A semi-Analytical Dispersion Model from a Steady Source of Short Duration

http://dx.doi.org/10.5902/2179460X13489This paper presents a semi-analytical unsteady-state solution to the atmospheric dispersion equation for substances subject to chemical-physical decay and that allows a description of the time evolution of the concentration field emitted from a source during a release of short duration. This solution is suitable for describing critical events relative to accidental release of toxic, flammable or explosive substances. The results of the sensitivity analysis and of validation testing, with a proper parameterization of the vertical profiles of the wind and eddy diffusivities, were evaluated against experimental data found in the literature.This paper presents a semi-analytical unsteady-state solution to the atmospheric dispersion equation for substances subject to chemical-physical decay and that allows a description of the time evolution of the concentration field emitted from a source during a release of short duration. This solution is suitable for describing critical events relative to accidental release of toxic, flammable or explosive substances. The results of the sensitivity analysis and of validation testing, with a proper parameterization of the vertical profiles of the wind and eddy diffusivities, were evaluated against experimental data found in the literature

Long-Term Correlations and Cross-Correlations in Meteorological Variables and Air Pollution in a Coastal Urban Region

In this work, we evaluated the evolution of some atmospheric pollutants (O3, NOx and PM10) over time and their relationship with four different climate variables (solar irradiation, air temperature, relative humidity and wind speed). To this end, we assessed the long-range dependence of those concentrations with a Detrended Fluctuation Analysis (DFA) and analyzed the cross-correlation of such dependence with the climate variables through a Detrended Cross-Correlation Coefficient Analysis (ρDCCA). The results show that air pollution tends to increase over time, impairing air quality and likely affecting human health. The results indicate a cross-correlation between air pollution and the climatic variables, which persisted for a certain period, with a greater correlation between O3 concentration and wind, mainly temperature, and a negative correlation with humidity for all monitoring stations. Moreover, unlike O3 and PM10, NOx concentrations always had a persistent behavior in the region of study for the entire analyzed period.N/

Study of the Wind Speed Forecasting Applying Computational Intelligence

The conventional sources of energy such as oil, natural gas, coal, or nuclear are finite and generate environmental pollution. Alternatively, renewable energy source like wind is clean and abundantly available in nature. Wind power has a huge potential of becoming a major source of renewable energy for this modern world. It is a clean, emission-free power generation technology. Wind energy has been experiencing very rapid growth in Brazil and in Uruguay; therefore, it’s a promising industry in these countries. Thus, this rapid expansion can bring several regional benefits and contribute to sustainable development, especially in places with low economic development. Therefore, the scope of this chapter is to estimate short-term wind speed forecasting applying computational intelligence, by recurrent neural networks (RNN), using anemometers data collected by an anemometric tower at a height of 100.0 m in Brazil (tropical region) and 101.8 m in Uruguay (subtropical region), both Latin American countries. The results of this study are compared with wind speed prediction results from the literature. In one of the cases investigated, this study proved to be more appropriate when analyzing evaluation metrics (error and regression) of the prediction results obtained by the proposed model

DISPERSÃO DE POLUENTES NA CAMADA LIMITE ESTÁVEL: O CASO DE UMA FONTE SUPERFICIAL

Atualmente, a compreensão acerca da estrutura física da Camada Limite Estável noturna (CLE)é muito limitada, tornando o estudo da dispersão de poluentes nesta camada muito importante. Istose deve, em parte, ao fato de que esta camada raramente encontra-se em estado de equilíbrio estacionário.Esta ausência de equilíbrio em relação às mudanças nas condições de contorno provocauma contínua evolução na sua estrutura. Como uma conseqüência tem-se à noite uma turbulênciamenos intensa do que durante o dia. O objetivo deste trabalho é obter uma formulação semi-empíricapara o parâmetro de dispersão vertical que é função da distância da fonte, da velocidade turbulentavertical e da escala de tempo integral Lagrangeana provenientes do espectro da turbulência. Paratestar a influência deste parâmetro de dispersão é utilizado o modelo de pluma Gaussiana e osresultados de concentrações superficiais são confrontados com dados observacionais do experimentode dispersão atmosférica de Prairie Grass.Palavras-chave: parâmetro de dispersão vertical; camada limite estável; modelo Gaussiano

A method for accurate measurement of chemical elements concentrations present in total suspended particulates and PM 10 in the atmosphere

In this paper we present an analytic method for theidentification and the quantification of chemical elements in theatmosphere present in TSP and PM10. To the present moment, themethod is applicable to a total of 16 metals. The method was used toanalyze TSP and PM10 samples collected in the city of Canoas, in thegreater Porto Alegre, Brazil. In order to obtain relative measures ofPM10 concentrations, the method was also used to analyze samples from a neutral location. The results were checked against electronicmicroscopy findings, having an excellent agreement. Geologicalanalysis of the electronic microscopy results was used to infer thepollutants source.Apresenta-se um método analítico para a identificação ea quantificação de elementos químicos presentes nas PartículasTotais Suspensas (TSP) na atmosfera e nos particulados PM10. Estemétodo foi aplicado para a quantificação de 16 metais emsuspensão na atmosfera da região de Canoas, RS. Os resultadosforam comparados com as análises de metais presentes naatmosfera do Centro Agrícola da ULBRA, na região de Montenegro(uma área agrícola na qual a direção predominante do ventospossibilita que ela seja considerada como um ponto com mínimapoluição atmosférica) . Usou- se, também, a técnica da microscopiaeletrônica para a comparação dos resultados e para a identificaçãoda origem de várias partículas poluentes

Modelo euleriano semi-analítico para a dispersão de contaminantes na camada limite planetária

Um modelo de dispersão analítico baseado na discretização da Camada Limite Planetária em N subcamadas é apresentado. Em cada subcamada a equação de difusão-adevcção é resolvida pelo emprego da transformada de Laplace, considerando um valor médio para o coeficiente de difusão e para a velocidade do vento. O presente modelo emprega perfis realísticos semi-empíricos para o coeficiente de difusão e velocidade do vento, de modo que a turbulência não homogênea pode ser utilizada. A performance do modelo é testada confrontando as simulações com os dados experimentais de Prairie Grass e Copenhagen. A aplicação de índices estatísticos (Hanna, 1989) mostra que o modelo analítico de dispersão proposto produz bons resultados.An analytical air quality dispersion model based on a discretization of the planetary boundary layer in N sub-layers is presented. In each sub-layer the diffusion-advection equation is solved by the Laplace Transform techniques, considering an average value for the vertical exchange coefficient and the wind speed. As a consequence, the present approach allows to employ realistic semi-empirical profiles for the eddy diffusivity and wind speed, in such manner that the inhomogeneous turbulence can be handle. The model performance have been evaluated using the well-known Copenhagen and Prairie Grass datasets. Then, the application of the statistical evalution procedure (Hanna, 1989) over the out coming results has show that the proposed analytical dispersion model produces a good fitting of the observational data

Exposure and dose assessment of school children to air pollutants in a tropical coastal-urban area

This study estimates exposure and inhaled dose to air pollutants of children residing in a tropical coastal-urban area in Southeast Brazil. For that, twenty-one children filled their time-activities diaries and wore the passive samplers to monitor NO2. The personal exposure was also estimated using data provided by the combination of WRF-Urban/GEOS-Chem/CMAQ models, and the nearby monitoring station. Indoor/outdoor ratios were used to consider the amount of time spent indoors by children in homes and schools. The model's performance was assessed by comparing the modelled data with concentrations measured by urban monitoring stations. A sensitivity analyses was also performed to evaluate the impact of the model's height on the air pollutant concentrations. The results showed that the mean children's personal exposure to NO2 predicted by the model (22.3 μg/m3) was nearly twice to those measured by the passive samplers (12.3 μg/m3). In contrast, the nearest urban monitoring station did not represent the personal exposure to NO2 (9.3 μg/m3), suggesting a bias in the quantification of previous epidemiological studies. The building effect parameterisation (BEP) together with the lowering of the model height enhanced the air pollutant concentrations and the exposure of children to air pollutants. With the use of the CMAQ model, exposure to O3, PM10, PM2.5, and PM1 was also estimated and revealed that the daily children's personal exposure was 13.4, 38.9, 32.9, and 9.6 μg/m3, respectively. Meanwhile, the potential inhalation daily dose was 570-667 μg for PM2.5, 684-789 μg for PM10, and 163-194 μg for PM1, showing to be favourable to cause adverse health effects. The exposure of children to air pollutants estimated by the numerical model in this work was comparable to other studies found in the literature, showing one of the advantages of using the modelling approach since some air pollutants are poorly spatially represented and/or are not routinely monitored by environmental agencies in many regions