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You searched for subject:(Ozone estimator). Showing records 1 – 2 of 2 total matches.

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Georgia Tech

1. Cheng, Ye. Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations.

Degree: PhD, Earth and Atmospheric Sciences, 2018, Georgia Tech

Tropospheric O3 and CO are major pollutants in the troposphere. Strong correlation between O3 and CO was observed during the DISCOVER-AQ aircraft experiment in July 2011 over the Washington-Baltimore area. The observed correlation does not vary significantly with time or altitude in the boundary layer. The observations are simulated well by a regional chemical transport model. We analyze the model results to understand the factors contributing to the observed O3-CO regression slope, which has been used in past studies to estimate the anthropogenic O3 production amount. We trace separately four different CO sources: primary anthropogenic emissions, oxidation of anthropogenic VOCs, oxidation of biogenic isoprene, and transport from the lateral and upper model boundaries. Modeling analysis suggests that the contribution from biogenic isoprene oxidation to the observed O3-CO regression slope is as large as that from primary anthropogenic CO emissions. As a result of decrease of anthropogenic primary CO emissions during the past decades, biogenic CO from oxidation of isoprene is increasingly important. Consequently, observed and simulated O3-CO regression slopes can no longer be used directly with an anthropogenic CO emission inventory to quantify anthropogenic O3 production over the United States. The consistent enhancement of O3 relative to CO observed in the boundary layer, as indicated by the O3-CO regression slope, provides a useful constraint on model photochemistry and emissions. As an extension, we analyze the scenario of O3-CO regression slopes in the entire United States and China regions. The O3-CO regression slope ~ 0.3 is simulated over the eastern outflow regions over the ocean. Over the eastern inland regions of both countries, the O3-CO regression slope is lower than that over the outflow region, reflecting in part continuous O3 production in the outflow region. The simulation result shows that the proportion of contribution from biogenic isoprene to the regressed O3-CO slopes various depending on the corresponding local emission scenario. While biogenic isoprene oxidation makes a comparable contribution as anthropogenic emissions in the eastern US, the latter dominates over eastern China. Over the western inland regions of both countries, the O3-CO regression slope can be higher than the eastern inland regions due to transport from lateral and upper boundaries. The observations of O3-CO regression slope provide the means to understand the relative importance of anthropogenic and biogenic emissions on O3 as well as transport. In addition to O3-CO, strong correlations and consistent linear regression slopes of O3-CH2O and CO-CH2O were also observed during the DISCOVER-AQ aircraft experiment in July 2011 over the Washington-Baltimore area. Same as CO, we also analyze the model results to understand the factors contributing to the observed O3-CH2O regression slope by tracing separately three different CH2O sources: primary secondary anthropogenic sources, biogenic isoprene oxidation, and transport from model… Advisors/Committee Members: Wang, Yuhang (advisor), Deng, Yi (committee member), Ng, Nga Lee (committee member), Weber, Rodney (committee member), Crawford, James (committee member).

Subjects/Keywords: Ozone; Formaldehyde; Carbon monoxide; Correlation; Regression slope; Anthropogenic; Biogenic; Ozone prediction; Ozone estimator; Alkanes; VOCs

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cheng, Y. (2018). Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61150

Chicago Manual of Style (16th Edition):

Cheng, Ye. “Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 02, 2020. http://hdl.handle.net/1853/61150.

MLA Handbook (7th Edition):

Cheng, Ye. “Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations.” 2018. Web. 02 Apr 2020.

Vancouver:

Cheng Y. Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Apr 02]. Available from: http://hdl.handle.net/1853/61150.

Council of Science Editors:

Cheng Y. Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61150

2. Ayari, Samia. Nonparametric estimation of the dependence function for multivariate extreme value distributions : Estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes.

Degree: Docteur es, Mathématiques, 2016, Aix-Marseille; Université de Tunis. Institut supérieur de gestion (Tunisie)

Dans cette thèse, nous abordons l'estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes. Dans une première partie, on adopte l’hypothèse classique stipulant que les variables aléatoires sont indépendantes et identiquement distribuées (i.i.d). Plusieurs estimateurs non paramétriques sont comparés pour une fonction de dépendance trivariée de type logistique dans deux différents cas. Dans le premier cas, on suppose que les fonctions marginales sont des distributions généralisées à valeurs extrêmes. La distribution marginale est remplacée par la fonction de répartition empirique dans le deuxième cas. Les résultats des simulations Monte Carlo montrent que l'estimateur Gudendorf-Segers (Gudendorf et Segers, 2011) est plus efficient que les autres estimateurs pour différentes tailles de l’échantillon. Dans une deuxième partie, on ignore l’hypothèse i.i.d vue qu’elle n'est pas vérifiée dans l'analyse des séries temporelles. Dans le cadre univarié, on examine le comportement extrêmal d'un modèle autorégressif Gaussien stationnaire. Dans le cadre multivarié, on développe un nouveau théorème qui porte sur la convergence asymptotique de l'estimateur de Pickands vers la fonction de dépendance théorique. Ce fondement théorique est vérifié empiriquement dans les cas d’indépendance et de dépendance asymptotique. Dans la dernière partie de la thèse, l'estimateur Gudendorf-Segers est utilisé pour modéliser la structure de dépendance des concentrations extrêmes d’ozone observées dans les stations qui enregistrent des dépassements de la valeur guide et limite de la norme Tunisienne de la qualité d'air NT.106.04.

In this thesis, we investigate the nonparametric estimation of the dependence function for multivariate extreme value distributions. Firstly, we assume independent and identically distributed random variables (i.i.d). Several nonparametric estimators are compared for a trivariate dependence function of logistic type in two different cases. In a first analysis, we suppose that marginal functions are generalized extreme value distributions. In a second investigation, we substitute the marginal function by the empirical distribution function. Monte Carlo simulations show that the Gudendorf-Segers (Gudendorf and Segers, 2011) estimator outperforms the other estimators for different sample sizes. Secondly, we drop the i.i.d assumption as it’s not verified in time series analysis. Considering the univariate framework, we examine the extremal behavior of a stationary Gaussian autoregressive process. In the multivariate setting, we prove the asymptotic consistency of the Pickands dependence function estimator. This theoretical finding is confirmed by empirical investigations in the asymptotic independence case as well as the asymptotic dependence case. Finally, the Gudendorf-Segers estimator is used to model the dependence structure of extreme ozone concentrations in locations that record several exceedances for both guideline and limit values of the Tunisian air quality standard…

Advisors/Committee Members: Boutahar, Mohamed (thesis director), Trabelsi, Abdelwahed (thesis director).

Subjects/Keywords: Valeurs extrêmes; Copules à valeurs extrêmes; Fonction de dépendance; Estimation non paramétrique; Simulation Monte Carlo; Estimateur de Pickands; Processus stationnaires; Pollution d’air; Modélisation de la structure de dépendance de l’ozone; Extreme values; Extreme values copulas; Dependence function; Nonparametric estimation; Monte Carlo simulation; Pickands estimator; Stationary processes; Air pollution; Ozone dependence structure modeling; 510

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ayari, S. (2016). Nonparametric estimation of the dependence function for multivariate extreme value distributions : Estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes. (Doctoral Dissertation). Aix-Marseille; Université de Tunis. Institut supérieur de gestion (Tunisie). Retrieved from http://www.theses.fr/2016AIXM4078

Chicago Manual of Style (16th Edition):

Ayari, Samia. “Nonparametric estimation of the dependence function for multivariate extreme value distributions : Estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes.” 2016. Doctoral Dissertation, Aix-Marseille; Université de Tunis. Institut supérieur de gestion (Tunisie). Accessed April 02, 2020. http://www.theses.fr/2016AIXM4078.

MLA Handbook (7th Edition):

Ayari, Samia. “Nonparametric estimation of the dependence function for multivariate extreme value distributions : Estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes.” 2016. Web. 02 Apr 2020.

Vancouver:

Ayari S. Nonparametric estimation of the dependence function for multivariate extreme value distributions : Estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes. [Internet] [Doctoral dissertation]. Aix-Marseille; Université de Tunis. Institut supérieur de gestion (Tunisie); 2016. [cited 2020 Apr 02]. Available from: http://www.theses.fr/2016AIXM4078.

Council of Science Editors:

Ayari S. Nonparametric estimation of the dependence function for multivariate extreme value distributions : Estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes. [Doctoral Dissertation]. Aix-Marseille; Université de Tunis. Institut supérieur de gestion (Tunisie); 2016. Available from: http://www.theses.fr/2016AIXM4078

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