COMPARISON OF SPMDsCOMPARISON OF SPMDsAND BIOTIC SAMPLERSAND BIOTIC SAMPLERS

USING GNOSTIC ANALUSING GNOSTI YSISC ANALYSIS

Institute of Public Health Ostrava Czech RepubliInstitute of Public Health Ostrava Czech RepublicNational reference laboratory for POPsNational reference laboratory for POPs

Tomas OcelkaTomas Ocelka PavelPavel KovanicKovanic

TomasTomas OcelkazuovaczOcelkazuovacz

11

1

22

TOPICSTOPICS

Sampling methods to beSampling methods to be comparedcompared

Objects of measuringObjects of measuring Problems of analysisProblems of analysis Gnostic analysisGnostic analysis Methodsrsquo features to be comparedMethodsrsquo features to be compared Results of comparisonResults of comparison

2

33

Geographic locationGeographic location

3

ndash

ndash

ndash -

ndash

ndash

ndashndash

Centre laboratoriesCentre laboratories accreditationaccreditation

Personnel over 140 5+Person 2 workplacesnel over 140 5+2 workplaces According to ČSN EN ISOIEC 17According 025to ČSN EN ISOIEC 17 025

ndash Over 200 paraOver 200 p metersarameters PCDDFs PCBs OCPs PBDE hellipPCDDFs PCBs OCPs PBDE hellip

ndash Recognized by ILAC EA IAFRecognized by ILAC EA IAF Sampling and TestingSampling and Testing

ndash IntegralIntegral - waterwater SPMDsSPMDs DGTsDGTs POCIPOC SIS

ndash Biotic organiBioti smsc organisms IntercalibrationIntercalibration

ndash Czech + InternationalCzech + International Data analysis (univariatemultivariate)Data analysis (univariatemultivariate)

ndash StatisStati ticalsticalndash GnosticGnostic

44

4

-

-

InstrumentatiInstrumenta ontion (worth over 6 mil USD)(worth over 6 mil USD)

GC-MSMS (ion-trap)

GCQ Polaris

Since 1996 (starting to POPs issue)

GC-HRMS (POPs)

- MAT 95XP

- since 2003

LC-MSMS (pharmacy pesticides)

- ThermoFinigan

- since 2006 55

5

ndash

ndash

ndashndash ndash

Data source for comparison ofData source for comparison of methodsmethods

All rivers within Czech RepuAll rivers wi blicthin Czech Republic scale (15)scale (15)

21 sampling pr21 sampling p ofilesrofiles Complementary to bioticComplementary to biotic

sampling system (since 1999)sampling system (since 1999) with abiotic (SPMDs DGTswith abiotic (SPMDs DGTs POCIS)POCIS) ndashndash sincsi e 2003nce 2003

AimsAimsndash Pilot application 2 years beforePilot application 2 years before

routine applicationroutine application ndash Parallel exposure of DreissenaParallel exposure of Dreissena

Polymorpha Benthos PlantsPolymorpha Benthos Plantsndash POPs (basic OCPs PCBs)POPs (basic OCPs PCBs)ndash POPs (other PCBsPOPs (other PCBs ndash coc ngong

PCDDFs PAHs PBDEPCDDFs PAHs PBD s)Es)

66

6

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

22

TOPICSTOPICS

Sampling methods to beSampling methods to be comparedcompared

Objects of measuringObjects of measuring Problems of analysisProblems of analysis Gnostic analysisGnostic analysis Methodsrsquo features to be comparedMethodsrsquo features to be compared Results of comparisonResults of comparison

2

33

Geographic locationGeographic location

3

ndash

ndash

ndash -

ndash

ndash

ndashndash

Centre laboratoriesCentre laboratories accreditationaccreditation

Personnel over 140 5+Person 2 workplacesnel over 140 5+2 workplaces According to ČSN EN ISOIEC 17According 025to ČSN EN ISOIEC 17 025

ndash Over 200 paraOver 200 p metersarameters PCDDFs PCBs OCPs PBDE hellipPCDDFs PCBs OCPs PBDE hellip

ndash Recognized by ILAC EA IAFRecognized by ILAC EA IAF Sampling and TestingSampling and Testing

ndash IntegralIntegral - waterwater SPMDsSPMDs DGTsDGTs POCIPOC SIS

ndash Biotic organiBioti smsc organisms IntercalibrationIntercalibration

ndash Czech + InternationalCzech + International Data analysis (univariatemultivariate)Data analysis (univariatemultivariate)

ndash StatisStati ticalsticalndash GnosticGnostic

44

4

-

-

InstrumentatiInstrumenta ontion (worth over 6 mil USD)(worth over 6 mil USD)

GC-MSMS (ion-trap)

GCQ Polaris

Since 1996 (starting to POPs issue)

GC-HRMS (POPs)

- MAT 95XP

- since 2003

LC-MSMS (pharmacy pesticides)

- ThermoFinigan

- since 2006 55

5

ndash

ndash

ndashndash ndash

Data source for comparison ofData source for comparison of methodsmethods

All rivers within Czech RepuAll rivers wi blicthin Czech Republic scale (15)scale (15)

21 sampling pr21 sampling p ofilesrofiles Complementary to bioticComplementary to biotic

sampling system (since 1999)sampling system (since 1999) with abiotic (SPMDs DGTswith abiotic (SPMDs DGTs POCIS)POCIS) ndashndash sincsi e 2003nce 2003

AimsAimsndash Pilot application 2 years beforePilot application 2 years before

routine applicationroutine application ndash Parallel exposure of DreissenaParallel exposure of Dreissena

Polymorpha Benthos PlantsPolymorpha Benthos Plantsndash POPs (basic OCPs PCBs)POPs (basic OCPs PCBs)ndash POPs (other PCBsPOPs (other PCBs ndash coc ngong

PCDDFs PAHs PBDEPCDDFs PAHs PBD s)Es)

66

6

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

33

Geographic locationGeographic location

3

ndash

ndash

ndash -

ndash

ndash

ndashndash

Centre laboratoriesCentre laboratories accreditationaccreditation

Personnel over 140 5+Person 2 workplacesnel over 140 5+2 workplaces According to ČSN EN ISOIEC 17According 025to ČSN EN ISOIEC 17 025

ndash Over 200 paraOver 200 p metersarameters PCDDFs PCBs OCPs PBDE hellipPCDDFs PCBs OCPs PBDE hellip

ndash Recognized by ILAC EA IAFRecognized by ILAC EA IAF Sampling and TestingSampling and Testing

ndash IntegralIntegral - waterwater SPMDsSPMDs DGTsDGTs POCIPOC SIS

ndash Biotic organiBioti smsc organisms IntercalibrationIntercalibration

ndash Czech + InternationalCzech + International Data analysis (univariatemultivariate)Data analysis (univariatemultivariate)

ndash StatisStati ticalsticalndash GnosticGnostic

44

4

-

-

InstrumentatiInstrumenta ontion (worth over 6 mil USD)(worth over 6 mil USD)

GC-MSMS (ion-trap)

GCQ Polaris

Since 1996 (starting to POPs issue)

GC-HRMS (POPs)

- MAT 95XP

- since 2003

LC-MSMS (pharmacy pesticides)

- ThermoFinigan

- since 2006 55

5

ndash

ndash

ndashndash ndash

Data source for comparison ofData source for comparison of methodsmethods

All rivers within Czech RepuAll rivers wi blicthin Czech Republic scale (15)scale (15)

21 sampling pr21 sampling p ofilesrofiles Complementary to bioticComplementary to biotic

sampling system (since 1999)sampling system (since 1999) with abiotic (SPMDs DGTswith abiotic (SPMDs DGTs POCIS)POCIS) ndashndash sincsi e 2003nce 2003

AimsAimsndash Pilot application 2 years beforePilot application 2 years before

routine applicationroutine application ndash Parallel exposure of DreissenaParallel exposure of Dreissena

Polymorpha Benthos PlantsPolymorpha Benthos Plantsndash POPs (basic OCPs PCBs)POPs (basic OCPs PCBs)ndash POPs (other PCBsPOPs (other PCBs ndash coc ngong

PCDDFs PAHs PBDEPCDDFs PAHs PBD s)Es)

66

6

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

ndash

ndash

ndash -

ndash

ndash

ndashndash

Centre laboratoriesCentre laboratories accreditationaccreditation

Personnel over 140 5+Person 2 workplacesnel over 140 5+2 workplaces According to ČSN EN ISOIEC 17According 025to ČSN EN ISOIEC 17 025

ndash Over 200 paraOver 200 p metersarameters PCDDFs PCBs OCPs PBDE hellipPCDDFs PCBs OCPs PBDE hellip

ndash Recognized by ILAC EA IAFRecognized by ILAC EA IAF Sampling and TestingSampling and Testing

ndash IntegralIntegral - waterwater SPMDsSPMDs DGTsDGTs POCIPOC SIS

ndash Biotic organiBioti smsc organisms IntercalibrationIntercalibration

ndash Czech + InternationalCzech + International Data analysis (univariatemultivariate)Data analysis (univariatemultivariate)

ndash StatisStati ticalsticalndash GnosticGnostic

44

4

-

-

InstrumentatiInstrumenta ontion (worth over 6 mil USD)(worth over 6 mil USD)

GC-MSMS (ion-trap)

GCQ Polaris

Since 1996 (starting to POPs issue)

GC-HRMS (POPs)

- MAT 95XP

- since 2003

LC-MSMS (pharmacy pesticides)

- ThermoFinigan

- since 2006 55

5

ndash

ndash

ndashndash ndash

Data source for comparison ofData source for comparison of methodsmethods

All rivers within Czech RepuAll rivers wi blicthin Czech Republic scale (15)scale (15)

21 sampling pr21 sampling p ofilesrofiles Complementary to bioticComplementary to biotic

sampling system (since 1999)sampling system (since 1999) with abiotic (SPMDs DGTswith abiotic (SPMDs DGTs POCIS)POCIS) ndashndash sincsi e 2003nce 2003

AimsAimsndash Pilot application 2 years beforePilot application 2 years before

routine applicationroutine application ndash Parallel exposure of DreissenaParallel exposure of Dreissena

Polymorpha Benthos PlantsPolymorpha Benthos Plantsndash POPs (basic OCPs PCBs)POPs (basic OCPs PCBs)ndash POPs (other PCBsPOPs (other PCBs ndash coc ngong

PCDDFs PAHs PBDEPCDDFs PAHs PBD s)Es)

66

6

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

-

-

InstrumentatiInstrumenta ontion (worth over 6 mil USD)(worth over 6 mil USD)

GC-MSMS (ion-trap)

GCQ Polaris

Since 1996 (starting to POPs issue)

GC-HRMS (POPs)

- MAT 95XP

- since 2003

LC-MSMS (pharmacy pesticides)

- ThermoFinigan

- since 2006 55

5

ndash

ndash

ndashndash ndash

Data source for comparison ofData source for comparison of methodsmethods

All rivers within Czech RepuAll rivers wi blicthin Czech Republic scale (15)scale (15)

21 sampling pr21 sampling p ofilesrofiles Complementary to bioticComplementary to biotic

sampling system (since 1999)sampling system (since 1999) with abiotic (SPMDs DGTswith abiotic (SPMDs DGTs POCIS)POCIS) ndashndash sincsi e 2003nce 2003

AimsAimsndash Pilot application 2 years beforePilot application 2 years before

routine applicationroutine application ndash Parallel exposure of DreissenaParallel exposure of Dreissena

Polymorpha Benthos PlantsPolymorpha Benthos Plantsndash POPs (basic OCPs PCBs)POPs (basic OCPs PCBs)ndash POPs (other PCBsPOPs (other PCBs ndash coc ngong

PCDDFs PAHs PBDEPCDDFs PAHs PBD s)Es)

66

6

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

ndash

ndash

ndashndash ndash

Data source for comparison ofData source for comparison of methodsmethods

All rivers within Czech RepuAll rivers wi blicthin Czech Republic scale (15)scale (15)

21 sampling pr21 sampling p ofilesrofiles Complementary to bioticComplementary to biotic

sampling system (since 1999)sampling system (since 1999) with abiotic (SPMDs DGTswith abiotic (SPMDs DGTs POCIS)POCIS) ndashndash sincsi e 2003nce 2003

AimsAimsndash Pilot application 2 years beforePilot application 2 years before

routine applicationroutine application ndash Parallel exposure of DreissenaParallel exposure of Dreissena

Polymorpha Benthos PlantsPolymorpha Benthos Plantsndash POPs (basic OCPs PCBs)POPs (basic OCPs PCBs)ndash POPs (other PCBsPOPs (other PCBs ndash coc ngong

PCDDFs PAHs PBDEPCDDFs PAHs PBD s)Es)

66

6

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

77

SAMPLING METHODSSAMPLING METHODSTO BE COMPAREDTO BE COMPARED

ThreeThree bioticbiotic methodsmethods BentosBentos DreissenaDreissena PlantsPlantsOneOne abioticabiotic method SPMDmethod SPMD(Semipermeable Membrane Measuring(Semipermeable Membrane Measuring

Device)Device)

7

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

The selectionThe selection

Concentrations of selected permanentConcentrations of selected permanentorganic pollutants (POPs) in severalorganic pollutants (POPs) in severallocations of Elbe river in Czech Republiclocations of Elbe river in Czech Republic

ppDDEppDDE PCB138 PCB180 PCB138 PCB180 PCB101 PCB2831PCB101 PCB2831 ppDDTppDDT ppDDDppDDD PCB52 PCB118 PCB52 PCB118

88

8

PROBLEMS OF ANALYSISPROBLEMS OF ANALYSIS

Small data samplesSmall data samples Different mean concentrationsDifferent mean concentrations Strong vaStrong v riabilityariability Different length of data vectorsDifferent length of data vectors Data censoring (Data censoring (egeg data below the LOD)data below the LOD) NonNon--homogeneous and outlying datahomogeneous and outlying data

99

9

SPECIFICSSPECIFICS of MATHEMATICAL GNOSTICSof MATHEMATICAL GNOSTICS

Theory ofTheory of individualindividual datadata andand smallsmall data samplesdata samples

RealisticRealistic assumptionsassumptions UncertaintyUncertainty a lack of knowledgea lack of knowledge ldquoLet data speakldquoLet data speak for themsefor thems lveselvesrdquordquo ResultsResults maximizing informationmaximizing information NaturalNatural robustnessrobustness

1010

10

1111

Comparison of two approachesComparison of two approaches

1111

11

GNOSTICGNOSTICDISTRIBUTION FUNCTIONSDISTRIBUTION FUNCTIONS

No a priori modelNo a priori model (e( verything from data)everything from data)

MaximumMaximum informationinformation

RobustnessRobustness in estimation of probabilityin estimation of probability

quantilesquantiles scale and location parameters scale and location parameters

bounds of data support and membershipbounds of data support and membership

intervalinterval

frac34frac34 RobustRobust correlationscorrelations 1212

12

1313

GNOSTICGNOSTICDISTRIBUTION FUNCTIONS IIDISTRIBUTION FUNCTIONS II

DataData homogeneityhomogeneity teststests

MarginalMarginal cluster analysiscluster analysis

CrossCross--sectionsection filteringfiltering

Applicability toApplicability to censoredcensored datadata

Applicability toApplicability to heteroscedasticheteroscedastic datadata

13

QUALITY OF METHODSQUALITY OF METHODSTO BE COMPAREDTO BE COMPARED

Relative sensitivity (Relative sensitivity (tresholdtreshold range) range)Homogeneity of resultsHomogeneity of resultsConsistency of resultsConsistency of results Internal (of methodrsquos own results)Internal (of methodrsquos own results) External (mutual consistency of methExternal (mutual consistency of met ods)hods)

InformativInformati enessveness of resultsof resultsPrecissionPrecission

1414

14

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

SPECIFICSSPECIFICS of MATHEMATICAL GNOSTICSof MATHEMATICAL GNOSTICS

Theory ofTheory of individualindividual datadata andand smallsmall data samplesdata samples

RealisticRealistic assumptionsassumptions UncertaintyUncertainty a lack of knowledgea lack of knowledge ldquoLet data speakldquoLet data speak for themsefor thems lveselvesrdquordquo ResultsResults maximizing informationmaximizing information NaturalNatural robustnessrobustness

1010

10

1111

Comparison of two approachesComparison of two approaches

1111

11

GNOSTICGNOSTICDISTRIBUTION FUNCTIONSDISTRIBUTION FUNCTIONS

No a priori modelNo a priori model (e( verything from data)everything from data)

MaximumMaximum informationinformation

RobustnessRobustness in estimation of probabilityin estimation of probability

quantilesquantiles scale and location parameters scale and location parameters

bounds of data support and membershipbounds of data support and membership

intervalinterval

frac34frac34 RobustRobust correlationscorrelations 1212

12

1313

GNOSTICGNOSTICDISTRIBUTION FUNCTIONS IIDISTRIBUTION FUNCTIONS II

DataData homogeneityhomogeneity teststests

MarginalMarginal cluster analysiscluster analysis

CrossCross--sectionsection filteringfiltering

Applicability toApplicability to censoredcensored datadata

Applicability toApplicability to heteroscedasticheteroscedastic datadata

13

QUALITY OF METHODSQUALITY OF METHODSTO BE COMPAREDTO BE COMPARED

Relative sensitivity (Relative sensitivity (tresholdtreshold range) range)Homogeneity of resultsHomogeneity of resultsConsistency of resultsConsistency of results Internal (of methodrsquos own results)Internal (of methodrsquos own results) External (mutual consistency of methExternal (mutual consistency of met ods)hods)

InformativInformati enessveness of resultsof resultsPrecissionPrecission

1414

14

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

1111

Comparison of two approachesComparison of two approaches

1111

11

GNOSTICGNOSTICDISTRIBUTION FUNCTIONSDISTRIBUTION FUNCTIONS

No a priori modelNo a priori model (e( verything from data)everything from data)

MaximumMaximum informationinformation

RobustnessRobustness in estimation of probabilityin estimation of probability

quantilesquantiles scale and location parameters scale and location parameters

bounds of data support and membershipbounds of data support and membership

intervalinterval

frac34frac34 RobustRobust correlationscorrelations 1212

12

1313

GNOSTICGNOSTICDISTRIBUTION FUNCTIONS IIDISTRIBUTION FUNCTIONS II

DataData homogeneityhomogeneity teststests

MarginalMarginal cluster analysiscluster analysis

CrossCross--sectionsection filteringfiltering

Applicability toApplicability to censoredcensored datadata

Applicability toApplicability to heteroscedasticheteroscedastic datadata

13

QUALITY OF METHODSQUALITY OF METHODSTO BE COMPAREDTO BE COMPARED

Relative sensitivity (Relative sensitivity (tresholdtreshold range) range)Homogeneity of resultsHomogeneity of resultsConsistency of resultsConsistency of results Internal (of methodrsquos own results)Internal (of methodrsquos own results) External (mutual consistency of methExternal (mutual consistency of met ods)hods)

InformativInformati enessveness of resultsof resultsPrecissionPrecission

1414

14

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

GNOSTICGNOSTICDISTRIBUTION FUNCTIONSDISTRIBUTION FUNCTIONS

No a priori modelNo a priori model (e( verything from data)everything from data)

MaximumMaximum informationinformation

RobustnessRobustness in estimation of probabilityin estimation of probability

quantilesquantiles scale and location parameters scale and location parameters

bounds of data support and membershipbounds of data support and membership

intervalinterval

frac34frac34 RobustRobust correlationscorrelations 1212

12

1313

GNOSTICGNOSTICDISTRIBUTION FUNCTIONS IIDISTRIBUTION FUNCTIONS II

DataData homogeneityhomogeneity teststests

MarginalMarginal cluster analysiscluster analysis

CrossCross--sectionsection filteringfiltering

Applicability toApplicability to censoredcensored datadata

Applicability toApplicability to heteroscedasticheteroscedastic datadata

13

QUALITY OF METHODSQUALITY OF METHODSTO BE COMPAREDTO BE COMPARED

Relative sensitivity (Relative sensitivity (tresholdtreshold range) range)Homogeneity of resultsHomogeneity of resultsConsistency of resultsConsistency of results Internal (of methodrsquos own results)Internal (of methodrsquos own results) External (mutual consistency of methExternal (mutual consistency of met ods)hods)

InformativInformati enessveness of resultsof resultsPrecissionPrecission

1414

14

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

1313

GNOSTICGNOSTICDISTRIBUTION FUNCTIONS IIDISTRIBUTION FUNCTIONS II

DataData homogeneityhomogeneity teststests

MarginalMarginal cluster analysiscluster analysis

CrossCross--sectionsection filteringfiltering

Applicability toApplicability to censoredcensored datadata

Applicability toApplicability to heteroscedasticheteroscedastic datadata

13

QUALITY OF METHODSQUALITY OF METHODSTO BE COMPAREDTO BE COMPARED

Relative sensitivity (Relative sensitivity (tresholdtreshold range) range)Homogeneity of resultsHomogeneity of resultsConsistency of resultsConsistency of results Internal (of methodrsquos own results)Internal (of methodrsquos own results) External (mutual consistency of methExternal (mutual consistency of met ods)hods)

InformativInformati enessveness of resultsof resultsPrecissionPrecission

1414

14

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

QUALITY OF METHODSQUALITY OF METHODSTO BE COMPAREDTO BE COMPARED

Relative sensitivity (Relative sensitivity (tresholdtreshold range) range)Homogeneity of resultsHomogeneity of resultsConsistency of resultsConsistency of results Internal (of methodrsquos own results)Internal (of methodrsquos own results) External (mutual consistency of methExternal (mutual consistency of met ods)hods)

InformativInformati enessveness of resultsof resultsPrecissionPrecission

1414

14

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

RELATIVE SENSITIVITYRELATIVE SENSITIVITY

Methodrsquos relative sensitivity dependsMethodrsquos relative sensitivity dependsOn the pollutantrsquos concentrationOn the pollutantrsquos concentrationOn the methodrsquos measuring domainOn the methodrsquos measuring domain

RS = (1RS = (1 ndashndash NCN) x 100 ()NCN) x 100 ()NCNC hellip number of data in the intervhellip number of data in th ale interval[sensitivity threshold[sensitivity threshold max(rangemax(range)])]

NN hellip all data of the sahellip all data of the s mpleample1515

15

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

HOMOGENIZATIONHOMOGENIZATIONTO BE OR NOT TO BETO BE OR NOT TO BE

Homogeneous dataHomogeneous datathe samethe same originorigin of true valuesof true valuesthe same nature of thethe same nature of the uncertaintyuncertainty

To homogenizeTo homogenize ProsPros

More certain main clusterMore certain main cluster ConsCons

Possible loss of informationPossible loss of informationRule homogenize and verifyRule homogenize and verify

1616

16

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

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n u

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am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

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2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

MEASURABILITYMEASURABILITY

Homogenization hellip elimination of outliersHomogenization hellip elimination of outliersMeasMeas = (1= (1 ndashndash (NL+NU)N) x 100 ()(NL+NU)N) x 100 ()NL hellipNL hellip number of lower outliersnumber of lower outliersNU hellipNU hellip number of upper outliersnumber of upper outliersN hellipN hellip number of the samplersquos datanumber of the samplersquos dataNN ndashndash NLNL ndashndash NU hellipNU hellip data of the main clusterdata of the main cluster

1717

17

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

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2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

1818

METHODS OF ANALYSISMETHODS OF ANALYSIS

GEOMETRYGEOMETRY(angles between vectors)(angles between vectors)

STATISTICSSTATISTICS(robust correlations)(robust correlations)

MATHEMATICAL GNOSTICS MATHEMATICAL GNOSTICS (robust correlations robust(robust correlations robustdistribution functions information distribution functions information and entropy of small data samples)and entropy of small data samples)

Dec Log (concentration) ugsampling system

1818

18

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

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nce

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2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

1919

Dec Log (concentration) ugsampling system

1919

19

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

2020

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2020

20

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

DIFFERENCES IN METHODSDIFFERENCES IN METHODS

Different accumulation of pollutantsDifferent accumulation of pollutantsbullbull different mean concentrationsdifferent mean concentrationsbullbull differentdifferent variabilitiesvariabilities

Different relations between meansDifferent relations between means Rare exception agreement in PCB118Rare exception agreement in PCB118 Impact of outliers to SPMDImpact of outliers to SPMD NONO

2121

21

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

METHODrsquoS CONSISTENCYMETHODrsquoS CONSISTENCY

Methods areMethods are consistentconsistent when they givewhen they givesimilar resultssimilar results

Measuring of similarityMeasuring of similarityCorrelations or (more generally)Correlations or (more generally)

mean angles between vectors of resultsmean angles between vectors of resultsSIMccSIMcc = 100 x= 100 x correlcoefficientcorrelcoefficient ()()SIMqaSIMqa = 100 x (1= 100 x (1 ndashndash |Ang|180) ()|Ang|180) ()

2222

22

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

GNOSTIC CORRELATIONSGNOSTIC CORRELATIONS

Data error inData error in gnosticgnostic irrelevanceirrelevanceirir = (2p= (2p -- 1)21)2

pp hellip probability of the data itemhellip probability of the data itemCorrelation coefficient of two samplesCorrelation coefficient of two samples

Gcc(MNGcc(MN) =) = ccir(m)ir(nccir(m)ir(n))(m in M n in N) cc (m in M n in N) cc statiststatist corcoefcorcoef

RobustnessRobustness-- 1 lt=1 lt= irir lt= + 1lt= + 1 2323

23

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

SIGNIFICANCESIGNIFICANCE OF CORRELATIONSOF CORRELATIONS

Problems false statistical modelProblems false statistical model (normality finite data support)(normality finite data support) small data samplessmall data samples unrobustnessunrobustness

Gnostic estimating of significanceGnostic estimating of significancefrac34frac34 fast auxiliaryfast auxiliary usinusi g Spearmanrsquosng Spearmanrsquos

robust estimate of significancerobust estimate of significancefrac34frac34 carefullycarefully distribution function ofdistribution function of

correlation coefficientscorrelation coefficients2424

24

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

2828

2929

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2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

2525

25

2525

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

28

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2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

QUANTILE VECTORSQUANTILE VECTORS

Make samplersquos distribution functionMake samplersquos distribution function Set a series of probabilitiesSet a series of probabilities p1hellipp1hellippNpN FindFind quantilesquantiles q1hellipq1hellipqNqN so thatso that PqkPqk==pkpk TakeTake q1hellipq1hellipqNqN as aas a quantilequantile vectorvectorAdvantagesAdvantages

Robustness making use of censored dataRobustness making use of censored dataindependence of data amount and ofindependence of data amount and of mean data value filtering effectmean data value filtering effect

2626

26

2727

27

2727

2828

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2828

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2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

2727

27

2727

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

2828

28

2828

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

2929

Co

nce

ntr

atio

n u

gs

am

plin

g s

yste

m

2929

29

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

EXTERNAL CONSISTENCYEXTERNAL CONSISTENCY

ApproachesApproaches CorrelationsCorrelations Angles betwAngl een MDes between MD--vectors of meansvectors of means Angles betweenAngles between quantilequantile vectovect rsro s Conjunction of typical data intervalsConjunction of typical data intervals Conjunction of data supportsConjunction of data supports

3030

30

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

INTERVAL ANALYSISINTERVAL ANALYSIS

1)1) Distribution functionsDistribution functions2)2) Interval analysisInterval analysis

a)a) Data supportData support (LB UB)(LB UB)b)b) Membership intervalMembership interval (LSB USB)(LSB USB)c)c) Interval of typical dataInterval of typical data (ZL UL)(ZL UL)d)d) Tolerance intervalTolerance interval (Z0L Z0U)(Z0L Z0U)

3)3) OverlappingOverlapping 100xconjunction(I1 I2)union(I1I2) ()100xconjunction(I1 I2)union(I1I2) ()

3131

31

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

INFORMATIVENESSINFORMATIVENESS

1)1) Data sampleData sample2)2) Distribution functionDistribution function3)3) ProbabilityProbability pp of an individual data itemof an individual data item4)4) Information of the data itemInformation of the data item

Info=(pInfo=(p log(plog(p) + (1) + (1--p)log(1p)log(1--p))logp (12)())log 12)5)5) InformativenessInformativeness of a data sampleof a data sample

100 x100 x MeM an(Infoean(Info) ()) ()3232

32

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

3333

EVALUATIONEVALUATION OF PRECISIONOF PRECISION

Weak variabilityWeak variability PrecPrec = 100 x (1= 100 x (1 ndashndash STDAVG) ()STDAVG) ()

((STDSTD hellip standard deviationhellip standard deviation AVGAVG helliphellip mean)mean)

Strong uncertaintyStrong uncertaintyPrecPrec = 100 x (1= 100 x (1 -- Mean(GWMean(GW) ) ()) ) ()

((GWGW helliphellip gnosticgnostic weight of data entropyweight of data entropy change caused by the uncertainty)change caused by the uncertainty)

0 lt= GW lt= 10 lt= GW lt= 1

33

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

SUMMARY COMPARISONSUMMARY COMPARISON

AverigeAverige of 14 evaluationsof 14 evaluations

MethodMethod NonNon--homdatahomdata HomogHomog data data

BentosBentos 609 609 627 627

DreissenaDreissena 645 645 675 675

PlantsPlants 642 642 689 689

SPMDSPMD 675 675 695 695

3434

34

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

3535

35

3535

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

RATINGRATIN OF METHODSG OF METHODS

FeatureFeatureExtconsistencyExtconsistencyIntconsistencyIntconsistencyInformativenessInformativenessPrecissionPrecissionHomogeneityHomogeneityRelsensitivityRelsensitivityMean ratingMean rating

BentosBentos DreissDreiss PlantsPlants SPMDSPMD44 33 11 2244 33 22 1111 33 44 2233 11 44 2222 44 33 1133 11 22 11

2828 2525 2727 15153636

36

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

ndash

ndash

ndash

ConclusionsConclusions

Passive sampling like SPMDs shown the bestPassive sampling like SPMDs shown the best results if there are no legal requirements for biotaresults if there are no legal requirements for biota biotic organisms can be replacedbiotic organisms can be replaced

Do not forget to analyze data preciselyDo not forget to analyze data precisely independently before your interpretationindependently before your interpretationndash Do not rely ONLY on functionality of any processingDo not rely ONLY on functionality of any processing

packagepackagendash Statistical approach has some limitations on small dataStatistical approach has some limitations on small data

sets (majority of monitoring studies)sets (majority of monitoring studies)

Any headache from analytical tools can beAny headache from analytical tools can be eliminated by expeeliminated by exp rienceeriencendash Try itTry it

3737

37

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

Further intentionsFurther intentions

Finalization of Gnostic analytical toolFinalization of Gnostic analytical tool with GUI (Swith GUI (S--Plus)Plus)

Extension to other platforms byExtension to other platforms by interfaceinterface

Linking to databases (LIMS GIS hellip)Linking to databases (LIMS GIS hellip) Training and disseminationTraining and dissemination Projects solutions and participationsProjects solutions and participations

ndashndash Join us 2Join us 2--FUN projectFUN project www2www2--funorgfunorg

3838

38

3939

hellip thank you for your attention

PCDDF

39

3939

hellip thank you for your attention

PCDDF

39