Environmental Sciences Europe最新文献

In this article we attempted to investigate the occurrence of metals (Fe, Mn, Zn, Pb, Cu, Cd) in the aquatic environment of the Kozłowa Góra Reservoir (southern Poland) under the influence of the Upper Silesian industry. The reservoir, as a shallow and polymictic unit, has not been the subject of as many extensive studies on the spatial distribution of metals as deep and dimictic reservoirs. The objective of the article is to fill this gap. In our work, we confirmed that the metals in the Kozłowa Góra Reservoir were of anthropogenic origin, and their highest contentin the sediments reflected old Brynica riverbed within the reservoir, which transports pollutants from the Silesian agglomeration. The metals detected in the sediments (a fraction f ≥ 0.06 mm) show positive correlations with organic matter. On the other hand, the metals in the fraction f < 0.2 mm had a positive correlation with a silty-clay fraction. During the study, we also found that only Zn, Cd, and Pb in the fraction f < 0.06 mm showed a mean positive correlation with the silty-clay fraction (f < 0.06 mm). Additionally, we developed the interfacial equilibrium model which allows for the calculation of metal content in the mineral and organic fractions of sediments. The highest concentrations of the analyzed metals occur in the organic fraction, exceeding the concentrations in the mineral fraction by 6 to 34 times. We also developed a model of metal multisorption in the sediment. We found that the accumulation of a given metal in sediment can be influenced by other metals contained in the water and/or sediment. In order to determine the mutual relationships between the metal content in water, in various sediment fractions, and the mass share of the mineral and organic fractions, we developed a cluster analysis method which allows for the isolation of synergistic relationships in the presence of antagonistic relationships. The content of metals in various granulometric fractions of the sediments of the Kozłowa Góra Reservoir we analyzed and showed that metals should be extracted from the fraction f < 0.2 mm, which is the most representative fraction.

This study employs the New Generation of Artificial Intelligence Innovation and Development Pilot Zones Policy (AIE policy) as a quasi-natural experiment to examine how firms deploy strategic greenwashing in the context of environmental information disclosure. A multi-period difference‑in‑differences model is utilized to assess the dynamic changes in firms’ greenwashing intensity before and after the AIE policy came into force. The results indicate that the AIE policy significantly contributes to reduced greenwashing levels. This inhibitory effect primarily operates by enhancing firms’ AI strategic orientation and AI technological capabilities. Notably, the suppressive effect is more pronounced for state‑owned enterprises, firms in heavily polluting industries, and those located in the eastern regions. This study concludes that an integrated environmental information disclosure regime linking policy, strategy, and technology is critical for corporate green transformation and high-quality development. The findings provide empirical support for synergistic environmental and AI policies while revealing how state-led emerging-tech initiatives reshape firms’ non-market strategies and disclosure ethics.

Background

The aim of this study was to investigate the presence of further, as yet unknown dissolved organic contaminants with isolated point-sources (e.g. industrial origin) in river water of the Rhine by implementing a large, distributed sampling campaign stretching along a large portion of the central river course. The analysis and data processing were based on a non-target screening approach with a focus on compounds of industrial origin which often show pronounced intensity changes over time due to production cycles. Many such compounds are by-products and are not included in online databases, making their identification challenging. This study describes the process of uncovering one such chemical group utilizing a non-target screening approach. Inter-agency cooperation was essential to localize emissions and determine the inter-regional distribution of the compounds.

Results

Both spatially-distributed sampling at 17 sites along the Rhine and Danube rivers as well as daily-composite and grab samples from 2018 to 2023 at further sites across Germany were used. Analysis was accomplished by LC-QToF- and LC-Orbitrap-MS and followed by data evaluation with different non-target software solutions including open-source solutions in R. The “discovery” of the contaminants in question began with the non-target data of the spatial sampling of the Rhine, which were first processed to create feature lists, which were then prioritized based on their intensity profiles along the river course. One group of prioritized features was selected for identification, first by interpretation of MS² fragmentation spectra followed by verification using a laboratory synthesis. This was a group of oligoacrylonitrile sulfonates originating from polymer fiber production, which to the best of our knowledge not previously detected in river water. Further spatial and time-series data showed the long-term and inter-regional occurrence of the oligoacrylonitrile sulfonates (4 to 13 acrylonitrile units) in the Rhine and Danube catchments. Consistent with the cessation of production at the measured sites in Germany, these compounds were no longer detected in the Rhine and Danube after 2021.

Conclusions

The study highlights the need for cooperation and pooling of resources to obtain the necessary temporally and spatially distributed data for successful identification, and source localization, of unknown contaminants by non-target screening.

Introduction

Oil exploration in the Niger Delta has resulted in severe contamination of environmental media, with polycyclic aromatic hydrocarbons (PAHs) recognized as priority pollutants by the U.S. EPA. This study assessed the levels and ecological risks of PAHs in soil, sediment, surface water, groundwater, and food crops from Ibaa, an oil-impacted community in the Niger Delta.

Methods

These samples were collected during the wet and dry seasons and analyzed for 16 priority PAHs using gas chromatography–mass spectrometry (GC–MS) following U.S. EPA protocols. Contamination factors, risk quotients (RQ), and diagnostic ratios were used to evaluate contamination levels and identify PAH sources. Results were compared with international standards from the WHO, USEPA, EU, and Canadian guidelines.

Results

Total PAH concentrations (Σ16PAHs) in soils ranged from 0.60–12.29 mg/kg, exceeding the Canadian agricultural soil guideline (0.1 mg/kg) by over 100 times. Surface water PAHs reached 0.693 mg/L, surpassing the WHO limit for drinking water (0.0002 mg/L) by more than 3000 times, while groundwater remained below but close to acceptable thresholds (RQ∑PAHs ≤ 0.157). PAHs in food crops (0.007–0.020 mg/kg) slightly exceeded the EU limit (0.01 mg/kg) but posed minimal ecological risk (RQ∑PAHs < 1). Soils and sediments in the dry season showed the highest ecological risk, with diagnostic ratios indicating a predominantly petrogenic source.

Conclusion and recommendation

The findings demonstrate persistent PAH contamination that threatens soil fertility, aquatic ecosystems, and food safety in Ibaa. The study indicates the potential for bioaccumulation and long-term exposure risks to local populations. Immediate remediation, strict regulatory enforcement, and continuous monitoring are recommended to mitigate ecological and health hazards in the Niger Delta.

Background

Water-dispersible soil colloids (WDC) play a significant role in various soil processes, such as in nutrient storage and release. Therefore, it is essential to investigate and characterize WDC to understand their behavior and characteristics. However, a diversity of methods used to extract WDC for characterization is found in literature, which consequently makes interstudy comparison of colloidal data challenging. In this study, we analyzed methodological data on WDC extraction obtained from literature through Principal Component Analysis (PCA) and K-means clustering to examine methodological trends and similarities. Selected extraction methods from the methodological clusters (and the type of dispersant used) were evaluated experimentally to assess how different extractions influence WDC characteristics, and in terms of their feasibility.

Results

PCA of methodological information showed that most methodological parameters were drivers of extraction method diversity. From the K-means clusters of the four Principal Components, three extraction methods were identified for comparison. The first method employed sedimentation and centrifugation to separate the WDC fraction, yielding 0.2–0.4% (w/w) of WDC relative to the fresh soil mass. The second method only used sedimentation and extracted the highest WDC quantity (0.6–2.0%), but with the highest proportion of particles with diameters > 1000 nm. The third method involved centrifugation and filtration, extracting 0.02–0.08% of WDC, with an average maximum colloidal particle diameter of 638 nm. While the use of different dispersants did not have an influence on WDC yield, it influenced the particle size distribution (PSD) of WDC extracted, specifically in organic soil. Furthermore, the individual influence of the extraction method and dispersant and their interaction effects on WDC elemental composition vary with soil and the element of concern.

Conclusion

Our findings demonstrate that extraction methods influence WDC characteristics in terms of yield, PSD, and elemental composition. The type of dispersant also affects the PSD of WDC in organic soil, and its influence on elemental composition varies depending on soil type and elemental component. Among the extraction methods compared, the first method emerged as the most balanced and reliable approach in extracting WDC in terms of yield and feasibility.

Twelve pharmaceuticals, including stimulant caffeine (CAFF), anti-inflammatories (naproxen (NPX), ibuprofen (IBU), and diclofenac (DCF)), antibiotics (anhydro-erythromycin (AETM), azithromycin (ATM), erythromycin (ETM), clindamycin (CMC), ciprofloxacin (CFC), sulfamethoxazole (SMX), and trimethoprim (TMP)), and the antiepileptic carbamazepine (CBZ), were analyzed in surface waters and sediments in the Ishmi River basin, Albania, across seasons during 2023 and 2024. This basin is characterized by limited wastewater treatment infrastructure, varying degrees of urban impact, and different environmental conditions. All targeted compounds were detected in water, with the highest concentrations observed near urban areas, particularly at the wastewater-impacted location LR1 for CAFF, IBU, NPX, and CFC with 22.5, 12.8, 2.7, and 1.8 µg L⁻¹, respectively. Sediment concentrations showed high levels of CFC and ATM, notably during spring with the highest concentrations of 1068 (LR1) and 396 ng g⁻¹ (IR2), respectively, suggesting strong seasonal and spatial variability. Partitioning behavior (K_d and K_oc) was investigated in relation to compound-specific (D_ow) and sediment-specific (pH, organic carbon, CaCO₃ and metal content) properties. Significant correlations were found, and multiple regression models successfully predicted in situ K_d values for NPX, IBU, CFC, AETM, and CMC. These findings underline the influence of environmental and sediment characteristics on environmental pharmaceutical distribution.

Accurate estimation of land surface temperature (LST) is essential for environmental monitoring and management applications. While recent advances in remote sensing have made the retrieval of LST from satellite images a common practice in Malaysia, the frequent existence of cloud cover throughout the year makes the retrieval challenging and results in a significant amount of missing LST data. In this study, multiple machine learning models, support vector regressor (SVR), multilayer perceptron (MLP) and random forest (RF) and deep learning models, long short-term memory (LSTM) and gated recurrent unit (GRU), were used to estimate the daytime LST based on significant meteorological and remote sensing variables selected using feature selection methods. This study was performed at two stations, Alor Setar and KLIA Sepang station, which are situated across the Peninsular Malaysia. The meteorological data were sourced from the Malaysian Meteorological Department (MMD), while the MODIS/Terra remote sensing data were retrieved from the Google Earth Engine (GEE) platform. Most of the variables demonstrated moderate to strong correlations with the daytime LST. Maximum air temperature (T_max) was found to be the most critical variable that cannot be ignored at both study stations. The significance of near infrared (NIR) and shortwave infrared bands (SWIR7) indicated that the daytime LST was strongly influenced by the differences in moisture content. All the models showed satisfactory capabilities in estimating the daytime LST. The overall coefficient of determination, R² and Kling–Gupta efficiency (KGE) obtained across the stations ranged from 0.644 to 0.794 and 0.584 to 0.873, respectively. For prediction errors, the values ranged from 1.237℃ to 1.656℃ for mean absolute error (MAE), 0.036 to 0.049 for normalized mean absolute error (NMAE), and 1.605℃ to 2.133℃ for root mean square error (RMSE). Comparing among the models, SVR outperformed the other models in the majority of the scenarios, followed by GRU, LSTM, MLP and RF. The present study has helped in expanding the available predictor variable space and confirmed the feasibility of using AI-based models for LST estimation in Peninsular Malaysia, supported by satisfactory predictive performance metrics reflected in low error values and favourable agreement between observed and predicted values.

Understanding the spatiotemporal drought variability is essential for effective climate resilience and resource management in drought-prone areas. Therefore, this study aimed to assess the dynamics of meteorological and agricultural droughts in the Rift Valley Lake Basin (RVLB) of Ethiopia by using the Standardized Precipitation Index (SPI-1, SPI-4) derived from CHIRPS data (1985–2024) and remote sensing-based vegetation indices (NDVI, VCI, TCI, VHI) from MODIS data (2001–2024). The data were processed using Google Earth Engine and spatial statistical tools, including Spearman correlation, ordinary least squares (OLS), and geographically weighted regression (GWR), to examine the relationships between drought indices and root zone soil moisture. The metrics demonstrated that CHIRPS accurately captures spatial and temporal rainfall variability. The findings revealed significant temporal and spatial variability in drought severity, with major events occurring in 1985, 1987, 1990, 2002, 2009, 2016, and 2020. The study highlighted the compounded effects of thermal and moisture stress on vegetation. Strong negative correlations were identified between land surface temperature (LST) and vegetation indices, emphasizing the role of high temperatures in exacerbating drought stress. VHI identified a larger drought-affected area (severe: 10%, moderate: 30.26%) than VCI (severe: 1.42%, moderate: 15.37%). The GWR model outperformed OLS (Adjusted R² = 0.95) with randomly distributed residuals (Moran’s I), capturing strong spatial relationships between TCI (3.19 in central and north; − 4.45 in south), SPI-1, and root zone soil moisture (~ 1.20). Predicted drought risk ranged from 0.05 to 5.78, peaking in the central basin where short-term rainfall deficits and high temperatures intensified severity. In conclusion, a multi-index, spatially explicit drought monitoring framework is crucial for early warning, targeted interventions, and adaptive agricultural planning. Integrating real-time remote sensing, climate-smart practices, and localized water management can strengthen resilience and support sustainable development under a changing climate.

Environmental deterioration can cause major issues like air pollution, water scarcity, land degradation, and socioeconomic disruptions in heavily mined places like Sindh, Pakistan's Thar coalfields. To overcome these obstacles, a novel strategy using contemporary monitoring and prediction technology is required. This study presents a novel framework for tracking and reducing the environmental effects of mining in poor nations by combining data from blockchain technology, Temporal Convolutional Networks (TCNs), and remote sensing. To ensure stakeholder confidence and accountability, the proposed architecture recodes environmental data using Blockchain Distributed Ledger Technology (BDLT) in a secure, transparent, immutable, and secure manner. The primary potential is to periodically monitor key metrics like vegetation loss, water depletion, and air quality using the Remote Sensing (RS) approach. However, by examining temporal data, TCNs are able to predict trends in environmental degradation and take pre-emptive steps to prevent damage. With a prediction performance of up to 97.3%, metrics such as the Normalised Difference Vegetation Index (NDVI), Air Quality Index (AQI), and water table depth are assessed with great accuracy. In addition to offering politicians and regulators useful information, the proposed architecture uses chaincode to guarantee adherence to environmental regulations. Furthermore, this paper offers a scalable and adaptable solution to environmental limitations in resource-rich places. It supports international sustainability objectives and sets the standard for more ethical mining methods in underdeveloping countries.

In recent years, the distribution and transport of microplastics (MPs) in aquatic environments have garnered significant research interest due to their interactions with sediments, which directly influence their migration pathways, deposition patterns, and ecological risks. This study reviews research on the co-migration of suspended sediments (SS) and MPs based on publications from the Web of Science and Engineering Village databases spanning 2011–2025. A co-occurrence network analysis of keywords was conducted using CiteSpace, and the literature was visualized accordingly. The study also investigates the distribution of MPs in sediments within Chinese waters as a case study. The spatiotemporal distribution of MPs is influenced by hydrological conditions (e.g., flow and runoff intensity) and MP properties (e.g., density, shape, polymer type). This paper provides a systematic overview of key physical processes in sedimentary environments, including MP aggregation, settling, burial, and resuspension. Hydrographic conditions, particle concentration, and material properties are identified as the primary factors governing their co-migration. At the microscopic level, interactions between MPs and SS are mainly controlled by van der Waals forces, electrostatic interactions, and covalent bonding. The co-migration of MPs and SS involves multi-mechanistic coupling governed by physical, chemical, and biological processes. This study offers a scientific basis for assessing pollution risks and developing effective management strategies.

Graphical Abstract