Actes publiés en ligne | National audience

Actes publiés en ligne | National audience

Feeding the world’s population sustainably is a major challenge of our society, and was stated as one of the key priorities for development cooperation by the European Union (EU) policy framework on food security. However, with the current trend of natural resource exploitation, food systems consume around 30% of final energy use, generating up to 30% of greenhouse gas (GHG) emissions. Given the expected increase of global population (nine billion people by 2050) and the amount of food losses and waste generated (one-third of global food production), improving the efficiency of food systems along the supply chain is essential to ensure food security. This study combines life-cycle assessment (LCA) and data envelopment analysis (DEA) to assess the efficiency of Spanish agri-food system and to propose improvement actions in order to reduce energy usage and GHG emissions. An average energy saving of approximately 70% is estimated for the Spanish agri-food system in order to be efficient. This study highlights the importance of the DEA method as a tool for energy optimization, identifying efficient and inefficient food systems. This approach could be adopted by administrations, policy-makers, and producers as a helpful instrument to support decision-making and improve the sustainability of agri-food systems.

Actes publiés en ligne | National audience

Air pollution is an important matter for local authorities in Bogotá (Colombia), with PM10 and PM2.5 being the most serious air pollutants in the city. In the present study, data mining algorithms were used to establish the most influential meteorological variables on air pollution in Bogotá, and to develop models to forecast PM10 and PM2.5 to help local authorities prevent human exposure to high levels of pollution. To achieve the main objectives, data were collected between 2010 and 2015 from 13 local stations in a monitoring network. A data quality analysis was done to determine the most and least polluted stations. Kennedy and Parque Simon Bolivar stations were selected as the most and least polluted stations, respectively, to use to develop the forecasting models. Principal Component Analysis (PCA) was used to determine the variables that most influenced the behaviour of the data. Then, models were developed to forecast average PM10 and PM2.5 concentrations for the next day using Artificial Neural Networks (ANN) and k-means clustering. The input variables for the ANN models were selected based on the PCA results. k-means clustering was applied to group the data, and the results were used as inputs to the forecasting models. It was possible to forecast average PM10 and PM2.5 concentrations for the next 24 h by developing forecasting models that used Multi-Layer Perceptron with the consideration of k-means clustering results. It was demonstrated that considering clustering results as input variables improves PM10 and PM2.5 forecasting models for the most polluted station. Finally, hourly forecast models for PM10 and PM2.5 were developed and evaluated at Kennedy station. The developed models can be used as references for the issuance of early warnings for high air pollution because of their ability to accurately predict high pollution incidents.

Present study investigated the physical and chemical characteristics of PM₁₀ at one of the critically polluted industrial clusters of India (Manali, Tamil Nadu). Chemical and morphological analysis were carried out for 103 PM₁₀ samples collected using high volume sampler during winter, summer, monsoon and post monsoon seasons (November 2014 to May 2016). The daily average PM₁₀ concentration at the study area exceeded the national ambient air quality standards (NAAQS) by 100, 68, 55, 78% of time during winter, summer, monsoon and post monsoon seasons, respectively. Results indicated dominance of SO₄²⁻ (5481 ng/m³) ions followed by NO₃²⁻ (4445 ng/m³), Na⁺ (4115 ng/m³) and NH₄⁺ (3993 ng/m³) with a strong correlation among SO₄²⁻, NO₃²⁻ and NH₄⁺ suggesting their common origin from industrial and vehicular exhausts. High SO₄²⁻ to NO₃²⁻ ratio indicated dominance of stationary sources at the study area. Metals such as Na, Fe, Ca, K and Al constituent 96% of total elements at the study site. Among toxic metals high concentration of Zn (681 ng/m³) followed by Ba (295 ng/m³), Mn (24.67 ng/m³), V (17.05 ng/m³) and Ni (14.37 ng/m³) were observed. Further, carcinogenic elements such as Cr, Cd, As, Ni and Pb were also observed at the study site with highest cancer risk for Cr. Factor analysis revealed the contributions from industrial processes, heavy oil combustion and petroleum refineries, crustal, marine aerosol and road dust re-suspension and metal industries. Further, morphological analysis showed various metal (C, Si, Fe, Mn, Ti, V) rich particles with irregular, spherical and elongated shapes.

We measured particulate matter concentrations (PMCs), including PM10, PM2.5, and PM1.0 fractions, on and under pedestrian bridges over two different types of roads: an urban expressway and an arterial road. The objectives of the study were to 1) assess the variability of PMCs measured at pedestrian bridges, 2) identify best-fit probability distribution curves of different measures of PMCs, and 3) quantify pedestrian exposure (by respiratory deposition dose (RDD) rate). Results showed that the average on-bridge PM concentrations were significantly higher than the under-bridge values over the urban expressway, although no similar trend observed over the arterial road. More specifically, average PM1.0 concentration on the bridge was 1.29 times of that under the bridge over the urban expressway. Gaussian distribution “best” fit the 30s average PMCs for PM10 on and under the pedestrian bridge over the urban expressway, and LogNormal distribution “best” fit the PMCs for PM10 on and under the bridge for the arterial road. The average RDD rates for PM10 (143.85 μg h⁻¹), PM2.5 (9.41 μg h⁻¹), and PM1.0 (3.78 μg h⁻¹) on the bridge over the urban expressway (OBUE) were higher by 47%, 41%, and 11%, respectively, than those on the bridge over the arterial road (OBAR). While only minor differences were noticed in the average RDDs of PM between on and under the pedestrian bridge over the arterial road, a significant difference in the average RDD rate was found between on and under pedestrian bridges over the urban expressway.

Waste collection presents a significant influence in the environmental sustainability of municipal solid waste (MSW) management. Conventional door-to-door collection consumes high amounts of fuel for waste transportation, thus generating significant direct greenhouse gas emissions (GHG). Pneumatic collection emerges as an alternative to conventional trucking system, comprised by an underground network of long distance pipelines that carries MSW fractions to a central collection plant where the waste is collected and compacted. Such systems represent a way of arranging waste collection in densely populated urban areas and have recently been used in the design of smart cities to control waste flows. While this technology apparently reduces direct air emissions, suffers from large energy demand derived from vacuum production for waste suction. This work compares both conventional door-to-door and pneumatic collection systems from a life cycle approach, obtaining that the latter accounts for 5 and 3 times more energy demand and CO2-eq. emissions than conventional collection, respectively. Results suggests that the electricity consumption and the origin of electricity have a significant influence on the results, since vacuum production is responsible for more than 99 % of the total impacts for pneumatic scenario, while diesel for trucking accounts to around 70 % of the conventional system impacts. Greener electricity mixes and less energy consuming materials are required in order to ensure the environmental sustainability of pneumatic systems. | The authors are grateful for the funding of the Spanish Ministry of Economy and Competitiveness through the Ceres-Procom: Food production and consumption strategies for climate change mitigation (CTM2016-76176- C2-1-R) (AEI/FEDER, UE). The authors wish to extend their acknowledgment to all people involved in the LIFE FENIX Project, as well as the European LIFE Financing Programme. In addition, they want to acknowledge the support of The Circular Lab founded by Ecoembes.

Unsustainable consumption and production patterns, together with industrialization and population growth, have increased the generation of municipal solid waste (MSW), causing several environmental problems. The European Waste Framework Directive (WFD) sets waste prevention, preparation for reuse and recycling as priority strategies. Nevertheless, still a great amount of MSW ends up in landfills and waste-to-energy (WtE) plants. WtE plants reduces waste volume and allows efficient recovery of energy, however, incineration results in various types of solid wastes, bottom, boiler and fly ashes (FA). Due to the concentration of dangerous substances, FA are treated by means of stabilisation/solidification (S/S), thermal treatments or combined treatments, to reduce their toxicity and to avoid negative impacts on the environment and human health. Among S/S alternatives, stabilisation with cement and carbonation are one of the most popular. To determine the environmental performance of these processes this paper conducted a life cycle assessment (LCA). The study evaluated FA stabilisation with cement and water and FA carbonation for 55 % and 100 % excess of CO2 in the flue gas at the outlet of the reactor, and pressures of 1, 5, 10, 15 and 20 bar. The results showed that the range of pressure between 3 and 4 bar, and 55 % excess of CO2 in the flue gas have an efficient performance. The comparison of FA carbonation and stabilization displayed that the latter has higher impacts than the alternative carbonation due mainly to the cement production and the reduction of lixiviation and CO2 capture in the ash.

Feeding the world’s population sustainably is a major challenge of our society and has been stated as one of the key priorities for development cooperation by the EU policy framework on food security. However, the current pattern of natural resources exploitation to meet humanity’s demand for food threatens long-term food security. Food systems represent around 30 % of final energy use, generating up to 30 % of greenhouse gas (GHG) emissions. Given the expected increase of global population (9 billion people by 2050) and the amount of food losses and waste generated (a third of global food production), improving the efficiency of food systems along the supply chain is essential to ensure food security. This work combines life cycle assessment (LCA) and data envelopment analysis (DEA) to assess the efficiency of Spanish agri-food system and propose improvement actions in order to reduce energy usage and GHG emissions. Results suggest that sweets and vegetable fats categories provide the largest nutritional energy to consumer per unit of embedded energy in its production. Around a 70 % average reduction target is estimated for the Spanish agri-food system to be efficient, with a similar reduction in related greenhouse gas emissions. | The authors are grateful for the funding of the Spanish Ministry of Economy and Competitiveness through the Ceres-Procom: Food production and consumption strategies for climate change mitigation (CTM2016-76176- C2-1-R) (AEI/FEDER, UE).