Publikasjoner

Introduction to hCOMET special issue, 'Comet assay in vitro'

Dusinska, Maria; Costa, Solange; Collins, Andrew

Introduction to PM monitoring methods. Powerpoint presentation. NILU F

Marsteen, L.

Introduction to recent EMEP activities of relevance for EANET. Powerpoint presentation. NILU F

Tørseth, K.

Introduction to special section on 'East Asia Climate and Environment'.

Isaksen, I.S.A.; Dalsøren, S.; Li, L.; Wang, W.-C.

Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972-2009.

Tørseth, K.; Aas, W.; Breivik, K.; Fjæraa, A.M.; Fiebig, M.; Hjellbrekke, A.-G.; Lund Myhre, C.; Solberg, S.; Yttri, K.E.

Introduction to workshop on national AQMS system for South Africa. NILU F

Endregard, G.

Introduction to WP2 – Exposure

Hanssen, Linda

Introduction: Redox interfaces in marine waters. Handbook of Environmental Chemistry, 22

Yakushev, E.V.; Newton, A.

Introduksjon om samarbeidet i Framsenteret og flaggskipet Miljøgifter. NILU F

Heimstad, E.S.

Intuitively tuned elastic bias correction of atmospheric inversion using Gaussian process prior: Application to accidental radioactive emissions

Brožová, Antonie; Šmídl, Václav; Tichý, Ondřej; Evangeliou, Nikolaos

Precise estimation of atmospheric pollutant releases is crucial for assessing the impact of environmental accidents. Atmospheric inversion typically relies on a linear model with a source–receptor sensitivity (SRS) matrix, which may contain significant errors or even completely fail to capture the real magnitude of the event. We propose a correction of the SRS matrix formulated as slight shifts in the observation locations, effectively warping the sensitivity field. To constrain these shifts and ensure data-driven corrections, we model them using a Gaussian process prior. This prior not only enforces smoothness and sparsity, but also enables posterior prediction of shifts at previously unseen locations. This key feature provides a mechanism for hyper-parameter tuning: the predicted shift field can be visualized on a map and assessed by an expert. We present a user-friendly framework that combines a Bayesian inversion model with correction and a tuning algorithm based on L-curve-like plots and the maps of predicted shifts. The proposed method is demonstrated on three case studies: the ETEX-I experiment, the 137Cs emissions during the 2020 Chernobyl wildfires, and the 106Ru release in 2017.

Inventory review 2005. Emission data reported to LRTAP Convention and NEC Directive. Initial review for HMs and POPs. EMEP/MSC-W Technical report, 1/2005

Vestreng, V.; Breivik, K.; Adams, M.; Wagner, A.; Goodwin, J.; Rozovskaya, O.; Pacyna J.M.

Inverse constraints for emission fluxes of atmospheric tracers estimated from concentration measurements and Lagrangian transport.

Pisso, I.; Patra, P.; Breivik, K.