THE WILD, WILD, WET!

SETAC Expert Advisory PanelSETAC Expert Advisory Panel

Performance Evaluation andPerformance Evaluation and

Data InterpretationData Interpretation

THE PERFECT WORLD

FOCUS ON DATA ANALYSIS

• STEP 1: STEP 1: GRAPH THE DATAGRAPH THE DATA

• STEP 2: STEP 2: Analyze the data by EPA flowchartsAnalyze the data by EPA flowcharts

• STEP 3: STEP 3: DO THE RESULTS MAKE SENSE?DO THE RESULTS MAKE SENSE?

SOFTWARE PROGRAMS

• Many software packages/programs are Many software packages/programs are availableavailable

• DO NOT assume they follow the EPA DO NOT assume they follow the EPA recommended analysisrecommended analysis

• DO verify the software by running DO verify the software by running example datasets from the methods example datasets from the methods manualsmanuals

STATISTICAL AND BIOLOGICAL SIGNIFICANCE

TOXIC VS. NON-TOXIC

• How are data from a WET test used to How are data from a WET test used to make a decision of toxicity?make a decision of toxicity?

– Two paths:Two paths:• Decision based on the observed result• Decision based on standard effect

WHO DECIDES WHICH PATH?

• The Permit WritersThe Permit Writers– BOTH approaches are supported by the BOTH approaches are supported by the

TSD and the methods manualTSD and the methods manual

OBSERVED RESULT

• Data from the test are used to Data from the test are used to determine if toxicity is present by determine if toxicity is present by hypothesis testinghypothesis testing

– HHOO: Effluent is not toxic: Effluent is not toxic

– HHaa: Effluent is toxic: Effluent is toxic

STANDARD EFFECT

• A A preselectedpreselected level of effect is level of effect is considered toxicconsidered toxic– Acute test:Acute test: 50 % effect50 % effect– Chronic test:Chronic test: 25 % effect25 % effect

THERE ARE INHERENT STRENGTHS AND

WEAKNESSES TO BOTH APPROACHES

COMPONENTS WHICH IMPACT THE NOEC

WHAT BIOLOGICAL CONCLUSIONS CAN BE

MADE FROM THE STATISTICAL ANALYSIS OF A SINGLE TOXICITY TEST?

The biological impact was The biological impact was significant in the beakersignificant in the beaker

THE LESS THAN PERFECT WORLD

INTRA- AND INTER-TEST VARIABILITY

TYPES OF VARIABILITY

• Intra-test : among and between Intra-test : among and between concentrationsconcentrations

• Inter-test: within one lab, same methodInter-test: within one lab, same method

• Inter-lab: between labs, same methodInter-lab: between labs, same method

• Method specific: within limits of methodMethod specific: within limits of method

INTRA-TEST VARIABILITY

Group N Mean s.d. CVcontrol 4 0.975 0.050 0.051

2 4 0.975 0.050 0.051

3 4 0.975 0.050 0.051

4 4 0.950 0.058 0.061

5* 4 0.675 0.150 0.222

6* 4 0.275 0.222 0.806

MSE = 0.033% %MSD = 13.9 %

INTRA-TEST VARIABILITY AND ENDPT. UNCERTAINTYEC Concentration Upper

95% CLLower

95% CL1 330 110 515

10 569 286 769

50 1107 841 1390

90 2156 1662 3731

99 3712 2509 9639

POINT ESTIMATE INTER-TEST VARIABILITY

1 3 5 7 9 11 13 15 17 19

95% UCI

95% LCI

HYPOTHESIS TEST INTER-TEST VARIABILITY

1 2 3 4 5 6 7 8 9

SOURCES OF INTRA-TEST VARIABILITY

• Genetic variabilityGenetic variability

• Organism handling and feedingOrganism handling and feeding

• Toxicity among and between treatmentsToxicity among and between treatments

• Non-homogeneous sample sourceNon-homogeneous sample source

SOURCES OF INTRA-TEST VARIABILITY

• Abiotic conditionsAbiotic conditions

• Dilution schemeDilution scheme

• Number of organisms/treatmentNumber of organisms/treatment

• Dilution water pathogensDilution water pathogens

SOURCES OF INTER-TEST VARIABILITY

• Intra-test sourcesIntra-test sources

• Analyst experience and practiceAnalyst experience and practice

• Organism age and healthOrganism age and health

• AcclimationAcclimation

• Dilution waterDilution water

• Sample qualitySample quality

• Test chamber characteristicsTest chamber characteristics

• Replicate volumeReplicate volume

• ProceduresProcedures

VARIABILITY AND POINT ESTIMATE UNCERTAINTY

Test #1 Test #2

Mean CV (%) 9.9 33.8

IC25 (%) 27.2 26.0

MSE 34.5 290.6

95% CI 25.7-28.5 17.2-31.3

HIGH VARIABILITY - LOW STATISTICAL POWER

Group n Mean(ug/ind)

s.d. CV%

Control 4 632 552 87.4

2 4 727 674 92.7

3 4 1080 408 37.7

4 4 564 493 87.5

5 4 748 235 31.4

% MSD = 131 %

LOW VARIABILITY - HIGH STATISTICAL POWER

Group n Meansurvival

s.d. CV

Control 8 1.000 0.000 0.000

2 8 1.000 0.000 0.000

3 8 1.000 0.000 0.000

4 8 1.000 0.000 0.000

5 8 1.000 0.000 0.000

6* 8 0.950 0.093 0.148

% MSD = 1.0 %

ACTIONS TO REDUCE VARIABILITY

• Increase number of reps/treatmentIncrease number of reps/treatment

• QA programQA program

• Establish and follow strict proceduresEstablish and follow strict procedures

• Maximize analyst skillMaximize analyst skill

• Contract lab selectionContract lab selection

• Additional QA/QC criteriaAdditional QA/QC criteria

EXAMPLES OF ADDITIONAL QC TEST CRITERIA

• Region IX: upper MSD limitsRegion IX: upper MSD limits

• Washington: upper MSD limits, Washington: upper MSD limits, change in alphachange in alpha

• N. Carolina: limit control CVs, N. Carolina: limit control CVs, C. dubia “PSC”C. dubia “PSC”

• Region VI: limit control CV, Region VI: limit control CV, increase # replicates,increase # replicates,

biological significancebiological significance

SUSPICIOUS DATA AND OUTLIER DETECTION

CONCERNS

• Outliers make interpretation of WET Outliers make interpretation of WET data difficult bydata difficult by– Increasing the variability in test responsesIncreasing the variability in test responses– Biasing mean responsesBiasing mean responses

IDENTIFYING OUTLIERS

• Graph raw data, Graph raw data, means and means and residualsresiduals

Raw Data and Means

Copper Concentration (ppb)0 100 200 300 400

Residuals

Copper Concentration (ppb)0 100 200 300 400

IDENTIFYING OUTLIERS• Formal statistical test - Chauvenet’s CriterionFormal statistical test - Chauvenet’s Criterion

– Using the previous mysid data, the critical values are:Using the previous mysid data, the critical values are:• Mean = .80, Std. Dev. = 0.302, n = 8

– Chauvenet’s Criterion Value = n/2 = 4Chauvenet’s Criterion Value = n/2 = 4

– Z-score = Z-score = 2.054 (two-tailed probability of 4 %)2.054 (two-tailed probability of 4 %)– The calculations are:The calculations are:

• Equation 1) (Z-score)(Std. Dev.) = (2.054)(0.302) = 0.620• Mean Equation 1 = 0.80 0.620 = 1.42 - 0.18• Outlier Range is >1.42 or <0.18

– A value of 0.2 is not an outlier.A value of 0.2 is not an outlier.

CAN A CAUSE BE ASSIGNED TO THE

OUTLIER(S) ?• Review analyst’s daily observationsReview analyst’s daily observations

• Check water chemistry dataCheck water chemistry data

• Check data entryCheck data entry

• Check calculationsCheck calculations

• If cause can be assigned to outlier, then If cause can be assigned to outlier, then reanalyze data without outlierreanalyze data without outlier

DETERMINE EFFECT ON TEST INTERPRETATION

• Keep all data unless cause is foundKeep all data unless cause is found

• Analyze data with and without suspect Analyze data with and without suspect datadata

• Determine effect of suspect data on test Determine effect of suspect data on test interpretationinterpretation

• Results reported will depend on effect of Results reported will depend on effect of outlier(s) on test interpretationoutlier(s) on test interpretation

REPORTING OF RESULTS• Insignificant EffectInsignificant Effect

– With OutlierWith Outlier• IC25 = 131 (96.9-158) ppb

• NOEC = 100 ppb• % MSD = 28.1 %

– Without OutlierWithout Outlier• IC25 = 124 (93.6-152) ppb

• NOEC = 100 ppb• % MSD = 20.9 %

• Report results with Report results with suspect data suspect data includedincluded

• Significant EffectSignificant Effect– With OutlierWith Outlier

• IC25 = 131 (96.9-158) ppb

• NOEC = 100 ppb• % MSD = 28.1 %

– Without OutlierWithout Outlier• IC25 = 106 (83.8-126) ppb

• NOEC = 50 ppb• % MSD = 12.2 %

• Report results from Report results from both analysesboth analyses

HORMESIS ANDNON-MONOTONIC CONCENTRATION

RESPONSES

WHAT IS HORMESIS ?

• Calabrese and Baldwin, 1998Calabrese and Baldwin, 1998

• General conceptGeneral concept

• OccurrenceOccurrence

• Typical CharacteristicsTypical Characteristics

TYPICAL TRAITS OF HORMESIS

• Hormetic - Hormetic - concentration rangeconcentration range

• Magnitude of Magnitude of hormetic stimulation hormetic stimulation

• Range from Range from maximum maximum stimulation to NOELstimulation to NOEL

Concentration

Max. Stimulation (30-60%)

Hormetic Range (10 x)

Max. Stimulationto NOEL Range

(4-5 x)

WHY IS HORMESIS DIFFICULT TO DETECT IN TOXICITY

TESTS?• Inadequate Inadequate

concentration seriesconcentration series• Inadequate description Inadequate description

of concentration - of concentration - responseresponse

• Inadequate statistical Inadequate statistical powerpower

• Hormesis is not the Hormesis is not the causecause

Well Defined Hormetic Response

Concentration100 1000

Poorly Defined "Hormetic" Response

Concentration100

EFFECTS OF NON-MONOTONIC DATA

NOEC >LOECSea Urchin Fertilization Data

Percent Effluent0 1 2 3 4 5 6

Statistically Significant Reduction

NOEC = 6.0 %LOEC = 0.36 %% MSD = 5.82 %IC25 = > 6.0 %

• Limited replicates Limited replicates (4)(4)

• Control/low Control/low concentration concentration variabilityvariability

• High Statistical High Statistical PowerPower

• NOEC > LOECNOEC > LOEC

HETEROGENEITY IN PROBIT ANALYSIS

• Limited replicates (5)Limited replicates (5)• Control/low Control/low

concentration variabilityconcentration variability• Significant chi-square Significant chi-square • Inflated confidence Inflated confidence

intervalsintervals• Reanalyze with non-Reanalyze with non-

parametric modelsparametric models

Significant Chi-Square for Heterogeneity

0.00.10.20.30.40.50.60.70.80.91.0

1 10 100 1000 10000

Dose ppb

SMOOTHING IN ICP ANALYSIS• Smoothing is used Smoothing is used

in all non-parametric in all non-parametric models.models.

• Smoothing Smoothing procedure averages procedure averages treatment responsestreatment responses

• Increases observed Increases observed toxicitytoxicity

Selenastrum Cell Growth Data

Percent Effluent0 20 40 60 80 100

Actual ResponseSmoothed Response

REMEDIES FOR PROBLEMS ASSOCIATED WITH NON-

MONOTONIC DATA• Better concentration series selectionBetter concentration series selection• Increase number of replicatesIncrease number of replicates• % MSD limits (NOEC’s)% MSD limits (NOEC’s)• Concentration-response curve criterionConcentration-response curve criterion• Use of more robust parametric modelsUse of more robust parametric models

Bailer and Oris, 1997Bailer and Oris, 1997Kerr and Meador, 1996Kerr and Meador, 1996Baird Baird et alet al., 1996., 1996

ANALYSIS OF MULTIPLECONTROL TOXICITY TESTS

WHEN ARE MULTIPLE CONTROLS USED?

• To compare “standard” and “alternative To compare “standard” and “alternative methods.methods.– Food, dilution water, sterilization, organism Food, dilution water, sterilization, organism

source, source, etcetc....– Control response is often not sufficient to Control response is often not sufficient to

determine differences.determine differences.– Use of reference toxicant tests is Use of reference toxicant tests is

recommended.recommended.

EFFECT OF KELP STORAGE ON SENSITIVITY TO COPPER

Fresh Stored

Effect Level1 5 10 15 25

10EC25

WHY ARE MULTIPLE CONTROLS USED?

• When manipulations are made to When manipulations are made to SOMESOME of the test concentrations. of the test concentrations.– Primarily used for salinity adjustments.Primarily used for salinity adjustments.– First rule, avoid if at all possible.First rule, avoid if at all possible.– Treat extra control as most manipulated Treat extra control as most manipulated

concentration.concentration.– Purpose is to determine if adjustments Purpose is to determine if adjustments

affected test results.affected test results.

BRINE ADDITION INMARINE TESTS

EffluentVolume(0 ppt)

BrineVolume(68 ppt)

SeawaterVolume(34 ppt) Salinity

SeawaterControl

0 ml 0 ml 1000 ml 34 ppt

0.625 % 6.25 ml 0 ml 993.75 ml 34 ppt

1.25 % 12.5 ml 0 ml 987.5 ml 34 ppt

2.5 % 25 ml 0 ml 975 ml 33 ppt

5 % 50 ml 0 ml 950 ml 32 ppt

10 % 100 ml 100 ml 800 ml 34 ppt

BrineControl

0 ml 100 ml +100 ml D.I.

800 ml 34 ppt

ANALYSIS OF TWO-CONTROL TOXICITY TESTS WHEN SOME CONCENTRATIONS

WERE MANIPULATED

N o Y es

Y esY es N o N o

A n a lyze IW C an d L ikeTrea ted C on cs . an d

C on tro l U s in gE P A F lowch arts

R ep eat Tes t

IW C Trea tedC on tro l V a lid ?

P oo l C on tro lsan d A n a lyze A ll D ata

U s in g E P A F lowch arts

A n a lyze IW C an d L ikeTrea ted C on cs . an d

C on tro l U s in gE P A F lowch arts

C on tro l t-Tes tN on -S ig n ifican t?

B oth C on tro lsV a lid ?

MOST SENSITIVE SPECIES

DETERMINATION

WHAT IS A MOST SENSITIVE SPECIES

SCREEN (MSSS)?

• A group of toxicity tests used to A group of toxicity tests used to determine the species/method most determine the species/method most capable of characterizing the toxicity capable of characterizing the toxicity associated with a dischargeassociated with a discharge

COMMON CONSIDERATIONS

• Test species selectionTest species selection

• Frequency/timing of initial and Frequency/timing of initial and subsequent screenssubsequent screens

• Changing effluent characteristicsChanging effluent characteristics

• Selection of data analysis methodsSelection of data analysis methods

MULTIPLE BIOLOGICAL ENDPOINT ANALYSIS

• Evaluate each Evaluate each biological endpointbiological endpoint

• Use most “toxic” Use most “toxic” endpointendpoint

Kelp Germination and Germ Tube Length

Statistical Endpoint

NOEC EC/IC25

100 GerminationTube Length

METHODS OF COMBINING MSSS RESULTS

• Proportion (X times Proportion (X times out of Y screens)out of Y screens)

• AveragingAveraging

Multiple MSSS Data Using FW Chronic Tests

Screen Number

1 2 3E

FH CD SC

Species Proportion (X/Y) Average Fathead Minnow (FH) 67 % (2/3) * 87 % Ceriodaphnia (CD) 33 % (1/3) 70 % *Selenastrum (SC) 0 % (0/3) 97 %

STATISTICAL ENDPOINTS FOR EVALUATING MSSS

• NOEC’sNOEC’s

• Point-estimatesPoint-estimates

• Effect at critical concentration (ECC)Effect at critical concentration (ECC)

• Probability of effect at critical Probability of effect at critical concentration (pECC)concentration (pECC)

NOEC’S

• Experimental QuestionExperimental Question

Which method/species is Which method/species is most likely to identify a change from most likely to identify a change from

control response?control response?

ADVANTAGES OF NOEC’S

• Common endpointCommon endpoint• Integrates effect and Integrates effect and

intra-test variabilityintra-test variability

MSSS Determination Using NOEC's

Species

FH CD SC

DISADVANTAGES OF NOEC’S

• Can not separate Can not separate biological effect and biological effect and statistical sensitivitystatistical sensitivity

• Can not averageCan not average• NOEC’s may not be NOEC’s may not be

environmentally environmentally relevantrelevant

Species

FH CD SC

NOEC EC/IC25

>100 >100

MSSS Determination Using NOEC's

POINT-ESTIMATES

Which method/species shows Which method/species shows the specified effect at the lowest the specified effect at the lowest

concentration?concentration?

ADVANTAGES OF POINT-ESTIMATES

• Evaluates a Evaluates a common effect levelcommon effect level

• Utilizes the entire Utilizes the entire concentration-concentration-response curve response curve (parametric models)(parametric models)

• Can use proportion Can use proportion or average analysisor average analysis

MSSS Using Point-Estimates

Concentration (%)

0 20 40 60 80 100

100 FH - EC/IC25 = 70 % *

CD - EC/IC25 = 90 %

SC - EC/IC25 = >100 %

DISADVANTAGES OF POINT-ESTIMATES

• Effect level selectionEffect level selection• Concentration-Concentration-

response requiredresponse required• SmoothingSmoothing• No consideration of No consideration of

endpoint precisionendpoint precision• EC values may not EC values may not

be environmentally be environmentally relevantrelevant

MSSS Using Point-Estimates

Concentration (%)0 20 40 60 80 100

100 FH - EC/IC25 = 70 % *

CD - EC/IC25 = 90 %

SC - EC/IC25 = >100 %

EFFECT AT CRITICAL CONCENTRATION (ECC)• Experimental QuestionExperimental Question

Which method/species shows Which method/species shows the greatest effect at the concentration the greatest effect at the concentration

of environmental concern?of environmental concern?

ADVANTAGES OF ECC

• Environmental Environmental relevancerelevance

• No concentration-No concentration-response requiredresponse required

MSSS Using Effect at the Critical Concentration

Concentration (%)

0 20 40 60 80 100

100 FH - ECC = 0 %CD - ECC = 10 % *SC - ECC = -3 %

DISADVANTAGES OF ECC

• Does not consider Does not consider certainty of certainty of response estimateresponse estimate

• Ability to obtain Ability to obtain effect estimate at effect estimate at IWC from point-IWC from point-estimate modelsestimate models

Species

FH CD SC

t Crit

MSSS Using Effect at the Critical Concentration

PROBABILITY OF ECC (pECC)

At the concentration of At the concentration of environmental concern, which environmental concern, which

method/species had the greatest effect method/species had the greatest effect at the lower 95 % confidence limit?at the lower 95 % confidence limit?

ADVANTAGES OF pECC

Species

FH CD SC

ECCpECC

MSSS Using Probability of Effect at the Critical Concentration

• Considers precision Considers precision of response of response estimate estimate

• Environmental Environmental relevancerelevance

• No concentration-No concentration-response requiredresponse required

DISADVANTAGES OF pECC

• Zero replicate Zero replicate variance variance

• Boot-strapping Boot-strapping • Obtaining 95% Obtaining 95%

confidence intervals confidence intervals at IWCat IWC

SpeciesFH CD SC

ECCpECC

MSSS Using Probability of Effect at the Critical Concentration

SUMMARY

• Discuss the MSSS procedure in detail Discuss the MSSS procedure in detail during permit developmentduring permit development

• Select variety of organism typesSelect variety of organism types• Initially test for trends in toxicityInitially test for trends in toxicity• Continue periodic screening Continue periodic screening • Select type of statistical analysis carefullySelect type of statistical analysis carefully• Make sure that statistical analysis and the Make sure that statistical analysis and the

raw results “make sense”raw results “make sense”

AGE-RELATED SENSITIVITY OF FISH IN ACUTE

WET TESTS

REVISIONS TO FISH AGES IN EPA ACUTE TEST

MANUALS• From: 1-90 days old in the 3rd edition From: 1-90 days old in the 3rd edition

of the acute manual (1985; EPA/600/4-of the acute manual (1985; EPA/600/4-85/013)85/013)

• To: 1-14 days old (or 9-14 days old for To: 1-14 days old (or 9-14 days old for silversides) in the 4th edition of the silversides) in the 4th edition of the acute manual (1993; EPA/600/4-acute manual (1993; EPA/600/4-90/027F)90/027F)

COMMONLY USEDTEST SPECIES

• Fathead minnowsFathead minnows

• Sheepshead minnowsSheepshead minnows

• Silversides (inland, atlantic, and Silversides (inland, atlantic, and tidewater)tidewater)

RATIONALE

• Younger life stage is generally more Younger life stage is generally more sensitive than older life stagesensitive than older life stage

• Reduction in range of acceptable ages Reduction in range of acceptable ages from 1-90 to 1-14 days will reduce from 1-90 to 1-14 days will reduce variabilityvariability

CONCERN

• Use of younger fish in NPDES testing Use of younger fish in NPDES testing may show an increase in apparent may show an increase in apparent toxicity, without any changes in effluent toxicity, without any changes in effluent conditionsconditions

COMMON QUESTIONS

• Are <14-day old fish more sensitive Are <14-day old fish more sensitive than <90-day old fish to toxicants?than <90-day old fish to toxicants?

• Does the use of <14-day old fish reduce Does the use of <14-day old fish reduce intertest variability when compared to intertest variability when compared to <90 day-old fish?<90 day-old fish?

• How does the sensitivity and precision How does the sensitivity and precision vary within the 1 to 14 day old age vary within the 1 to 14 day old age range?range?

SENSITIVITY OF 14, 30, AND 90 DAY-OLD

FATHEAD MINNOWSCopper

Age (days)14 30 90

Unionized Ammonia

Age (days)14 30 90

INTER-TEST PRECISION OF 14, 30, AND 90-Day Old

FATHEAD MINNOWSCopper

Age (days)14 30 90

Unionized Ammonia

Age (days)14 30 90

SENSITIVITY OF 1-14 DAY-OLD

FATHEAD MINNOWSSodium Pentachlorophenol

Age (days)1 4 7 10 14

Hexavalent Chromium

Age (days)1 4 7 10 14

01234567

Unionized Ammonia

Age (days)1 4 7 10 14

BB B B

AA A A

B BB B

INTER-TEST PRECISIONOF 1-14 DAY-OLD

FATHEAD MINNOWS

Age Range (days)1 - 14 4 - 14 7 - 14 10 - 14

0.6NaPCP

SDSNH3

SUMMARY

• 14-day old fathead minnow larvae are 14-day old fathead minnow larvae are more sensitive to copper & ammonia more sensitive to copper & ammonia than 90 day- old fish.than 90 day- old fish.

• The inter-test precision of 90 day old The inter-test precision of 90 day old fish is equal or better than 14 day-old fish is equal or better than 14 day-old fish for copper & ammonia.fish for copper & ammonia.

SUMMARY(CONTINUED)

• Within the 1-14 day age range, 1 day-Within the 1-14 day age range, 1 day-old larvae are less sensitive to several old larvae are less sensitive to several toxicants.toxicants.

• The sensitivity of these toxicants The sensitivity of these toxicants becomes constant after 4-7 days of age.becomes constant after 4-7 days of age.

• Maximum inter-test precision for these Maximum inter-test precision for these toxicants is observed when the age toxicants is observed when the age range is limited to 7 -14 day old larvae.range is limited to 7 -14 day old larvae.

THE CHRONIC TEST GROWTH ENDPOINT

CHANGE IN GROWTH ENDPOINT CALCULATION

Pre-Nov., 1995 ApproachPre-Nov., 1995 Approach

Growth = Growth = D.W. surviving organismsD.W. surviving organisms

# surviving organisms# surviving organisms

Post-Nov., 1995 ApproachPost-Nov., 1995 Approach

Growth = Growth = D.W. surviving organismsD.W. surviving organisms

# initial organisms# initial organisms

EFFECT ON MEAN TREATMENT RESPONSES

Treatment %Mortality

BeforePromulgation

AfterPromulgation

Control 5.1 325 308

2 2.6 353 341

3 5.0 345 329

4 17.9 387 306

5 47.5 319 167

INTRA-TREATMENT VARIABILITY AND WEIGHT

CALCULATIONS

Observations

Before

OLD MSE/NEW MSE RATIO

1 2 3 4 5 6 7 8 9 10

Ref. Tox.

Effluent

EFFECTS ON HYPOTHESIS TEST ENDPOINTS

BeforePromulgation

AfterPromulgation

Test #

%MSD NOEC %MSD NOEC

1 16.4 50 16.7 50

2 10.8 10 29.1 10

3 11.9 5 39.0 5

4 19.7 25 18.5 25

EFFECTS ON HYPOTHESIS TEST ENDPOINTSBefore Promulgation After Promulgation

Test # %MSD NOEC Avg.wgt. atNOEC

%MSD NOEC Avg.wgt. atNOEC

1 20.9 100 296 23.4 100 296

2 19.5 100 268 25.1 100 233

3 22.1 100 254 24.1 100 227

4 21.4 100 387 22.8 100 313

EFFECTS ON POINT ESTIMATE ENDPOINTS

BeforePromulgation

AfterPromulgation

Test #

IC25 95%CI IC25 95%CI

1 56.2 45.4-79.3 48.3 43.3-61.9

2 NC NC 12.4 6.4-13.8

3 NC NC 4.2 1.5-7.3

4 33.7 28.2-40.6 30.0 19.4-35.0

EFFECTS ON POINT ESTIMATE ENDPOINTS

BeforePromulgation

AfterPromulgation

Test #

IC25 95%CI IC25 95%CI

1 291 NC 234 191-262

2 386 NC 176 140-256

3 227 179-258 138 111-155

4 >400 NC 144 104-162

NOEC/IC25 RELATIONSHIP

Test # TestType

NOEC IC25Before

IC25After

1 Effluent 50% 56.2 48.3

2 Effluent 25% 33.7 30.0

3 Ref. Tox. 100 ppb 291 234

4 Ref. Tox. 100 ppb 386 176

5 Ref. Tox. 100 ppb 227 138

6 Ref. Tox. 100 ppb >400 144

IMPACT ON TEST INTERPRETATION

• Hypothesis Test Results - most cases Hypothesis Test Results - most cases show little change, but not alwaysshow little change, but not always

• Point Estimate Results - usually Point Estimate Results - usually increases predicted toxicityincreases predicted toxicity

ISSUES RELATED TO CHANGE IN APPROACH

• Test growth or biomass?Test growth or biomass?

• Accurate representation of growth?Accurate representation of growth?

• Correlation between new results and Correlation between new results and instream responses?instream responses?

ISSUES RELATED TO CHANGE IN APPROACH

• Conflict between new results and Conflict between new results and unchanged effluent quality?unchanged effluent quality?

• Effect on reference toxicant control Effect on reference toxicant control chartscharts

• Relationship between NOEC and IC25Relationship between NOEC and IC25

ANOMALOUS PATTERNS OF SURVIVAL IN SHORT-TERM

CHRONIC WET TESTS WITH FATHEAD MINNOWS

WHERE HAS THE “PROBLEM” BEEN SHOWN?• Effluent toxicity tests where receiving Effluent toxicity tests where receiving

water is used as test dilution water water is used as test dilution water (diluent)(diluent)

• ““Once through” cooling waters, where Once through” cooling waters, where receiving water is used for cooling and receiving water is used for cooling and then dischargedthen discharged

• Ambient toxicity testsAmbient toxicity tests

COMMON CHARACTERISTICS

• Observed in fathead minnow short-term chronic WET Observed in fathead minnow short-term chronic WET tests, but not in acute WET teststests, but not in acute WET tests

• Not observed in concurrently performed Not observed in concurrently performed ceriodaphniaceriodaphnia short-term chronic testsshort-term chronic tests

• High variability in survival between replicates within a High variability in survival between replicates within a concentrationconcentration

• Concentration-effects relationship is often non-Concentration-effects relationship is often non-monotonicmonotonic

• Mortality is often first seen on day 4 of the test in RW Mortality is often first seen on day 4 of the test in RW controlscontrols

INDUSTRIAL EFFLUENT #1SURVIVAL ENDPOINT (7 D)

Survival vs. Effluent Concentration

0 11 22 43 72 100

Effluent Concentration (%)

Laboratory water Receiving water

Coefficient of Variation vs. Effluent Concentration

101520

2530354045

0 11 22 43 72 100

INDUSTRIAL EFFLUENT #1GROWTH ENDPOINT

Growth vs. Effluent Concentration

0 11 22 43 72 100

101520253035404550

0 11 22 43 72 100

INDUSTRIAL EFFLUENT #1DAILY SURVIVAL (%)

0102030405060708090

LAB.WATER

REC.WATER

• Majority of mortality Majority of mortality occurred on Test occurred on Test Days 3 and 4Days 3 and 4

• Final survival for Final survival for four replicates in four replicates in receiving water was receiving water was 70, 30, 40, and 70%70, 30, 40, and 70%

INDUSTRIAL EFFLUENT #2SURVIVAL ENDPOINT (7 D)

Survival vs. Effluent Concentration

0 6.25 12.5 25 50 100

INDUSTRIAL EFFLUENT #2GROWTH ENDPOINT

Growth

Growth vs. Effluent Concentration

0 6.25 12.5 25 50 100

WISCONSIN DNR PROGRAM

• For chronic WET tests performed For chronic WET tests performed between 1988-1998, 26% showed between 1988-1998, 26% showed unacceptable receiving water control unacceptable receiving water control survival survival

• Only 2.9% of lab controls failed to meet Only 2.9% of lab controls failed to meet survival criterionsurvival criterion

• Effects are not seasonalEffects are not seasonal

MICROBIOLOGICAL EXAMINATION OF FISH

• Aeromonas hydrophilaAeromonas hydrophila

• Flexibacter aurantic and F. columnarisFlexibacter aurantic and F. columnaris

• Flavobacterium sp.Flavobacterium sp.

• Saprolegnia sp.Saprolegnia sp.

WHAT WORKS TO ELIMINATE THE PROBLEM?• Filtration (0.2 µ; some success with Filtration (0.2 µ; some success with

0.45µ)0.45µ)

• AutoclavingAutoclaving

• UV disinfectionUV disinfection

• HeatingHeating

• AntibioticsAntibiotics

HOW ARE PEOPLE HANDLING THIS PROBLEM?

• Use laboratory water as test dilution and Use laboratory water as test dilution and control water for all WET testingcontrol water for all WET testing

• Use laboratory water, after receiving Use laboratory water, after receiving water problems have been shownwater problems have been shown

• Perform concurrent testing in both Perform concurrent testing in both laboratory water and receiving waterlaboratory water and receiving water

HOW ARE PEOPLE HANDLING THE PROBLEM?

(CONTINUED)

• Accept “problem” tests for compliance, but don’t use Accept “problem” tests for compliance, but don’t use them to determine “pass/fail”them to determine “pass/fail”

• Manipulate receiving water sample, before use in testManipulate receiving water sample, before use in test

SUMMARY• Interpretation of fathead minnow short-term Interpretation of fathead minnow short-term

chronic tests may be complicated by presence chronic tests may be complicated by presence of a “biological agent”of a “biological agent”

• This problem has been observed in many This problem has been observed in many areas, but it is not known how widespread is its areas, but it is not known how widespread is its occurrenceoccurrence

• Phenomenon is being studied by different Phenomenon is being studied by different investigators and may result in investigators and may result in recommendations for test method modificationsrecommendations for test method modifications

THE WILD, WILD, WET!

Documents

Transcript of THE WILD, WILD, WET!

Wet weather works well for worms the big wet

Wire Magazine Issue #21.2013 Wet N Wild Swimwear

Looking for wild grapes on the banks of the Tigris …...chromatography, gas chromatography, and wet chemical tests. over the past 25 years, coupling mass spectrometry to liquid chromatography

THE WILD WILD EAST 2011

Meghalaya Wet & Wild ChaloHoppo to a · ﬁne. You must ensure that you are trying out all the available seasonal fruits and local wild leaves. Read this blog to know about the food

Wet and Wild…and goodbye to dignity

UNIVERSITY OF CAMBRIDGE INTERNATIONAL EXAMINATIONS … · TAMBORINE NATIONAL PARKS To BRISBANE Dreamworld Sanctuary Cove Warner Bros. Movie World Wet ‘n’ Wild Mount Tamborine

WET AND WILD WEEK ROADS STILL CLOSED BY THE NUMBERS.pdfWET AND WILD WEEK HUNTER SES CALL OUTS 6018 From Monday to Thursday 4.30pm 3500 jobs remaining SES WORKERS 500 on the ground,

Regulation of Wild Animal Wet Markets in Selected ......United Arab Emirates, and the United Kingdom. These jurisdictions do notappear to have wet markets at which wild animals are

Remaining Vital in a Pandemic Prone World€¦ · Wild Animal Wet Markets and Factory Farms. ... Civet Cats SARS - 2002 MERS - 2012.. HUMANS ... health professionals in the world,

What's W What's Wet on the Wet on the Web!eb!

Edible Wild Plants in Northwestern Ontario: A Primer · Wild ginger grows in average, medium to wet, well-drained soil, in part shade to full shade. Wild ginger prefers constantly

Wet and Wild on Taveuni

Wet n' wild aaron and joseph

ushakamarineworld.co.zaushakamarineworld.co.za/images/PDFs/Platinum_Pass/ush_plat_pass... · Pass for both Sea World and Wet 'n Wild and the Play Pass for uShaka Kids World! So sign

40 - Wet Wet Wet - Love is all around.pdf

APPENDIX 4E - Preliminary Final Report · of the new H2O Zone including the amazing Tornado. This Wet ‘N’ Wild increase was particularly pleasing as a new competitive water park

Wet & Wild Ready To Escape? - British Mountaineering Council · diverse world of the BMC International Meet. 26 Wild and Wet River crossings - how to reach the other bank, with Plas

Wild Rhode Island · comes with a code that accesses the Project Wet Portal, an excellent supplementary resource for teachers. ‘Project WET’ is correlated with NGSS, STEM and

Wild Blueberry Best Management Practices for Insects17 the blueberry landscape • forest edges –BMF, BLB • weedy fields –GH, BMF • low, wet areas –SRW, BSW • coast –