Engenharia Ambiental

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http://repositorio.ufes.br/handle/10/17610

Programa de Pós-Graduação em Engenharia Ambiental

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URL do programa: http://www.ambiental.ufes.br/pos-graduacao/PPGEA

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Análise da influência da especificação do uso e ocupação do solo e do uso da técnica de assimilação de dados meteorológicos na performance do modelo WRF
(Universidade Federal do Espírito Santo, 2017-04-06) Aylas, Georgynio Yossimar Rosales; Albuquerque, Taciana Toledo de Almeida; Reis Junior, Neyval Costa; Santos, Jane Meri; Moreira, Davidson Martins; Pimentel, Luiz Claudio Gomes
Air quality models require accurate meteorological elds and geographic data to make the correctly modeling of chemical transport. For this purpose, the Metropolitan Region of Greater Victoria (RMGV) uses the numerical mesoscale model WRF. The physical and biological property of the land surface has been progressively a ected as a consequence of the change in land use. This is mainly due to urbanization and farming and forest practices. However, the problem about the use of parameters such as soil coverage provided by the USGS is that they are available with ultimate update date in 1993. In addition, there is the high computational cost about the assimilating of real data (observational meteorological data) to obtain more improve on the forecasts. For this purpose this study has the main goal evaluate the input datas regarding to land use and cover, together whit speci cation the meteorological assimilation data (pontual observations) to evaluate the performance of the WRF model for the Região Metropolitana da Grande Vitória (RMGV). As part of the work of generating accurate spatial data, satellite image analysis was performed. These provide excellent data quality, with enough information to generate the desired geographic data. Therefore, we have worked speci cally for a small 120km x 120km area that covers the RMGV whole and is centered at the Airport Station. On the other hand, to make the data treatment with the objective of implementing the new geographic database for the RMGV, several steps were followed. These was grouped on the preprocessing of images for the land use and cover with the 24 categories of land use suggested by the USGS and the equivalent to the brazilian sistem for the RMGV area. However, for soil granulometry and texture it had no greater problem than accurate the existing information, as well as to Topography. As well as, for the four-dimensional data assimilation (FDDA), the Newtonian relaxation or nudging (continuous data assimilation method that adjusts the model's dynamic variables gradually for observations by adding one or more prognostic equations) were generated les containing the reports of the surface meteorological data of the Aeroporto station. As a consequence, the modeling performance of the direction and speed of the wind and temperature. In consequence, the modeling performance of the speed and direction of the wind and temperature, using to modeling with updated geographical database has a slight improve compared with using USGS data source. Although, not every month they hit the ags for the suggested indicators for all seasons. However, to evaluate the modeling performance of wind direction and speed and temperature for all seasons, making use of the updated geographic data with the in uence of nudging a ecting the domain d01 improvement in every month. Thus, when the in uence of nudging for domain d02 is used, it improves modeling even more than when was used together domains d01 and d02 is in uenced.
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.
Avaliação da qualidade do ar e as emissões atmosféricas em grandes cidades de América do Sul
(Universidade Federal do Espírito Santo, 2019-05-08) Pelaez, Luisa Maria Gomez; Santos, Jane Meri; https://orcid.org/0000000339332849; http://lattes.cnpq.br/0120226021957540; https://orcid.org/0000-0001-7872-7208; http://lattes.cnpq.br/5774215235034727; Andrade, Maria de Fatima; https://orcid.org/; http://lattes.cnpq.br/; Albuquerque, Taciana Toledo de Almeida; https://orcid.org/0000-0002-6611-0283; http://lattes.cnpq.br/1339985577872129; Roa, Nestor Yezid Rojas; https://orcid.org/0000-0001-7804-0449; http://lattes.cnpq.br/
Air pollution is the largest and most persistent environmental problem in South America and in the world. Exposure to PM2.5 is considered the largest environmental health risk. According to World Health Organization (WHO), in 2016, the 91% of the urban population lived in cities that exceed the minimum levels established by this Organization for PM2.5. This research presents a review of the long-and short-term concentrations of air pollutants such as nitrogen dioxide (NO2), Sulphur dioxide (SO2), particulate matter (PM10 and PM10), carbon monoxide (CO) and ozone (O3) recorded between 2010 and 2017 in automatic monitoring networks of eleven metropolitan regions of South America (Including 3 of 3 from 28 and 34 large mega-cities in the world). Despite efforts to monitor air quality, in some cities were found large gaps in the diffusion, consistency and presentation of information provided by the environmental authorities. In comparison with the annual WHO Air Quality Guidelines (WHO-AQG), the particulate matter (PM2.5 and PM2.5) registered in all cities, exceeded during every year of the period studied, the NO2 has been exceeded by 4 of the 11 cities that had record. The guideline of the daily average of who to SO2 was exceeded only by Vitoria (over the past two years), cities like Rio de Janeiro and Belo Horizonte, had exceedances above the Interim Target 1 (IT-1). The ozone (average running averages of 8 hours) always been below the WHO-AQG, but Rio de Janeiro, São Paulo and Belo Horizonte, had every year atypical data of exceedance the WHO-AQG and IT-1. In the emissions review was found that 6 of 11 cities has no recent inventories available, in the 5 that have no inventory was analyzed the 2012 information of Emissions Database for Global Atmospheric Research (EDGAR). The inventories found are updated in the maximum until the year 2016, which makes it more difficult for environmental authorities generate environmental policies and make effective air quality management. Most of the inventory was developed with emission factors from other cities and even other countries, but despite the fit with local conditions is necessary investment in the development of local factors which make it possible to comply with the principles of quality, transparency, accuracy, consistency, comparability and completeness.
Avaliação das parametrizações físicas do modelo WRF para a camada limite atmosférica para a região Metropolitana da Grande Vitória
(Universidade Federal do Espírito Santo, 2017-04-05) Velásquez, Juan Felipe Medina; Reis Junior, Neyval Costa; Albuquerque, Taciana Toledo de Almeida; Santos, Jane Meri; Pimentel, Luiz Claudio Gomes; Moreira, Davidson Martins
The main objective of this work is to evaluate the performance of the different PBL physical parameterizations available in the Weather Research and Forecasting model (WRF) 3.6.1., in order to identify which one best represents the meteorological conditions of the Metropolitan Region of Grande Vitória (RMGV) in the two most representative periods of the region, winter and summer. The achieve this goal, were made a total of 34 simulations, 12 for the winter period (07/2010) e 12 for the summer period (02/2016), with which all the PBL parameterizations were evaluated, except QNSE (Quasi-normal Scale Elimination) and the MYNN (Mellor-Yamada Nakanishi Niino) level 2.5 and 3, with their respective CLS parameterization available in the model. For these simulations, tow nested domains were used where the major domain has a spatial resolution of 5 km, forming a 5 x 5 km domain with 49 x 49 cells covering the entire state of Espírito Santo, part of the Minas Gerais, Rio de Janeiro and Bahia and the smaller domain has a spatial resolution of 1 km, forming a 1 x 1 km domain with 120 x 120 cells comprising the entire RMGV. both with a vertical structure represented by 21 vertical layers and centered at the coordinates 20,25 ° S and 40,29 ° W. In order to achieve the main goal, the model data obtain with each parameterization were compared with the data measured in the stations belonging to RMAQAR and the airport of surface temperature (2 m), wind and speed direction (10 m), using the statistical parameters mentioned in subsection 4.3 of this dissertation. The results showed that the parameterization that best represented the values of the meteorological variables previously mentioned for the summer period was the parameterization used in the modeling M_1 that corresponds to the YSU schemes for CLA and the improved MM5 for the CLS, with the Carapina station presenting simulated values closer to the real values observed. On the other hand, for the winter period, the parameterization that best represented the values of the mentioned meteorological variables was the parameterization used in the modeling M_12 that corresponds to the schemes UW for the CLA and the MM5 for the CLS, being Cariacica and airport the stations that presented an accuracy between the simulated data and the real data observed. The results presented by both parameterizations show that the best results are presented for wind speed, followed by surface temperature and wind direction. These results suggest the need to test the other physical parameterizations available in the model in order to improve the prediction results of the meteorological variables for the RMGV and thus to have better results at the moment of using this data in dispersion models.
Avaliação de desempenho do modelo fotoquímico CMAQ utilizando diferentes condições de contorno em uma região urbana e industrializada
(Universidade Federal do Espírito Santo, 2016-04-13) Pedruzzi, Rizzieri; Henderson, Barron; Albuquerque, Taciana Toledo de Almeida; Reis Júnior, Neyval Costa; Andrade, Maria de Fátima
The purpose of this study was to evaluate the influence of the boundary conditions (BCON) in the CMAQ model simulations over the Metropolitan Region of Grande Vitória (RMGV) for MP10 and O3 pollution. It was made four scenarios of August 2010 with different boundary conditions. The first scenario (M1) using fixed, time-independent boundary conditions with zero concentration (zero) for all pollutants; a second scenario (M2) with fixed, timeindependent concentration values, with average values from monitoring stations from RMGV and from Aracruz’s stations on north and Anchieta’s stations on south; the third scenario (M3) used boundary conditions varying with time from a previous simulation with CMAQ over a larger area, centered on RMGV; and finally, the fourth scenario (M4) usin g boundary conditions varying with time from simulations of global model GEOS-Chem. All scenarios used the same meteorology conditions and pollutant emissions, meteorological conditions was generated by the model WRF version 3.6.1 and pollutant emissions inventory are from the official emissions inventory of RMGV. The air quality simulations were made with a domain 61 x 79 km centered on coordinates -20,25ºS, -40,28ºW with a resolution of 1 km, using the CB05 and Aero6 and still analyzer CMAQ processes (PROCAN). The results were compared with the measured data in monitoring stations from RMGV. The results showed that for PM10, the boundary conditions were not so influential on the simulated concentrations, with small variations of concentrations between the tests, but, in general, M3 and M4 methods achieved the best results for statistics, however, the M2 method is not totally wrong but should be cautious in using this method. It was observed different behaviors between monitoring stations, where there are some that have been overestimated values in a few hours and others with understated concentrations, occurred probably because the grid size associated with weather conditions and temporal variation of emissions. For ozone, it was noted that the boundary conditions had a large influence on modeled concentrations, and may also influence the increased of production of O3 not only by chemical reactions, but also by advection processes and atmospheric diffusion. It was observed on scenario M1 that the O3 modeled concentrations were very small and do not represent reality. On M2 and M3 scenarios, concentrations were overestimated, on monitoring stations and in areas near the boundaries of the domain, mainly in the western portion. The M4 scenario achieved the best results of concentrations and statistics, which is the most advisable when the goal is to evaluate the ozone. As the high resolution domain applied to CMAQ had only 61x79km was noted that the boundary conditions affect directly across the grid field, especially for ozone, by using the process analysis preprocessor (PROCAN). When the boundary concentrations are high, regardless of being fixed or varying with time, the advection processes and turbulent diffusion adds a large amount of mass in domain's borders, overestimating the modeled concentrations.
Avaliação de desempenho dos esquemas de camada limite planetária do modelo WRF para a Região Metropolitana de Salvador - BA
(Universidade Federal do Espírito Santo, 2018-02-26) Kitagawa, Yasmin Kaore Lago; Nascimento, Erick Giovani Sperandio; Moreira, Davidson Martins; Albuquerque, Taciana Toledo de Almeida; Gonçalves, Marcelo Albano Moret Simões
Exchanges of moisture, heat, and momentum occur within the planetary boundary layer (PBL)through mixing associated with turbulent eddiesthat influence the way in which lower-tropospheric thermodynamic and kinematic structures evolve. Howeversuch eddies operate on spatiotemporal scales that cannot be explicitly represented on grid scales and time steps employed in most mesoscale models. As such, their effects are expressed in these models via the use of PBL parameterization schemes. In this way, the present work used the Weather Research and Forecasting (WRF)model to find a set of physical parameterization that represents thelocalatmospheric circulation of theMetropolitan Region of Salvador (RMS) which counts with several industrial complexes that affect the air quality of the region. In addition, the workexpects to contributescientifically to researches related toatmospheric processes represented by numerical models that occur in tropical and coastal regions.Since there wasno previouswork performing any assessment in this area using WRF model, the performance of six PBL schemes were evaluated –Boucheau e Lacarrere (BOU), Grenier e Bretherton (GBM), Mellor-Yamada-Janjić (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN2), Bretherton e Park(UW) e Yonsei University (YSU).The model performance was performed using statistical metrics of mean bias (MB), root mean square error (RMSE), mean absolute gross error (MAGE), correlation coefficient (r) and index of agreement (IOA) between simulated and observed data in the surface meteorological stations of the region through the meteorological variables air temperature at 2 meters (T2), wind speed and direction at 10 meters (WS10 and WD10).MYJ scheme showedthe best performance for WS10, while BOU and UW schemes stood out for WD10. Regarding T2, MYNN2 and, again, MYJ, presented the best results.However, it is worth to point out that results indicate that there were no statistically significant differences using different PBL schemes.Analysis of simulated PBLheights (PBLH) and vertical profiles of water vapor, potential temperature and wind speed and direction were also made, it was observed that the WRF model produced low values of PBLH, neverthelessit was inferred that even so the model was able to present satisfactory results for the behavior of vertical profiles.It was concluded that the WRF model presented good performance and was able to simulate the atmospheric conditions that characterize RMS.
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 comparativo entre coeficientes de difusão verticais na simulação da dispersão de poluentes em uma camada limite convectiva
(Universidade Federal do Espírito Santo, 2014-01-01) Leite, Maria de Fátima Silva; Moreira, Davidson Martins; Albuquerque, Taciana Toledo de Almeida; Gonçalves, Marcelo Albano Moret Simões
This work presents simulations for the pollutant dispersion in the Convective Boundary Layer (CBL) with a stationary three-dimensional semi-analytical solution, obtained by solving the advection-diffusion equation.The equation was solved by combining the techniques ADMM (Advection Diffusion Multilayer Method), based on the discretization of CLC in sublayers, each sublayer where the advection-diffusion equation is solved by the Laplace transform technique and GITT (Generalized Integral Transform Technique), a hybrid method that solves a broad class of direct and inverse problems. The new technique is then called GIADMT (Generalized Integral Advection Diffusion Multilayer Technique). The objective was to compare and analyze some of vertical diffusion coefficients, and their applicability in the three-dimensional concentration equation obtained by GIADMT method. Comparison entres appropriate to the atmosphere in unstable conditions vertical diffusion coefficients (Kz) was presented. The results were compared with experimental data of Copenhagen (Gryning and Lick, 1984; Gryning et al, 1987; Gryning and Lick, 2002) in order to verify the performance of the model under the various parameterizations of atmospheric turbulence. The comparisons showed better results when employing the parameterization suggested by Degrazia et al. (2001) [A].
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
Influence of traffic emissions on the street-level concentrations in an urban built neighborhood in Brazil: a MUNICH application
(Universidade Federal do Espírito Santo, 2023-10-30) Cevolani, Karina Tonoli; Goulart, Elisa Valentim; https://orcid.org/0000000200510778; http://lattes.cnpq.br/0014236670973457; https://orcid.org/0000-0002-0335-570X; http://lattes.cnpq.br/4723349502456604; Albuquerque, Taciana Toledo de Almeida; http://lattes.cnpq.br/1339985577872129; Calderon, Mario Eduardo Gavidia
Air pollution is already considered the main environmental threat for human health. Especially in urban areas, exposure to high concentrations can be more frequently due to the proximity to traffic emissions and limited dispersion. Combination of air quality deterioration and rapidly urban growth may result in a increase of clinical cases. In order to analyze the influence of traffic emissions in Enseada do Suá, an urban neighborhood located in Vitória city/Brazil, simulations with MUNICH were performed to November and December/2019. Based on that, three additional emissions scenarios were proposed to provide subsidies for strategies for pollution control. Emissions data required by MUNICH was defined based on the emission inventory released by the State Institute of Environment and Water Resources (Base year 2015). Particularly, volatile organic emissions were chemically speciated following factors associated with combustion of Brazilian fuels available in the literature. Meteorological data and background concentrations were obtained from modelings with WRF-Urban model version 4.1.5 and Community Multiscale Air Quality (CMAQ) model version 5.3.2, respectively. Street network and buildings were obtained from Vitória city hall’website. MUNICH simulations indicated that peaks of NO2, PM10 and PM2.5 concentrations were associated with low values of planetary boundary layer and/or friction velocity. In addition, a decrease by roughly 30% on the aspect ratio in a primary street (street 119 which is near to the air quality station placed at Enseada do Suá) resulted a decrease by 13.0%, 13.7% and 8.9% on the average concentrations of PM2.5, PM10 and NO2 over streets 112 and 119 (near the Enseada do Suá station). A better agreement between modeled and observed NO2 concentrations were achieved when a correction on background concentrations were applied, which suggests that this pollutant is more affected by urban background. Regarding PM10 and PM2.5, results indicated a potential overestimation of resuspension emission. Hence, it is a key factor to obtain higher concentrations over the study area. With respect to the additional traffic emission scenarios, simulations showed that all urban mobility scenarios resulted in reductions on the air concentration. For all analyzed pollutants, largest decreases were obtained for null emissions on primary streets, which are the most relevant for the total emission rate estimated for Enseada do Suá. In this scenario, average PM10, PM2.5 and NO2 concentrations over street segments may be reduced by up 66.1%, 79.6% and 92.5%, respectively. However, violations to Brazilian standards and WHO air quality guidelines - 2021 (WHO 2021) were noted. Larger number of exceedings to the WHO 2021 in comparison to the current standards in Brazil suggest a need to update the national legislation. Finally, more ambitious strategies are required to control pollution in the study area aiming human welfare.
Influência dos compostos orgânicos voláteis no potencial de formação do ozônio troposférico na região da Grande Vitória-ES
(Universidade Federal do Espírito Santo, 2014-06-06) Galvão, Elson Silva; Santos, Jane Meri; Albuquerque, Taciana Toledo de Almeida; Fornaro, Adalgiza
In the present study, the ozone forming potential (OFP) from volatile organic compounds (VOCs) in theatmosphereof Metropolitan Region of Grande Vitória(RMGV) has been studied on the periods between02/05/2013 to 04/05/2013 and 05/16/2013 to 06/21/2013. Each species of VOC has a particular reactivity on ozone formingpotential, so the knowledge of these species in the local atmosphere is of great importance in proposing measures to control of air pollution. Passive samplers were installed at three of air quality monitoring stations (RAMQAr) of RMGV, and a total of 96 samplings were collected between the two sampling periods. Bythe characterization and quantification of VOC was possible to quantify the OFPapplying the scale ofmaximumincrementalreactivity (MIR) proposed by Carter (1994). The results indicatedthat the predominant group of VOCs in the RMGV is the organic acids, followed by alcohols and substituted aromatics. Among the VOCs with the greatest OFP, the undecane, toluene , ethylbenzene and m, p-xylene compounds are the most abundant in RMGV, presentingthe means of 0,855 μg m-3, 0,365 μg m-3, 0,259 μg m-3and0,289 μg m-3, respectively, on the period 05/16/2013 to 06/21/2013. The results also indicatedthat the BTEX group consisting of benzene, toluene, ethylbenzene and m,p-xylenes are belowof the limitsconsidered harmful to the health of the population in RMGV . The ozone formingpotential in RMGV calculated from all the precursorsVOC group was 22.55 μg m-3insummer period and 32.11 μg m-3in the winter period. The toluene and o-xylene compounds are responsible for approximately 37 % of all OFPproduced from precursors VOC group. The assessment of the VOC/NOxratio revealed that the overall value of thatratio inRMGV is approximately 1.71, indicating that the region has a VOC-limiting condition for the production of ozone. Therefore, the adoption of measures only toremove NOxfrom the atmosphere of RMGV could favor reactions between OH and VOC, forming radicals that enable regenerate NO2increasingthenet rate of ozone production in the region.
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.

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