IMDEA Water Institute, Av. Punto Com, 2, Parque Científico Tecnológico, 28805 Alcalá de Henares, Madrid, Spain.
METfilter S.L., Autovía A49 Sevilla-Huelva Km 28, 41820 Carrión de los Céspedes, Sevilla, Spain.
Int J Environ Res Public Health. 2021 May 19;18(10):5415. doi: 10.3390/ijerph18105415.
METland is a new variety of Constructed Wetland (CW) for treating wastewater where gravel is replaced by a biocompatible electroconductive material to stimulate the metabolism of electroactive bacteria. The system requires a remarkably low land footprint (0.4 m/pe) compared to conventional CW, due to the high pollutant removal rate exhibited by such microorganisms. In order to predict the optimal locations for METland, a methodology based on Multi-Criteria Evaluation (MCE) techniques applied to Geographical Information Systems (GIS) has been proposed. Seven criteria were evaluated and weighted in the context of Analytical Hierarchy Process (AHP). Finally, a Global Sensitivity Analysis (GSA) was performed using the Sobol method for resource optimization. The model was tested in two locations, oceanic and Mediterranean, to prove its feasibility in different geographical, demographic and climate conditions. The GSA revealed as conclusion the most influential factors in the model: (i) land use, (ii) distance to population centers, and (iii) distance to river beds. Interestingly, the model could predict best suitable locations by reducing the number of analyzed factors to just such three key factors (responsible for 78% of the output variance). The proposed methodology will help decision-making stakeholders in implementing nature-based solutions, including constructed wetlands, for treating wastewater in rural areas.
METland 是一种新型的人工湿地（CW），用于处理废水，其中砾石被生物相容性导电材料取代，以刺激电活性细菌的新陈代谢。与传统 CW 相比，该系统需要的土地足迹非常小（0.4 m/pe），这是由于这种微生物表现出的高污染物去除率。为了预测 METland 的最佳位置，提出了一种基于多标准评估（MCE）技术并应用于地理信息系统（GIS）的方法。在层次分析法（AHP）的背景下，对七个标准进行了评估和加权。最后，使用 Sobol 方法对资源优化进行了全局敏感性分析（GSA）。该模型在两个地点，海洋和地中海进行了测试，以证明其在不同地理、人口和气候条件下的可行性。GSA 的结论是，模型中最具影响力的因素是：（i）土地利用，（ii）与人口中心的距离，以及（iii）与河床的距离。有趣的是，通过将分析因素减少到仅这三个关键因素（占输出方差的 78%），该模型可以预测最合适的位置。该方法将有助于决策利益相关者实施基于自然的解决方案，包括人工湿地，用于处理农村地区的废水。
