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Grouping strategies are needed for per- and polyfluoroalkyl substances (PFAS), in part, because it would be time and resource intensive to test and evaluate the more than 4700 PFAS on the global market on a chemical-by-chemical basis. In this paper we review various grouping strategies that could be used to inform actions on these chemicals and outline the motivations, advantages and disadvantages for each. Grouping strategies are subdivided into (1) those based on the intrinsic properties of the PFAS (e.g. persistence, bioaccumulation potential, toxicity, mobility, molecular size) and (2) those that inform risk assessment through estimation of cumulative exposure and/or effects. The most precautionary grouping approach of those reviewed within this article suggests phasing out PFAS based on their high persistence alone (the so-called “P-sufficient” approach). The least precautionary grouping approach reviewed advocates only grouping PFAS for risk assessment that have the same toxicological effects, modes and mechanisms of action, and elimination kinetics, which would need to be well documented across different PFAS. It is recognised that, given jurisdictional differences in chemical assessment philosophies and methodologies, no one strategy will be generally acceptable. The guiding question we apply to the reviewed grouping strategies is: grouping for what purpose? The motivation behind the grouping (e.g. determining use in products vs. setting guideline levels for contaminated environments) may lead to different grouping decisions. This assessment provides the necessary context for grouping strategies such that they can be adopted as they are, or built on further, to protect human and environmental health from potential PFAS-related effects.
2020
2012
2017
2012
2005
Stratospheric injection of biomass fire smoke followed by long-range transport: MOZAIC case studies.
2005
2009
2015
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
Street Emission Ceiling (SEC) exercise. Phase 3 report on station pair data analysis, comparison with emissions estimates, street typology and guidance on how to use it. ETC/ACC Technical paper, 2006/7
2007
2019
2018
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
South Asia is a major contributor to global methane (CH4) emissions, yet its emissions remain poorly constrained, limiting targeted mitigation. Current bottom-up inventories do not consistently capture the magnitude and seasonality of CH4 emissions in this region, particularly during the monsoon. Here we quantify South Asian CH4 emissions for 2020 using column observations from a satellite instrument (TROPOMI), a Lagrangian transport model (FLEXPART), and a Bayesian inversion system (FLEXINVERT+). We estimate a posteriori emission of 73.0 ± 0.7 Tg yr−1 for South Asia, including 35.6 ± 0.5 Tg yr−1 for India and 13.2 ± 0.4 Tg yr−1 for Bangladesh. Agriculture and wetlands contribute substantially to the regional budget, with the flux increments coincident with rice-growing areas and inundated lowlands. The inversion indicates pronounced monsoon-modulated seasonality in South Asia: posterior fluxes are higher than the prior by about 19.3 Tg CH4 (an increase of ∼ 70 %) during June–September and lower during January–May by ∼ 46 %. Localized enhancements seen over the lower Indus Basin align with runoff patterns, while the seasonal peaks here are absent in inventories. By resolving monsoon seasonality with satellite constraints, our results point towards key uncertainties in the South Asian CH4 budget and underscore the need for process-based, seasonally responsive inventories to inform mitigation strategies and reconcile bottom-up and top-down estimates.
2026
2018