A manutenção da qualidade ambiental está diretamente relacionada ao conhecimento e controle das variáveis que interferem no problema, sejam elas resultantes das ações do homem sobre o ambiente ou de suas transformações naturais. Este conhecimento sobre o meio ambiente pode ser obtido através de monitoramento e, no caso dos corpos d’água, a eficácia do monitoramento depende em especial de um projeto e operação adequados do sistema de informações, que é constituído pela aquisição, manipulação, análise e utilização da informação obtida. No que se refere à aquisição da informação, ainda existem grandes lacunas a serem preenchidas na decisão sobre onde, quando e o que monitorar. Neste trabalho são enfocadas as atividades básicas do projeto da rede de monitoramento, ou seja, a localização das estações, a seleção das variáveis, a freqüência e a duração da amostragem, procurando-se buscar um conjunto de diretrizes de aplicação que seja exeqüível e eficaz para o projeto e manutenção de redes de monitoramento, uma vez que não se dispõe atualmente na literatura, de procedimento geral que vise solucionar este problema. A localização das estações de monitoramento na bacia hidrográfica, denominada de macrolocalização, a determinação de freqüências amostrais e a definição das variáveis a serem monitoradas, são tarefas que fazem parte do projeto da rede de monitoramento da qualidade da água. A rede de monitoramento, por sua vez, é parte do sistema de informações sobre qualidade da água, cujo objetivo é a descrição dos fenômenos físicos, químicos e biológicos relacionados à qualidade do corpo d’água. A eficiência da metodologia discutida neste trabalho é demonstrada e discutida através de estudos de casos. Dentre as contribuições deste trabalho podem-se destacar: (i) a ampliação do trabalho de POMEROY e ORLOB, para a localização de estações de monitoramento baseando-se na área e características de drenagem da bacia hidrográfica; (ii) a adaptação do procedimento de otimização no processo de macrolocalização de SHARP; (iii) a dedução da equação da entropia para análise de múltiplas variáveis; (iv) o desenvolvimento de um aplicativo computacional para a análise e avaliação de desempenho de estações de monitoramento baseado no conceito de entropia; e (v) a proposição de uma metodologia geral de dimensionamento e redimensionamento de redes de monitoramento de qualidade da água . Neste trabalho são descritas, discutidas e aplicadas as metodologias disponíveis para a macrolocalização e as recentemente propostas para o redimensionamento de redes de monitoramento, baseadas no conceito de entropia, objetivando a produção de diretrizes de projeto de redes de monitoramento, especialmente para países em desenvolvimento. Assim sendo, neste trabalho não se faz somente uma análise de métodos disponíveis para o projeto de redes de monitoramento mas também é proposta uma metodologia de dimensionamento e redimensionamento de redes de monitoramento para bacias com poucos dados e para bacias com dados de qualidade da água. Desta forma espera-se contribuir com países em processo de início e organização de seus sistemas de informação ambiental, e também para aqueles com suas redes já em operação, facilitando-se a decisão quanto à destinação de recursos para a adequação da realidade financeira aos objetivos de operação e manutenção destes sistemas. / The maintenance of the environmental quality is directly related to the knowledge and control of the variables affected by the problem, either as a result of the human action on the environment, or due to natural transformations. Knowledge on the environment can be obtained through monitoring. The effectiveness of the monitoring especially depends on the project of the information system and its operation. Data acquisition, manipulation, analysis, as well as the use of the generated information, are all activities that must be prepared by the project. As far as the acquisition of information is concerned, there are research gaps about the decision on where, when and what to monitor. This study will focus on the basic activities of the monitoring network project, that is, the location of the stations, the variable selection and the sampling frequency. The objective is to find out a group of application guidelines that has a practical use and is effective both to the project and maintenance of monitoring networks, since a general procedure to solve this problem is not available in the literature. Water quality monitoring networks are part of the water resources information system, with the objective of describing the physical, chemical and biological phenomena related to the quality of the water body. The efficiency of the methodology discussed in this study is demonstrated and discussed through case studies. The most important contributions of this study are: (i) the expansion of the POMEROY and ORLOB studies to locate monitoring stations based on the watershed area and channel network; (ii) the extended use of the optimization procedure proposed by SHARP to improve the macrolocation process; (iii) the deduction of the entropy equation to analyze the multiple variables; (iv) the software to analyze and to propose a new design for monitoring networks, based on the entropy concept; and (v) the proposal of a general methodology to the water quality monitoring network design and redesign. The processes and methodologies proposed in this study were developed to be applied in developing countries. Usually such countries are in the process of initialization and organization of their environmental information systems, and we hope that this study will be able to bring satisfactory benefits, taking into account that optimization is necessary to bring the costs down and to make the monitoring effort sustainable.

Thesis (M. Sc.)--University of Hong Kong, 1999. / Includes bibliographical references.

published_or_final_version / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

The CMS Tracker is an all silicon detector and it is the biggest of its kind to be built. The system consists of over 15,000 individual detector modules giving rise to readout through almost 107 channels. The data generated by the Tracker system is close to 650 MB at 40 MHz. This has created a challenge for the CMS collaborators in terms of data storage for analysis. To store only the interesting physics data the readout rate has to be reduced to 100 Hz where the data has to be ltered through a monitoring system for quality checks. The Tracker being the closest part of the detector to the interaction point of the CMS creates yet another challenge that needs the data quality monitoring system. As it operates in a very hostile environment the silicon detectors used to detect the particles will be degraded. It is very important to monitor the changes in the sensor behaviour with time so that to calibrate the sensors to compensate for the erroneous readings. This thesis discusses the development of a monitoring system that will enable the checking of data generated by the tracker to address the issues discussed above. The system has two parts, one dealing with the data used to monitor the Tracker and a second one that deals with statistical methods used to check the quality of the data.

Denton, Texas consistently reaches ozone nonattainment levels. This has led to a large focus of air pollution monitoring efforts in the region, with long-range transport being explored as a key contributor. For this study, the University of North Texas Discovery Park campus was chosen as a prospective location for an extensive air quality monitoring station. Sixteen years of ozone and meteorological data for five state-run monitoring sites within a 25 mile radius, including the nearest Denton Airport site, was gathered from TCEQ online database for the month of April for the years 2000 to 2015. The data was analyzed to show a historical, regional perspective of ozone near the proposed site. The maximum ozone concentration measured at the Denton Airport location over the 16 year period was measured at 96 ppb in 2001. Experimental ozone and meteorological measurements were collected at the Discovery Park location from March 26 to April 3 and April 8 to April, 2016 and compared to the Denton Airport monitoring site. A time lag in ozone trends and an increase in peak ozone concentrations at the proposed location were observed at the proposed site in comparison to the Denton Airport site. Historical and experimental meteorological data agreed in indicating that southern winds that rarely exceed 20 miles per hour are the predominant wind pattern. Back trajectories, wind roses, pollution roses, and bivariate plots created for peak ozone days during experimental periods support long range transport as a considerable cause of high ozone levels in Denton. Furthermore, a study of the precursor characteristics at the Denton Airport site indicated the site was being affected by a local source of nitrogen dioxide that was not affecting the proposed location. The differences in the Denton Airport site and the proposed site indicate that further monitoring at Discovery Park would be insightful. An outline of an expansive mobile monitoring station and suggestions for effective utilization are provided to guide future studies in Denton and the surrounding North Texas region.

Student Number : 0107471X - MSc research report - School of Civil and Environmental Engineering - Faculty of Engineering and the Built Environment / Water plays a global role in an enormous variety of ways. More importantely it also plays a fundamental role on a regional and local scales where it has a profound effect on the environment and socio economic development. It is a prerequisite for many of the mans activities and as such it must be managed and protected accordingly. In this water quality plays a fundamental role and water quality monitoring is a foundation stone of any serious efforts to manage water resources on any scale. The research conducted focused on water quality monitoring in the SADC region. It looked at the current practice and the gaps present with respect to the real needs and international best practice. SADC region is characterized by trans-boundary water systems where pollution of water resources is often not understood properly nor has been seriously studied. A regional Water Quality Monitoring Program is advocated to address these issues and is seen as one of the prerequisites for effective water resources management in the SADC region. Existing Water Quality Monitoring in individual countries of the SADC region has been studied on the basis of existing (limited) information and gaps with respect to international best practice have been identified. Recommendations have been made regarding the establishment of the SADC Water Quality Monitoring program. A set of principles on which this should be based have been formulated. It has been concluded that SADC countries do not have an appropriate water quality monitoring in place and that what does exist is not in line with the best practice recommendations. It is suggested that without an appropriate regional water quality monitoring program water quality could become one of the limiting factors of the future economic development in the region.

Neste trabalho é proposto e avaliado um sistema capaz de monitorar de forma não-destrutiva, não-invasiva, individualizada, em tempo real e em ambiente industrial, a qualidade de soldas produzidas através do processo de solda a ponto resistiva (PSPR), diminuindo ou mesmo eliminando a necessidade dos testes destrutivos, reduzindo custos e aumentando a produtividade. Este sistema de monitoramento é baseado em reconhecimento de padrões, através de redes neurais artificiais do tipo Perceptron multicamadas. As características do processo usadas na entrada da rede neural são os parâmetros ajustados de um modelo matemático parametrizável, criado com o intuito de refletir as propriedades fundamentais da grandeza do processo passível de ser medida e monitorada em tempo real, neste caso a curva de resistência dinâmica. Estes valores ajustados dos parâmetros do modelo são ainda relacionados com os estados ou condições do processo, de forma a permitir a identificação de possíveis causas para falhas detectadas. Para avaliar e validar este sistema, usaram-se dados reais obtidos na produção de lotes de contatos elétricos através do PSPR. Os resultados obtidos mostram que o sistema proposto é capaz de monitorar satisfatoriamente a qualidade do processo investigado, com erro médio quadrático de 16,5 N, na estimação da força de cisalhamento suportada pela solda, no pior caso. O sistema também mostrou-se capaz de identificar a causa para soldas cuja qualidade estimada foi considerada baixa, com taxa de acerto acima de 97%. Esse sistema proposto não contém especificidades de nenhum processo produtivo e, portanto, tem potencial para ser aplicado em outros processos, além do PSPR. / In this work a non-destructive, non-invasive, individualized, real-time system has been proposed and evaluated to monitor the quality of welds produced by resistance spot welding process (RSWP) in industrial environment. This system is able to reduce or eliminate the need for destructive tests, leading to cost reduction and increase in productivity. This monitoring system is based on pattern recognition with multilayer Perceptron artificial neural networks (ANN). The process features used as input of the ANN are adjusted parameters of a parametric mathematic model created to reflect the fundamental properties of the process variable that is measurable in real time, in this work, the dynamic resistance curve. The adjustable model parameters values are related with the process states and conditions, so that it is possible to identify the causes for detected bad quality. In order to evaluate and validate the proposed system, real data obtained in the production of electric contacts by RSWP were used. The results show that the proposed system is capable of properly monitoring the investigated process quality, with a mean square error of 16.5 N, in the estimation of the shear force supported by the weld, in the worst case. The system proved to be able to identify the causes for detected bad quality, with a reliability of more than 97%. The proposed system contains no productive process specificities, and, therefore, can be applied to other processes.

Appropriately constructed pollutant export models can help set management priorities for catchments, identify critical pollutant source areas, and are important tools for developing and evaluating economically viable ways of minimising surface water pollution.¶ This thesis presents a comparison, an evaluation and an integration of models for predicting the export of environmental pollutants, in particular sediment, through river systems. A review of the capabilities and limitations of current water quality modelling approaches is made. Several water quality and quantity modelling approaches are applied and evaluated in the catchment of the upper Murrumbidgee River.¶ The IHACRES rainfall-runoff model and a simple hydrologic routing model are applied with the aim of developing a capacity to predict streamflow at various catchment scales and to enable integration with other pollutant load estimation techniques. Methods for calculating pollutant loads from observed pollutant concentration and modelled streamflow data are also investigated. Sediment export is estimated using these methods over a 10-year period for two case study subcatchments. Approaches for water quality sampling are discussed and a novel monitoring program using rising stage siphon samplers is presented. Results from a refinement of the Sediment River Network model in the upper Murrumbidgee catchment (SedNet-UM) are presented. The model provides a capacity to quantify sediment source, transport and to simulate the effects of management change in the catchment. The investigation of the model includes rigorous examination of the behaviour of the model through sensitivity assessment and comparison with other sediment modelling studies. The major conclusion reached through sensitivity assessment was that the outputs of the model are most sensitive to perturbation of the hydrologic parameters of the model.¶ The SedNet-UM application demonstrates that it is possible to construct stream pollutant models that assist in prioritising management across catchment scales. It can be concluded that SedNet and similar variants have much potential to address common resource management issues requiring the identification of the source, propagation and fate of environmental pollutants. In addition, incorporating the strengths of a conceptual rainfall-runoff model and the semi-distributed SedNet model has been identified as very useful for the future prediction of environmental pollutant export.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. A mathematical model based on the Multiple Cell Approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. A mathematical model based on the Multiple Cell Approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.