A systems-based analysis to rethink the European environmental risk assessment of regulated chemicals using pesticides as a pilot case.

A growing body of scientific literature stresses the need to advance current environmental risk assessment (ERA) methodologies and associated regulatory frameworks to better address the landscape-scale and long-term impact of pesticide use on biodiversity and the ecosystem. Moreover, more collaborative and integrative approaches are needed to meet sustainability goals. The One Health approach is increasingly applied by the European Food Safety Authority (EFSA) to support the transition towards safer, healthier and more sustainable food. To this end, EFSA commissioned the development of a roadmap for action to establish a European Partnership for next-generation, systems-based Environmental Risk Assessment (PERA). Here, we summarise the main conclusions and recommendations reported in the 2022 PERA Roadmap. This roadmap highlights that fragmentation of data, knowledge and expertise across regulatory sectors results in suboptimal processes and hinders the implementation of integrative ERA approaches needed to better protect the environment. To advance ERA, we revisited the underlying assumptions of the current ERA paradigm; that chemical risks are generally assessed and managed in isolation with a substance-by-substance, realistic worst-case and tiered approach. We suggest optimising the use of the vast amount of information and expertise available with pesticides as a pilot area. It is recommended to as soon as possible adopt a systems-based approach, i.e. within the current regulatory framework, to spark a step-wise transition towards an ERA framed at a system level of ecological and societal relevance. Tangible systems-based and integrative steps are available. For instance, the rich sources of existing data for prospective and retrospective ERA of pesticides could be used to reality-benchmark existing and new ERA methods. To achieve these goals, collaboration among stakeholders across scientific disciplines and regulatory sectors must be strengthened.

Life cycle and economic assessment of tidal energy farms in early design phases: Application to a second-generation tidal device.

Ocean currents are emerging as key contributors to renewable energy generation. However, technologies for harvesting tidal current energy are still in the early stages of development. In this context, environmental and economic studies on tidal energy converters (TECs) are crucial to further advance tidal technology and facilitate its entry into the market. This article presents a life cycle and economic assessment of a 34.5 MW tidal farm project comprising 23 second-generation tidal devices, each with a rated power of 1.5 MW. The tidal system was simulated using primary data from the full-scale floating platform Atir. The Atir is a pre-commercial tidal device designed with a steel trimaran and a submerged section for TEC installation. An assessment of 18 environmental impact categories was conducted using the ReCiPe 2016 MidPoint method, with process flow systems modelled using SimaPro v9.2.0.1 software. The environmental assessment indicates emissions of 42.11 g CO2eq per kWh, primarily stemming from manufacturing processes that demand substantial amounts of steel. The economic analysis reveals a Levelized Cost of Electricity (LCOE) of 0.125 EUR/kWh, consistent with European Commission projections. Although the platform structure represents a high initial investment, the lower maintenance costs of the Atir device provide long-term savings and, overall, result in a competitive LCOE. The study also introduces a methodological framework for harmonised environmental and economic assessments in tidal energy projects, proving crucial in supporting decision-making processes.

Navigating challenges and solutions for metal-halide and carbon-based electrodes in perovskite solar cells (NCS-MCEPSC): An environmental approach.

The urgent need to shift to renewable energy is highlighted by rising global energy use and environmental issues like global warming from fossil fuel dependency. Perovskite solar cells (PSCs) stand out as a promising option, providing high efficiency and potential for cost-effective production. This study delves into the environmental concerns and viable solutions linked with metal-halide PSCs (M-PSCs) and carbon-based electrode PCSs (C-PSCs). It showcases the swift progress in PSC technology, highlighting its potential to deliver efficient and economical renewable energy options. Yet, the environmental implications of these technologies, especially the utilization of toxic lead (Pb) in M-PSCs and the issues of stability and degradation in C-PSCs, represent considerable hurdles for their broad application and sustainability. The paper details the recent advances in PSCs, focusing on enhancements in device efficiency and stability through innovative material combinations and device designs. Nonetheless, the environmental hazards linked to the dispersal of toxic substances from compromised or deteriorating PSCs into the ecosystem raise significant concerns. In particular, the risk of Pb from M-PSCs contaminating soil and aquatic ecosystems is a pressing issue for human and environmental health, spurring investigations into alternative materials and methods to diminish these impacts. The authors examine several strategies, including the introduction of Pb-free perovskites, encapsulation methods to block the escape of hazardous substances, and the recycling of PSC elements. The study stresses the necessity of aligning technological innovations with considerations for the environment and health, calling for ongoing research into PSC technologies that are sustainable and safe. This review highlights the need for detailed assessments of PSC technologies, focusing on their renewable energy contributions, environmental impacts, and strategies to mitigate these effects. The authors call for a cohesive strategy to develop PSCs that are efficient, cost-effective, eco-friendly, and safe for widespread use.

Healthy environments for athleTes (HEAT): environmental conditions along a 90 km ultra-marathon event, South Africa.

This paper provides an overview of the HEAT (Healthy Environments for AthleTes) project, which aims to understand the impact of environmental conditions on athlete health and performance during major sporting events such as long-distance running, cycling, and triathlons. In collaboration with the SAFER (Strategies to reduce Adverse medical events For the ExerciseR) initiative, the HEAT project carried out a field campaign at the 2022 Comrades Marathon in the KwaZulu-Natal province of South Africa. The measurement campaign deployed seven weather stations, seven PM2.5 monitors and one spore trap along the 90 km route to capture spatially representative measurements of complex micro-climates, allergenic aerospora, and particulate matter exposure. The results indicate that runners were exposed to moderate risk heat stress conditions. Novel findings from this initial campaign shows elevated and potentially harmful PM2.5 levels at spectator areas, possibly coinciding with small fire events around the race day festivities. Our findings show values PM2.5 levels over the WHO 24-h guidelines at all stations, while 2000 µg/m3 at two stations. However, the lack of an acute exposure standard means direct health impacts cannot be quantified in the context of a sport event. The HEAT project highlights important aspects of race day monitoring; regional scale climatology has an impact on the race day conditions, the microclimatic conditions (pollution and meteorology) are not necessarily captured by proximity instruments and direct environmental measurements are required to accurately capture conditions along the route.

Machine learning-based life cycle assessment for environmental sustainability optimization of a food supply chain.

Effective resource allocation in the agri-food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted in recent studies. This hybrid framework is valuable not only for assessing food supply chains but also for improving them toward a more sustainable system. Yet, an essential step in the optimization process is defining the optimization boundaries, or minimum and maximum quantities for the variables. Usually, the boundaries for optimization variables in these studies are obtained from the minimum and maximum values found through interviews and surveys. A deviation in these ranges can impact the final optimization results. To address this issue, this study applies the Delphi method for identifying variable optimization boundaries. A hybrid environmental assessment framework linking LCA, multilayer perceptron artificial neural network, the Delphi method, and genetic algorithm was used for optimizing the pomegranate production system. The results indicated that the suggested framework holds promise for achieving substantial mitigation in environmental impacts (potential reduction of global warming by 46%) within the explored case study. Inclusion of the Delphi method for variable boundary determination brings novelty to the resource allocation optimization process in the agri-food sector. Integr Environ Assess Manag 2024;00:1-11. © 2024 SETAC.

[The environmental impact of digital technology and artificial intelligence, in the time of digital pathology]. / Impacts environnementaux du numérique et de l'intelligence artificielle, à l'heure de la pathologie digitale.

While digitization and artificial intelligence represent the future of our specialty, future is also constrained by global warming and overstepping of planetary limits, threatening human health and the functioning of the healthcare system. The report by the Délégation ministérielle du numérique en santé and the French government's ecological planning of the healthcare system confirm the need to control the environmental impact of digital technology. Indeed, despite the promises of dematerialization, digital technology is a very material industry, generating greenhouse gas emissions, problematic consumption of water and mineral resources, and social impacts. The digital sector is impacting at every stage: (i) manufacture of equipment; (ii) use; and (iii) end-of-life of equipment, which, when recycled, can only be recycled to a very limited extent. This is a fast-growing sector, and the digitization of our specialty is part of its acceleration and its impact. Understanding the consequences of digitalization and artificial intelligence, and phenomena such as the rebound effect, is an essential prerequisite for the implementation of a sober, responsible, and sustainable digital pathology. The aim of this update is to help pathologists better understand the environmental impact of digital technology. As healthcare professionals, we have a responsibility to combine technological advances with an awareness of their impact, within a systemic vision of human health.

Current Concerns about Microplastics and Nanoplastics: A Brief Overview.

The widespread and increasing use of plastic-based goods in the present-day world has been raising many concerns about the formation of microplastics, their release, their impacts on the environment and, ultimately, on living organisms. These concerns are even greater regarding nanoplastics, i.e., nanosized microplastics, which may have even greater impacts. In this brief review, although without any claim or intention to exhaustively cover all the aspects of such a complex and many-sided issue, the very topical problem of the formation of microplastics, and the even more worrisome nanoplastics, from polymer-based products was considered. The approach is focused on a terse, straightforward, and easily accessible analysis oriented to the main technological engineering aspects regarding the sources of microplastics and nanoplastics released into the environment, their nature, some of the consequences arising from the release, the different polymers involved, their technological form (i.e., products or processes, with particular attention towards unintentional release), the formation mechanisms, and some possible mitigation pathways.

A call from 40 public health scientists for an end to the continuing humanitarian and environmental catastrophe in Gaza.

An under-recognised aspect of the current humanitarian catastrophe in Gaza is the impact of the war on the environment and the associated risks for human health. This commentary contextualises these impacts against the background of human suffering produced by the overwhelming violence associated with the use of military force against the general population of Gaza. In calling for an immediate cessation to the violence, the authors draw attention to the urgent need to rebuild the health care system and restore the physical and human infrastructure that makes a liveable environment possible and promotes human health and well-being, especially for the most vulnerable in the population. Environmental remediation should therefore form one of the most important parts of international efforts to assist reconstruction, through which we hope Palestinians and Israelis will achieve lasting peace, health, and sustainable development, all as part of accepted international human rights obligations.

Environmental impact assessment of ocean energy converters using quantum machine learning.

The depletion of fossil energy reserves and the environmental pollution caused by these sources highlight the need to harness renewable energy sources from the oceans, such as waves and tides, due to their high potential. On the other hand, the large-scale deployment of ocean energy converters to meet future energy needs requires the use of large farms of these converters, which may have negative environmental impacts on the ocean ecosystem. In the meantime, a very important point is the volume of data produced by different methods of collecting data from the ocean for their analysis, which makes the use of advanced tools such as different machine learning algorithms even more colorful. In this article, some environmental impacts of ocean energy devices have been analyzed using machine learning and quantum machine learning. The results show that quantum machine learning performs better than its classical counterpart in terms of calculation accuracy. This approach offers a promising new method for environmental impact assessment, especially in a complex environment such as the ocean.

Socio-environmental externalities of sewage waste management.

Conventional sewage management is expensive and inefficient, putting the environment and public health at risk, making access to sewage services difficult for everyone. Reusing sewage waste has agricultural and economic potential, but can contain harmful contaminants if not treated properly. This review is based on the hypothesis that the destination of sewage waste generates environmental and social externalities, which have not yet been widely compared. With the aim of identifying, from the literature, the socio-environmental externalities generated by different sewage waste management approaches, a systematic review of the literature was carried out, including 244 documents, with 50 % of these discussing impacts of conventional treatment and 37 % analyzing the reuse of waste. The main impacts and externalities were evaluated in three situations: untreated sewage, treated sewage, and reused waste. The results indicate that sewage waste has an underutilized economic value and can generate revenue, reduce operational costs and electricity expenses. Six negative externalities generated by conventional sewage treatment were identified: health costs; environmental cleaning; carbon offsetting; damage to tourism; damage to fishing and agriculture; and real estate depreciation. In reuse, there is a risk of two negative externalities: health costs and environmental cleaning, but two positive externalities were also identified: the reduction of phosphate rock mining and the neutralization of carbon credits. The complexity of the transition to sustainable sewage treatment practices is highlighted given the lack of consensus on the safe use of sewage waste, the lack of regulatory standardization, implementation costs and differences in regional parameters, highlighting the need for preliminary experimentation in a multidisciplinary and contextualized approach, considering comparative externalities among the available sewage waste management possibilities.

Melatonin and resveratrol alleviate molecular and metabolic toxicity induced by Bisphenol A in endometrial organoids.

Bisphenol A (BPA), a widespread environmental contaminant, poses concerns due to its disruptive effects on physiological functions of the uterine endometrium. In contrast, melatonin (MT) and Resveratrol (RSV) are under scrutiny for their potential protective roles against BPA-induced damage. For the efficacy and ethical concerns in the animal test, endometrial organoids, three-dimensional models mimicking endometrium, serve as crucial tools for unraveling the impact of environmental factors on reproductive health. This study aimed to comprehensively characterize the morphological, molecular and metabolic responses of porcine endometrial organoids to BPA and assess the potential protective effects of MT and RSV. Porcine uteri were prepared, digested with collagenase, mixed with Matrigel, and incubated at 38°C with 5 % CO2. Passaging involved dissociation through trypsin-EDTA treatment and subculturing. The culture medium was refreshed every 2-3 days. To investigate the environmental impact on reproductive health, endometrial organoids were treated with BPA (0.5 µM), MT (with/without BPA at 0.1 µM), and/or RSV (10 µM). Various molecular screening using gene expression, western blotting, immunofluorescence staining, and metabolites profiling were assessed the effects of BPA, MT, and RSV in terms of cell viability, morphology, reproductivity, and metabolism alteration in the endometrial organoids. As expected, BPA induced structural and molecular disruptions in organoids, affecting cytoskeletal proteins, Wnt/ß-catenin signaling, and epithelial/mesenchymal markers. It triggered oxidative stress and apoptotic pathways, altered miRNA expression, and disrupted the endocannabinoid system. The level of glucose, galactose, and essential amino acids were increased or decreased by approximately 1.5-3 times in BPA-treated groups compared to the control groups (p-value < 0.05), indicating metabolic changes. Moreover, MT and RSV treated groups exhibited protective effects, mitigating BPA-induced disruptions across multiple pathways. For the first time, our study models endometrial organoids, advancing understanding of environmental impacts on reproductive health.

Environmental impacts and performance assessment of recycled fine aggregate concrete.

Natural disasters and human demolition create vast amounts of construction and demolition waste (CDW), with a substantial portion being concrete waste. Managing this concrete waste is a daunting challenge for developing countries with limited resources, aiming to mitigate its harmful environmental effects. Therefore, the proposed approach involves using recycled fine aggregates (RFA) instead of fresh fine aggregates (FFA) in concrete, which aligns closely with achieving sustainable environmental objectives. Extensive laboratory tests were conducted to assess the effects of adding RFA to concrete. The influence of 0 to 100% RFA replacement and different curing times was investigated on compressive strength, tensile strength, resistance against chloride ion penetration and chemicals exposure, and quality of aggregates. So, around 30%, 35%, 20%, and 79% reductions in compression strength, tensile strength, modulus of elasticity, and workability were estimated when 100% RFA was used in recycled aggregate concrete (RAC). However, according to results analyses, the performance of RAC is reliable up to 50% of RFA in proposed conditions and mix design. In addition, major environmental impacts such as global warming potential, aquatic eutrophication, and aquatic acidification were reduced by 47%, 40%, and 18%, respectively, for concrete having 50% RFA than concrete having 100% FFA.

Data on the economic lifelines and environmental impacts of home-based enterprises in Nigeria: A case study of Ikot-Ekpene, Akwa-Ibom state, Nigeria.

Home-based enterprises (HBEs), which involve the use of one's residence for commercial activities, have become a prominent research focus in the fields of entrepreneurship, sustainability, and environmental management. Despite this, the lack of data impairs the ability to accurately evaluate the economic merits of this rapidly growing business model relative to its environmental effects. This article, detailing data on the demographic features of HBE operators in Ikot-Ekpene, Akwa Ibom State, Nigeria, contributes to ongoing data mining efforts towards realistic framing of HBEs. The current data represent HBEs from five Ikot-Ekpene neighborhoods: Uruk-Uso, Ifuho, G.R.A, Ibiakpan Akanawan, and Ikot Ekpene Urban. The target population of the survey included operators of HBEs and residents. A total of 400 questionnaires were issued through systematic random sampling across the five neighborhoods. The entire sample yielded 330 valid responses, which underwent a descriptive statistical analysis, compiled, and presented in tables and bar charts. Analysis of the data provided insight into the rating of the economic relevance of HBEs in relation to their associated environmental impacts. These data provide helpful insights for researchers and policymakers involved in regulating and controlling activities in the urban sphere. They also serve as a reference point for more rigorous studies on environmental management and urban informality in cities.

Modeling the environmental impacts of Asparagopsis as feed, a cow toilet and slurry acidification in two synthetic dairy farms.

Intensive dairy farming, particularly enteric fermentation and manure management, is a major contributor to negative impacts on the local and global environment. A wide range of abatement measures has been proposed to reduce livestock-related emissions, yet the individual and combined effects of these innovations are often unknown. In this study, we performed an attributional life cycle assessment of three innovative measures modeled in two synthetic German dairy farm systems: Feeding of the seaweed Asparagopsis, installing an in-house cow toilet system, and performing on-field slurry acidification. These measures were modeled both individually and in combination to account for single and cumulative effects and compared to a reference scenario under current practices. Our results showed that feeding high levels of Asparagopsis and the combination of all three measures were most effective at reducing global warming potential (20-30 %), while only the latter mitigated eutrophication (6-9%) and acidification potential (14-17 %). The cow toilet required additional adapted manure management (separated storage and injection of urine) to effectively reduce eutrophication (8-10 %) and acidification potential (19-23 %) and to decrease global warming potential (3-4%) and abiotic depletion (4-5%). Slurry acidification slightly affected all considered environmental impact categories. All three measures involved trade-offs, either between LCA impact categories (global warming potential vs. abiotic depletion), the location of impacts (off- vs. on-farm), or the emission reduction in individual gases (ammonia vs. nitrous oxide). Measure combinations could compensate for the observed trade-offs. Our study highlights the potential of novel abatement measures but also shows the interdependencies of measures in different stages. This calls for a revisiting of current priorities in funding and legislation, which often focus on single objectives and measures (e.g. ammonia reduction) toward the preferential use of measures that are effective without driving trade-offs or improving resource efficiency.

Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

Life cycle assessment of sewage sludge treatment and disposal technologies based on carbon emissions and environmental impacts.

This study aimed to create a comprehensive evaluation method for sewage sludge (SS) treatment and disposal technologies, considering carbon emission and environmental impacts. Life cycle assessment (LCA) were conducted on six SS treatment and disposal technologies in China. The assessments used the IPCC emission factor approach to calculate carbon emissions and the CML2001 method to determine environmental impact factors. Additionally, a colour-coded method was implemented to quantify the evaluation results. The study found that S1 (anaerobic digestion + land application) had the lowest carbon emissions and environmental impact, making it the optimal technology. The S1 scenario had carbon emissions of 669 kg CO2(t DS)-1 and environmental impacts of 5.20E-10. A sensitivity analysis was conducted to show the impacts of each unit in the six technologies on total carbon emissions and environmental impacts. The results showed that landfilling has a high sensitivity to carbon emissions and environmental impacts. Therefore, controlling greenhouse gases and toxic substances in sludge landfills is crucial for reducing carbon emissions and environmental pollution.

Life cycle assessment to evaluate the integral water cycle in industrial supply: A real case study.

Wastewater recycling technologies are developed in areas where the necessity of water resources cannot be satisfied by natural sources. Nevertheless, nowadays trends and European Union Plans show an increasing interest on using these technologies to reduce environmental impacts. This manuscript aims to address the question of the real environmental results of using these technologies and the differences between each specific case using the Life Cycle Assessment (LCA) methodology. A real case study is analyzed to answer this question: the integral water cycle of a northern of Spain, comparing a traditional water supply system (system I), and an alternative wastewater regeneration plant (system II). System II presents a higher impact for all categories (between 1.2 and 37 times higher), except for land use, where it is reduced by 53 %. These results show a larger impact produced by the alternative system due to higher energy and chemical product consumption. Energy consumption is the main factor causing the highest impact in most of the impact categories for both studied systems, including the one associated to the water resource consumption. It accounts for at least 50 % of the total impact for each system in 7 of the 16 evaluated impact categories. In terms of climate change, energy consumption is not particularly significant in system I, but it is for system II, where it represents around 50 % of that impact. In the categories where the impact is not determined by energy consumption, chemical product consumption and waste and discharge treatment are the most relevant factors. In this sense, this paper highlights the importance of analysing each case specifically and underscores the usefulness of using LCA methodology as a tool to improve decision-making in resource management, with water resources emerging as a crucial focal point.

Environmental reporting in Italian thermal power plants: insights from a comprehensive analysis of EMAS environmental statements.

The global energy sector heavily relies on fossil fuels, significantly contributing to climate change. The ambitious European emissions' reduction targets require sustainable processes and alternatives. This study presents a comprehensive analysis of 73 Italian thermal power plants registered to the European Eco-Management and Audit Scheme (EMAS) aimed at assessing EMAS effectiveness in addressing and quantifying the environmental impacts of this relevant industrial sector. The analysis was based on EMAS environmental statements, publicly disclosing verified and certified data, with the secondary objective of evaluating if EMAS could be an efficient tool to improve the plants' environmental performances. An inventory of technical and environmental aspects, adopted indicators, and allocated budgets was based on 2023 data. A strong correlation was found between the significance of the environmental aspects and the number of adopted indicators. Gaps were observed in describing aspects like "biodiversity" and "local issues". Improvement objectives and budget allocation showed discrepancies and lacked correlation with the significance of the related environmental aspects. "Energy production" accounted for 68% of the total allocated budget; "environmental risks", "emissions to air", "electricity consumption", and "local issues" were also key focus areas. Insufficient information on emission control technologies and progress tracking of improvement objectives was detected. This study highlights the need for thermal power installations to improve the selection of appropriate indicators and to better relate allocated budget to improvement objectives when implementing EMAS. Such measures would facilitate the quantification of the effective environmental impacts of the energy production sector, supporting future research on this topic, allowing stakeholders a better comparison among plants, and driving industry-wide improvements.

Impacts assessment of nitrification inhibitors on U.S. agricultural emissions of reactive nitrogen gases.

Fertilizer-intensive agriculture leads to emissions of reactive nitrogen (Nr), posing threats to climate via nitrous oxide (N2O) and to air quality and human health via nitric oxide (NO) and ammonia (NH3) that form ozone and particulate matter (PM) downwind. Adding nitrification inhibitors (NIs) to fertilizers can mitigate N2O and NO emissions but may stimulate NH3 emissions. Quantifying the net effects of these trade-offs requires spatially resolving changes in emissions and associated impacts. We introduce an assessment framework to quantify such trade-off effects. It deploys an agroecosystem model with enhanced capabilities to predict emissions of Nr with or without the use of NIs, and a social cost of greenhouse gas to monetize the impacts of N2O on climate. The framework also incorporates reduced-complexity air quality and health models to monetize associated impacts of NO and NH3 emissions on human health downwind via ozone and PM. Evaluation of our model against available field measurements showed that it captured the direction of emission changes but underestimated reductions in N2O and overestimated increases in NH3 emissions. The model estimated that, averaged over applicable U.S. agricultural soils, NIs could reduce N2O and NO emissions by an average of 11% and 16%, respectively, while stimulating NH3 emissions by 87%. Impacts are largest in regions with moderate soil temperatures and occur mostly within two to three months of N fertilizer and NI application. An alternative estimate of NI-induced emission changes was obtained by multiplying the baseline emissions from the agroecosystem model by the reported relative changes in Nr emissions suggested from a global meta-analysis: -44% for N2O, -24% for NO and +20% for NH3. Monetized assessments indicate that on an annual scale, NI-induced harms from increased NH3 emissions outweigh (8.5-33.8 times) the benefits of reducing NO and N2O emissions in all agricultural regions, according to model-based estimates. Even under meta-analysis-based estimates, NI-induced damages exceed benefits by a factor of 1.1-4. Our study highlights the importance of considering multiple pollutants when assessing NIs, and underscores the need to mitigate NH3 emissions. Further field studies are needed to evaluate the robustness of multi-pollutant assessments.

Exploring tradeoffs among diet quality and environmental impacts in self-selected diets: a population-based study.

PURPOSE: Proposed sustainable diets often deviate dramatically from currently consumed diets, excluding or drastically reducing entire food groups. Moreover, their environmental sustainability tends to be measured only in terms of greenhouse gases emissions. The aim of this study was to overcome these limitations and identify a cluster of already adopted, relatively healthy diets with substantially lower environmental impacts than the average diet. We also aimed to estimate the reduction in multiple environmental impacts that could be achieved by shifting to this diet cluster and highlight possible tradeoffs among environmental impacts. METHODS: The diet clusters were identified by applying energy-adjusted multiple factor analysis and hierarchical clustering to the dietary data of the National FinHealth 2017 Study (n = 5125) harmonized with life cycle assessment data on food products from Agribalyse 3.0 and Agri-Footprint using nutrient intakes and global warming potential, land use, and eutrophication of marine and freshwater systems as the active variables. RESULTS: We identified five diet clusters, none of which had the highest overall diet quality and lowest impact for all four environmental indicators. One cluster, including twenty percent of the individuals in the sample was identified as a "best compromise" diet with the highest diet quality and the second lowest environmental impacts of all clusters, except for freshwater eutrophication. The cluster did not exclude any food groups, but included more fruits, vegetables, and fish and less of all other animal-source foods than average. Shifting to this cluster diet could raise diet quality while achieving significant reductions in most but not all environmental impacts. CONCLUSION: There are tradeoffs among the environmental impacts of diets. Thus, future dietary analyses should consider multiple sustainability indicators simultaneously. Cluster analysis is a useful tool to help design tailored, socio-culturally acceptable dietary transition paths towards high diet quality and lower environmental impact.