Publikasjoner

Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations

Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH4 emissions in Australia amount to 6.84 ± 0.51 Tg CH4 yr−1 and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH4 yr−1, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.

Slik kan mose vise luft­forurensing

Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection

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.

Lufktvalitetsmålinger i omgivelsene til Hydro Årdal. Måling av svevestøv, arsen og nikkel i kalenderåret 2024

NILU har på oppdrag fra Hydro Aluminium AS Årdal Metallverk utført målinger av svevestøv (PM2.5, PM10), arsen (As), nikkel (Ni) og gassformig fluorid (HF) i omgivelsesluft i Øvre Årdal. Målingene pågikk i perioden 12. januar 2024 – 2. januar 2025 ved Årdal VGS. Konsentrasjonene av de målte komponentene var under de individuelle grenseverdier, målsettingsverdier og luftkvalitetskriterier i måleperioden. Vurderinger rundt spredningsberegningene fra 2021 og måleresultatene fra 2024 viser godt samsvar mellom beregninger og målinger for As, mens beregnet Ni er overestimert sammenlignet med målingene. For svevestøv er beregningene i finfraksjonen PM2.5 litt underestimert sammenlignet med målingene, for PM10 samsvarer beregningene godt med hva som er målt.

NILU

Construction of an enterprise-level global supply chain database

Data tracing global supply chains, commonly captured in input–output models, is a foundational resource across economic, regulatory, investment, defense, and environmental applications. Such models provide insight into interdependency and environmental burden-shifting, forming part of the empirical basis for policies such as Scope 3 embodied emissions targets, supply chain transparency, life cycle assessments, and product declarations. Current approaches, based on national statistics, remain constrained by sector-level resolution, limiting their precision and utility in certain applications. Here, we document the construction of an enterprise-level multi-regional input–output (EMRIO) table. This database merges official national input–output tables with publicly available firm-level production and transaction data, creating a globally consistent account of purchases and sales across 9,466 companies, 86,305 subsegments, and 121 countries. The finer resolution allows supply chain transactions to be represented in greater detail, providing an additional resource for analyses and policy tools requiring more disaggregated supply chain information.

Miljøeksponering for KMR-stoffer

The active layer soils of Greenlandic permafrost areas can function as important sinks for volatile organic compounds

Permafrost is a considerable carbon reservoir harboring up to 1700 petagrams of carbon accumulated over millennia, which can be mobilized as permafrost thaws under global warming. Recent studies have highlighted that a fraction of this carbon can be transformed to atmospheric volatile organic compounds, which can affect the atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols. In this study, active layer soils from the seasonally unfrozen layer above the permafrost were collected from two distinct locations of the Greenlandic permafrost and incubated to explore their roles in the soil-atmosphere exchange of volatile organic compounds. Results show that these soils can actively function as sinks of these compounds, despite their different physiochemical properties. Upper active layer possessed relatively higher uptake capacities; factors including soil moisture, organic matter, and microbial biomass carbon were identified as the main factors correlating with the uptake rates. Additionally, uptake coefficients for several compounds were calculated for their potential use in future model development. Correlation analysis and the varying coefficients indicate that the sink was likely biotic. The development of a deeper active layer under climate change may enhance the sink capacity and reduce the net emissions of volatile organic compounds from permafrost thaw.

Modelling the influence of suburban sprawl vs. compact city development upon road network performance and traffic emissions

Road traffic externalities are an important consequence of land-use and transport interactions and may be especially induced by their inefficient combinations. In this study, we integrate land-use, transport and emission modelling tools (the LUTEm framework) to assess how suburban expansion vs. inward densification scenarios influence journey parameters, road network performance and traffic emissions. Case-study simulations for Warsaw (Poland) underscore the negative consequences of suburban sprawl development, which are hardly mitigated by additional land-use or transport interventions, such as rebalancing of population-workplace distribution or road capacity reductions. On the other side, compact city development lowers global traffic congestion and emissions, but can also raise the risks of traffic externalities in central city area unless complemented with further interventions such as improved public transport attractiveness. This study aims to enrich the understanding of how integrating the land-use development and transport interventions can ultimately influence travel parameters and reduce urban road traffic externalities.

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

Efficient use of a Lagrangian particle dispersion model for atmospheric inversions using satellite observations of column mixing ratios

Satellite instruments for measuring atmospheric column mixing ratios have improved significantly over the past couple of decades, with increases in pixel resolution and accuracy. As a result, satellite observations are being increasingly used in atmospheric inversions to improve estimates of emissions of greenhouse gases (GHGs), particularly CO2 and CH4, and to constrain regional and national emission budgets. However, in order to make use of the increasing resolution in inversions, the atmospheric transport models used need to be able to represent the observations at these finer resolutions. Here, we present a new and computationally efficient methodology to model satellite column average mixing ratios with a Lagrangian particle dispersion model (LPDM) and calculate the Jacobian matrices describing the relationship between surface fluxes of GHGs and atmospheric column average mixing ratios, as needed in inversions. The development will enable a more accurate representation of satellite observations (especially high-resolution ones) via the use of LPDMs and, thus, help improve the accuracy of emission estimates obtained by atmospheric inversions. We present a case study using this methodology in the FLEXPART (FLEXible PARTicle dispersion model) LPDM and the FLEXINVERT inversion framework to estimate CH4 fluxes over Siberia using column average mixing ratios of CH4 (XCH4) from the TROPOMI (TROPOspheric Monitoring Instrument) instrument aboard the Sentinel-5P satellite. The results of the inversion using TROPOMI XCH4 are evaluated against results using ground-based observations.

NatureScape Nature-Based Solutions Transformation Lab at Linderud Gård community garden

Airborne measurements of trace organic species in the upper troposphere over Europe: The impact of deep convection

Critical review of the atmospheric composition observing capabilities for monitoring and forecasting

WMO