Fant 733 publikasjoner. Viser side 1 av 31:
Geopolitical events have shown to threaten European energy security in 2022. In Norway, accustomed to low energy prices, the southern part saw 4 times higher electricity prices in 2022 than long term average, whereas in the north, energy prices remained stable. This offers an opportunity to examine the effect of price on household energy consumption and PM2.5 emissions from the residential sector. In the south, electricity consumption went down by 10% while in the north it remained unchanged relative to expected values. While the documented correlation between increased electricity prices and reduced consumption is well-established, our study uniquely captures a substantial shift towards wood as an alternative energy source. In the south, wood for heating increased by approximately 40%, effectively replacing half of the electricity saved. This increase happened despite prices being curbed by strong government subsidies on electricity. Faced with higher energy costs in Europe, we simulate a scenario where consumers across Europe look for affordable energy. With gas and electricity prices predicted to remain well above long-term averages until 2030, biomass will be an attractive option. Our study shows how a shift can endanger Europe's Zero-Pollution strategy, and the need for initiatives targeting the reduction of residential biomass heating.
Elsevier
2024
2024
Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of −0.3 μg/m3 vs. −1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of −7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.
Elsevier
2025
Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland
The present study describes the data sets produced in Warsaw, Poland with the aim of developing tools and methods for the implementation of human-centred and data-driven solutions to the enhancement of sustainable mobility transition. This study focuses on school commutes and alternatives to private cars for children drop off and pick up from primary schools. The dataset enables the complex analysis of interactions between determinants of transport mode choice, revealed choices, and air quality impact. We draw on four data collection methods, namely, (i) air quality and noise sensors’ measurements, (ii) in-person observations of transport behaviours, (iii) travel diaries, and (iv) social surveys. Moreover, all trip data from travel diaries are complemented with the calculated attributes of alternative travel modes. The data produced in the project can be also combined with publicly available information on air quality, public transport schedules, and traffic flows. The present data sets help to open new venues for interdisciplinary analyses of sustainable mobility transition effectiveness and efficiency.
Springer Nature
2024
European pollen reanalysis, 1980–2022, for alder, birch, and olive
The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980–2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.
Springer Nature
2024
Per- and polyfluoroalkyl substances (PFAS) are persistent anthropogenic contaminants, some of which are toxic and bioaccumulative. Perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) can form during the atmospheric degradation of precursors such as fluorotelomer alcohols (FTOHs), N-alkylated perfluoroalkane sulfonamides (FASAs), and hydrofluorocarbons (HFCs). Since PFCAs and PFSAs will readily undergo wet deposition, snow and ice cores are useful for studying PFAS in the Arctic atmosphere. In this study, 36 PFAS were detected in surface snow around the Arctic island of Spitsbergen during January–August 2019 (i.e., 24 h darkness to 24 h daylight), indicating widespread and chemically diverse contamination, including at remote high elevation sites. Local sources meant some PFAS had concentrations in snow up to 54 times higher in Longyearbyen, compared to remote locations. At a remote high elevation ice cap, where PFAS input was from long-range atmospheric processes, the median deposition fluxes of C2–C11 PFCAs, PFOS and HFPO–DA (GenX) were 7.6–71 times higher during 24 h daylight. These PFAS all positively correlated with solar flux. Together this suggests seasonal light is important to enable photochemistry for their atmospheric formation and subsequent deposition in the Arctic. This study provides the first evidence for the possible atmospheric formation of PFOS and GenX from precursors.
2024
We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of −0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period.
2024
Toward Standardization of a Lung New Approach Model for Toxicity Testing of Nanomaterials
This study represents an attempt toward the standardization of pulmonary NAMs and the development of a novel approach for toxicity testing of nanomaterials. Laboratory comparisons are challenging yet essential for identifying existing limitations and proposing potential solutions. Lung cells cultivated and exposed at the air-liquid interface (ALI) more accurately represent the physiology of human lungs and pulmonary exposure scenarios than submerged cell and exposure models. A triculture cell model system was used, consisting of human A549 lung epithelial cells and differentiated THP-1 macrophages on the apical side, with EA.hy926 endothelial cells on the basolateral side. The cells were exposed to silver nanoparticles NM-300K for 24 h. The model used here showed to be applicable for assessing the hazards of nanomaterials and chemicals, albeit with some limitations. Cellular viability was measured using the alamarBlue assay, DNA damage was assessed with the enzyme-modified comet assay, and the expression of 40 genes related to cell viability, inflammation, and DNA damage response was evaluated through RT2 gene expression profiling. Despite harmonized protocols used in the two independent laboratories, however, some methodological challenges could affect the results, including sensitivity and reproducibility of the model.
MDPI
2024
SuperDARN Radar Wind Observations of Eastward-Propagating Planetary Waves
An array of SuperDARN meteor radars at northern high latitudes was used to investigate the sources and characteristics of eastward-propagating planetary waves (EPWs) at 95 km, with a focus on wintertime. The nine radars provided the daily mean meridional winds and their anomalies over 180 degrees of longitude, and these anomalies were separated into eastward and westward waves using a fast Fourier transform (FFT) method to extract the planetary wave components of zonal wavenumbers 1 and 2. Years when a sudden stratospheric warming event with an elevated stratopause (ES-SSW) occurred during the winter were contrasted with years without such events and composited through superposed epoch analysis. The results show that EPWs are a ubiquitous—and unexpected—feature of meridional wind variability near 95 km. Present even in non-ES-SSW years, they display a regular annual cycle peaking in January or February, depending on the zonal wavenumber. In years when an ES-SSW occurred, the EPWs were highly variable but enhanced before and after the onset.
MDPI
2024
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Numerical methods and simulation codes are essential for the advancement of our understanding of complex atmospheric processes. As technology and computer hardware continue to evolve, the development of sophisticated code is vital for accurate and efficient simulations. In this paper, we present the recent advancements made in the FLEXible PARTicle dispersion model (FLEXPART), a Lagrangian particle dispersion model, which has been used in a wide range of atmospheric transport studies over the past 3 decades, extending from tracing radionuclides from the Fukushima nuclear disaster, to inverse modelling of greenhouse gases, and to the study of atmospheric moisture cycles.
This version of FLEXPART includes notable improvements in accuracy and computational efficiency. (1) By leveraging the native vertical coordinates of European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) instead of interpolating to terrain-following coordinates, we achieved an improvement in trajectory accuracy, leading to a ∼8 %–10 % reduction in conservation errors for quasi-conservative quantities like potential vorticity. (2) The shape of aerosol particles is now accounted for in the gravitational settling and dry-deposition calculation, increasing the simulation accuracy for non-spherical aerosol particles such as microplastic fibres. (3) Wet deposition has been improved by the introduction of a new below-cloud scheme, by a new cloud identification scheme, and by improving the interpolation of precipitation. (4) Functionality from a separate version of FLEXPART, the FLEXPART CTM (chemical transport model), is implemented, which includes linear chemical reactions. Additionally, the incorporation of Open Multi-Processing parallelisation makes the model better suited for handling large input data. Furthermore, we introduced novel methods for the input and output of particle properties and distributions. Users now have the option to run FLEXPART with more flexible particle input data, providing greater adaptability for specific research scenarios (e.g. effective backward simulations corresponding to satellite retrievals). Finally, a new user manual (https://flexpart.img.univie.ac.at/docs/, last access: 11 September 2024) and restructuring of the source code into modules will serve as a basis for further development.
2024
The blood enzyme glutamate-oxaloacetate transaminase (GOT) has been postulated as an effective therapeutic to protect the brain during stroke. To demonstrate its potential clinical utility, a new human recombinant form of GOT (rGOT) was produced for medical use.
We tested the pharmacokinetics and evaluated the protective efficacy of rGOT in rodent and non-human primate models that reflected clinical stroke conditions.
We found that continuous intravenous administration of rGOT within the first 8 h after ischemic onset significantly reduced the infarct size in both severe (30%) and mild lesions (48%). Cerebrospinal fluid and proteomics analysis, in combination with positron emission tomography imaging, indicated that rGOT can reach the brain and induce cytoprotective autophagy and induce local protection by alleviating neuronal apoptosis.
Our results suggest that rGOT can be safely used immediately in patients suspected of having a stroke. This study requires further validation in clinical stroke populations.
2024
Large stocks of soil carbon (C) and nitrogen (N) in northern permafrost soils are vulnerable to remobilization under climate change. However, there are large uncertainties in present-day greenhouse gas (GHG) budgets. We compare bottom-up (data-driven upscaling and process-based models) and top-down (atmospheric inversion models) budgets of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) as well as lateral fluxes of C and N across the region over 2000–2020. Bottom-up approaches estimate higher land-to-atmosphere fluxes for all GHGs. Both bottom-up and top-down approaches show a sink of CO2 in natural ecosystems (bottom-up: −29 (−709, 455), top-down: −587 (−862, −312) Tg CO2-C yr−1) and sources of CH4 (bottom-up: 38 (22, 53), top-down: 15 (11, 18) Tg CH4-C yr−1) and N2O (bottom-up: 0.7 (0.1, 1.3), top-down: 0.09 (−0.19, 0.37) Tg N2O-N yr−1). The combined global warming potential of all three gases (GWP-100) cannot be distinguished from neutral. Over shorter timescales (GWP-20), the region is a net GHG source because CH4 dominates the total forcing. The net CO2 sink in Boreal forests and wetlands is largely offset by fires and inland water CO2 emissions as well as CH4 emissions from wetlands and inland waters, with a smaller contribution from N2O emissions. Priorities for future research include the representation of inland waters in process-based models and the compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more and well-distributed in situ GHG measurements and improved resolution in upscaling techniques.
American Geophysical Union (AGU)
2024
Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 and to the annual hourly concentration in 2019 over the Oslo metropolitan region (1026 ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.
Cambridge University Press
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.
Elsevier
2024
Data Fusion for Enhancing Urban Air Quality Modeling using Large-Scale Citizen Science
Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.
Elsevier
2024
2024
Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.
Nature Portfolio
2024
PFAS Exposure is Associated with a Lower Spermatic Quality in an Arctic Seabird
Several studies have reported an increasing occurrence of poly- and perfluorinated alkyl substances (PFASs) in Arctic wildlife tissues, raising concerns due to their resistance to degradation. While some research has explored PFAS’s physiological effects on birds, their impact on reproductive functions, particularly sperm quality, remains underexplored. This study aims to assess (1) potential association between PFAS concentrations in blood and sperm quality in black-legged kittiwakes (Rissa tridactyla), focusing on the percentage of abnormal spermatozoa, sperm velocity, percentage of sperm motility, and morphology; and (2) examine the association of plasma levels of testosterone, corticosterone, and luteinizing hormone with both PFAS concentrations and sperm quality parameters to assess possible endocrine disrupting pathways. Our findings reveal a positive correlation between the concentration of longer-chain perfluoroalkyl carboxylates (PFCA; C11–C14) in blood and the percentage of abnormal sperm in kittiwakes. Additionally, we observed that two other PFAS (i.e., PFOSlin and PFNA), distinct from those associated with sperm abnormalities, were positively correlated with the stress hormone corticosterone. These findings emphasize the potentially harmful substance-specific effects of long-chain PFCAs on seabirds and the need for further research into the impact of pollutants on sperm quality as a potential additional detrimental effect on birds.
2024
2024
Quantification Approaches in Non-Target LC/ESI/HRMS Analysis: An Interlaboratory Comparison
Nontargeted screening (NTS) utilizing liquid chromatography electrospray ionization high-resolution mass spectrometry (LC/ESI/HRMS) is increasingly used to identify environmental contaminants. Major differences in the ionization efficiency of compounds in ESI/HRMS result in widely varying responses and complicate quantitative analysis. Despite an increasing number of methods for quantification without authentic standards in NTS, the approaches are evaluated on limited and diverse data sets with varying chemical coverage collected on different instruments, complicating an unbiased comparison. In this interlaboratory comparison, organized by the NORMAN Network, we evaluated the accuracy and performance variability of five quantification approaches across 41 NTS methods from 37 laboratories. Three approaches are based on surrogate standard quantification (parent-transformation product, structurally similar or close eluting) and two on predicted ionization efficiencies (RandFor-IE and MLR-IE). Shortly, HPLC grade water, tap water, and surface water spiked with 45 compounds at 2 concentration levels were analyzed together with 41 calibrants at 6 known concentrations by the laboratories using in-house NTS workflows. The accuracy of the approaches was evaluated by comparing the estimated and spiked concentrations across quantification approaches, instrumentation, and laboratories. The RandFor-IE approach performed best with a reported mean prediction error of 15× and over 83% of compounds quantified within 10× error. Despite different instrumentation and workflows, the performance was stable across laboratories and did not depend on the complexity of water matrices.
American Chemical Society (ACS)
2024
The FAIR principles as a key enabler to operationalize safe and sustainable by design approaches
Safe and sustainable development of chemicals, (advanced) materials, and products is at the heart of achieving a healthy future environment in line with the European Green Deal and the Chemicals Strategy for Sustainability. Recently, the Joint Research Center (JRC) of the European Commission (EC) developed the safe and sustainable by design (SSbD) framework for definition of criteria and evaluation procedure proposed to be established in Research and Innovation (R&I) activities. The framework aims to support the design of chemicals, materials and products that provide desirable functions (or services), while simultaneously minimizing the risk for harmful impacts to human health and the environment. While many industrial sectors already consider such aspects during R&I, the framework aims to harmonize safety and sustainability assessment across diverse sectors and innovation strategies to meet the mentioned overarching policy goals. A cornerstone to successfully implement and operationalize the SSbD framework lies in the availability of high-quality data and tools, and their interoperability, aspects which also play a key role in ensuring transparency and thereby trust in the assessment outcomes. Availability of data and tools depend on their machine-actionability in terms of findability, accessibility, interoperability, and reusability, in line with the FAIR principles. The principles were developed in order to harmonize digitalization across all data domains, supporting unanticipated data-driven “seamless” integration of information and generation of new knowledge. Here we discuss the essentiality of FAIR data and tools to operationalize SSbD providing views and examples of activities within the European Partnership for the Assessment of Risks from Chemicals (PARC). The discussion covers five areas previously brought up in relation to the SSbD framework, and which are highly dependent on implementation of the FAIR principles; (i) digitalization to leverage innovation towards a green transition; (ii) existing data sources and their interoperability; (iii) navigating SSbD with data from new scientific developments (iv) transparency and trust through automated assessment of data quality and uncertainty; and (v) “seamless” integration of SSbD tools.
Royal Society of Chemistry (RSC)
2024
Whereas inhalation exposure to organic contaminants can negatively impact human health, knowledge of their spatial variability in the ambient atmosphere remains limited. We analyzed the extracts of passive air samplers deployed at 119 unique sites in Southern Canada between 2019 and 2022 for 353 organic vapors. Hierarchical clustering of the obtained data set revealed four archetypes of spatial concentration variability in the outdoor atmosphere, which are indicative of common sources and similar atmospheric dispersion behavior. “Point Source” signatures are characterized by elevated concentration in the vicinity of major release locations. A “Population” signature applies to compounds whose air concentrations are highly correlated with population density, and is associated with emissions from consumer products. The “Water Source” signature applies to substances with elevated levels in the vicinity of water bodies from which they evaporate. Another group of compounds displays a “Uniform” signature, indicative of a lack of major sources within the study area. We illustrate how such a data set, and the derived spatial patterns, can be applied to support the identification of sources, the quantification of atmospheric emissions, the modeling of air quality, and the investigation of potential inequities in inhalation exposure.
2024
Ammonia emission estimates using CrIS satellite observations over Europe
Over the past century, ammonia (NH3) emissions have increased with the growth of livestock and fertilizer usage. The abundant NH3 emissions lead to secondary fine particulate matter (PM2.5) pollution, climate change, and a reduction in biodiversity, and they affect human health. Up-to-date and spatially and temporally resolved information on NH3 emissions is essential to better quantify their impact. In this study we applied the existing Daily Emissions Constrained by Satellite Observations (DECSO) algorithm to NH3 observations from the Cross-track Infrared Sounder (CrIS) to estimate NH3 emissions. Because NH3 in the atmosphere is influenced by nitrogen oxides (NOx), we implemented DECSO to estimate NOx and NH3 emissions simultaneously. The emissions are derived over Europe for 2020 on a spatial resolution of 0.2°×0.2° using daily observations from both CrIS and the TROPOspheric Monitoring Instrument (TROPOMI; on the Sentinel-5 Precursor (S5P) satellite). Due to the limited number of daily satellite observations of NH3, monthly emissions of NH3 are reported. The total NH3 emissions derived from observations are about 8 Tg yr−1, with a precision of about 5 %–17 % per grid cell per year over the European domain (35–55° N, 10° W–30° E). The comparison of the satellite-derived NH3 emissions from DECSO with independent bottom-up inventories and in situ observations indicates a consistency in terms of magnitude on the country totals, with the results also being comparable regarding the temporal and spatial distributions. The validation of DECSO over Europe implies that we can use DECSO to quickly derive fairly accurate monthly emissions of NH3 over regions with limited local information on NH3 emissions.
2024
Metal pollution is a global environmental issue with adverse biological effects on wildlife. Long-term studies that span declines in metal emissions due to regulation, resulting in varying levels of environmental contamination, are therefore well-suited to investigate effects of toxic metals, while also facilitating robust analysis by incorporating fluctuating environmental conditions and food availability. Here, we examined a resident population of tawny owls in Norway between 1986 and 2019. Tail feathers from females were collected annually, resulting in over 1000 feathers. Each feather served as an archive of local environmental conditions during molt, including the presence of metals, and their dietary ecology, proxied by stable isotopes of nitrogen (δ15N) and carbon (δ13C), as well as corticosterone levels (CORTf), the primary avian glucocorticoid and a measure of physiological stress. We analyzed feathers to examine how exposure to toxic metal(loid)s (Al, As, Cd, Hg, and Pb) and variability in dietary proxies modulate CORTf. Using structural equation modelling, we found that increased Al concentrations and δ15N values, linked directly to increased CORTf. In opposite, we found that increased Hg concentrations and δ13C related to decreased CORTf concentrations. δ15N was indirectly linked to CORTf through Al and Hg, while δ13C was indirectly linked to CORTf through Hg. This supports our hypothesis that metal exposure and dietary ecology may individually or jointly influence physiological stress. Notably, our results suggest that dietary ecology has the potential to mediate the impact of metals on CORTf, highlighting the importance of considering multiple variables, direct and indirect effects, when assessing stress in wildlife. In conclusion, feathers represent an excellent non-destructive biomonitoring strategy in avian wildlife, providing valuable insights not easily accessible using other methods. Further research is warranted to fully comprehend implications of alterations in CORTf on the tawny owl's health and fitness.
Elsevier
2024