1. 1 Chemometric Methods for Environmental Pollution Monitoring Dmitry E. Bykov Samara State Technical University Samara, Russia

2. 2 Outlines I. Introduction II. Wastes recovering III. Wastes conversion IV. Wastes cancellation V. Wastes management VI. Landfills management VII. Conclusions

3. 3 The Goals This lecture has two main objectives: To give information about our R & D activities; To get your advices how to apply chemometrics

4. 4 Samara is a large industrial city

5. 5 Samara State Technical University SSTU 17 000 Students Since 1914

6. 6 SSTU Structure SSTU Research & Analysis Center of Industrial Ecology Faculty Faculty of Chemical Technology Institute Department Department of Industrial Ecology

7. 7

8. 8 Design the processes and equipment for waste treatment industrial sewage cleaning Reengineering of out-of-date technologies Ecological auditing and improvement of ecological management in industry Department Research Activities

9. 9 Development activities

10. 10 Public Activities

11. 11 Research & Analysis Center of Industrial Ecology (RACIE)

12. 12 RACIE Activities Chemical analysis of topsoil, wastes, sewage, and ground water Development of standards that regulate the pressure on the environment by human activities Designing the up-to-date landfills for industrial and domestic wastes

13. 13 II. Wastes Recovering The goals are purification and regeneration

14. 14 Sleeper plant sewage purification Waste emulsion regeneration Copper contaminated sorbent regeneration Used enamel regeneration Hydrolyzed salomass regeneration High foul blowoff sewage purification Sleeper plant sewage purification High foul blowoff sewage purification Tasks solved

15. 15 Sleeper plant sewage purification Sleeper plants sewage water contains up to 10% of tars. To purify it extraction with xylene is applied.

16. 16 Equilibrium in the water/tar/xylol system Tar concentration in water, kg/m 3 Extraction tie-line 10 30 50 4.03.0 2.0 1.0 70 90 Pseudoequilibrium area Tar concentration in xylene, kg/m 3 Suspended matter concentration 100 mg/l 300 mg/l 500 mg/l

17. 17 Tar extraction Sewage water 100% 91% 9% Tar Water Xylene Sewage+Xylene 100% 91.2% 8.1% 0.7% Emulsion 9% 79.1% 19.7% 1.2% Extract 7% 2.4% 88.7% 8.8% Refined water 84% 99.96% 0.02%

18. 18 High foul blowoff sewage purification

19. 19 Process parameters (Input)  T – Temperature  Ph – Acidity  PAA – Flocculant (polyacrylamid) concentration  K-2 – Coagulant concentration

20. 20 Purified water quality (Output)  D – Optical density  Al – Concentration of aluminium ions Al 3+  Fe – Concentration of ferric compounds

21. 21 Conventional univariate approach - I Output parameters versus acidity. Other input parameters are constants T = 20°C [K-2] = 50 mg/l [PAA] = 2 mg/l

22. 22 Conventional univariate approach - II Output parameters versus temperature. Other input parameters are constants pH = 6 [K-2] = 40 mg/l [PAA] = 2 mg/l

23. 23 Conventional univariate approach - III Output parameters versus PAA concentration. Other input parameters are constants T = 20°C pH = 6 [K-2] = 40 mg/l

24. 24 Conventional univariate approach - IV Output parameters versus K-2 concentration. Other input parameters are constants T = 20°C pH = 6 [PAA] = 2 mg/l

25. 25 Optimal process setup  Temperature T=35°C  Acidity Ph= 6  PAA concentration [PAA]=2 mg/l  K-2 concentration [K-2]= 40 mg/l

26. 26 Chemometrics related problem Would MSPC approach be useful there?

27. 27 PLS2 Model Loadings Plot Input parameters Output parameters T, pH, PAA, K-2 Fe, D, Al

28. 28 Predicted optical density

29. 29 Predicted concentration of aluminium ions

30. 30 Predicted concentration of ferric compounds

31. 31 III. Wastes conversion The goal is utilization

32. 32 Tasks solved Soap stock utilization Conversion of plastic-insulated cable scraps 1,2-dichlorpropane processing Polychlorethanes processing

33. 33 Soap stock utilization Soap stock is a waste of oils and fats refining This is a valuable product, which should utilized

34. 34 Conventional method of utilization H 2 SO 4 Oil refining Fat refining Stock gathering Soap stock Deoxidation Mixed soap stock Fat separation Laundry soap Mixture of saturated and unsaturated fatty acids, neutral fat Waste is utilized into not valuable soap

35. 35 Soap stock composition Vegetable oil production wastes Fat production wastes Stock composition is different for oil and fat

36. 36 Oil production wastes utilization Waste is utilized into valuable dry oil Oil refining Soap stock Deoxidation Fat separation Etherification polymerization oxidization Mixture of saturated fatty acids and neutral fat Desiccant Glycerin О 2 Compounding Oxidized oil Dry oil

37. 37 Fat production wastes utilization Waste is utilized into valuable products Soap stock Hydro- genation Fat separation Mixture of saturated fatty acids and neutral fat Neutralization Calcium stearate Fat production Commercial stearin Са О

38. 38 Chemometrics related problem Will MSPC approach be useful in this case?

39. 39 IV. Wastes cancellation The goal is wastes annihilation

40. 40 Tasks solved Oil polluted lands reclamation Sewage sludge utilization

41. 41 Oil polluted lands reclamation We have: Oil polluted lands that should be reclamated A lot of activated sludge that should be utilized Let’s mix them up!

42. 42 Oil polluted lands reclamation Mixture Oil polluted soil Activated sludge

43. 43 Oil conversion ES is enzyme-substrate complex E is enzyme (catalase) S is substrate (oil) Р is oil decomposition product

44. 44 Chemometrics related problem The problem looks similar to biofuel production. Will this similarity be helpful?

45. 45 More on lands reclamation Konstantin Chertes Samara State Technical University, Samara, Russia Possibilities of application of multidimensional data analysis methods to substantiate directions of degraded land recultivation

46. 46 V. Wastes management The goal is collection and sorting

47. 47 Wastes sources

48. 48 Wastes distribution within industry

49. 49 Domestic refuse composition

50. 50 Domestic refuse break up Total (100 %) Collected (83 %)Disposed (76 %)Recycled (7 %)

51. 51 Waste collection system in Samara

52. 52 Wastes traverser station

53. 53 Polymer wastes composition Polymer wastes weight portion is 10 % Polymer wastes cost portion is 60 %

54. 54 Chemometrics related problem How to automate the wastes sorting? Will NIR spectroscopy be helpful there?

55. 55 More on waste sorting and recycling Nataliya Ryumina Samara State Technical University, Samara, Russia Sorting of polymers according to the types by the method of near infrared spectroscopy

56. 56 VI. Landfills management The goal is ecological risk assessment

57. 57 Well-run landfill Kinel

58. 58 Illegal dump Bezenchuk

59. 59 How to estimate a landfill state? measuredevaluated ash contentage densitypeculiarities temperature depth humidity pH

60. 60 Prediction of maturity (age)

61. 61 PCA-based classification

62. 62 Chemometrics related problem How to perform sampling on landfills? Will sampling theory be helpful there? 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 18 19 20 21

63. 63 More on landfill state evaluation Olga Tupicina Samara State Technical University, Samara, Russia Chemometrics-based evaluation of man- caused formations’ stability Evgeniy Michailov Samara State Technical University, Samara, Russia Ecological assessment of waste fields with multivariate analysis - feasibility study

64. 64 VII. Conclusions Numerous cases that are of interest in ecology and waste management have been presented Our first chemometric experience inspire us to use it more and more We are entirely open for co-operation in ecological chemometrics It is great to see so many outstanding scientists here!