Eco-toxicological impact of “metals” on the aquatic and terrestrial ecosystem: A comparison between eight different methodologies for Life Cycle Impact Assessment (LCIA)

The assessment of eco-toxicological impacts related to the emission of contaminants into the environment has peculiar characteristics in the context of Life Cycle Assessment (LCA), and many different Life Cycle Impact Assessment (LCIA) methodologies can be used. However, previous studies recognized the presence of high uncertainties in the process, in particular when the analysis focuses on metal emissions. Metals are diverse inorganic substances relevant for their characteristic environmental chemistry, their high persistency into the environment and their toxicity at even low environmental concentrations. This study aims at expanding the discussion about how, and how differently, various methods for LCIA are estimating the impacts related to metals emissions, with a focus on eco-toxicological impacts. A comparison between eight different LCIA methods is presented in relative terms, using a contribution analysis at two levels: general and specific. Results show that there is a poor agreement between methods in the determination of the total eco-toxic impact attributable to metals, and in defining which metal provokes the highest eco-toxic impact per unitary emission. Furthermore, the characterization phase is critical in determining the disagreement between methods. However, some similarities were found: there is the tendency in most methods to associate the largest share of the total eco-toxicological impacts to metals. Theoretical and practical differences between methods are discussed and suggestions for the choice of the proper, case-specific, LCIA method are provided.     

Evaluation of Saccharomyces cerevisiae strains as probiotic agent with aflatoxin B1 adsorption ability for use in poultry feedstuffs

In this study the aflatoxin B₁ (AFB₁) removal capacity, the tolerance to salivary and gastrointestinal conditions, autoaggregation and coaggregation with pathogenic bacteria of Saccharomyces cerevisiae strains isolated from broiler feces, were evaluated. Only four of twelve isolated strains were identified as Saccharomyces cerevisiae using molecular techniques. The results obtained in AFB₁ binding studies indicated that the amount of AFB₁ removed was both strain and mycotoxin-concentration dependent. Therefore, a theoretical model was applied in order to select the most efficient strain to remove AFB₁ in a wide range of mycotoxin concentration. The results indicated that S. cerevisiae 08 and S. cerevisiae 01 strains were the most efficient microorganisms in the mycotoxin removal. Viability on simulated salivary and gastrointestinal conditions was investigated and S. cerevisiae 08 strain showed the best results, achieving 98% of total survival whereas S. cerevisiae 01 reached only 75%. Autoaggregation and coaggregation assays showed S. cerevisiae 08 as the most appropriate strain, mainly because it was the unique strain able to coaggregate with the four bacterial pathogens assayed. Consequently, S. cerevisiae 08 is the best candidate for future in vivo studies useful to prevent aflatoxicosis. Further quantitative in vitro and in vivo studies are required to evaluate the real impact of yeast-binding activity on the bioavailability of AFB₁ in poultry. However, this study could be useful in selecting efficient strains in terms of AFB₁ binding and provide an important contribution to research into microorganisms with potential probiotic effects on the host.     

Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles

Within the European research project ARTEMIS, significant works have been conducted to analyse the hot emissions of pollutant from the passenger cars regarding the driving cycles and to propose modelling approaches taking into account large but heterogeneous datasets recorded in Europe. The review and analysis of a large range of test cycles enabled first the building-up of a set of contrasted cycles specifically designed for characterizing the influence of the driving conditions. These cycles were used for the measurement of the pollutants emission rates from nine passenger cars on a chassis dynamometer.Emissions measured on 30 vehicles tested on cycles adapted to their motorization (i.e., cycles for high- or low-powered cars, inducing thus a significant difference in the dynamic) were also considered for analysing the influence of the cycles and of the kinematic parameters on the hot emission rates of the regulated pollutants (CO, HC, NO_x, CO₂, PM). An analyses of variance demonstrated the preponderance of the driving type (urban, rural road, motorway), of the vehicle category (fuel, emission standard) and emitting status (high/normal emitter) and thus the pertinence of analysing and modelling separately the corresponding emissions. It also demonstrated that Urban driving led systematically to high diesel emission rates and to high CO₂, HC and NO_x emissions from petrol cars. Congested driving implied high CO₂ (diesel and petrol) and high diesel NO_x emission. On motorway, the very high speeds generated high CO₂, while unsteady speeds induced diesel NO_x and petrol CO over-emissions. A search for pertinent kinematic parameters showed that urban diesel emissions were mostly sensitive to stops and speed parameters, while petrol emissions were rather sensitive to acceleration parameters. On the motorway, diesel NO_x and CO₂ emissions rates increased with the speed variability and occurrence of high speeds, while CO₂ and CO over-emission from petrol cars were linked to the occurrence of strong acceleration at high speeds.A modelling approach based on partial least square regression was tested, which demonstrates its ability to discriminate satisfactorily the emissions according to dynamic related parameters and in particular when considering the two-dimensionnal distribution of instantaneous speed and acceleration.Finally, a strategy was proposed to analyse the large but heterogeneous set of hot emission data collected within the ARTEMIS project. The approach consisted in analysing the similarity of the numerous cycles as regards their kinematic, grouping them into reference test patterns through an automatic clustering, and then computing reference emissions for these patterns. These principles enabled the development of a method to compute the emissions at a low spatial scale, i.e. the so-called traffic situation approach, which was implemented in the European Artemis model for estimating the cars’ pollutant emissions.     

Environmental modelling of use of treated organic waste on agricultural land: a comparison of existing models for life cycle assessment of waste systems.

Modelling of environmental impacts from the application of treated organic municipal solid waste (MSW) in agriculture differs widely between different models for environmental assessment of waste systems. In this comparative study five models were examined concerning quantification and impact assessment of environmental effects from land application of treated organic MSW: DST (Decision Support Tool, USA), IWM (Integrated Waste Management, U.K.), THE IFEU PROJECT (Germany), ORWARE (ORganic WAste REsearch, Sweden) and EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies, Denmark). DST and IWM are life cycle inventory (LCI) models, thus not performing actual impact assessment. The DST model includes only one water emission (biological oxygen demand) from compost leaching in the results and IWM considers only air emissions from avoided production of commercial fertilizers. THE IFEU PROJECT, ORWARE and EASEWASTE are life cycle assessment (LCA) models containing more detailed land application modules. A case study estimating the environmental impacts from land application of 1 ton of composted source sorted organic household waste was performed to compare the results from the different models and investigate the origin of any difference in type or magnitude of the results. The contributions from the LCI models were limited and did not depend on waste composition or local agricultural conditions. The three LCA models use the same overall approach for quantifying the impacts of the system. However, due to slightly different assumptions, quantification methods and environmental impact assessment, the obtained results varied clearly between the models. Furthermore, local conditions (e.g. soil type, farm type, climate and legal regulation) and waste composition strongly influenced the results of the environmental assessment.