Evaluating the viability of establishing container-based sanitation in low-income settlements

Container-based sanitation (CBS) services operate in a number of low-income urban settlements across the globe, providing sanitation services where other on-site and off-site sanitation systems face logistical and environmental restrictions. The viability of each CBS service is influenced by a number of location specific factors. Drawing on an initial review of existing CBS services, this paper identifies and evaluates these factors in relation to establishing CBS in a new service location. By applying a weighted scoring matrix to these factors, the potential viability of CBS services has been assessed for urban informal settlements in Kathmandu Valley, Nepal. The viability of CBS services in these settlements was found to be most influenced by the current availability of basic sanitation facilities, the unfamiliarity with paying for sanitation services and the universally adopted practice of anal cleansing with water. The process and scoring matrix developed and subsequently applied in Nepal, are recommended as part of the pre-feasibility stage assessment where a CBS service is being considered as a sanitation option in new locations

Additional file 1: Figure S1. of The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle

Relative abundance (%) of 20 groups of functional genes representing 204 selected genes (number of animals, n = 50 samples). The sum of the relative abundance (%) of genes grouping within the same function is shown in this figure. Figure S2A. Total abundance of 204 selected genes based on diet treatments (n = 50). *P value < 0.05. Figure S2B. Shannon index diversity of 204 selected genes based on diet treatments (n = 50). *P value < 0.05, °P value < 0.1. Figure S3. Canonical Variate analysis (CVA) on the structure of 204 genes selected based on breed, age, weight, Proteobacteria ratio, FCR and methane grouping (n = 50). Figure S4. Factors explaining the significant differences observed for Proteobacteria ratio (n = 50). Figure S5. Microbial community composition at the phylum level (n = 50). Table S1. Characteristics of the cattle used in the experiment. Table S2. Groups of AMR genes significantly correlated with abundance of the Proteobacteria phylum and Proteobacteria ratio. Table S3. The relative abundance of AMR genes. Table S4, Proteobacteria populations strongly correlated with the Proteobacteria ratio. Table S5. Functional genes significantly correlated with Proteobacteria ratio (PLS). Table S6. Cluster distribution of functional genes significantly different between diets. (DOCX 60 kb