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Spatial patterns of conditions leading to peak O3 concentrations revealed by clustering analysis of modeled data

Authors
  • Pineda Rojas, Andrea L.1
  • Leloup, Julie A.2
  • Kropff, Emilio3
  • 1 Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera, UMI-IFAECI/CNRS, Facultad de Ciencias Exactas y Naturales, CONICET, UBA, Ciudad Universitaria, Pab. II, piso 2, Buenos Aires, 1428, Argentina , Buenos Aires (Argentina)
  • 2 Sorbonne Universités, UPMC University Paris 6, LOCEAN/IPSL, UMR 7159, CNRS-IRD-MNHN, Paris, France , Paris (France)
  • 3 Fundación Instituto Leloir - IIBBA/CONICET, Buenos Aires, Argentina , Buenos Aires (Argentina)
Type
Published Article
Journal
Air Quality Atmosphere & Health
Publisher
Springer-Verlag
Publication Date
May 04, 2019
Volume
12
Issue
6
Pages
743–754
Identifiers
DOI: 10.1007/s11869-019-00694-9
Source
Springer Nature
Keywords
License
Yellow

Abstract

Air quality models are currently the best available tool to estimate ozone (O3) concentrations in the Metropolitan Area of Buenos Aires (MABA). While the DAUMOD-GRS has been satisfactorily evaluated against observations in the urban area, a Monte Carlo (MC) analysis showed that it is the region around the MABA, where the lack of observations impedes model testing, that concentrates not only the greatest estimated O3 peak levels but also the largest model uncertainty. In this work, we apply clustering analysis to these MC outcomes in order to study the spatial patterns of conditions leading to peak ozone hourly concentrations. Results show that families of conditions distribute, as emissions, radially around the city. A cluster exhibiting an O3 morning peak dominates in low-emission areas, a behavior that can be explained both from theory and from the few monitoring campaigns carried out in the city. Its distinct dynamics compared with the typical O3 diurnal profile occurring in the urban area suggests the need of new ozone measurements in the surroundings of the MABA which could contribute to improve our understanding of O3 formation drivers in this region. The results illustrate the potential of applying clustering analysis on large ensembles of modeled data to better understand the variability in model solutions.

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