Found 2534 publications. Showing page 2 of 254:
Short-, medium-, and long-chain chlorinated paraffins (CPs) (SCCPs, MCCPs, and LCCPs) and dechloranes are chemicals of emerging concern; however, little is known of their bioaccumulative potential compared to legacy contaminants in marine mammals. Here, we analyzed SCCPs, MCCPs, LCCPs, 7 dechloranes, 4 emerging brominated flame retardants, and 64 legacy contaminants, including polychlorinated biphenyls (PCBs), in the blubber of 46 individual marine mammals, representing 10 species, from Norway. Dietary niche was modeled based on stable isotopes of nitrogen and carbon in the skin/muscle to assess the contaminant accumulation in relation to diet. SCCPs and dechlorane-602 were strongly positively correlated with legacy contaminants and highest in killer (Orcinus orca) and sperm (Physeter macrocephalus) whales (median SCCPs: 160 ng/g lw; 230 ng/g lw and median dechlorane-602: 3.8 ng/g lw; 2.0 ng/g lw, respectively). In contrast, MCCPs and LCCPs were only weakly correlated to recalcitrant legacy contaminants and were highest in common minke whales (Balaenoptera acutorostrata; median MCCPs: 480 ng/g lw and LCCPs: 240 ng/g lw). The total contaminant load in all species was dominated by PCBs and legacy chlorinated pesticides (63–98%), and MCCPs dominated the total CP load (42–68%, except 11% in the long-finned pilot whale Globicephala melas). Surprisingly, we found no relation between contaminant concentrations and dietary niche, suggesting that other large species differences may be masking effects of diet such as lifespan or biotransformation and elimination capacities. CP and dechlorane concentrations were higher than in other marine mammals from the (sub)Arctic, and they were present in a killer whale neonate, indicating bioaccumulative properties and a potential for maternal transfer in these predominantly unregulated chemicals.
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)
2024
Two Stage Feature Engineering to Predict Air Pollutants in Urban Areas
Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: (a) root mean square error (RMSE), (b) mean absolute error (MAE), and (c) R-squared (R 2 ). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R 2 ) and the lowest error scores for both RMSE and MAE.
IEEE (Institute of Electrical and Electronics Engineers)
2024
An AI-Enhanced Systematic Review of Climate Adaptation Costs: Approaches and Advancements, 2010–2021
This study addresses the critical global challenge of climate adaptation by assessing the inadequacies in current methodologies for estimating adaptation costs. Broad assessments reveal a significant investment shortfall in adaptation strategies, highlighting the necessity for precise cost analysis to guide effective policy-making. By employing the PRISMA 2020 protocol and enhancing it with the prismAId tool, this review systematically analyzes the recent evolution of cost assessment methodologies using state-of-the-art generative AI. The AI-enhanced approach facilitates rapid and replicable research extensions. The analysis reveals a significant geographical and sectoral disparity in research on climate adaptation costs, with notable underrepresentation of crucial areas and sectors that are most vulnerable to climate impacts. The study also highlights a predominant reliance on secondary data and a lack of comprehensive uncertainty quantification in economic assessments, suggesting an urgent need for methodological enhancements. It concludes that extending analyses beyond merely verifying that benefits exceed costs is crucial for supporting effective climate adaptation. By assessing the profitability of adaptation investments, it becomes possible to prioritize these investments not only against similar interventions but also across the broader spectrum of public spending.
MDPI
2024
High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage
In the modern “omics” era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography–HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.
2024
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 modeled meteorological parameters such as planetary boundary layer height, wind velocity, temperature. The overall model evaluation shows a mean absolute error of 7.77 μg/m3, a median bias of 0.6 μg/m3 and a Spearman rank correlation of 0.66. The model performance is found to be influenced by NO2 concentration levels, with the most reliable predictions at concentration levels of 10–40 μg/m3 with a bias of
2024
2024
Understanding thermal comfort expectations in older adults: The role of long-term thermal history
Understanding how long-term thermal history affects thermal comfort expectations in older adults (65+) has implications for designing energy-efficient spaces in a changing climate. A growing number of studies focus on thermal sensation/preference votes to represent the current thermal comfort expectations, often overlooking their limitations. This study, however, investigates how factors shaping long-term thermal history link to the current 65+ adults indoor thermal comfort expectations during exposure to heat, by focusing on the upper limit of thermally acceptable temperature range, represented by a self-reported temperature threshold at which 65+ adults believe to feel uncomfortable by indoor heat (Tit). To find Tit, we use answers to “Above what temperature do you start feeling too hot indoors?” by survey respondents in Warsaw (n = 678) and Madrid (n = 527), who lived in their apartment ≥5 years. Statistically, we find indoor factors affecting long-term thermal experiences more significant in explaining 65+ Tit, when compared to outdoor factors such as distance to water, vegetation, or surface thermal radiance. Better-insulated buildings were associated with a lower Tit [...]
Elsevier
2024