1. Decision Support in the Reuse of Sewage Sludge in Agriculture Aida Valls, Xavier Mercadé, Marta Schuhmacher, Ana Passuello Universitat Rovira i Virgili, Tarragona, Spain

2. Contents The problem: WWTP sewage sludge disposal SMES: Sludge Management Expert System 1) Filtering of data 2) Multi-criteria decision process  Construction of performance criteria  Aggregation of partial performaces into a global score 3) Visualization tools Conclusions

3. WWTP sewage sludge disposal Sewage sludge can be applied to agricultural soils to improve their quality. To keep sustainability, countries are encouraged to do this activity. Ecological and human impact must be considered:  metals and organic compounds may be transferred to the soil, which may cause contamination (nitrification) To determine the best scenario for sludge application, a decision support system is presented.

4. Filtering Fuzzy Expert System Aggregation of criteria evaluations Visualization The system is composed by 4 modules:

5. Input data To assess those criteria many variables have been considered. Sludge PropertiesSoil and Landscape Properties Other Variables Metals concentration Pops concentration Treatment type Organic Matter pH Nutrients Metals concentration Texture Organic Matter pH Nutrients Carbonates Groundwater nitrification Temperature Precipitation Crop type Application type Population type Population density Distance to urban areas Management costs Transport costs Fertilizer costs Legal maximum levels Filtering Fuzzy Expert System Aggregation of criteria evaluations Visualization

6. 1. F ILTERING S TAGE European legal regulations on the amount of chemical and organic contaminants that are present in each sludge and soil samples. Sludge or soils above the limits are discarded from the rest of the analysis. A report is generated.

7. 2. MCDA. The family of criteria Criteria have been organized hierarchically. At the first level, 3 groups are distinguished:  Economical issues Costs derived from the application, transport and management, and profits for non using other fertilizers  Environmental impacts Ecological impact on soil and groundwater (GW) contamination  Human health risks Social impact and consequences on human health due to direct exposition or ingestion of pollutants

8. The family of criteria

9. Two types of criteria Simple criteria (S): g:   [0..10] Composite criteria (C): g:    ...    [0..10] Depend on the interactions between some soil and sludge properties

10. Simple criteria

11. Composite criteria The performace score is a consequence of the combination of soil and sludge properties (or other variables) Interactions between several variables must be properly modeled We proposed to use Fuzzy Expert Systems Valls, A., Schuhmacher, M., Pijuan, J., Passuello, A., Nadal, M., Sierra, J., Preference assessment for the management of sewage sludge application on agricultural soils, International Journal of Multicriteria Decision Making, Indersicence Publishers, ISSN (Print): Vol 1. No1. pp 4-24. 2010 Filtering Fuzzy Expert System Aggregation of criteria evaluations Visualization

12. A fuzzy expert system approach Rules are used to model the dependencies between the groups of variables Linguistic variables permit to model uncertainty The properties of each alternative are introduced into the fuzzy expert system Rules are activated and a conclusion is obtained, regarding the performance of the alternative

13. Input linguistic variables

14. Output linguistic variable for performance evaluation

15. A fuzzy expert system approach

16. 2. MCDA. Aggregation Logic Scoring of Preferences (LSP) Proposed by J. Dujmovic (1973) Performance scores are aggregated in a compositional way following the hierarchy of criteria Different logical operators can be chosen at each aggregation point Aggregation method based on continuous preference logic (CPL)  Simultaneity (andness), Replaceability (orness), Mandatory requirements and Sufficient requirements Filtering Fuzzy Expert System Aggregation of criteria evaluations Visualization

17. LSP aggregation operators Type of polarization Level of polarizationSymbold Ornessc Andness Disjunctive polarization (Partial disjunction) StrongestD10 Very StrongD++0,93750,0625 StrongD+0,8750,125 Medium StrongD+-0,81250,1875 MediumDA0,750,25 Medium WeakD-+0,68750,3125 WeakD-+0,6250,375 Very weakD--0,56250,4375 Neutrality A0,5 Conjunctive polarization (Partial conjunction) Non mandatory Very weakC--0,43750,5625 WeakC-0,3750,625 Mandatory requirements Medium WeakC-+0,31250,6875 MediumCA0,250,75 Medium StrongC+-0,18750,8125 StrongC+0,1250,875 Very StrongC++0,06250,9375 StrongestC01

21. Filtering Fuzzy Expert System Aggregation of criteria evaluations Visualization

23. Conclusions The SMES system analyses the physical and chemical properties of the sludge and the soil in order to calculate the degree of suitability of each case study, taking into account that some soils are more suitable to receive some kind of sewage sludge than others. A combination of different techniques has been used to solve the problem. Some criteria related to territorial information are included in the multi-criteria analysis. The SMES software is being developed by Microart S.L. (Barcelona, Spain).

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