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2005

Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions

Lopez-Aparicio, Susana; Grythe, Henrik; Vogt, Matthias; Pierce, Matthew; Vallejo, Islen

In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution emission inventory for residential wood combustion (RWC). The first method is a webcrawler that extracts openly online available real estate data in a systematic way, and thereafter structures them for analysis. The webcrawler reads online Norwegian real estate advertisements and it collects the geo-position of the dwellings. Dwellings are classified according to the type (e.g., apartment, detached house) they belong to and the heating systems they are equipped with. The second method is a model trained for image recognition and classification based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official statistics, i.e., open fireplaces, stoves produced before 1998 and stoves produced after 1998. The model recognizes and classifies the wood appliances with a precision of 81%, 85% and 91% for open fireplaces, old stoves and new stoves, respectively. Emission factors are heavily dependent on technology and this information is therefore essential for determining accurate emissions. The collected data are compared with existing information from the statistical register at county and national level in Norway. The comparison shows good agreement for the proportion of residential heating systems between the webcrawled data and the official statistics. The high resolution and level of detail of the extracted data show the value of open data to improve emission inventories. With the increased amount and availability of data, the techniques presented here add significant value to emission accuracy and potential applications should also be considered across all emission sectors.

2018

WeBIOPATR 2020. The Eighth WeBIOPATR Workshop & Conference. Particulate Matter: Research and Management. Abstracts of Keynote Invited Lectures and Contributed Papers.

Jovasevic-Stojanovic, Milena; Davidovic, Milos; Bartonova, Alena; Smith, Simon (eds.)

Vinca Institute of Nuclear Sciences

2021

Bok

WG5 session on source apportionment and planning

Guerreiro, Cristina; Pisoni, E.; Belis, C.; Pirovano, G.; Monteiro, A.; Clappier, A.; Thunis, P.

2019

What caused a record high PM10 episode in northern Europe in October 2020?

Zwaaftink, Christine Groot; Aas, Wenche; Eckhardt, Sabine; Evangeliou, Nikolaos; Hamer, Paul David; Johnsrud, Mona; Kylling, Arve; Platt, Stephen Matthew; Stebel, Kerstin; Uggerud, Hilde Thelle; Yttri, Karl Espen

In early October 2020, northern Europe experienced an episode with poor air quality due to high concentrations of particulate matter (PM). At several sites in Norway, recorded weekly values exceeded historical maximum PM10 concentrations from the past 4 to 10 years. Daily mean PM10 values at Norwegian sites were up to 97 µg m−3 and had a median value of 59 µg m−3. We analysed this severe pollution episode caused by long-range atmospheric transport based on surface and remote sensing observations and transport model simulations to understand its causes. Samples from three sites in mainland Norway and the Arctic remote station Zeppelin (Svalbard) showed strong contributions from mineral dust to PM10 (23 %–36 % as a minimum and 31 %–45 % as a maximum) and biomass burning (8 %–16 % to 19 %–21 %). Atmospheric transport simulations indicate that Central Asia was the main source region for mineral dust observed in this episode. The biomass burning fraction can be attributed to forest fires in Ukraine and southern Russia, but we cannot exclude other sources contributing, like fires elsewhere, because the model underestimates observed concentrations. The combined use of remote sensing, surface measurements, and transport modelling proved effective in describing the episode and distinguishing its causes.

2022

What is the effect of phasing out long-chain per- and polyfluoroalkyl substances on the concentrations of perfluoroalkyl acids and their precursors in the environment? A systematic review

Land, Magnus; de Wit, Cynthia A.; Bignert, Anders; Cousins, Ian T.; Herzke, Dorte; Johansson, Jana H.; Martin, Jonathan W.

There is a concern that continued emissions of man-made per- and polyfluoroalkyl substances (PFASs) may cause environmental and human health effects. Now widespread in human populations and in the environment, several PFASs are also present in remote regions of the world, but the environmental transport and fate of PFASs are not well understood. Phasing out the manufacture of some types of PFASs started in 2000 and further regulatory and voluntary actions have followed. The objective of this review is to understand the effects of these actions on global scale PFAS concentrations.

2018

What is the impact of mercury contamination on human health in the Arctic?

Stow, J.; Krümmel, E.; Leech, T.; Donaldson, S.; Hansen, J.C.; Van Oostdam, J.; Gilman, A.; Odland, J.Ø.; Vaktskjold, A.; Dudarev, A.; Ayotte, P.; Berner, J.E.; Bonefeld-Jørgensen, E.C.; Carlsen, A.; Dewailly, E.; Donaldson, S.G.; Furgal, C.; Gilman, A.; Muckle, G.; Ólafsdóttir, K.; Pedersen, H.S.; Rautio, A.; Sandanger, T.M.; Savolainen, M.; Skinner, K.; Tikhonov, C.; Weber, J.-P.; Weihe, P.

2011

What is the status of the Mediterranean Sea and its atmosphere? What has been learned from over-water intensive mercury measurements along 6000 km cruise path.

Pirrone, N.; Ammiraglia, L.; Breg, T.; Ceccarini, C.; Cipriani, F.; Costa, P.; Fajon, V.; Ferrara, Gardfeldt, K.; Gensini, M.; Horvat, M.; Kotnik, J.; Logar, M.; Mamane, Y.; Melamed, E.; Yossef, O.; Pesenti, E.; Sommar, J.; Sekkesæter, S.; Sprovieri, F.; Valdal, A.K.

2001

What makes a good OSSE? NILU F

Lahoz, W.A.

2012

What we have learned in validating Aerosol_cci pixel level uncertainties?

Stebel, K.; Povey, A.; Popp, T.; Capelle, V.; Clarisse, L.; Heckel, A.; Kinne, S.; Klueser, L.; Kolmonen, P.; Kosmale, M.; de Leeuw, G.; North, P. R. J.; Pinnock, S.; Sogacheva, L.; Thomas, G.; Vandenbussche, S.

2017

Where are we in the definition of the optimal satellite instrument to measure ozone for air quality?

Attie, J.-L.; El Amraoui, L.; Lahoz, W.; Quesada, S.; Ricaud, P.; Zbinden, R.

2016

Where does mercury in the Arctic environment come from, and how does it get there?

Munthe, J.; Goodsite, M.; Berg, T.; Chételat, J.; Dastoor, A.; Douglas, T.; Durnford, D.; Goodsite, M.; Macdonald, R.; Muir, D.; Outridge, P.; Pacyna, J.; Ryzhkov, A.; Skov, H.; Steffen, A.; Sundseth, K.; Travnikov, O.; Wängberg, I.; Wilson, S.

2011

Where does the optically detectable aerosol in the European Arctic come from?

Stock, M.; Ritter, C.; Aaltonen, V.; Aas, W.; Handorff, D.; Herber, A.; Treffeisen, R.; Dethloff, K.

2014

White-Tailed Eagle (Haliaeetus albicilla) Body Feathers Document Spatiotemporal Trends of Perfluoroalkyl Substances in the Northern Environment

Sun, Jiachen; Bossi, Rossana; Bustnes, Jan Ove; Helander, Björn; Boertmann, David; Dietz, Rune; Herzke, Dorte; Jaspers, Veerle; Labansen, Aili Lage; Lepoint, Gilles; Schulz, Ralf; Sonne, Christian; Thorup, Kasper; Tøttrup, Anders; Zubrod, Jochen P.; Eens, Marcel; Eulaers, Igor

2019

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