Human consumption of pharmaceuticals leads to continuous emissions of active pharmaceutical ingredients (APIs) to the aquatic environment, primarily via wastewater treatment plants (WWTPs). However, temporal and spatial patterns of environmental API concentrations are challenging to assess using conventional chemical measurements. Chemical risk assessments are, consequently, typically based on low-resolution data and often overlook API concentrations of environmental concern. We, therefore, developed a new method, combining emission and hydrodynamic modeling to estimate spatiotemporal variations. The method was applied in a case study in a Swedish lake receiving water from a WWTP (capacity 200,000 person equivalents), including >500 prescription APIs. The emission model was validated using ten different APIs measured monthly in the WWTP effluent, and of 103 measured API concentrations 102 were predicted within a factor of 10. The full method was validated against 321 historical measurements from the lake, covering 20 different APIs where 233 (73%) and 319 (99%) of the predicted concentrations were within a factor of 10 and 100 of the measurements. Thus, our method enables predictions of environmental concentrations of APIs, accurate enough to supplement and guide environmental monitoring, directly from human prescription data. Furthermore, we demonstrate that API concentrations vary by orders of magnitude over time and space, directly impacting risk management and monitoring.

This study, conducted in Combomune, Mozambique, investigated land use and land cover changes (LULCC) from 2002 to 2021 and their causes, utilizing Landsat satellite imagery and the land change modeler (LCM). The research mapped vegetation changes and analysed identified explanatory variables. Sentinel-2 imagery was employed to identify charcoal production sites during 2016–2021. The findings indicate significant landscape transformations, with a 20.4

Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups—algae, aquatic invertebrates and fish—and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC 50 /EC 10 ), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.

PurposeThis study focuses on updating, improving, and expanding the extrapolation factors needed to convert various acute or chronic effect concentration indicators into consistent chronic EC10eq (effect concentration inducing a 10% response over background) for use in life cycle assessment (LCA). Our main objectives include (1) to present a detailed approach for the harmonization of ecotoxicity data, with a focus on deriving extrapolation factors, and (2) to estimate both generic and species group-specific extrapolation factors, facilitating the conversion of effect concentration indicator groups (EC10eq and EC50eq) into chronic EC10eq.MethodsExperimental ecotoxicity data were sourced from CompTox Version 2.1.1, which integrates toxicity information from ToxValDB v9.1.1, and the information from REACH registration dossiers. We developed a framework for harmonizing ecotoxicity data, ensuring uniformity and high quality of aquatic ecotoxicity information from these sources. Through linear regression analysis, both generic and species group-specific extrapolation factors were then derived.Results and discussionHarmonization of ecotoxicity data yielded a streamlined dataset with 339,729 datapoints for 10,668 chemicals, reflecting a 54% reduction in raw datapoints. The geometric mean-based aggregation process produced 79,001 aggregated effect concentration datapoints at the species level, 41,303 at the species group level, and 23,215 at the effect concentration indicator level for these chemicals. This process facilitated the derivation of 3 generic and 24 species group-specific extrapolation factors, allowing for the conversion of effect concentration indicator groups (EC10eq and EC50eq) to a chronic EC10eq across two exposure classes (acute vs. chronic) and species groups, as defined in the US EPA ECOTOX knowledgebase, including algae, amphibians, fish, crustaceans, insects/spiders, invertebrates, molluscs, and worms.ConclusionsThe harmonization of ecotoxicity data and the derived extrapolation factors permit the integration of diverse datapoints with varying effect concentration indicators and exposure durations into USEtox ecotoxicity characterization factors. This has the potential to enhance substance coverage for characterizing ecotoxicity effects across chemicals in LCA frameworks by permitting wider species coverage. More generally, this is part of global efforts to extend the potential for quantitative assessment of environmental impacts of chemicals in an LCA framework.

The environmental management cycles for chemicals and climate change (EMC4 ) is a suggested conceptual framework for integrating climate change aspects into chemical risk management. The interaction of climate change and chemical risk brings together complex systems that are imperfectly understood by science. Making management decisions in this context is therefore difficult and often exacerbated by a lack of data. The consequences of poor decision-making can be significant for both environmental and human health. This article reflects on the ways in which existing chemicals management systems consider climate change and proposes the EMC4 conceptual framework, which is a tool for decision-makers operating at different spatial scales. Also presented are key questions raised by the tool to help the decision-maker identify chemical risks from climate change, management options, and, importantly, the different types of actors that are instrumental in managing that risk. Case studies showing decision-making at different spatial scales are also presented highlighting the conceptual framework's applicability to multiple scales. The United Nations Environment Programme's development of an intergovernmental Science Policy Panel on Chemicals and Waste has presented an opportunity to promote and generate research highlighting the impacts of chemicals and climate change interlinkages. Integr Environ Assess Manag 2024;20:433-453. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Anthropogenic activities and their influences on aquatic systems is an important topic, especially considering the growing interest in using the earth's resources in a sustainable way. One of those anthropogenic activities is the introduction of renewable technologies into the aquatic environment such as instream turbines. Environmental studies around those technologies are often still ongoing due to their novelty. During the spring of 2018, juvenile individuals of two salmonid species, Atlantic salmon and brown trout were released upstream a vertical axis instream turbine in the river Dal (Dalälven) in eastern Sweden. The aim of this study was to investigate the swimming behavior of the salmonids around a small‐scale prototype vertical axis instream turbine. The swimming pattern and the possible response of avoiding the vertical axis instream turbine were documented with a multi beam sonar. A control area, next to the turbine, was used as reference. No consistent results were shown for trout as they were passing the control area with a statistically high variation, and specimens were rarely observed in proximity of the turbine, neither if the turbine was operating nor at stand still. Salmon clearly avoided the operating turbine, but did not avoid the turbine when it was at stand still, and was often observed swimming straight through the turbine area. These findings indicate that operating this type of instream turbine in a river affects the swimming behavior of Atlantic salmon but is unlikely to affect its migration paths. For brown trout, the statistical results are inconclusive, although data indicate a response of avoiding the turbine. The species are in little risk to suffer physical harm as no fish entered the rotating turbine, despite very turbid water conditions.

Environmental safety assessments, as mandated by many regulations, require that toxicity data is generated for up to three trophic levels, algae, aquatic invertebrates, and fish. Conducting these tests in vivo is resource-intensive, time-consuming, and causes undue suffering. Computational methods are fast and cost-efficient alternatives, however, their adaptation in regulatory settings has been slow, both due to low accuracy and narrow applicability domains. Here we present a new method for predicting chemical toxicity based on molecular structure. The method is based on a transformer, capturing structural features associated with toxicity, followed by a deep neural network that predicts the corresponding effect concentrations. After training on data from tens of thousands of exposure experiments, the model shows high predictive performance for each of the three trophic levels. Compared to commonly used QSAR methods, the model has both a larger applicability domain and a considerably lower error. In addition, training the model on data that combines multiple types of effect concentrations further improves the performance. We conclude that transformer-based models have the potential to significantly advance computational predictions of chemical toxicity and make in silico approaches a more attractive alternative when compared to animal-based exposure experiments.

In chemical risk assessment, measured or modelled environmental concentrations are compared to environmental exposure limits (EELs), such as Predicted No Effect Concentrations (PNECs) or hazardous concentrations for 5% of species (HC05s) derived from species sensitivity distributions (SSDs). However, for many chemicals the EELs include large uncertainties or, in the worst case, the necessary data for their estimation are completely missing. This makes the assessment of chemical risks and any subsequent implementation of management strategies challenging. In this study we analyzed the uncertainty of EELs and its impact on chemical risk assessment. First, we compared three individual EEL datasets, two primarily based on experimental data and one based on computational predictions. The comparison demonstrates large disagreements between EEL data sources, with experimentally derived EELs differing by more than seven orders of magnitude. In a case-study, based on the predicted emissions of 2005 chemicals, we showed that these uncertainties lead to significantly different risk assessment outcomes, including large differences in the magnitude of the total risk, risk driver identification, and the ranking of use categories as risk contributors. We also show that the large data-gaps in EEL datasets cannot be covered by commonly used computational approaches (QSARs). We conclude that an expanded framework for interpreting risk characterization outcomes is needed. We also argue that the large data-gaps present in ecotoxicological data need to be addressed in order to achieve the European zero pollution vision as the growing emphasis on ambient exposures will further increase the demand for accurate and well-established EELs.

Chemical emissions from households originate from a wide range of sources and results in highly diverse mixtures. This makes traditional monitoring based on analytical chemistry challenging, especially for compounds that appear in low concentrations. We therefore developed a method for predicting emissions of chemicals from households into wastewater, relying on consumption patterns from multiple data sources. The method was then used to predict the emissions of chemical preparations, chemicals leaching from textiles and prescription pharmaceuticals in Sweden. In total we predicted emissions of 2007 chemicals with a combined emission of 62,659 tonnes per year - or 18 g/person and day. Of the emitted chemicals, 2.0% (w/w) were either classified as hazardous to the environment or were both persistent and mobile. We also show that chemical emissions come from a wide range of uses and that the total emission of any individual chemical is determined primarily by its use pattern, not by the total amount used. This emphasizes the need for continuous updates and additional knowledge generation both on emission factors and excretion rates as well as a need for improved reporting on the intended use of individual chemicals. Finally, we scrutinize the model and its uncertainty and suggest areas that need improvement to increase the accuracy of future emission modelling. We conclude that emission modelling can help guide environmental monitoring and provide input into management strategies aimed at reducing the environmental effect caused by hazardous chemicals.

In comparative life cycle assessment (LCA) studies of materials, there is a mismatch between the current practice and existing guidelines regarding functional unit definition. The purpose of this study is to develop a practice-based framework for defining functional units in comparative LCAs of materials and provide guidance regarding in which situations different functional unit types are relevant. A literature review of comparative LCAs of materials identified three types of functional units: (i) the reference flow functional unit, (ii) the property functional unit, and (iii) the performance functional unit. These functional unit types, of which only the latter strictly complies with LCA guidelines, represent varying degrees of functional equivalence and technological maturity. The most relevant functional unit type depends on the goal of the study. We suggest that screening assessments of whether materials have comparable environmental impacts can apply reference flow functional units. Material comparisons for certain application areas with some important properties can apply property functional units. For comparisons of end products, performance functional units can be applied. However, even in such cases, complete functional equivalence can hardly be achieved due to more or less relevant product differences. The applicability of the framework is demonstrated for the case of comparing cemented carbide and polycrystalline diamond hard materials.

Off-grid systems, and mini-grids in particular, are expected to play a significant role in improving electricity access to one billion people until 2040. One of the major challenges for mini-grids is associated with their high costs, low financial viability and local development impact. Productive use of electricity can be an important driver of local development and impacts the total load in a mini-grid. By using a mixture of high-resolution (minutes) measurements and long-term data (years) on electricity expenditures and purchased electricity from a mini-grid in the Tanzanian highlands, we analyse the technical and economic impact from household and productive use of electricity, respectively. The high-resolution data is analysed using performance indicators and the long-term data using regression tools. We find that a mixture of household use and productive use of electricity provides both technical and economic benefits for the operator. In addition, we find that while productive use customers only represent 25% of the customers, they generate 44% of the operator's income. Furthermore, productive use of electricity customers are also likely responsible for the peak demand in the mini-grid, which occurs during day time. Lastly, we find empirical evidence suggesting that expenditures and demand are unit elastic, which has implications on economic policies for supporting rural electrification.

Claims for ocean space are growing while marine ecosystems suffer from centuries of insufficient care. Human pressures from runoff, atmospheric emissions, marine pollution, fishing, shipping, military operations and other activities wear on habitats and populations. Ecosystem-based marine spatial planning (MSP) has emerged worldwide as a strategic instrument for handling conflicting spatial claims among competing sectors and the environment. The twofold objective of both boosting the blue economy and protecting the environment is challenging in practice and marine planners need decision support. Cumulative Impact Assessment (CIA) was originally developed to provide an overview of the human imprint on the world's ocean ecosystems. We have now added a scenario component to the CIA model and used it within Swedish ecosystem-based MSP. This has allowed us to project environmental impacts for different planning alternatives throughout the planning process, strengthening the integration of environmental considerations into strategic decision-making. Every MSP decision may entail a local shift of environmental impact, causing positive or negative consequences for ecosystem components. The results from Swedish MSP in the North Sea and Baltic Sea illustrate that MSP certainly has the potential to lower net cumulative environmental impact, both locally and across sea basins, as long as environmental values are rated high and prevailing pressures derive from activities that are part of MSP. By synthesizing innumerous data into comprehensible decision support that informs marine planners of the likely environmental consequences of different options, CIA enables ecosystem-based MSP in practice.

Mini-grids are considered important in order to improve access to electricity in developing countries. Their technical and organizational independence from the large national grids and their interactions with local social, economic, and environmental factors suggests that system dynamics is a useful method of analysis. However, the successful implementation of mini-grids in rural electrification has partly been prevented due to complexity issues, making problem formulation difficult. Most problem-solving methods, such as system dynamics, require well-defined problems. Previous work on the problem formulation process in system dynamics is limited. This work presents a conceptual framework for tackling complexity and uncertainties in rural electrification. The conceptual framework is general and draws on research in conceptual modelling and system dynamics. The focus is on the learning that can be achieved from a system description and how it can be used to tackle complexity by reducing uncertainties and improving knowledge.

Recognition of human excreta as a resource, rather than as waste, has led to the emergence of a range of new and innovative nutrient recovery solutions. Nevertheless, the management of human excreta remains largely rooted in current sanitation and wastewater management approaches, which often makes nutrient recovery an add-on to existing infrastructures. In this paper, we argue that framing human excreta management as a resource recovery challenge within waste management obscures important trade-offs. We explore the factors that would be brought to the fore by reframing human excreta management as part of food and farming systems. We find that such a reframing would accentuate (at least) six aspects of critical importance that are currently largely overlooked. Recognizing that the proposed framing may also have its limitations, we argue that it has the potential to better guide human excreta management towards long-term global food, soil, and nutrient security while reducing the risk of compromising other priorities related to human and environmental health.

Recognition of human excreta as a resource, rather than as waste, has led to the emergence of a range of new and innovative nutrient recovery solutions. Nevertheless, the management of human excreta remains largely rooted in current sanitation and wastewater management approaches, which often makes nutrient recovery an add-on to existing infrastructures. In this paper, we argue that framing human excreta management as a resource recovery challenge within waste management obscures important trade-offs. We explore the factors that would be brought to the fore by reframing human excreta management as part of food and farming systems. We find that such a reframing would accentuate (at least) six aspects of critical importance that are currently largely overlooked. Recognizing that the proposed framing may also have its limitations, we argue that it has the potential to better guide human excreta management towards long-term global food, soil, and nutrient security while reducing the risk of compromising other priorities related to human and environmental health.

Tidal current technologies have the potential to provide highly predictable energy, since tides are driven by lunar cycles. However, before implementing such technologies on a large scale, their environmental performance should be assessed. In this study, a prospective life cycle assessment (LCA) was performed on a 12 MW tidal energy converter array of Minesto Deep Green 500 (DG500) prototypes, closely following the Environmental Product Declaration (EPD) standards, but including scenarios to cover various design possibilities. The global warming potential (GWP) of the prototype array was in the range of 18.4–26.3 gCO2-eq/kWhe. This is comparable with other renewable energy systems, such as wind power. Material production processes have the largest impact, but are largely offset by recycling at the end of life. Operation and maintenance processes, including the production of replacement parts, also provide major contributions to environmental impacts. Comparisons with other technologies are limited by the lack of a standardized way of performing LCA on offshore power generation technologies.

WindChoir ar ett projekt som utvecklar ett GIS verktyg for kumulativ miljopaverkan baserat pa en enkel additiv model. Data hamtas fran manga olika datakallor som medger en hog spatial upplosning (10'10 m). For ca 25 stressors (paverkansfaktorer) anvands modeller for att ge GIS-skikt som representerar stressorernas intensitet. Modeller anvands ocksa for att representera de ca 45 endpoints (skyddsobjekt) som ingar i analysen. Slutresultatet blir den samlade miljopaverkan av alla inkluderade stressors pa alla inkluderade endpoints.

Synthetic diamond is a superhard material found in applications such as cutting and grinding of rocks, minerals, metals and plastics, but also in windows and lenses in analytical equipment. Since the 1950s, when the synthesis of diamond via the high-pressure high-temperature (HPHT) synthesis was introduced, the use of synthetic diamond has gained increasing importance within industry, for example in cutting processes. Today, synthetic diamond dominates the industrial diamond use over its natural counterpart and the production is mainly via HPHT synthesis. Chemical vapor deposition (CVD) synthesis is also applied but to a lesser extent due to various technological issues, including the tendency of CVD diamond coatings to wear and tear in tooling applications. Nevertheless, a lot of research exist in the area of synthetic diamond film production via CVD synthesis addressing technological issues and CVD diamond has been described as the potential facilitator of a new diamond age. In the strive for sustainable production, it is important to investigate the environmental impacts associated with various products and life cycle assessment (LCA) is commonly applied for this purpose. Until now, however, no study has conducted LCA of conventional synthetic diamond production via HPHT synthesis and so far, only laboratory-scale LCA results have been presented for one CVD synthesis alternative. The aim of this study is to conduct a cradle-to-gate LCA for synthetic diamond production both via the conventional HPHT synthesis and the potentially emerging CVD synthesis. For the latter route of synthesis, prospective (or ex ante) LCA was conducted and a predictive scenario was constructed focusing on microwave CVD. Future scenarios were constructed, e.g. by modelling the background energy system both using a fossil-based mix, representing the currently dominating Chinese production, and a renewable energy mix in an explorative scenario. A comprehensive uncertainty analysis was furthermore conducted for the included parameters. Environmental impacts were compared between the two routes of synthesis and hotspots in synthetic diamond production identified. The results from this research can be applied as a foundation for further LCA studies of synthetic diamond products.

Drawing on the emerging scarcity, abundance, and sufficiency (SAS) framework, this study explores how various consumer behaviors with potential environmental impacts relate to subjective evaluations of psychological resources such as economic resources, time, social networks, and emotional support. Assuming that individuals may "trade" the costs and efforts of green consumption, including the buying of eco-labeled goods, altered eating habits, and choice of transportation mode, against such psychological resources, we investigate the relationships between green consumer choices and resource evaluations using hierarchical regression analysis of data from an online panel survey. The results suggest that green consumer behaviors are positively related to subjectively evaluated resources such as feelings of economic sufficiency and other, more "relational" resources, including social networks and emotional support. Performing such behaviors may therefore lead to psychological gains. These findings do paint a rather positive picture of environmental behaviors, since they may thus be viewed as having a personal positive trade-off. Although directional effects cannot be firmly established, our study suggests that pro-environmental behavior may increase wellbeing and experienced prosperity. Future studies should further investigate these causalities and implications of these suggested relationships.

The purpose of this synthesis paper is to present the motivations and conceptual basis for research on socio-technical-ecological systems (STES), addressing the need for interdisciplinary studies targeting the technological mediation of human–environment relationships. The background is the very limited number of collaborations between scholars of social-ecological systems and sociotechnical systems (SES), despite repeated calls for bridging work. The synthesis builds on an in-depth review of previous literature, interdisciplinary exchanges, and empirical examples. The result is arguments for why a sociotechnical understanding of ‘technology’ is of central importance for SES studies, related to how technology: (1) mediates human–environment relationships; (2) brings ambivalence to these relationships; (3) enhances and transforms human agency and provides a source of constitutive power; (4) changes scalar relationships, enabling our interaction with and impact on the natural world across time and space. Furthermore, we present an STES analytical approach which starts from symmetrical attention to technology, society, and environment, specifically targeting interfaces and relationships of critical relevance for SES scholars, and address counterarguments that we have encountered. We conclude that a shift to STES research will enhance our knowledge of system interfaces that are often overlooked, opening further avenues for research and real-world interventions.