Doutorado em Engenharia Ambiental

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Análise de componentes principais em séries temporais multivariadas com heteroscedasticidade condicional e outliers : uma aplicação para a poluição do ar, na Região da Grande Vitória, Espírito Santo, Brasil
(Universidade Federal do Espírito Santo, 2016-04-01) Monte, Edson Zambon; Reisen, Valdério Anselmo; Bondon, Pascal; Ispány, Márton; Munaro, Celso José; Albuquerque, Taciana Toledo de Almeida; Reis Junior, Neyval Costa
Issues relating to air quality have become increasingly important, since many health problems come from air pollution. In addition, air pollution contributes to the degradation of the environment, contributing to the greenhouse effect. Thus, several studies adopting technical statistics have been conducted in order to contribute in the making of public and private actors with regard to combating pollution, prevention of high concentrations and formulation of laws for this purpose. The classical principal component analysis (PCA) is a statistical methodologies adopted. The PCA is used for dimensional reduction, cluster analysis, regression analysis, among others. However, among the studies that have adopted the classical PCA, a common feature is to neglect the conditional heteroscedasticity and/or the presence of additive outliers, which may lead to spurious results (misleading), since the estimated autocovariance matrix may be biased (estimated incorrectly). It is possible to note that the time series related to air pollution tend to present conditional heteroscedasticity and additive outliers. Then, the first paper of this thesis proposed to apply a multivariate filter VARFIMA-GARCH to the original data and use the classical PCA on residuals of the VARFIMA-GARCH model. Besides the volatility, this model was used to filter the temporal correlation and the long memory behavior. The application of the PCA on the residuals of the VARFIMA-GARCH model was more consistent with the environmental characteristics of the Greater Victoria Region (GVR), Esp´ırito Santo, Brazil, than the application using the original data The second paper, that is the core of this thesis, the technique of principal volatility components (PVC), proposed by Hu e Tsay (2014), was extended for a robust approach (RPVC), in order to capture the volatility present in the multivariate time processes, but considering the effects of additive outliers on conditional covariance, since these outliers may mask (“hide”) the conditional heteroscedasticity or even produce spurious volatility. The proposed RPVC improved the predictions of PM10 exceedance days in the Laranjeiras station, in the GVR.
Análise espectral de séries temporais de concentrações de poluentes atmosféricos com dados faltantes
(Universidade Federal do Espírito Santo, 2019-08-22) Pinto, Wanderson de Paula; Reisen, Valderio Anselmo; https://orcid.org/; http://lattes.cnpq.br/9401938646002189; https://orcid.org/0000-0001-5267-227X; http://lattes.cnpq.br/3452133768614018; Franco, Glaura da Conceicao; https://orcid.org/; http://lattes.cnpq.br/0913222654204695; Junior, Neyval Costa Reis; https://orcid.org/0000000261594063; http://lattes.cnpq.br/4944106074149720; Albuquerque, Taciana Toledo de Almeida; https://orcid.org/; http://lattes.cnpq.br/1339985577872129; Palma, Wilfredo; Bondon, Pascal; Ispány, Marton
Air pollution has significantly affected living beings, even when their values are below what is allowed by regulators. In this regard, air quality issues have become increasingly important as a number of health problems arise from air pollution. In this way, several studies applied time series analysis techniques have been carried out, aiming to contribute as tools in the decision making of the public and private agents with respect to the prevention of high concentrations, the control of air pollution and the formulation legislation for this purpose. One of the sta tistical methodologies adopted is the spectral analysis, which is used to identify properties of the dataset, such as seasonality. However, it is noted that among studies that have adopted this technique, a common feature is to neglect the presence of missing data, which may lead to un derestimation of the accuracy of the results. Note that in the time series related to atmospheric pollution a frequent problem is the presence of missing data, usually due to the failure of the monitoring equipment. Thus, this paper concentrates on the study of methodologies used to estimate the autocorrelation function and the spectral density of univariate time series in the presence or absence of missing data. The suggested estimators are based on the Amplitude Modulated methodology, proposed by Parzen (1963), and in the Lomb-Scargle (LOMB, 1976; SCARGLE, 1982) periodogram. In addition, we proposed estimators of autocovarianance and autocorrelation functions of time series, considering the connection between the time domain and frequency by means of the relation between the autocovariance function and the spectral density. Thus, in the first article of this thesis were presented three methods to estimate the au tocorrelation function of univariate stationary time series in the presence of missing data. The theoretical properties of the estimators were evaluated and their performances for finite sam ples investigated through a numerical simulation study. Finally, it was proposed the application of these methodologies to evaluate a time series of concentrations of PM10 of the Region of Greater Vit´ oria (RGV), Esp´ ırito Santo, Brazil, with missing data. The second article presents an estimation method for the autocorrelation and autocovariance functions of time series con sidering the connection between time domain and frequency. The asymptotic properties of the method are evaluated through a Monte Carlo simulation study for different sample sizes and percentages of missing data. In the third article, which is the main contribution of this thesis, two methods were proposed to estimate the spectral density function of stationary time series in the presence of missing data. The effect of the percentage of missing data on the employed estimators was studied. The methods were analyzed through simulations and an application to actual PM10 data monitored at the RGV was also considered. allowed by regulators. In this regard, air quality issues have become increasingly important as a number of health problems arise from air pollution. In this way, several studies applied time series analysis techniques have been carried out, aiming to contribute as tools in the decision making of the public and private agents with respect to the prevention of high concentrations, the control of air pollution and the formulation legislation for this purpose. One of the statistical methodologies adopted is the spectral analysis, which is used to identify properties of the dataset, such as seasonality. However, it is noted that among studies that have adopted this technique, a common feature is to neglect the presence of missing data, which may lead to un derestimation of the accuracy of the results. Note that in the time series related to atmospheric pollution a frequent problem is the presence of missing data, usually due to the failure of the monitoring equipment. Thus, this paper concentrates on the study of methodologies used to estimate the autocorrelation function and the spectral density of univariate time series in the presence or absence of missing data. The suggested estimators are based on the Amplitude Modulated methodology, proposed by Parzen (1963), and in the Lomb-Scargle (LOMB, 1976; SCARGLE, 1982) periodogram. In addition, we proposed estimators of autocovarianance and autocorrelation functions of time series, considering the connection between the time domain and frequency by means of the relation between the autocovariance function and the spectral density. Thus, in the first article of this thesis were presented three methods to estimate the autocorrelation function of univariate stationary time series in the presence of missing data. The theoretical properties of the estimators were evaluated and their performances for finite samples investigated through a numerical simulation study. Finally, it was proposed the application of these methodologies to evaluate a time series of concentrations of PM10 of the Region of Greater Vit´ oria (RGV), Esp´ ırito Santo, Brazil, with missing data. The second article presents an estimation method for the autocorrelation and autocovariance functions of time series considering the connection between time domain and frequency. The asymptotic properties of the method are evaluated through a Monte Carlo simulation study for different sample sizes and percentages of missing data. In the third article, which is the main contribution of this thesis, two methods were proposed to estimate the spectral density function of stationary time series in the presence of missing data. The effect of the percentage of missing data on the employed estimators was studied. The methods were analyzed through simulations and an application to actual PM10 data monitored at the RGV was also considered.
Análise fatorial em series temporais com long-memory, outliers e sazonalidade : aplicação em poluição do ar na região da Grande Vitória-ES
(Universidade Federal do Espírito Santo, 2015-07-20) Sgrancio, Adriano Marcio; Reisen, Valdério Anselmo; Zielgmann, Flávio Augusto; Reis Junior, Neyval Costa; Albuquerque, Taciana Toledo de Almeida; Bovas, Abraham; Thavanesswaran, Aerambamoorthy
Studies about air pollution typically involve measurements and analysis of pollutants, such as PM10 (particulate matter), SO2 (sulfur dioxide) and others. These data typically have important features like serial correlation, long dependency, seasonality and occurence of atypical observations, and many others, which may be analyzed by means of multivariate time series. In this context, a robust estimator of fractional robust autocovariance matrix of long dependence and seasonal frequency for SARFIMA model is proposed. The model is compared to SARMA model and is applied to SO2 concentrations. In addition of the mentioned features the data present high dimensionality in relation to sample size and number of variables. This fact complicates the analisys of the data using vector time series models. In the literature, the approach to mitigate this problem for high dimensional time series is to reduce the dimensionality using the factor analysis and principal component analysis. However, the long dependence characteristics and atypical observations, very common in air pollution series, is not considered by the standard factor analysis method. In this context, the standard factor model is extended to consider time series data presenting long dependence and outliers. The proposed method is applied to PM10 series of air quality monitoring network of the Greater Vitoria Region - ES.
Caracterização química e morfológica de partículas sedimentadas na Região Metropolitana da Grande Vitória - ES
(Universidade Federal do Espírito Santo, 2013-06-27) Conti, Melina Moreira; Reis Junior, Neyval Costa; Kerr, Américo Adlai Franco Sansigolo; Andrade, Maria de Fátima; Albuquerque, Taciana Toledo de Almeida; Santos, Jane Méri
The main objective of this thesis is to study the dustfall in the Metropolitan Region of the Great Vitória (RMGV),including an alysisordepositionflux, physico-chemical characterization, size distribution and morphologicalcharacterization to classifysource categories withhigh degree of similarityandcontributing to identifythe main sources of the region. The samples were collected between the months of May and November 2010 atfour sitesin RMGV, by using copper plates and samplers based on the American Standard ASTMD1739-98 (2004). The deposition flux was determinedby gravimetric methodbased on the Brazilian Standard ABNT MB3402 (1991). The analysis of the chemical composition andmorphology were performedby Scanning Electron Microscopy coupled with individual-particle X-ray analysis(SEM/EDS). Threemaingroups of particles were found:particles with high contentofSi and Al with low concentrations of K, Ti and Fe; particles with high content of C; and particles with highcontent ofFe.The first group is probably aluminum silicatematerials related to the crust and dirt roads. The second group the particles may have anorganic origin, related to biogenic processes, burning or coal handling. For the third group, the particlesareprobablyrelated to iron-ore processingand steel manufacturingin the region. It wasalso found a significant presence of NaCl particles, C-Caand high content of Ca-Mg. It was observed that more than 95% of the sampled setted the particles are smaller than 10 μm pelleted, however, more than 95% of deposited mass correspondsto particles larger than 10 μm.Regarding the determination of originof the particles, a comparisonbetween the results obtained here and previous CMB source apportionment study, performed by using CMB, indicates that the results obtained by both techniques are fairlyconsistent, but the use of SEM/EDSenables better source separation/identification, since it relies not only on chemical composition but also on morphological characteristics of the particles.
Desenvolvimento de ferramentas computacionais para simulação da dispersão de gases liberados por veículos espaciais no Centro de Lançamento de Alcântara
(Universidade Federal do Espírito Santo, 2016-10-11) Nascimento, Erick Giovani Sperandio; Albuquerque, Taciana Toledo de Almeida; Moreira, Davidson Martins; Santos, Jane Méri; Reis Junior, Neyval Costa; Fisch, Gilberto Fernando; Goulart, Antônio Gledson de Oliveira
During the launch of rockets and spacecrafts, huge and hot clouds are generated near the ground level, and are composed by buoyant exhaust products, such as alumina, carbon monoxide and hydrogen chloride. This process takes a few minutes to occur, and generally populated areas nearby the launching center may be exposed to high levels of hazardous pollutant concentrations within few minutes to less than a couple of hours. Due to the specificity of the representation of the source term – which is the rocket exhaust cloud – and since a receptor can be impacted in less than one hour, common air quality models were not designed to deal with such a unique problem. Furthermore, the cloud may be transported to farther distances and impact receptors in longer time and space scales. Thus, the launching centers around the globe, like spaceports, need to operationally assess the short and long range impacts of rocket launch events in the environment through meteorological and air quality modeling. For this end, this work presents the development of a new model called Model for Simulating the Rocket Exhaust Dispersion – MSRED. It is based on a semi analytical three dimensional solution of the advection-diffusion equation, incorporating a modern three dimensional parameterization of the atmospheric turbulence, designed to simulate the formation, rise, expansion, stabilization and dispersion of rocket exhaust clouds for short range assessment, being able to directly read meteorological data from WRF (Weather Research and Forecasting) model output. And, for the long range and chemical transport modelling, the MSRED was built to be integrated to the Community Multi-scale Air Quality (CMAQ) model, by generating a ready-to-use initial conditions file to be input to CMAQ. Simulations and analysis were carried out in order to evaluate the application of this integrated modeling system for different rocket launch cases and atmospheric conditions, for the Alcântara Launching Center (CLA, the Brazilian gate to the space) region. This hybrid, modern and multidisciplinary system is the basis of a modeling framework that can be operationally employed at any launching center in the world, for pre- and post-launching simulations of the environmental effects of rocket operations
Estudo da camada limite atmosférica em regiões metropolitanas costeiras com simulações de brisa marítima
(Universidade Federal do Espírito Santo, 2014-09-26) Salvador, Nadir; Reis Junior, Neyval Costa; Moreira, Davidson Martins; Santos, Jane Meri; Goulart, Antonio Gledson Oliveira; Silva Neto, Antônio José da; Albuquerque, Taciana Toledo de Almeida
The main objective of this work was to identify and characterize the daily evolution of the Atmospheric Boundary Layer in the Great Region of Vitória (RGV), state of Espírito Santo, Brazil, and the Region of Dunkerque (RD), department of Nord Pas-de-Calais, France, evaluating the accuracy of parameterizations used in Weather Research and Forecasting (WRF) model to detect the formation and attributes of Thermal Internal Boundary Layer (CLI) formed by sea breezes. The RGV has complex relief in a coastal region of rugged topography and a chain of mountains parallel to the coast. The RD has a simple relief in a coastal region with small peaks not higher than 150 meters, all along the domain of study. To evaluate the results of the predictions made by the model, the results of two campaigns were used: one held in Dunkerque-FR, in July 2009, using a light detection and ranging (LIDAR) system, sonic detection and ranging (SODAR) and a surface meteorological station (EMS) data; another one held in Vitória-BR, in July 2012, also using a LIDAR, SODAR and EMS data. The simulations were performed using three PBL parameterizations schemes, two nonlocal closure, Yonsei University (YSU) and Asymmetric Convective Model 2 (ACM2), and a local closure, Mellor Yamada Janjic (MYJ) and two land surface schemes (CLS), Rapid Update Cycle (RUC) and Noah. As per the RGV as for RD, simulations with the six possible combinations were made for the periods in which the campaigns were made, using four nested domains, with the three largest square with 1863 km, 891 km and 297 km of side dimensions, grids 27 km, 9 km and 3 km, respectively, and the study domain, with dimensions 81 km in North-South direction and 63 km in the East-West grid 1 km, with 55 vertical levels up to approximately 13,400 m, more concentrated near the ground. The results of this study showed that: a) depending on the configuration adopted, the computational effort may increase too, though without a large increase in the accuracy of the results; b) for the RD, the simulation using the MYJ and Noah parameterizations produced the best estimation for CLI. Simulations using the ACM2 and YSU parameterizations, inferred the sea breeze entry with a maximum delay of three hours; c) for the RGV, the simulation that used the YSU and Noah parameterization made the best inferences about the CLI. The results show that it is necessary to evaluate in advance the computational effort required for certain settings and the accuracy of specific sets of parameterizations for each region. The differences are associated with the ability of different parameterizations capturing surface data from global, essential information for determining the intensity of vertical turbulent mixing and surface soil temperature, suggesting that a better representation of land use is crucial to improve estimations of the CLI and other parameters as input in models of dispersion of air pollutants.
ESTUDO DA EXPOSIÇÃO DE CRIANÇAS À POLUIÇÃO ATMOSFÉRICA NA REGIÃO METROPOLITANA DE VITÓRIA
(Universidade Federal do Espírito Santo, 2022-06-28) Kitagawa, Yasmin Kaore Lago; Moreira, Davidson Martins; https://orcid.org/0000000209025218; http://lattes.cnpq.br/2331953711858907; https://orcid.org/0000000285502573; http://lattes.cnpq.br/; Fisch, Gilberto Fernando; https://orcid.org/; http://lattes.cnpq.br/; Santos, Jane Meri; https://orcid.org/0000000339332849; http://lattes.cnpq.br/0120226021957540; Albuquerque, Taciana Toledo de Almeida; https://orcid.org/; http://lattes.cnpq.br/1339985577872129; Nascimento, Erick Giovani Sperandio; Pimentel, Luiz Claudio Gomes
abstract
Estudo do transporte atmosférico de MP10 e SO2 com os modelos WRF/CMAQ em regiões costeiras urbanas
(Universidade Federal do Espírito Santo, 2015-01-01) Loriato, Ayres Geraldo; Reis Junior, Neyval Costa; Albuquerque, Taciana Toledo de Almeida; Santos, Jane Meri; Moreira, Davidson Martins; Landulfo, Eduardo; Pimentel, Luiz Claudio Gomes
This work's main objective is to study atmospheric transportation of MP10 and SO2 in urban coastal areas using WRF/CMAQ modeling. Two main areas were chosen for this purpose. One is Great Vitória Area (GVA), in Espírito Santo State, Brasil; the other is Great Dunkerque Area (GDA), in Nord Pas-de-Calais, France. GVA is surrounded by a mountain range parallel to the coast, which makes its topography complex and rugged. On the other hand, GDA's topography is much smoother. Modeling inputs encompassed IEMA-ES' inventory of atmospheric pollutants emissions for GVA, and Nord Pas-de-Calais' inventory of ground level emissions named " Cadastre_totaux_3km_A2008 _M2010_V2_SNAPN2" for GDA. Both inventories showed restrictions, however. GVA's showed high traffic lanes resuspension in comparison with several studies, so those data were altered. Ground level data and large grid area (9 km2) of GDA inventory didn't allow for satisfying modeling results. Modeled results were validated by comparing them with two experimental campaigns: one performed in the city of Dunkerque, North of France, on September 2009; the other in Vitória, Southeast of Brazil, on July 2012. Experimental data were obtained through the use of Light Detection and Ranging (LIDAR), Sonic Detection and Ranging (SODAR), Surface Meteorological Stations (SMS) and atmospheric monitoring stations. Results of this work showed that: a) there is a need for continuous improvement on regional inventories of emissions, adapting them to specific local characteristics and focusing on obtaining parameters required for photochemical modeling; b) the direction and magnitude of velocity vectors obtained from meteorological modeling have a high impact on pollutant concentrations modeling; c) air quality in both GVA and GDA deserve attention, especially regarding MP10 concentrations. Based on monitoring stations data, the situation seems more critical in GDA; d) modeling in GVA was better than in GDA according to validation results; e) sea breeze inflow caused significant alteration on pollutants concentration, which was observed analyzing MP10 and SO2 dispersion dynamics. This phenomenon was more distinctive in GVA, where the sea breeze caused an oscillatory motion on the pollution plume, moving it to the urban agglomeration most densely populated neighborhoods. In GDA, sea breeze inflow wasn`t a daily phenomenon, and on the day when it occurred there was a change of almost 180º in the pollution plume direction of movement. In addition to vertical turbulence increase, which has already been studied by many authors, this study also focus on influence of sea-breeze of plume dynamics effects on dispersing atmospheric pollutants in coastal areas.
Formação e transporte de material particulado na região metropolitana da Grande Vitória/ES : utilização e avaliação de desempenho do Modelo CMAQ
(Universidade Federal do Espírito Santo, 2015-03-31) Santiago, Alexandre Magalhães; Albuquerque, Taciana Toledo de Almeida; Reis Junior, Neyval Costa; Santos, Jane Meri; Moreira, Davidson Martins; Landulfo, Eduardo; Pimentel, Luiz Claudio Gomes
This study aimed to evaluate the formation and transport of particulate matter in the Metropolitan Area of Greater Vitória (RMGV) using The Models-3 Community Multiscale Air Quality Modeling System (CMAQ). In particular, it was investigated how particulate material respond to changes in vehicle and industrial sources emission. During winter 2012 (22-31 of July) an experimental campaign was conducted with a LIDAR to characterize the behavior of Atmospheric Boundary Layer (CLA) and a SODAR to measure the vertical structure of the atmosphere in RMGV. Also data collected by weather and air quality stations in the region were used to validate the numerical results. Three-dimensional meteorological fields were modeled using meteorological model Weather Research and Forecasting (WRF) in version 3.4.1 during the period 15-31 July 2012. There was four scenarios with nested grid resolution of 27 kilometers (70 × 70 cells), 9 km (100 x 100 cells), 3 km (100 x 100 cells), 1 km (120 x 120 cells) and all with 21 vertical levels. For the simulations with the CMAQ it was used the 1km domain resolution with 79 x 61 cells, which covers the towns of Cariacica, Laranjeiras, Serra, Viana, Vila Velha and Vitória. The simulations with the CMAQ model were conducted from 22nd to 31st July, 2012 (240 hours). The SMOKE model was applied to build an inventory of emissions, spatially and temporally resolved to RMGV using the official state inventory emissions. The air quality simulations used measured concentrations as initial and boundary conditions. AERO4 and Carbon Bond V options available in version 4.6 of CMAQ model were used for description of the aerosol processes, chemistry of aqueous and gaseous phase. Three different scenarios were simulated: considering the current emission inventory (base case), considering the exclusion of sources of vehicle emissions (scenario 1) and considering the exclusion of industrial emissions (scenario 2). The results show maximum values of particle concentration at Ponta de Tubarão due to the industrial zone, however, it was clear the vehicle influence in the region. The PM10 concentration reduced in relation to the base case and the two emission reduction scenarios, 85% and 24% for Laranjeiras, 82% and 25% for Enseada and 89% and 23% for Cariacica, in the absence vehicle emissions and without the presence of industrial emissions, respectively. PM2.5 concentration was reduced, in relation to the base case, to approximately 75.4% and 19.4% for Laranjeiras, 74.5% and 19.9% for Enseada and 79.1% and 7.8% for Cariacica without the presence of vehicle emissions and without the presence of industrial emissions, respectively. Modeled results have shown that the carbonaceous fraction of the RMGV particulate material is approximately 60% of the total mass of MP10, then the modeled scenarios of suppression of major source caused greatest impact on the concentration of organic and elemental carbon concentrations than the sulfate, nitrate and ammonium concentration which were very small in the base case and remained without significant changes. These results reinforce the necessity to update the inventory of emission sources of RMGV, which considers the vehicular source as the main source of MP in the region. Policies control of MP concentration must consider the role of organic aerosols and the elemental carbon, as these represent the largest fraction of the total mass of MP10
Modelo ARFIMA espaço-temporal em estudos de poluição do ar
(Universidade Federal do Espírito Santo, 2013-08-28) Monroy, Nátaly Adriana Jiménez; Subba Rao, Tata; Reisen, Valdério Anselmo; Santos, Jane Meri; Vasconcellos, Klaus Leite Pinto; Andrade Filho, Marinho Gomes de; Albuquerque, Taciana Toledo de Almeida
In air pollution studies is frequent to observe data measured on time over several spatial locations. This is the case of measures of air pollutant concentrations obtained from monitoring networks. The dynamics of these kind of observations can be represented by statistical models, which consider the dependence between observations at each location or region and their neighbor locations, as well as the dependence between the observations sequentially measured. In this context, the class of the Space-Time Autoregressive Moving Average (STARMA) models is very useful since it explains the underlying uncertainty in systems with a complex variability on time and space scales. The process with STARMA representation is an extension of the univariate ARMA time series. In this case, besides the modeling of the single series on time, their evolution over a spatial grid is also considered. The application of the STARMA models in air pollution studies is not much explored. This thesis proposes a class of space-time models which consider the long memory dependence usually observed in time series of air pollutant concentrations. This model is applied to real series of daily average concentrations of PM10 and SO2 at Greater Vit´oria Region, ES, Brazil. The results obtained showed that the dispersion dynamics of the studied pollutants can be well described using the STARMA and STARFIMA models, here proposed. These class of models allowed to estimate the influence of the pollutants on the pollution levels over the neighbor regions. The STARFIMA process showed to be appropriate for the series under study since they have long memory characteristics. Taking into account the long memory properties lead to a significant improvement of the forecasts, both on time and space.
O uso e interpretação de análise de componentes principais, em séries temporais, com enfoque no gerenciamento da qualidade do ar
(Universidade Federal do Espírito Santo, 2013-08-09) Zamprogno, Bartolomeu; Reis Junior, Neyval Costa; Reisen, Valdério Anselmo; Ziegelmann, Flávio Augusto; Santos, Jane Meri; Albuquerque, Taciana Toledo de Almeida; Manriquez, Wilfredo Omar Palma
This work was motivated by the application of principal component analysis technique in different contexts of area air pollution, especially in the use of network management. This statistical methodology in practical terms produces information with accuracies in making important decisions for quality air. This technique is commonly used, as well as in the regression analysis as a tool for analysis and interpretation of the phenomena of the data. However, according to the statistical literature that fosters basis for the use of this tool in any area of application, the technique requires the assumption in this case the use of independent variables, a characteristic which is hardly observed in practical situations in the field of air pollution. In general, the data available for troubleshooting management network, identification of pollutant source, studies spatio-temporal association and the number of hospitalizations for respiratory pollutants are by series displaying structure of short and long time dependence, that is, autocorrelation. The research results show, in the field of time that the technique of principal components analysis, depending on the structure autocorrelation of the series, can be based on spurious results. When the structure is weak, the autocorrelation effect of autocorrelation is practically zero, so that the method can be used without further problems. In the context of the use of the technique of time series analysis in the frequency domain was reported the extension of existing methods for the case of time series data memory long. The results show that the use of frequency domain methods can be used, but some considerations should be observed and some types of applications, the air pollution, deserve further study because of the difficulty of interpreting the frequency domain.
Obust methods in multivariate time series
(Universidade Federal do Espírito Santo, 2019-08-22) Cotta, Higor Henrique Aranda; Reisen, Valderio Anselmo; https://orcid.org/0000-0002-8313-7648; http://lattes.cnpq.br/9401938646002189; https://orcid.org/0000000203222317; http://lattes.cnpq.br/2488791027245465; Franco, Glaura da Conceicao; https://orcid.org/0000-0002-7994-8448; http://lattes.cnpq.br/0913222654204695; Junior, Neyval Costa Reis; https://orcid.org/0000000261594063; http://lattes.cnpq.br/4944106074149720; Albuquerque, Taciana Toledo de Almeida; https://orcid.org/0000-0002-6611-0283; http://lattes.cnpq.br/1339985577872129; Palma, Wilfredo; Bondon, Pascal; Ispány, Marton; Renaux, Alexandre
This manuscript proposes new robust estimation me thods for the autocovariance and autocorrelation ma trices functions of stationary multivariates time se ries that may have random additives outliers. These functions play an important role in the identification and estimation of time series model parameters. Ran dom additive outliers can impact the level of one or more components of the multivariate vector. This in creases the overall variability of the series, which has an impact on the periodogram matrix and leads to a decrease in the values of the autocorrelation ma trix function. We first propose new estimators of the autocovariance and of autocorrelation matrices func tions constructed using a spectral approach conside ring the periodogram matrix periodogram which is the natural estimator of the spectral density matrix. As in the case of the classic autocovariance and autocor relation matrices functions estimators, these estima tors are affected by aberrant observations. Thus, any identification or estimation procedure using them is di rectly affected, which leads to erroneous conclusions. To mitigate this problem, we propose the use of robust statistical techniques to create estimators resistant to aberrant random observations. As a first step, we propose new estimators of auto covariance and autocorrelation functions of univariate time series. The time and frequency domains are lin ked by the relationship between the autocovariance function and the spectral density. As the periodogram is sensitive to aberrant data, we get a robust esti mator by replacing it with the M-periodogram. The M-periodogram is obtained by replacing the Fourier coefficients related to periodogram calculated by the standard least squares regression with the ones cal culated by the M-robust regression. The asymptotic properties of estimators are established. Their perfor mances are studied by means of numerical simula tions for different sample sizes and different scena rios of contamination. The empirical results indicate that the proposed methods provide close values of those obtained by the classical autocorrelation func tion when the data is not contaminated and it is re sistant to different contamination scenarios. Thus, th estimators proposed in this thesis are alternative me thods that can be used for time series with or without outliers. The estimators obtained for univariate time series are then extended to the case of multivariate series. This extension is simplified by the fact that the calculation of the cross-periodogram only involves the Fourier co efficients of each component from the univariate se ries. Again, the duality relationship between time and frequency domains is considered via the link between the autocovariance matrix function and the spectral density matrix stationary multivariate time series. The M-periodogram matrix is a robust periodogram matrix alternative to build robust estimators of the autoco variance and autocorrelation matrices functions. The asymptotic properties are studied and numerical ex periments are performed. As an example of an appli cation with real data, we use the proposed functions to adjust an autoregressive model by the Yule-Walker method to Pollution data collected in the Vit´ oria re gion Brazil (particles smaller than 10 micrometers in diameter, PM10). Finally, the robust estimation of the number of fac tors in large factorial models is considered in order to reduce the dimensionality. It is well known that the values random additive outliers affect the covariance and correlation matrices and the techniques that de pend on the calculation of their eigenvalues and ei genvectors, such as the analysis principal compo nents and the factor analysis, are affected. Thus, in the presence of outliers, the information criteria pro posed by Bai & Ng (2002) tend to overestimate the number of factors. To alleviate this problem, we pro poseto replace the standard covariance matrix with the robust covariance matrix proposed in this manus cript. Our Monte Carlo simulations show that, in the absence of contamination, the standard and robust methods are equivalent. In the presence of outliers, the number of estimated factors increases with the non-robust methods while it remains the same using robust methods. As an application with real data, we study pollutant concentrations PM10 measured in the ˆ Ile-de-France region of France.
Particles emission and transport due to wind erosion on beds and stockpiles : local and non-local aspects of turbulent fluid flow dynamics
(Universidade Federal do Espírito Santo, 2024-05-03) Morais, Cristina Lima de; Furieri, Bruno ; https://orcid.org/0000-0002-9736-0250; http://lattes.cnpq.br/6585455298349085; Caliman, Maria Clara Schuwartz Ferreira ; https://orcid.org/0000-0001-9323-5041; http://lattes.cnpq.br/9245815665956760; Harion, Jean-Luc ; https://orcid.org/0000-0003-4317-9146; http://lattes.cnpq.br/; Co-orientador4; ID do co-orientador4; Lattes do co-orientador4; Santos, Jane Meri ; https://orcid.org/0000-0003-3933-2849; http://lattes.cnpq.br/0120226021957540; Orientador2; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/0000-0003-4557-9952; http://lattes.cnpq.br/3583322289076958; Albuquerque, Taciana Toledo de Almeida ; https://orcid.org/0000-0002-6611-0283; http://lattes.cnpq.br/1339985577872129; Reis Junior, Neyval Costa ; https://orcid.org/0000-0002-6159-4063; http://lattes.cnpq.br/4944106074149720; Nieckele, Angela Ourivio ; https://orcid.org/0000-0003-4836-8931; http://lattes.cnpq.br/6935503527357767; Sartim, Rafael ; https://orcid.org/0000-0002-5678-2027; http://lattes.cnpq.br/0380809249444032; 5º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 6º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 7º membro da banca; https://orcid.org/; http://lattes.cnpq.br/
This study aims to provide a comprehensive understanding of particle behavior in both local and non-local contexts. We analyze the influence of different configurations of successive stockpiles on the re-emission potential of particles from their surroundings using the USEPA model. This analysis is important for industries that store granular materials in open yards, as it helps to understand the impact of stockpile arrangements on particle emission. However, the USEPA model relies on an empirical equation and has gaps in its consideration of non-erodible particles and their effects on turbulent structures. Additionally, the friction velocity distribution used in the model is derived from previous RANS numerical simulations, which may not be the most accurate. Moving from a non-local to a local perspective, we assess the influence of non erodible particles on the development of fluid flow structures at the particle level. We examine the role of turbulent structures in particle entrainment through LES simulations, which provide detailed information about the flow field. These simulations allowed us to generate probability density distributions of friction velocity for smooth and rough beds, which can be used to improve or develop more accurate environmental stochastic prediction models. Finally, we evaluate the similarity requirements of wind flow, particle entrainment, and particle transport in wind tunnel experiments to ensure they accurately represent real-scale scenarios. By addressing these similarity requirements, we discuss the conditions under which wind tunnel experiments can reliably simulate real conditions, which is essential for the future applicability of our findings
Qualidade do ar interno da Estação Antártica Comandante Ferraz (EACF) e sua relação com os materiais de construção e as atividades humanas
(Universidade Federal do Espírito Santo, 2015-12-04) Pagel, Érica Coelho; Alvarez, Cristina Engel de; Reis Junior, Neyval Costa; Santos, Jane Meri; Albuquerque, Taciana Toledo de Almeida
abstract
Simulação numérica da evaporação de gotas em meios porosos : uma abordagem baseada em modelos de escalas de poros
(Universidade Federal do Espírito Santo, 2014-08-29) Sartim, Rafael; Goulart, Elisa Valentim; Reis Junior, Neyval Costa; Freitas, Edmilson Dias de; Carciofi, Bruno Augusto Mattar; Albuquerque, Taciana Toledo de Almeida; Moreira , Davidson Martins
The study of the impact and evaporation of liquid droplets on porous surfaces, especially for hazardous chemical compounds, is an important research topic for environmental sciences. This class of problems may poses some imitations for the use of conventional modeling techniques used for drying on porous media, since size of the droplets impinged on the porous media is of the order of a few millimeters (10-3 – 10-4 m) and the pore size is usually of the order of a few micrometers (10-4 – 10-5 m). This has motivated the development and use of “pore-scale models”, which attempt to simulate the transport and evaporation at the pore scale. This work presents a computational model for the drying of liquid droplets embedded on porous media based on a pore-scale model. The model formulation is based on the work originally proposed by Prat (1993). In order to increase the generality of the model, gravitational and viscous effects were also considered, as well as, the formation of liquid film, as proposed by Yiotis et al. (2007). The numerical results indicate that the viscous and gravitational effects are not really relevant for this class of problems. In general, the results obtained by the model without the presence of liquid films is in better agreement with experimental data than the results obtained by the model with the presence of liquid films.
Simulação numérica da transferência de massa de compostos odorantes através da interface de um sistema multifásico líquido-gás
(Universidade Federal do Espírito Santo, 2015-12-04) Feroni, Rita de Cassia; Santos, Jane Meri; Reis Junior, Neyval Costa; Nieckele, Angela Ourivio; Goulart, Antonio Gledson Oliveira; Moreira, Davidson Martins; Albuquerque, Taciana Toledo de Almeida
The emission odorous compounds from liquid-gas interface can be represented from multiphasic flow and the generation of turbulence occurs close to the liquid-gas interface. Other characteristics are the likely configurations of the deformable interface with the presence or not of waves. The major part of the resistance to mass transport through liquid-gas interface is limited to a thin sub-layer from both sides of interface, in a few millimeters of dimension, where the turbulence is small and the diffusion processes become dominant, the relation between turbulence flow and the interface configuration (related to Reynolds number) together with the properties of the compound, as the diffusivity (related to Schmidt number) and solubility (related to Henry’s constant), are primordial to the study of mass transfer. In this work, in order to investigate the relation between the parameters quoted above, a multiphasic mass transfer assay was made using the computational model ANSYS-CFX 14.5, applying the turbulence model for large eddy simulation (LES). The mathematic model was validated with data of direct numeric simulations (DNS) from the work of Komori et al. (2010). The results show that the mass transfer is strongly affected by the presence of waves on interface, leading to the formation of turbulent structures in the liquid side. Thereby, values of mass transfer coefficients (kl) for different Reynolds numbers (210, 86 and 43) show large differences in order of magnitude. Likewise, considering odorous compounds with different values for Henry’s constants, this work show that the mass transfer also has a strongly dependence with this parameter. Higher values of kl are found for odorous compounds such as hydrogen sulfide in which the mass transfer is controlled by liquid phase, followed by ammonia in which the mass transfer is controlled by both liquid and gas phases and the isovaleric acid with mass transfer controlled by the gas phase.
Uma nova abordagem na equação de dispersão de poluentes atmosféricos usando a transformação gás partícula e a difusão anômala
(Universidade Federal do Espírito Santo, 2019-03-22) Santos, Cassia Aparecida Gobeti dos; Moreira, Davidson Martins; Silva Neto, Antônio José da; Santos, Jane Meri; Albuquerque, Taciana Toledo de Almeida
In this research it is proposed a new equation of fractional diffusion-advection which describes the atmospheric dispersion of contaminants including the formation of secondary pollutants (sulfur dioxide and sulfate) and their removal processes in the atmosphere. It is proposed to use the methodology used by Alam and Seinfeld (1981), which uses linear algebra to decouple the system of equations from the secondary pollutant. To solve the equations found, it uses the ADMM (Advection Diffusion Multilayer Model) method, which consists of a semi-analytical solution based on the discretization of the planetary boundary layer (CLP) in sub-layers, so that the diffusion-advection equation can be solved using the Laplace transform and the technician GITT in a planetary boundary layer vertically nonhomogeneous. Simulations proves the influence of dry and wet deposition, including the order of the derivative, on the concentration of primary and secondary pollutants, representing step forward in the understanding of the pollutants dispersion in the atmosphere with lower computational costs due to semi-analytical solution characteristic.

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