Dissertations / Theses on the topic 'Air pollution modelling and control'

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.

Urban air pollution and climate are closely connected due to shared generating processes (e.g., combustion) for emissions of the driving gases and aerosols. They are also connected because the atmospheric lifecycles of common air pollutants such as CO, NOx and VOCs, and of the climatically important methane gas (CH4) and sulfate aerosols, both involve the fast photochemistry of the hydroxyl free radical (OH). Thus policies designed to address air pollution may impact climate and vice versa. We present calculations using a model coupling economics, atmospheric chemistry, climate and ecosystems to illustrate some effects of air pollution policy alone on global warming. We consider caps on emissions of NOx, CO, volatile organic carbon, and SOx both individually and combined in two ways. These caps can lower ozone causing less warming, lower sulfate aerosols yielding more warming, lower OH and thus increase CH4 giving more warming, and finally, allow more carbon uptake by ecosystems leading to less warming. Overall, these effects significantly offset each other suggesting that air pollution policy has a relatively small net effect on the global mean surface temperature and sea level rise. However, our study does not account for the effects of air pollution policies on overall demand for fossil fuels and on the choice of fuels (coal, oil, gas), nor have we considered the effects of caps on black carbon or organic carbon aerosols on climate. These effects, if included, could lead to more substantial impacts of capping pollutant emissions on global temperature and sea level than concluded here. Caps on aerosols in general could also yield impacts on other important aspects of climate beyond those addressed here, such as the regional patterns of cloudiness and precipitation.<br>Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).<br>This research was supported by the U.S Department of Energy, U.S. National Science Foundation, and the Industry Sponsors of the MIT Joint Program on the Science and Policy of Global Change: Alstom Power (France), American Electric Power (USA), BP p.l.c. (UK/USA), ChevronTexaco Corporation (USA), DaimlerChrysler AG (Germany), Duke Energy (USA), J-Power (Electric Power Development Co., Ltd.) (Japan), Electric Power Research Institute (USA), Electricité de France, ExxonMobil Corporation (USA), Ford Motor Company (USA), General Motors (USA), Mirant (USA), Murphy Oil Corporation (USA), Oglethorpe Power Corporation (USA), RWE/Rheinbraun (Germany), Shell International Petroleum (Netherlands/UK), Statoil (Norway), Tennessee Valley Authority (USA), Tokyo Electric Power Company (Japan), TotalFinaElf (France), Vetlesen Foundation (USA).

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems<br>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.<br>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

This thesis examines the implementation of air pollution control at the local level within the UK. Focusing upon the local air pollution control regime (LAPC) that is regulated by local authorities under the provisions of Part One of the Environmental Protection Act 1990, the research assesses whether this system has been effective in controlling localised emissions from industrial processes. To place the research in context, it essentially evaluates the implementation of LAPC with reference to its main objectives: to reduce industrial emissions and improve local air quality, to provide greater clarity and certainty for business and to allow the public greater access to local environmental information. The methodology adopted to undertake the research was based upon grounded theory and naturalistic inquiry. As such, the research programme was structured in phases where initially, essential baseline data was collected on the LAPC system. This quantitative data provided the foundations from which more detailed qualitative work was undertaken in the field in subsequent phases of the research. This allowed the research to build towards theory utilising an emergent, yet structured research design. As the research progressed, a range of complementary methods were used to collect data about the LAPC system. Initially, the research critically evaluated the major Reports that had been written on LAPC in order to establish benchmarks from which implementation of the LAPC system could be assessed. To keep the research within defined limits, a case study approach was adopted, using Lancashire as the case study area. Under the first phase of the research a questionnaire was used to survey the process operators within the region in order to collect baseline information about their processes and to obtain their views and perceptions of the LAPC system. Under the second phase qualitative research was carried out in the field. This involved detailed, semi-structured, faceto-face interviews with the process operators and the DETR and examination of documentary evidence within the Public Registers held by Lancashire's local authorities. The phased approach allowed the research to explore progressively deeper into the key areas associated with the implementation of the LAPC system. In particular, the research examined local authority enforcement and administration of the LAPC system to assess their regulatory performance and to determine the strategies they adopt to achieve compliance. The research also focused upon the process operators to assess their capacity to comply with the duties and responsibilities placed upon them under LAPC. The work offers a new perspective as it focuses upon both regulators and operators alike and examines their performance from the outset of LAPC to its closing stages. The research findings allow conclusions to be drawn on the relationship that exists between these "stakeholders" and the regulatory conditions that exist within the system. The research findings suggest that local authorities and process operators have had difficulty in discharging their duties under the LAPC system. For various reasons, the capacity of the regulators and the operators to achieve compliance has been compromised. The difficulties that the stakeholders have faced in striving to come to terms with the LAPC system have influenced their compliance strategies and affected the relationship that exists between them. This research identifies and examines these difficulties and assesses their performance under such conditions.

"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."<br>Master of Information Technology by Research

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.

El crecimiento industrial ha acarreado grandes avances tecnológicos para nuestra sociedad. Lamentablemente, el precio a pagar por estos avances ha sido un aumento significativo de los niveles de contaminación del aire en todo el mundo, afectando tanto a zonas urbanas como a las zonas rurales. Por lo general, la monitorización de la calidad aire se realiza mediante estaciones de monitorización fijas. Sin embargo, este método es demasiado costoso, poco escalable y difícil de implementar en nuestras ciudades, las cuales están cada vez más pobladas. El uso de Mobile CrowdSensing, paradigma en el cual la monitorización la realizan los propios usuarios, permite realizar monitorización ambiental utilizando sensores móviles integrados en vehículos. Los posibles escenarios se pueden dividir en dos: entornos urbanos, donde hay un amplio conjunto de vehículos disponibles, y entornos rurales o industriales, donde el tráfico vehicular es escaso y está limitado a las principales arterias de transporte. Teniendo en cuenta estos dos escenarios, esta tesis propone una arquitectura, llamada EcoSensor, que permite monitorizar la contaminación del aire utilizando pequeños sensores de bajo coste instalados en diferentes tipos de vehículos, tales como bicicletas, automóviles o autobuses del sistema de transporte público, en el caso de entornos urbanos, y en drones o UAS en entornos rurales. La arquitectura propuesta está compuesta por tres componentes: un sensor de bajo coste para capturar datos de contaminación, un smartphone para realizar un preprocesamiento de la información y para transmitir los datos hacia un servidor central, y el servidor central, encargado de almacenar y procesar la información de contaminación ambiental. Para entornos urbanos, analizamos diferentes alternativas con respecto al diseño de una unidad de monitorización de bajo coste basada en plataformas de prototipado comerciales como RaspberryPi o Arduino, junto con sensores también de precio reducido. En la tesis realizamos un análisis, y proponemos un proceso, para llevar a cabo la monitorización ambiental utilizando la arquitectura propuesta. Este proceso abarca cuatro operaciones básicas: captura de datos, conversión de unidades, reducción de la variabilidad temporal, e interpolación espacial. Para entornos rurales, proponemos el uso de drones como unidades de sensorización móviles. Específicamente, equipamos el drone con capacidades de monitorización a través de un microordenador RaspberryPi y sensores de calidad del aire de bajo coste. Finalmente, se propone un algoritmo llamado PdUC para controlar el vuelo del UAV con el objetivo de realizar monitorización ambiental, identificando las áreas más contaminadas, y tratando de ese modo de mejorar la precisión general y la velocidad de monitorización. Además, proponemos una mejora a este algoritmo, denominada PdUC-D, basada en la discretización del área a monitorizar dividiéndola en pequeñas áreas (tiles), donde cada tile se monitoriza una sola vez, evitando así realizar muestreos redundantes. En general, verificamos que la monitorización móvil es una aproximación eficiente y fiable para monitorizar la contaminación del aire en cualquier entorno, ya sea usando vehículos o bicicletas en entornos urbanos, o UAVs en entornos rurales. Con respecto al proceso de monitorización ambiental, validamos nuestra propuesta comparando los valores obtenidos por nuestros sensores móviles de bajo coste con respecto a los valores típicos de referencia ofrecidos por las estaciones de monitorización fijas para el mismo período y ubicación, comprobando que los resultados son semejantes, y están acuerdo a lo esperado. Además, demostramos que PdUC-D, permite guiar autónomamente un UAV en tareas de monitorización del aire, ofreciendo un mejor rendimiento que los modelos de movilidad típicos, reduciendo tanto los errores de predicción como el tiempo para cubrir el área completa,<br>Industrial growth has brought unforeseen technological advances to our society. Unfortunately, the price to pay for these advances has been an increase of the air pollution levels worldwide, affecting both urban and countryside areas. Typically, air pollution monitoring relies on fixed monitoring stations to carry out the pollution control. However, this method is too expensive, not scalable, and hard to implement in any city. The Mobile Crowdsensing (MCS) approach, a novel paradigm whereby users are in charge of performing monitoring tasks, allows environment monitoring to be made using small sensors embedded in mobile vehicles. The possible scenarios can be divided into two: urban scenarios, where a wide set of vehicles are available, and rural and industrial areas, where vehicular traffic is scarce and limited to the main transportation arteries. Considering these two scenarios, in this thesis we propose an architecture, called EcoSensor, to monitor the air pollution using small sensors installed in vehicles, such as bicycles, private cars, or the public transportation system, applicable to urban scenarios, and the use of an Unmanned Aerial System (UAS) in rural scenarios. Three main components compose our architecture: a low-cost sensor to capture pollution data, a smartphone to preprocess the pollution information and transmit the data towards a central server, and the central server, to store and process pollution information. For urban scenarios, we analyze different alternatives regarding the design of a low-cost sensing unit based on commercial prototyping platforms such as Raspberry Pi or Arduino, and Commercial Off-the-shelf (COTS) air quality sensors. Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation. For rural scenarios, we propose the use of an Unmanned Aerial Vehicle (UAV) as a mobile sensor. Specifically, we equip the UAV with sensing capabilities through a Raspberry Pi microcomputer and low-cost air quality sensors. Finally, we propose an algorithm, called Pollution-driven UAV Control (PdUC), to control the UAV flight for monitoring tasks by focusing on the most polluted areas, and thereby attempting to improve the overall accuracy while minimizing flight time. We then propose an improvement to this algorithm, called Discretized Pollution-driven UAV Control (PdUC-D), where we discretize the target area by splitting it into small tiles, where each tile is monitored only once, thereby avoiding redundant sampling. Overall, we found that mobile sensing is a good approach for monitoring air pollution in any environment, either by using vehicles or bicycles in urban scenarios, or an UAVs in rural scenarios. We validate our proposal by comparing obtained values by our mobile sensors against typical values reported by monitoring stations at the same time and location, showing that the results are right, matching the expected values with a low error. Moreover, we proved that PdUC-D, our protocol for the autonomous guidance of UAVs performing air monitoring tasks, has better performance than typical mobility models in terms of reducing the prediction errors and reducing the time to cover the whole area.Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation.<br>El creixement industrial ha implicat grans avanços tecnològics per a la nostra societat. Lamentablement, el preu que cal pagar per aquests avanços ha sigut un augment significatiu dels nivells de contaminació de l'aire a tot el món, que afecta tant zones urbanes com zones rurals. En general, el monitoratge de la qualitat aire es fa mitjançant estacions de monitoratge fixes. No obstant això, aquest mètode és massa costós, poc escalable i difícil d'implementar a les nostres ciutats, les quals estan cada vegada més poblades. L'ús de Mobile CrowdSensing (MCS), paradigma en el qual el monitoratge el duen a terme els mateixos usuaris, permet realitzar monitorització ambiental tenint sensors mòbils integrats en vehicles. Els possibles escenaris es poden dividir en dos: entorns urbans, on hi ha un ampli conjunt de vehicles disponibles, i entorns rurals o industrials, on el trànsit vehicular és escàs i està limitat a les principals artèries de transport. Tenint en compte aquests dos escenaris, aquesta tesi proposa una arquitectura, anomenada EcoSensor, que permet monitorar la contaminació de l'aire utilitzant petits sensors de baix cost instal·lats en diferents tipus de vehicles, com ara bicicletes, automòbils o autobusos del sistema de transport públic, en el cas d'entorns urbans, i en UAVs (Unmanned Aerial Vehicles) en entorns rurals. L'arquitectura proposada està composta per tres components: un sensor de baix cost per a capturar dades de contaminació, un smartphone per a realitzar un preprocessament de la informació i per a transmetre les dades cap a un servidor central, i el servidor central, encarregat d'emmagatzemar i processar la informació de contaminació ambiental. Per a entorns urbans, analitzem diferents alternatives pel que fa al disseny d'una unitat de monitoratge (sensor mòbil) de baix cost basada en plataformes de prototipatge comercials com Raspberry Pi o Arduino, juntament amb sensors també de preu reduït. En la tesi fem una anàlisi, i proposem un procés, per a dur a terme el monitoratge ambiental utilitzant l'arquitectura proposada. Aquest procés abasta quatre operacions bàsiques: captura de dades, conversió d'unitats, reducció de la variabilitat temporal, i interpolació espacial. Per a entorns rurals, proposem l'ús de drons o Unmanned Aerial Vehicles (UAVs) com a unitats de sensorització mòbils. Específicament, equipem el dron amb capacitats de monitoratge a través d'un microordinador Raspberry Pi i sensors de qualitat de l'aire de baix cost. Finalment, es proposa un algorisme anomenat PdUC (Pollution-driven UAV Control) per a controlar el vol del UAV amb l'objectiu de realitzar monitoratge ambiental, que identifica les àrees més contaminades i que, d'aquesta manera, tracta de millorar la precisió general i la velocitat de monitoratge. A més, proposem una millora a aquest algorisme, denominada PdUC-D, basada en la discretització de l'àrea a monitorar dividint-la en xicotetes àrees (tiles), on cada tile es monitora una sola vegada, fet que evita dur a terme mostrejos redundants. En general, verifiquem que el monitoratge mòbil és una aproximació eficient i fiable per a monitorar la contaminació de l'aire en qualsevol entorn, ja siga usant vehicles o bicicletes en entorns urbans, o UAVs en entorns rurals. Pel que fa al procés de monitoratge ambiental, validem la nostra proposta comparant els valors obtinguts pels nostres sensors mòbils de baix cost pel que fa als valors típics de referència oferits per les estacions de monitoratge fixes per al mateix període i ubicació, i es comprova que els resultats són semblants, i estan d'acord amb el resultat esperat. A més, es demostra que PdUC-D permet guiar autònomament un UAV en tasques de monitoratge de l'aire, oferint un millor rendiment que els models de mobilitat típics, reduint tant els errors de predicció com el temps per a cobrir l'àrea completa, i aconseguint una major precisió dins de les àrees més<br>Alvear Alvear, ÓP. (2018). Mobile Sensing Architecture for Air Pollution Monitoring [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107928<br>TESIS

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.<br>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.

Air quality in the Shenandoah Valley has deteriorated in recent years. The valley exceeds the National Ambient Air Quality Standards for ozone (O3) a few days each year, and with stricter fine particulate matter (PM2.5) standards coming into effect, the valley risks exceeding those as well. Visibility is poor in the valley region, and the haze obscures the spectacular vistas from the Shenandoah National Park. To solve the growing problem local governments in the valley joined forces to find economically and politically feasible ways to reduce air pollution. In this study we aim to provide the scientific basis for air quality management strategies through modeling the sensitivity of various pollutants to changes in emissions. We distinguish between locally generated versus regionally transported air pollution as well as assess the impacts of proposed local air pollution control measures on ambient air quality in the valley. The first part of this thesis assesses air pollutant emissions in the Shenandoah Valley. Emissions were assigned to one of 14 source categories and allocated by county or city. Biogenic sources were responsible for 56% of the volatile organic compounds (VOCs) emitted in the valley. VOCs are important because they, together with nitrogen oxides (NOx) react to form O3 in the presence of sunlight. On-road and off-road mobile sources were the largest anthropogenic sources of VOCs as well as 63% of the NOx. PM2.5 emissions were not dominated by any single source, but fuel combustion, dust, and agriculture were important contributors. The second part of this thesis focuses on modeling ambient air pollution concentrations in the Shenandoah Valley based on the emissions generated in the first portion. We developed a set of three alternative emissions scenarios for comparison to the base case. We first zeroed anthropogenic emissions in the valley, allowing us to determine how much pollution was produced by local sources versus transported into the valley from upwind areas. We then developed a scenario that contained nine different pollution reduction strategies being considered by local governments. Finally we modeled a similar scenario in which we predicted the impact of ten proposed greenhouse gas reduction strategies on concentrations of O3 and PM2.5. We found that PM2.5 concentrations fell when emissions in the valley were reduced, but O3 did not. PM2.5 concentrations fell by 26-57% for the Zero Case and by 10-27% for the other two cases, depending on the time of year and location. Conversely for O3 there was either no change in most seasons or a small increase in concentrations in the fall. These results suggest that PM2.5 in the valley can be controlled with local measures but O3 is a more geographically wide problem.<br>Master of Science

<p>As air traffic is forecasted to increase, air traffic control software subsequently needs to be more sophisticated. To efficiently push development forward, testing is important in order to determine usability. The tests need to be adapted to fit a particular purpose and carried out with methods that preserve the validity of the results. </p><p>This thesis describes an implementation project carried out at the EUROCONTROL Experimental Centre, Bretigny-sur-Orge, France. The purpose of the project is to create an application that enables a user to create datasets of air traffic to be used for these tests. The application allows for manual work or bulk imports from external data sources. Furthermore it compiles scenarios as output datasets intended for prototype air traffic control software developed at Linköping University. </p><p>The application design rationale and development process is described. Some time is spent on demonstrating the flexibility of the application and how its usage fits in a bigger picture.</p>