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Contains fulltext : 285572.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 295062.pdf (Publisher’s version ) (Open Access)

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Micro- and nanoplastics (MNPs) emissions and pollution are a growing concern due to their potential impact on ecosystems and human health, particularly in soil. This study conducts a comprehensive bibliometric analysis of 2,451 publications spanning from 2006 to 2023. The aim is to assess the research landscape, trends, contributors, and collaborative efforts related to MNPs in soil. Moreover, it examines the extensive research on the effects of MNPs on soil organisms, including earthworms, nematodes, and other fauna as well as the physical–chemical impacts, nanoscale interactions, and ecotoxicological effects on soil microorganisms. Utilizing network analysis, this study explores the global distribution of research across countries, institutions, authors, and keywords, shedding light on the interconnected scientific exploration. The findings reveal a consistent rise in research output over the past decade, reflecting worldwide interest in soil MNPs pollution. It also identifies influential authors and interdisciplinary clusters, highlighting their significant collaborations. Moreover, it pinpoints key institutions and leading journals in this area. Keyword co-occurrence and time-series analysis uncover seven significant research clusters. All provide insights into crucial MNPs aspects and their environmental and health implications. Our findings guide future research and inform strategies to combat MNPs pollution in soils, underscore the need for interdisciplinary approaches to address this complex challenge. In essence, our comprehensive bibliometric analysis serves as a valuable resource, it benefits researchers, policy stakeholders by promoting further research and guiding strategies to mitigate MNPs pollution in soils, in support of ecosystem preservation and human health protection.

Bridging applied ecology and ecotoxicology is key to protect ecosystems. These disciplines show a mismatch, especially when evaluating pressures. Contrasting to applied ecology, ecotoxicological impacts are often characterized for whole species assemblages based on Species Sensitivity Distributions (SSDs). SSDs are statistical models describing per chemical across-species sensitivity variation based on laboratory toxicity tests. To assist in the aligning of the disciplines and improve decision-support uses of SSDs, we investigate taxonomic-group-specific SSDs for algae/cyanobacteria/aquatic plants, invertebrates, and vertebrates for 180 chemicals with sufficient test data. We show that splitting improves pollution impact assessments for chemicals with a specific mode of action and, surprisingly, for narcotic chemicals. We provide a framework for splitting SSDs that can be applied to serve in environmental protection, life cycle assessment, and management of freshwater ecosystems. We illustrate that using split SSDs has potentially large implications for the decision-support of SSD-based outputs around the globe.

Although several models of pesticide uptake into plants are available, there are few modeling studies on the bioconcentration of metabolites in plants. Ignoring metabolites in plant uptake models can result in an underestimation of the parent compound's overall impacts on human health associated with pesticide residues in harvested food crops. To address this limitation, we offer a metabolite-based plant uptake model to predict the bioconcentration of the parent compound and its metabolites in plants. We used the uptake of glyphosate and its major metabolite (aminomethylphosphonic acid, AMPA) into potato as an example. The analysis of variability revealed that soil properties (affecting the soil sorption coefficient), dissipation half-life in soil, and metabolic half-life in the potato had a significant impact on the simulated AMPA concentration in the potato, indicating that regional variability could be generated in the plant bioconcentration process of metabolites. The proposed model was further compared using the non-metabolite model. The findings of the comparison suggested that the non-metabolite model, which is integrated with the AMPA bioconcentration process, can predict the AMPA concentration in the potato similarly to the proposed model. In conclusion, we provide insight into the bioconcentration process of metabolites in tuber plants from a modeling viewpoint, with some crucial model inputs, such as biotransformation and metabolic rate constants, requiring confirmation in future studies. The modeling demonstration emphasizes that it is relevant to consider bioaccumulation of metabolites, which can propagate further into increased overall residues of harmful compounds, especially in cases where metabolites have higher toxicity effect potency than their respective parent compounds.

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8-46% of marketed chemicals based on 1-10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

Soil ecosystems are crucial for providing vital ecosystem services (ES), and are increasingly pressured by the intensification and expansion of human activities, leading to potentially harmful consequences for their related ES provision. Micro- and nanoplastics (MNPs), associated with releases from various human activities, have become prevalent in various soil ecosystems and pose a global threat. Life Cycle Assessment (LCA), a tool for evaluating environmental performance of product and technology life cycles, has yet to adequately include MNPs-related damage to soil ES, owing to factors like uncertainties in MNPs environmental fate and ecotoxicological effects, and characterizing related damage on soil species loss, functional diversity, and ES. This study aims to address this gap by providing as a first step an overview of the current understanding of MNPs in soil ecosystems and proposing a conceptual approach to link MNPs impacts to soil ES damage. We find that MNPs pervade soil ecosystems worldwide, introduced through various pathways, including wastewater discharge, urban runoff, atmospheric deposition, and degradation of larger plastic debris. MNPs can inflict a range of ecotoxicity effects on soil species, including physical harm, chemical toxicity, and pollutants bioaccumulation. Methods to translate these impacts into damage on ES are under development and typically focus on discrete, yet not fully integrated aspects along the impact-to-damage pathway. We propose a conceptual framework for linking different MNPs effects on soil organisms to damage on soil species loss, functional diversity loss and loss of ES, and elaborate on each link. Proposed underlying approaches include the Threshold Indicator Taxa Analysis (TITAN) for translating ecotoxicological effects associated with MNPs into quantitative measures of soil species diversity damage; trait-based approaches for linking soil species loss to functional diversity loss; and ecological networks and Bayes

Occupational injuries and illnesses are major risk factors for human health impacts worldwide, but they have not been consistently nor comprehensively considered in life cycle impact assessment (LCIA) methods. In this study, we quantified occupational health impacts as disability-adjusted life years (DALYs) for nonfatal injuries and illnesses in all US industries. We further applied an economic input-output model of the US economy to develop a new data set of characterization factors (CFs) that links direct and indirect occupational health impacts to product life cycle final demand. We found that the CF data set varies significantly by industry, ranging from 6.1 to 298 DALYs per billion dollars. About 20% of final demand in the US economic system contributes nearly 50% of the total impacts of occupational health, suggesting occupational health impacts are concentrated in a small portion of industries. To verify the feasibility of the CFs and demonstrate their importance, we included a case of an office chair. The occupational health impacts caused by nonfatal injuries and illnesses during the production of an office chair are of the same order of magnitude as those caused by chemical emissions across the chair's life cycle, with 1.1 × 10-5 and 1.4 × 10-5 DALYs per chair, respectively. Results and data sets derived from this study support the integration of occupational health impacts with LCIA methods.

This systematic literature review aimed to provide an overview of the characteristics and methods used in studies applying the Disability-Adjusted Life Years (DALY) concept for infectious diseases within European Union (EU)/European Economic Area (EEA)/European Free Trade Association (EFTA) countries and the United Kingdom. Electronic databases and grey literature were searched for articles reporting the assessment of DALY and its components. We considered studies in which researchers performed DALY calculations using primary epidemiological data input sources. We screened 3,053 studies of which 2,948 were excluded and 105 studies met our inclusion criteria. Of these studies, 22 were multi-country and 83 were single-country studies, of which 46 were from the Netherlands. Food- and water-borne diseases were the most frequently studied infectious diseases. Between 2015 and 2022, the number of burden of infectious disease studies was 1.6 times higher compared to that published between 2000 and 2014. Almost all studies (97%) estimated DALYs based on the incidence- and pathogen-based approach and without social weighting functions; however, there was less methodological consensus with regards to the disability weights and life tables that were applied. The number of burden of infectious disease studies undertaken across Europe has increased over time. Development and use of guidelines will promote performing burden of infectious disease studies and facilitate comparability of the results.

Ongoing efforts focus on quantifying plastic pollution and describing and estimating the related magnitude of exposure and impacts on human and environmental health. Data gathered during such work usually follows a receptor perspective. However, Life Cycle Assessment (LCA) represents an emitter perspective. This study examines existing data gathering and reporting approaches for field and laboratory studies on micro- and nanoplastics (MNPs) exposure and effects relevant to LCA data inputs. The outcomes indicate that receptor perspective approaches do not typically provide suitable or sufficiently harmonised data. Improved design is needed in the sampling, testing and recording of results using harmonised, validated and comparable methods, with more comprehensive reporting of relevant data. We propose a three-level set of requirements for data recording and reporting to increase the potential for LCA studies and models to utilise data gathered in receptor-oriented studies. We show for which purpose such data can be used as inputs to LCA, particularly in life cycle impact assessment (LCIA) methods. Implementing these requirements will facilitate proper integration of the potential environmental impacts of plastic losses from human activity (e.g. litter) into LCA. Then, the impacts of plastic emissions can eventually be connected and compared with other environmental issues related to anthropogenic activities.

Background Degradation kinetics of pesticides in plants are crucial for modeling mechanism-based pesticide residual concentrations. However, due to complex open-field conditions that involve multiple pesticide plant uptake and elimination processes, it is difficult to directly measure degradation kinetics of pesticides in plants. To address this limitation, we proposed a modeling approach to estimating degradation rate constants of pesticides in plants, using potato as a model crop. An operational tool was developed to backward-estimate degradation rate constants, and three pesticides were selected to perform example simulations. Results The simulation results of thiamethoxam indicated that the growth dynamics of the potato had a significant impact on the degradation kinetic estimates when the pesticide was applied during the early growth stage, as the size of the potato determined the uptake and elimination kinetics via diffusion. Using mepiquat, we demonstrated that geographical variations in weather conditions and soil properties led to significant differences in the dissipation kinetics in both potato plants and soil, which propagated the variability of the degradation rate constant. Simulation results of chlorpyrifos differed between two reported field studies, which is like due to the effect of the vertical distribution of the residue concentration in the soil, which is not considered in the majority of recent studies. Conclusions Our proposed approach is adaptable to plant growth dynamics, pre-harvest intervals, and multiple pesticide application events. In future research, it is expected that the proposed method will enable region-specific inputs to improve the estimation of the degradation kinetics of pesticides in plants.

BACKGROUND: Pesticide residues in animal feed can endanger animal health and compromise the safety of livestock products for human consumption. Even though policymakers such as the European Union and the World Health Organization have established maximum residue levels (MRLs) for pesticides in both human food and animal feed, there is no systematic management of pesticides in animal feed that considers the entire supply chain. In response, we propose a framework for defining consistent MRLs for pesticides in animal feed that assesses the impact of defined MRLs on upstream (e.g., MRLs in feed crops) and downstream (e.g., MRLs in livestock products) sectors of the livestock-product supply chain. RESULTS: The MRLs determined for the selected pesticides in the feed of cattle and sheep as case study animals indicate that lipophilic pesticides tend to have lower MRLs than hydrophilic pesticides, primarily due to the relatively high toxicity and biotransfer factors of lipophilic pesticides. In addition, we observe that, primarily for lipophilic pesticides, upstream and downstream regulations are not aligned in terms of defining MRLs in feed using current MRLs in crops with relevance to feed and foods of animal origin. CONCLUSION: Some of the current pesticide regulations in the livestock-product supply chain need to be re-evaluated to ensure that MRLs in the upstream sector (i.e., crops) do not result in unacceptable residues in the downstream sector (i.e., MRLs in livestock products affecting animal and human health). Finally, we provide recommendations for optimizing the derivation of MRLs in feed, including the evaluation of residue fate during feed and food manufacturing processes.