Found 10166 publications. Showing page 407 of 407:
The influence of aerosols on the Arctic system remains associated with significant uncertainties, particularly concerning black carbon (BC). The polar aerosol station “Island Bely” (IBS), located in the Western Siberian Arctic, was established to enhance aerosol monitoring. Continuous measurements from 2019 to 2022 revealed the long-term effects of light-absorbing carbon. During the cold period, the annual average light-absorption coefficient was 0.7 ± 0.7 Mm−1, decreasing by 2–3 times during the warm period. The interannual mean showed a peak in February (0.9 ± 0.8 Mm−1) then 10 times the lower minimum in June and exhibited high variability in August (0.7 ± 2.2 Mm−1). An increase of up to 1.5 at shorter wavelengths from April to September suggests contribution from brown carbon (BrC). The annual mean equivalent black carbon (eBC) demonstrated considerable interannual variability, with the lowest in 2020 (24 ± 29 ng m−3). Significant difference was observed between Arctic haze and Siberian wildfire periods, with record-high pollution levels in February 2022 (110 ± 70 ng m−3) and August 2021 (83 ± 249 ng m−3). Anthropogenic BC contributed 83 % to the total for the entire study period, and gas flaring, domestic combustion, transportation, and industrial emissions dominated. During the cold season, > 90 % of surface BC was attributed to anthropogenic sources, mainly gas flaring. In contrast, during the warm period, Siberian wildfires contributed to BC concentrations by 48 %. In August 2021, intense smoke from Yakutian wildfires was transported at high altitudes during the region's worst fire season in 40 years.
2025
Reliable quantification of polychlorinated alkanes (PCAs) remains a major challenge, hindering environmental research across diverse matrices. Each sample can contain over 500 homologue groups, collectively producing >1000 m/z ratios that require interference checks. High-resolution mass spectrometry methods vary in ionization signals and data formats and require specialized algorithms for quantification. CPxplorer streamlines data processing through the integration of three modules: (1) CPions generates target ion sets and isotopic thresholds for compound identification into the next module; (2) Skyline performs instrument-independent data integration, interference evaluation, and homologue profiling; and (3) CPquant deconvolves homologues and reports concentrations using reference standards and homologue profiles from Skyline. Evaluation of the workflow with NIST-SRM-2585 dust and ERM-CE100 fish tissue material yielded comparable results across raw data formats from different instruments. Further applications of CPxplorer across diverse matrices, including indoor dust, organic films, silicone wrist bands, and food samples, demonstrated the usefulness in biological and environmental monitoring. Compared to existing tools limited to qualitative detection, CPxplorer enables quantitative outputs, reduces processing time, and expands functionality to PCA-like substances (e.g., BCAs) and PCA degradation products (e.g., OH-PCAs). CPxplorer reduces learning barriers, empowers users to quantify PCAs across various analytical instruments, and contributes to generating comparable results in the field.
2025
Airborne microplastics on the move: Urban Europe as a source to remote regions
This study presents a comprehensive assessment of unique parallel measurements of surface airborne and deposited microplastics (AMPs) across urban and remote sites in Norway, employing pyrolysis-GC/MS for polymer-specific analysis. MPs were detected in nearly all samples, with significantly higher concentrations and fluxes observed in urban areas like Oslo, where tire wear particles (TWP) dominated (>90 % of AMP mass). Seasonal peaks in TWP coincided with the transition to winter tires, while remote sites showed consistent but lower AMP levels, indicating long-range transport (LRT) from European source regions. Parallel measurements of suspended and deposited AMPs revealed consistent polymer signatures, highlighting common sources and transport pathways. Although urban TWP contributions to PM2.5 were generally low, episodic events reached up to 30 %, raising concerns about human exposure. The dual dataset enabled a robust cross-validation of atmospheric loading estimates and facilitated integration into advanced transport models for remote sites. Our findings confirm AMPs as significant components of urban air pollution and subsequent carriers of chemical and biological contaminants to remote regions, emphasizing the need for targeted monitoring and mitigation strategies.
2025
Temporal changes in per and polyfluoroalkyl substances and their associations with type 2 diabetes
We assessed temporal changes of PFAS and associations with T2DM over a period of 30 years in a nested case–control study with repeated measurements. Logistic regression was used to assess associations between 11 PFAS and T2DM at five time-points in 116 cases and 139 controls (3 pre- and 2 post-diagnostic time-points in cases). Mixed linear models were applied to assess if changes in PFAS were related to T2DM status. In the pre-diagnostic time-point T3 (2001), future cases had higher concentrations of PFHpA, PFNA, PFHxS and PFHpS compared to controls. In the post-diagnostic time point T5 (2015/16), PFNA and PFOS were higher in prevalent cases. PFHxS and PFHpS were positively associated with future T2DM at the pre-diagnostic time-point T3, whereas PFTrDA were inversely associated with future T2DM at T1 (1986/87) and prevalent T2DM at T4 (2007/8). Temporal changes in PFAS across the study period showed that cases experienced a greater increase in pre-diagnostic concentrations of PFHpA, PFTrDA, PFHxS and PFOSA, as well as a larger post-diagnostic decrease in PFOSA, compared to controls. This study is the first to show that temporal changes in PFAS are associated with T2DM status for certain PFAS, and associations between PFAS and T2DM vary according to sample year.
2025
Ocean carbon capture isn’t ready to clean up our mess, report finds
Scientists say ocean carbon capture isn’t ready for prime time and warn deep emissions cuts still have to come first
2025
2025
2025
2026
2026
Nitrogen dioxide (NO2) is a well-known air pollutant, mostly elevated by car traffic in cities. To date, small, reliable, cost-efficient multipollutant sensors with sufficient power and accuracy for community-based atmospheric studies are still lacking. The HAPADS (highly accurate and autonomous programmable platforms for providing air pollution data services) platforms, developed and tested in real conditions, can be a possible approach to solving this issue. The developed HAPADS platforms are equipped with three different NO2 sensors (7E4-NO2–5, SGX-7NO2, MICS-2711 MOS) and a combined ambient air temperature, humidity, and pressure sensor (BME280). The platforms were tested during the driving test, which was conducted across various roads, including highways, expressways, and national and regional routes, as well as major cities and the countryside, to analyse the environmental conditions as much as possible (Poland, 2024). The correlation coefficient r was more than 0.8, and RMSE (root mean squared error) was in the 3.3–4.3 μg/m3 range during the calibration process. The results obtained during the driving tests showed R2 of 0.9–1.0, which proves the ability of HAPADS platforms to work in the hard environmental conditions (including high rain and snow, as well as sun and a wide range of temperatures and humidity).
2026
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
New Approach Methodologies (NAMs) are gaining significant momentum globally to reduce animal testing and enhance the efficiency and human relevance of chemical safety assessment. Even with substantial EU commitment from regulatory agencies and the academic community, the full regulatory adoption of NAMs remains a distant prospect. This challenge is further complicated by the fact that the academic world, oriented toward NAMs development, and regulatory agencies, focused on practical application, frequently operate in separate spheres. Addressing this disconnect, the present paper, developed within the European Partnership for the Assessment of Risks from Chemicals (PARC), provides a clear overview of both the available non-animal tests and current evaluation practices for genotoxic and carcinogenic hazard assessment, while simultaneously highlighting existing regulatory needs, gaps, and challenges toward greater human health protection and the replacement of animal testing through NAMs adoption.
The analysis reveals a complex landscape: while the EU is deeply committed to developing and adopting NAMs, as outlined in its Chemical Strategy for Sustainability and supported by initiatives like PARC, prescriptive regulations such as Classification, Labelling and Packaging (CLP) and Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) still heavily mandate in vivo animal data for hazard classification, particularly for germ cell mutagenicity and carcinogenicity. This reliance creates a “too-short-blanket-problem,” where efforts to reduce animal testing may impact human health protection because of the current in vivo-based classification criteria. In contrast, sectors such as cosmetics and certain European Food Safety Authority (EFSA)-regulated products demonstrate greater flexibility toward progressive integration of NAMs. While the deep mechanistic understanding of genotoxicity and carcinogenicity has significantly advanced the integration of alternatives to animal tests into regulatory chemical hazard assessment, their broader and full implementation faces considerable challenges due to both scientific complexities (i.e., the development and validation of fit-for-purpose NAMs) and existing legislative provisions.
2026
Abstract Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar techniques such as K‐means, density‐based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100 × 100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present a scalable hierarchical clustering implementation that we apply to two year‐long, hourly atmospheric data sets: model concentration and deposition timeseries at 290,520 locations over Alberta and Saskatchewan (538 540 grid); and 366,427 multi‐pollutant observations from 51 national air pollution surveillance stations located across Canada. When combined with other information such as emissions source locations, orography, and prevailing meteorological conditions, the method yields coherent, interpretable structures. In the case of model time series, the clustering provides regions of similar air quality (airsheds) which can be used to inform air quality monitoring network placement, or regions of similar deposition which can inform critical load assessment as well as monitoring site locations. In the case of the multi‐pollutant observations, we show that a single low‐primary pollutant cluster appears the most frequently at all but one of 51 stations across Canada, accounting for 62% of all station‐hours, while elevated SO 2 appears in factor profiles at certain monitoring locations near industrial and shipping activity. Together, these results demonstrate that hierarchical clustering can efficiently summarize patterns relevant to airshed mapping and source apportionment at previously unreachable scales.
2026