Publications

Scene-to-Scene classification variability in spatiotemporal glacier surface facies mapping in Svalbard

Wankhede, Sagar Filipe; Jolly, Christo; Balakrishna, Keshava; Jawak, Shridhar Digambar

Årsrapport 2024. Nasjonalt referanselaboratorium for luftkvalitetsmålinger

Marsteen, Leif; Johnsrud, Mona; Hak, Claudia; Dauge, Franck Rene; Tørnkvist, Kjersti Karlsen; Vo, Dam Thanh; Amundsen, Filip

Denne rapporten oppsummerer oppgavene til Nasjonalt referanselaboratorium for luftkvalitetsmålinger (NRL), delkontrakt 1b, for året 2024.

NILU

Global greenhouse gas reconciliation 2022

Deng, Zhu; Ciais, Philippe; Hu, Liting; Martinez, Adrien; Saunois, Marielle; Thompson, Rona Louise; Tibrewal, Kushal; Peters, Wouter; Byrne, Brendan; Grassi, Giacomo; Palmer, Paul I.; Luijkx, Ingrid T.; Liu, Zhu; Liu, Junjie; Fang, Xuekun; Wang, Tengjiao; Tian, Hanqin; Tanaka, Katsumasa; Bastos, Ana; Sitch, Stephen; Poulter, Benjamin; Albergel, Clement; Tsuruta, Aki; Maksyutov, Shamil; Janardanan, Rajesh; Niwa, Yosuke; Zheng, Bo; Thanwerdas, Joel; Belikov, Dmitry; Segers, Arjo; Chevallier, Frédéric

n this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data until February 2023 by compiling data from periodical United Nations Framework Convention on Climate Change (UNFCCC) inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by national communications and biennial update reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4, and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study and an improved managed-land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slowly declining or stable trend in emissions. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).

Estimate Rooftop Heights from Orthophotos: a Machine Learning Approach

Belaid, Mohamed-Bachir; Ebrahimi, Babak

Developing the chemistry module for 27 fluorinated greenhouse gases (F-gases): Reactions, emissions, and implementation in GEOS-Chem

Li, Yali; Zhu, Lei; Li, Juan; Chen, Yuyang; Western, Luke M.; Young, Dickon; Mühle, Jens; Weiss, Ray F.; Krummel, Paul B.; Lunder, Chris Rene; Liu, Song; Li, Xicheng; Fu, Weitao; Zhang, Peng; Zhang, Xue; Zhang, Jiaming; Wu, Xingyi; Huang, Yuchen; Shen, Huizhong; Ye, Jianhuai; Wang, Chen; Fu, Tzung-May; Yang, Xin

Critical Insights into Untargeted GC-HRMS Analysis: Exploring Volatile Organic Compounds in Italian Ambient Air

Cerasa, Marina; Balducci, Catia; Moneta, Benedetta Giannelli; Santoro, Serena; Perilli, Mattia; Nikiforov, Vladimir

This study critically examines the workflow for untargeted analysis of volatile organic compounds (VOCs) in ambient air, from sampling strategies to data interpretation by using GC-HRMS. While untargeted approaches are well-established in liquid chromatography (LC) due to advanced-deconvolution tools and extensive metabolomic libraries, their application in gas chromatography (GC) remains less developed, particularly for VOCs. The high structural isomerism of VOCs and the relative novelty of GC-based untargeted methodologies present unique challenges, including limited software tools and reference libraries. Air samples from suburban and rural sites in central Italy were analyzed to explore chemical diversity and address methodological gaps. This study evaluates critical decisions, such as sampling strategies, extraction techniques, and data-processing workflows, highlighting the limitations of automated deconvolution tools and the need for manual validation. Results revealed distinct source contributions, with suburban areas showing higher levels of anthropogenic compounds and rural areas dominated by biogenic emissions. This work underscores the potential of GC-HRMS untargeted analysis to advance environmental chemistry, while addressing key pitfalls and providing practical recommendations for reliable application. By bridging methodological gaps, it offers a roadmap for future studies aiming to integrate untargeted and targeted approaches in air quality research.

Air quality monitoring for air quality policy. Technical support document on the use of reference and non-reference methods, and on the quality assurance process to meet relevant data quality objectives for regulated air pollutants

Tarrasón, Leonor; Geiger, Jutta; Vercauteren, Jordy; Baldan, Annarita; Kyllönen, Katriina; Panteliadis, Pavlos; Stacey, Brian; Green, Jo; Jursins, Jekabs; Marsteen, Leif; Johnsrud, Mona

This document provides technical details and support for the implementation of air quality monitoring under the Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality and cleaner air for Europe (recast) (AAQD, Directive (EU) 2024/2881). It presents an overview of current knowledge and best practices, signposting to existing technical guidance on air quality monitoring and to sources of ongoing technical guidance development. This document does not formulate any legal provisions and as such, it does not have a legally binding value.

Publications Office of the European Union/European Commission. Directorate-General for Environment

Monitoring of environmental contaminants in freshwater food webs (MILFERSK), 2024 Overvåkning av miljøgifter i ferskvann (MILFERSK), 2024

Økelsrud, Asle; Grung, Merete; Bæk, Kine; Rundberget, Thomas; Enge, Ellen Katrin; Hanssen, Linda; Johansen, Ingar

This report presents data from the fourth year of a five-year period of the MILFERSK program. In 2024, the monitoring program focused on the sampling and analysis of the benthic food chain in Lake Mjøsa, encompassing the following sample types: Chironomids, Ruffe, Perch, Pike and the stomach contents of ruffe. Additionally, brown trout from the pelagic zone in Lake Mjøsa were collected and analyzed, with the contaminant levels compared to samples of brown trout from the reference lake, Femunden. The concentrations of 175 individual compounds/isomers were determined, with frequent detections of specific per- and polyfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), mercury (Hg), and siloxanes exhibiting biomagnifying properties throughout the food chain. Certain contaminants, such as quaternary ammonium compounds, were found in higher concentrations in sediment and lower trophic levels. Concentrations of chlorinated paraffins (CPs), particularly medium-chain chlorinated paraffins (MCCPs) were higher in chironomids, ruffe, and the livers of perch and pike, compared to levels observed in 2021 and 2022, with an increase up the food chain in 2024. A slight downward trend in perfluorooctane sulfonate (PFOS) concentrations was observed in Lake Mjøsa from 2014 – 2024. Additionally, a lower length-adjusted mercury concentration was noted for brown trout in Lake Mjøsa during the period from 2015 to 2024, compared to the preceding nine years (2006 – 2014).

Norsk institutt for vannforskning (NIVA)

Activities of the Aerosol Clouds and Trace gases European Research Infrastructure (ACTRIS) Expert Group on Satellite Cal/Val

Balis, Dimitris; Kazadzis, Stelios; Amiridis, Vassilis; Apituley, Arnoud; Baars, Holger; Dandocsi, Alexandru; Putaud, Jean-Philippe; Höhler, Kristina; Liberti, Gianluigi; Marenco, Franco; Marinou, Eleni; Nicolae, Doina; Papagiannopoulos, Nikolaos; Pfitzenmaier, Lucas; Rodriguez-Gomez, Alejandro; Stebel, Kerstin; Sicard, Michael; Verhoelst, Tijl; Wandinger, Ulla; Wegener, Robert

Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks

Chatterjee, Ayan; Thambawita, Vajira L B; Riegler, Michael; Halvorsen, Pål

In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.

Recent Global Trends in Urban Nitrogen Dioxide Observed from Space

Schneider, Philipp; Hassani, Amirhossein; Walker, Sam-Erik; Solberg, Sverre; Stebel, Kerstin

Monitoring of the atmospheric ozone layer and natural ultraviolet radiation. Annual report 2024

Svendby, Tove Marit; Fjæraa, Ann Mari; Schulze, Dorothea; Bäcklund, Are; Johnsen, Bjørn

This report summarizes the results from the Norwegian monitoring programme on stratospheric ozone and UV radiation measurements. The ozone layer has been measured at three locations since 1979: In Oslo/Kjeller, Tromsø/Andøya and Ny-Ålesund. The UV measurements started in 1995. The results show that there was a significant decrease in stratospheric ozone above Norway between 1979 and 1997. After that, the ozone layer stabilized at a level ~2% below pre-1980 level. The year 2024 was characterized by high total ozone values most of the year, especially in the Arctic stations in March. For Ny-Ålesund, 2024 showed the highest annual average total ozone value since systematic ground-based ozone measurements started in 1997.

NILU