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Found 10066 publications. Showing page 99 of 403:

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Year  
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QUILT: Measurement and model results for the arctic winter stratosphere 2001/2002.

Arlander, D.W.; Tørnkvist, K.K.; Roozendael, M. Van, Hendrick, F.; Wittrock, F.; Richter, A.; Goutail, F.; Lefevre, F.; Pfeilsticker, K.; Wagner, T.; Chipperfield, M.; Roscoe, H.K.; Denis, L.; Gil, M.; Puentedura, O.; Landgraf, J.; Laat, J. de, Ravegnani, F.; Petritoli, A.; Johnston, P.V.; Kreher, K.

2002

Query-driven Qualitative Constraint Acquisition

Belaid, Mohamed-Bachir; Belmecheri, Nassim; Gotlieb, Arnaud; Lazaar, Nadjib; Spieker, Helge

Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.

2024

Quantitative imaging of volcanic plumes - results, needs, and future trends.

Platt, U.; Lübcke, P.; Kuhn, J.; Bobrowski, N.; Prata, F.; Burton, M.; Kern, C.

2015

Quantitative analysis of the 16-17 September 2013 resuspended ash event in Iceland.

Kylling, A.; Beckett, F.; Sigurdardottir, G.M.; von Loewis, S.; Witham, C.

2015

Quantitative analysis of Microplastics including Tire Wear Particles in Northern Atlantic Air with Pyrolysis-GC/MS

Isabel, Gossmann; Schulz, Janina; Herzke, Dorte; Nikiforov, Vladimir; Held, Andreas; Wurl, Oliver; Scholz-Böttcher, Barbara

2023

Quantitative Analysis of Microplastics including Tire Wear Particles in Northern Atlantic Air with Pyrolysis-GC/MS

Gossmann, Isabel; Herzke, Dorte; Held, Andreas; Schulz, Janina; Nikiforov, Vladimir; Georgi, Christoph; Evangeliou, Nikolaos; Eckhardt, Sabine; Gerdts, Gunnar; Wurl, Oliver; Scholz-Böttcher, Barbara

2023

Quantifying wet scavenging processes in aircraft observations of nitric acid and cloud condensation nuclei.

Garrett, T.J.; Avey, L.; Palmer, P.I.; Stohl, A.; Neuman, J.A.; Brock, C.A.; Ryerson, T.B.; Holloway, J.S.

2006

Quantifying the source/receptor link for the IAGOS-MOZAIC observation database.

Auby, A.; Sauvage, B.; Thouret, V.; Boulanger, D.; Eckhardt, S.; Darras, S.; Turquety, S.

2013

Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe

Solberg, Sverre; Walker, Sam-Erik; Schneider, Philipp; Guerreiro, Cristina

In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.

2021

Quantifying subnational CO2 emissions by assimilating regional measurements in a global high-resolution inverse model

Nayagam, Lorna Raja; Maksyutov, Shamil; Oda, Tomohiro; Janardanan, Rajesh; Yoshida, Yukio; Trisolino, Pamela; Zeng, Jiye; Kaiser, Johannes; Matsunaga, Tsuneo

2024

Quantifying methane emissions from the Arctic Ocean seabed to the atmosphere.

Platt, S.; Pisso, I.; Schmidbauer, N.; Hermansen, O.; Silyakova, A.; Ferré, B.; Vadakkepuliyambatta, S.; Myhre, G.; Mienert, J.; Stohl, A.; Myhre, C.L.

2016

Quantifying European SF6 emissions from 2005 to 2021 using a large inversion ensemble

Vojta, Martin; Plach, Andreas; Thompson, Rona Louise; Purohit, Pallav; Stanley, Kieran; O'Doherty, Simon; Young, Dickon; Pitt, Joe; Arduini, Jgor; Lan, Xin; Stohl, Andreas

Abstract. Sulfur hexafluoride (SF6) is a highly potent and long-lived greenhouse gas whose atmospheric concentrations are increasing due to human emissions. In this study, we determine European SF6 emissions from 2005 to 2021 using a large ensemble of atmospheric inversions. To assess uncertainty, we systematically vary key inversion parameters across 986 sensitivity tests and apply a Monte Carlo approach to randomly combine these parameters in 1003 additional inversions. Our analysis focuses on high-emitting countries with robust observational coverage – UK, Germany, France, and Italy – while also examining aggregated EU-27 emissions. SF6 emissions declined across all studied regions except Italy, largely attributed to EU F-gas regulations (2006, 2014), however, national reports underestimated emissions: (i) UK emissions dropped from 68 (47–77) t yr−1 in 2008 to 19 (15–26) t yr−1 in 2018, aligning with the reports from 2018 onward; (ii) French emissions fell from 78 (51–117) t yr−1 (2005) to 35 (19–54) t yr−1 (2021), exceeding reports by 88 %; (iii) Italian emissions fluctuated (25–48 t yr−1), surpassing reports by 107 %; (iv) German emissions declined from 182 (155–251) t yr−1 (2005) to 97 (88–104) t yr−1 (2021), aligning reasonably well with reports; (v) EU-27 emissions decreased from 403 (335–501) t yr−1 (2005) to 225 (191–260) t yr−1 (2021), exceeding reports by 20 %. A substantial drop from 2017 to 2018 mirrored the trend in southern Germany, suggesting regional actions were taken as the 2014 EU regulation took effect. Our sensitivity tests highlight the crucial role of dense monitoring networks in improving inversion reliability. The UK system expansions (2012, 2014) significantly enhanced result robustness, demonstrating the importance of comprehensive observational networks in refining emission estimates.

2025

Quantifying effect of traffic measures using individual exposure modeling. NILU F

Clench-Aas, J.; Bartonova, A.; Klæboe, R.; Kolbenstvedt, M.

1999

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