assessments.

Statistical models describing the effect of environmental risk factors on NoV levels in

shellfish are rarely encountered in the peer-reviewed literature. Our regression models

predict comparatively elevated contamination with NoV when mean annual human

population exceeds 80,000, when the catchment area exceeds 32,000 hectares, when the

size of impacting continuous discharge exceeds 2,000m3/day, and when there are more

than two continuous sources of sewage contamination directly to the estuary. The

relationship with mean annual human population may be confounded by variations in

population such as tourism although it is important to note that STW are designed to

accommodate multiples of dry weather flow and this is assumed for all sewerage systems.

It is important to note that the levels of explained variance in the regression models were

low and a high number of data points were outside the 95% prediction intervals. There are

two main reasons why R2 values for these models were low. Firstly, prediction of

microbiological contamination on the basis of demographic, physical and meteorological

variables typically has coefficients of determination lower than 50%. Furthermore, the

experimental protocol for the collection of oyster samples was not originally intended to

capture the worst-case scenario of sewage contamination. The models represent however

the mean change in NoV levels in response to change in risk factors. The relationships are

site-specific and would depend upon the location of pollution sources in relation to the

oyster beds. This type of information is of practical use to the industry in helping to choose

lower risk areas for oyster production and for informing the possible NoV contamination

risk of areas for new farming operations.

We were able to extend the analysis regarding CSO impact by comparing the mean NoV

contamination levels in oysters with the number of intermittent discharges occurring for 10

30


sites for which we were able to obtain robust spill data. We found a significant correlation

between the frequency of CSO discharges and the degree of contamination with NoV.

Although this is an important finding the analysis has some limitations. At these 10 sites,

the variation in mean levels of NoV was <1log10. Information provided by the Environment

Agency indicates that from April 2010 to March 2011 the designated shellfish water and

bathing water sites that were monitored in E&W were impacted by 16 intermittent

discharges on average (P. Simmons, EA pers. comm.). It would be informative to study a

broader range of sites with a wider range of NoV contamination levels. Since not all CSOs

potentially impacting the sites were monitored it is possible that unmonitored CSOs may

also make a contribution which is not accounted for. The 10 sampling sites examined here

were also potentially impacted by continuous discharges. In this situation, both types of

discharge (continuous and intermittent) are likely to contribute to the overall NoV

contamination burden. Of relevance to this scenario Ventrone et al. (2013) studied the

dynamics of NoV uptake in oysters maintained in laboratory conditions and found that

there is no significant difference between chronic and episodic events of sewage

contamination on NoV uptake. Further studies are required to confirm the relationship

between CSO discharges and the degree of NoV contamination and the relative

importance of continuous versus intermittent discharges. In practice, this is likely to be

site-specific and depend on the volume, treatment level, and proximity of the sources to

the oyster harvesting areas. Further work could also consider the proportion of

separate/combined sewerage systems in the catchments draining to shellfish waters.

Combined sewerage systems are expected to have relatively higher number of CSOs

whereas separate systems may have a greater risk of misconnections which could

outweigh the impact of an intermittent discharge over time.

Following the analysis of human faecal inputs we examined the impact of hydrometric

parameters (rainfall and river flows) which are likely to influence the transmission of these

inputs to the oyster growing sites and also, in some cases, the likelihood of CSO

discharges occurring. Our analysis revealed significant new information on the differential

effect of hydrometric factors on the levels of E. coli and NoV in shellfish. Hydrometric

parameters had a consistent correlation with the likelihood of E. coli contamination in

oysters, as has been previously reported (Campos et al. 2011), but by contrast very little

effect on the likelihood of contamination with NoV. For E. coli, rainfall is significant in

enhancing the wet weather connectivity between the reservoirs of bacterial contamination

in the catchments, which include both human and agricultural (diffuse) sources, and the

shellfishery. Rainfall also enhances river flows thus potentially carrying more

contamination to the fishery. However, from our results, rainfall and river flows do not

appear to have the same impact on NoV contamination in shellfish. River inputs seem to

contribute only a small proportion of NoV loads to the receiving waters, particularly those

that do not receive discharges of sewage pollution from point sources in the catchment

during the summer, when the prevalence of the virus is at its lower levels. In contrast,

during the winter (peak of NoV prevalence), our results show that high rainfall-river flow

impacts actually correlate with a reduction in NoV contamination in shellfish. This contrasts

with studies that have reported increased levels of faecal indicator bacteria (Campos et al.

31


2013) and NoV (Miossec et al. 2000) during and shortly after rainfall events when periods

of regulatory non-compliance are more likely to occur. However, this does not always

occur as in some sites shellfish accumulate lower levels of E. coli during rainfall possibly

due to either a suspension of filtration activity during periods of low salinity or to the

dilution of impacting sewage plumes (Lee and Morgan, 2003). Another factor that may

have influenced these results is the site-specific variability of the volumes of freshwater

entering the study estuaries versus the volumes of water exchanged during the tidal

cycles. In the context of this study, it was not possible to identify the relationships between

NoV contamination levels and the water mixing/flushing characteristics of the study sites.

To evaluate this, NoV levels for a larger number of sampling points in each study site

would be required.

For NoV, agricultural sources are unlikely to be a significant risk for NoV GI and GII

contamination because these genogroups are found in humans (GII.11 are also found in

pigs), however human diffuse sources higher up the catchment may be. However, in some

study catchments, it seems likely that a substantial proportion of E. coli was associated

with diffuse pollution from agricultural land. In many rural parts of England and Wales, it

has been found that improved grassland and associated livestock are key sources of

faecal indicator bacteria to coastal waters, particularly during high-flows (Kay et al. 2010).

Microbial source tracking approaches could provide information on the relative contribution

of human and non-human inputs of faecal pollution from point and diffuse sources and in

assisting the development of more targeted pollution remediation plans in hydrological

catchments or shellfisheries.

Finally, we attempted to apply our analysis of key risk factors to investigate possible

management criteria which could be used to better control NoV risk in a case study for one

of the sampling sites (site 13). The results from this case study indicated that the most

contaminated period for NoV generally coincided with the oyster harvest season

(November–February). From a risk management point of view, modification of the harvest

season is problematical since the peak of maturation for this species occurs in June/July

and spawning takes place from June to September. The oyster population dynamics may

not be sustainable if the fishery was harvested during the summer months. The correlation

of NoV contamination with river flows at this site suggests an alternative strategy could be

to base harvest decisions on this predictor of NoV contamination. It is also important to

note that the underlying causes of contamination at this fishery were the moderately high

volumes of continuous sewage discharges and the high number of CSO discharges.

In summary, focusing future sewerage infrastructure improvements on advanced forms of

sewage treatment (membranes for advanced filtration, optimised forms of activated sludge

involving nitrogen control) and reducing the frequency of CSO discharges could contribute

to strategies to reduce NoV contamination in shellfish waters. Additional public health

protection could also potentially be gained through prediction of episodes of NoV

contamination combined with NoV testing prior to marketing. From this study, the

parameters that could be used to develop models for forecasting NoV contamination in

shellfish waters could include water temperature, storm overflow inputs and potentially

32


river flows where these contribute to contaminant loading from pollution sources higher in

the catchments.

5. Conclusions

Water temperature, river flows, consented dry weather flows of sewage discharged,

catchment area, number of continuous and intermittent discharges and catchment

human population all influenced the degree of NoV contamination of oysters in the

catchments studied. The partial contribution of each of these risk factors is complex

and site-specific.

The strength of the relationships between NoV levels and the risk factors was different

from that between E. coli and the same risk factors. Therefore, reliance on E. coli as a

regulatory tool is likely to be only partially effective at managing NoV risk. Further, it

suggests that distinct sets of measures are probably required to manage the risk of

contamination associated with bacteria versus viral pathogens. Such measure(s) may

include regulatory limit(s) for NoV, enhancement of post-harvest treatments to reduce

NoV in shellfish, restriction of commercial harvesting during periods of high risk of NoV

contamination or a combination of these.

There were no substantial differences in the way risk factors influence NoV levels in

shellfish between genogroups I and II. Therefore, the cumulative total of both

genogroups is an appropriate measure of the NoV load impacting the fisheries in the

context of risk management.

Larger catchments (>32,000 hectares), with mean annual human population of more

than 80,000, with more than 50 intermittent discharges and with more than two large

(dry weather flow>2,000m3/day) continuous sources of sewage pollution directly to the

estuary where shellfish waters are located were associated with higher risk of NoV

contamination.

An association between the number of CSO discharges and NoV contamination was

observed at a sub-selection of sampling sites. However, further site specific work is

necessary to understand the relative importance of continuous and intermittent

discharges to NoV contamination.

A predictive model that integrated water temperature and river flow data with additional

information on sources of sewage pollution was able to offer predictive capability for

NoV contamination at one sampling site. It is possible that this approach could be

used at other sites to develop near real-time predictions of NoV contamination to

assist local risk management decisions.

33


6. Acknowledgements

Thanks to the Food Standards Agency England for permission to use the norovirus

surveillance data for this study. Also thanks to Allan Reese (Cefas) for advice on statistical

methodology, Mark Yeomans (Office for National Statistics) for supplying Census data,

Helen Florek (Environment Agency Wales) and Joanne Walker (Environment Agency

England) for supplying rainfall data, Paul Simmons (Environment Agency) for supplying

CSO spill data and Natalie Adams for supplying data on norovirus outbreaks in hospitals.

Intellectual input and review by the following individuals is gratefully acknowledged: Elaine

Connolly (Defra), Mandy Pike (Seafish), Fergus O’Brien (Welsh Water), Michelle Hull

(Department of the Environment, Northern Ireland), Angela Halpenny (Northern Ireland

Water). Special thanks also to Dr. James Ebdon (University of Brighton) for peer review of

the report. His thoughtful and constructive comments are much appreciated.

This study was funded by Defra under the Memorandum of Understanding Advice and

Evidence on Shellfisheries (FC003A).

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http://www.hpa.org.uk/hpr/archives/2013/hpr50-5113.pdf

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8. Appendices

Appendix I Seasonality of total levels of norovirus in oyster tissues from thirty-one

monitoring points in England and Wales.

Tukey’s test 95% confidence intervals

Site code n ANOVA Winter-summer differences

1 24 F3,20=6.17; p=0.004 S

2 18 -

3 17 -

4 17 -

5 21 F3,17=1.80; p=0.186 NS

6 24 F3,20=13.42; p=0.000 S

7 24 F3,20=4.93; p=0.010 S

8 22 -

9 23 F3,19=3.19; p=0.047 S

10 24 F3,20=14.54; p=0.000 S

11 24 F3,20=2.96; p=0.057 NS

12 20 F3,16=4.09; p=0.025 S

13 19 -

14 17 -

15 17 -

16 23 F3,19=2.30; p=0.110 NS

17 23 F3,19=7.67; p=0.001 S

18 22 -

19 23 F3,19=13.36; p=0.000 S

20 22 F3,18=2.96; p=0.060 NS

21 23 F3,19=12.53; p=0.000 S

22 22 F3,18=9.89; p=0.000 S

23 22 F3,18=11.70; p=0.000 S

24 20 F3,16=0.14; p=0.935 NS

25 15 -

26 23 F3,19=5.62; p=0.006 S

27 24 F3,20=7.68; p=0.001 S

28 24 F3,20=13.06; p=0.000 S

29 20 -

30 21 -

31 23 F3,19=1.39; p=0.278 NS

- insufficient data. S-significant; NS-not significant. Spring: March–May; summer: June–August; autumn: September–November; winter: December–February.

39


Appendix II Linear models of relationships between levels of total norovirus (GI+GII) in oyster tissues and water temperature. site 1- log10 total NoV=2.504-0.6449*log10temperature; R

2=48%;

site 2 - log10 total NoV=4.899-2.184*log10temperature; R2=56.1%.

site 3 - log10 total NoV=4.753-2.090*log10temperature; R2=52.4%



site 7 - log10 total NoV=3.541-1.2429*log10temperature; R2=46%















site 24 - log10 total NoV=2.441+0.0704*log10temperature; R2=0%








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Appendix III One-way analysis of variance for log10-transformed norovirus versus temperature range. Source DF SS MS F P

Tempclass 5 56.589 11.318 35.87 0.000

Error 520 164.070 0.316

Total 525 220.658

S = 0.5617 R-Sq = 25.65% R-Sq(adj) = 24.93%

Individual 99% CIs For Mean Based on

Pooled StDev

Level N Mean StDev ----+---------+---------+---------+-----

0 1 2.7453 * (-----------------*-----------------)

1 54 2.9278 0.7121 (--*-)

2 145 2.5287 0.6500 (-*)

3 165 2.1591 0.5478 (*)

4 148 1.9131 0.4248 (-*)

5 13 1.8381 0.2254 (----*----)

----+---------+---------+---------+-----

1.60 2.40 3.20 4.00

Pooled StDev = 0.5617

Grouping Information Using Tukey Method

Tempclass N Mean Grouping

1 54 2.9278 A

0 1 2.7453 A B C D E

2 145 2.5287 B

3 165 2.1591 E

4 148 1.9131 D

5 13 1.8381 C D E

Means that do not share a letter are significantly different.

Tukey 95% Simultaneous Confidence Intervals

All Pairwise Comparisons among Levels of Tempclass

Individual confidence level = 99.54%

Tempclass = 0 subtracted from:

Tempclass Lower Center Upper -+---------+---------+---------+--------

1 -1.4329 0.1826 1.7980 (-------------*------------)

2 -1.8227 -0.2165 1.3896 (------------*-------------)

3 -2.1917 -0.5862 1.0193 (------------*------------)

4 -2.4382 -0.8322 0.7739 (------------*------------)

5 -2.5683 -0.9072 0.7539 (------------*-------------)

-+---------+---------+---------+--------

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-2.4 -1.2 0.0 1.2


Tempclass Lower Center Upper

2 -0.6543 -0.3991 -0.1439

3 -1.0197 -0.7688 -0.5178

4 -1.2692 -1.0147 -0.7602

5 -1.5843 -1.0898 -0.5953

Tempclass -+---------+---------+---------+--------

2 (-*-)

3 (-*-)

4 (--*-)

5 (---*---)

-+---------+---------+---------+--------

-2.4 -1.2 0.0 1.2



3 -0.5519 -0.3697 -0.1875

4 -0.8027 -0.6156 -0.4286

5 -1.1541 -0.6907 -0.2273

Tempclass -+---------+---------+---------+--------

3 (-*)

4 (-*)

5 (---*---)

-+---------+---------+---------+--------

-2.4 -1.2 0.0 1.2



4 -0.4272 -0.2460 -0.0647

5 -0.7821 -0.3210 0.1401

Tempclass -+---------+---------+---------+--------

4 (-*)

5 (---*---)

-+---------+---------+---------+--------

-2.4 -1.2 0.0 1.2


Tempclass Lower Center Upper -+---------+---------+---------+--------

5 -0.5381 -0.0751 0.3880 (--*---)

-+---------+---------+---------+--------

-2.4 -1.2 0.0 1.2

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