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Background
Hazard and risk assessment of nanomaterials (NMs) face challenges due to, among others, the numerous existing nanoforms, discordant data and conflicting results found in the literature, and specific challenges in the application of strategies such as grouping and read-across, emphasizing the need for New Approach Methodologies (NAMs) to support Next Generation Risk Assessment (NGRA). Here these challenges are addressed in a study that couples physico-chemical characterization with in vitro investigations and in silico similarity analyses for nine nanoforms, having different chemical composition, sizes, aggregation states and shapes. For cytotoxicity assessment, three methods (Alamar Blue, Colony Forming Efficiency, and Electric Cell-Substrate Impedance Sensing) are applied in a cross-validation approach to support NAMs implementation into NGRA.
Results
The results highlight the role of physico-chemical properties in eliciting biological responses. Uptake studies reveal distinct cellular morphological changes. The cytotoxicity assessment shows varying responses among NMs, consistent among the three methods used, while only one nanoform gave a positive response in the genotoxicity assessment performed by comet assay.
Conclusions
The study highlights the potential of in silico models to effectively identify biologically active nanoforms based on their physico-chemical properties, reinforcing previous knowledge on the relevance of certain properties, such as aspect ratio. The potential of implementing in vitro methods into NGRA is underlined, cross-validating three cytotoxicity assessment methods, and showcasing their strength in terms of sensitivity and suitability for the testing of NMs.
BioMed Central (BMC)
2024
We investigate the concentration fluctuations of passive scalar plumes emitted from small, localised (point-like) steady sources in a neutrally stratified turbulent boundary layer over a rough wall. The study utilises high-resolution large-eddy simulations for sources of varying sizes and heights. The numerical results, which show good agreement with wind-tunnel studies, are used to estimate statistical indicators of the concentration field, including spectra and moments up to the fourth order. These allow us to elucidate the mechanisms responsible for the production, transport and dissipation of concentration fluctuations, with a focus on the very near field, where the skewness is found to have negative values – an aspect not previously highlighted. The gamma probability density function is confirmed to be a robust model for the one-point concentration at sufficiently large distances from the source. However, for ground-level releases in a well-defined area around the plume centreline, the Gaussian distribution is found to be a better statistical model. As recently demonstrated by laboratory results, for elevated releases, the peak and shape of the pre-multiplied scalar spectra are confirmed to be independent of the crosswind location for a given downwind distance. Using a stochastic model and theoretical arguments, we demonstrate that this is due to the concentration spectra being directly shaped by the transverse and vertical velocity components governing the meandering of the plume. Finally, we investigate the intermittency factor, i.e. the probability of non-zero concentration, and analyse its variability depending on the thresholds adopted for its definition.
Cambridge University Press
2024
2025
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
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
2024
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
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
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
Data fusion for enhancing urban air quality modeling using large-scale citizen science data
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
State of the Climate in 2023 : Global Climate
American Meteorological Society (AMS)
2024
State of the Climate in 2023: The Arctic
American Meteorological Society (AMS)
2024
2024
A growing number of studies have reported that routinely monitored per- and polyfluoroalkyl substances (PFAS) are not sufficient to explain the extractable organic fluorine (EOF) measured in human blood. In this study, we address this gap by screening pooled human serum collected over 3 decades (1986–2015) in Tromsø (Norway) for >5000 PFAS and >300 fluorinated pharmaceuticals. We combined multiple analytical techniques (direct infusion Fourier transform ion cyclotron resonance mass spectrometry, liquid chromatography-Orbitrap-high-resolution mass spectrometry, and total oxidizable precursors assay) in a three-step suspect screening process which aimed at unequivocal suspect identification. This approach uncovered the presence of one PFAS and eight fluorinated pharmaceuticals (including some metabolites) in human serum. While the PFAS suspect only accounted for 2–4% of the EOF, fluorinated pharmaceuticals accounted for 0–63% of the EOF, and their contribution increased in recent years. Although fluorinated pharmaceuticals often contain only 1–3 fluorine atoms, our results indicate that they can contribute significantly to the EOF. Indeed, the contribution from fluorinated pharmaceuticals allowed us to close the organofluorine mass balance in pooled serum from 2015, indicating a good understanding of organofluorine compounds in humans. However, a portion of the EOF in human serum from 1986 and 2007 still remained unexplained.
2024
The adverse outcome pathway (AOP) framework plays a crucial role in the paradigm shift of toxicity testing towards the development and use of new approach methodologies. AOPs developed for chemicals are in theory applicable to nanomaterials (NMs). However, only initial efforts have been made to integrate information on NM-induced toxicity into existing AOPs. In a previous study, we identified AOPs in the AOP-Wiki associated with the molecular initiating events (MIEs) and key events (KEs) reported for NMs in scientific literature. In a next step, we analyzed these AOPs and found that mitochondrial toxicity plays a significant role in several of them at the molecular and cellular levels. In this study, we aimed to generate hypothesis-based AOPs related to NM-induced mitochondrial toxicity. This was achieved by integrating knowledge on NM-induced mitochondrial toxicity into all existing AOPs in the AOP-Wiki, which already includes mitochondrial toxicity as a MIE/KE. Several AOPs in the AOP-Wiki related to the lung, liver, cardiovascular and nervous system, with extensively defined KEs and key event relationships (KERs), could be utilized to develop AOPs that are relevant for NMs. However, the majority of the studies included in our literature review were of poor quality, particularly in reporting NM physicochemical characteristics, and NM-relevant mitochondrial MIEs were rarely reported. This study highlights the potential role of NM-induced mitochondrial toxicity in human-relevant adverse outcomes and identifies useful AOPs in the AOP-Wiki for the development of AOPs for NMs.
Elsevier
2024
IEEE (Institute of Electrical and Electronics Engineers)
2025
This study investigates the efficacy of supramolecular solvent (SUPRAS) in extracting a diverse spectrum of organic contaminants from indoor dust. Initially, seven distinct SUPRAS were assessed across nine categories of contaminants to identify the most effective one. A SUPRAS comprising Milli-Q water, tetrahydrofuran, and hexanol in a 70:20:10 ratio, respectively, demonstrated the best extraction performance and was employed for testing a wider array of organic contaminants. Furthermore, we applied the selected SUPRAS for the extraction of organic compounds from the NIST Standard Reference Material (SRM) 2585. In parallel, we performed the extraction of NIST SRM 2585 with conventional extraction methods using hexane:acetone (1:1) for non-polar contaminants and methanol (100%) extraction for polar contaminants. Analysis from two independent laboratories (in Norway and the Czech Republic) demonstrated the viability of SUPRAS for the simultaneous extraction of twelve groups of organic contaminants with a broad range of physico-chemical properties including plastic additives, pesticides, and combustion by-products. However, caution is advised when employing SUPRAS for highly polar contaminants like current-use pesticides or volatile substances like naphthalene.
Springer
2024
Satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO2, however they typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled ‘S-MESH’ (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and
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.
Elsevier
2024
2024
Energetic particle precipitation influences global secondary ozone distribution
The secondary ozone layer is a global peak in ozone abundance in the upper mesosphere-lower thermosphere (UMLT) around 90-95 km. The effect of energetic particle precipitation (EPP) from geomagnetic processes on this UMLT ozone remains largely unexplored. In this research we investigated how the secondary ozone responds to EPP using satellite observations. In addition, the residual Mean Meridional Circulation (MMC) derived from model simulations and the atomic oxygen [O], atomic hydrogen [H], temperature measurements from satellite observations were used to characterise the residual circulation changes during EPP events. We report regions of secondary ozone enhancement or deficit across low, mid and high latitudes as a result of global circulation and transport changes induced by EPP. The results are supported by a sensitivity test using an empirical model.
Springer Nature
2024
2024