Using fuzzy cognitive maps to promote nature-based solutions for water quality improvement in developing-country communities

Abstract

An adequate strategy for water quality improvement must consider a range of political, economic, social, technological, environmental, and legal (PESTEL) concepts. Nature-based solutions have emerged as promising tools to improve water quality while considering these factors. In this context, fuzzy cognitive maps and the PESTEL approach have been merged to i) identify the principal concepts that affect water quality from different perspectives, and ii) theoretically explore the use of artificial floating islands as a measure of nature-based solution combined with different policies, to find strategies to improve water quality given local conditions. For this purpose, three Ecuadorian communities are used as scenarios. The communities are located in different geographical regions, i.e., páramo (an alpine tundra ecosystem), coastal mangrove, and tropical rainforest. From literature-based research, 40 PESTEL concepts are identified, then local experts recognize relevant concepts related to water quality deterioration regarding local conditions, and the communities develop fully democratic social cognitive maps. The cognitive map from the páramo community is constructed with 17 concepts mainly driven by environment (23%) and economy (23%). The major problem identified is natural water pollutants with the highest centrality value (ci = 12.22). The mangrove community uses 19 concepts influenced by policy (48%), and the major issue is human exposure to environmental pollutants (ci = 16.27). The rainforest community uses 15 concepts related to the economy (40%), and the major problem is the violation of environmental legislation (ci = 15.96). Our pioneer work to predict the future of water management shows that in the worst-case scenario, more than 85% of concepts are affected in all communities. However, the implementation of policy strategies in combination with artificial floating islands demonstrates a large potential for improving water quality. With this study, we provide a novel, inclusive, and locally adapted framework to guide future water management and contribute to achieving the Sustainable Development Goal SDG 6.