English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  An unsupervised machine learning-based methodology to study the seasonal dispersal pathways of short-lived pollutants from Major Population Centers

Poulidis, A., Daskalakis, N., Kanakidou, M., Vrekoussis, M. (2023): An unsupervised machine learning-based methodology to study the seasonal dispersal pathways of short-lived pollutants from Major Population Centers, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3643

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020899 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020899_1
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Poulidis, Alexandros1, Author
Daskalakis, Nikos1, Author
Kanakidou, Maria1, Author
Vrekoussis, Mihalis1, Author
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

Content

show
hide
Free keywords: -
 Abstract: The rapid increase in human population since 1900 has occurred along with a rapid increase in urbanization - the process of migration from rural into urban areas. The fossil fuel combustion-based emissions affecting areas with high population densities induce a significant health risk for local populations. Protection of human health requires better knowledge of local and regional impacts of urban pollution dispersal. This study addresses the issue of short-lived pollutant transport (e.g. NOx) by constructing a methodology to study the seasonality of common dispersal pathways from Major Population Centers (MPCs); initially applied to 5 MPCs in south America during 2018. For each city, ERA5 reanalysis data were used to drive the FLEXPART emission transport model to simulate the dispersal of near-surface emissions. Simulations are performed for a total of 8 hours from release in the early morning and in the evening, to capture the effects of commuting. A total of 100,000 trajectories per release time per city were analyzed to create representative average trajectories. The k-means clustering algorithm was then applied to categorize the emissions per MPC. Clustering for each MPC led to a robust grouping of trajectories that were seen to reflect climatological and topographic phenomena during the simulation period and exhibit strong seasonality. This finding supports potential applicability of the proposed methodology for a global analysis of MPC emissions.

Details

show
hide
Language(s): eng - English
 Dates: 2023-07-112023-07-11
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.57757/IUGG23-3643
 Degree: -

Event

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Place of Event: Berlin
Start-/End Date: 2023-07-11 - 2023-07-20

Legal Case

show

Project information

show

Source 1

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Potsdam : GFZ German Research Centre for Geosciences
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -