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Evgeniy Michailov Samara State Technical University, Samara, Russia Ecological assessment of waste fields with multivariate analysis - feasibility study.

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Presentation on theme: "Evgeniy Michailov Samara State Technical University, Samara, Russia Ecological assessment of waste fields with multivariate analysis - feasibility study."— Presentation transcript:

1 Evgeniy Michailov Samara State Technical University, Samara, Russia Ecological assessment of waste fields with multivariate analysis - feasibility study

2 WSC-52 Man-caused formations

3 WSC-53 Objects for investigation 1.Illegal dump Bezenchuk 2.Modern, well-run landfill Kinel 3.Poorly run landfill Otradniy

4 WSC-54 Sampling hole 1 metre n metre n-1 metre

5 WSC-55 Variables variables measured variables measured variables evaluated variables ash content volumetric weight temperature depth humidity pH ash content volumetric weight temperature depth humidity pH stratum lense topsoil stratum lense topsoil age

6 WSC-56 Evaluated variables CHEMOMETRICS-BASED EVALUATION OF MAN- CAUSED FORMATIONS’ STABILITY Olga Tupicina Samara State Technical University, Samara, Russia CHEMOMETRICS-BASED EVALUATION OF MAN- CAUSED FORMATIONS’ STABILITY Olga Tupicina Samara State Technical University, Samara, Russia

7 WSC-57 Age → maturity Maturity=1-exp(-k*Age) k=1/5 Maturity=1-exp(-k*Age) k=1/5 Age can be evaluated for waste only Age of topsoil? →use the maturity Maturity of topsoil is 1

8 WSC-58 Goals and methods X1 X2 Y measuredevaluated PCA PLS

9 WSC-59 Illegal dump Bezenchuk Life cycle more then 25 years environmental protection system is absend Amount of waste is more than 90 thousand m 3 Area 30 hectares Life cycle more then 25 years environmental protection system is absend Amount of waste is more than 90 thousand m 3 Area 30 hectares

10 WSC-510 Scheme of dump Bezenchuk 2 regions of sewage sludge 2 regions of sewage sludge topsoiltopsoil

11 WSC-511 Samples and variables Bezenchuk data set 123 samples (21 holes) Bezenchuk data set 123 samples (21 holes) 9 variables 6 measured variables 6 measured variables 3 evaluated variables ash content volumetric weight temperature depth humidity ash content volumetric weight temperature depth humidity lens topsoil lens topsoil maturity

12 WSC-512 PCA X1X2 PCA

13 WSC-513 PCA Bezenchuk data set

14 WSC-514 Lenses and topsoil sewage sludge topsoil

15 WSC-515 PLS X1 Y PLS

16 WSC-516 PLS Bezenchuk data set

17 WSC-517 Scores & loadings

18 WSC-518 Result PCA allows revealing the lens and topsoil groups using only measured variables PLS regression provides us with maturity prediction

19 WSC-519 Modern, well-run landfill Kinel Life cycle about 10 years Environmental protection system exist Amount of waste is more than 1300 thousand m 3 Area 13 hectares Life cycle about 10 years Environmental protection system exist Amount of waste is more than 1300 thousand m 3 Area 13 hectares

20 WSC-520 Samples and variables Kinel data set 105 samples (12 holes) Kinel data set 105 samples (12 holes) 6 variables 4 measured variables 4 measured variables 2 evaluated variables ash content volumetric weight temperature depth ash content volumetric weight temperature depth layer age

21 WSC-521 PCA X1X2 PCA

22 WSC-522 PCA. Kinel data set

23 WSC-523 … without samples of industrial waste

24 WSC-524 Scores plot Ash Weight Temperature Depth

25 WSC groups of waste

26 WSC-526 PLS X1 X2 Y PLS +

27 WSC-527 PLS Regression

28 WSC-528 Result PCA discriminates between industrial and domestic wastes PCA reveals four waste layers existing in this landfill PLS regression provides us with waste age prediction

29 WSC-529 Poorly run landfill Otradniy Life cycle more then 45 years Environmental protection system is absent Amount of waste is more than 300 thousand m 3 Area 8 hectares Life cycle more then 45 years Environmental protection system is absent Amount of waste is more than 300 thousand m 3 Area 8 hectares

30 WSC-530 Samples and variables Otradniy data set 84 samples (13 holes) Otradniy data set 84 samples (13 holes) 7 variables 5 measured variables 5 measured variables 2 evaluated variables ash content volumetric weight temperature depth humidity pH ash content volumetric weight temperature depth humidity pH layers maturity

31 WSC-531 PLS X1 X2 Y PLS

32 WSC-532 PLS Regression Weight

33 WSC-533 Result PLS regression provides us with maturity prediction and gives the waste layers’ stratification

34 WSC-534 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


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