Publikasjoner

A GEOSS perspective on air quality and health in Europe: the EGIDA methodology.

Vik, A.F.; Quante, M.; Moldanova, J.; Matthias, V.

A GIS based Air Quality Management System. NILU F

Sivertsen, B.; Naseer, A.A.

A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories

Aerosol particles are essential constituents of the Earth's atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence times, resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in situ near-surface segment of the atmospheric observation system. This paper will provide the widest effort so far to document variability of climate-relevant in situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High-quality data from almost 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single-scattering albedo, and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information, including data provision procedures, quality control and analysis, data policy, and usage of the ground-based aerosol measurement network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus.

Vet, R.; Artz, R.S.; Carou, S.; Shaw, M.; Ro, C.-U.; Aas, W.; Baker, A.; Bowersox, V.C.; Dentener, F.; Galy-Lacaux, C.; Hou, A.; Pienaar, J.J.; Gillett, R.; Forti, M.C.; Gromov, S.; Hara, H.; Khodzher, T.; Mahowald, N.M, Nickovic, S.; Rao, P.S.P.; Reid, N.W.

A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009.

Sharma, S.; Gray, D. K.; Read, J. S.; O'Reilly, C. M.; Schneider, P.; Qudrat, A.; Gries, C.; Stefanoff, S.; Hampton, S. E.; Hook, S.; Lenters, J. D.; Livingstone, D. M.; Mcintyre, P. B.; Adrian, R.; Allan, M. G.; Anneville, O.; Arvola, L.; Austin, J.; Bailey, J.; Baron, J. S.; Brookes, J.; Chen, Y.; Daly, R.; Dokulil, M.; Dong, B.; Ewing, K.; De Eyto, E.; Hamilton, D.; Havens, K.; Haydon, S.; Hetzenauer, H.; Heneberry, J.; Hetherington, A. L.; Higgins, S. N.; Hixson, E.; Izmest'eva, L. R.; Jones, B. M.; Kangur, K.; Kasprzak, P.; Köster, O.; Kraemer, B. M.; Kumagai, M.; Kuusisto, E.; Leshkevich, G.; May, L.; Macintyre, S.; Müller-Navarra, D.; Naumenko, M.; Noges, P.; Noges, T.; Niederhauser, P.; North, R. P.; Paterson, A. M.; Plisnier, P.-D.; Rigosi, A.; Rimmer, A.; Rogora, M.; Rudstam, L.; Rusak, J. A.; Salmaso, N.; Samal, N. R.; Schindler, D. E.; Schladow, G.; Schmidt, S. R.; Schultz, T.; Silow, E. A.; Straile, D.; Teubner, K.; Verburg, P.; Voutilainen, A.; Watkinson, A.; Weyhenmeyer, G. A.; Williamson, C. E.; Woo, K. H.

A global historical inventory of individual PBDEs: From production towards emissions.

Abbasi, G.; Breivik, K.

A global re-analysis of regionally resolved emissions and atmospheric mole fractions of SF6 for the period 2005–2021

We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of −0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period.

A global SCIAMACHY-based trend analysis of tropospheric NO2 over megacities. NILU F

Schneider, P.; R. van der A.

A global strategy for atmospheric interdisciplinary research in the European research area, AIRES in ERA. Air pollution report, 76; EUR 19436

Hov, Ø.; Amanatidis, G.T.; Angeletti, G.; Brasseur, G.; Harris, N.; Mégie, G, Schumann, U.; Slania, S. (eds.)

A high-resolution dynamic probabilistic material flow analysis of seven plastic polymers; A case study of Norway

Plastic pollution has long been identified as one of the biggest challenges of the 21st century. To tackle this problem, governments are setting stringent recycling targets to keep plastics in a closed loop. Yet, knowledge of the stocks and flows of plastic has not been well integrated into policies. This study presents a dynamic probabilistic economy-wide material flow analysis (MFA) of seven plastic polymers (HDPE, LDPE, PP, PS, PVC, EPS, and PET) in Norway from 2000 to 2050. A total of 40 individual product categories aggregated into nine industrial sectors were examined. An estimated 620 ± 23 kt or 114 kg/capita of these seven plastic polymers was put on the Norwegian market in 2020. Packaging products contributed to the largest share of plastic put on the market (∼40%). The accumulated in-use stock in 2020 was about 3400 ± 56 kt with ∼60% remaining in buildings and construction sector. In 2020, about 460 ± 22 kt of plastic waste was generated in Norway, with half originating from packaging. Although ∼50% of all plastic waste is collected separately from the waste stream, only around 25% is sorted for recycling. Overall, ∼50% of plastic waste is incinerated, ∼15% exported, and ∼10% landfilled. Under a business-as-usual scenario, the plastic put on the market, in-use stock, and waste generation will increase by 65%, 140%, and 90%, respectively by 2050. The outcomes of this work can be used as a guideline for other countries to establish the stocks and flows of plastic polymers from various industrial sectors which is needed for the implementation of necessary regulatory actions and circular strategies. The systematic classification of products suitable for recycling or be made of recyclate will facilitate the safe and sustainable recycling of plastic waste into new products, cap production, lower consumption, and prevent waste generation.

A history about Lagrangian modelling - transport of hazardous substances through the atmosphere

A hybrid CNN-transformer model with adaptive activation function for potato leaf disease classification

Abstract Potato plants are highly vulnerable to numerous diseases that can substantially affect both yield and quality. Conventional approaches for detecting these diseases are often labor-intensive, slow, and prone to inaccuracies, particularly under variable environmental conditions. This study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet) , which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases. Furthermore, an adaptive parametric activation function, referred to as Adaptive Flatten p-Mish (AFpM) , is proposed to enhance the model’s learning flexibility and representational capacity. When evaluated on the PlantVillage and Mendeley datasets, PLDNet attains classification accuracies of 99.54% and 87.50%, respectively, surpassing contemporary state-of-the-art models and activation techniques. The proposed framework exhibits strong generalization performance and offers a scalable, efficient approach for automated plant disease identification. To highlight the novelty, the proposed AFpM activation function introduces a learnable parameter enabling adaptive nonlinearity, improving over Mish, Swish, and PFpM activation functions through dynamic gradient control. AFpM improves accuracy by 2.52% on Mendeley dataset, and 1.93% on PlantVillage dataset compared to PFpM, and by more than 3% compared to Swish and Mish.

A knowledge based approach for PM2.5 air pollution effects analysis.

Oprea, M.; Liu, H.Y.