An improved life cycle impact assessment (LCIA) approach for assessing aquatic eco-toxic impact of brine disposal from seawater desalination plants

Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. Integr Environ Assess Manag 2016;12:580-590. Published 2015. This article is a US Government work and is in the public domain in the USA.

Residues in field crops grown and harvested for human consumption are the main contributor to overall human exposure toward agricultural pesticides for the general population. However, exposure from crop residues is currently not considered in life cycle assessment practice. We therefore present a consistent framework for characterizing human toxicological impacts associated with pesticides applied to agricultural crops in the frame of life cycle impact assessment based on state-of-the-art data and methods. We combine a dynamic multicrop plant uptake model designed for evaluating human exposure to residues for a wide range of pesticide-crop combinations with latest findings of pesticide dissipation kinetics in crops and post-harvest food processing. Outcome is a set of intake fractions and characterization factors for 875 organic pesticides and six major food crops along with specific confidence intervals for each factor. Intake fractions aggregating exposure via crop residues and exposure via fractions lost to air and soil for pesticides applied to agricultural crops vary between 10−8 and 10−1 kg intake per kilogram applied as a function of pesticide and crop. Intake fractions are typically highest for lettuce and tomato and lowest for potato due to differences in application times before crop harvest and soil as additional barrier for uptake into potato tubers. Uncertainty in intake fractions is mainly associated with dissipation dynamics in crops, where results demonstrate that using pesticide- and crop-specific data is crucial. Combined with the uncertainty in effect modeling, characterization factors per pesticide and crop show squared geometric mean standard deviations ranging from 38 to 15,560 over a variability range across pesticide-crop combinations of 10 orders of magnitude. Our framework is operational for use in current life cycle impact assessment models, is made available for USEtox, and closes an important gap in the assessment of human exposure to pesticides. For ready use in life cycle assessment studies, we present pesticide-crop combination-specific characterization factors normalized to pesticide mass applied and provide default data for application times and loss due to post-harvest food processing. When using our data, we emphasize the need to consult current pesticide regulation, since each pesticide is registered for use on certain crops only, which varies between countries.

Aggregated indicator to assess land use impacts in life cycle assessment (LCA) based on the economic value of ecosystem services

Most life cycle impact assessment (LCIA) approaches in life cycle assessment (LCA) are developed for western countries. Their LCIA approaches and characterization methodologies for different impact categories may not be necessarily relevant to African environmental conditions and particularly not for the timber sector in Ghana. This study reviews the relevance of existing impact categories and LCIA approaches, and uses the most relevant for the timber sector of Ghana. The study reviewed 23 life cycle inventories (LCIs) and LCAs on forestry, timber, and wood products for relevant impact categories and LCIA approaches for their relevance to the specific conditions in Ghana. This study uses an earlier LCI study of the timber industry as a starting point for an additional LCIA. We next performed a correlation and regression analysis to learn whether wood wastes may function as a reasonable single indicator for land use as proxy for biodiversity loss and the other impact categories. The literature review shows that no LCI or LCA studies were developed for Africa or the tropics. The LCIA approaches in the reviewed LCAs are indeed shown to take their basis in the environmental problems in western countries and characterization methodologies relating to how these problems manifest themselves in the western world. Characterization methodologies for different impact categories in CML-2000 and other LCIA approaches may not be necessarily relevant to African tropical environmental conditions and particularly not for the timber sector in Ghana. This situation hampers the reliability of our LCIA and points to a serious research gap in LCIA development in general. We applied the scientifically well-recognized CML 2000 to the earlier LCI results and characterized the preliminary selected impact categories of global warming, acidification, eutrophication, photochemical oxidant formation, and human toxicity. The correlation analysis indicated that wood waste is indeed strongly correlated with land use as proxy for biodiversity loss and also positively correlated with the other five potential impact results. It can be concluded that wood waste production is a major driving force for biodiversity loss and a sufficiently good single indicator for all other environmental performance indicators in the timber sector of Ghana. This study and the previous LCI paper are pioneering a field not yet explored, since the correct environmental performance indicators are not yet developed or adapted to tropical conditions. The development of LCIA approaches in the tropics may be the start of a never-ending journey in LCA research in Africa, particularly Ghana.

The biosphere is progressively subjected to a variety of pressures resulting from anthropogenic activities. Habitat conversion, resulting from anthropogenic land use, is considered the dominant factor driving terrestrial biodiversity loss. Hence, adequate modelling of land use impacts on biodiversity in decision-support tools, like life cycle assessment (LCA), is a priority. State-of-the-art life cycle impact assessment (LCIA) characterisation models for land use impacts on biodiversity translate natural habitat transformation and occupation into biodiversity impacts. However, the currently available models predominantly focus on total habitat loss and ignore the spatial configuration of the landscape. That is, habitat fragmentation effects are ignored in current LCIAs with the exception of one recently developed method. Here, we review how habitat fragmentation may affect biodiversity. In addition, we investigate how land use impacts on biodiversity are currently modelled in LCIA and how missing fragmentation impacts can influence the LCIA model results. Finally, we discuss fragmentation literature to evaluate possible methods to include habitat fragmentation into advanced characterisation models. We found support in available ecological literature for the notion that habitat fragmentation is a relevant factor when assessing biodiversity loss. Moreover, there are models that capture fragmentation effects on biodiversity that have the potential to be incorporated into current LCIA characterisation models. To enhance the credibility of LCA biodiversity assessments, we suggest that available fragmentation models are adapted, expanded and subsequently incorporated into advanced LCIA characterisation models and promote further efforts to capture the remaining fragmentation effects in LCIA characterisation models.

Hotspots analysis and critical interpretation of food life cycle assessment studies for selecting eco-innovation options and for policy support

Global Resource Indicator for life cycle impact assessment: Applied in wind turbine case study

In current life cycle impact assessment, the focus of ecotoxicity is on cold-blooded species. We developed a method to calculate characterization factors (CFs) for the impact assessment of chemical emissions on warm-blooded predators in freshwater food chains. The method was applied to 329 organic chemicals. The CF for these predators was defined as a multiplication of the fate factor (FF), exposure factor (XF), bioaccumulation factor (BF), and effect factor (EF). Fate factors and XFs were calculated with the model USES-LCA 2.0. Bioaccumulation factors were calculated with the model OMEGA, for chemical uptake via freshwater, food, and air. Effect factors were calculated based on experimental, median lethal doses (LD50). The concentration buildup (CB) of the chemicals (i.e., FF, XF, and BF over the 3 routes of exposure) showed a range of 7 to 9 orders of magnitude, depending on the emission compartment. Effect factors displayed a range of 7 orders of magnitude. Characterization factors ranged 9 orders of magnitude. After emissions to freshwater, the relative contribution of the uptake routes to CB were 1% (90% confidence interval [CI]: 0%-2%) for uptake from air, 43% (11%-50%) for uptake from water, and 56% (50%-87%) for uptake from food. After an emission to agricultural soil, the contribution was 11% (0%-80%) for uptake from air, 39% (5%-50%) for uptake from water, and 50% (11%-83%) for uptake from food. Uptake from air was mainly relevant for emissions to air (on average 42%, 90% CI: 5%-98%). Characterization factors for cold-blooded species were typically 4 orders of magnitude higher than CFs for warm-blooded predators. The correlation between both types of CFs was low, which means that a high relative impact on cold-blooded species does not necessarily indicate a high relative impact on warm-blooded predators. Depending on the weighing method to be considered, the inclusion of impacts on warm-blooded predators can change the relative ranking of toxic chemicals in a life cycle assessment.

Feeding a growing, increasingly affluent population while limiting environmental pressures of food production is a central challenge for society. Understanding the location and magnitude of food production is key to addressing this challenge because pressures vary substantially across food production types. Applying data and models from life cycle assessment with the methodologies for mapping cumulative environmental impacts of human activities (hereafter cumulative impact mapping) provides a powerful approach to spatially map the cumulative environmental pressure of food production in a way that is consistent and comprehensive across food types. However, these methodologies have yet to be combined. By synthesizing life cycle assessment and cumulative impact mapping methodologies, we provide guidance for comprehensively and cumulatively mapping the environmental pressures (e.g., greenhouse gas emissions, spatial occupancy, and freshwater use) associated with food production systems. This spatial approach enables quantification of current and potential future environmental pressures, which is needed for decision makers to create more sustainable food policies and practices.

Bridging the gap between life cycle inventory and impact assessment for toxicological assessments of pesticides used in crop production

Chapter 11 - Life cycle assessment of biomethane technology

Purpose Political interest in the future availability of natural resources has spiked recently, with new documents from the European Union, United Nations Environment Programme and the US National Research Council assessing the supply situation of key raw materials. As resource efficiency is considered a key element for sustainable development, suitable methods to address sustainability of resource use are increasingly needed. Life cycle thinking and assessment may play a principal role here. Nonetheless, the extent to which current life cycle impact assessment methods are capable to answer to resource sustainability challenges is widely debated. The aim of this paper is to present key elements of the ongoing discussion, contributing to the future development of more robust and comprehensive methods for evaluating resources in the life cycle assessment (LCA) context.

The Life Cycle Assessment (LCA) is an impact research methodology that focuses on the life cycle of a product (by extension, services), and is standardized by the ISO 14000 Series. This methodology has been applied in so many areas related to sustainable development, in order to evaluate the environmental, economic and social aspects of the processes of production and distribution of products and service goods. Despite this wide range of applications, the technique still presents weaknesses, especially in the question of the evaluation and expression of the uncertainties present in the various phases of the studies and inherent to the stochastic or subjective variations of the data sources and the generation of models, sometimes reducing the consistency and accuracy of the proposed results. In the present study, we will evaluate a methodology to deal with the best expression of such uncertainties in LCA studies, focusing on the Life Cycle Inventory (LCI) phase. The hypothesis explored is that the application of the Monte Carlo Simulation and Fuzzy Set Theory to the estimation and analysis of stochastic uncertainties in LCA allows a better expression of the level of uncertainty in terms of the Guide to Expression of Uncertainty in Measurements [11], in situations where the original life cycle inventory does not specify the initial uncertainties. The iron ore transport was selected as a process unit by means of an off-road- truck (OHT) with a load capacity of 220 tons and a power of 1700 kW, acting on the route between the mine and the primary crushing of a mining company, in the city of Congonhas (MG). Monte Carlo simulations and Fuzzy Set Theory applications were performed using spreadsheets (MS Excel). The LCA study was conducted in OpenLCA 1.6 (open source) software from data inventories of ELCD database 3.2, also freely accessible. The results obtained were statistically compared using Hypothesis Test and Variance Analysis to identify the effect of the techniques on the results of the Life Cycle Impact Assessment (LCIA) and a Sensitivity Analysis was performed to test the effect of the treatment and function of the distribution of probabilities in the expression of the parameters associated with the items of the original life cycle inventory. Research indicates that inventories with treated data may have their uncertainty expressed to a lesser degree than that expressed in the original inventory, with no change in the final values of the Life Cycle Impact Assessment (LCIA). The treatment of life cycle inventory data through Monte Carlo Simulation and Fuzzy Set Theory resulted in the possibility of expressing the LCI results with a degree of uncertainty lower than that used to express the uncertainty under the standards. Data treatment through Monte Carlo simulation with normal probability distribution showed the lowest values of uncertainty expression with significant difference in relation to the original inventory, at a significance level of 1%.

PurposeAn adequate matching between the nomenclature of elementary flows in life cycle inventory (LCI) databases and life cycle impact assessment (LCIA) methods is key for ensuring the proper application of life cycle assessment (LCA). However, the nomenclature of elementary flows lacks harmonization among the LCA community. This paper aims at defining mapping rules and discussing main challenges related to the process of systematically mapping LCI nomenclatures to LCIA methods and models addressing biodiversity impacts.MethodsEight LCIA methods and models addressing biodiversity loss are analyzed: five comprehensive LCIA methods (i.e., LC-IMPACT, Impact World + , Ecological Scarcity 2013, ReCiPe 2016, and Stepwise), one land use intensity-specific LCIA model; and two approaches adapting the GLOBIO model to LCIA. These models and methods are mapped to two LCI nomenclatures (ecoinvent v3.6 as implemented in Simapro and Environmental Footprint (EF) 3.0). A mapping tool was developed to support the process of (a) mapping elementary flows by name, Chemical Service number or available synonyms; (b) implementing specific mapping rules regarding compartment/sub-compartment, and substance name; (c) mapping elementary flows to manually defined proxies (e.g., synonyms, spelling corrections and similar substances); and (d) assigning characterization factors (CFs). The process entails analyzing a case study to identify uncharacterized elementary flows.Results and discussionWe present a mapping of LCIA methods and models addressing impacts on biodiversity loss with specific LCI nomenclatures. Mapping rules are proposed for elementary flows regarding chemicals, carbon emissions, land use, water use, and particulate matter. Specific aspects to be considered in mapping elementary flows in LCIA and LCI nomenclatures are discussed. Main gaps in LCI nomenclatures are associated to toxicity and climate change impacts. The EF 3.0 was more aligned than ecoinvent 3.6 with the LCIA methods and models regarding elementary flows coverage and regionalization level. Analyzing uncharacterized flows revealed further coverage needs for “Chemical, organic” (between 19 and 20% uncharacterized flows), “Chemical, inorganic” (between 9 and 18% uncharacterized flows) and “Chemical, radioactive” (between 9 and 14% uncharacterized flows).ConclusionsThis paper contributes to the operationalization of LCIA methods and models addressing biodiversity impacts by proposing a systematic mapping process and rules for a better LCIA-LCI connection. Different development pathways of LCI (e.g., focused on substance name detail) and LCIA (e.g., towards improved regionalization level) have stretched the gap between both nomenclatures. Recommendations are provided identifying further efforts towards the harmonization of the nomenclature of elementary flows in the LCA community.

Application of life cycle assessment in municipal solid waste management: A worldwide critical review