Ações de educação ambiental para conservação e preservação do Córrego Bela Vista, Piacatu-SP

Pós-graduação em Geografia - FCT

Estudo para correção dos sensores da rede de monitoramento do campo elétrico em função do campo elétrico de tempo bom

A monitoring network of atmospheric electric field covering the Vale do Paraiba region was implemented. The sensors were located on different sites with different altitude and geographic topology. The present work reports the study carried on those sensors in order to verify the necessity of using some correction factor to the measured local electric field intensity due to effects of local environment. The measurements were done in continuous 24 hours per day with the data recorded on registers in each device accumulating information during a period of four months. The relation between the electric field values by each sensor was compared to the reference located on Sao Jose dos Campos city using the same period. In a graphical analysis using the local field intensity and the reference, the data were fitted to a straight line obtained by minimum square method. Variation up to 95% was observed between the field values in some sensors. Another method was proposed, comparing the mean values of the electric field in a function of time. The variation in some sensors reached up to 133%. We conclude that the variations are due to local atmospheric conditions and no correction factor is required on the electric field sensors

A non-homogeneous poisson model with spatial anisotropy applied to ozone data from Mexico City

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

Numerical simulations of magma chamber dynamics at Campi Flegrei, and associated seismicity, deformation and gravity changes

Understanding the complex relationships between quantities measured by volcanic monitoring network and shallow magma processes is a crucial headway for the comprehension of volcanic processes and a more realistic evaluation of the associated hazard. This question is very relevant at Campi Flegrei, a volcanic quiescent caldera immediately north-west of Napoli (Italy). The system activity shows a high fumarole release and periodic ground slow movement (bradyseism) with high seismicity. This activity, with the high people density and the presence of military and industrial buildings, makes Campi Flegrei one of the areas with higher volcanic hazard in the world. In such a context my thesis has been focused on magma dynamics due to the refilling of shallow magma chambers, and on the geophysical signals detectable by seismic, deformative and gravimetric monitoring networks that are associated with this phenomenologies. Indeed, the refilling of magma chambers is a process frequently occurring just before a volcanic eruption; therefore, the faculty of identifying this dynamics by means of recorded signal analysis is important to evaluate the short term volcanic hazard. The space-time evolution of dynamics due to injection of new magma in the magma chamber has been studied performing numerical simulations with, and implementing additional features in, the code GALES (Longo et al., 2006), recently developed and still on the upgrade at the Istituto Nazionale di Geofisica e Vulcanologia in Pisa (Italy). GALES is a finite element code based on a physico-mathematical two dimensional, transient model able to treat fluids as multiphase homogeneous mixtures, compressible to incompressible. The fundamental equations of mass, momentum and energy balance are discretised both in time and space using the Galerkin Least-Squares and discontinuity-capturing stabilisation technique. The physical properties of the mixture are computed as a function of local conditions of magma composition, pressure and temperature.The model features enable to study a broad range of phenomenologies characterizing pre and sin-eruptive magma dynamics in a wide domain from the volcanic crater to deep magma feeding zones. The study of displacement field associated with the simulated fluid dynamics has been carried out with a numerical code developed by the Geophysical group at the University College Dublin (O’Brien and Bean, 2004b), with whom we started a very profitable collaboration. In this code, the seismic wave propagation in heterogeneous media with free surface (e.g. the Earth’s surface) is simulated using a discrete elastic lattice where particle interactions are controlled by the Hooke’s law. This method allows to consider medium heterogeneities and complex topography. The initial and boundary conditions for the simulations have been defined within a coordinate project (INGV-DPC 2004-06 V3_2 “Research on active volcanoes, precursors, scenarios, hazard and risk - Campi Flegrei”), to which this thesis contributes, and many researchers experienced on Campi Flegrei in volcanological, seismic, petrological, geochemical fields, etc. collaborate. Numerical simulations of magma and rock dynamis have been coupled as described in the thesis. The first part of the thesis consists of a parametric study aimed at understanding the eect of the presence in magma of carbon dioxide in magma in the convection dynamics. Indeed, the presence of this volatile was relevant in many Campi Flegrei eruptions, including some eruptions commonly considered as reference for a future activity of this volcano. A set of simulations considering an elliptical magma chamber, compositionally uniform, refilled from below by a magma with volatile content equal or dierent from that of the resident magma has been performed. To do this, a multicomponent non-ideal magma saturation model (Papale et al., 2006) that considers the simultaneous presence of CO2 and H2O, has been implemented in GALES. Results show that the presence of CO2 in the incoming magma increases its buoyancy force promoting convection ad mixing. The simulated dynamics produce pressure transients with frequency and amplitude in the sensitivity range of modern geophysical monitoring networks such as the one installed at Campi Flegrei . In the second part, simulations more related with the Campi Flegrei volcanic system have been performed. The simulated system has been defined on the basis of conditions consistent with the bulk of knowledge of Campi Flegrei and in particular of the Agnano-Monte Spina eruption (4100 B.P.), commonly considered as reference for a future high intensity eruption in this area. The magmatic system has been modelled as a long dyke refilling a small shallow magma chamber; magmas with trachytic and phonolitic composition and variable volatile content of H2O and CO2 have been considered. The simulations have been carried out changing the condition of magma injection, the system configuration (magma chamber geometry, dyke size) and the resident and refilling magma composition and volatile content, in order to study the influence of these factors on the simulated dynamics. Simulation results allow to follow each step of the gas-rich magma ascent in the denser magma, highlighting the details of magma convection and mixing. In particular, the presence of more CO2 in the deep magma results in more ecient and faster dynamics. Through this simulations the variation of the gravimetric field has been determined. Afterward, the space-time distribution of stress resulting from numerical simulations have been used as boundary conditions for the simulations of the displacement field imposed by the magmatic dynamics on rocks. The properties of the simulated domain (rock density, P and S wave velocities) have been based on data from literature on active and passive tomographic experiments, obtained through a collaboration with A. Zollo at the Dept. of Physics of the Federici II Univeristy in Napoli. The elasto-dynamics simulations allow to determine the variations of the space-time distribution of deformation and the seismic signal associated with the studied magmatic dynamics. In particular, results show that these dynamics induce deformations similar to those measured at Campi Flegrei and seismic signals with energies concentrated on the typical frequency bands observed in volcanic areas. The present work shows that an approach based on the solution of equations describing the physics of processes within a magmatic fluid and the surrounding rock system is able to recognise and describe the relationships between geophysical signals detectable on the surface and deep magma dynamics. Therefore, the results suggest that the combined study of geophysical data and informations from numerical simulations can allow in a near future a more ecient evaluation of the short term volcanic hazard.

Modeling, design and experimental characterization of Micro-Electro-Mechanical-Systems for gas chromatographic applications

Design parameters, process flows, electro-thermal-fluidic simulations and experimental characterizations of Micro-Electro-Mechanical-Systems (MEMS) suited for gas-chromatographic (GC) applications are presented and thoroughly described in this thesis, whose topic belongs to the research activities the Institute for Microelectronics and Microsystems (IMM)-Bologna is involved since several years, i.e. the development of micro-systems for chemical analysis, based on silicon micro-machining techniques and able to perform analysis of complex gaseous mixtures, especially in the field of environmental monitoring. In this regard, attention has been focused on the development of micro-fabricated devices to be employed in a portable mini-GC system for the analysis of aromatic Volatile Organic Compounds (VOC) like Benzene, Toluene, Ethyl-benzene and Xylene (BTEX), i.e. chemical compounds which can significantly affect environment and human health because of their demonstrated carcinogenicity (benzene) or toxicity (toluene, xylene) even at parts per billion (ppb) concentrations. The most significant results achieved through the laboratory functional characterization of the mini-GC system have been reported, together with in-field analysis results carried out in a station of the Bologna air monitoring network and compared with those provided by a commercial GC system. The development of more advanced prototypes of micro-fabricated devices specifically suited for FAST-GC have been also presented (silicon capillary columns, Ultra-Low-Power (ULP) Metal OXide (MOX) sensor, Thermal Conductivity Detector (TCD)), together with the technological processes for their fabrication. The experimentally demonstrated very high sensitivity of ULP-MOX sensors to VOCs, coupled with the extremely low power consumption, makes the developed ULP-MOX sensor the most performing metal oxide sensor reported up to now in literature, while preliminary test results proved that the developed silicon capillary columns are capable of performances comparable to those of the best fused silica capillary columns. Finally, the development and the validation of a coupled electro-thermal Finite Element Model suited for both steady-state and transient analysis of the micro-devices has been described, and subsequently implemented with a fluidic part to investigate devices behaviour in presence of a gas flowing with certain volumetric flow rates.

Sviluppo di tecniche per la progettazione delle reti di monitoraggio della qualita' dell'aria

L’obiettivo del lavoro consiste nell’implementare una metodologia operativa volta alla progettazione di reti di monitoraggio e di campagne di misura della qualità dell’aria con l’utilizzo del laboratorio mobile, ottimizzando le posizioni dei dispositivi di campionamento rispetto a differenti obiettivi e criteri di scelta. La revisione e l’analisi degli approcci e delle indicazioni fornite dalla normativa di riferimento e dai diversi autori di lavori scientifici ha permesso di proporre un approccio metodologico costituito da due fasi operative principali, che è stato applicato ad un caso studio rappresentato dal territorio della provincia di Ravenna. La metodologia implementata prevede l’integrazione di numerosi strumenti di supporto alla valutazione dello stato di qualità dell’aria e degli effetti che gli inquinanti atmosferici possono generare su specifici recettori sensibili (popolazione residente, vegetazione, beni materiali). In particolare, la metodologia integra approcci di disaggregazione degli inventari delle emissioni attraverso l’utilizzo di variabili proxy, strumenti modellistici per la simulazione della dispersione degli inquinanti in atmosfera ed algoritmi di allocazione degli strumenti di monitoraggio attraverso la massimizzazione (o minimizzazione) di specifiche funzioni obiettivo. La procedura di allocazione sviluppata è stata automatizzata attraverso lo sviluppo di un software che, mediante un’interfaccia grafica di interrogazione, consente di identificare delle aree ottimali per realizzare le diverse campagne di monitoraggio

INTEGRATED ASSESSMENT OF ANTHROPOGENIC, CLIMATE, AND POLICY INDUCED CHANGES OF PHOSPHORUS EXPORT IN THE UNITED STATES LAURENTIAN GREAT LAKES WATERSHEDS

Anthropogenic activities have increased phosphorus (P) loading in tributaries to the Laurentian Great Lakes resulting in eutrophication in small bays to most notably, Lake Erie. Changes to surface water quality from P loading have resulted in billions of dollars in damage and threaten the health of the world’s largest freshwater resource. To understand the factors affecting P delivery with projected increasing urban lands and biofuels expansion, two spatially explicit models were coupled. The coupled models predict that the majority of the basin will experience a significant increase in urban area P sources while the agriculture intensity and forest sources of P will decrease. Changes in P loading across the basin will be highly variable spatially. Additionally, the impacts of climate change on high precipitation events across the Great Lakes were examined. Using historical regression relationships on phosphorus concentrations, key Great Lakes tributaries were found to have future changes including decreasing total loads and increases to high-flow loading events. The urbanized Cuyahoga watersheds exhibits the most vulnerability to these climate-induced changes with increases in total loading and storm loading , while the forested Au Sable watershed exhibits greater resilience. Finally, the monitoring network currently in place for sampling the amount of phosphorus entering the U.S. Great Lakes was examined with a focus on the challenges to monitoring. Based on these interviews, the research identified three issues that policy makers interested in maintaining an effective phosphorus monitoring network in the Great Lakes should consider: first, that the policy objectives driving different monitoring programs vary, which results in different patterns of sampling design and frequency; second, that these differences complicate efforts to encourage collaboration; and third, that methods of funding sampling programs vary from agency to agency, further complicating efforts to generate sufficient long-term data to improve our understanding of phosphorus into the Great Lakes. The dissertation combines these three areas of research to present the potential future impacts of P loading in the Great Lakes as anthropogenic activities, climate and monitoring changes. These manuscripts report new experimental data for future sources, loading and climate impacts on phosphorus.

Table 2 Overview of the established BIOTA Observatories in Africa & Appendix

The international, interdisciplinary biodiversity research project BIOTA AFRICA initiated a standardized biodiversity monitoring network along climatic gradients across the African continent. Due to an identified lack of adequate monitoring designs, BIOTA AFRICA developed and implemented the standardized BIOTA Biodiversity Observatories, that meet the following criteria (a) enable long-term monitoring of biodiversity, potential driving factors, and relevant indicators with adequate spatial and temporal resolution, (b) facilitate comparability of data generated within different ecosystems, (c) allow integration of many disciplines, (d) allow spatial up-scaling, and (e) be applicable within a network approach. A BIOTA Observatory encompasses an area of 1 km2 and is subdivided into 100 1-ha plots. For meeting the needs of sampling of different organism groups, the hectare plot is again subdivided into standardized subplots, whose sizes follow a geometric series. To allow for different sampling intensities but at the same time to characterize the whole square kilometer, the number of hectare plots to be sampled depends on the requirements of the respective discipline. A hierarchical ranking of the hectare plots ensures that all disciplines monitor as many hectare plots jointly as possible. The BIOTA Observatory design assures repeated, multidisciplinary standardized inventories of biodiversity and its environmental drivers, including options for spatial up- and downscaling and different sampling intensities. BIOTA Observatories have been installed along climatic and landscape gradients in Morocco, West Africa, and southern Africa. In regions with varying land use, several BIOTA Observatories are situated close to each other to analyze management effects.

Urban forest health survey on Roznik in Ljubljana in 2013 by two methods

Urban forest health was surveyed on Roznik in Ljubljana (46.05141 N, 14.47797 E) in 2013 by two methods: ICP Forests and UFMO. ICP Forests is most commonly used monitoring programme in Europe - the International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests, which is based on systematic grid. UFMO method - Urban Forests Management Oriented method was developed in the frame of EMoNFUr Project - Establishing a monitoring network to assess lowland forest and urban plantations in Lombardy and urban forest in Slovenia (LIFE10 ENV/IT/000399). UFMO is based on non-linear transects (GPS tracks). ICP forests monitoring plots were established in July 2013 in the urban forest Roznik in Ljubljana .The 32 plots are located on sampling grid 500 × 500 m. The grid was down-scaled from the National Forest Monitoring survey, which bases on national sample grid 4 × 4 km. With the ICP forests method the following parameters for each tree within the 15 plots were gathered according to the ICP forests manual for Visual assessment of crown condition and damaging agents: tree species, percentage of defoliation, affected part of the tree, specification of affected part, location in crown, symptom, symptom specification, causal agents / factors, age of damage, damage extent, and damage extent on the trunk. With the UFMO method, the following parameters for each tree that needed sylviculture measure (felling, pruning, sanitary felling, thinning, etc.) were recorded: tree species, breast diameter, causal agent / damaging factor, GPS waypoint and GPS track. For overall picture in the urban forest health problems, also other biotic and abiotic damaging factors that did not require management action were recorded.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Comparison and Assessment of Two Emission inventories for the Madrid Region

Emission inventories are databases that aim to describe the polluting activities that occur across a certain geographic domain. According to the spatial scale, the availability of information will vary as well as the applied assumptions, which will strongly influence its quality, accuracy and representativeness. This study compared and contrasted two emission inventories describing the Greater Madrid Region (GMR) under an air quality simulation approach. The chosen inventories were the National Emissions Inventory (NEI) and the Regional Emissions Inventory of the Greater Madrid Region (REI). Both of them were used to feed air quality simulations with the CMAQ modelling system, and the results were compared with observations from the air quality monitoring network in the modelled domain. Through the application of statistical tools, the analysis of emissions at cell level and cell – expansion procedures, it was observed that the National Inventory showed better results for describing on – road traffic activities and agriculture, SNAP07 and SNAP10. The accurate description of activities, the good characterization of the vehicle fleet and the correct use of traffic emission factors were the main causes of such a good correlation. On the other hand, the Regional Inventory showed better descriptions for non – industrial combustion (SNAP02) and industrial activities (SNAP03). It incorporated realistic emission factors, a reasonable fuel mix and it drew upon local information sources to describe these activities, while NEI relied on surrogation and national datasets which leaded to a poorer representation. Off – road transportation (SNAP08) was similarly described by both inventories, while the rest of the SNAP activities showed a marginal contribution to the overall emissions.

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Investigação de aquífero fraturado para entendimento de fluxo e transporte de contaminantes clorados: estudo de caso em Valinhos, SP

Casos de contaminação de aquíferos fraturados são bastante complexos, tendo em vista a heterogeneidade das redes de fraturas, e no geral, sua investigação demanda a utilização de técnicas pouco usuais, como por exemplo o imageamento acústico e a perfilagem de velocidade de fluxo de água. Na área de estudo, localizada em Valinhos/SP, o uso inadequado de solventes organoclorados no passado ocasionou a contaminação do aquífero raso em duas áreas, e o aparecimento de concentrações no aquifero profundo levaram a condução do atual trabalho, que teve como principal objetivo a elaboração de um modelo conceitual de fluxo de água e transporte de contaminantes no aquífero cristalino. Previamente à investigação do aquífero fraturado, foi realizada uma análise de trabalhos existentes, incluindo a interpretação de lineamentos, levantamentos geológicos além de perfilagens geofísicas de superfície. Em cada área investigada, foi realizada a perfuração de um poço profundo e aplicadas as técnicas de perfilagens de raios gama, cáliper, flowmeter, imageamento acústico, além da filmagem do poço e realização de ensaios hidráulicos nos dois pontos perfurados. Para caracterização química do aquífero fraturado, foram realizadas coletas de água subterrânea em intervalos selecionados com a utilização de obturadores pneumáticos. As cargas hidráulicas medidas durante a amostragem também auxiliaram no entendimento da direção do fluxo de água. O aquífero cristalino é formado por rochas gnáissicas e se encontra bastante fraturado e intemperizado, principalmente na porção superficial da rocha (até aproximadamente 65,0 m) onde as maiores velocidades de fluxo de água também foram observadas. A rocha sã possui uma menor densidade de fraturas e predominância de minerais mais claros. As fraturas de baixo a médio angulo de mergulho (Grupo 1) são as mais frequentes em ambas as perfurações e possuem direção principal N-S a NE-SW. São observadas, no geral, exercendo grande influência sobre o fluxo de água, principalmente na porção alterada do gnaisse. Fraturas com ângulo elevado de mergulho, classificadas como Grupo 2 (paralelas à foliação) e Grupo 3 (direção NW à W), são também observadas ao longo de toda a perfuração estabelecendo a conexão hidráulica entre as fraturas do Grupo 1. Em menor proporção, são ainda verificadas fraturas com ângulos de mergulho >40 ° pertencente aos Grupos 4 (NE-SW), 5 (E-W), 6 (NW-SE) e 7 (E-W). O fluxo de água subterrânea se mostrou descendente na porção superior da rocha alterada e ascendente na porção mais profunda, possivelmente direcionando a água subterrânea para a região de transição da rocha mais alterada para a rocha sã (entre 61 a 65 m de profundidade). Apesar do fluxo ascendente em profundidade, o bombeamento de poços tubulares existentes no entorno ao longo dos anos, favoreceu a migração dos contaminantes para porções mais profundas. Os contaminantes observados no poço tubular P6 possuem maior semelhança com os contaminantes observados na Área 2, e ambos estão localizados entre lineamentos NW-SE, indicando uma possível influência dos lineamentos no controle sobre o fluxo de água. No entanto, para entendimento do transporte dos contaminantes em área, é necessário um adensamento da rede de monitoramento, levando em consideração a heterogeneidade do meio e as incertezas relacionadas à extrapolação dos dados para áreas não investigadas.