Environmental Sciences Europe最新文献

Persistent and toxic chemicals remain a significant pollution concern, underscored by the European Union's Zero Pollution Action Plan. Daily exposure to complex chemical mixtures starts early and continues throughout life, for instance for consumer products such as toys, plasticware, furniture, and synthetic fibres. EU-funded research projects like COPHES/DEMOCOPHES, HBM4EU, and PARC have documented population exposure to these substances. The outcomes of such research initiatives have contributed to highlighting the adverse health impacts of Substances of Concern (SoCs), leading to several regulatory actions within the EU. SoCs include hazardous chemicals such as carcinogens, mutagens, endocrine disruptors, and “persistent, bioaccumulative, and mobile” (PBM) chemicals. The digital transformation in chemicals management has resulted in policies that mandate electronic submissions of chemical risk assessment-relevant data, and the creation of industry-specific databases like the Substances of Concern in Products (SCIP) database, established by the European Chemicals Agency (ECHA) under the revised Waste Framework Directive (WstFD). These databases describe SoCs and their link with products, offering a comprehensive view of chemical quantities, emission sources, exposure pathways, and other relevant data, contingent on robust data governance. Effective chemical risk assessment requires characterizing hazards, exposure sources and levels, and drawing conclusions concluding on potential risks, supported by a well-defined problem formulation and monitoring. This includes setting objectives and defining the scope of the risk assessment and decision-making, particularly regarding early warning signal detection for the purpose of public health protection. Successful risk assessment hinges on access to robust, traceable, accessible, and interoperable data across scientific disciplines and regulatory frameworks. This paper discusses the challenges of aggregating human health risk assessment-relevant chemical information from multiple sources, especially from the perspective of data fusion and reuse. It presents findings from a research project focused on utilizing chemicals datasets from various governmental and scientific sources. The study highlights the need for improved data presentation and availability to enhance usability for all stakeholders. Recommendations are made for the EU Commission, ECHA, industry, and academia to support harmonized data practices, increased transparency, and the development of sustainable chemical applications fostering safer market introductions. These recommendations can also be useful to other data providers that care about the reusability of the data they publish or manage.

Rotational shifting cultivation (RSC) is commonly practiced in northern Thailand for upland rice cultivation, primarily for household consumption. However, the potential health risks from heavy metal contamination in these soils have not been thoroughly explored. This study aimed to evaluate the contamination of six heavy metals (Arsenic (As), Mercury (Hg), Cadmium (Cd), Lead (Pb), Copper (Cu), and Chromium (Cr)) in upland rice across RSC fields with varying fallow periods and assess the associated health risks from rice consumption. Four RSC fields with 5, 6, 10, and 12-year fallow periods were examined. The RSC-6Y and RSC-12Y fields were used for upland rice cultivation in 2022, while the RSC-5Y and RSC-10Y fields were cultivated in 2023. The geo-accumulation index (I_geo) was calculated, and translocation factors (TF) were assessed for the transfer of heavy metals from soil to straw (TF_Straw/Soil), straw to grain (TF_Grain/Straw), and soil to grain (TF_Grain/Soil). The results indicated that after burning vegetation in the RSC fields, the highest concentrations of Pb, Cr, and Hg were found in the ash. In RSC soils, Cu, Cr, As, Pb, and Hg levels were below reference standards, with Cd undetected. In rice grains, the order of concentration was Pb > Cu > Cr > As, with Hg and Cd undetected. Pb levels in rice grains exceeded the safety threshold. I_geo values indicated no contamination to moderate contamination across sites, with negative I_geo values for Cr and Cu, and zero values for Cd. The TF results showed limited transfer of As, Hg, and Cd from soil to rice plants (TF_Straw/Soil < 0.1), but notable transfer for Pb, Cr, and Cu. Pb was readily transferred from soil to grain (TF_Grain/Soil), posing a potential health risk. The study highlights potential non-carcinogenic and carcinogenic risks from heavy metal exposure, particularly Pb, and underscores the need for further research to determine contamination sources and mitigation strategies.

The interest in and use of biological materials in crop production is increasing globally at a rapid pace. In many cases, testing methods for conventional chemicals are applied to microbial-based biopesticides because specific microbial test methods are lacking. However, not all methods are easily transferred to microbial-based products. An evaluation was conducted to determine if OECD (Organization for Economic Co-operation and Development) Guidance Document No. 239 on honey bee larval toxicity could be adapted to microbial pesticides. In our study, five microbes, including B. pumilus, B. thuringiensis, B. velezensis, Paenibacillus larvae, and Ascophaera apis were grown on agar media and spotted with either honey bee in vitro larval diet or royal jelly. We observed that the honey bee larval bee diet and royal jelly did not inhibit the fungal honey bee pathogen Ascophaera apis, yet inhibited the growth of bacteria, including a known honey bee larval pathogen. This finding may make the test unreliable for certain biopesticides. The OECD is considering biopesticide-specific testing guidelines, and the EPA has indicated it to update itsbiopesticide bee test guidelines. However, additional research is needed to determine which options may be feasible and provide the best improvements.

Background

Microplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran.

Methods

The research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set.

Results

The findings underscore the proposed algorithm’s supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model’s precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics.

Conclusions

The study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.

Background

Acute fish toxicity test (AFT) is one of the cornerstones of environmental risk assessment (ERA) of chemicals for the aquatic environment. Despite many efforts to find an alternative able to fully replace the test, there is still lasting pressure from stakeholders for AFT results.

Results

Here, we present the results of a case study with eight pharmaceuticals from various pharmaceutical groups with different levels of expected toxicity to fish. Selected compounds were tested in two validated alternative tests—fish embryo toxicity test with zebrafish (Danio rerio) (zFET) and in vitro RTgill-W1 assay according to their corresponding OECD guidelines TG 236 and TG 249, respectively. Data for AFT were collected from PubMed and ECOTOX knowledgebase databases, and acute toxicity to fish was further predicted in silico by the ECOSAR program. Predicted environmental risks (risk quotients, RQ, calculated using the exposure data from NORMAN) from both zFET and RTgill-W1 well correlated with the average RQs based on AFT LC50s. The strongest and most significant correlation was observed while comparing the AFT results with the median of combined alternative methods (zFET, RTgill-W1, ECOSAR).

Conclusions

This proposed approach combining experimental data with modeling could serve as a reliable tool for predictions of environmental risks promoting the 3R alternatives to acute fish toxicity testing.

Vitamin C, also referred to as ascorbic acid (AsA), is recognized for its capacity to cure and avert scurvy, and it is crucial for regular human growth and development. In various crops, AsA participates in stress response mechanisms mediated by abscisic acid and has been discovered to have a crucial function in the morphogenesis, growth, development, and production of male gametes in plants. GDP-D-mannose 3′,5′-epimerase (GME) is essential in the synthesis of vitamin C. Our research identified 91, 83, 51, and 46 genes, respectively, found in G. barbadense (GbGMEs), G. hirsutum (GhGMEs), G. arboretum (GaGMEs), and G. raimondii (GrGMEs). Plants resulting from VIGS infection with GhGME31D clearly showed yellowing, water loss and wilting of leaves and black spots on stems. Measurement of MDA and AsA levels indicated that the plants were more damaged. This indicates that AsA has a substantial impact on plant growth and development.

At present, the research of sustainable development is developing from a single local problem to a multi-scale, transdisciplinary and comprehensive study. The evaluation and monitoring of its progress need to adopt multi-disciplinary research methods and multi-dimensional, multi-scale identification mechanism. Landsenses ecology is an emerging scientific system that uses the basic principles of ecology to study the sustainable development of land-use planning, construction, and management from the aspects of natural elements, physical senses, psychological perceptions, social economy, process and risk. It provides an effective way for the multi-disciplinary integration research of the relationship between human and ecosystem, and provides an important method and theory for the sustainable transformation research of environmental system and social–economic system, and plays an important role in guiding and realizing the beneficial impact of human on natural ecosystem. This study describes 57 articles published in peer-reviewed journals between 2016 and 2024, using qualitative content analysis, and discusses the impact of landsenses ecology on the way sustainable development is perceived and practiced. The results suggest that the role of landsenses ecology in the creation of sustainable vision resonance and behavior is crucial to the study of sustainable transformation and will help to explore effective strategies for using intrinsic sustainable transformation as a deep leverage point.

Background

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are under development in oilseed rape (Brassica napus), camelina (Camelina sativa), and pennycress (Thlaspi arvense).

Main findings

Environmental risk scenarios starting with hazard identification are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness-related traits. In case of a changed oil composition, an increase or decrease of polyunsaturated fatty acids (PUFA) may lead to risks for health and survival of pollinators. Regarding fitness-related traits, other risks were identified, i.e. an increased spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions.

Conclusion

It is shown that for environmental risk scenarios of the technological specificities of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will, also in future, require risk assessment and monitoring of individual traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially limit the scale of releases. This is especially relevant for NGT Brassicaceae in Europe, which is a centre of diversity of this plant family.

Fertilizers are an essential input in agriculture as they can enhance crop yields. However, their use also poses significant environmental risks. To thoroughly explore the intensity of fertilizer use and its potential threats to the ecological environment, this study analyzed the environmental risks of fertilizer use from a temporal and spatial perspective based on fertilizer application data in China from 1978 to 2022. Additionally, the contribution of fertilizer application in Chinese farmland to greenhouse gas N₂O emissions was quantified using IPCC emission factor methodology. The results indicated that fertilizer application intensity and N₂O emissions in China initially increased and then decreased from 1978 to 2022. Despite the implementation of various fertilizer control measures at the policy level, such as the Zero Growth of Fertilizer Action in 2015 and the Efficiency-Increasing Action for Reducing Fertilizer Use in 2022, the intensity of fertilizer application in China still exceeded international safety standards by 1.33-fold in 2022, reaching 298.79 kg/hm². Furthermore, N₂O emissions amounted to 50.17 × 10⁴t, accounting for 16% of China's total agricultural greenhouse gas emissions that year. Correlation and regression analyses demonstrated that with increasing fertilizer application, crop production exhibits an inverted U-shaped growth trend, indicating limited effectiveness of high-intensity fertilizer use in increasing crop yields. These findings highlight the profound greenhouse effect resulting from the use of agricultural nitrogen fertilizer. Therefore, this study proposed technical and policy-level mitigation measures to address the issues caused by excessive fertilizer application, aiming to provide insights for controlling agricultural non-point source pollution and preserving the agroecological environment.

The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization.

Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration.

It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments.