Wastewater-based epidemiology (WBE) is an approach that can be used to estimate COVID-19 prevalence in the population by detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. As the WBE approach uses pooled samples from the study population, it is an inexpensive and non-invasive mass surveillance method compared to individual testing. Thus, it offers a good complement in low- and middle-income countries (LMICs) facing high costs of testing or social stigmatization, and it has a huge potential to monitor SARS-CoV-2 and its variants to curb the global COVID-19 pandemic. The aim of this review is to systematize the current evidence about the application of the WBE approach in mass surveillance of COVID-19 infection in LMICs, as well as its future potential. Among other parameters, population size contributing the fecal input to wastewater is an important parameter for COVID-19 prevalence estimation. It is easier to back-calculate COVID-19 prevalence in the community with centralized wastewater systems, because there can be more accurate estimates about the size of contributing population in the catchment. However, centralized wastewater management systems are often of low quality (or even non-existent) in LMICs, which raises a major concern about the ability to implement the WBE approach. However, it is possible to mobilize the WBE approach, if large areas are divided into sub-areas, corresponding to the existing wastewater management systems. In addition, a strong coordination between stakeholders is required for estimating population size respective to wastewater management systems. Nevertheless, further international efforts should be leveraged to strengthen the sanitation infrastructures in LMICs, using the lessons gathered from the current COVID-19 pandemic to be prepared for future pandemics.

The achievement of water sustainability necessitates the development and application of comprehensive assessment tools to monitor and evaluate the impact of water resource management. This article presents a comprehensive comparative analysis of various water sustainability indices, emphasizing their underlying principles, methodologies, and potential applications. Our study reveals the diverse landscape of existing indices, illustrating that even indices with similar names can vary significantly in scope and methodology. Via a systematic review of 124 publications, this study provides insights into existing composite indices related to water sustainability, highlighting their specific applications and potential contributions to water resource management and sustainability. The information gathered from the selected papers was synthesized and analyzed thematically to identify common patterns through keyword co-occurrence mapping, SDG mapping, standard review protocols, and cluster analyses. Through a cluster analysis, we identified six distinct clusters of indices, highlighting the need for careful consideration in selecting appropriate ones. Moreover, our analysis of co-occurring keywords underscores the close relationship between sustainable development, water resources, water supply, and water conservation within the context of water-related indices. Notably, these indices address not only sustainable development goal six but also a number of other interconnected goals. It was also found that “sustainability index” is a common name for different nature water indices. This review also identifies research gaps in the existing literature. However, significant limitations exist, including a lack of historical application and future projections for many current water sustainability indicators. Without the ability to track changes over time and project the future, identifying areas of improvement and measuring progress toward long-term water sustainability goals becomes challenging. Furthermore, many indices are complex and designed for watershed or regional levels, limiting their adaptability to different contexts. Despite these challenges, indices remain valuable tools for assessing and managing water resources sustainably, addressing various dimensions of sustainability, and supporting decision-making processes across different sectors and contexts.

Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience.

This study investigated the potential adaptation measures for farmers in the Mwanga and Same Districts of Tanzania and then assessed their positive and negative interactions towards potential contributions to the selected sustainable development goal (SDG) indicators of no poverty and zero hunger. A total of 200 household surveys were conducted, and 36 participants were interviewed as key informants. Moreover, four focus group discussions were conducted to identify potential adaptation strategies in the studied areas. The literature and expert judgement approaches were used to understand and assess the positive and negative interactions between adaptation strategies and the selected indicators of SDGs. A seven-point scale of SDG interactions was used to determine the interactions between identified adaptation strategies and selected SDG indicators. Qualitative data were subjected to content analysis, whereas quantitative data were analyzed through descriptive statistics. The finding revealed that some of the adaptation strategies (i.e., stream bank crop cultivation, valley bottom crop cultivation, and cultivating crops near water sources) considered potential at the household levels and had strong negative interactions on achieving SDG 2.4.1. Further, most farmers hardly employed strategies (i.e., use of extension officers, concrete irrigation channels, crop insurance schemes, and credit schemes) that had strong positive interactions on the selected SDG indicators. Moreover, most of the identified strategies (i.e., early maturity crops, planting drought-resistant crops, use of improved varieties, mixing improved and local varieties, mixing short and long duration varieties, and crop diversification) are enabling strategies (+1), which, despite their importance, may constrain (−1) the income of small-scale farmers, food security, and poverty reduction. The importance of other strategies (i.e., irrigation infrastructures) must be addressed for better yields and positive impacts. Hence, achieving SDGs 1 and 2 in the studied areas will require the integration of different adaptation strategies that complement each other, and not by promoting only some strategies as used or suggested before. For example, the emphasis on using improved varieties and crop diversification should be complemented by access to credit schemes, irrigation infrastructures, crop insurance, and extension services at the village level.