evgeniy michailov samara state technical university, samara, russia ecological assessment of waste...
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![Page 1: Evgeniy Michailov Samara State Technical University, Samara, Russia Ecological assessment of waste fields with multivariate analysis - feasibility study](https://reader037.vdocuments.site/reader037/viewer/2022103022/56649c985503460f94954775/html5/thumbnails/1.jpg)
Evgeniy MichailovEvgeniy Michailov
Samara State Technical University, Samara, RussiaSamara State Technical University, Samara, Russia
Ecological assessment of waste fields with multivariate analysis - feasibility study
![Page 2: Evgeniy Michailov Samara State Technical University, Samara, Russia Ecological assessment of waste fields with multivariate analysis - feasibility study](https://reader037.vdocuments.site/reader037/viewer/2022103022/56649c985503460f94954775/html5/thumbnails/2.jpg)
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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