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In Lithuania the amount of waste generation is increasing every year. According to national strategy, all wastes should be disposed in new regional landfills. Landfills pollutes environment with leachate and landfill gas and odours. Landfill gas consists of odorous compounds and one of them is hydrogen sulphide (H2S). Hydrogen sulphide is highly toxic and affects the nervous system with low threshold. As the landfill gas and leachate generation was word widely investigated before this work, it is not necessary to provide new researches on them. The measurements of H2S generation were provided in Jerubaiciai landfill. For the measurements was used “site-on” measurement method, measurements were provided with equipment GD/MG 7, in 51 measurement points and 2 monitoring wells, during different seasons of the year. Results of the measurement shows, that amount of H2S varies in different areas of landfill and during different seasons. The results of dispersion modeling achieved with dispersion model AERMOD, provided under calm weather conditions and under wind dominated in that session winter speed and direction, during different seasons of the year shows, that H2S spreads from landfill in longest distances from landfill’s section during summer (almost in distance equal to 2.5 km the H2S concentration is higher then Highest Allowable Concentration ). In autumn and spring this distance is equal to 1.5 km, and in winter – 800 m. / Susidarančių komunalinių atliekų kiekis Lietuvoje kiekvienais metais didėja. Pagal nacionalinę strategiją, visos komunalinės atliekos Turi būti deponuojamos regioniniuose sąvartynuose, kurie teršia aplinka filtratu iš sąvartyno išsiskiriančiomis dujomis bei kvapais, kurių veina iš sudedamųjų dalių yra sieros vandenilis (H2S). H2S matavimai buvo atlikti Jerubaičių sąvartyne. Iš sąvartyno išsiskiriantis H2S kiekis buvo tiriamas jo išsiskyrimo vietoje, t.y. sąvartyno teritorijoje. Šis matavimo metodas buvo pasirinktas remiantis tuo, kad iš sąvartyno išsiskiriančios taršos dydis ir poveikis priklauso nuo daugelio aplinkos faktorių. Matavimai, naudojant prietaisą GD/MG 7, buvo atlikti 59 matavimo taškuose ir 2 monitoringo šuliniuose, skirtingais metų laikai. Gauti tyrimų rezultatai parodė, kad šios medžiagos kiekis yra skirtingas įvairiose sąvartyno zonose bei įvairiais metų laikais. Norint ištirti H2S sklaidą buvo atliktas skaitmeninis dispersijos modeliavimas naudojant programą AERMOD. Jo metu vienu atveju buvo pasirinktos stabilios meteorologinės sąlygos, o kitu pasirinkti dominuojančios konkrečiu metų laiku vėjo kryptys ir greičiai. Modeliavimo rezultatai parodė, kad vasarą H2S didžiausia leistina koncentracija pasiekiama tik maždaug 2,5 kilometrų, rudenį ir pavasarį 1,5 kilometrų, o žiemą - už 800 metrų atstumu nuo sąvartyno teritorijos.

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Odor problems are a common complaint from residents living near landfills. Many compounds can cause malodorous conditions. However, hydrogen sulfide (h2s) has been identified as a principal odorous component from construction and demolition (c&d)debris landfills. Although several studies have reported the ambient concentrations of h2s near c&d landfills, few studies have quantified emission rates of h2s. The most widely used and proven technique for measuring gas emission rates from landfills is the flux chamber method. Typically the flux chamber is a cylindrical enclosure device with a spherical top which limits the gas emission area. Pure zero grade air is introduced into the chamber, allowed to mix with emitting gases captured from the landfill surface, and then transported to the exit port where concentrations can be measured. Flux measurements using the flux chamber were performed at five different c&d landfills from june to august, 2003. The flux rates of h2s measured in this research were three to six orders of magnitude lower than the flux rates of methane reported in the literature. In addition to the h2s flux measurements, dispersion modeling was conducted, using the epa dispersion model, industrial source complex short term (iscst3), in order to evaluate impacts on landfill workers and communities around the landfills. The modeling results were analyzed to estimate the potential ground level maximum h2s concentrations for 1-hr and 3-min periods and the frequency (occurrences per year) above the h2s odor detection threshold for each landfill. Odor complaints could be expected from four among five landfills selected for this study, based on 0.5-ppb odor detection threshold.

In the event of a radioactive release in an urban environment the consequent response mustbe swift and precise. As soon as first responders have correct information, they can make anaccurate risk assessment. However, if the position, release rate and time of the radioactiverelease is unknown it is hard to know how the pollutant will spread. This thesis aims to testa model which approximates these three unknowns using weather data (wind and rain) as wellas measurement data collected at sensors placed around an urban environment. An atmospheric dispersion model based on an existing Reynolds Averaged Navier-Stokes modelis set up in two geometries of different complexity to create forward mode synthetic depositiondata and adjoint mode concentration fields resulting from a fixed dry deposition velocity andscavenging effect for wet deposition. Variations of time- and space-dependent rainfall is simu-lated. The resulting data is used in an existing optimization model, where a parameter studyis conducted regarding regularization coefficients. This thesis shows that the optimization model accurately estimates position and its approximaterelease rate of a 2D geometry of radioactive releases using a logarithmic optimization approach,and fail to do so using a linear optimization approach. The logarithmic optimization model alsoapproximately estimates position and release rate in a 3D geometry. Regularization parametersshould be within the range of 0.1 and 1.2 depending on rain. More rain requires smallerparameters and will estimate a lower release rate. Time-dependent rainfall is shown to have amajor negative effect on simulation time.iii

Doctor of Philosophy / Department of Biological and Agricultural Engineering / Ronaldo G. Maghirang / Emission of particulate matter (PM) and various gases from open-lot beef cattle feedlots is becoming a concern because of the adverse effects on human health and the environment; however, scientific information on feedlot emissions is limited. This research was conducted to estimate emission rates of PM[subscript]10 from large cattle feedlots. Specific objectives were to: (1) determine feedlot PM[subscript]10 emission rates by reverse dispersion modeling using AERMOD; (2) compare AERMOD and WindTrax in terms of their predicted concentrations and back-calculated PM[subscript]10 emission rates; (3) examine the sensitivity of both AERMOD and WindTrax to changes in meteorological parameters, source location, and receptor location; (4) determine feedlot PM[subscript]10 emission rates using the flux-gradient technique; and (5) compare AERMOD and computational fluid dynamics (CFD) in simulating particulate dispersion from an area source. PM[subscript]10 emission rates from two cattle feedlots in Kansas were determined by reverse dispersion modeling with AERMOD using PM[subscript]10 concentration and meteorological measurements over a 2-yr period. PM[subscript]10 emission rates for these feedlots varied seasonally, with overall medians of 1.60 and 1.10 g /m[superscript]2 -day. Warm and prolonged dry periods had significantly higher PM emissions compared to cold periods. Results also showed that the PM[subscript]10 emissions had a diurnal trend; highest PM[subscript]10 emission rates were observed during the afternoon and early evening periods. Using particulate concentration and meteorological measurements from a third cattle feedlot, PM[subscript]10 emission rates were back-calculated with AERMOD and WindTrax. Higher PM[subscript]10 emission rates were calculated by AERMOD, but their resulting PM[subscript]10 emission rates were highly linear (R[superscript]2 > 0.88). As such, development of conversion factors between these two models is feasible. AERMOD and WindTrax were also compared based on their sensitivity to changes in meteorological parameters and source locations. In general, AERMOD calculated lower concentrations than WindTrax; however, the two models responded similarly to changes in wind speed, surface roughness, atmospheric stability, and source and receptor locations. The flux-gradient technique also estimated PM[subscript]10 emission rates at the third cattle feedlot. Analyses of PM[subscript]10 emission rates and meteorological parameters indicated that PM[subscript]10 emissions at the feedlot were influenced by friction velocity, sensible heat flux, temperature, and surface roughness. Based on pen surface water content measurements, a water content of at least 20% (wet basis) significantly lowered PM[subscript]10 emissions at the feedlot. The dispersion of particulate from a simulated feedlot pen was predicted using CFD turbulence model ([kappa]-[epsilon] model) and AERMOD. Compared to CFD, AERMOD responded differently to wind speed setting, and was not able to provide detailed vertical concentration profiles such that the vertical concentration gradients at the first few meters from the ground were negligible. This demonstrates some limitations of AERMOD in simulating dispersion for area sources such as cattle feedlots and suggests the need to further evaluate its performance for area source modeling.

La surveillance de la qualité de l'air est actuellement effectuée avec des mesures de concentration et à partir d'outils de modélisation de la dispersion atmosphérique. Ces modèles numériques évaluent les concentrations des polluants avec une résolution spatio-temporelle plus fine que les mesures. Néanmoins, les estimations fournies par ces modèles sont moins précises que les mesures. Dans ce projet de recherche, nous avons étudié les approches de couplage d'échelles et d'assimilation de données pour améliorer les estimations fournies par le modèle de dispersion atmosphérique SIRANE, dédié à l'échelle urbaine. L'approche de couplage d'échelles consiste à déterminer les conditions aux limites d'une simulation à partir d'une autre simulation à plus grande échelle. Au cours de ce travail de thèse, nous avons analysé trois méthodes afin de coupler le modèle urbain SIRANE et le modèle à méso-échelle CHIMERE. Cette étude montre que ces méthodes permettent potentiellement d'estimer la qualité de l'air à l'échelle urbaine de manière plus satisfaisante que les modèles à méso-échelle (utilisés seuls). Cependant, elles n'améliorent pas forcément la modélisation des conditions aux limites d'une simulation à l'échelle urbaine et les estimations fournies par celles-ci. Cela est a priori lié au fait que les estimations fournies par le modèle CHIMERE ne sont pas suffisamment satisfaisantes sur notre cas d'étude. Il est néanmoins possible que ces méthodes améliorent les résultats à l'échelle urbaine en utilisant une simulation à l'échelle régionale de meilleure qualité. L'approche d'assimilation de données consiste à combiner les mesures et les données modélisées afin de déterminer la meilleure estimation de l'état d'un système. Durant cette thèse, nous avons étudié trois méthodes d'assimilation de données : la méthode de débiaisement, la méthode que nous avons nommée modulation de la contribution des sources et la méthode Best Linear Unbiased Estimator. Cette étude indique que ces méthodes permettent globalement d'améliorer les estimations fournies par le modèle SIRANE. L'étude de sensibilité vis-à-vis du nombre de mesures utilisées lors de l'assimilation de données indique qu'en général, plus ce nombre est élevé plus les résultats sont satisfaisants. Enfin, les résultats montrent que les performances statistiques associées à ces trois méthodes d'assimilation de données sont globalement comparables entre elles sur notre cas d'étude. / Air quality monitoring is currently carried out with concentration measurements and with atmospheric dispersion modeling tools. These numerical models evaluate pollutant concentrations with a finer spatio-temporal resolution than measurements. Nevertheless, the estimates provided by these models are less accurate than measurements. In this research project, we studied multiscale coupling and data assimilation approaches to improve the estimates provided by the SIRANE atmospheric dispersion model, dedicated to the urban scale. The multiscale coupling approach consists in determining the boundary conditions of a simulation from another simulation on a larger scale. In this thesis work, we analyzed three methods for coupling the SIRANE model with the CHIMERE mesoscale model. This study shows that these methods can potentially estimate the air quality at the urban scale more satisfactorily than the mesoscale models (used alone). However, they do not necessarily improve the modeling of the boundary conditions of a simulation at the urban scale and the estimates provided by them. This is a priori due to the fact that the estimates provided by the CHIMERE model are not sufficiently good on our case study. It is possible, however, that these methods improve the results at the urban scale by using a better simulation at the regional scale. The data assimilation approach consists of combining the measurements and the modelled data to determine the best estimate of the system state. During this thesis, we studied three data assimilation methods : the unbiased method, the method that we called source apportionment modulation, and the Best Linear Unbiased Estimator method. This study indicates that these methods generally improve the estimates provided by the SIRANE model. The sensitivity study on the number of measurements used during the data assimilation indicates that, in general, higher is this number, more satisfactory are the results. Finally, the results show that the statistical performances associated with these three data assimilation methods are globally comparable on our case study.

Os derramamentos de petróleo são consequência inevitável e indesejável da produção e transporte do petróleo e seus derivados. A maioria desses derramamentos são relativamente pequenos, mas alguns deles são grandes o suficiente para causar significativo impacto ambiental. Nessas situações, os modelos computacionais são ferramentas importantes para estimar a trajetória, dimensionamento e comportamento do óleo derramado no ambiente marinho, sendo determinantes na elaboração de planos de ação e trabalho das equipes de resposta. O transporte e destino de óleo offshore derramado são regidos majoritariamente, no curto período, por processos de transporte e de transformação físico-químicos e no longo período por processos de degradação biológica, de acordo com as condições ambientais locais (oceânicas e atmosféricas). Os principais processos que atuam sobre as manchas de óleo offshore incluem, no curto período, advecção, difusão turbulenta, espalhamento superficial, evaporação, dissolução, emulsificação, sedimentação e a interação de mancha de óleo com a linha da costa. O STFM (Spill, Transport and Fate Model) foi o modelo computacional desenvolvido nesse trabalho. Os algoritmos foram desenvolvidos com base em formulações físico-químicas propostas na literatura, sendo testadas as proposições de diversos autores e selecionadas as equações que apresentaram melhores resultado para integrar o conjunto físico-químico que compõe o STFM. Os resultados do trabalho mostraram que o STFM apresentou desempenho superior aos demais modelos testados na descrição do espalhamento e difusão dando mais estabilidade à mancha por utilizar a derivação de Dodge para a proposta de espalhamento de Fay e substituir o método usual de Randon Walk por Randon Flight (avançado no tempo) na forma canônica dada por Lynch. O algoritmo do STFM também traz outra evolução importante ao incluir um modelo de evaporação baseado nas equações empíricas de Fingas, substituindo as atuais parametrizações baseadas no ADIOS2 e nos métodos de pseudocomponentes. / Oil and its by-products spills are an inevitable and undesirable consequence of their production and transportation. Even though these spills are relatively small, some of them are large enough to cause significant environmental impact. Taken this into account, the computational models are important tools to estimate the trajectory, dimensioning and behavior of the oil spilled in the marine environment, being also determinants to elaborate action plans for response teams work. The transportation and fate of oil spills are governed in the short term by physical-chemical transport and transformation processes and in the long term by biological degradation processes, according to local environmental conditions (oceanic and atmospheric). The main processes that act on offshore oil spills include, in the short term, advection, turbulent diffusion, surface scattering, evaporation, dissolution, emulsification, sedimentation and the interaction of oil slick according to the coast line. The Spill, Transport and Fate Model (STFM) was the computational model developed in this work. The algorithms were developed based on physicochemical formulations proposed in literature, being the propositions of several authors tested and the equations which presented the best results were selected to integrate the physical-chemical set that makes up the STFM. The STFM results presented superior performance, giving more stability to the stain, compared to the other models tested in the scattering and diffusion description, by using the Dodge derivation for the Fay spreading proposal and by replacing the usual \"Randon Walk\" method by \"Randon Flight\" (advanced in time) in the canonical form given by Lynch. The STFM algorithm also brings forward another important evolution by including an evaporation model based on Fingas empirical equations, replacing the current parameterizations based on ADIOS2 and pseudo component methods.

Os derramamentos de petróleo são consequência inevitável e indesejável da produção e transporte do petróleo e seus derivados. A maioria desses derramamentos são relativamente pequenos, mas alguns deles são grandes o suficiente para causar significativo impacto ambiental. Nessas situações, os modelos computacionais são ferramentas importantes para estimar a trajetória, dimensionamento e comportamento do óleo derramado no ambiente marinho, sendo determinantes na elaboração de planos de ação e trabalho das equipes de resposta. O transporte e destino de óleo offshore derramado são regidos majoritariamente, no curto período, por processos de transporte e de transformação físico-químicos e no longo período por processos de degradação biológica, de acordo com as condições ambientais locais (oceânicas e atmosféricas). Os principais processos que atuam sobre as manchas de óleo offshore incluem, no curto período, advecção, difusão turbulenta, espalhamento superficial, evaporação, dissolução, emulsificação, sedimentação e a interação de mancha de óleo com a linha da costa. O STFM (Spill, Transport and Fate Model) foi o modelo computacional desenvolvido nesse trabalho. Os algoritmos foram desenvolvidos com base em formulações físico-químicas propostas na literatura, sendo testadas as proposições de diversos autores e selecionadas as equações que apresentaram melhores resultado para integrar o conjunto físico-químico que compõe o STFM. Os resultados do trabalho mostraram que o STFM apresentou desempenho superior aos demais modelos testados na descrição do espalhamento e difusão dando mais estabilidade à mancha por utilizar a derivação de Dodge para a proposta de espalhamento de Fay e substituir o método usual de Randon Walk por Randon Flight (avançado no tempo) na forma canônica dada por Lynch. O algoritmo do STFM também traz outra evolução importante ao incluir um modelo de evaporação baseado nas equações empíricas de Fingas, substituindo as atuais parametrizações baseadas no ADIOS2 e nos métodos de pseudocomponentes. / Oil and its by-products spills are an inevitable and undesirable consequence of their production and transportation. Even though these spills are relatively small, some of them are large enough to cause significant environmental impact. Taken this into account, the computational models are important tools to estimate the trajectory, dimensioning and behavior of the oil spilled in the marine environment, being also determinants to elaborate action plans for response teams work. The transportation and fate of oil spills are governed in the short term by physical-chemical transport and transformation processes and in the long term by biological degradation processes, according to local environmental conditions (oceanic and atmospheric). The main processes that act on offshore oil spills include, in the short term, advection, turbulent diffusion, surface scattering, evaporation, dissolution, emulsification, sedimentation and the interaction of oil slick according to the coast line. The Spill, Transport and Fate Model (STFM) was the computational model developed in this work. The algorithms were developed based on physicochemical formulations proposed in literature, being the propositions of several authors tested and the equations which presented the best results were selected to integrate the physical-chemical set that makes up the STFM. The STFM results presented superior performance, giving more stability to the stain, compared to the other models tested in the scattering and diffusion description, by using the Dodge derivation for the Fay spreading proposal and by replacing the usual \"Randon Walk\" method by \"Randon Flight\" (advanced in time) in the canonical form given by Lynch. The STFM algorithm also brings forward another important evolution by including an evaporation model based on Fingas empirical equations, replacing the current parameterizations based on ADIOS2 and pseudo component methods.