Environmental Sciences Europe最新文献

Background

Over the past few centuries, human impacts on river ecosystems have resulted in significant physical and ecological degradation of aquatic ecosystems. While the implementation of restoration measures has led to morphological improvements in rivers, positive biological responses, especially for benthic macroinvertebrates, are often limited. Alongside morphological degradation, recovery of invertebrate diversity is constrained by other factors acting at larger spatial scales, such as chemical pollution. Yet the extent to which chemical pollution limits the recovery of aquatic communities in restored river sections remains poorly studied. The application of current water quality monitoring methods may lead to inaccurate predictions of the effects of such stressors on river ecosystems. Therefore, effect-based methods (EBMs) are proposed as a complementary screening tool for the integrative assessment of river status.

Results

In this study, we provided a comprehensive assessment of the effects of local restoration measures by integrating ecological metrics, ecotoxicological tools, and chemical analyses. Although restoration measures were found to have a positive effect on habitat quality, no recovery of good ecological status in restored sections was found. The main pressure acting as a limiting factor for the improvement of benthic macroinvertebrate communities was found to be the prevailing chemical pollution. The effect of restoration age on benthic invertebrate communities and, for the first time, on ecotoxicological effects in response to hydromorphological river restoration showed no effect on ecological metrics or toxicity parameters.

Conclusion

In summary, the success of restoration needs to be assessed using a more integrative and multi-dimensional approach to identify limiting factors and to differentiate cases where restoration measures have not yet had time to take effect from cases where additional interventions may be required. The present results underscore the necessity for restoration initiatives to address not only individual stressors, such as morphological degradation, but also to incorporate comprehensive ecotoxicological monitoring to identify and mitigate any adverse effects that may compromise their effectiveness.

Background

Transportation plays a pivotal role in global economic activities, facilitating mobility, trade, and sustainable development. However, it is also responsible for 15% of global greenhouse gas (GHG) emissions, with rapid urbanization and economic growth in Africa expected to further intensify these emissions. South Africa, Egypt, Nigeria, and Algeria together account for over 50% of Africa’s total emissions, emphasizing the need for detailed analysis and effective policy interventions. This study aims to predict GHG emissions from the transportation sector in these top-emitting African countries by analyzing demographic, economic, energy-related, and transportation-specific factors. Data from 1995 to 2022 were sourced from the World Development Indicators (WDI) and the International Organization of Motor Vehicle Manufacturers (OICA). Seven machine learning models were tested, including ordinary least squares (OLS), ridge regression (RR), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), histogram-based gradient boosting (HGB), random forest (RF), and bagging regressor (BR). The models were assessed using fourfold cross-validation and performance metrics, such as mean absolute percentage error (MAPE), relative root mean square error (rRMSE), and R².

Results

Results show that CatBoost demonstrated superior performance, achieving an R² of 0.960, MAPE of 4.9%, and rRMSE of 6.7%. Feature importance analysis identified passenger cars in use and GDP (PPP) as the most significant contributors to GHG emissions from transportation. The findings further reveal that all evaluated factor categories, including demographic, economic, energy, and transportation-specific, exert a significant impact on GHG emissions from transportation, with transportation-specific factors exhibiting the greatest influence. Moreover, when comparing the relative impact of nations on the predictive model, Egypt (EGY) was found to have the highest influence among countries analyzed.

Conclusions

These findings provide critical insights for policymakers in Africa, particularly in the top-emitting countries, to revise energy investments and design targeted strategies for mitigating transportation-sector emissions.

Graphical Abstract

Many regulatory agencies establish maximum residue levels (MRLs) for pesticides in livestock products to protect human health. However, the relationships between pesticide MRLs across different products from the same livestock animal and their impact on animal health remain unknown. To address these questions, we developed compatibility, livestock-health, and human-health mechanisms within a regulation-oriented framework. For the compatibility mechanism, which aims to prevent inconsistencies in the MRLs of pesticides set for raw livestock products, we simulated uncertainty intervals for the suggested MRL ratios in raw products from cattle and sheep. For the livestock and human-health mechanisms, we introduced pesticide-specific elimination-equivalent (PSEE) exposure slopes to evaluate animal exposure risks, based on the legal limits of pesticides. Simulation results find that the median suggested MRL ratio of bovine meat (with ~ 20% fat content) to liver (with ~ 3.6% fat content) was 4.90, while the median ratio of liver to kidney was 1.18, indicating that the ratios were determined by the log K_OW of the pesticides and the fat content of livestock products. The compatible intervals for DDT's suggested MRL ratios were [3.80, 6.42] for meat/liver and [4.51, 7.62] for meat/kidney. The calculated PSEE exposure slopes reveal that, in general, lipophobic pesticides have steeper PSEE exposure slopes, meaning higher exposure is required to reach the default residue level. For example, assuming a reservation coefficient (ζ) of 1.0, the PSEE exposure slopes of oxamyl (log K_OW = − 0.47) are 0.12 and 0.16 kg kg⁻¹d⁻¹ for bovine milk and liver, respectively, while DDT (log K_OW = 6.91) shows values of 0.066 and 0.064 kg kg⁻¹d⁻¹. These findings provide a new perspective for improving the comprehensive regulatory process.

Background

Respiratory infections (RIs) are a significant burden on public health, and the COVID-19 pandemic complicated their management. Here, we investigate associations between air pollution exposure and RI health outcomes.

Methods

We analysed 8.2 million RI episodes (limited to respiratory diseases, as classified by the 10th revision of the International Classification of Diseases: J00-J22) in Taizhou and Huzhou (2017–2022), collapsing repeat visits within 14 days. A generalized additive model (GAM) was used to explore the relationships between daily 24-h average Particulate Matter with a diameter < 2.5 µm (PM_2.5) and nitrogen dioxide (NO₂) exposure (single lags [0–3] and moving averages over the same period) and health outcomes. Additionally, multivariable logistic regression was employed to assess the risk (defined as exceeding the median hospital stays and severe illness during hospitalization) attributable to demographic factors among inpatient cases.

Results

The study encompassed 8,198,574 cases admitted; males constituted 51.2% and children under 5 years comprised 31.2%. Children and adolescents aged 5–18 exhibited a 4.965% (95% Confidence Interval [CI]: 4.449–5.483) increase in daily admissions for every 10 μg/m³ rise in PM_2.5 and a 7.789% (7.092–8.491) increase for the same change in NO₂. For each 10 μg/m³ increase in PM_2.5, the length of stay (LOS) increased by 6.435 days for children under 5 and 11.206 days for those over 65. Urban residents showed a longer LOS (adjusted odds ratio, aOR: 1.079, 1.047–1.112) but a reduced risk of severe illness (aOR: 0.833, 0.772–0.899) compared to their rural counterparts. The elderly (aged 65 and above) demonstrated the highest risk of severe illness compared to adults aged 19–34; this was particularly pronounced during the pandemic response phase (aOR: 11.900, 7.928–18.960).

Conclusions

Air pollutant levels are correlated with a spectrum of respiratory health outcomes, and these associations exhibit substantial demographic disparities. The risk of severe illness among children under 5 years and the elderly was significantly elevated, underscoring the urgent need for health authorities to formulate targeted strategies aimed at protecting these at-risk groups.

Fishponds are essential ecosystems, inhabited by many different groups of biota and having commercial and non-commercial functions. Therefore, managing fishponds can be a crucial parameter affecting all biota inhabiting a given fishpond, and macrozoobenthos and zooplankton can be the most affected communities. Thus, this study investigates the effect of traditional management with high fish density and cereal co-feeding (business as usual management, BAU) and adjusted fishpond management (NP (natural production) and NPF (natural production with supplementary feeding)) on macrozoobenthos, zooplankton, and phytoplankton dynamics. This study was carried out in four fishponds for three seasons. Our results showed that seasonal community development was affected by management type, where BAU management had different community patterns compared to NP and NPF management. In contrast, taxa richness of all communities was comparable among the management. BAU management favored smaller zooplankton species compared to the adjusted NP and NPF. Notably, macrozoobenthos and phytoplankton also showed significant differences between NP and NPF management. To sum up, fishpond management is an essential driver for macrozoobenthos and zooplankton communities in fishponds.

Irrigation is an important adaptation measure to global climate change, aimed at ensuring the sustainable development of the agricultural sector and addressing the food crisis. At the same time, methods and practices of sustainable management of water resources, soils, and soil fertility are of critical importance. The application of such approaches supports soil health, enables the delivery of essential ecosystem services, and contributes to overall ecosystem stability. This article analyses the issues of sustainable management of soil and water resources both internationally and within Ukraine. The study involves a bibliometric analysis of literature in the Scopus database, using selected keywords and a defined date range to examine the development of irrigation worldwide. Bibliographic network maps based on keywords were constructed, and clusters representing different aspects of irrigation research were identified. These clusters allow for the tracing of trends and the evolution of focus areas within this research domain. Ukraine is characterised by low coefficients of water resource availability. This study outlines the conceptual approaches, strategic directions, and current state of irrigation development in Ukraine, taking into account contemporary challenges. One of the key challenges facing the development of irrigation, irrigated agriculture, and the sustainable use of water and soil resources is the ongoing military conflict. The bibliometric analysis conducted reveals an increasing number of global publications on this topic during the period 1990–2023. The findings of this research can be used to inform irrigation development planning in Ukraine and to support decision-making at various levels of governance to ensure the sustainable management of water and soil resources.

Background

The widespread use and frequent detection of the herbicide metazachlor (MTZ) in aquatic environments have raised concerns about its impacts on non-target organisms. Despite its regular presence in freshwater ecosystems, the ecotoxicological effects of MTZ on benthic macroinvertebrates are mostly unknown. We investigated the sublethal impacts of two environmentally relevant concentrations of MTZ (5 µg/L and 100 µg/L) on sediment-dwelling larvae of mayfly Ephemera danica, collected from natural habitats over two consecutive years during their critical physiological stage of emergence, while also considering their natural parasite infections.

Results

We observed a significant increase in the mortality of individuals exposed to MTZ, following two separate ten-day exposure experiments conducted in 2022 and 2023 under controlled conditions. Furthermore, in both trials, MTZ-induced stress responses at the pre-emergence developmental stage resulted in significant decreases in larval glycogen content, while glutathione S-transferase activity increased. Infections of E. danica with microsporidian parasites had no significant impact on the sublethal endpoints; however, the number of infected individuals was low in the different treatment groups (low statistical power). Moreover, microsporidian infection rates in E. danica differed significantly between the two years.

Conclusions

MTZ affects the survival and biochemical responses of E. danica larvae at field-realistic concentrations, while the varying prevalence of microsporidian infection highlights the potential effects of parasitism on study outcomes. Thereby, we advocate the use of ecologically relevant testing approaches for a better understanding of the ecological implications of herbicides in aquatic ecosystems.

Graphical abstract

Artificial intelligence (AI) is being developed fast and applied in several areas including education and healthcare with excellent potential for use in fields that require complex analytics, particularly in the case of climate change. Recent developments in AI, such as ChatGPT and OpenAI, machine vision technologies and deep learning, among others, may be deployed in various contexts, including climate change. Of specific interest is the role played by foundation models (FMs), which may help to augment intelligence on climate change and reduce the social risks of adaptation and mitigation initiatives. This article discusses the potential applications of FMs in climate change research and management and illustrates the need for further studies. FMs, built on large unlabelled data sets and enabled by transfer learning, offer versatility in handling complex tasks. Specifically, FMs can aid in climate data analysis, modelling future scenarios, assessing risks, and supporting decision-making processes. Despite their potential, challenges such as data privacy, algorithm bias, and energy consumption require careful consideration. The article emphasizes the importance of interdisciplinary efforts to address these challenges and maximize the positive impact of FMs in mitigation and adaptation. AI, including advanced models like FMs, holds significant promise for addressing climate change challenges.

Background

Trifluoroacetate (TFA) is a substance of rising concern, because TFA is widespread and high TFA levels have been detected in groundwater, causing an imminent threat, e.g. to organisms in groundwater. Potential precursors of TFA contain at least one carbon-bound trifluoromethyl moiety, which includes several pesticides. However, only for few pesticides empirical evidence for TFA formation is available.

This paper aims to provide an estimation of TFA emissions from pesticides used in the EU by applying two approaches.

The estimation approach was based on application and crop cultivation data and was used to calculate (a) the theoretical TFA formation in g/ha for typical pesticidal uses in the EU and (b) TFA formation in t by considering crop cultivation areas in the EU.

In addition, a worst-case groundwater modelling approach was developed to assume the TFA leaching potential from particular pesticide applications with the model commonly used in pesticide regulation. For the FOCUS modelling approach, we used harmonised EU endpoints for physico-chemical parameters of pesticide active substances and added TFA as metabolite, considering a worst-case formation of 100% from precursors.

Results

The lowest TFA formation potential in g/ha was determined for insecticides, while fungicides showed a strong variation. Taking crop production areas into account, five herbicides were identified as the most important TFA sources.

The modelled TFA concentrations in groundwater were above 10 µg/L for most substances except for insecticides which predominately range from 0.1 µg/L to 10 µg/L. This is far above the legal limit of 0.1 µg/L for relevant pesticide metabolites in groundwater.

Considering the results of both approaches, we prioritised nine out of 24 active substances as the most crucial ones regarding TFA leaching in the EU: diflufenican, flonicamid, fluazifop-P-butyl, fluazinam, flufenacet, fluopyram, flutolanil, picolinafen and trifloxystrobin.

Conclusions

Our estimated TFA emissions indicated potentially unacceptable leaching of TFA into groundwater. The most important active substances identified here can be used as priority substances to address this topic. Overall, it becomes clear that improved regulatory assessment of TFA is necessary in pesticide risk assessment.

Dumpsite leachate plumes (DLPs) remain one of the major threats to environmental health, compromising the integrity of soil, surface-groundwater systems, and human health. This study exploits three main geophysical methods (electrical resistivity, magnetic survey, and Very Low Frequency) and hydrochemical analysis (HCA) alongside an oral interview to evaluate the impacts of the DLPs on the immediate environment and the associated health risks within Kaduna Metropolis, Nigeria. Based on geophysical investigations, the subsurface resistivities significantly vary between 2.3–9.2 and 17–114 Ωm, the total magnetic intensity (TMI) varies between 37,600–44,500 and 31,095–37,300 nT, while the equivalent current density (ECD) varies between 30.0–45.0 and – 50 to 30, respectively. Low resistivity, high ECD, and high TMI zones were delineated and mapped as leachate plume-accumulated regions, occurring within the depths of 0.0–7.0 m. The geophysical data highlight zones with elevated magnetic anomalies, resistivity contrasts, and conductive anomalies linked to the suspected DLP accumulated zones. HCA examined the contents of trace elements in the leachate plumes and groundwater samples independently, which vary considerably between 11.17–21.73 and 0.017–2.919 mg/l, respectively. The combined data shows that nearly 75% of the populace residing within a 200 m radius of the investigated dumpsites are potentially exposed to trace elements with (Fe > 1.5, Pb > 1.2, Cd > 0.08, Cr > 0.6 mg/l) in groundwater and (Fe > 2.0, Pb > 1.6, Cd > 0.5, Cr > 1.0 mg/l) in surface water. In addition, the leachate plume contaminants have significantly endangered the integrity of the subsurface in nearly 50% of the area within a 200 m radius of the dumpsites to a depth of > 3.0 m, which inversely compromises the regional groundwater and public health systems. Based on the findings of this study, a safe depth/distance was examined and suggested as a temporary solution. Also, for geophysical standardization, it requires that drinking water sources should be located at least 15 m deep in the soil and 100 m away from dumpsites. Similarly, open and unlined dumpsites should be avoided, while sanitary landfills should be sited at least 6 m above the water table. In conclusion, the overall integrity of surface/groundwater and public health status is largely determined by the overall sanitation of its ambient environment.