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Item type:Publication, Wastewater treatment decentralization: is this the right direction for megacities in the Global South?(Elsevier, 2021-07-15)The centralization-decentralization dichotomy in wastewater treatment management has been a recurrent topic of discussion in the urban context. The escalation of environmental hazards linked to increasing mismanaged wastewater flows in emerging or developing cities has vivified this conundrum. It is argued that there is a wide range of parameters to identify the optimal level of centralization-decentralization that must be implemented. In many cases, this prevents decision-makers from having a clear picture of the most appropriate management choices that must be undertaken. Hence, the main objective of the current discussion consists of an in-depth comparison between centralized wastewater treatment systems and decentralized systems with source separation in urban environments of the Global South. Moreover, a set of actions that should be considered in order to upgrade wastewater treatment systems amidst the existence of numerous economic, social and environmental constraints are analyzed. Considering the constraints of megacentralization as a preferred option, we argue that decision-makers should restrain from entering a centralization-decentralization dichotomy, seeing the process as a gradient between the two concepts. In fact, we advocate combining the benefits of each of the two perspectives to generate an adaptive management, site-specific solution for urban environments. For this, the inclusion of quantitative management tools, such as life-cycle environmental or cost management methodologies, in multi-objective optimization models, constitutes an interesting path forward towards fostering comprehensive policy support. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review(Elsevier B.V., 2024-10-01)The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multi-Criteria Decision Framework for Circular Wastewater Systems in Emerging Megacities of the Global South(Elsevier BV, 2023-12-05)Lima faces increasing water stress due to demographic growth, climate change and outdated water management infrastructure. Moreover, its highly centralized wastewater management system is currently unable to recover water or other resources. Hence, the p... - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Circular Economy in the Global South: Co-creating a research agenda from the Industrial Ecology Society(Wiley, 2026-04-01)The Global South faces unique sustainability challenges especially around the use and management of resources, providing a timely opportunity to understand and adapt Circular Economy (CE) approaches and initiatives, to chart a research agenda for these regions. During the 12th International Conference on Industrial Ecology (IE), ISIE Singapore 2025, we hosted the first special session to unpack the variety of research and key issues related to CE in the Global South. The session aimed to create shared learning and co-creation of a research agenda to prioritize the necessary elements for an effective, inclusive, and just CE transition. A total of 24 participants contributed to the session, representing 16 nationalities. In this paper, we discuss the key outcomes from the session, focusing on the most pressing research gaps, desirable characteristics of a comprehensive CE research agenda, and enablers needed to ensure alignment with both global priorities and local realities. Three core areas emerged as priorities for the IE community's CE research agenda in the Global South: (1) improving data integrity and availability, especially addressing data collection methodologies for uncovering blind-spots such as those related to the informal economy; (2) combining quantitative IE tools with qualitative, transdisciplinary approaches to better address complex circular challenges; and (3) recognizing and integrating cultural practices and ancestral knowledge drawing on CE principles. By articulating these priorities, our IE society can support more grounded, context-sensitive, and inclusive CE research practices that respond to the specific needs and contributions of the Global South.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying random forest to forecast municipal solid waste generation from household fuel consumption(Elsevier BV, 2025-09-01)Accurately forecasting municipal solid waste (MSW) generation is essential for designing efficient waste management systems and promoting sustainable urban development. As cities expand and consumption patterns shift, reliable data-driven approaches are increasingly necessary to address the complexities of MSW generation. This study applied the random forest (RF) algorithm, a machine learning technique, to predict MSW generation at the household level. RF was selected for its capacity to handle non-linear relationships, imbalanced datasets, and outliers. The analysis focused on data from year 2019, avoiding distortions associated with the COVID-19 pandemic. The model integrated per capita MSW data with household fuel consumption indicators (i.e., natural gas, electricity, and liquefied petroleum gas) and demographic variables such as age, education level, and monthly expenditure. The case study focused on the city of Lima, Peru, using 80% of the data for training and 20% for testing, with hyperparameters optimized via 5-fold cross-validation. The final model explained 55% of the variance in MSW generation (R² = 0.55). This result reflects the model's ability to capture significant drivers of variability, although it leaves room for refinement due to factors not included in the analysis, such as cultural practices or seasonality. Among the predictors, household monthly expenditure on cooking fuels emerged as the most influential variable, reinforcing the connection between resource consumption and waste generation. These findings highlight the potential of integrating socioeconomic indicators into predictive models to enhance their reliability. By improving forecasting capabilities, this study supports targeted policies for urban waste management and sustainable resource use.2