Holistically Managing Pathogens and Nutrients in Urbanizing Tropical Towns: Can Sanitation Technologies Create Safer Conditions for Beach Recreation?

Abstract

Rapidly urbanizing coastal communities are prone to overpopulation and unrestrained growth that result in pathogen and nutrient emissions, which impair beach water quality and jeopardize human and environmental health. Decision makers face complex, context-dependent choices when selecting sanitation technologies with varying abilities to remove pathogens and nutrients. The goal of this study was to identify context-appropriate sanitation solutions that manage pathogens and nutrients to ensure safe beach swimming conditions in an urbanizing coastal town that relies on tourism. Quantitative microbial risk assessment was utilized to determine the pathogen log10 reduction values (LRVs) required for safe swimming. A lack of consensus in the literature about an aggregation parameter in the norovirus dose–response model resulted in predicted LRVs that differed by as much as 3 orders of magnitude. Local experts identified nine context-appropriate infrastructure scenarios, and their ability to reduce pathogen and nutrient loadings was modeled. The model showed that spatially targeted sanitation infrastructure scenarios could effectively meet LRV targets. Seasonal increases in population were predicted to greatly impact pathogen and nutrient loadings. Top-performing scenarios that managed both pathogens and nutrients included centralized treatment with disinfection and integrated resource recovery. Future research is needed to understand the scenarios’ sociocultural feasibility and costs.

Quantitative sustainable design (QSD) for the prioritization of research, development, and deployment of technologies: a tutorial and review

“…For example, to mitigate GHG emissions, different scenarios, each consisting of varying technologies, have been evaluated for decarbonization. 61 Similar applications of scenario analysis can be found in other topics (e.g., water and wastewater treatment, 46,145,279,280 renewable energy, 199,254,257,281 waste management 215 ), or for a certain technique (e.g., using different scenarios of electricity mix in LCA to assess the environmental sustainability of electric vehicles 282 ). In essence, scenario analysis divides the entire problem space into discrete, representative sub-spaces.…”

“…61 Similar applications of scenario analysis can be found in other topics ( e.g. , water and wastewater treatment, 46,145,279,280 renewable energy, 199,254,257,281 waste management 215 ), or for a certain technique ( e.g. , using different scenarios of electricity mix in LCA to assess the environmental sustainability of electric vehicles 282 ).…”

Quantitative Sustainable Design (QSD) for the Prioritization of Research, Development, and Deployment of Technologies: A Tutorial and Review

“…For example, to mitigate GHG emissions and curb global warming within 1.5°C above the pre-industrial level, different scenarios, each consisting of varying technologies, have been evaluated for decarbonization. 61 Similar applications of scenario analysis can be found in other topics (e.g., water and wastewater treatment, 46,159,298,299 renewable energy, 217,260,279,300 waste management 232 ), or for a certain technique (e.g., using different scenarios of electricity mix in LCA to assess the environmental sustainability of electric vehicles 301 ). In essence, scenario analysis divides the entire problem space into discrete, representative sub-spaces.…”

“…55,154 More specifically, to assess local human health risk due to pathogens, QSD inputs and process algorithms can be used to simulate pathogen concentrations, after which quantitative microbial risk assessment (QMRA) 155 can be performed to quantify the human health risk. QMRA can be performed with generic (e.g., using generic pathogen concentrations in feces for evaluating water and sanitation systems 156 ) or site-specific data (e.g., [157][158][159] and Example 2 157 in Table 2). Additionally, there are examples where QMRA is hybridized with LCA 56,160,161 to characterize trade-offs between human health risk and environmental impacts.…”