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2003
2004
2003
2014
This workshop is part of a Nordic Council of Ministers (NMR) funded project which supports the development and communication of scientific research between Nordic countries on the topic of 'data assimilation in regional scale atmospheric chemistry models'. The four institutes involved, NILU (Norway), met.no (Norway), DMU (Denmark) and SMHI (Sweden), all have active programmes in data assimilation. The intention of this project and workshop is to bring together these institutes to share knowledge and experience within a Nordic context and to further
support development in this research area. In total 21 people attended the workshop, including invited experts in data assimilation from Europe. The workshop showed itself to be successful, being both informative and helpful to the participants. This report consolidates the presentations and discussions that took place during the workshop.
2006
2011
2011
2007
Data assimilation. Cross-cutting issue milestone report 6.8 (M.5). Air4EU - Air Quality Assessment for Europe: from local to continental scale. Air4EU-M.5.
2006
Data fusion for enhancing urban air quality modeling using large-scale citizen science data
Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.
Elsevier
2024
Data fusion in the environmental domain. IFIP Advances in Information and Communication Technology, 413
2013
2016