Quantitative Environmental Assessment Research Articles

Integrating environmental issues into the design of mobility plans: Insights from French practices

Local authorities have a strategic role in mitigating the environmental impacts of the transport sector. However, they struggle to integrate environmental issues into their decision-making processes, especially planning. In the European context of the Sustainable Urban Mobility Plan approach and Strategic Environmental Assessments (SEAs), this paper scrutinises three French localities to determine the current best practices and limitations for designing mobility plans and integrating environmental issues. Several limitations are identified: (1) limited expertise in defining and characterising actions and objectives, which complexifies plans' design, understanding, and monitoring; (2) a lack of a framework to conduct long-term quantitative environmental assessments and to use the results to influence decision effectively; and (3) monitoring processes are barely described in the documents, and the planning horizon where objectives are defined is not in sync with the indicators’ mandatory evaluation period. This French case study thus reveals that European planning practices must be further analysed and improved to deal with the rising environmental concerns, e.g. through an operational framework to design mobility plans with effective integration of environmental issues.

Qualitative and quantitative simulation of best management practices (BMPs) for contaminated megasite remediation using the SiteWise™ tool

Remediation activities, particularly in megasites, may induce substantial secondary environmental impacts that must be addressed for green and sustainable remediation (GSR) practices. Only limited studies are available quantitatively assessing the environmental footprint and environmental benefits of implementing Best Management Practices (BMPs) in megasite remediation. This study used the SiteWise™ tool, a quantitative environmental footprint assessment for scenario simulation and benefit quantification of BMPs, on a contaminated megasite in Hebei Province, China. We observed a considerable environmental footprint and energy from the remediation. Taking the final implementation alternative (Alt 1) as an example, which is characterized by combining multiple remediation techniques, the greenhouse gas (GHG) emissions reached 113,474 t, the energy used was 2,082,841 million metric British thermal units (MMBTU), and other air pollutant emissions (NOx, SOx, and PM10) amounted to 856 t. Further BMP analyses highlighted the benefits of substituting the conventional solidification/stabilization agent with willow woodchip-based biochar, which could reduce GHG emissions by 50,806 t and energy used by 926,648 MMBTU. The overall environmental benefits of implementing all applicable BMPs in the remediation were significant, with a 66.85%, 50.15%, and 56.05% reduction in GHG emissions, energy used, and other air pollutants, respectively. Our study provides insights into quantifying the environmental footprint and exploring emission reduction pathways for contaminated megasite remediation. It also offers a feasible path for quantifying the environmental benefits of BMPs, promoting the development of GSR of contaminated sites.

Automatic Plastic Greenhouse Extraction from Gaofen-2 Satellite Images with Fully Convolution Networks and Image Enhanced Index

Plastic greenhouses (PGs) play a vital role in modern agricultural development by providing a controlled environment for the cultivation of food crops. Their widespread adoption has the potential to revolutionize agriculture and impact the local environment. Accurate mapping and estimation of PG coverage are critical for strategic planning in agriculture. However, the challenge lies in the extraction of small and densely distributed PGs; this is often compounded by issues like irrelevant and redundant features and spectral confusion in high-resolution remote-sensing imagery, such as Gaofen-2 data. This paper proposes an innovative approach that combines the power of a full convolutional network (FC-DenseNet103) with an image enhancement index. The image enhancement index effectively accentuates the boundary features of PGs in Gaofen-2 satellite images, enhancing the unique spectral characteristics of PGs. FC-DenseNet103, known for its robust feature propagation and extensive feature reuse, complements this by addressing challenges related to feature fusion and misclassification at the boundaries of PGs and adjacent features. The results demonstrate the effectiveness of this approach. By incorporating the image enhancement index into the DenseNet103 model, the proposed method successfully eliminates issues related to the fusion and misclassification of PG boundaries and adjacent features. The proposed method, known as DenseNet103 (Index), excels in extracting the integrity of PGs, especially in cases involving small and densely packed plastic sheds. Moreover, it holds the potential for large-scale digital mapping of PG coverage. In conclusion, the proposed method providing a practical and versatile tool for a wide range of applications related to the monitoring and evaluation of PGs, which can help to improve the precision of agricultural management and quantitative environmental assessment.

Implementation of the European Union´s packaging and packaging waste regulation: A decision support framework combining quantitative environmental sustainability assessment methods and socio-technical approaches

The recent proposal for a European Union Regulation on packaging and packaging waste has a two-fold aims: i) to reduce the negative environmental impacts of packaging and packaging waste and ii) to improve the functioning of the internal market. Life Cycle Assessment (LCA) has historically been able to address the specific objectives of the proposal and is recognized as a key decision support tool in the circular economy context. In this short communication, it is argued that the new EU regulation calls for systemic changes, i.e. changes that are not simply just technological changes, but changes that might give rise to conflicts among actors and that are reliant on the active involvement of end-users. Therefore, to tackle the challenges in the implementation of the requirements of the new regulation a multi-disciplinary research approach able to address both technical aspects and social relations is needed. Accordingly, a conceptual framework combining quantitative environmental sustainability assessment tools (i.e. LCA and Material Flow Analysis) and qualitative socio-technical approaches (i.e. Actor-Network mapping and Practice Theory) is presented. The added value of such combination is illustrated in relation to two cases relevant for packaging, i.e. take-away food and food waste sorting. To exploit the full potential of LCA as a support to the decision-making process, the need to move beyond a product-centric analysis and instead adopt a socio-material perspective is outlined and the packaging sector has the potential to be a frontrunner in this context.

Data-driven based estimation of waste-derived ceramic concrete from experimental results with its environmental assessment

The significant requirement for natural resources, specifically as ingredients of cement, is accelerating due to the considerable growth of the construction sector. Further, cement production adversely affects climate change due to the generation of bulk CO2 emissions. At the same time, a significant quantum of ceramic waste is generated either in the ceramic production process or due to the demolition of ceramic products each year. The unavailability of an adequate way to dispose of this ceramic waste negatively impacts the environment and landfills. Numerous researchers have explored the potential of utilizing this ceramic waste powder as a partial cement replacement to reduce the allied issues. Hence, in the current study, the supervised machine learning (ML) algorithms, i.e., Decision Tree (DT), AdaBoost (AdB), Bagging (Bg), Random Forest (RF), Gradient Boosting (GB) and XGBoost (XGB) are employed for predicting the Compressive Strength (CS) of ceramic waste powder concrete (CWPC). The performance of models is also assessed by using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash Sutcliffe efficiency (NSE). The k-fold cross-validation technique is applied afterwards to validate the model's performance. For predicting the CS of CWPC, the RF algorithm is the most effective among the employed algorithms, with a higher R2 value of 0.97 and significantly lesser RMSE and MAE values of 1.40 and 1.13, respectively. SHAP analysis shows that the curing days feature has the highest influence on the CS of CWPC. As per quantitative Environmental Impact Assessment (EIA), the concrete with 10% CWP content can have 6.78%, 8.68%, 7.18%, and 7.19% reduced impacts on natural resources, climate change, ecosystem quality, and human health, respectively. Moreover, the effects on non-renewable energy resources, depletion of the ozone layer, and global warming can also primarily be reduced by a maximum of 7%, 6%, and 9%, respectively. The application of ML techniques for estimating the CS of CWPC would benefit the field of civil engineering in terms of conserving resources, effort, and time.

Life Cycle Impact Assessment of Solution Combustion Synthesis of Titanium Dioxide Nanoparticles and Its Comparison with More Conventional Strategies.

This paper represents the first attempt to quantitatively and reliably assess the environmental sustainability of solution combustion synthesis (SCS) with respect to other soft chemistry strategies, which are more conventionally employed in the preparation of engineered oxide nanomaterials, namely hydrolytic and non-hydrolytic sol-gel syntheses (i. e., HSGS and NHSGS). Indeed, although SCS is well known to rely on significant reduction in the energy as well as time required for the obtainment of the desired nanocrystals, its quantitative environmental assessment and a detailed comparison with other existing synthetic pathways represents an absolute novelty of high scientific desirability in order to pursue a more sustainable development in the inorganic chemistry as well as materials science research fields. TiO2 nanoparticles were selected as the material of choice, for the production of which three slightly modified literature procedures were experimentally reproduced and environmentally evaluated by the application of the comprehensive life cycle assessment (LCA) methodology. Particularly, SCS was compared from an environmental perspective with sol-gel approaches performed both in water and in benzyl alcohol. The results of the present study were also framed among those recently obtained in a systematic study assessing seven further chemical, physical, and biological routes for the synthesis of TiO2 nanoparticles, comprising also flame spray pyrolysis (typically used in industrial productions), highlighting and quantifying the excellent environmental performances of SCS.

Toward sustainable reprocessing and valorization of sulfidic copper tailings: Scenarios and prospective LCA

There has been increasing attention recently to reprocessing of mining waste, which aims to recover potentially valuable materials such as metals and other byproducts from untapped resources. Mining waste valorization may offer environmental advantages over traditional make-waste-dispose approaches. However, a quantitative environmental assessment for large-scale reprocessing, accounting for future trends and a broad set of environmental indicators, is still lacking. This article assesses the life cycle impacts and resource recovery potential associated with alternative waste management through mine tailings reprocessing at a regional scale. Sulfidic copper tailings in the EU were selected as a case study. We perform prospective life cycle assessments of future reprocessing scenarios by considering emerging resource recovery technologies, market supply & demand forecasts, and energy system changes. We find that some reprocessing and valorization technologies in future scenarios may have reduction potentials for multiple impact indicators. However, results for indicators such as climate change and energy-related impacts suggest that specific scenarios perform sub-optimally due to energy/resource-intensive processes. The environmental performance of reprocessing of tailings is influenced by technology routes, secondary material market penetration, and choices of displaced products. The trade-off between climate change and energy related impacts, on the one hand, and toxicity impacts, on the other hand, requires critical appraisal by decision makers when promoting alternative tailings reprocessing. Implementing value recovery strategies for building material production, can save up to 3 Mt. CO2-eq in 2050 compared to business as usual, helping the copper sector mitigate climate impacts. Additional climate mitigation efforts in demand-side management are needed though to achieve the 1.5 °C climate target. This work provides a scientific basis for decision-making toward more sustainable reprocessing and valorization of sulfidic tailings.

Pixel–Scene–Pixel–Object Sample Transferring: A Labor-Free Approach for High-Resolution Plastic Greenhouse Mapping

As an important agriculture technique, plastic greenhouse (PG) has been widely used to increase crop yield and improve food security status in the world. The high-resolution spatial information of PG is of great significance to precise agricultural management and quantitative environmental assessment. Many studies have examined the role that remote sensing technology could play in mapping and monitoring PG coverage. However, these methods, which employ either the traditional machine learning algorithms or the deep learning models, depend on massive manually labeled samples. To address this problem, this paper proposes a new cross-scale sample transferring method to generate high-resolution samples for automated PG mapping. The proposed method aims to transfer reliable label information from Sentinel-2 images (10-m) to high-resolution images (0.2-m) in a pixel-scene-pixel-object (PSPO) transferring process. In the proposed PG mapping workflow, the low-resolution label information of PG/non-PG can be obtained from an advanced plastic greenhouse index (APGI) which is calculated in Sentinel-2 images, and then the label information is transferred to the corresponding high-resolution images using the proposed PSPO transferring method. Finally, the transferred high-resolution samples are used to train the deep semantic segmentation model and produce PG mapping results. The whole process is labor-free which requires no manually labeled samples. The experimental results on three collected datasets show that the proposed approach can automatically generate accurate and reliable high-resolution samples, and the final PG mapping results can achieve an OA (overall accuracy) of 89.52% ~ 97.65% and F1 score of 84.13% ~ 94.03%, which is comparable to the fully supervised semantic segmentation model.

Water and carbon risks within hydropower development on national scale

• The positive and negative environmental benefits of hydropower are co-existed, and it cannot be ignored. • This study firstly assessed environmental burden at national scale, including water footprint and carbon footprint. • Machine learning offers a new approach: estimating environmental burden of hydropower with inadequate data. • Hydropower replacing thermal power cannot achieve complete cleanliness. The United Nations has proposed Sustainable Development Goals (SDGs), which aim to achieve coordinated green development in energy, economic and environmental dimensions. Hydropower is currently the world's most important renewable energy source, it has made up for the electricity shortage and created great economic value, but at the same time, the environmental impacts occurred cannot be ignored. However, current studies focused on a single or a few specific projects, it has not achieved quantitative environmental assessment on regional scale. To fill this gap, we selected China, the world's largest developing country, as the case for the first time to assess the hydropower water footprint (WF) and carbon footprint (CF) at both spatial and temporal dimensions. The results showed that total WF & CF of hydropower in China are 13.90 billion m 3 (closes to half annually runoff of the Yellow River) and 413.39 billion kg eqCO 2 (is equivalent to burning 1.5 billion t of coal), with intensity of 53.95 m 3 /MWh and 125.89 kg eqCO 2 /MWh respectively. The hydropower WF alone is more than regional available water occurred in 1/4 provinces of China. The emission reduction effect of hydropower is overestimated by 11.72 %, this should be considered in plans that hydropower replacing thermal power. Considering the CF of hydropower itself, 25–53 % of the regional carbon emission reduction target would not be achieved. From a global perspective, there about 1/3 countries’ hydropower WF exceed 10 % of the water resource availability, and about 1/4 countries’ hydropower CF exceeds 5 % of carbon emission.

Comparative Life Cycle Assessment of intra-row and inter-row weeding practices using autonomous robot systems in French vineyards

Viticulture, as well as other crops, is facing obligation to reduce the use of herbicides and to develop alternatives solutions to chemical weed control. These alternatives can be achieved by mechanical weeding either using tractors or weeding robots. This paper provides a quantitative environmental impact assessment using Life Cycle Assessment of intra-row and inter-row weeding practices using autonomous robot systems in three French vineyards. Twenty-seven scenarios were built to assess chemical, mechanical or a combination of the two for intra-row weeding management combined with mechanical weeding or mowing as inter-row management. Results shows that scenarios using weeding robots for the intra-row management have greater impacts than conventional ones on mineral resources, human toxicity, freshwater ecotoxicity and marine eutrophication due to the manufacture, the lifetime (when assumed short) and the functionality of robots. However, these same scenarios have fewer impacts than conventional ones on climate change, fossil resources, ozone depletion, acidification and particle formation, especially when robots are used on plots closed to the winery. This study had shed some light on the relative impacts of a robotic machine intended to replace tractors and vine straddles tractors in certain tillage operations. In particular, it may allow the implementation of an eco-design approach for the use of robots, making it possible to find the operating point that could minimize the overall impact of robotic solutions.

Quantitative Environmental Assessment of Explosive Residues from the Detonation of Insensitive High Explosive Filled 155 mm Artillery Shell

AbstractInsensitive High‐Explosive (IHE) typically comprises up to five constituents including 2,4‐dinitroanisole (DNAN), 3‐nitro‐1,2,4‐triazol‐5‐one (NTO), and 1,3,5‐trinitroperhydro‐1,3,5‐triazine (RDX), which are mixed in various ratios to achieve desired performance and increase insensitivity. Insensitive munitions, which are designed to detonate on command and not accidentally, are currently in use in military operations and training areas around the world. However, there is minimal literature available on the physiochemical behavior of these materials in the environment, therefore the actual consequence of residues being deposited post‐detonation is still an unexplored area of research. Three 155 mm artillery shells filled with an IHE mixture of 53 % NTO, 32 % DNAN, and 15 % RDX were detonated in an inert sand arena to collect and quantify residues. Post detonation, approximately 0.02 % NTO, and 0.07 % DNAN were deposited in the environment which may rapidly accumulate dependent on the number of rounds fired. This is of concern due to the toxicity of DNAN and its degradation products, and the potential for increased acidity of soil and discoloration of watercourses from NTO contamination.

Adopting an integrated building energy simulation and life cycle assessment framework for the optimisation of facades and fenestration in building envelopes

Delivering sustainability in the buildings and construction sector requires innovation in the materials used and tools adopted to help promote low-carbon, zero-energy and resource-efficient buildings. This study has proposed an integrated building energy simulation (BES) and life cycle assessment (LCA) framework to inform the selection of sustainable facades and fenestration for a building. The influence of distinct facade systems, enhanced glazing options and varying window-to-wall (WWR) ratios are examined for a theoretical office nearly zero-energy building (NZEB) located in a temperate maritime climate. The embodied and operational environmental impacts – carbon, energy and resources – are quantified and compared for each facade system. The results presented an 8.3% reduction in operational energy demands with enhanced glazing yet translated to a 10% increase in the embodied impacts; equating to an additional 7.0–7.6 months towards the cumulative environmental payback. The optimal conditions for combined heating and cooling performance in the building was a 0.2 variable WWR layout. This can reduce the total energy consumption by 2% or 4.8 kWh/m 2 /year when compared to equivalent uniform WWR configurations. The proposed integrated BES-LCA framework provides guidance for NZEB facade and fenestration assessment, demonstrating the value of quantitative environmental assessments of building components during the design process. Further framework enhancements can ensure additional life cycle considerations are accounted for, such as circularity, life cycle costing and geospatial considerations. • Proposed framework integrating building energy simulation and life cycle assessment. • Energy, carbon and resource impacts determined for three distinct NZEB facades. • 8.3% reduction in operational demands achieved from additional 10% embodied burdens. • The optimal fenestration strategy was triple glazing and a 0.2 window-to-wall ratio. • A masonry insulated cavity wall facade presented the lowest impacts.

Environmental impact assessment of milk packaging in Canada

The present study reports quantitative environmental impact assessments for types of milk packaging presently available in Canada: 4 L packages of pillow pouches (‘milk bags’, containing three 1.33 L pillow pouches), 4 L and 2 L high-density polyethylene (HDPE) jugs, and 2 L and 1 L screw-cap gable-top liquid packaging board (LPB) cartons. The purpose of the investigation was to inform consumers and other stakeholders of the relative environmental impacts of the milk packaging choice. The simplified life cycle assessment consisted of quantification of the energy consumption, global warming potential (quantified as CO2 equivalent emissions) and water consumption associated with the functional unit of milk use for one average Canadian household of 2.6 people for one year (163.4 L). The steps considered included primary production and processing of the materials, and delivery to the city (Toronto, Ontario, Canada or Halifax, Nova Scotia, Canada) where they would be filled with milk and the milk would be consumed. Four different end-of-life scenarios for each of the five different types of packaging were considered: 100% recycled; 100% incinerated (where the energy of incineration is captured with 100% efficiency); 100% landfill (CO2 not released); and actual recycling rate at that location, with remainder in landfill. The embodied energy, GHG emissions and water consumption factors all indicate that pillow pouches have the lowest impact of the investigated types of packaging for milk in Canada, regardless of location. The main reason for the significantly lower impact of the pouches is their lower mass per functional unit compared with jugs and cartons. Pillow pouches are better across all impact categories than the alternatives, even if the pillow pouches are disposed in the landfill or incinerated and the alternatives are fully recycled.

Environmental impacts of the future German energy system from integrated energy systems optimization and dynamic life cycle assessment

Mitigating climate change requires a fundamental transformation of our energy systems. This transformation should not shift burdens to other environmental impacts. Current energy models account for environmental impacts using Life Cycle Inventories (LCIs) that typically rely on historic processes. Thus, the LCIs are static and do not reflect improvements in underlying background processes, e.g., in the energy supply. Dynamic Life Cycle Assessment (LCA) incorporates changes in the LCI and allows for a consistent assessment of future energy systems. We integrate dynamic LCA in a national energy system optimization and discuss the differences between employing static and dynamic LCA in energy system optimization and assessment. Dynamic LCA leads to lower environmental impacts in most categories (e.g., climate change: -18%) and is required for a quantitative environmental assessment. However, our analysis shows that static LCA is sufficient to identify general trends in energy system optimization and assessment for Germany till 2050.

Steps from Zero Carbon Supply Chains and Demand of Circular Economy to Circular Business Cases

The European Green Deal requires a zero-carbon Europe until 2050. Consequently, all processes conducted, and actions taken need to ensure zero carbon emissions. Beside the areas of energy systems, logistics and living, it certainly also includes industry sectors, industrial processes and their entire supply chains. Zero carbon supply chains mean net zero carbon emission from scope 1, 2 and 3. The key question for future business is therefore how to reduce the carbon emissions of all procured components down to zero, beside all procured and consumed energies (scope 1 and 2). A promising approach is a circular economy, if primary produced materials will not be provided carbon neutral or will increase in price. However, today the development of circular business models often is a chicken and egg problem. It needs a clear business case to invest into changes, adaptions, substitutions or life cycle system adjustments of linear products (take, make, dispose) to turn them into circular products or product systems (avoid, re-use, recycle, recover). The paper presents approaches to support companies seeking carbon neutral products in a carbon neutral Europe via identifying business cases for circular products. An evaluation matrix is presented allowing the identification of circularity status depending on the selected products or sector perspective. The matrix contains evaluation criteria based on the circularity building blocks by the Ellen MacArthur Foundation. Based on the results, hot spots and weak points are identified and allow entering a six-step-approach for business-case identification. The steps refer to life cycle thinking, quantitative environmental assessment, simulation, creativity methods and end up identifying profitable business cases for circularity. The presented approach combines life cycle assessment, circular economy and the development of business models and is partly developed and applied during the European H2020 project AVANGARD.

Case Study for the Predictive Environmental Risk Assessment of Hexamethylenetetramine Release to the Yodo River during a Massive Tsunami Attack

Untreated hexamethylenetetramine (HMT) was discharged into the Tone River in the central area of Japan, and the risk management plan in the watershed area has been strengthened because HMT is the precursor of formaldehyde (FA) regulated by Japanese water supply law. The release of HMT could occur not only in steady but also in unsteady environmental conditions. In this context, no quantitative environmental risk assessments have dealt with the combined events of FA precursor outflow and natural disasters, such as a massive tsunami attack. In this study, we estimated the time course changes of HMT concentrations at the near water treatment plant (WTP) intakes due to tsunami run-up in the river after HMT discharge from facilities along the river during a massive tsunami attack, then potential ecological and health impacts were estimated. This method has a strong analytical ability to reveal the relationship between wave source and inland water run-up in consideration of 3D density flow. For ecological risk, the half maximal effective concentration (EC50) was employed. We found that HMT concentration would not reach a level of concern even after the accident. For potential health risk in terms of the possible maximum inlet concentration of HMT at the WTP, the FA formation amount was 5.3 × 10−2 mg/L, which was below the water quality standard.

Informative Environment Qualifying Index

Improvement in the quality of the environment and, as a result, in the quality of life, including environmental impact assessments and environmental management, play an important role in the practical implementation of environmental regulations. The goal of this paper is to develop a new type of quantitative environmental impact assessment method to describe changes in environmental elements as well as the environment in an objective and reliable manner for various projects, investments, plans and proposals. An easily adaptable method was sought which provides a clear and well-interpretable result on the condition of and foreseeable changes in the environment. The algorithm operates using the limit values of environmental elements set forth by national regulations. The evaluation is independent of the number of environmental parameters chosen as it was included in the informativity rates of the method. The process results in an aggregated index for qualifying the total environment but, nonetheless, the affected environmental elements and measured environmental parameters can be analysed independently. The Informative Environment Qualifying Index evaluates the environmental parameters in proportion to the strictness of the limit values. The final assessment of the total environment is performed by using varying intervals, therefore, the different cases can be compared to each other. Experts interpret the results as well as explain the changes in the state of the environment and, therefore, identify the cause-effect relationships.

Probabilistic environmental risk assessment of microplastics in marine habitats

Microplastics are ubiquitous in the environment and given the large number of published hazard and exposure studies, quantitative environmental risks assessments of microplastics become feasible. We present here the first environmental risk assessment for marine waters based only on measured concentrations. The Thevariability and uncertainty of the measured data was accounted for in the exposure assessment, while probabilistic species sensitivity distributions were used for hazard assessment, from which a probability distribution was extracted for the predicted no-effect concentration (PNEC). By dividing the exposure distribution by the PNEC-distribution, we were able to calculate probabilistic risk characterisation ratios for each water body in which measurements were performed. Results show a good coverage of the world's major water bodies by measured exposure concentrations (MECs), while the hazard assessment could be improved by aligning the type of particles tested in hazard studies (size, form, polymer) to those actually found in the oceans. Overall, the mean predicted no-effect concentration (PNEC) is 3.84·106 part m−3, with Oryzias melastigma being the most sensitive species (calculated mean NOEC of 3.90·106 part m−3). Interestingly, the only type of dose descriptor that could be extracted from the literature for particles above 10−20 μm was the highest observed no effect concentration (HONEC), which indicates a very low or null toxicity of these larger MPs towards marine organisms. The mean MEC is 1.5·103 part m−3, the highest concentrations being measured in the Atlantic and Pacific Ocean. Although there is a very small overlap of the probability distribution associated with the RCR (0.00002 % of the data points), the mean RCR is 4·10-4 and therefore risks are unlikely given the available data. However, as increasing amounts of plastic reach the environment, RCRs can be expected to increase in the future.

Promoting sustainable consumption with LCA – A social practice based perspective

Quantitative environmental assessments are crucial in working effectively towards sustainable production and consumption patterns. Over the last decades, life cycle assessments (LCA) have been established as a viable means of measuring the environmental impacts of products along the supply chain. In regard to user and consumption patterns, however, methodological weaknesses have been reported and, several attempts have been made to improve LCA accordingly, for example, by including higher order effects and behavioural science support. In a discussion of such approaches, we show that there has been no explicit attention to the concepts of consumption, often leading to product-centred assessments. We introduce social practice theories in order to make consumption patterns accessible to LCA. Social practices are routinised actions comprising interconnected elements (materials, competences, and meanings), which make them conceivable as one entity (e.g. cooking). Because most social practices include some sort of consumption (materials, energy, air), we were able to develop a framework which links social practices to the life cycle inventory of LCA. The proposed framework provides a new perspective of quantitative environmental assessments by switching the focus from products or users to social practices. Accordingly, we see the opportunity in overcoming the reductionist view that people are just users of products, and instead we see them as practitioners in social practises. This change could enable new methods of interdisciplinary research on consumption, integrating intend-oriented social sciences and impact-oriented assessments. However, the framework requires further revision and, especially, empirical validation.

Probabilistic Groundwater Flow, Particle Tracking and Uncertainty Analysis for Environmental Receptor Vulnerability Assessment of a Coal Seam Gas Project

The production of coalbed methane, or coal seam gas (CSG) in Australia increased 250-fold since the 1990s to around 1502 petajoules in 2019 and continues to expand. Groundwater flow in the aquifers intersected by gas wells could potentially facilitate a transport pathway for migration of contaminants or poorer quality water from deeper formations. While regulatory and mitigation mechanisms are put in place to minimize the risks, quantitative environmental impact assessments are also undertaken. When many gas wells are drilled in a wide area where many potential receptors are also spatially distributed, potential source-receptor combinations are too numerous to undertake detailed contamination risk assessment using contaminant transport modelling. However, valuable information can be gleaned from the analysis of groundwater flow directions and velocities to inform and prioritise contamination risk assessment and can precede computationally challenging stochastic contaminant transport modelling. A probabilistic particle tracking approach was developed as a computationally efficient screening analysis of contamination pathways for a planned CSG development near Narrabri in northern New South Wales, Australia. Particle tracking was run iteratively with a numerical groundwater flow model across a range of plausible parameter sets to generate an ensemble of estimated flow paths through the main Great Artesian Basin aquifer in the area. Spatial patterns of path lines and spatial relationships with potential receptors including neighbouring groundwater extraction wells and hydrologically connected ecological systems were analysed. Particle velocities ranged from 0.5 to 11 m/year and trajectories indicated dedicated contaminant transport modeling would be ideally focused at the local scale where wells are near potential receptors. The results of this type of analysis can inform the design of monitoring strategies and direct new data collection to reduce uncertainty and improve the effectiveness of adaptive management strategies and early detection of impacts.