Evgeniy MichailovEvgeniy Michailov

Samara State Technical University, Samara, RussiaSamara State Technical University, Samara, Russia

Ecological assessment of waste fields with multivariate analysis - feasibility study

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Man-caused formationsMan-caused formations

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OObjectbjectss for investigationfor investigation

1.1. Illegal dump BezenchukIllegal dump Bezenchuk

2.2. Modern, well-run landfillModern, well-run landfill Kinel Kinel

3.3. Poorly run landfill OtradniyPoorly run landfill Otradniy

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SSamplingampling

hole

1 metre

n metre

n-1 metre

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VariablesVariables

variablesvariables

measured

variables

measured

variablesevaluated variables

evaluated variables

ash content volumetric

weight temperature

depth humidity

pH

ash content volumetric

weight temperature

depth humidity

pH

stratumlense

topsoil

stratumlense

topsoil

ageage

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Evaluated variables

CHEMOMETRICS-BASED EVALUATION OF MAN-CAUSED FORMATIONS’ STABILITYOlga Tupicina Samara State Technical University , Samara, Russia

CHEMOMETRICS-BASED EVALUATION OF MAN-CAUSED FORMATIONS’ STABILITYOlga Tupicina Samara State Technical University , Samara, Russia

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Age Age → maturity→ maturity

Maturity=1-exp(-k*Age)

k=1/5

Maturity=1-exp(-k*Age)

k=1/5

Age can be evaluated for wasteAge can be evaluated for waste onlyonly

Age of topsoil? Age of topsoil? →→use the maturityuse the maturity

Maturity of topsoil is 1Maturity of topsoil is 1

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Goals and methodsGoals and methods

X1 X2 Y

measured evaluatedPCA

PLS

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Illegal dump BezenchukIllegal dump Bezenchuk

Life cycle more then 25 years

environmental protection system

is absend

Amount of waste is more than 90 thousand

m3

Area 30 hectares

Life cycle more then 25 years

environmental protection system

is absend

Amount of waste is more than 90 thousand

m3

Area 30 hectares

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Scheme of dump BezenchukScheme of dump Bezenchuk

1

2 3

4

5

6

7

8

9 11

12

13

14

15

16

17

1819

20

2121

2 regions of

sewage sludge

2 regions of

sewage sludge

topsoiltopsoil topsoiltopsoil

sewage sewage sludgesludge sewage sewage

sludgesludge

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Samples and variablesSamples and variables

Bezenchuk data set

123 samples (21 holes)

Bezenchuk data set

123 samples (21 holes)

9 variables9 variables

6 measured variables

6 measured variables

3 evaluated variables

3 evaluated variables

ash content volumetric

weight temperature

depth humidity

ash content volumetric

weight temperature

depth humidity

lenstopsoil

lenstopsoil

maturitymaturity

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PCAPCA

X1 X2

PCA

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PCA Bezenchuk data setPCA Bezenchuk data set

Scores

-4

-2

0

2

4

-4 -2 0 2 4

PC1

PC2

Bezenchuk 0, X-exp:63%,25%

X-loadings

Ash

-8C

Depth

Humidity+28C

0

0.3

0.6

0.9

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

PC1

PC2

Bezenchuk 0, X-exp: 63%, 25%

Influence

0

0.05

0.1

0.15

0 0.02 0.04 0.06 0.08 0.1LeverageBezenchuk 0, PC: 4,4

Residual X-variance Residual Variance

0

0.5

1

0 1 2 3 4 5PCs

X-variance

Bezenchuk 0, Variable: c. Total v. Total

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Lenses and topsoilLenses and topsoilScores

-4

-2

0

2

4

-4 -2 0 2 4

PC1

PC2

Bezenchuk 0, X-exp:63%,25%

sewage sludgesewage sludge

topsoiltopsoil

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PLSPLS

X1 Y

PLS

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PLS Bezenchuk data setPLS Bezenchuk data setScores

-4

-2

0

2

4

-4 -2 0 2 4

PC1

PC2

Bezenchuk 2, X-exp: 61%, 26% Y-exp: 83%, 3%

X- and Y-loadings

Depth

Humidity+28C

Maturity

Ash

Weight

-8C

-0.9

-0.6

-0.3

0

0.3

-0.5 -0.25 0 0.25 0.5

PC1

PC2

Bezenchuk 2, X-exp: 61%, 26% Y-exp: 83%, 3%

Root Mean Square Error

0.07

0.075

0.08

0.085

1 2 3 4PCs

RMSE

RMSEC

RMSEP

Bezenchuk 2, Variable c.Maturity v.Maturity

0.3

0.6

0.9

1.2

0.4 0.6 0.8 1 1.2Measured Y

Predicted Y

Bezenchuk 2, (Y-var, PC): (Maturity, 2)

Elements: 123Correlation: 0.9244RMCEP: 0.0779

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Scores & loadingsScores & loadings

Scores

-4

-2

0

2

4

-4 -2 0 2 4

PC1

PC2

Bezenchuk 2, X-exp: 61%, 26% Y-exp: 83%, 3%

X- and Y-loadings

Depth

Humidity

+28C

Maturity

Ash

Weight

-8C

-0.9

-0.6

-0.3

0

0.3

-0.5 -0.25 0 0.25 0.5

PC1

PC2

Bezenchuk 2, X-exp: 61%, 26% Y-exp: 83%, 3%

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ResultResult

PCA allows revealing the lens and topsoil groups PCA allows revealing the lens and topsoil groups

using only measured variablesusing only measured variables

PLS regression provides us with maturity PLS regression provides us with maturity

predictionprediction

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Modern, well-run landfillModern, well-run landfill KinelKinel

Life cycle about 10 years

Environmental protection system exist

Amount of waste is more than 1300

thousand m3

Area 13 hectares

Life cycle about 10 years

Environmental protection system exist

Amount of waste is more than 1300

thousand m3

Area 13 hectares

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Samples and variablesSamples and variables

Kinel data set

105 samples (12 holes)

Kinel data set

105 samples (12 holes)

6 variables6 variables

4 measured variables

4 measured variables

2 evaluated variables

2 evaluated variables

ash content volumetric

weight temperature

depth

ash content volumetric

weight temperature

depth

layerlayer ageage

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PCAPCA

X1 X2

PCA

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PCA. Kinel data setPCA. Kinel data setScores

-2

0

2

4

-4 -2 0 2 4

PC1

PC2

Kinel 0, X-exp: 88%, 7%

X-loadings

WeightAsh

+28

Depth

-0.5

0

0.5

1

-0.5 -0.25 0 0.25 0.5

PC1

PC2

Kinel 0, X-exp: 88%, 7%

Influence

0

0.03

0.06

0.09

0 0.1 0.2 0.3 0.4 0.5 0.6LeverageKinel 0, PC: 3,3

Residual X-variance Residual Variance

0

0.5

1

0 1 2 3PCs

X-variance

Kinel 0, Variable: c. Total v. Total

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… … without samples of industrial wastewithout samples of industrial waste

Scores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 1, X-exp: 93%, 5%

X-loadings

Ash

Weight

Depth

+28

-0.5

0

0.5

1

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

PC1

PC2

Kinel 1, X-exp: 93%, 5%

Influence

0

0.02

0.04

0.06

0 0.05 0.1 0.15 0.2 0.25 0.3LeverageKinel 1, PC: 3,3

Residual X-variance Residual Variance

0

0.5

1

0 1 2 3PCs

X-variance

Kinel 1, Variable: c. Total v. Total

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Scores plotScores plot

Scores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 1, X-exp: 93%, 5%

AshAsh

Scores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 1, X-exp: 93%, 5%

WeightWeight

Scores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 1, X-exp: 93%, 5%

TemperatureTemperature

Scores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 1, X-exp: 93%, 5%

DepthDepth

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4 groups of waste4 groups of wasteScores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 1, X-exp: 93%, 5%

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PLSPLS

X1 X2 Y

PLS

++

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PLS RegressionPLS Regression

Scores

-2

-1

0

1

-4 -2 0 2 4

PC1

PC2

Kinel 3, X-exp: 92%, 3% Y-exp: 95%, 3%

X- and Y-loadings

Ash

Weight

+28

Depth

Plast

Age

-1

-0.5

0

0.5

1

-0.5 -0.25 0 0.25 0.5

PC1

PC2

Kinel 3, X-exp: 92%, 3% Y-exp: 95%, 3%

Root Mean Square Error

0.2

0.4

0.6

0.8

1 2 3PCs

RMSE RMSEC

RMSEP

Kinel 3, Variable c.Age v.Age

0

2

4

6

8

10

12

0 2 4 6 8 10 12Measured Y

Predicted Y

Kinel 3, (Y-var, PC): (Age, 2)

Elements: 103Correlation: 0.9907RMCEP: 0.4279

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ResultResult

PCA discriminates between industrial and PCA discriminates between industrial and

domestic wastesdomestic wastes

PCA reveals four waste layers existing in this PCA reveals four waste layers existing in this

landfill landfill

PLS regression provides us with waste age PLS regression provides us with waste age

predictionprediction

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Poorly run landfill OtradniyPoorly run landfill Otradniy

Life cycle more then 45 years

Environmental protection system is

absent

Amount of waste is more than 300 thousand

m3

Area 8 hectares

Life cycle more then 45 years

Environmental protection system is

absent

Amount of waste is more than 300 thousand

m3

Area 8 hectares

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Samples and variablesSamples and variables

Otradniy data set

84 samples (13 holes)

Otradniy data set

84 samples (13 holes)

7 variables7 variables

5 measured variables

5 measured variables

2 evaluated variables

2 evaluated variables

ash content volumetric

weight temperature

depth humidity

pH

ash content volumetric

weight temperature

depth humidity

pH

layerslayers maturitymaturity

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PLSPLS

X1 X2 Y

PLS

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PLS RegressionPLS RegressionScores

-2

0

2

4

-4 -3 -2 -1 0 1 2 3

PC1

PC2

Otradniy 1, X-exp: 44%, 28% Y-exp: 60%, 9%

Scores

-2

0

2

4

-4 -3 -2 -1 0 1 2 3

PC1

PC2

Otradniy 1, X-exp: 44%, 28% Y-exp: 60%, 9%

Weight

Weight

X- and Y-loadings

Humidity

Ash

Maturity

Depth

Weight

pH

-0.5

0

0.5

1

-0.6 -0.3 0 0.3 0.6

PC1

PC2

Otradniy 0, X-exp: 49%, 23% Y-exp: 60%, 7%

0.3

0.6

0.9

1.2

0.4 0.6 0.8 1 1.2Measured Y

Predicted Y

Otradniy 0, (Y-var, PC): (Maturity, 2)

Elements: 84Correlation: 0.7908RMCEP: 0.1100

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ResultResult

PLS regression provides us with maturity PLS regression provides us with maturity

prediction and gives the waste layers’ prediction and gives the waste layers’

stratification stratification

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Conclusions

Chemometric methods give possibility :► to explore the structure of man-caused

formation► to reveal the specific areas and strata► to predict the age or maturity of samples

The obtained results confirm the conventional methods of landfill exploration