3. Producción
Browse
6 results
Search Results
- Some of the metrics are blocked by yourconsent settings
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, Low energy and carbon hydroponic tomato cultivation in Northern Spain: Nutritional and environmental assessment(American Chemical Society, 2024-01-15)The agri-food sector is one of the most impactful on the environment in terms of greenhouse gas emissions and is one of the largest consumers of energy and natural resources. In this context, the objective of this study was to evaluate the environmental impacts of hydroponic tomatoes cultivated in northern Spain using a waste product as biomass to heat the greenhouses and solar energy for powering the irrigation system to identify their main environmental hotspots and improvement measures. For this purpose, life cycle assessment (LCA) has been used. The functional units (FUs) chosen were 1 kg of tomatoes and an alternate FU based on nutrition, e.g., NRF9.3. The influence of including biogenic CO2 emissions has been considered using a “-1/+1″ approach to assess the uptake and release of biogenic carbon throughout the whole life cycle. The main results showed that greenhouse gas emissions varied between 1.4 kg CO2 equiv and 2.5 kg CO2 equiv considering biogenic carbon, whereas the total energy demand of the production of hydroponic tomatoes was 9.7 MJ. Fertilization and the greenhouse structure were identified as the main contributors to environmental burdens; hence, improvement opportunities were focused on these critical points. - 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, Life cycle assessment of organic chocolate production in Peru(Elsevier BV, 2025-12-01)Limited studies have been conducted in Latin America related to the environmental profile of cocoa and chocolate production using Life Cycle Assessment (LCA). The current study conducts a cradle-to-gate LCA of the production of organic chocolate products in Peru, considering cocoa cultivation practices by a group of 21 female producers located in central Peru in the year 2022. Data were collected on-site at cultivation sites and processing plant using questionnaires with the technical staff. Beyond fossil and biogenic emissions linked to cultivation, transport of dried cocoa, and manufacturing activities at the chocolate producing plant, carbon capture on fields by cocoa and shading trees was modeled and included in the carbon balance. A total of 8 impact categories were selected, considering different environmental compartments. Results for global warming using the main scenario show a range of values from 4.33 kg CO 2 eq per kilogram of final chocolate product to 4.88 kg CO 2 eq. Most impacts are derived from the production of dry cocoa beans and, to a lesser extent, upstream sugarcane production. However, important differences were evident when the individual cocoa producers were analyzed, with agroforestry systems presenting lower greenhouse gas (GHG) emissions than cocoa monocrops. Regarding water scarcity, the activities at the chocolate processing plant were found to contribute more than water use at the cocoa cultivation sites. For other impact categories, toxicity emissions at the cultivation site were relatively low given the organic characteristics of the fields, which do not use conventional pesticides. The post-harvest management of the cocoa pods (i.e., composting) is a critical source of GHG emissions. Hence, adequate composting conditions maintain methane emissions low, but direct return of the pods to the field can generate a substantial increase in GHG emissions. Carbon sequestration from above ground biomass, mainly from shading and cocoa trees, appears to mitigate an important fraction of these emissions if shading is homogeneous and sufficiently dense across the fields. • A Life Cycle Assessment was conducted on the production of organic chocolate in Peru. • A group of 21 producers was sampled for organic cocoa practices in central Peru. • A full characterization of the biogenic carbon cycle in cultivation sites was modeled. • Global warming results show better results for agroforestry systems and cocoa pod husk composting practices. • Manufacturing stage impacts are dominated by water use, cooling agents and upstream sugar production.2 - 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