Data Reduced Method for Cost Comparison of Wastewater Management Scenarios–Case Study for Two Settlements in Jordan and Oman

Abstract

Safe access to sanitation is at the core of the United Nations Sustainable Development Goal (SDG) #6. Currently, it is estimated that this goal cannot be met by 2030. Despite all kinds of administrational hurdles, meeting SDG#6 depends on considerable investment. In order to get a chance at fulfilling SGD#6, the most cost-effective wastewater management has to be identified. Wastewater can be managed in different ways ranging from central treatment plants connected to individual households through sewer networks down to tanker fleets servicing each household. Depending on the geographical setting, investment costs, operation and maintenance as well as social acceptance there is no single best solution. Instead, identifying the optimal wastewater management is highly localized and country-specific or even settlement-specific. Within this study we present a data-reduced scenario generation and assessment for wastewater management based on the ALLOWS method that can be applied to individual settlements. Results provide cost-ranked wastewater management scenarios that enable decision makers to select the most cost-effective and feasible scenario. Our study starts with a detailed step-by-step methodology of a data-reduced ALLOWS approach. The approach is applied to two small settlements in Jordan and Oman with comparable population size for which a set of five scenarios along a decentralization gradient is defined and generated: centralized, semi-centralized, decentralized, and on-site/tanker. Considering spatial specificities and country-specific cost data, the five scenarios are cost-ranked and discussed in view of the two settlement settings. For Jordan specific treatment costs range from 3.8 to 6.9 USD/m3 of treated water and for Oman from 2.3 to 10.1 USD/m3. Although the scenario ranking differs, for both settlements the decentralized scenario is identified as the most cost effective, where wastewater is treated on-site for less-populated parts and by small cluster treatment plants for higher density parts. Further, potential extensions providing users with more functionality depending upon data availability for the data-reduced ALLOWS method are discussed. Using globally available data, the data-reduced can be applied worldwide. In view of SDG#6, we present a methodology that closes the gap between country-scale investment estimates and the most cost-effective wastewater management scenarios at settlement level.