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The alamar blue assay in the context of safety testing of nanomaterials
The Alamar Blue (AB) assay is widely used to investigate cytotoxicity, cell proliferation and cellular metabolic activity within different fields of toxicology. The use of the assay with nanomaterials (NMs) entails specific aspects including the potential interference of NMs with the test. The procedure of the AB assay applied for testing NMs is described in detail and step-by-step, from NM preparation, cell exposure, inclusion of interference controls, to the analysis and interpretation of the results. Provided that the proper procedure is followed, and relevant controls are included, the AB assay is a reliable and high throughput test to evaluate the cytotoxicity/proliferation/metabolic response of cells exposed to NMs.
2022
The Chinese Loess Plateau (CLP) in northern China is home to one of the most prominent loess records in the world, reflecting past eolian dust activity in East Asia. However, their interpretation is hampered by ambiguity in the origin of loess-forming dust and an incomplete understanding of the circulation forcing dust accumulation. In this study, we used a novel modeling approach combining a dust emission model FLEXDUST with simulated back trajectories from FLEXPART to trace the dust back to where it was emitted. Over 21 years (1999–2019), we modeled back trajectories for fine (∼2 μm) and super-coarse (∼20 μm) dust particles at six CLP sites during the peak dust storm season from March to May. FLEXPART source-receptor relationships are combined with the dust emission inventory from FLEXDUST to create site-dependent high-resolution maps of the source contribution of deposited dust. The nearby dust emission areas were found to be the main source of dust to the CLP. Dust deposition across the CLP was found to predominantly occur via wet removal, with also some super-coarse dust from distant emission regions being wet deposited following high-level tropospheric transport. The high topography located on the downwind side of the emission area plays an essential role in forcing the emitted super-coarse dust upward. On an interannual scale, the phase of the Arctic Oscillation in the preceding winter was found to have a strong association with the spring deposition rate on the CLP, while the strength of the East Asian Winter Monsoon was less influential.
2024
In vivo Mammalian Alkaline Comet Assay: Method Adapted for Genotoxicity Assessment of Nanomaterials
The in vivo Comet assay measures the generation of DNA strand breaks under conditions in which the DNA will unwind and migrate to the anode in an electrophoresis assay, producing comet-like figures. Measurements are on single cells, which allows the sampling of a diversity of cells and tissues for DNA damaging effects. The Comet assay is the most common in vivo method for genotoxicity assessment of nanomaterials (NM). The Method outlined here includes a recommended step-by-step approach, consistent with OECD 489, taking into consideration the issues impacting assessment of NM, including choice of cells or systems, handling of NM test articles, dose determination, assay methods and data assessment. This method is designed to be used along with the accompanying “Common Considerations” paper, which discusses issues common to any genotoxicity assay using NM as a test article.
2022
2025
Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks
In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.
2025
Are ingredients of personal care products likely to undergo long-range transport to remote regions?
Personal care products (PCPs) contain contaminants of emerging concern. Despite increasing reports of their presence in polar regions, the behavior of PCP ingredients under cold environmental conditions remains poorly understood. Snow collected around Villum Research Station at Station Nord, Greenland, between December 2018 and June 2019 was extracted in a stainless steel clean-room and analyzed for seven fragrance materials, four organic UV-filters and an antioxidant using gas chromatography-tandem mass spectrometry. All twelve target PCPs were detected, with elevated concentrations during two sampling events potentially tied to air mass transport from northern Europe and the northern coasts of Russia. To contextualize the presence of these PCP chemicals in high Arctic snow, we estimated their (i) partitioning properties as a function of temperature, (ii) equilibrium phase distribution and dominant deposition processes in the atmosphere at temperatures above and below freezing, and (iii) potential for long-range environmental transport (LRET). Even though most PCPs are deemed to be gas phase chemicals predominantly deposited as vapors, rapid atmospheric degradation is expected to limit their LRET. On the other hand, the less volatile octocrylene is expected to be sorbed to atmospheric particles, removed via wet and dry particle deposition, and possibly exhibit a higher potential for LRET by being protected from attack by photooxidants. The contrast between consistent detection of PCP chemicals in high Arctic snow and relatively low estimated LRET potential emphasizes the need for further research on their real-world atmospheric behavior under cold conditions.
2025
The apportionment of equivalent black carbon (eBC) to combustion sources from liquid fuels (mainly fossil; eBCLF) and solid fuels (mainly non-fossil; eBCSF) is commonly performed using data from Aethalometer instruments (AE approach). This study evaluates the feasibility of using AE data to determine the absorption Ångström exponents (AAEs) for liquid fuels (AAELF) and solid fuels (AAESF), which are fundamental parameters in the AE approach. AAEs were derived from Aethalometer data as the fit in a logarithmic space of the six absorption coefficients (470–950 nm) versus the corresponding wavelengths. The findings indicate that AAELF can be robustly determined as the 1st percentile (PC1) of AAE values from fits with R2 > 0.99. This R2-filtering was necessary to remove extremely low and noisy-driven AAE values commonly observed under clean atmospheric conditions (i.e., low absorption coefficients). Conversely, AAESF can be obtained from the 99th percentile (PC99) of unfiltered AAE values. To optimize the signal from solid fuel sources, winter data should be used to calculate PC99, whereas summer data should be employed for calculating PC1 to maximize the signal from liquid fuel sources. The derived PC1 (AAELF) and PC99 (AAESF) values ranged from 0.79 to 1.08, and 1.45 to 1.84, respectively. The AAESF values were further compared with those constrained using the signal at mass-to-charge 60 (m/z 60), a tracer for fresh biomass combustion, measured using aerosol chemical speciation monitor (ACSM) and aerosol mass spectrometry (AMS) instruments deployed at 16 sites. Overall, the AAESF values obtained from the two methods showed strong agreement, with a coefficient of determination (R2) of 0.78. However, uncertainties in both approaches may vary due to site-specific sources, and in certain environments, such as traffic-dominated sites, neither approach may be fully applicable.
2025
2019
Recommendation technologies are widespread in streaming services, e-commerce, social media, news, and content management. Besides recommendation generation, its presentation is also important. Most research and development focus on the technical aspects of recommendation generation; therefore, a gap exists between recommendation generation and its effective presentation and user interaction. This study focuses on how personalized recommendations can be presented and interacted with in a music recommendation system using interactive visual interfaces. Interactive interface modeling with User-Centered Design (UCD) in a recommendation system is essential for creating a user-friendly, engaging, and personalized experience. By involving users in the recommendation process and considering their feedback, the system can deliver more relevant content, foster user trust, and improve overall user satisfaction and engagement. In this study, the visual interface design and development of a personalized music recommendation prototype (MusicReco) are presented using an iterative UCD approach, involving twenty end-users, one researcher, three academic professionals, and four experts. As the study is more inclined toward the recommendation presentation and visual modeling, we used a standard content-based filtering algorithm on the publicly available Spotify dataset for music recommendation generation. End-users helped to mature the MusicReco prototype to a basic working version through continuous feedback and design inputs on their needs, context, preferences, personalization, and effective visualization. Moreover, MusicReco captures the idea of mood-based tailored recommendations to encourage end-users. Overall, this study demonstrates how UCD can enhance the presentation and interaction of mood-based music recommendations, effectively engaging users with advancements in recommendation algorithms as a future focus.
2025
Spatial distribution of Dechlorane Plus and dechlorane related compounds in European background air
The highly chlorinated chemical Dechlorane Plus (DP) was introduced as a replacement flame retardant for Mirex, which is banned through the Stockholm Convention (SC) for its toxicity (T), environmental persistence (P), potential for bioaccumulation (B) and long-range environmental transport potential (LRETP). Currently, Dechlorane Plus is under consideration for listing under the Stockholm Convention and by the European Chemical Agency as it is suspected to also have potential for P, B, T and LRET. Knowledge of atmospheric concentrations of chemicals in background regions is vital to understand their persistence and long-range atmospheric transport but such knowledge is still limited for Dechlorane Plus. Also, knowledge on environmental occurrence of the less described Dechlorane Related Compounds (DRCs), with similar properties and uses as Dechlorane Plus, is limited. Hence, the main objective of this study was to carry out a spatial mapping of atmospheric concentrations of Dechlorane Plus and Dechlorane Related Compounds at background sites in Europe. Polyurethane foam passive air samplers were deployed at 99 sites across 33 European countries for 3 months in summer 2016 and analyzed for dechloranes. The study showed that syn- and anti-DP are present across the European continent...
2023
Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer
Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study’s objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual’s stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.
2025
Silicon alloys are produced by carbothermic reduction of quartz in a submerged arc furnace. This high-temperature pyrolytic process is a source of polycyclic aromatic hydrocarbons (PAHs), which are a group of aromatic organic molecules with known mutagenic and carcinogenic properties. In this study, the emission of oxy- and nitro-PAHs from a pilot-scale Si furnace, with varying process conditions such as oxygen level, flue gas recirculation (FGR), and off-gas flow, was investigated. Analysis shows the presence of both oxy- and nitro-PAH species in all experiments, believed to be formed from radical-induced substitution reactions initiated by SiO combustion and NOx formation. During Si production without FGR, the levels of oxy- and nitro-PAHs range between 1.1 and 4.4 μg Nm−3, independent of the flue gas flow rate. With increasing FGR (0–82.5%) and decreasing oxygen level (20.7–13.3%), the concentrations of both oxy- and nitro-PAHs increase to 36.6 and 65.9 μg Nm−3, respectively. When the levels of substituted PAHs increase, species such as 4-nitropyrene and 1,2-benzanthraquinone are in abundance compared to their parent PAHs. Experiments at lower flue gas flow (500 Nm3 h−1 versus 1000 Nm3 h−1) generally produce less substituted PAHs, as well as SiO2 particulate matter and NOx, where the latter two parameters have a 99% correlation in this study.
2023
2022
Carbonaceous aerosol (total carbon, TCp) was source apportioned at nine European rural background sites, as part of the European Measurement and Evaluation Programme (EMEP) Intensive Measurement Periods in fall 2008 and winter/spring 2009. Five predefined fractions were apportioned based on ambient measurements: elemental and organic carbon, from combustion of biomass (ECbb and OCbb) and from fossil-fuel (ECff and OCff) sources, and remaining non-fossil organic carbon (OCrnf), dominated by natural sources.
OCrnf made a larger contribution to TCp than anthropogenic sources (ECbb, OCbb, ECff, and OCff) at four out of nine sites in fall, reflecting the vegetative season, whereas anthropogenic sources dominated at all but one site in winter/spring. Biomass burning (OCbb + ECbb) was the major anthropogenic source at the central European sites in fall, whereas fossil-fuel (OCff + ECff) sources dominated at the southernmost and the two northernmost sites. Residential wood burning emissions explained 30 %–50 % of TCp at most sites in the first week of sampling in fall, showing that this source can be the dominant one, even outside the heating season. In winter/spring, biomass burning was the major anthropogenic source at all but two sites, reflecting increased residential wood burning emissions in the heating season. Fossil-fuel sources dominated EC at all sites in fall, whereas there was a shift towards biomass burning for the southernmost sites in winter/spring.
Model calculations based on base-case emissions (mainly officially reported national emissions) strongly underpredicted observational derived levels of OCbb and ECbb outside Scandinavia. Emissions based on a consistent bottom-up inventory for residential wood burning (and including intermediate volatility compounds, IVOCs) improved model results compared to the base-case emissions, but modeled levels were still substantially underestimated compared to observational derived OCbb and ECbb levels at the southernmost sites.
Our study shows that natural sources are a major contributor to carbonaceous aerosol in Europe, even in fall and in winter/spring, and that residential wood burning emissions are equally as large as or larger than that of fossil-fuel sources, depending on season and region. The poorly constrained residential wood burning emissions for large parts of Europe show the obvious need to improve emission inventories, with harmonization of emission factors between countries likely being the most important step to improve model calculations for biomass burning emissions, and European PM2.5 concentrations in general.
2019
Urban herring gulls (Larus argentatus) are exposed to contaminants from aquatic, terrestrial and anthropogenic sources. We aim to assess if differences in urbanisation affect ecological niche and contaminant concentrations in female herring gulls. Furthermore, we investigated maternal transfer from mothers to eggs for all the target compounds, including chlorinated paraffins (CPs) and cyclic volatile methyl siloxane (cVMSs), which to our knowledge have not been assessed in herring gulls previously. We compare concentrations of legacy and emerging contaminants and metals in blood and eggs between two herring gull colonies located 51 km apart, in the urban influenced Norwegian Oslofjord. While both colonies are within an urbanised area, the inner fjord is more so, as it is surrounded by Oslo, the capital and largest city in Norway Stable isotopes of carbon and nitrogen indicated a more marine ecological niche in the outer than the inner fjord colony, although with overlap. Persistent organic pollutant (POP) concentrations were similar in the inner and outer fjord colonies, while the short-chained chlorinated paraffins (SCCP), which are recently added to the Stockholm convention and contaminants of emerging concern (CECs) varied, with higher concentrations of SCCP and the cVMS decamethylcyclopentasiloxane (D5) in females and eggs of the inner fjord colony. Per- and polyfluorinated substances (PFAS) concentrations were higher in the outer fjord colony, likely linked to releases from a point-source (airport and waste management facility with open access to food waste). In blood, chlorinated paraffins contributed most the total lipophilic contaminants (inner: 78%, outer: 56%), while polychlorinated biphenyls (PCBs) were the most abundant lipophilic contaminants in eggs (inner: 62%, outer: 46%). Dechloranes and brominated flame retardants (BFRs) were detected in few samples. Maternal transfer, assessed by egg to blood ratios, of cVMSs were similar to the POPs with mean log ratio 0.39 (D5), while it was lower for SCCPs, with log ratios-0.77. Our results indicate comparable POP exposure of the herring gulls in the inner and outer Oslofjord, likely due to overlap in ecological niches between the colonies and wide distribution of POPs. The differences between the colonies in concentrations of PFAS, cVMS and CPs shows that point source exposures and urban influence may be more important than ecological niche for these compounds.
2021
2019
2022
The impact of atmospheric reactive nitrogen (Nr) deposition on carbon (C) sequestration in soils and biomass of unfertilized, natural, semi-natural and forest ecosystems has been much debated. Many previous results of this dC∕dN response were based on changes in carbon stocks from periodical soil and ecosystem inventories, associated with estimates of Nr deposition obtained from large-scale chemical transport models. This study and a companion paper (Flechard et al., 2020) strive to reduce uncertainties of N effects on C sequestration by linking multi-annual gross and net ecosystem productivity estimates from 40 eddy covariance flux towers across Europe to local measurement-based estimates of dry and wet Nr deposition from a dedicated collocated monitoring network. To identify possible ecological drivers and processes affecting the interplay between C and Nr inputs and losses, these data were also combined with in situ flux measurements of NO, N2O and CH4 fluxes; soil NO−3
leaching sampling; and results of soil incubation experiments for N and greenhouse gas (GHG) emissions, as well as surveys of available data from online databases and from the literature, together with forest ecosystem (BASFOR) modelling.
Multi-year averages of net ecosystem productivity (NEP) in forests ranged from −70 to 826 g C m−2 yr−1 at total wet + dry inorganic Nr deposition rates (Ndep) of 0.3 to 4.3 g N m−2 yr−1 and from −4 to 361 g C m−2 yr−1 at Ndep rates of 0.1 to 3.1 g N m−2 yr−1 in short semi-natural vegetation (moorlands, wetlands and unfertilized extensively managed grasslands). The GHG budgets of the forests were strongly dominated by CO2 exchange, while CH4 and N2O exchange comprised a larger proportion of the GHG balance in short semi-natural vegetation. Uncertainties in elemental budgets were much larger for nitrogen than carbon, especially at sites with elevated Ndep where Nr leaching losses were also very large, and compounded by the lack of reliable data on organic nitrogen and N2 losses by denitrification. Nitrogen losses in the form of NO, N2O and especially NO−3
were on average 27 % (range 6 %–54 %) of Ndep at sites with Ndep < 1 g N m−2 yr−1 versus 65 % (range 35 %–85 %) for Ndep > 3 g N m−2 yr−1. Such large levels of Nr loss likely indicate that different stages of N saturation occurred at a number of sites. The joint analysis of the C and N budgets provided further hints that N saturation could be detected in altered patterns of forest growth. Net ecosystem productivity increased with Nr deposition up to 2–2.5 g N m−2 yr−1, with large scatter associated with a wide range in carbon sequestration efficiency (CSE, defined as the NEP ∕ GPP ratio). At elevated Ndep levels (> 2.5 g N m−2 yr−1), where inorganic Nr losses were also increasingly large, NEP levelled off and then decreased. The apparent increase in NEP at low to intermediate Ndep levels was partly the result of geographical cross-correlations between Ndep and climate, indicating that the actual mean dC∕dN response at individual sites was significantly lower than would be suggested by a simple, straightforward regression of NEP vs. Ndep.
2020
It is unclear whether the Eurasian snow plays a role in the tropospheric driving of sudden stratospheric warming (SSW). The major SSW event of February 2018 is analyzed using reanalysis datasets. Characterized by predominant planetary waves of zonal wave 2, the SSW developed into a vortex split via wave–mean flow interaction. In the following two weeks, the downward migration of zonal-mean zonal wind anomalies was accompanied by a significant transition to the negative phase of the North Atlantic Oscillation, leading to extensive cold extremes across Europe. Here, we demonstrate that anomalous Siberian snow accumulation could have played an important role in the 2018 SSW occurrence. In the 2017/18 winter, snow depths over Siberia were much higher than normal. A lead–lag correlation analysis shows that the positive fluctuating snow depth anomalies, leading to intensified “cold domes” over eastern Siberia (i.e., in a region where the climatological upward planetary waves maximize), precede enhanced wave-2 pulses of meridional heat fluxes (100 hPa) by 7–8 days. The snow–SSW linkage over 2003–19 is further investigated, and some common traits among three split events are found. These include a time lag of about one week between the maximum anomalies of snow depth and wave-2 pulses (100 hPa), high sea level pressure favored by anomalous snowpack, and a ridge anchoring over Siberia as precursor of the splits. The role of tropospheric ridges over Alaska and the Urals in the wave-2 enhancement and the role of Arctic sea ice loss in Siberian snow accumulation are also discussed.
2020
Fourteen legacy organochlorine (OC) contaminants and 12 perfluoroalkyl substances (PFASs) were measured in eggs of tawny owls (Strix alueco) in central Norway (1986–2019). We expected OCs to have reached stable equilibrium levels due to bans, and that recent phase-out of some PFASs would have slowed the increase of these compounds. ∑OC comprised on average approximately 92% of the measured compounds, whereas ∑PFAS accounted for approximately 8%. However, whereas the ∑OC to ∑PFAS ratio was approximately 60 in the first 5 years of the study, it was only approximately 11 in the last 5 years. Both OC pesticides and polychlorinated biphenyls (PCBs) showed substantial declines over the study period (~85%–98%): hexachlorocyclohexanes and chlordanes seemed to be levelling off, whereas p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE) and hexachlororbenzene (HCB), and most PCB congeners still seemed to decline at a more or less constant rate. While the concentration of perfluorooctane sulfonic acid (PFOS), the dominating PFAS, was reduced by approximately 43%, other perfluorinated sulfonates (PFSAs) showed only minor changes. Moreover, the median concentrations of seven perfluorinated carboxylic acids (PFCAs) increased approximately five-fold over the study period. Perfluorononanoic acid and perfluoroundecanoate acid, however, seemed to be levelling off in recent years. In contrast, perfluorododecanoic acid, perfluorodecanoate acid, perfluorotridecanoic acid, and perfluorotetradecanoic acid seemed to increase more or less linearily. Finally, perfluorooctanoic acid (PFOA) was increasingly likely to be detected over the study period. Hence, most legacy OCs and PFOS have not reached a lower threshold with stable background levels, and voluntary elimination of perfluoroalkyl carboxylates still has not resulted in declining levels in tawny owls in central Norway.
2022
Background Previous studies have reported associations between certain persistent organic pollutants (POPs) and type 2 diabetes mellitus (T2DM). Polybrominated diphenyl ethers (PBDEs) are a class of POPs that are found in increasing concentrations in humans. Although obesity is a known risk factor for T2DM and PBDEs are fat-soluble, very few studies have investigated associations between PBDEs and T2DM. No longitudinal studies have assessed associations between repeated measurements of PBDE and T2DM in the same individuals and compared time trends of PBDEs in T2DM cases and controls. Objectives To investigate associations between pre- and post-diagnostic measurements of PBDEs and T2DM and to compare time trends of PBDEs in T2DM cases and controls. Methods Questionnaire data and serum samples from participants in the Tromsø Study were used to conduct a longitudinal nested case-control study among 116 T2DM cases and 139 controls. All included study participants had three pre-diagnostic blood samples (collected before T2DM diagnosis in cases), and up to two post-diagnostic samples after T2DM diagnosis. We used logistic regression models to investigate pre- and post-diagnostic associations between PBDEs and T2DM, and linear mixed-effect models to assess time trends of PBDEs in T2DM cases and controls. Results We observed no substantial pre- or post-diagnostic associations between any of the PBDEs and T2DM, except for BDE-154 at one of the post-diagnostic time-points (OR = 1.65, 95% CI: 1.00, 2.71). The overall time trends of PBDE concentrations were similar for cases and controls. Discussion The study did not support PBDEs increasing the odds of T2DM, prior to or after T2DM diagnosis. T2DM status did not influence the time trends of PBDE concentrations.
2023
Insight into processes determining the exposure of organohalogenated contaminants (OHCs) in wildlife might be gained from comparing predators in different ecosystems. This study compared two avian predator species with similar food chain lengths: the goldeneye duck (Bucephala clangula) and the tawny owl (Strix aluco) breeding in adjacent freshwater- and terrestrial ecosystems in central Norway. We measured lipophilic organochlorines (OCs) and protein-bound perfluorinated substances (PFASs) in eggs of the two species over 21 years (1999–2019). Across years, the proportional distribution of OCs (∼90% of the ΣOHC load) relative to PFASs (∼10%) was similar in the two species. Moreover, ΣOC concentrations were similar between the species, but PFAS compounds were 2–12 times higher in the goldeneyes than in tawny owls. OC-pesticides dominated in tawny owls (∼60% of ΣOC), whereas persistent polychlorinated biphenyl (PCBs) congeners were the main OC components in goldeneyes (∼70% of ΣOC). The lipid-normalized concentrations of most OC-pesticides and the less persistent PCB101 declined significantly in both species. Hexachlorobenzene (HCB), p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE), and more persistent PCBs decreased in tawny owls, while they tended to increase in goldeneyes. The increase in HCB was particulary robust. Among the PFASs, contrasted temporal trends were found across the species for four out of 11 compounds: PFOS declined while most perfluorocarboxylic acids (PFCAs) increased in tawny owls. In contrast, most PFASs were stable in goldeneyes. Moreover, there was no annual covariance between the OHC exposure in the two species: i.e., high concentrations in one species in a given year did not translate into high concentrations in the other. Hence, the two avian predators in adjacent ecosystems seem to be subject to different processes determining the OHC exposure, probably related to variation in diet and climate, long-range transport of different contaminants, and emissions of pollution locally.
2022
Three-dimensional (3D) cloud structures may impact atmospheric trace gas products from ultraviolet–visible (UV–Vis) sounders. We used synthetic and observational data to identify and quantify possible cloud-related bias in NO2 tropospheric vertical column density (TVCD). The synthetic data were based on high-resolution large eddy simulations which were input to a 3D radiative transfer model. The simulated visible spectra for low-earth-orbiting and geostationary geometries were analysed with standard retrieval methods and cloud correction schemes that are employed in operational NO2 satellite products. For the observational data, the NO2 products from the TROPOspheric Monitoring Instrument (TROPOMI) were used, while the Visible Infrared Imaging Radiometer Suite (VIIRS) provided high-spatial-resolution cloud and radiance data. NO2 profile shape, cloud shadow fraction, cloud top height, cloud optical depth, and solar zenith and viewing angles were identified as the metrics being the most important in identifying 3D cloud impacts on NO2 TVCD retrievals. For a solar zenith angle less than about 40∘ the synthetic data show that the NO2 TVCD bias is typically below 10 %, while for larger solar zenith angles the NO2 TVCD is low-biased by tens of percent. The horizontal variability of NO2 and differences in TROPOMI and VIIRS overpass times make it challenging to identify a similar bias in the observational data. However, for optically thick clouds above 3000 m, a low bias appears to be present in the observational data.
2022
Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.
2020