Author(s): Luis Izquierdo Horna, Ramzy Kahhat Abedrabbo, Ian Vázquez Rowe
(Español) 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 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.
Open linkAuthor(s): (Español) Alejandro Deville del Águila, Ian Vázquez Rowe, (Español) Angel Avadí, Ramzy Kahhat Abedrabbo
The anchoveta (Engraulis ringens) fishery in Peru, which is almost entirely devoted to the production of fishmeal and fish oil, is one of the largest fisheries in the world. It is volatile in terms of fishing stock availability, and the fishmeal industry has been subject to technological changes to upgrade its efficiency and reduce costs to maintain its competitiveness. The objective of this study is to apply the Life Cycle Assessment methodology to the production and exportation of fishmeal and fish oil products related to a relevant producer in Peru, representing 10 % of national production. A set of 169 vessels targeting E. ringens were inventoried, 88 % of which are owned by third parties, and four factories belonging to the company were assessed for the years 2019 and 2021. Ecoinvent was the selected database to support the life cycle inventory, and ReCiPe 2016 and IPCC 2021 were the methods applied to compute the environmental impacts. The results show that fuel combustion in fishmeal and fish oil production was the dominating activity in most of the impact categories analyzed. In terms of greenhouse gas emissions, it was found that, on average, approximately 320 kg CO2eq and 4430 kg CO2eq are emitted due to the production of 1 t of fishmeal and 1 t of fish oil, respectively, when an energy allocation is followed. The fishery accounted for ca. 45 % of greenhouse gas emissions and dominated most of the impact categories, showing greater influence of the fishing stage than in previous studies. The reasons behind are linked to the combined influence of improvements in the energy matrix of the plants, by prioritizing natural gas over diesel and residual fuel oils, and a slightly higher fuel use intensity of the fishing fleet. E. ringens quality was found to be an important parameter, as low protein or fat yields translate into substantially higher impacts. Finally, although Peruvian fishmeal and fish oil remain as one of the lowest environmental footprint products among animal feed, future work is needed to understand the effects that climate change and El Niño-Southern Oscillation events have on this industry.
Open linkAuthor(s): (Español) Alejandro Deville del Águila, Ian Vázquez Rowe, Ramzy Kahhat Abedrabbo
Microplastic (MP) pollution has been largely documented in aquaculture systems, farmed animals, fishmeal, and feed, as well as in humans due to ingestion from food, including seafood, although a skew remains with fisheries and supply chains more commonly assessed for MP pollution in the Global North. In this sense, the main objective of this short communication is to explore how Peru, the biggest fishmeal, and fish oil (FMFO) producer worldwide, performs in terms of plastic pollution in fisheries and derived seafood products. For this, the available scientific literature has been analyzed. Our analysis suggests that studies in Peru are scarce, and more research must be undergone to evaluate the full extent of plastic pollution in its seafood supply chains. The literature analyzed suggests that pelagic species are more vulnerable to MP exposure and ingestion, and that a gradient in terms of closeness to the coast and depth of the fishery may be determining the level of occurrence and abundance of MPs in Peruvian fisheries. Furthermore, the combination of lack of measures for controlling plastic leakage to the ocean in Peru, with the closeness to the coast of most fishing grounds makes the Peruvian fishing industry highly vulnerable to plastic pollution. In this sense, as the Peruvian FMFO industry overwhelmingly targets anchoveta (Engraulis ringens), a pelagic fish, MP pollution of FMFO products must be monitored, as occurrence could lead to an introduction of MPs in aquaculture products worldwide and subsequent human consumption.
Open linkAuthor(s): (Español) André Torre, Ian Vázquez Rowe, Ramzy Kahhat Abedrabbo
Wastewater treatment plants (WWTPs) are not specifically designed to tackle microplastics (MPs), leaving them in aquatic ecosystems. The novelty of our study is a critical review of the effectiveness of conventional activated sludge (CAS), membrane bioreactors (MBRs), and activated granular sludge (AGS) in managing MPs within WWTPs. We bridge a gap in scientific literature by assessing MP removal and resilience to MPs. Our scope extends beyond MPs management, evaluating these technologies against environmental, economic, and social criteria. Findings show that MBR outperforms CAS and AGS in MP removal but faces challenges with smaller MPs due to fouling and secondary pollution. AGS shows similar removal rates to CAS but often superior resilience to MPs, given its higher decontamination capabilities. Environmentally, AGS may better reduce indirect greenhouse gas (GHG) emissions due to lower energy and chemical demands. Moreover, AGS exhibits higher resource recovery potential (e.g., biopolymers, phosphates). Socially, MBR excels in pathogen removal, reducing waterborne disease risks. Economically, AGS is the most cost-effective technology regarding both operational and capital expenditures. However, MPs can impact these criteria by reducing nutrient removal efficiency and increasing both direct and indirect GHGs. MPs create “plastisphere” habitats, reducing pathogen removal and compromising water safety. Moreover, MPs increase energy and chemical use, especially in MBR systems due to fouling concerns.
Open linkAuthor(s): Joan Sanchez Matos, Ian Vázquez Rowe, Ramzy Kahhat Abedrabbo
Water scarcity is a critical environmental challenge which will be exacerbated by the effects of climate change and increased human demand. Hence, more precise and realistic methods of quantifying this impact are necessary. In this sense, the present study proposes updated water scarcity characterization factors (CFs) for watersheds in Peru using the AWARE method. The novelty is linked to the consideration of present and future conditions, as well as quasi-cyclical climatic events such as El Niño.
Open linkAuthor(s): Ian Vázquez Rowe, (Español) Claudia Cucchi Quispe, Eduardo Parodi Gonzales Prada, Ramzy Kahhat Abedrabbo y []
(Español) Volcanic events with an important affectation of urban areas and other land areas with important human activity have been rare in Europe in the past century. This has led to a lack of comprehensive analysis of the social, economic and environmental damages that these types of events can cause on specific human communities. In the present study, we apply an industrial ecology approach to calculate the damage linked to the Cumbre Vieja volcanic eruption in the Canary Islands in September 2021. Therefore, the main objective was to apply the multi dimensional damage assessment (MDDA) methodology to quantify the degree of damage that has been exerted by the eruption in the island of La Palma (Spain) through the inclusion of environmental damage endpoints with other sustainable development variables (i.e., social and economic dimensions). Data were obtained from different sources, including the cadastre of La Palma, local data on derived health, as well as data obtained from the global ecosystem dynamics investigation of NASA, among other sources. Thereafter, damage endpoints were all converted to disability-adjusted life years (DALYs). Results show that direct gaseous emissions from the volcano were responsible for a significant amount of total DALYs, above 90% in all scenarios, followed by damage linked to economic losses, as well as social losses related to morbidity. Other environmental damages played a minor part in the total damage exerted by the volcano. The results demonstrate the importance of air quality indicators in the aftermath of an eruption in densely populated areas; in contrast, the impact associated with infrastructure loss played a minor role in total damage. Although challenges remain when providing a holistic quantification of total damage linked to volcanic disasters, the MDDA method constitutes a promising systematic standardized and transparent damage quantification tool that allows computing a deterministic damage evaluation that can aid in natural hazard risk assessment. In fact, it is considered that the method has the potential to be used as a holistic decision tool to aid in mitigating disaster risk.
Open linkAuthor(s): Joan Sanchez Matos, Ian Vázquez Rowe, Ramzy Kahhat Abedrabbo
(Español) Peru is one of the most diverse countries in the world in terms of food production, but also suffers a wide range of food security challenges, including malnutrition, the impact of natural hazards, and rising food prices. People living in poverty conditions are the main victims of these problems, which trigger undernutrition, obesity, and diet-related non-transmittable diseases. Despite these challenges, Peru lacks historical food intake data. Therefore, in the current study, we assess the diet quality evolution in the period 2008–2021 based on apparent household purchases extracted from the National Household Survey. The results reveal significant variations in the consumption of certain food items and groups, and the consequences of these changes are discussed in environmental and human health terms. The consumption of lower environmental impact animal protein, such as chicken, eggs, and marine fish, has increased by 37%, 69%, and 29%, respectively; whereas the consumption of high environmental impact foods, such as beef and other red meat, has decreased. Moreover, consumption of less processed carbohydrate sources (e.g., legumes, fruits, and vegetables) has risen, while refined sugar and sugar sweetened beverages have decreased significantly (almost 45%). Regional differences were also visible; hence, cities on the Northern coast and the Amazon basin had similar consumption habits, whereas Central/Southern coastal and Andean cities had closer consumption patterns. On average, this improvement was reflected in the increase in calories (9.9%) and macronutrient intake (up to 15%), but at the socioeconomic level, food inequality persists, with consumption of many food groups below minimum thresholds in lower socioeconomic strata.
Download publication (3.10 MB)Author(s): Luis Izquierdo Horna, Ramzy Kahhat Abedrabbo, Ian Vázquez Rowe
(Español) In most cities worldwide, household food waste constitutes a significant portion of municipal solid waste (MSW). However, its management often proves inadequate due to the insufficient resources allocated to waste management systems, the omission of the resource potential in MSW, and the lack of recognition of household food waste drivers for forecasting generation in specific geographical contexts. This research aims to identify social, economic, and environmental variables serving as proxies to forecast household food waste generation. To achieve this, a multiple linear regression model was proposed to assess the relationship between cooking fuel type (i.e., liquefied petroleum gas, natural gas, and electricity), land use categories (i.e., commercial, industrial, and residential), population density, expenditure on in-house food consumption, and household food waste generation. Three alternate modeling scenarios were considered based on available data, with Lima, Peru, serving as a case study. The results indicate that the combined consumption of liquefied petroleum gas and natural gas, and electricity consumption, along with residential land use, were the most influential variables. Finally, for a comprehensive understanding of the studied phenomenon, it is crucial to analyze and consider the intricate dynamics of societal consumption patterns.
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