Found 10054 publications. Showing page 392 of 403:
Citizen-operated low-cost air quality sensors (LCSs) have expanded air quality monitoring through community engagement. However, still challenges related to lack of semantic standards, data quality, and interoperability hinder their integration into official air quality assessments, management, and research. Here, we introduce FILTER, a geospatially scalable framework designed to unify, correct, and enhance the reliability of crowd-sourced PM2.5 data across various LCS networks. FILTER assesses data quality through five steps: range check, constant value detection, outlier detection, spatial correlation, and spatial similarity. Using official data, we modeled PM2.5 spatial correlation and similarity (Euclidean distance) as functions of geographic distance as benchmarks for evaluating whether LCS measurements are sufficiently correlated/consistent with neighbors. Our study suggests a −10 to 10 Median Absolute Deviation threshold for outlier flagging (360 h). We find higher PM2.5 spatial correlation in DJF compared to JJA across Europe while lower PM2.5 similarity in DJF compared to JJA. We observe seasonal variability in the maximum possible distance between sensors and reference stations for in-situ (remote) PM2.5 data correction, with optimal thresholds of ∼11.5 km (DJF), ∼12.7 km (MAM), ∼20 km (JJA), and ∼17 km (SON). The values implicitly reflect the spatial representativeness of stations. ±15 km relaxation for each season remains feasible when data loss is a concern. We demonstrate and validate FILTER's effectiveness using European-scale data originating from the two community-based monitoring networks, sensor.community and PurpleAir with QC-ed/corrected output including 37,085 locations and 521,115,762 hourly timestamps. Results facilitate uptake and adoption of crowd-sourced LCS data in regulatory applications.
2025
Stress is a common human reaction to demanding circumstances, and prolonged and excessive stress can have detrimental effects on both mental and physical health. Heart rate variability (HRV) is widely used as a measure of stress due to its ability to capture variations in the time intervals between heartbeats. However, achieving high accuracy in stress detection through machine learning (ML), using a reduced set of statistical features extracted from HRV, remains a significant challenge. In this study, we aim to address these challenges by proposing lightweight ML models that can effectively detect stress using minimal HRV features and are computationally efficient enough for IoT deployment. We have developed ML models incorporating efficient feature selection techniques and hyper-parameter tuning. The publicly available SWELL-KW dataset has been utilized for evaluating the performance of our models. Our results demonstrate that lightweight models such as k-NN and Decision Tree can achieve competitive accuracy while ensuring lower computational demands, making them ideal for real-time applications. Promisingly, among the developed models, the k-nearest neighbors (k-NN) algorithm has emerged as the best-performing model, achieving an accuracy score of 99.3% using only three selected features. To confirm real-world deployability, we benchmarked the best model on an 8 GB NVIDIA Jetson Orin Nano edge device, where it retained 99.26% accuracy and completed training in 31 s. Furthermore, our study has incorporated local interpretable model-agnostic explanations to provide comprehensive insights into the predictions made by the k-NN-based architecture.
2025
Car tire particles and their additives: biomarkers for recent exposure in marine environments
Car tire particles represent an important category of microplastics that is difficult to alleviate. The particles stem from abrasion during driving, so-called tire wear particles (TWPs), down-cycled end-oflife tire granulate, popular as low-cost infill on sports fields, or degradation products from discarded tires. The material contains a variety of additives and chemical residues from the manufacturing process, including metals, especially high concentrations of zinc, polycyclic aromatic hydrocarbons (PAHs), and benzothiazoles, but also para-phenylenediamines (PPDs) and numerous other organic chemicals. In urbanized areas, TWPs are emitted from roads, and granulates disperse from artifical sports fields and other urban surfaces to the environment, suggesting that runoff to coastal systems is likely and a route of exposure to marine organisms. Recent experimental studies show tire rubber
particles in marine animals from different functional groups in addition to uptake of tire-related organic chemicals into biological tissues. These include bivalves, crabs, and fish, representing different body sizes, marine habitats, and feeding modes, and thus varying exposure scenarios. Our findings from GC-HRMS SIM chromatography demonstrate that different marine species ingest tire rubber particles, and that several tire additives are taken up into tissues post-ingestion. Although the organic chemicals do not seem to bioaccumulate, they are specific and bioavailable chemicals in tire materials. Mapping of tire rubber particle distributions in coastal systems, dose-response toxicity
testing and risk assessments of environmental concentrations are thus warranted, also with a view to potential trophic transfer and implications for human health.
2025
Data Report 2023. Particulate matter, carbonaceous and inorganic compounds
This report presents an overview of annual statistical summaries and methods for sampling and analysis of particulate matter, carbonaceous and inorganic compounds measured under the EMEP monitoring programme in 2023.
NILU
2025
Measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) combined with a global 12-box model of the atmosphere have long been used to estimate global emissions and surface mean mole fraction trends of atmospheric trace gases. Here, we present annually updated estimates of these global emissions and mole fraction trends for 42 compounds through 2023 measured by the AGAGE network, including chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, nitrogen trifluoride, methane, nitrous oxide, and selected other compounds. The data sets are available at https://doi.org/10.5281/zenodo.15372480 (Western et al., 2025). We describe the methodology to derive global mole fraction and emissions trends, which includes the calculation of semihemispheric monthly mean mole fractions, the mechanics of the 12-box model and the inverse method that is used to estimate emissions from the observations and model. Finally, we present examples of the emissions and mole fraction data sets for the 42 compounds.
2025
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.
2025
A global assemblage of regional prescribed burn records — GlobalRx
Abstract Prescribed burning (RxB) is a land management tool used widely for reducing wildfire hazard, restoring biodiversity, and managing natural resources. However, RxB can only be carried out safely and effectively under certain seasonal or weather conditions. Under climate change, shifts in the frequency and timing of these weather conditions are expected but analyses of climate change impacts have been restricted to select few regions partly due to a paucity of RxB records at global scale. Here, we introduce GlobalRx, a dataset including 204,517 RxB records from 1979–2023, covering 16 countries and 209 terrestrial ecoregions. For each record, we add a comprehensive suite of meteorological variables that are regularly used in RxB prescriptions by fire management agencies, such as temperature, humidity, and wind speed. We also characterise the environmental setting of each RxB, such as land cover and protected area status. GlobalRx enables the bioclimatic range of conditions suitable for RxB to be defined regionally, thus unlocking new potential to study shifting opportunities for RxB planning and implementation under future climate.
2025
Human biomonitoring (HBM) provides an integrated chemical exposures assessment considering all routes and sources of exposure. The accurate interpretation and comparability of biomarkers of exposure and effect depend on harmonized, quality-assured sampling, processing, and analysis. Currently, the lack of broadly accepted guidance on minimum information required for collecting and reporting HBM data, hinders comparability between studies. Furthermore, it prevents HBM from reaching its full potential as a reliable approach for assessing and managing the risks of human exposure to chemicals.
The European Chapter of the International Society of Exposure Science HBM Working Group (ISES Europe HBM working group) has established a global human biomonitoring community network (HBM Global Network) to develop a guidance to define the minimum information to be collected and reported in HBM, called the “Minimum Information Requirements for Human Biomonitoring (MIR-HBM)”. This work builds on previous efforts to harmonize HBM worldwide.
The MIR-HBM guidance covers all phases of HBM from the design phase to the effective communication of results. By carefully defining MIR for all phases, researchers and health professionals can make their HBM studies and programs are robust, reproducible, and meaningful. Acceptance and implementation of MIR-HBM Guidelines in both the general population and occupational fields would improve the interpretability and regulatory utility of HBM data. While implementation challenges remain—such as varying local capacities, and ethical and legal differences at the national levels, this initiative represents an important step toward harmonizing HBM practice and supports an ongoing dialogue among policymakers, legal experts, and scientists to effectively address these challenges. Leveraging the data and insights from HBM, policymakers can develop more effective strategies to protect public health and ensure safer working environments.
2025
Detection of Body Shaming in Social Media: Traditional Machine Learning vs. Transformer-based Models
2025
2025
Fungus-farming termites cultivate a Termitomyces fungus monoculture in enclosed gardens (combs) free of other fungi, except during colony declines, where Pseudoxylaria spp. stowaway fungi appear and take over combs. Here, we determined Volatile Organic Compounds (VOCs) of healthy Macrotermes bellicosus nests in nature and VOC changes associated with comb decay during Pseudoxylaria takeover. We identified 443 VOCs and unique volatilomes across samples and nest volatilomes that were mainly composed of fungus comb VOCs with termite contributions. Few comb VOCs were linked to chemical changes during decay, but longipinocarvone and longiverbenone were only emitted during comb decay. These terpenes may be involved in Termitomyces defence against antagonistic fungi or in fungus-termite signalling of comb state. Both comb and Pseudoxylaria biomass volatilomes contained many VOCs with antimicrobial activity that may serve in maintaining healthy Termitomyces monocultures or aid in the antagonistic takeover by Pseudoxylaria during colony decline. We further observed a series of oxylipins with known functions in the regulation of fungus germination, growth, and secondary metabolite production. Our volatilome map of the fungus-farming termite symbiosis provides new insights into the chemistry regulating complex interactions and serves as a valuable guide for future work on the roles of VOCs in symbioses.
2025
prismAId is an open-source tool designed to streamline systematic literature reviews by leveraging generative AI models for information extraction. It offers an accessible, efficient, and replicable method for extracting and analyzing data from scientific literature, eliminating the need for coding expertise. Supporting various review protocols, including PRISMA 2020, prismAId is distributed across multiple platforms – Go, Python, Julia, R – and provides user-friendly binaries compatible with Windows, macOS, and Linux. The tool integrates with leading large language models (LLMs) such as OpenAI’s GPT series, Google’s Gemini, Cohere’s Command, and Anthropic’s Claude, ensuring comprehensive and up-to-date literature analysis. prismAId facilitates systematic reviews, enabling researchers to conduct thorough, fast, and reproducible analyses, thereby advancing open science initiatives.
2025
2025
Citizen-operated low-cost sensors for estimating outdoor particulate matter infiltration
Fine particulates observed indoors exhibit high variability, influenced by both indoor emission sources and the infiltration of outdoor particles through open spaces and the incomplete building insulation. This study examines the relationship between indoor and outdoor PM2.5 levels in Legionowo, Poland, using data from low-cost air quality sensors operated by citizens. The indoor PM2.5 was lower than outdoor levels (median PM2.5: 1.9–17.3 μg m–3 indoors and 6.7–27.9 μg m–3 outdoors), with occasional peaks attributed to potential indoor emission sources. Statistical analysis identified emission events—particularly during cooking and household-heating periods—occurring more frequently from October to April. During this period, nearly 17% of indoor PM2.5 measurements were attributed to indoor emission sources after 18:00 LT, representing a 7% increase compared to the May–September period. In the absence of indoor sources, outdoor particles accounted for 29% to 75% of indoor concentrations, highlighting the significance of infiltration. This research emphasizes how citizen-generated data using low-cost sensors, after post-processing, can provide decision-ready information as for example outdoor particles’ infiltration factors for each building. The knowledge of the infiltration factor enables the determination of the contribution of indoor and outdoor sources to each resident’s exposure to airborne PM. This information can help decision-makers in devising interventions such as prioritizing indoor ventilation, reducing indoor activities resulting in increased exposure, and addressing outdoor pollution sources.
2025
Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra
Abstract This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO 2 fluxes were measured with eddy covariance alongside spectral and meteorological data, and surface temperature. A random forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. The results showed that surface temperature and CO 2 flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly), while the PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. The NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy ( R 2 up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting the potential to combine machine learning with process-based modelling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems.
2025