Ecotoxicological Classification of Sediments using Fuzzy Logic

Ecotoxicological Classification of Sediments using

Fuzzy Logic

S. Keiter1, T. Braunbeck2, S. Heise3, S. Pudenz4, W. Manz5, H. Hollert1

1Institute for Environmental Research (Biology V), RWTH Aachen University2Aquatic Ecology and Toxicology, University of Heidelberg3Department of Life Sciences, Hamburg University of Applied Sciences4Westlakes Scientific Consulting Ltd., Department of Environmental Science5Institute of integrated natural sciences, University of Koblenz-Landau

212.10.2011 – Rio Claro, Brazil

Index

1. Background

2. What is Fuzzy Logic?

3. The application of Fuzzy‐Logic

− Data selection

− Fuzzification

− Rule base

− Inference

− Defuzzification

4. Ecological relevance

5. Results

6. Conclusions

3

Ehingen: grayling catch and stocking

Stoc

king

(fis

hes/

a)

0

500

1000

1500

2000

2500

3000

3500

stocking

1980 1985 1990 1995 2000

Cat

ch (f

ishe

s/a)

0

100

200

300

400

500

600

catch

Negative trend of the fish catch since 1980

Background

Fish decline in the upper Danube River


4

Positive development of the water quality since 1968

Background


1968 1974 1981 1986 1991 1998 2004

Sigmaringen ‐ III ‐ IV II ‐ III II III II ‐ III II – III

Lauchert II II II II II II II

Schwarzach ‐ III ‐ IV II ‐ III II ‐ III II ‐ III II ‐ III II

Riedlingen ‐ III ‐ IV II ‐ II ‐ III II II

Rottenacker ‐ III II II II II II

Ehingen III ‐ IV III ‐ IV II ‐ III II II II II

Öpfingen ‐ ‐ II ‐ III II ‐ III II ‐ III II II


5

Background


Water qua

lity

Fish catchReasons?


6

Background

Test strategy

Field Studies:

Macrozoobenthos

Chemical analysis:

Heavy metals, PAHs, PCBs, PCDD/Fs, limnological

parameters

In situ parameters:

Histopathology of the liver, micronucleus test using

erythrocytes

Effect‐directed‐analysis (EDA):

Separation of non‐persistent and persistent pollutants

In vitro biotests:

Cytotoxicity, mutagenicity, genotoxicity, embryotoxicity, dioxin‐like activity, toxicity

towards bacteria

Weight‐of‐EvidenceStudy


7

Sediments

Dioxins Heavy metals

PesticidesPAHs

PCBs

Sediments are a sink and source for many pollutants

sediment

Why sediments?


8

Exposure

Bioavailable hazard potential

Native

Freeze dried

Sediment contact tests:

• Comet assay

• Fish Embryo Toxicity Test

(FET)

• EROD assay

• …

Exposure scenarios

Overall hazard potential

Specific biotests:

• Comet assay

• Fish Embryo Toxicity Test

(FET)

• EROD assay

• …

Extraction


9

Background

Ecotoxicoligical assessment of sediments based on results of bioassays


10

Background

(a) Strongly polluted: Schwarzach, Rottenacker, Ehingen and Öpfingen

(b) Moderately polluted: Sigmaringen, Ingolstadt, Jochenstein and Bad Abbach

(c) Low polluted: Lauchert and Riedlingen

In conclusion, a very heterogenic contamination for the different sites



11

Background

Classification using Fuzzy Logic

However, what does low, moderately and strongly polluted mean?



(a) Strongly polluted: Schwarzach, Rottenacker, Ehingen and Öpfingen

(b) Moderately polluted: Sigmaringen, Ingolstadt, Jochenstein and Bad Abbach

(c) Low polluted: Lauchert and Riedlingen

In conclusion, a very heterogenic contamination for the different sites

12

Index


1. Background



− Data selection

− Fuzzification

− Rule base

− Inference

− Defuzzification


5. Results

6. Conclusions

13

What is Fuzzy Logic?

Fuzzy Logic (Fuzzy‐Set Theory)

Developed by L. Zadeh in 1965

Fuzzy Logic serves the possibility to perform a mathematical modeling of the uncertainties of imprecise words/descriptions (linguistic uncertainties)

Example from a technical application:

Imprecise terms like “slightly dirty” or “really dirty" are by Fuzzy Logic translated into the right amount of water and/or washing powder.


14


392119

Within Fuzzy Logic a value belongs to a fuzzy set with a defined gradual membership degree between zero and one.


15


Structure

Fuzzification

Rule base

Defuzzification

Inference

Crispy valuesCrispy values

fuzzy values fuzzy values


16

Index


1. Background



− Data selection

− Fuzzification

− Rule base

− Inference

− Defuzzification


5. Results

6. Conclusions

17

Data selection

No redundant information

Fish egg test Comet assayEROD assay Neutral red assay

Correlation analysis

For statistical reasons as much data as possible should be selected

Neutral red assay EROD assay FET (native) FET (extracts)

Comet assay 0.43 0.37 0.62 0.16

Neutral red assay ‐ 0.52 0.28 0.07

EROD assay ‐ ‐ 0.26 ‐0.25

FET (native) ‐ ‐ ‐ ‐0.16


18

Fuzzification

Boxplot method Empiric method

Definition of the toxicity levels (linguistic variables)

Calculation of the range of selected toxicity levels


19

Fuzzification

Example: Neutral red assay

Variability is:± 20 % (α < 0.05)

Variability of the negativ‐ and/or positiv control of each biotest.

Calculation of the gradual membership range


20

Fuzzification

Membership function of the Neutral red assay

A sample with an EC50 of 25 mg/L, shows membership values of: µ (strongly toxic) = 0.3µ (moderately toxic) = 0.7


21

Rule base

IF

and

FET (native)non‐toxic

EROD assaynon‐toxic

Comet assaynon‐toxic

and

and

and

Class 1

THEN

Neutral red assaynon‐toxic

FET (extracts)non‐toxic

IF

and

FET (extracts)toxic

FET (native)toxic

EROD assaytoxic

Comet assaytoxic

and

and

and

Class 5

THEN

Neutral red assaytoxic

Assumptions and conclusions


22

Rule‐base

Toxicity leveles

non‐toxicmoderatly‐

toxicstrongly‐toxic

Class 15 ‐ ‐

4 1 ‐

4 ‐ 1

Class 23 1 1

3 2 ‐

2 3 ‐

Class 3

3 ‐ 2

2 ‐ 3

1 3 1

2 2 1

2 1 2

1 2 2

1 4 ‐

‐ 4 1

‐ 5 ‐

Class 4‐ 3 2

1 1 3

‐ 2 3

Class 51 ‐ 4

‐ 1 4

‐ ‐ 5

243 combinations


23

Inference

In order to come to the right conclusion (class), for all found membership values the related rules will be determined.

Test Unit Value nt mt t

Comet assay CDI 0.21 0.5 0.5 0

EROD assay BioTEQ [pg/g] 618 0.75 0.25 0

Neutralrot NR50 [mg/ml] 46.3 0.01 0.99 0

FET Test (native) LC50 [mg/ml] 214 1.0 0 0

FET Test (extract) LC50 [mg/ml] 169 1.0 0 0

More than one rule is true!


24

Inference

Aggregation:

Completion grade of the assumptions

Summary of all inference steps

Accumulation:

Completion grade of the rules

Implication:

Completion grade of the conclusions


25

Inference

Result of the inference


Class 1Class 2Class 3Class 4Class 5

26

Defuzzification

Very often the result of the inference step needs to be transfered into clear/chrispy value or to concret policy rules.

∫

∫= b

aout

b

aout

dxxµ

dxxxµx

)(

)(

∑=

=n

iix

nx

1

max1Mean of maxima

Center of gravity


Membership

Membership

Center of gravity

27

Index


1. Background



− Data selection

− Fuzzification

− Rule base

− Inference

− Defuzzification


5. Results

6. Conclusions

28

Ecological relevance

Note: there is a need for further investigations and discussions about criteria and rules to account for the ecological relevance

Weighing of biotests


29


Possibilities to consider the ecological relevance

Rule base:A positive effect in one test leads to a worse class

IF

and



Comet assaymoderately‐toxic

and

and

and

Class 1

THEN


FET (extract)non‐toxic

WENN

and



Comet assaymoderately‐toxic

and

and

and

Class 2

THEN


FET (extract)non‐toxic


30


Shifting of threshold values

Possibilities to consider the ecological relevance


31

Index


1. Background



− Data selection

− Fuzzification

− Rule base

− Inference

− Defuzzification


5. Results

6. Conclusions

32

Results

Sediments of the rivers Rhine and Danube


33

Results

Comet assay

Neutral red assay

EROD assay

FET (native)

FET (extract)

Mean

Boxplot method (wo. weighing) 0.66 0.75 0.69 0.48 0.03 0.52

Empiric method (wo. weighing) 0.75 0.79 0.66 0.53 0.21 0.59

Empiric method (with weighing) 0.76 0.69 0.77 0.51 0.14 0.57

Correlation between classes and biotest results


34

Index


1. Background



− Data selection

− Fuzzification

− Rule base

− Inference

− Defuzzification


5. Results

6. Conclusions

35

Conclusions

Consideration of the range from biotest responses.

Creation of gradual memberships by the variability of the used test systems.

Consideration of the ecological relevance by shifting the threshold values.

Fuzzy Logic classification models serves the possibility to consider the uncertainties of biological effect data and their ecological relevance.

Integration of expert‐knowledge from mechanism‐specific effects.



37

Inferenz

1. Aggregation:

Hier werden die Erfülltheitsgrade der einzelnen Prämissen ausdrücke jeder Regel zu einem Erfülltheitsgrad der Gesamtprämisse zusammengefasst.

Die Auswertung jeder zutreffenden Regel (Inferenzschritt) besteht aus drei Schritten

Zugehörigkeit Auto1 Auto2 Auto3 Auto4 Auto5 Auto6

A. Leistung (PS) 0,1 0,6 0,0 0,9 1,0 0,0

B. Alter 0,8 0,2 0,9 0,4 0,3 1,0

C. Preis 0,4 0,8 0,3 0,6 0,7 0,4

D. Image 0,3 0,4 0,0 0,7 0,8 0,8

Operatoren Auto1 Auto2 Auto3 Auto4 Auto5 Auto6

Arith. Mittel 0,400 0,500 0,300 0,650 0,700 0,550

Minimum 0,100 0,200 0,000 0,400 0,300 0,000

Algeb. Produkt 0,0096 0,0384 0,0000 0,1512 0,1680 0,0000

Minimum: min [µA(x), µB(x), µC(x), µD(x)]Algeb. Produkt: µA(x) × µB(x) × µC(x) × µD(x)

Prämissen

Aggregations‐operatoren


38

Inferenz

2. Implikation:

Bei der Implikation wird ‐ aufbauend auf dem zuvor errechneten Erfülltheitsgrad der Prämisse ‐der Erfülltheitsgrad der zugehörigen Konklusion ermittelt. Dieser Schritt bildet den logischen Schluss "WENN A DANN B" ab.


Minimum: min [µA(x), µB(x), µC(x), µD(x)]Algeb. Produkt: µA(x) × µB(x) × µC(x) × µD(x)

Aggregations‐operatoren


39

Inferenz

3. Akkumulation:

Es existieren oft mehrere Regeln mit derselben Konklusion (z.B. „Klasse 1"), aber mit unterschiedlichen Erfülltheitsgrad. Daher müssen die verschiedenen Erfülltheitsgrade der Regeln zu einem Gesamterfülltheitsgrad zusammengefasst werden.


Maximum: max [µA(x), µB(x), µC(x), µD(x)]Algeb. Summe: [µA(x) + µB(x) + µC(x) + µD(x)] - µA(x) × µB(x) × µC(x) × µD(x)

Akkumulations‐operatoren


Documents

Ecotoxicological Classification of Sediments using Fuzzy Logic