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Computing Geographical Networks Generated by Air‐Mass Movement
As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on t...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584379/ https://www.ncbi.nlm.nih.gov/pubmed/37859755 http://dx.doi.org/10.1029/2023GH000885 |
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author | Richard, H. Martinetti, D. Lercier, D. Fouillat, Y. Hadi, B. Elkahky, M. Ding, J. Michel, L. Morris, C. E. Berthier, K. Maupas, F. Soubeyrand, S. |
author_facet | Richard, H. Martinetti, D. Lercier, D. Fouillat, Y. Hadi, B. Elkahky, M. Ding, J. Michel, L. Morris, C. E. Berthier, K. Maupas, F. Soubeyrand, S. |
author_sort | Richard, H. |
collection | PubMed |
description | As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on their aerodynamic properties and their reaction to environmental conditions. In this article we present an approach and an accompanying web application called tropolink for measuring the extent to which distant locations are potentially connected by air‐mass movement. This approach is based on the computation of trajectories of air masses with the HYSPLIT atmospheric transport and dispersion model, and on the computation of connection frequencies, called connectivities, in the purpose of building trajectory‐based geographical networks. It is illustrated for different spatial and temporal scales with three case studies related to plant epidemiology. The web application that we designed allows the user to easily perform intensive computation and mobilize massive archived gridded meteorological data to build weighted directed networks. The analysis of such networks allowed us for example, to describe the potential of invasion of a migratory pest beyond its actual distribution. Our approach could also be used to compute geographical networks generated by air‐mass movement for diverse application domains, for example, to assess long‐term risk of spread from persistent or recurrent sources of pollutants, including wildfire smoke. |
format | Online Article Text |
id | pubmed-10584379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105843792023-10-19 Computing Geographical Networks Generated by Air‐Mass Movement Richard, H. Martinetti, D. Lercier, D. Fouillat, Y. Hadi, B. Elkahky, M. Ding, J. Michel, L. Morris, C. E. Berthier, K. Maupas, F. Soubeyrand, S. Geohealth Research Article As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on their aerodynamic properties and their reaction to environmental conditions. In this article we present an approach and an accompanying web application called tropolink for measuring the extent to which distant locations are potentially connected by air‐mass movement. This approach is based on the computation of trajectories of air masses with the HYSPLIT atmospheric transport and dispersion model, and on the computation of connection frequencies, called connectivities, in the purpose of building trajectory‐based geographical networks. It is illustrated for different spatial and temporal scales with three case studies related to plant epidemiology. The web application that we designed allows the user to easily perform intensive computation and mobilize massive archived gridded meteorological data to build weighted directed networks. The analysis of such networks allowed us for example, to describe the potential of invasion of a migratory pest beyond its actual distribution. Our approach could also be used to compute geographical networks generated by air‐mass movement for diverse application domains, for example, to assess long‐term risk of spread from persistent or recurrent sources of pollutants, including wildfire smoke. John Wiley and Sons Inc. 2023-10-18 /pmc/articles/PMC10584379/ /pubmed/37859755 http://dx.doi.org/10.1029/2023GH000885 Text en © 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Article Richard, H. Martinetti, D. Lercier, D. Fouillat, Y. Hadi, B. Elkahky, M. Ding, J. Michel, L. Morris, C. E. Berthier, K. Maupas, F. Soubeyrand, S. Computing Geographical Networks Generated by Air‐Mass Movement |
title | Computing Geographical Networks Generated by Air‐Mass Movement |
title_full | Computing Geographical Networks Generated by Air‐Mass Movement |
title_fullStr | Computing Geographical Networks Generated by Air‐Mass Movement |
title_full_unstemmed | Computing Geographical Networks Generated by Air‐Mass Movement |
title_short | Computing Geographical Networks Generated by Air‐Mass Movement |
title_sort | computing geographical networks generated by air‐mass movement |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584379/ https://www.ncbi.nlm.nih.gov/pubmed/37859755 http://dx.doi.org/10.1029/2023GH000885 |
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