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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.
Frontiers Media S.A.
2025
Advancing Genotoxicity Assessment by Building a Global AOP Network
John Wiley & Sons
2025
2025
We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.
Springer Nature
2025
CAMS Assessment Report on European Air Quality 2024
The full report provides reference information on air quality in Europe in 2024. The purpose of the report is to present a consistent and accurate estimate of European air quality focusing on key indicators and on the origin of selected pollution episodes. It is intended to support air quality experts in their reporting under air quality legislation by providing an overview over the status of European transboundary air pollution. It contains updated information on key indicators for background air quality for the main regulatory pollutants: ozone (O3), nitrogen dioxide (NO2), particulate matter of 10 micrometres or less in diameter (PM10) and particulate matter of 2.5 micrometres or less in diameter (PM2.5).
Copernicus Atmosphere Monitoring Service (CAMS)
2025
Vil et endret kosthold påvirke miljøgifter i arktiske sjøfugler?
Norges forskningsråd
2025
Tiltaksutredning for lokal luftkvalitet i Bærum 2025-2030
Stiftelsen NILU har, i samarbeid med Transportanalyse AS, utarbeidet trafikk- og luftkvalitetsberegninger for Oslo og Bærum kommuner. Arbeidet omfatter en kartlegging av luftkvaliteten ved trafikkberegninger og utslipps- og spredningsberegninger for relevante forurensningskomponenter (PM10, PM2,5 og NO2) for Dagens situasjon 2022, Referansesituasjonen 2030 og for 2030 med tiltak. Det er beregnet risiko for overskridelse av dagens grenseverdier i forurensningsforskriften og for grenseverdier i revidert EU-direktiv som vil innføres fullt fra 2030.
NILU
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.
Springer
2025
Unprecedented shifts in aerosol pollution sources in China under a decade of clean air actions
China is a major hotspot of black carbon (BC) emissions, contributing to climate warming and risk to public health. Here, our dual-isotope-constrained observations indicate stringent air pollution controls have drastically reduced coal-burning in North China over the past decade, marking a transition to a “post-coal” era compared to earlier 2012–2014. However, biomass-burning fraction (fbb) for north/central/east winter hazes has doubled from earlier (north/east) ~20%, with significantly higher fbb during polluted winters. Comparisons between observation and transport modelling show good alignment in BC concentrations but substantial discrepancies in source attribution (i.e., fbb). Leveraging radiocarbon measurements, advanced atmospheric modelling, and a Bayesian approach, our study identifies biases stemming from misallocated residential fuel types in emission inventories. These findings underscore the untapped potential to mitigate BC emissions by targeting rural biomass burning, while providing critical insights into BC source evolution to refine emission inventories and formulate effective air quality policies for China and other nations facing severe air pollution.
Springer Nature
2025
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
2025
Monitoring of long-range transported air pollutants in Norway. Annual Report 2024
This report presents results from the monitoring of atmospheric composition and deposition of air pollution in 2024, and focuses on main components in air and precipitation, particulate and gaseous phase of inorganic constituents, particulate carbonaceous matter, ground level ozone and particulate matter.
NILU
2025
2025