Justin Henriques, SIE Department, University of Virginia Decision Analysis and Its Applications to Systems Engineering H

1. Revisions to a decision aid for the selection of sustainable water and wastewater infrastructure in low-income communities in developing countries Justin Henriques, SIE Department, University of Virginia Decision Analysis and Its Applications to Systems Engineering HRA INCOSE, Hampton, VA November 18, 2009

2. Overview • Research Motivation • Re-developed Capacity Factor Analysis • Case Study: Cimahi, Indonesia (2008)

3. I. Research Motivation • Since 1990, only small improvements have been made due to increasing population (WHO/UNICEF 2006). • High critical failure (30% to 60%) of installed water infrastructure in developing countries, often attributed to inappropriate technology (Davis 1995). This, of course, makes progress difficult. • 884 million people lack access to safe drinking water (WHO 2008) • 2.5 billion who lack access to improved sanitation (WHO 2000) • Lack of access leads to approx. 30,000 deaths per day and 75% of all disease related illnesses in developing countries (WHO 2000).

4. I. Research Motivation: Example, Mbeere Kenya • Familiar story in many developing communities: 5 broken well pumps in 1-2 mile radius - main source of water in the community • Technical failure symptom of inability to manage technology • Multiple factor system failure: financial, human resource, socio-cultural (transience) • Observed the need for a Integrated approach that systematically incorporated relevant factors into your selection essential infrastructure Repairing the bore whole, Mbeere, Kenya. July ’06 Child receives clean water, Mbeere, Kenya. July ’06

5. II. Capacity Factor Analysis (CFA) • CFA is a decision support model for the systematic selection of appropriate technologies for water and sanitation services in developing communities. • def. developing communities

6. II. CFA: Capacity Factors (Criteria)

7. II. CFA: Technology Assessment - Unit Operation DWS - water to be used for direct human consumption that does not pose a substantial threat to human health through microbiological, chemical, or other contaminated sources. • Source • Procurement • Storage • Treatment • Distribution Unit operation essential system components that are necessary for the provision of a service 4

8. II. CFA: Sample from Community Assessment

9. II. CFA: Technology Assessment Sample DWS Requirements and Benchmarks

10. II. CFA: Technology Assessment Source Sample List of Rated Technology: Source

11. II. CFA: Scoring Technology and Communities 1. score of the i-th capacity factor where: i = capacity factor {1, 2, …, 8} j = criterion (or requirement) within each capacity factor fi = score of the i-th capacity factor Cij = score of the j-th criterion of the i-th capacity factor wj = weight of the criterion Cij, where 0 < wj ≤ 1, and Σwj = 1 for j =1, …, n. 2. Community Score (row vector): f = [f1, f2, …, f8] 3. Rated Technology Matrix: where: A = technology rating matrix of all rated technologies for a single unit operation K = set of kth technologies where k=1, 2, …, n-1, N technologies fi = score of the i-th capacity factor

12. II. CFA: Matching Rule 1. Produce Subset of feasible technology options Rule: Set: 2. Order Set (T) by property x where: x = the squared difference between the score of the community assessment and technology rating k = kth technology where k=1, …, n-1, N technology t = {t: tx1 tx2 …, txN, t  T},

13. II. CFA: Interactive Software … …

14. II. CFA: Web-based Interactive software CFAModel.org

15. III. Case Study: Cimahi, Indonesia • Suburb of Bandung, West Java, Indonesia. • Poverty and a rapidly increasing population of approximately half a million have caused a strain on MSS • Cimahi’s designated as the final disposal site (FDS) of solid waste from Bandung, Indonesia. • The FDS is known for inadequate management practices, 2005 landslide of the solid waste resulted in 140 casualties of residents in FDS surrounding area (BBC 2006) Caption: Indonesian men collect plastic rubbish for recycling on the Citarum river, in Cimahi, West Java province (Global Media, 2007)

16. III. Case Study: Cimahi Indonesia Perform the community assessment to determine the CCL for both DWS* and GWR. Determine the optimal service options using benchmarks and requirements developed in this research for GWR and DWS. Compare results of current GWR and DWS technology for each unit operation to those chosen by the model Present relevant findings to local decision making entities in the community. Caption. Validation Hypotheses

17. III. Case Study: Cimahi, Indonesia NYC Manhattan Island Bronx Bronx Park Neighborhood Watch

18. III. Case Study: DWS Assessment - Kota Cimahi

19. III. Case Study: Cimahi, Indonesia

20. III. Case Study: DWS Technology Recommendations Caption: Tank with piped network distribution in RW6 Caption: Sandfilter with Chlorination in RW6

21. III. Case Study: Failure of Current DWS in Cimahi * * * * indicates that multiple units of the technology would need to be purchased to achieve service level

22. III. Case Study: DWS Recommendation for Cimahi Caption: Example of low environmental capacity in Leuwigajah Caption: Example of high socio-cultural capacity in Leuwigajah 1 By increase the CFA Community assessment Environmental Capacity Factor score from 2 to 3 2 By increase the CFA Community assessment Economic Capacity Factor score from 2 to 4 3 By increase the CFA Community assessment Economic Capacity Factor score from 2 to 3

23. III. Case Study: CFA Informal Validation

24. End References: • Ahmad, Tisan. 2004. Technology assessment for sustainable sanitation services in lower-income communities. Ph.D. diss., University of Virginia. • Berndtsson, Justyna C. 2006/4. Experiences from the implementation of a urine separation system: Goals, planning, reality. Building and Environment 41, no. 4: 427-437. • Bouabid, M. A. 2004. Community assessment for sustainable sanitation systems in low-income countries. • Buede, Dennis M., Knovel. 2000. The engineering design of systems. • Davis, Jan, Franðcois Brikkâe, Mary Boesveld, and IRC International Water and Sanitation Centre. 1995. Making your water supply work : Operation and maintenance of small water supply systems. The Hague, The Netherlands: IRC International Water and Sanitation Centre. • Global Media, Dadang Tri/REUTERS., • http://www.theglobeandmail.com/servlet/story/RTGAM.20070608.wwip0609/PhotoGallery01?slot=15 Access10-29 07 • Jefferson, B., S. Judd, and C. Diaper. 2001. Treatment methods for grey water. In Decentralised sanitation and reuse : Concepts, systems and implementation. London: IWA. • Louis, Garrick E. 2002. Risk analysis for capacity development in less industrialized countries. In SRA annual meeting. • Rogers, JeffreyWilliam. 2005. A standardized performance assessment and evaluation model for community water systems. • Sibeyn, Jop. Greedy Algorithms http://users.informatik.uni-halle.de/~jopsi/dinf503/chap8.shtml. Accessed 10-31-07 • Water Environment Federation (WEF). Glossary of water terms.Internet on-line. Available from <http://www.wef.org/AboutWater/ForThePublic/WaterTerms/#g>. [4/28/2008, 2008]. • WHO and UNICEF Joint Water Supply and Sanitation Monitoring Programme. 2006. Meeting the MDG Drinking Water and Sanitation Target - the Urban and Rural Challenge of the Decade. Geneva, Switzerland; New York. • World Health Organization, Unicef, Water Supply and Sanitation Collaborative Council, and WHO/UNICEF Joint Water Supply and Sanitation Monitoring Programme. 2000. Global water supply and sanitation assessment 2000 report.