Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations
An application of a simple urban air quality model (DAUMOD-GRS) shows that summer maximum O3 hourly concentrations (Cmax) above 40 ppb occur outside the Metropolitan Area of Buenos Aires (MABA) where the absence of observations impedes model testing. In addition, those relatively high values present...
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2017
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS17737_v2017-October_n_p566_PinedaRojas http://hdl.handle.net/20.500.12110/paper_NIS17737_v2017-October_n_p566_PinedaRojas |
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paper:paper_NIS17737_v2017-October_n_p566_PinedaRojas2023-06-08T16:39:39Z Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations Air quality modelling Buenos Aires Clustering analysis Monte Carlo analysis Ozone Air quality Monte Carlo methods Ozone Quality control Uncertainty analysis Air quality modelling Buenos Aires Clustering analysis Model uncertainties Monte carlo analysis Peak ozone concentration Probability assessments Urban air quality Atmospheric movements An application of a simple urban air quality model (DAUMOD-GRS) shows that summer maximum O3 hourly concentrations (Cmax) above 40 ppb occur outside the Metropolitan Area of Buenos Aires (MABA) where the absence of observations impedes model testing. In addition, those relatively high values present the greatest model uncertainty caused by possible errors in model input variables. In order to tackle this issue, a probability assessment of Cmax values exceeding 40 ppb is performed applying a Monte Carlo analysis. On the other hand, a non-hierarchical (k-means) clustering analyses is applied to analyse the Monte Carlo outcomes. Results show three main clusters with a marked spatial distribution resembling that of the ozone precursor species emissions, which highlights an important role of the emissions on the regimes under which modelled Cmax values in the MABA can occur. © 2018 Hungarian Meteorological Service. All Rights Reserved. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS17737_v2017-October_n_p566_PinedaRojas http://hdl.handle.net/20.500.12110/paper_NIS17737_v2017-October_n_p566_PinedaRojas |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Air quality modelling Buenos Aires Clustering analysis Monte Carlo analysis Ozone Air quality Monte Carlo methods Ozone Quality control Uncertainty analysis Air quality modelling Buenos Aires Clustering analysis Model uncertainties Monte carlo analysis Peak ozone concentration Probability assessments Urban air quality Atmospheric movements |
spellingShingle |
Air quality modelling Buenos Aires Clustering analysis Monte Carlo analysis Ozone Air quality Monte Carlo methods Ozone Quality control Uncertainty analysis Air quality modelling Buenos Aires Clustering analysis Model uncertainties Monte carlo analysis Peak ozone concentration Probability assessments Urban air quality Atmospheric movements Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
topic_facet |
Air quality modelling Buenos Aires Clustering analysis Monte Carlo analysis Ozone Air quality Monte Carlo methods Ozone Quality control Uncertainty analysis Air quality modelling Buenos Aires Clustering analysis Model uncertainties Monte carlo analysis Peak ozone concentration Probability assessments Urban air quality Atmospheric movements |
description |
An application of a simple urban air quality model (DAUMOD-GRS) shows that summer maximum O3 hourly concentrations (Cmax) above 40 ppb occur outside the Metropolitan Area of Buenos Aires (MABA) where the absence of observations impedes model testing. In addition, those relatively high values present the greatest model uncertainty caused by possible errors in model input variables. In order to tackle this issue, a probability assessment of Cmax values exceeding 40 ppb is performed applying a Monte Carlo analysis. On the other hand, a non-hierarchical (k-means) clustering analyses is applied to analyse the Monte Carlo outcomes. Results show three main clusters with a marked spatial distribution resembling that of the ozone precursor species emissions, which highlights an important role of the emissions on the regimes under which modelled Cmax values in the MABA can occur. © 2018 Hungarian Meteorological Service. All Rights Reserved. |
title |
Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
title_short |
Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
title_full |
Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
title_fullStr |
Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
title_full_unstemmed |
Clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
title_sort |
clustering of atmospheric and emission conditions that lead to modelled peak ozone concentrations |
publishDate |
2017 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS17737_v2017-October_n_p566_PinedaRojas http://hdl.handle.net/20.500.12110/paper_NIS17737_v2017-October_n_p566_PinedaRojas |
_version_ |
1769175848646082560 |