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Fant 9758 publikasjoner. Viser side 75 av 391:

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Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Tichy, Ondrej; Šmídl, Václav; Hofman, Radek; Evangeliou, Nikolaos

John Wiley & Sons

2018

Source term determination with elastic plume bias correction

Tichý, Ondřej; Šmídl, Václav; Evangeliou, Nikolaos

2022

Source regions of fire emission observed during aircraft campaigns and in-situ networks

Eckhardt, Sabine; Evangeliou, Nikolaos; Sollum, Espen; Stebel, Kerstin

2024

Source Quantification of South Asian Black Carbon Aerosols with Isotopes and Modeling

Dasari, Sanjeev; Andersson, August; Stohl, Andreas; Evangeliou, Nikolaos; Bikkina, Srinivas; Holmstrand, Henry; Budhavant, Krishnakant; Salam, Abdus; Gustafsson, Örjan

2020

Source identification, ship campaigns, snow/ice BC as seen in FLEXPARTv10

Evangeliou, Nikolaos; Eckhardt, Sabine; Stohl, Andreas; Popovicheva, Olga; Sevchenko, V. P.; Eleftheriadis, K.; Sitnikov, N.; Kopeikin, V.M.; Lisitzin, A. P.; Novigatsky, A.N.; Pankratova, N.V.; Starodymova, Dina P.; Kalogridis, Athina C.

2019

Source identification of short-lived air pollutants in the Arctic using statistical analysis of measurement data and particle dispersion model output.

Hirdman, D.; Sodemann, H.; Eckhardt, S.; Burkhart, J.F.; Jefferson, A.; Mefford, T.; Quinn, P.K.; Sharma, S.; Ström, J.; Stohl, A.

2010

Source identification and airborne chemical characterisation of aerosol pollution from long-range transport over Greenland during POLARCAT summer campaign 2008.

Schmale, J.; Schneider, J.; Ancellet, G.; Quennehen, B.; Stohl, A.; Sodemann, H.; Burkhart, J. F.; Hamburger, T.; Arnold, S. R.; Schwarzenboeck, A.; Borrmann, S.; Law, K. S.

2011

Source identification and airborne chemical characterisation of aerosol pollution from long-range transport over Greenland during POLARCAT summer campaign 2008.

Schmale, J.; Schneider, J.; Ancellet, G.; Quennehen, B.; Stohl, A.; Sodemann, H.; Burkhart, J.; Hamburger, T.; Arnold, S.R.; Schwarzenböck, A.; Borrmann, S.; Law, K.S.

2011

Source attribution using FLEXPART and carbon monoxide emission inventories: SOFT-IO version 1.0.

Sauvage, B.; Fontaine, A.; Eckhardt, S.; Auby, A.; Boulanger, D.; Petetin, H.; Paugam, R.; Athier, G.; Cousin, J.-M.; Darras, S.; Nédélec, P.; Stohl, A.; Turquety, S.; Cammas, J.-P.; Thouret, V.

2017

Source Attribution of VOCs in the Canadian Oil Sands using Hierarchical Clustering

Makar, Paul; Liggio, John; Leithead, Amy; Wentzell, Jeremy; Stroud, Craig; Soares, Joana; Akingunola, Ayodeji; Zhang, Junhua; Moran, Michael; Li, Shao-Meng

2019

Source apportionment to support air quality planning: Strengths and weaknesses of existing approaches

Thunis, Philippe; Clappier, A.; Tarrasón, Leonor; Cuvelier, Cornelis; Monteiro, Ana; Pisoni, Enrico; Wesseling, Joost; Belis, Claudio A.; Pirovano, Guido; Janssen, Stijn; Guerreiro, Cristina; Peduzzi, Emanuela

Information on the origin of pollution constitutes an essential step of air quality management as it helps identifying measures to control air pollution. In this work, we review the most widely used source-apportionment methods for air quality management. Using theoretical and real-case datasets we study the differences among these methods and explain why they result in very different conclusions to support air quality planning. These differences are a consequence of the intrinsic assumptions that underpin the different methodologies and determine/limit their range of applicability. We show that ignoring their underlying assumptions is a risk for efficient/successful air quality management as these methods are sometimes used beyond their scope and range of applicability. The simplest approach based on increments (incremental approach) is often not suitable to support air quality planning. Contributions obtained through mass-transfer methods (receptor models or tagging approaches built in air quality models) are appropriate to support planning but only for specific pollutants. Impacts obtained via “brute-force” methods are the best suited but it is important to assess carefully their application range to make sure they reproduce correctly the prevailing chemical regimes.

Elsevier

2019

Source apportionment to support air quality management practices. A fitness-for-purpose guide (V 4.0).

Clappier, A.; Thunis, P.; Pirovano, G.; Riffault, V.; Gilardoni, S.; Pisoni, E.; Guerreiro, Cristina; Monteiro, A.; Dupont, H; Waersted, E.; Hellebust, S.; Stocker, J.; Eriksson, A.; Angyal, A.; Bonafe, G.; Montanari, F.; Matejovica, J.; Bartzis, J.; Gianelle, V.

Information on the origin of pollution is an essential element of air quality management that helps identifying measures to control air pollution. In this document, we review the most widely used source-apportionment (SA) methods for air quality management. The focus is on particulate matter but examples are provided for NO2 as well. Using simple theoretical examples, we explain the differences between these methods and the circumstances where they give different results and thus possibly different conclusions for air quality management. These differences are a consequence of the assumptions that underpin each methodology and determine/limit their range of applicability. We show that ignoring these underlying assumptions is a risk for efficient/successful air quality management when the methods are used outside their scope or range of applicability.

Publications Office for the European Union

2022

Source apportionment on PM2.5 aerosols measured in the urban area of Belgrade.

Cvetkovic, A.; Jovasevic-Stojanovic, M.; Bartonova, A.; Markovic, D.A.

2011

Source apportionment of the summer time carbonaceous aerosol at Nordic rural background sites.

Yttri, K.E.; Simpson, D.; Nøjgaard, J.K.; Kristensen, K.; Genberg, J.; Stenström, K.; Swietlicki, E.; Hillamo, R.; Aurela, M.; Bauer, H.; Offenberg, J.H.; Jaoui, M.; Dye, C.; Eckhardt, S.; Burkhart, J.F.; Stohl, A.; Glasius, M.

2011

Source apportionment of PM2,5 at Abu Dhabi sites. NILU PP

Hak, C.; Lopez-Aparicio, S.

2013

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