Tesis sobre el tema "Air pollution modelling"

The present study deals with air pollution modelling over complex topography, both from the phenomenological and numerical point of view. The theme of air pollution modelling has been faced at first from a phenomenological point of view. Then a numerical approach for the resolution of the diffusion-advection equation has been followed. Two different methods have been explored: puff-models and lagrangian particle models. The eulero-lagrangian puff-model CALPUFF (released by Earth Tech) has been used as a reference: closures and parametrizations adopted by this software have been tested over complex terrain and some minor changes have been introduced into the original code. A further step was the development of a lagrangian particle-tracking program, suitable for not homogenous not stationary flows, and also adapted to complex terrain cases, accounting for vertical skewed turrbulence in any atmospheric stability class. Langevin equation were solved following Thomson's (1987) approach. Special attention was put on near field dispersion processes. In fact, lagrangian models turn out to be the most advanced numerical schemes for pollutant transport simulations but at now only suitable for short term simulations, at least in complex errain where high spatial resolution is needed. An extension for the lagrangian model has been then developed, using the so called "kernel method"; this feature improves considerably the calculation performance, dramatically reducing computation time, so that simulations also become praticable for longer temporal scales; nevertheless it seems the kernel method seems to lead to unreliable results for narrow valleys or very steep slopes, so results cannot be generalized. Moreover, the problem of the determination of vertical profiles of turbulent diffusivity on complex orography has been faced. Both a local approach and a global one (suitable for compact valleys) for the estimate of eddy diffusivity in valley have been investigated. The first one has been adopted in the lagrangian problem previously developed. Since atmospheric turbulence is mostly generated by solar thermal flux, a procedure for the calculation of the effective solar radiation was developed. The method, which can be introduced into meteorological models which use complex orography as input, takes into account for shadowed areas, soil coverage and the possible precense of clouds which filter and reduce the incoming solar radiation. Tests have been carried out using a modified version of model CALMET (EarthTech Inc.). Results are in agreement with turbulence data acquired by means of a sonic anemometer during a field campain performed by the Department. Finally, the analysis of near field dispersion over complex terrain has been extended to the urban context, adopting, basically, the same conceptual tools on a smaller scale. A finite volume three-dimensional numerical model has been developed and tested in simulating dispersion of traffic derived pollutants in the town of Trento. For ground level sources geometry of the domain and emission condition turn out to be very important with respect to meteorological conditions (especially atmospheric stability). The roughness, i.e. the buildings of the study area has been therefore explicitely considered, using a high resolution deigital elevation map of the urban area. This approach has turned out to be necessary for near field dispersion, when the emission source is located inside the roughness and the impact area entirely fall inside the near field. Here a comparison has been made between the predicted numerical solution and data measured by air quality stations which are present in the urban area, showing a good agreement. A further refinement of the study has lead to the development of a two-dimensional x-z lagrangian model at the "street scale", for the study of canyon effects which tends to trap pollutant inside an urban canyon with behaviours which typically depends on geometric features, atmospheric turbulence and wind speed.

A street canyon is a typical urban configuration with surrounding buildings along the street, where emissions from vehicles are normally released. Buildings are the artificial obstacles to the urban atmospheric flow and give rise to limited ventilation, especially for deep street canyons. This study implements a large-eddy simulation (LES) coupled with a reduced chemical scheme (the LES-chemistry model) to investigate the processing, dispersion and transport of reactive pollutants in a deep street canyon. Spatial variation of reactive pollutants are significant due to the existence of unsteady multiple vortices and pollutant concentrations exhibit significant contrasts within each vortex. In practical applications of using one-box model, the hypothesis of a well-mixed deep street canyon is shown to be inappropriate. A simplified two-box model (vertically segregated) is developed and evaluated against the LES-chemistry model to represent key photochemical processes with timescales similar to and smaller than the turbulent mixing timescale. The two-box model provides the capability of efficiently running a series of emission scenarios under a set of meteorological conditions. In addition, a box model with grid-averaged emissions of street canyons is compared with a two-box model considering each street canyon independently (horizontally segregated) to evaluate uncertainties when grid-averaged emissions are adopted in a grid-based urban air quality model. This study could potentially support traffic management, urban planning strategies and personal exposure assessment.

This thesis is motivated by the particular modelling requirements of data collected by a General Practitioner who wished to study the relationship between incidences of asthma and air pollution in Glyn Neath, a small mining village in South Wales. We consider the need to model the function of an individual's peak expiratory flow in such a way that the possible influence of airborne pollutants is testable, using only the binary time series of attacks available for each patient. Korn and Whittemore (1979) presented a threshold model which considered an individual's resistance to an 'onslaught' of pollution. A subtle adaptation of the principles of their research has allowed this methodology to be adapted to the requirements of this thesis. We present a model which is motivated by medically-based criteria and is capable of generating events corresponding to acute episodes of asthma. Statistical analysis of the model introduces correlated random variables with survival probabilities requiring the integration of the appropriate multi-dimensional Normal probability density function. We develop a novel approach for approximating the correlation structure which allows this integration to be reduced to a single dimension. For parameter estimation we consider the method of maximum likelihood and examine the properties of the maximum likelihood estimates. Initial exploration of the estimates indicate that they are substantially biased and hence further refinement of the approximated correlation structure is necessary. The research has achieved its original aim of developing medically based statistical methods.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems
Half of the world’s population already lives in cities, and by 2050 two-thirds of the world’s population are expected to further move into urban areas. This urban growth leads to various environmental, social and economic challenges in cities, hampering the Quality of Life (QoL). Although recent trends in technologies equip us with various tools and techniques that can help in improving quality of life, air pollution remains the ‘biggest environmental health risk’ for decades, impacting individuals’ quality of life and well-being according to World Health Organisation (WHO). Many efforts have been made to measure air quality, but the sparse arrangement of monitoring stations and the lack of data currently make it challenging to develop systems that can capture within-city air pollution variations. To solve this, flexible methods that allow air quality monitoring using easily accessible data sources at the city level are desirable. The present thesis seeks to widen the current knowledge concerning detailed air quality monitoring by developing approaches that can help in tackling existing gaps in the literature. The thesis presents five contributions which address the issues mentioned above. The first contribution is the choice of a statistical method which can help in utilising existing open data and overcoming challenges imposed by the bigness of data for detailed air pollution monitoring. The second contribution concerns the development of optimisation method which helps in identifying optimal locations for robust air pollution modelling in cities. The third contribution of the thesis is also an optimisation method which helps in initiating systematic volunteered geographic information (VGI) campaigns for detailed air pollution monitoring by addressing sparsity and scarcity challenges of air pollution data in cities. The fourth contribution is a study proposing the involvement of housing companies as a stakeholder in the participatory framework for air pollution data collection, which helps in overcoming certain gaps existing in VGI-based approaches. Finally, the fifth contribution is an open-hardware system that aids in collecting vehicular traffic data using WiFi signal strength. The developed hardware can help in overcoming traffic data scarcity in cities, which limits detailed air pollution monitoring. All the contributions are illustrated through case studies in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under the broader framework of the open smart city and for urban health research.

Previous work on biological monitors for atmospheric lead has been reviewed. Experiments with hair-net and flat nylon-mesh envelope monitors failed to confirm a claim of reproducibility by previous workers, a percentage relative standard deviation of 24.5 for hair-net monitors and 13.4 to 56.7 for flat nylon-mesh envelopes was achieved. An homogenised acid-washed moss monitor in a diffusion tube produced a significant increase in lead deposition. Replicate diffusion tube monitors initially showed poor reproducibility. Homogenisation of the moss to < 1mm and maintained saturation produced a range of % RSD's of 8.6 to 12.8. Samples unwashed with acid showed more depostion than washed samples. Replicate monitors in which moss was replaced with various physical media displayed poor reproducibility. It seems unlikely that such passive monitors can replace established air filtration methods. At the commencement of this study in 1981, two types of dust gauge were in use in the North East Derbyshire area, they were the B.S. Standard Deposit Gauge and the B.S. Direction Dust Gauge. Sampling errors of the British Standard Deposit Gauge had been established by previous workers as being considerable. Proposed models to apply a wind direction correction to amounts of dust collected by Directional Gauges have inherent weaknesses. A new model was developed and its performance improved with successive refinements of input data. Wind data from a localised weather station was applied to the model and showed to improve the performance compared to using remote weather station data. Daily smoke and sulphur dioxide levels at 6 sites covering a 3 year period were used to develop a model which employs data collected at one monitoring station to predict levels in other parts of the area. Precipitation acidity has been measured within the area since 1950. Previous work on precipitation acidity data has been reviewed and an increase in precipitation acidity for the area was indicated. A rationalised air pollution monitoring network is proposed to fulfil the air quality management function for the N.E.D.D.C.

Exposures to air pollution have adverse effects on health. Traditionally, epidemiological studies used monitoring data to investigate the relationship between air pollution and health. In recent decades, modelling tools have been developed to predict pollutant concentrations for population exposure assessments. Whilst gradual improvements have been made to these techniques, such as dispersion and land use regression (LUR), results have exhibited spatial inconsistencies at times. The processes involved are often time- and data- consuming, and outputs generally do not account for short-term variations in pollution. Improving model prediction capabilities can avoid exposure misclassifications, and provide better estimates for health risk assessment. The aim of this project is to increase the accuracy and efficiency of current exposure modelling techniques to capture spatial and temporal variability of urban air pollution. As part of this study, air pollution models were developed in a GIS framework for London for PM10, NOX and NO2, using dispersion, LUR, hybrid and Bayesian statistical methods. Predictors derived from traffic, land use, population datasets were incorporated in a geographical information system for modelling. For the first time, newly available city-wide datasets were used to extract enhanced geographical variables, including building height/ area, street canyon and detailed urban green space, which may have significant influence on pollution in local dispersion environment. Developed models were cross-validated and compared to concentrations obtained from routine monitoring network. LUR models were found to have higher prediction capabilities over other techniques, providing accurate explanations of spatial variability in urban air pollution. Significant improvements in model performance were seen with addition of buildings and street configuration variables, particularly for traffic-related pollutants. LUR require less computational demands than conventional dispersion methods; therefore can be easily applied over large urban areas. Introducing Bayesian statistical techniques has enabled spatio-temporal predictions which accounted uncertainties, allowing detection of pollution trends and episodes.

Includes bibliography.
Limited research on dispersion modelling for the Cape Town metropolitan area has been undertaken. This thesis deals with air-pollution aspects in relation to dispersion modelling, as well as with the input requirements and application of a dispersion model in the Greater Cape Town region. An EPA approved Gaussian plume model, the Industrial Source Complex Short Term 2 (ISCST2), was chosen for the pollution simulation. The model requires one point meteorological measurements and can accommodate multiple point, line and area sources. Meteorological data used in the study were collected from D. F. Malan airport for the years 1991 and 1992. However, required parameters, such as the mixing height and the atmospheric stability class, are not readily available and thus needed to be calculated. Three methods for determining the mixing heights and three methods for determining atmospheric stability class were used in the model and the accuracy for each combination was assessed. Appropriate emission information for use with dispersion modelling is not available for the Greater Cape Town area. Therefore, the compilation of an emission inventory formed a considerable part of this study. Emission data from the large industries was collected with the collaboration of the Cape Town City Council's Air Pollution Control and of the Air Pollution Group of the Western Cape Regional Services Council. The rest of the sources (i.e. residential, vehicular and industrial), were grouped into areas, and their emissions were based on their fuel consumption.

There is increased concern regarding the effect of traffic related pollution on public heath. As the number of vehicles on the roads continues to rise, it is becoming increasingly more important to identify areas where the population may be at a greater risk to raised levels of pollution and areas where the implementation of policy to control and monitor levels of pollution would be beneficial. Traditionally, levels of air pollution have been established through dispersion modelling or monitoring. However, for modelling traffic related pollution for large populations, these methods have proved inappropriate. Three new approaches have been developed to model traffic related air pollution and are reported in this thesis. The approaches have been developed in a Geographical Information System (GIS) and involve generating detailed maps of the pollution surface from monitored data and information about the pollution sources. The new methods are compared against the geostatistical technique kriging. The first approach combines spatial interpolation from monitoring sites and dispersion modelling, linking the dispersion model to the GIS, the second combines GIS techniques for filtering data and spatial interpolation, and the third uses a combination of GIS techniques for filtering and statistical techniques. The three approaches are tested and validated by predicting levels of pollution at monitoring sites not used to develop the models. It was found that the new approaches provided more reliable estimates of pollution at unsampled locations than kriging, with the last of these proving to be the most effective. The adjusted r2 values for kriging, interpolation and dispersion, interpolation and filtering, and filtering and statistics were found to be 0.44, 0.63, 0.67 and 0.82 respectively. The approaches therefore have clear potential in the areas of air pollution management and epidemiology, where the maps can be used to help identify locations where levels of pollution exceed air quality standards, assess the relationship between air pollution and health outcome and examine the risk of exposure to raised levels of pollution.

Motivated by the potential danger of high air pollution concentrations (episodes) on human health and the environment, the overall aim of this thesis is to gain a greater understanding of and insight into the formation of such episodic conditions via proposing new extreme value statistical models. The modelling and prediction of air pollution episodes' occurrence, strength and dur~tion are formidable problems in the urban atmospheric media due to the combination of many complex simultaneously working physical and chemical processes involved in their formations. It has been long observed that conventional statistical methods may not be suitable for solving these problems, thus initiating the application of more flexible approaches. In the last couple of decades Extreme Value Theory (EVT) has been widely used with great success to overcome some of the aforementioned issues. However, even the most recent EVT models cannot deal with all the aspects of these problems. The objective of this research is to specify the requirements of new extreme value models by taking into account the demerits of the old ones, to develop such new models and validate their adequacy on real datasets. To place this research in relation to the wide-ranging existing literature and to identify the model requirements, a comprehensive review on EVT and its applications in air pollution modelling has been conducted. Based on the gaps identified in the literature, four extreme value models are proposed in the Peaks over Threshold context, which are either improvements on existing models or completely new ones involving new theoretical results in the background. Based on these models, and their possible amalgamations, the occurrence times, the strengths and the durations of episodes can be modelled and predicted. The relationship between these characteristics and meteorological as well as traffic conditions are identified, which are considered as the most significant contributors to these events.

Air pollution is not only a major risk to the environment, but also a major environmental risk to the health of the population in developed and developing countries. The health impact of both short-term and long-term exposure to air pollution has been the focus of much research in the past few decades, which has investigated the relationship between specific air pollutants, such as carbon monoxide (CO), nitrogen dioxide (NO_2), particulate matter (PM_2.5 and PM_10), and sulphur dioxide (SO_2), to cardiovascular and respiratory diseases. The health impact of short-term exposure is conducted through time series studies, whereas long-term exposure is investigated through cohort studies. Cohort studies are considered the gold-standard research design since inference is made at the individual level and can directly assess cause and effect. However, cohort studies are costly and require a long follow-up period meaning they take a long time to conduct. To counteract these limitations, spatial ecological studies are used instead, which make use of routinely available disease data and air pollutant concentrations at a small areal level, such as census tracts or postcodes. This is to ensure the population under study is relatively homogeneous within the areal unit in terms of socio-demographic characteristics, and thus complements inference from a cohort study. These studies quantify the health impact of exposure to air pollution by relating geographical contrasts between air pollutant concentrations and disease risk across the chosen spatial resolution. The disease data are counts of the numbers of disease cases occurring in each areal unit, and Poisson log-linear models are used to assess the pollutant-health relationship. Other covariate information, such as socio-economic deprivation, is also included to help explain the spatial pattern in disease risk. However, the residual disease risk after the covariate effects have been accounted for tends to contain spatial autocorrelation, which has to be modelled in order to make sound inferences. Residual spatial autocorrelation is typically modelled by a set of random effects that utilise a neighbourhood matrix in order to induce spatial autocorrelation into the model. There are a number of specifications to model this, but this thesis makes use of the Leroux specification due to its flexibility in being able to model both strong and weak spatial autocorrelation. An important issue with using a spatial ecological study design is the estimation of spatially representative pollutant concentrations that are available in each areal unit. Studies can typically use measured data from fixed-location monitors that are spatially sparse and do not provide a pollutant concentration for each areal unit; or they make use of modelled concentrations available at a fine grid square resolution, which are known to contain biases and no measure of uncertainty. There have been numerous statistical approaches to combine both sets of information in order to estimate accurate and spatially representative concentrations. This thesis will develop previous methodology that utilises extra data sources in order to improve the prediction performance of the model for use in a Scottish context. The overarching aim of this thesis is to investigate the cardio-respiratory health effects of long-term exposure to air pollution in West Central Scotland, UK. As the majority of air pollution in this region results from vehicle emissions, nitrogen dioxide (NO_2), a traffic-related gaseous pollutant, will be used to measure air pollution. Models investigating its health effect will incorporate predicted measures of NO_2 developed in this thesis. The sensitivity of the pollutant-health effect to the choice of NO_2 concentrations, indicator of deprivation, and choice of spatial model will be investigated. Changing these factors has been shown to modify estimated pollutant-health effects.\\ Findings in this thesis demonstrated that improvements in the accuracy of fine scale spatial prediction of NO_2 concentrations can be made by utilising extra sources of data in addition to the commonly-used monitoring stations. In addition, the estimated pollutant-health effect is not robust to the choice of the aforementioned factors and the choice of these factors can have a major impact on the resulting pollutant-health effects. This justified the combination of all statistical models into a single effect size, which estimated a small, but positive effect of NO_2 concentrations on cardio-respiratory ill health. However, the estimated NO_2-health relationship was not substantial, possibly due to the NO_2 concentrations in West Central Scotland being too low. Greater variation in the exposure would be needed to observe substantial health impacts.

This thesis develops, tests and applies methods for space-time modelling of exposure to air pollution using GIS. This involves linkage of five main sub-models: a traffic model, a model of urban air pollution - combining local and ‘background’ pollution models - a network analysis tool for modelling exposure during journeys, and a time-activity model. The model can provide exposure estimates for individuals or population groups. The study took place entirely within Northampton, UK. The model used to estimate hourly PM10 concentrations at outdoor locations gave a moderate fit to monitored data. Results were shown to be comparable with the best results from other studies. This research also found a strong, linear relationship between concentrations of PM10 during simultaneous monitoring of walking and in-car concentrations. This relationship was used to calibrate modelled outdoor pollution levels to give in-car concentrations. Modelled journey- time exposures for walking performed equally with predictions made using a fixed- site monitor located close to journey routes. The model did not perform as well as the fixed-site monitor in predicting in-car exposures. The application of the model to a walk-to-school policy, in which modelled local traffic levels were reduced by 20%, demonstrated that the benefits of the reduction were not spread evenly across a sample of schoolchildren, but varied depending on the route used to school and the location of homes and schools. For those switching between car and walk there may be positive or negative effects of the policy in terms of savings in average hourly exposures, depending on their specific journey and time activity patterns. The results from this research showed that, although the model worked reasonably well in estimating exposures, a number of improvements are needed. These include better models of background concentrations, more detailed models of in-car conditions, and extending exposure modelling to include dose-response estimates

Air pollution is an important determinant of health. There is convincing, and growing, evidence linking the risk of disease, and premature death, with exposure to various pollutants including fine particulate matter and ozone. Knowledge about the health and environmental risks and their trends is important stimulus for developing environmental and public health policy. In order to perform studies into the risks of environmental hazards on human health study there is a requirement for accurate estimates of exposures that might be experienced by the populations at risk. In this thesis we develop spatio-temporal models within a Bayesian framework to obtain accurate estimates of such exposures. These models are set within a hierarchical framework in a Bayesian setting with different levels describing dependencies over space and time. Considering the complexity of hierarchical models and the large amounts of data that can arise from environmental networks mean that inference using Markov Chain Monte Carlo (MCMC) may be computational challenging in this setting. We use both MCMC and Integrated Nested Laplace Approximations (INLA) to implement spatio-temporal exposure models when dealing with high–dimensional data. We also propose an approach for utilising the results from exposure models in health models which allows them to enhance studies of the health effects of air pollution. Moreover, we investigate the possible effects of preferential sampling, where monitoring sites in environmental networks are preferentially located by the designers in order to assess whether guideline and policies are being adhered to. This means the data arising from such networks may not accurately characterise the spatial-temporal field they intend to monitor and as such will not provide accurate estimates of the exposures that are potentially experienced by populations. This has the potential to introduce bias into estimates of risk associated with exposure to air pollution and subsequent health impact analyses. Throughout the thesis, the methods developed are assessed using simulation studies and applied to real–life case studies assessing the effects of particulate matter on health in Greater London and throughout the UK.

"The aims of this research were to investigate the association between air pollution and respiratory health effects using a spatial approach, and to derive a composite indicator of ambient air pollution."
Master of Information Technology by Research

"The aims of this research were to investigate the association between air pollution and respiratory health effects using a spatial approach, and to derive a composite indicator of ambient air pollution."
Master of Information Technology by Research

Atmospheric dispersion models are being increasingly used by local authorities in the United Kingdom as part of their urban air quality management programmes. Output from dispersion models now forms a vital part of any environmental impact assessment, road improvement or traffic management scheme or environmental health study. This study is centred primarily on Northampton, a county town in Southern England and is concerned with the limitations of meteorological data available to local authorities. The first part of the study investigates the variation in certain key meteorological parameters both within Northampton and between synoptic stations up to 70 kilometres away. The second part examines modelling outcomes using different sets of meteorological data and evaluates the performance of an urban dispersion model in relation to monitored air quality data. Special emphasis is placed on the use of cloud cover as a meteorological input variable. A small case study of monitoring and modelling work carried out in the London Borough of Richmond is also presented

Atmospheric pollution causes large impacts on human health and societal economic interests and it is a threat for the ecosystems and the climate of the Earth. Improving the understanding of pollution dynamics is necessary to desing efficient air quality strategies that reduce the impacts of air pollution. This Ph.D. Thesis identifies the typical atmospheric conditions at synoptic scale that affect the Iberian Peninsula (IP) and uses them to explain the dynamics of the most relevant gaseous pollutants in Spain (nitrogen dioxide NO2, sulphur dioxide SO2, and ozone O3) by means of air quality modelling. Circulation type classifications (CTC) summarise the continuum of atmospheric circulation into a discrete number of typical circulation types (CTs). For the 1983-2012 climatic period, a CTC is derived to be useful in the characterization of air quality dynamics over the IP. Sensitivity tests to classification techniques (principal components, correlation analysis, clustering) and other factors affecting the CTC (temporal and spatial resolution, domain size, etc.) are performed to objectivize the choice of the automatic set-up that maximizes its quality. The six identified CTs -described in terms of frequency, persistence, transitions, and location of pressure systems- are consistent with CTs found in the literature. The temporal stability of the CTC, evaluated following a cross-validation process that compares the results of the climatic and yearly CTs, leads to the identification of a representative year (2012). A representative day for each CT in 2012 is identified using an objective score that minimizes the differences of the daily and the average surface pressure CT grid. The study of NO2, SO2, and O3 dynamics performed on the representative day of each CT focuses on the biggest Spanish urban areas (Madrid and Barcelona) and heavy industrial/electricity-generation areas such as Asturias (northern Spain) and the Algeciras bay (southern Spain). The state-of-the-art CALIOPE Air Quality Forecast System (CALIOPE-AQFS) that provides high-resolution data on emissions, meteorology, and pollutant concentration over Spain is the main tool used in the characterisation of pollution dynamics. The modelling system is also used to quantify the contribution of specific sources of pollutants -coal-fired power plants and on-road transport- by means of a brute-force approach and an emission-based source apportionment, respectively. The CTs control the transport patterns of SO2/NO2/O3 in Spanish continental and Atlantic areas, whereas in Mediterranean coastal areas and over complex-terrains a combination of synoptic and mesoscale dynamics (sea-land and mountain-valley breezes) explains the pollutant concentration patterns. The power plants' contribution to surface concentration (up to 55 µgSO2 m-3 and 32 µgNO2 m-3) occurs mainly close to the source (< 20 km) related to vertical diffusion when the emission is injected within the planetary boundary layer. However, the SO2/NO2 plumes can reach distances higher than 250 km. The daily maximum O3 concentration attributed to the on-road transport emissions from Madrid and Barcelona contribute up to 24% and 8% to total O3 concentration, respectively, but it is particularly significant (up to 80-100 µg m-3 in an hour) to the O3 concentration peak during the central hours of the day in April-September. The long-range transport of O3 to the IP is controlled by the CTs and its concentration is very significant in the area of influence of Madrid and Barcelona, particularly under cold CTs (70-96%). This Ph.D. Thesis has proven that CALIOPE-AQFS (1) is useful to characterise the 3-D dynamics of primary and secondary pollutants in Spain under typical CTs; (2) is able to attribute and quantify air pollution to its sources via brute force and source apportionment; and (3) has the potential to help in the design of specific, science-based abatement strategies that minimize air pollution impacts.
La contaminación atmosférica genera perjuicios en la salud humana, en los intereses económicos de la sociedad y constituye una amenaza para los ecosistemas y el clima de la Tierra. Avanzar en la comprensión de la dinámica de la contaminación facilita el diseño de estrategias de calidad del aire que reduzcan sus impactos. Esta Tesis Doctoral identifica objetivamente patrones típicos de circulación atmosférica (PT) que afectan a la Península Ibérica (PI) a escala sinóptica para explicar la dinámica de los principales contaminantes gaseosos en España (dióxido de nitrógeno NO2, dióxido de azufre SO2 y ozono O3) mediante modelización de la calidad del aire. Las clasificaciones sinópticas (CS) discretizan el continuo de la circulación atmosférica en un catálogo de PT. Para el período climático 1983-2012, se establece una CS útil para el estudio de la dinámica de la contaminación atmosférica en la PI. Tests de sensibilidad para técnicas automáticas de clasificación (análisis de componentes principales, de correlación y clustering) y para otros factores que afectan a la CS (resolución temporal y espacial, tamaño del dominio, etc.) objetivizan la elección de la configuración que maximiza su calidad. Los seis PT identificados - descritos en términos de frecuencia, persistencia, transiciones y ubicación de los sistemas de presión - son consistentes con la literatura. La evaluación de la estabilidad temporal de la clasificación, mediante un proceso de validación cruzada que compara los PT climáticos con PT identificados en CS anuales, permite identificar un año representativo (2012). Un día representativo de cada PT es elegido gracias a un algoritmo que minimiza las diferencias de la malla de presiones diaria respecto de la del PT promedio. El estudio de la dinámica de NO2, SO2 y O3 se realiza en el día representativo de cada PT focalizando en las principales áreas urbanas de España (Madrid y Barcelona) y en importantes áreas industriales y/o de generación eléctrica (Asturias, bahía de Algeciras). El sistema de CALIdad del aire OPeracional para España (CALIOPE) que proporciona datos de alta resolución sobre emisiones, meteorología y concentración de contaminantes es la principal herramienta utilizada en el estudio. CALIOPE permite cuantificar la contribución de determinadas fuentes de emisión, centrales térmicas de carbón y transporte rodado, mediante un enfoque de fuerza bruta y de asignación de fuentes, respectivamente. Los PT controlan el transporte de SO2/NO2/O3 en áreas atlánticas y continentales de España mientras que en zonas costeras mediterráneas y/o de topografía compleja, una combinación de procesos sinópticos y de mesoescala (brisas marinas y de valle) explica los patrones de contaminación. La contribución de SO2 y NO2 de las centrales térmicas a la concentración en superficie (hasta 55 µg m-3 y 32 µg m-3, respectivamente) se produce principalmente cerca de la fuente (<20 km) por difusión vertical de la emisión cuando ésta se inyecta en la capa límite planetaria. Sin embargo, los penachos de SO2/NO2 pueden alcanzar distancias superiores a los 250 km. La contribución máxima diaria de O3 atribuido a emisiones del transporte rodado de Madrid y Barcelona alcanza el 24% y el 8%, respectivamente pero es particularmente significativa (hasta 80-100 µg m-3 en una hora) a mediodía durante el pico de concentración de O3. El transporte a larga distancia de O3 hacia la PI es controlado por los PT y su contribución es muy importante en el área de influencia de Madrid y Barcelona, en particular bajo los PT fríos (70-96%). Esta Tesis Doctoral ha demostrado que CALIOPE es (1) útil para caracterizar la dinámica 3-D de contaminantes primarios y secundarios en España bajo diferentes PT; (2) capaz de atribuir y cuantificar la contaminación a sus fuentes a través de fuerza bruta y atribución de fuentes; y (3) potencialmente útil en el diseño de estrategias de mitigación específicas que minimicen los impactos de la contaminación atmosférica.

Thesis (MScEng)--University of Stellenbosch, 2000.
ENGLISH ABSTRACT: Air pollution is a major concern III the Cape Metropole. A major contributor to the air pollution problem is road transport. For this reason, a national vehicle emissions study is in progress with the aim of developing a national policy regarding motor vehicle emissions and control. Such a policy could bring about vehicle emission control and regulatory measures, which may have far-reaching social and economic effects. Air pollution models are important tools 10 predicting the effectiveness and the possible secondary effects of such policies. It is therefore essential that these models are fundamentally sound to maintain a high level of prediction accuracy. Complex air pollution models are available, but they require spatial, time-resolved information of emission sources and a vast amount of processing power. It is unlikely that South African cities will have the necessary spatial, time-resolved emission information in the near future. An alternative air pollution model is one that is based on the Gaussian Plume Model. This model, however, relies on gross simplifying assumptions that affect model accuracy. It is proposed that statistical and mathematical analysis techniques will be the most viable approach to modelling air pollution in the Cape Metropole. These techniques make it possible to establish statistical relationships between pollutant emissions, meteorological conditions and pollutant concentrations without gross simplifying assumptions or excessive information requirements. This study investigates two analysis techniques that fall into the aforementioned category, namely, Non-linear Time Series Analysis (specifically, the method of delay co-ordinates) and Singular Spectrum Analysis (SSA). During the past two decades, important progress has been made in the field of Non-linear Time Series Analysis. An entire "toolbox" of methods is available to assist in identifying non-linear determinism and to enable the construction of predictive models. It is argued that the dynamics that govern a pollution system are inherently non-linear due to the strong correlation with weather patterns and the complexity of the chemical reactions and physical transport of the pollutants. In addition to this, a statistical technique (the method of surrogate data) showed that a pollution data set, the oxides of Nitrogen (NOx), displayed a degree of non-linearity, albeit that there was a high degree of noise contamination. This suggested that a pollution data set will be amenable to non-linear analysis and, hence, Non-linear Time Series Analysis was applied to the data set. SSA, on the other hand, is a linear data analysis technique that decomposes the time series into statistically independent components. The basis functions, in terms of which the data is decomposed, are data-adaptive which makes it well suited to the analysis of non-linear systems exhibiting anharmonic oscillations. The statistically independent components, into which the data has been decomposed, have limited harmonic content. Consequently, these components are more amenable to prediction than the time series itself. The fact that SSA's ability has been proven in the analysis of short, noisy non-linear signals prompted the use of this technique. The aim of the study was to establish which of these two techniques is best suited to the modelling of air pollution data. To this end, a univariate model to predict NOx concentrations was constructed using each of the techniques. The prediction ability of the respective model was assumed indicative of the accuracy of the model. It was therefore used as the basis against which the two techniques were evaluated. The procedure used to construct the model and to quantify the model accuracy, for both the Non-linear Time Series Analysis model and the SSA model, was consistent so as to allow for unbiased comparison. In both cases, no noise reduction schemes were applied to the data prior to the construction of the model. The accuracy of a 48-hour step-ahead prediction scheme and a lOO-hour step-ahead prediction scheme was used to compare the two techniques. The accuracy of the SSA model was markedly superior to the Non-linear Time Series model. The paramount reason for the superior accuracy of the SSA model is its adept ability to analyse and cope with noisy data sets such as the NOx data set. This observation provides evidence to suggest that Singular Spectrum Analysis is better suited to the modelling of air pollution data. It should therefore be the analysis technique of choice when more advanced, multivariate modelling of air pollution data is carried out. It is recommended that noise reduction schemes, which decontaminate the data without destroying important higher order dynamics, should be researched. The application of an effective noise reduction scheme could lead to an improvement in model accuracy. In addition to this, the univariate SSA model should be extended to a more complex multivariate model that explicitly encompasses variables such as traffic flow and weather patterns. This will explicitly expose the inter-relationships between the variables and will enable sensitivity studies and the evaluation of a multitude of scenarios.
AFRIKAANSE OPSOMMING: Die hoë vlak van lugbesoedeling in die Kaapse Metropool is kommerwekkend. Voertuie is een van die hoofoorsake, en as gevolg hiervan word 'n landswye ondersoek na voertuigemissie tans onderneem sodat 'n nasionale beleid opgestel kan word ten opsigte van voertuigemissie beheer. Beheermaatreëls van so 'n aard kan verreikende sosiale en ekonomiese uitwerkings tot gevolg hê. Lugbesoedelingsmodelle is van uiterste belang in die voorspelling van die effektiwiteit van moontlike wetgewing. Daarom is dit noodsaaklik dat hierdie modelle akkuraat is om 'n hoë vlak van voorspellingsakkuraatheid te handhaaf. Komplekse modelle is beskikbaar, maar hulle verg tyd-ruimtelike opgeloste inligting van emmissiebronne en baie berekeningsvermoë. Dit is onwaarskynlik dat Suid-Afrika in die nabye toekoms hierdie tydruimtelike inligting van emissiebronne gaan hê. 'n Alternatiewe lugbesoedelingsmodel is dié wat gebaseer is op die "Guassian Plume". Hierdie model berus egter op oorvereenvoudigde veronderstellings wat die akkuraatheid van die model beïnvloed. Daar word voorgestel dat statistiese en wiskundige analises die mees lewensvatbare benadering tot die modellering van lugbesoedeling in die Kaapse Metropool sal wees. Hierdie tegnieke maak dit moontlik om 'n statistiese verwantskap tussen besoedelingsbronne, meteorologiese toestande en besoedeling konsentrasies te bepaal sonder oorvereenvoudigde veronderstellings of oormatige informasie vereistes. Hierdie studie ondersoek twee analise tegnieke wat in die bogenoemde kategorie val, naamlik, Nie-lineêre Tydreeks Analise en Enkelvoudige Spektrale Analise (ESA). Daar is in die afgelope twee dekades belangrike vooruitgang gemaak in die studieveld van Nie-lineêre Tydreeks Analise. 'n Volledige stel metodes is beskikbaar om nie-lineêriteit te identifiseer en voorspellingsmodelle op te stel. Dit word geredeneer dat die dinamika wat 'n besoedelingsisteem beheer nie-lineêr is as gevolg van die sterk verwantskap wat dit toon met weerpatrone asook die kompleksiteit van die chemiese reaksies en die fisiese verplasing van die besoedelingstowwe. Bykomend verskaf 'n statistiese tegniek (die metode van surrogaatdata) bewyse dat 'n lugbesoedelingsdatastel, die okside van Stikstof (NOx), melineêre gedrag toon, alhoewel daar 'n hoë geraasvlak is. Om hierdie rede is die besluit geneem om Nie-lineêre Tydreeks Analise aan te wend tot die datastel. ESA daarenteen, is 'n lineêre data analise tegniek. Dit vereenvoudig die tydreeks tot statistiese onafhanklike komponente. Die basisfunksies, in terme waarvan die data vereenvoudig is, is data-aanpasbaar en dit maak hierdie tegniek gepas vir die analise van nielineêre sisteme. Die statisties onafhanklike komponente het beperkte harmoniese inhoud, met die gevolg dat die komponente aansienlik makliker is om te voorspel as die tydreeks self. ESA se effektiwitiet is ook al bewys in die analise van kort, hoë-graas nie-lineêre seine. Om hierdie redes, is ESA toegepas op die lugbesoedelings data. Die doel van die ondersoek was om vas te stel watter een van die twee tegnieke meer gepas is om lugbesoedelings data te analiseer. Met hierdie doelwit in sig, is 'n enkelvariaat model opgestel om NOx konsentrasies te voorspel met die gebruik van elk van die tegnieke. Die voorspellingsvermoë van die betreklike model is veronderstelom as 'n maatstaf van die model se akkuraatheid te kan dien en dus is dit gebruik om die twee modelle te vergelyk. 'n Konsekwente prosedure is gevolg om beide die modelle te skep om sodoende invloedlose vergelyking te verseker. In albei gevalle was daar geen geraasverminderings-tegnieke toegepas op die data nie. Die akuraatheid van 'n 48-uur voorspellingsmodel en 'n 100-uur voorspellingsmodel was gebruik vir die vergelyking van die twee tegnieke. Daar is bepaal dat die akkuraatheid van die ESA model veel beter as die Nie-lineêre Tydsreeks Analise is. Die hoofrede vir die ESA se hoër akkuraatheid is die model se vermoë om data met hoë geraasvlakke te analiseer. Hierdie ondersoek verskaf oortuigende bewyse dat Enkelvoudige Spektrale Analiese beter gepas is om lugbesoedelingsdata te analiseer en gevolglik moet hierdie tegniek gebruik word as meer gevorderde, multivariaat analises uitgevoer word. Daar word aanbeveel dat geraasverminderings-tegnieke, wat die data kan suiwer sonder om belangrike hoë-orde dinamika uit te wis, ondersoek moet word. Hierdie toepassing van effektiewe geraasverminderings-tegniek sal tot 'n verbetering in model-akkuraatheid lei. Aanvullend hiertoe, moet die enkele ESA model uitgebrei word tot 'n meer komplekse multivariaat model wat veranderlikes soos verkeersvloei en weerpatrone insluit. Dit sal die verhoudings tussen veranderlikes ten toon stel en sal sensitiwiteit-analises en die evaluering van menigte scenarios moontlik maak.

Abstract This thesis employs a statistical regression method to estimate models for testing the hypothesis of the thesis of vehicle emissions interdependencies. The thesis at the beginnings, reviews critically the formation of emissions in gasoline-fuelled engines, and also reviews existing and emerging models of automotive emissions. The thesis then, presents the relationships between the urban transport system and vehicle emissions. Particularly, it summarises different types of emissions and the contributory factors of the urban transport system to such emissions. Subsequently, the thesis presents the theory of vehicle emissions interdependencies and the empirical framework for testing the hypothesis of the thesis. The scope of testing the hypothesis of the thesis is only limited to gasoline-fuelled conventional vehicles in the urban transport environment. We use already available laboratory-based testing dataset of 542 passenger vehicles, to investigate the hypothesis of the thesis of vehicle emissions interdependencies. HC, CO, and NOX emissions were collected under six test drive-cycles, for each vehicle before and after vehicles were tuned. Prior to using any application, we transform the raw dataset into actionable information. We use three steps, namely conversion, cleaning, and screening, to process the data. We use classification and regression trees (CART) to narrow down the input number of variables in the models formulated for investigating the hypothesis of the thesis. We then, utilise initial results of the analysis to fix any remaining problems in the data. We employ three stage least squares (3SLS) regression to test the hypothesis of the thesis, and to estimate the maximum likelihood of vehicle variables and other emissions to influence HC, CO, and NOX emissions simultaneously. We estimate twelve models, each of which consists of a system of three simulations equations that accounts for the endogenous relations between HC, CO and NOX emissions when estimating vehicle emissions simultaneously under each test drive-cycle. The major contribution of the thesis is to investigate the inter-correlations between vehicle emissions within a well controlled data set, and to test the hypothesis of vehicle emissions interdependencies. We find that HC, CO, and NOX are endogenously or jointly dependent in a system of simultaneous-equations. The results of the analysis demonstrate that there is strong evidence against the null hypothesis (H0) in favour of the alternative hypothesis (H1) that HC, CO, and NOX are statistically significantly interdependent. We find, for the thesis sample, that NOX and CO are negatively related, whereas HC and CO emissions are positively related, and HC and NOX are positively related. The results of the thesis yield new insights. They bridge a very important gap in the current knowledge on vehicle emissions. They advance not only our current knowledge that HC, CO, and NOX should be predicted jointly since they are produced jointly, but also acknowledge the appropriateness of using 3SLS regression for estimating vehicle emissions simultaneously. The thesis measures the responses of emissions to changes with respect to changes in the other emissions. We investigate emission responses to a one percent increase in an emission with respect to the other emissions. We find the relationship between CO and NOX is of special interest. After vehicles were tuned, we find those vehicles that exhibit a one percent increase in NOX exhibit simultaneously a 0.35 percent average decrease in CO. Similarly, we find that vehicles which exhibit a one percent increase in CO exhibit simultaneously a 0.22 percent average decrease in NOX. We find that the responses of emission to changes with respect to other emissions vary with various test drive-cycles. Nonetheless, a band of upper and lower limits contains these variations. After vehicle tuning, a one percent increase in HC is associated with an increase in NOX between 0.5 percent and 0.8 percent, and an increase in CO between 0.5 percent and one percent Also, for post-tuning vehicles, a one percent increase in CO is associated with an increase in HC between 0.4 percent and 0.9 percent, and a decrease in NOX between 0.07 percent and 0.32 percent. Moreover, a one percent increase in NOX is associated with increase in HC between 0.8 percent and 1.3 percent, and a decrease in CO between 0.02 percent and 0.7 percent. These measures of the responses are very important derivatives of the hypothesis investigated in the thesis. They estimate the impacts of traffic management schemes and vehicle operations that target reducing one emission, on the other non-targeted emissions. However, we must be cautious in extending the results of the thesis to the modern vehicles fleet. The modern fleet differs significantly in technology from the dataset that we use in this thesis. The dataset consists of measurements of HC, CO, and NOX emissions for 542 gasoline-fuelled passenger vehicles, under six test drive-cycles, before and after the vehicles were tuned. Nevertheless, the dataset has a number of limitations such as limited model year range, limited representations of modal operations, and limitations of the measurements of emissions based only on averages of test drive-cycles, in addition to the exclusion of high-emitter emission measurements from the dataset. The dataset has a limited model year range, i.e., between 1980 and 1991. We highlight the age of the dataset, and acknowledge that the present vehicle fleet varies technologically from the vehicles in the dataset used in this thesis. Furthermore, the dataset has a limited number of makes - Holden, Ford, Toyota, Nissan, and Mitsubishi. There are also a limited number of modal operations. The model operations presented in the dataset are cold start, warming-up, and hot stabilised driving conditions. However, enrichment episodes are not adequately presented in the test-drive cycles of the dataset. Moreover, the dataset does not take into account driving behaviour influences, and all measurements are cycle-based averages. The emission measurements of laboratory-based testings are aggregated over a test drive cycle, and the test drive-cycle represents an average trip over an average speed. The exclusion of the measurements of high emitting vehicles from the dataset introduces further limitations. Remote sensing studies show that 20 percent of the on-road vehicle fleet is responsible for 80 percent of HC and CO emissions. The findings of the thesis assist in the identification of the best strategies to mitigate the most adverse effects of air-pollution, such as the most severe pollution that have the most undesirable pollution effects. Also, they provide decision-makers with valuable information on how changes in the operation of the transport system influence the urban air-quality. Moreover, the thesis provides information on how vehicle emissions affect the chemistry of the atmosphere and degrade the urban air-quality.

Air quality monitoring is one of several important tasks carried out in the area of environmental science and engineering. Accordingly, the development of air quality predictive models can be very useful as such models can provide early warnings of pollution levels increasing to unsatisfactory levels. The literature review conducted within the research context of this thesis revealed that only a limited number of widely used machine learning algorithms have been employed for the modelling of the concentrations of atmospheric gases such as ozone, nitrogen oxides etc. Despite this observation the research and technology area of machine learning has recently advanced significantly with the introduction of ensemble learning techniques, convolutional and deep neural networks etc. Given these observations the research presented in this thesis aims to investigate the effective use of ensemble learning algorithms with optimised algorithmic settings and the appropriate choice of base layer algorithms to create effective and efficient models for the prediction and forecasting of specifically, ground level ozone (O3). Three main research contributions have been made by this thesis in the application area of modelling O3 concentrations. As the first contribution, the performance of several ensemble learning (Homogeneous and Heterogonous) algorithms were investigated and compared with all popular and widely used single base learning algorithms. The results have showed impressive prediction performance improvement obtainable by using meta learning (Bagging, Stacking, and Voting) algorithms. The performances of the three investigated meta learning algorithms were similar in nature giving an average 0.91 correlation coefficient, in prediction accuracy. Thus as a second contribution, the effective use of feature selection and parameter based optimisation was carried out in conjunction with the application of Multilayer Perceptron, Support Vector Machines, Random Forest and Bagging based learning techniques providing significant improvements in prediction accuracy. The third contribution of research presented in this thesis includes the univariate and multivariate forecasting of ozone concentrations based of optimised Ensemble Learning algorithms. The results reported supersedes the accuracy levels reported in forecasting Ozone concentration variations based on widely used, single base learning algorithms. In summary the research conducted within this thesis bridges an existing research gap in big data analytics related to environment pollution modelling, prediction and forecasting where present research is largely limited to using standard learning algorithms such as Artificial Neural Networks and Support Vector Machines often available within popular commercial software packages.

Though nitrogen oxides (NOx ) emissions from aircraft make a relatively small contribution to the overall NOx emissions when compared to surface emissions, they constitute the only man-made pollutant at high altitude. Excessive NOx emissions might cause ozone depletion in the stratosphere and ozone increases in the troposphere, both topics of increasing environment concern in recent years. To tackle the problem of NOx emissions and their effects upon the environment, the spatial distribution of NOy (NOx + RN03) needs to be fully understood. The most significant source of NOx emissions is due to long range civil subsonic aircraft during their cruise phase. Detailed studies were performed for seven cases, including five single flight and two global scale scenarios, using the most widely used B747-400 aircraft which are powered by 4 three-shaft turbofan engines. Using models of aircraft performance, cruise phase NOx emissions are derived and employed as input to run a three-dimensional chemistry transport model. In order to achieve realistic results, all scenarios were carried out using a one-year period of simulation to study the transport and dispersion of NOy emitted from aircraft exhausts. For a better understanding of the atmospheric behaviour of NOy , two other cases were also carried out, a one-month simulation during both summer and winter situations. The results show that the upper troposphere may certainly be perturbed by aircraft NOx emissions at cruise altitude. The relative increase in NOx concentration is estimated to be of the order of 40% in the meridional average and up to 160% in regions covered with heavy air traffic such as Europe, the USA and the North Atlantic. This thesis investigates the quantity of long range civil subsonic aircraft NOx pollutants at cruise altitude and their subsequent implications for the upper troposphere. It discusses the concept of atmospheric NOy pollution chemistry, using computer programs developed for both total aircraft performance and 3-D chemistry transport modelling, and combinations thereof. Simulations have been conducted to predict the relationship between the NOx emissions and the environment. In addition, the thesis highlights the most important variables and derives a procedure to model the emissions from subsonic aircraft flight and to assess their impacts upon the atmosphere. Finally, some complicated problems which need further work are also mentioned.

One important component for successful air quality modelling is the utilisation of a reliable meteorological simulator. Evaluating the model with respect to its overall performance in predicting natural processes is no easy task. The problem is twofold, firstly there is the availability and suitability of field data with which to compare a model with and secondly there is the method of evaluation. The Air Pollution Model (TAPM), developed by the CSIRO was used to simulate the winds in Southeast Queensland (SEQ). The complex nature of the airshed makes it difficult to compare modelled data with observational data as the observational data may be influenced by local phenomena. Evaluation of the model through the use of standard statistics and monthly and seasonal statistics illustrated that overall the model predicted the annual average wind speeds and temperatures well. Through the use of synoptic clustering, more detail on model performance was gained and it was found that TAPM predicted sea breezes that occurred on high pollution days. The sensitivity of the model to the selection of input parameters such as soil type, land use, vegetation, and rain processes was also investigated.

One important component for successful air quality modelling is the utilisation of a reliable meteorological simulator. Evaluating the model with respect to its overall performance in predicting natural processes is no easy task. The problem is twofold, firstly there is the availability and suitability of field data with which to compare a model with and secondly there is the method of evaluation. The Air Pollution Model (TAPM), developed by the CSIRO was used to simulate the winds in Southeast Queensland (SEQ). The complex nature of the airshed makes it difficult to compare modelled data with observational data as the observational data may be influenced by local phenomena. Evaluation of the model through the use of standard statistics and monthly and seasonal statistics illustrated that overall the model predicted the annual average wind speeds and temperatures well. Through the use of synoptic clustering, more detail on model performance was gained and it was found that TAPM predicted sea breezes that occurred on high pollution days. The sensitivity of the model to the selection of input parameters such as soil type, land use, vegetation, and rain processes was also investigated.