Datensatz #850520

IntelliAQ

Artificial Intelligence for Air Quality

Coordinator	Forschungszentrum Jülich
Grant period	2018-10-01 - 2023-09-30
Funding body	European Union
Call number	ERC-2017-ADG
Grant number	787576
Identifier	G:(EU-Grant)787576

Note: The IntelliAQ project will develop novel approaches for the analysis and synthesis of global air quality data based on deep neural networks. The foundation of this project is the world’s largest collection of surface air quality measurements, which was recently assembled by the principal investigator and plays a pivotal role in the ongoing first comprehensive Tropospheric Ozone Assessment Report (TOAR). This database will be complemented with data from the world’s leading effort to collect global air pollutant measurements in near realtime and combined with high-resolution geodata, weather information, and satellite retrievals of atmospheric composition in order to characterize individual measurement locations and regional air pollution patterns. State-of-the-art deep learning methods will be applied to this unprecedented dataset in order to 1) fill observation gaps in space and time, 2) provide short-term forecasts of air quality, and 3) assess the quality of air pollutant information from diverse measurements. The combination of diverse data sources is unique, and the project will be the first to apply the full potential of deep neural networks on global air quality data. The achievement of the three IntelliAQ objectives will shift the analysis of global air pollutant observations to a new level and provide a basis for the future development of innovative air quality services with robust scientific underpinning. Due to the heterogeneity of the multivariate data, lack of structure, and generally unknown uncertainty of the input data, the project also poses challenges for existing deep learning methods, and will thus lead to new developments in this field. Direct outcomes of the project will be a substantial improvement of global air quality information including methods to assess the quality of air pollution measurements, and a new data-driven method for forecasting air quality at local scales.

Recent Publications

All known publications ...
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Dissertation / PhD Thesis Betancourt, C. (Corresponding author)FZJ*
Mapping and Interpolation of Tropospheric Ozone Data with Machine Learning Methods
Bonn : Universitäts- und Landesbibliothek Bonn 167 p. (2023) [10.48565/BONNDOC-179]2023 = Dissertation, Universität Bonn, 2023 OpenAccess Files BibTeX | EndNote: XML, Text | RIS

Journal Article Becker, J. S. ; DeLang, M. N. ; Chang, K.-L. ; Serre, M. L. ; Cooper, O. ; Wang, H. ; Schultz, M. G.FZJ* ; Schröder, S.FZJ* ; Lu, X. ; Zhang, L. ; Deushi, M. ; Josse, B. ; Keller, C. A. ; Lamarque, J.-F. ; Lin, M. ; Liu, J. ; Marécal, V. ; Strode, S. A. ; Sudo, K. ; Tilmes, S. ; Zhang, L. ; Brauer, M. ; Jason West, J. (Corresponding author)
Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration
Elementa 11(1), 00025 (2023) [10.1525/elementa.2022.00025]2023 OpenAccess Files BibTeX | EndNote: XML, Text | RIS

Journal Article Betancourt, C.FZJ* ; Li, C. W. Y. ; Kleinert, F. ; Schultz, M. G. (Corresponding author)FZJ*
Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data
Environmental science & technology 57, 18246-18258 (2023) [10.1021/acs.est.3c05104] special issue: "Data Science for Advancing Environmental Science, Engineering and Technology"2023 OpenAccess Files BibTeX | EndNote: XML, Text | RIS

Journal Article Leufen, L. H. (Corresponding author)FZJ* ; Kleinert, F.FZJ* ; Schultz, M. G.FZJ*
O3ResNet: A Deep Learning–Based Forecast System to Predict Local Ground-Level Daily Maximum 8-Hour Average Ozone in Rural and Suburban Environments
Artificial Intelligence for the Earth Systems 2(3), 1 - 16 (2023) [10.1175/AIES-D-22-0085.1]2023 OpenAccess Files BibTeX | EndNote: XML, Text | RIS

Journal Article Gong, B. (Corresponding author)FZJ* ; Langguth, M.FZJ* ; Ji, Y.FZJ* ; Mozaffari, A.FZJ* ; Stadtler, S.FZJ* ; Mache, K.FZJ* ; Schultz, M. G.FZJ*
Temperature forecasting by deep learning methods
Geoscientific model development 15(23), 8931 - 8956 (2022) [10.5194/gmd-15-8931-2022]2022 Files Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

Conference Presentation (Invited) Cooper, O. (Corresponding author) ; Schröder, S. (Corresponding author)FZJ* ; Romberg, M.FZJ* ; Selke, N.FZJ* ; Leufen, L. H.FZJ* ; Ahring, J. ; Mozaffari, A.FZJ* ; Schultz, M.FZJ*
TOAR-II Overview and Database
2022Spring 2022 Meetings of the Task Force on Hemispheric Transport of Air Pollution, virtualvirtual, virtual, 17 May 2022 - 25 May 20222022-05-172022-05-25 Files Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

Conference Presentation (Invited) Schröder, S. (Corresponding author)FZJ* ; Mozaffari, A.FZJ* ; Romberg, M.FZJ* ; Selke, N.FZJ* ; Leufen, L. H.FZJ* ; Ahring, J. ; Schultz, M.FZJ*
TOAR-II data portal for global measurements of ozone and its precursors
2022CEOS Atmospheric Composition Virtual Constellation AC-VC-18, virtualvirtual, Belgium, 14 Mar 2022 - 18 Mar 20222022-03-142022-03-18 Files Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

Journal Article Kleinert, F. (Corresponding author)FZJ* ; Leufen, L. H.FZJ* ; Lupascu, A. ; Butler, T. ; Schultz, M. G.FZJ*
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Geoscientific model development 15(23), 8913 - 8930 (2022) [10.5194/gmd-15-8913-2022] special issue: "Benchmark datasets and machine learning algorithms for Earth system science data"2022 Files Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

Poster (After Call) Selke, N. (Corresponding author)FZJ* ; Leufen, L. H.FZJ* ; Mozaffari, A.FZJ* ; Schröder, S.FZJ* ; Schultz, M.FZJ*
Geodata enrichment for air quality
2022Living Planet Symposium 2022, LPS2022, BonnBonn, Germany, 23 May 2022 - 27 May 20222022-05-232022-05-27 Files Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

Journal Article Wittenburg, P. (Corresponding author) ; Hardisty, A. ; Mozaffari, A.FZJ* ; Peer, L. ; Skvortsov, N. ; Spinuso, A. ; Zhao, Z.
Editors’ Note: Special Issue on Canonical Workflow Frameworks for Research
Data Intelligence 4(2), 149 - 154 (2022) [10.1162/dint_e_00122]2022 Files Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

All known publications ...
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Datensatz erzeugt am 2018-07-20, letzte Änderung am 2023-02-07

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