Found 10066 publications. Showing page 30 of 403:
Recently, chlorinated paraffins with carbon chain lengths in the range C14–17 and chlorination levels at or exceeding 45 per cent chlorine by weight have been proposed for listing under the Stockholm Convention. To aid the process of determining the identification of sum polychlorinated alkanes ΣPCAs C14-17 under the regulation (i.e. number of chlorines), there is a need for data from environmental samples that specifies the homologue group profiles, not just ΣPCAs.
In this report we present data on PCAs with a focus on ΣPCAs C14-17 from the Norwegian Environment Agency’s monitoring programmes in more detail than available in the programmes reports, focusing on homologue group patterns and chlorination degree. The programmes are i) Environmental pollutants in the terrestrial and urban environment ii) Atmospheric contaminants iii) Environmental contaminants in an urban fjord. Data presented are from the 2022 (Halvorsen et al., 2023; Heimstad et al., 2023; Ruus, 2023) and 2023 (reports in prep) programmes.
NILU
2024
There is a need for baseline information about how much plastics are ingested by wildlife and potential negative consequences thereof. We analysed the frequency of occurrence (FO) of plastics >1 mm in the stomachs of five pursuit-diving seabird species collected opportunistically.
Atlantic puffins (Fratercula arctica) found emaciated on beaches in SW Norway had the highest FO of plastics (58.8 %), followed by emaciated common guillemots (Uria aalge; 9.1 %) also found beached in either SW or SE Norway. No plastics were detected in razorbills (Alca torda), great cormorants (Phalacrocorax carbo), and European shags (Gulosus aristotelis) taken as bycatch in northern Norway. This is the first study to report on plastic ingestion of these five species in northern Europe, and it highlights both the usefulness and limitations of opportunistic sampling. Small sample sizes, as well as an unbalanced sample design, complicated the interpretation of the results.
2024
In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.
The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.
2024
2024
2024
The 2023 Screening Programme aimed to investigate the presence of emerging environmental concerning substances 1) at so-called “hot spot” locations, and 2) in marine top predators. Part 1 covered emerging substances that were considered for EU regulation; persistent, mobile and toxic; identified as problematic (Sweden); and UV-stabilizers. Sampling sites were a wastewater treatment plant, indoor dust, agricultural soils, and consumer products. Part 2 covered substances found in the 2021 Screening Programme as well as selected through the LIfeAPEX project. Additionally, in part 2, several classical legacy contaminants were included. A unique sample set was assembled with different types of tissue from whales (killer whale, sperm whale, fin whale, humpback whale, white beaked dolphin, and harbor porpoise) and sharks (greenland shark, porbeagle shark, and spiny dogfish). Highlights from the results in part 1 covered a high detection frequency and concentrations of one phthalate (CAS 6422-86-2) and two UV-stabilizers (CAS 154702-15-5 and 103597-45-1). In part 2, only a small number of the emerging substances were identified, while many of the legacy substances were found at high levels.
Norsk institutt for vannforskning (NIVA)
2024
Hulun Lake, the largest inland steppe lake in China, is encountering severe water quality degradation. Estuaries play important roles in material and energetic exchange between rivers and lakes. The water quality at the estuaries of Hulun Lake directly reflects the impact of both human activities and natural factors on the lake’s overall water quality, especially during rainfall events. From July 28, 2021, to August 4, 2021, water samples from 62 sites were collected in the three estuaries of Hulun Lake before and after a moderate rainfall event. 13 water parameters, including dissolved oxygen (DO), Turbidity (Tur), Total Nitrogen (TN), Total Phosphorus (TP), Total Organic Nitrogen (TON), and Total Organic Phosphorus (TOP) were measured. The spatio-temporal distribution of water quality in the estuaries was assessed based on water quality index (WQI). Besides, an improved approach integrating stepwise linear regression (SLR) and principal component analysis (PCA) was utilized to construct a WQImin model for an effective assessment of water quality in these estuaries. Furthermore, the absolute principal component scores-multiple linear regression (APCS-MLR) model was employed to identify and quantify the environmental drivers underlying the water quality in the estuaries. The results of WQI indicated that the water quality of the sites in the estuaries of Hulun Lake was “medium” or “poor”, both before and after the rainfall, with a general deterioration in water quality in response to the rainfall. The simplified WQImin model consisted of 5 crucial parameters (i.e., TN, TP, ammonium (NH4+-N), Tur, and permanganate index (CODMn)), and it performed well without parameter weights. Spatial differences in some water parameters among the estuaries were detected, which were attributed to the natural factors and human activities upstream. The principal environmental factors affecting the water quality in the estuaries consisted of hydrodynamic processes, internal phosphorus release, external phosphorus input, external nitrogen input, nitrification in the estuaries, and external organic input and internal organic release. Therefore, we propose basin management strategies such as limiting grazing pressure, adopting enclosed pasture, wetland restoration, optimizing water renewal cycle in Hulun Lake, and transboundary water quality management to tackle water contamination in Hulun Lake.
2024
This report gives an overview of annual statistics and results from the monitoring programme of ozone in EMEP 2022.
NILU
2024
This report documents the EMEP VOC monitoring carried out in 2022. The levels of the measured in 2022 are presented as well as the 2022-status and history of the VOC programme. The geographical pattern of the species in Europe is discussed as well as the long-term trend during the last 20 years.
NILU
2024
Negative correlation between soil salinity and soil organic carbon variability
Soil organic carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC < 150 g kg−1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m−1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg−1 above the mean predicted SOC (= 18.47 g kg−1) to 0.46 g kg−1 below (~−430%), while for noncroplands, such decline is sharper, from 0.96 above = 35.96 g kg−1 to 4.99 below (~−620%). Although salinity’s significance in explaining SOC variability is minor (<6%), we estimate a one SD increase in salinity of topsoil samples (0 to 7 cm) correlates with respective declines of ~4.4% and ~9.26%, relative to and. The decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needle-leaved trees are estimated with the highest decline in noncroplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index as the most significant parameters in explaining the SOC’s variability. The findings provide insights into SOC dynamics under increased soil salinity, improving understanding of SOC stock responses to land degradation and climate warming.
2024
A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements
Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available.
2024
Monitoring of environmental contaminants in air and precipitation. Annual report 2023
This report presents air monitoring data from 2023 for the Norwegian monitoring programme "Atmospheric contaminants". The results covers 16 groups comprising of 260 organic compounds (regulated and non-regulated) as well as 14 heavy metals, and a selection of organic chemicals of concern.
NILU
2024
The ubiquitous and global ecological footprint arising from the rapidly increasing rates of plastic production, use, and release into the environment is an important modern environmental issue. Of increasing concern are the risks associated with at least 16,000 chemicals present in plastics, some of which are known to be toxic, and which may leach out both during use and once exposed to environmental conditions, leading to environmental and human exposure. In response, the United Nations member states agreed to establish an international legally binding instrument on plastic pollution, the global plastics treaty. The resolution acknowledges that the treaty should prevent plastic pollution and its related impacts, that effective prevention requires consideration of the transboundary nature of plastic production, use and pollution, and that the full life cycle of plastics must be addressed. As a group of scientific experts and members of the Scientists' Coalition for an Effective Plastics Treaty, we concur that there are six essential “pillars” necessary to truly reduce plastic pollution and allow for chemical detoxification across the full life cycle of plastics. These include a plastic chemical reduction and simplification, safe and sustainable design of plastic chemicals, incentives for change, holistic approaches for alternatives, just transition and equitable interventions, and centering human rights. There is a critical need for scientifically informed and globally harmonized information, transparency, and traceability criteria to protect the environment and public health. The right to a clean, healthy, and sustainable environment must be upheld, and thus it is crucial that scientists, industry, and policy makers work in concert to create a future free from hazardous plastic contamination.
2024
Pioneering an effect-based early warning system for hazardous chemicals in the environment
Existing regulatory frameworks often prove inadequate in promptly identifying contaminants of emerging concern (CECs) and determining their impacts on biological systems at an early stage. The establishment of Early Warning Systems (EWSs) for CECs is becoming increasingly relevant for policy-making, aiming to proactively detect chemical hazards and implement effective mitigation measures. Effect-based methodologies, including bioassays and effect-directed analysis (EDA), offer valuable input to EWSs by pinpointing the relevant toxicity drivers and prioritizing the associated risks. This review evaluates the analytical techniques currently available to assess biological effects, and provides a structured plan for their systematic integration into an EWS for hazardous chemicals in the environment. Key scientific advancements in effect-based approaches and EDA are discussed, underscoring their potential for early detection and management of chemical hazards. Additionally, critical challenges such as data integration and regulatory alignment are addressed, emphasizing the need for continuous improvement of the EWS and the incorporation of analytical advancements to safeguard environmental and public health from emerging chemical threats.
2024
2024
2024