Dissertations / Theses on the topic 'Air pollution processes and air quality measurement'

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Bachelors
Engineering and Computer Science
Environmental Engineering

Beginning in March 1999 at Georgia Institute of Technology, the ASACA (Assessment of Spatial Aerosol Composition in Atlanta) program has provided PM₂. ₅ concentration and speciation using particle concentration monitoring in and around metropolitan Atlanta. Since 1999, three of the ASACA sites have collected PM₂. ₅ in an urban setting: Fort McPherson (FT, SW), South Dekalb (SD, SE), and Tucker (TU, NE). In January 2007, TU was retired and Fire Station 8 (FS8, NE) was employed as the new urban site. Starting in 2002, PM₂. ₅ concentrations have also been characterized at a rural site, Fort Yargo (YG). Water-soluble ionic species and carbonaceous species concentrations are collected daily on filters using a three-channel particulate composition monitor (PCM). From 1999 to 2008, average PM₂. ₅ concentrations range from 12.9 µg/m3 at YG to 15.4 µg/m3 at TU. Sulfate and organic matter are the main components of Atlanta PM, contributing around 26% and 31% respectively to PM mass. Overall ASACA data quality increased from around 5 in 1999 to a value of 9 in 2005. Seasonal PM data quality appears to be significantly affected by volatility of secondary aerosol species during warm months because ionic data quality regularly decreases in the summer. PM is more sensitive to total sulfate concentration than nitrate and ammonia year-round.

Over the last several years studies have shown that the quality of indoor air may be worse than outdoor air. People spend as much as 90% of their time indoors, therefor, the associated health risk due to indoor air pollution may be greater than the risk due to outdoor air pollution. Building designs have altered dramatically over the last two decades resulted in "tighter" buildings that rely on sophisticated mechanical systems to provide for the quantity of air required throughout the building. These changes over the years could result in an increased number of complaints received regarding Sick Building Syndrome symptoms. The World Health Organization (WHO) estimates that up to 30% of office buildings world-wide may have significant problems regarding poor indoor air quality (IAQ). This study involves a literature study of the major indoor air pollutants regarding the source of the pollutant, the associated health effects, the measuring techniques available and the results of previous studies conducted on the specific pollutant. Measurements will be taken in two sealed buildings, one an old and the other a new building to identify the major pollutants. A questionnaire was compiled specifically for building occupants and completed by the occupants of both buildings. From the results obtained a step-by-step method for solving indoor air quality (IAQ) problems was proposed. The method was applied and evaluated in a case study of a problem building where indoor air quality related problems were experienced. The results of the study revealed that the major indoor air pollutants are present in old as well as new buildings. The study also revealed that some office workers might be more susceptible than others to the medical reactions cause to human beings by these pollutants. Some concentrations are higher in new buildings than in old buildings. The responses from the questionnaire was evaluated against the results obtained from the measurement study. The step-by-step method in the case study provided a more systematic approach at solving IAQ problems at buildings. Solving indoor air quality problems is a very practical issue and does not necessarily require an investment of expensive high technology equipment, but might merely require a practical approach. Environmental Health Officers can play a major role in providing expert advice when scrutinizing building plans. Environmental Health Officers should empower themselves with the knowledge to do inspections or investigations in office buildings by using the step-by-step method for investigating indoor air quality problems. By addressing indoor air quality problems in buildings, the workers in healthy buildings can increase their productivity with lasting effects on a company’s bottom line.

Thesis (MTech (Environmental Management))--Cape Peninsula University of Technology, 2015.
The integrity of natural air has been compromised due to the deposition of chemical, biological and particulate substances from natural and anthropogenic sources. Adverse health consequences arising from the exposure of plants, animals and human to elevated atmospheric concentrations have been reported severally. The ambient baseline levels of many air contaminants in urban and industrial layouts of many Africa cities, especially Zimbabwe have not been fully characterized. Information on levels of these contaminants and their real time variability is therefore scarce and scanty. In this study, the ambient air concentration levels of selected gaseous pollutants in the vicinity of a fertilizer production facility in Zimbabwe were investigated. Nine sampling stations were systematically and randomly identified for the measurement of selected air pollutants (SO2, NO2, and NH3) the fertilizer production factory to capture air quality data on all wind directions. The electrochemical Drager Sensors which rely on electrochemical measuring transducer for measuring concentration of gases under atmospheric conditions was used for the measurement of NH3, SO2, and NO2. The ambient air monitored was allowed to diffuse through a membrane into the sensor liquid electrolytes, containing a sensing electrode, a counter electrode, a reference electrode, and an electronic potentiostat-circuit which ensures constant electrical voltage between the sensing electrode and the reference electrode. The flow of electrons, generated by the reaction is proportional to the concentration of the measured gas. The observed concentrations of NH3, SO2, and NO2 measured within and around the study site were very variable. Levels of NH3 ranged between 0.36 - 7.36 ppm; corresponding values for SO2 and NO2 were 0.02 - 84.61 ppm and 0.61 - 34.78 ppm respectively. These concentrations were significantly higher (p < 0.05) than measured (NH3; 0.01 - 0.05 ppm: SO2; 0.03 - 0.18 ppm: NO2; 0.17 - 1.30 ppm) at the control sampling station about 5 km from the industry. Isokinetic and dissipation of the measured gases, governed by the processes of molecular diffusion and convection, confirmed a common pattern of distance dissipation. Thus, the cloud concentrations of NH3, SO2, and NO2 within the facility were higher than observed distances away from the fertilizer factory.
Zimbabwe Presidential Scholarship

Both long-term and short-term exposure to fine particulate matter (PM2.5) has been shown to increase the rate of respiratory and cardiovascular illness, premature death, and hospital admissions from respiratory causes. It is important to understand what contributes to ambient PM2.5 level to establish effective regulation, and air quality model can provide guidance based on the best scientific understanding available. However, PM2.5 simulations in air quality models have often found performance less than desirable, particularly for organic carbon levels. Here, some of major shortcomings of current air quality model will be addressed and improved by using CMAQ, receptor models, and regression analysis. Detailed source apportionment of PM2.5 performed using the CMAQ-tracer method suggests that wood combustion and mobile sources are the largest sources of PM2.5, followed by meat cooking and industrial processes. Biases in emission estimates are investigated using tracer species, such as organic molecular markers and trace metals that are used in receptor models. Comparison of simulated and observed tracer species shows some consistent discrepancies, which enables us to quantify biases in emissions and improve CMAQ simulations. Secondary organic aerosol (SOA) is another topic that is investigated. CMAQ studies on organic aerosol usually underestimate organic carbon with larger than a 50% bias. Formation of aged aerosol from multigenerational semi-volatile organic carbon is added to CMAQ, significantly improving performance of organic aerosol simulations.

Ambient and indoor air pollution is a major cause of premature mortality, and has been associated with more than three million preventative deaths per year worldwide. Most of these health impacts are from the effects from fine particulate matter. It is suspected that PM2.5 health effects vary by composition, which depends on the mixture of pollutants emitted by sources. This has led to efforts to estimate relationships between sources of PM2.5 and health effects. The health effects of PM2.5 may be preferentially dependent on specific species; however, recent work has suggested that health impacts may actually be caused by the net effect of the mixture of pollutants which make up PM2.5. Recently, there have been efforts to use source impacts from source apportionment (SA) studies as a proxy for these multipollutant effects. Source impacts can be quantified using both receptor and chemical transport models (RMs and CTMs), and have both advantages and limitations for their use in health studies. In this work, a technique is developed that reconciles differences between source apportionment (SA) models by ensemble-averaging source impacts results from several SA models. This method uses a two-step process to calculate the ensemble average. An initial ensemble average is used calculate new estimates of uncertainties for the individual SA methods that are used in the ensemble. Next, an updated ensemble average is calculated using the SA method uncertainties as weights. Finally, uncertainties of the ensemble average are calculated using propagation of errors that includes covariance terms. The ensemble technique is extended to include a Bayesian formulation of weights used in ensemble-averaging source impacts. In a Bayesian approach, probabilistic distributions of the parameters of interest are estimated using prior distributions, along with information from observed data. Ensemble averaging results in updated estimates of source impacts with lower uncertainties than individual SA methods. Overall uncertainties for ensemble-averaged source impacts were ~45 - 74%. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R2=0.66) and water soluble potassium (R2=0.63) than source impacts estimated using more traditional methods, and more closely agreed with observed total mass. Power spectra of the time series of biomass burning source impacts suggest that profiles/factors associated with this source have the greatest variability across methods and locations. A secondary focus of this work is to examine the impacts of biomass burning. First a field campaign was undertaken to measure emissions from prescribed fires. An emissions factor of 14±17 g PM2.5/kg fuel burned was determined. Water soluble organic carbon (WSOC) was highly correlated with potassium (K) (R2=.93) and levoglucosan (R2=0.98). Results using a biomass burning source profile derived from this work further indicate that source apportionment is sensitive to levels of potassium in biomass burning source profiles, underscoring the importance of quantifying local biomass burning source profiles. Second, the sensitivity of ambient PM2.5 to various fire and meteorological parameters in was examined using the method of principle components regression (PCR) to estimate sensitivity of PM2.5 to fire data and, observed and forecast meteorological parameters. PM2.5 showed significant sensitivity to PB, with a unit-based sensitivity of 3.2±1 µg m-3 PM2.5 per 1000 acres burned. PM2.5 had a negative sensitivity to dispersive parameters such as wind speed.

Fine particulate matter (PM2.5) has been associated with a variety of problems that include adverse health effects, reduction in visibility, damage to buildings and crops, and possible interactions with climate. Although stringent air quality regulations are in place, policy makers need efficient tools to test a wide range of control strategies. Sensitivity analysis provides predictions on how the interdependent concentrations of various PM2.5 components and also gaseous pollutant species will respond to specific combinations of precursor emission reductions. The Community Multiscale Air Quality Model (CMAQ) was outfitted with the Decoupled Direct Method in 3D for calculating sensitivities of particulate matter (DDM-3D/PM). This method was evaluated and applied to high PM2.5 episodes in the Southeast United States. Sensitivities of directly emitted particles as well as those formed in the atmosphere through chemical and physical processing of emissions of gaseous precursors such as SO2, NOx, VOCs, and NH3 were calculated. DDM-3D/PM was further extended to calculate receptor oriented sensitivities or the Area of Influence (AOI). AOI analysis determines the geographical extent of relative air pollutant precursor contributions to pollutant levels at a specific receptor of interest. This method was applied to Atlanta and other major cities in Georgia. The tools developed here (DDM-3D/PM and AOI) provide valuable information to those charged with air quality management.

Time-series studies of ambient air pollution and acute health outcomes utilize measurements from fixed outdoor monitoring sites to assess changes in pollution concentration relative to time-variable health outcome measures. These studies rely on measured concentrations as a surrogate for population exposure. The degree to which monitoring site measurements accurately represent true ambient concentrations is of interest from both an etiologic and regulatory perspective, since associations observed in time-series studies are used to inform health-based ambient air quality standards. Air pollutant measurement errors associated with instrument precision and lack of spatial correlation between monitors have been shown to attenuate associations observed in health studies. Characterization and adjustment for air pollution measurement error can improve effect estimates in time-series studies. Measurement error was characterized for 12 ambient air pollutants in Atlanta. Simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. This method allows for pollutant-specific quantification of impacts of measurement error on health effect estimates, both the assessed strength of association and its significance. To inform on the amount and type of error present in Atlanta measurements, air pollutant concentrations were simulated over the 20-county metropolitan area for a 6-year period, incorporating several distribution characteristics observed in measurement data. The simulated concentration fields were then used to characterize the amount and type of error due to spatial variability in ambient concentrations, as well as the impact of use of different exposure metrics in a time-series epidemiologic study. Finally, methodologies developed for the Atlanta area were applied to air pollution measurements in Dallas, Texas with consideration for the impact of this error on a health study of the Dallas-Fort Worth region that is currently underway.

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.

Atmospheric aerosols are the major component in the shorter-term variability governing the radiative balance of the climate system, particularly on regional scales. However, knowledge of the myriad of properties and processes associated with aerosols is often limited, which results in major uncertainties when assessing their climate effects. One such aspect is the chemical make-up of the atmospheric aerosol burden.Airborne measurements of aerosol properties across Northern Europe are presented here in order to facilitate constraint of the properties, processes and effects of aerosols in this highly populated and industrialised region. An Aerodyne Aerosol Mass Spectrometer (AMS) delivered highly time-resolved measurements of aerosol chemical components, which included organic matter, sulphate, nitrate and ammonium.The chemical composition of the aerosol burden was strongly determined by the dominant meteorological conditions in Northern Europe. Pollution loadings in North- Western Europe were strongly enhanced when air masses originated from Continental Europe. Conversely, much cleaner conditions were associated with air masses from the Atlantic Ocean.Organic matter was found to be ubiquitous across Northern Europe and predominantly secondary in nature, which is consistent with other analyses in polluted regions of the Northern Hemisphere. Furthermore, its concentration was generally comparable to, or exceeded that of, sulphate. Significant chemical processing of the organic aerosol component was observed. Highly oxidised secondary organic aerosol dominated, as the distance from source and photochemical processing increased.Ammonium nitrate was found to be a major component of the aerosol burden in Northern Europe, with peak contributions occurring in North-Western Europe, due to the co-location of its emission precursors (NH3 and NOx) in the region. Ammonium nitrate was found to be the dominant sub-micron chemical constituent during periods associated with enhanced pollution episodes. Its concentration was shown to be modulated by the thermodynamic structure of the lower troposphere, with enhanced concentrations prevalent at the top of the boundary layer. This phenomenon greatly enhanced the radiative impact of the aerosol burden; the increased mass and water uptake by the aerosol significantly amplified the aerosol optical depth in the region.The results presented in this thesis highlight a highly dynamic region, where major changes in emissions have played a significant role in determining the chemical composition of the aerosol burden. As substantial reductions in sulphur dioxide emissions have occurred over the past two decades in Northern Europe, the relative contribution of sulphate aerosols to the regional aerosol burden has decreased. Consequently, it is more pertinent to consider the roles of organic matter and ammonium nitrate, as their influence becomes more pronounced than sulphate on regional and global climate.

Les réseaux de capteurs sans fil (RCSF) sont largement utilisés dans les applications environnementales où l’objectif est de détecter un phénomène physique tel que la température, l’humidité, la pollution de l’air, etc. Dans ce contexte d’application, l’utilisation de RCSF permet de comprendre les variations du phénomène et donc être en mesure de prendre des décisions appropriées concernant son impact. En raison des limitations de ses méthodes de suivi traditionnelles et de sa grande variabilité spatiale et temporelle, la pollution de l'air est considérée comme l'un des principaux phénomènes physiques qui restent à étudier et à caractériser. Dans cette thèse, nous considérons trois applications concernant l’utilisation de RCSF pour le suivi de la pollution de l’air : la cartographie en temps réel de la qualité de l’air, la détection de dépassements de seuils des polluants et la correction de modèles physiques qui simulent le phénomène de dispersion de la pollution. Toutes ces applications nécessitent de déployer et d’ordonnancer minutieusement les capteurs afin de mieux comprendre la pollution atmosphérique tout en garantissant un coût de déploiement minimal et en maximisant la durée de vie du réseau. Notre objectif est de résoudre les problèmes de déploiement et d'ordonnancement tout en tenant compte des caractéristiques spécifiques du phénomène de la pollution de l’air. Nous proposons pour chaque cas d'application une approche efficace pour le déploiement de noeuds capteurs et puits. Nous proposons également une approche d’ordonnancement adaptée au cas de la correction de modèles physiques. Nos approches d'optimisation prennent en compte la nature physique de la pollution atmosphérique et intègrent les données réelles fournies par les plateformes existantes de suivi de la qualité de l’air. Dans chacune de nos approches d’optimisation, nous utilisons la programmation linéaire en nombres entiers pour concevoir des modèles d’optimisation adaptés à la résolution de petites et moyennes instances. Pour traiter les grandes instances, nous proposons des heuristiques en utilisant des techniques de relaxation linéaire. Outre nos travaux théoriques sur le suivi de la pollution atmosphérique, nous avons conçu et déployé dans la ville de Lyon un réseau de capteurs de pollution économe en énergie. Sur la base des caractéristiques de notre système et des jeux de données de la pollution atmosphérique, nous avons évalué l’efficacité de nos approches de déploiement et d’ordonnancement. Nous présentons et discutons dans cette thèse les résultats d'évaluation de performances ainsi que des lignes directrices pour la conception de systèmes de suivi de la pollution de l’air. Parmi nos principales conclusions, nous soulignons le fait que la taille optimale du réseau de capteurs dépend du degré de variation des concentrations de pollution dans la région de déploiement
Wireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical phenomenon such as temperature, humidity, air pollution, etc. In this context of application, the use of WSN allows to understand the variations of the phenomenon over the monitoring region and therefore be able to take adequate decisions regarding the impact of the phenomenon. Due to the limitations of its traditional costly monitoring methods in addition to its high spatial and temporal variability, air pollution is considered as one of the main physical phenomena that still need to be studied and characterized. In this thesis, we consider three main applications regarding the use of WSN for air pollution monitoring: 1) the construction of real time air quality maps using sensor measurements; 2) the detection of pollution threshold crossings; and 3) the correction of physical models that simulate the pollution dispersion phenomenon. All these applications need careful deployment and scheduling of sensors in order to get a better knowledge of air pollution while ensuring a minimal deployment cost and a maximal lifetime of the deployed sensor network. Our aim is to tackle the problems of WSN deployment and scheduling while considering the specific characteristics of the air pollution phenomenon. We propose for each application case a new efficient approach for the deployment of sensor and sink nodes. We also propose a WSN scheduling approach that is adapted to the case of physical models’ correction. Our optimization approaches take into account the physical nature of air pollution dispersion and incorporate real data provided by the existing pollution sensing platforms. As part of each approach, we use integer linear programming to derive optimization models that are well adapted to solving small and medium instances. To deal with large instances, we propose heuristic algorithms while using linear relaxation techniques. Besides our theoretical works on air pollution monitoring, we design from scratch and deploy in the Lyon city a cost-effective energy-efficient air pollution sensor network. Based on the characteristics of our monitoring system in addition to real world air pollution datasets, we evaluate the effectiveness of our deployment and scheduling approaches and provide engineering insights for the design of WSN-based air pollution monitoring systems. Among our conclusions, we highlight the fact that the size of the optimal sensor network depends on the degree of the variations of pollution concentrations within the monitoring region

Mobile source emissions are a major contributor to global and local air pollution. Governments and regulatory agencies have been increasing the stringency of regulations in the transportation sector for the last ten years to help curb transportation sector air pollution. The need for regulations has been emphasized by scientific research on the impacts from ambient pollution, especially research on the effect of particulate matter on human health. The particulate emissions from diesel vehicles, diesel particulate matter (DPM) is considered a known or probable carcinogen in various countries and increased exposure to DPM is linked to increased cardiovascular health problems in humans. The toxicity of vehicle emissions and diesel particulate emissions in particular, in conjunction with an increased awareness of potential petroleum fuel shortages, international conflict over petroleum fuel sources and climate change science, have all contributed to the increase of biodiesel use as an additive to or replacement for petroleum fuel. The goal of this research is to determine how this increased use of biodiesel in the particular emission testing setup impacts urban air quality. To determine if biodiesel use contributes to a health or climate benefit, both the size range and general composition were investigated using a comprehensive comparison of the particulate component of the emissions in real time. The emissions from various biodiesel and diesel mixtures from a common diesel passenger vehicle were measured with a cavity ring-down transmissometer (CRDT) coupled with a condensation particle counter, a SMPS, a nephelometer, NOx, CO, CO2, and O3 measurements. From these data, key emission factors for several biodiesel and diesel fuel mixtures were developed. This approach reduces sampling artifacts and allows for the determination of optical properties, particle number concentration, and size distributions, along with several important gas phase species' concentrations. Findings indicate that biodiesel additions to diesel fuel do not necessarily have an air quality benefit for particulate emissions in this emission testing scenario. The often cited linear decrease in particulate emissions with increasing biodiesel content was not observed. Mixtures with half diesel and half biodiesel tended to have the highest particulate emissions in all size ranges. Mixtures with more than 50% biodiesel had slightly lower calculated mass for light absorbing carbon, but this reduction in mass is most likely a result of a shift in the size of the emission particles to a smaller size range, not a reduction in the total number of particles. Evaluation of the extensive optical properties from this experimental set-up indicates that biodiesel additions to diesel fuel has an impact on emission particle extinction in both visible and near-IR wavelengths. The B99 mixture had the smallest emission factor for extinction at 532 nm and at 1064 nm. For the extinction at 532 nm, the trend was not linear and the emission factor peaked at the B50 mixture. Results from intensive properties indicate that emissions from B5 and B25 mixtures have Ångström exponents close to 1, typical for black carbon emissions. The mixtures with a larger fraction of biodiesel have Ångström exponent values closer to 2, indicating more absorbing organic matter and/or smaller particle size in the emissions. Additional experimental testing should be completed to determine the application of these results and emission factors to other diesel vehicles or types of diesel and biodiesel fuel mixtures.

Thesis (M.S.) -- University of Tennessee, Knoxville, 2005.
Title from title page screen (viewed on June 28, 2005). Thesis advisor: Wayne T. Davis. Document formatted into pages (ix, 82 p. : ill. (some col.), col. maps). Vita. Includes bibliographical references (p. 58-67).

M.Sc.
The evaluation of population exposure to air pollution is a fundamental reason for management and control of regional air quality. The purpose of this study was to determine the exposure of the local population to PM-10 emissions from sources within the Vaal Triangle using a Geographic Information System (GIS). The emission inventory compiled by van Nierop for the calendar year of 1992 (van Nierop, 1994) was used as input data for these calculations. The Industrial Source Complex Short Term Model (ISCST) was applied for dispersion calculations of annual PM-10 emissions. The ReGIS package was applied to determine the applicability of GIS as a management tool. Annual average PM-10 concentration contours were calculated for the different air pollution source groups within the Vaal Triangle. The combined source group resulted in the highest population exposure from annual average PM-10 concentrations. Population exposure from high- (> 200 m), medium- (10 to 200 m) and low- (< 10 m) elevation air pollution source groups were determined. The medium-elevation source group resulted in high population exposure followed by the low-elevation source group. The high-elevation source group had very low population exposure as a result. The population exposures from all the industrial sources within the Vaal Triangle were calculated and found to be very high. Annual average PM-10 concentrations from domestic fuel combustion sources were surprisingly low, resulting in low population exposure. ReGIS was found to be inadequate for the task and is not recommended for further use. Despite this, GIS was found to be a powerful decision-making tool and other GIS software packages should be explored for future research.

D.Phil. (Geography)
The Infrared Atmospheric Sounding Interferometer (lASI), operational in polar-orbit since 2006 on the European MetOp-A satellite, is the most advanced of its kind in space. It has been designed to provide soundings of the troposphere and lower stratosphere at nadir in a spectral interval of 0.25 em" across the range 645-2 760 em". Fine spectral sampling such as this is imperative in the sounding of trace gases. Since its launch, the routine retrievals of greenhouse, species from IASI measurements have made a valuable contribution to atmospheric chemistry studies at a global scale. The main contribution of this thesis is the development of a new trace gas retrieval scheme for IASI measurements. The goal was to improve on the global operational scheme in terms of the algorithm complexity, speed of calculation and spatial resolution achieved in the final solution. This schemedirectly retrieves column integrated trace gas densities at single field-of-view (FOV) from IASI measurements within a 10% accuracy limit. The scheme is built on the Bayesian framework of probability and based on the assumption that the inversion of total column values, as apposed to gas profiles, is a near-linear problem. Performance of the retrieval scheme is demonstrated on simulated noisy measurements for carbon monoxide (CO). Being a linear solution, the scheme is'highly dependent on the accuracy of the a priori. A statistical estimate of the a priori was computed using a principal component regression analysis with 50 eigenvectors. The corresponding root-mean-square (RMS) error of the a priori was calculated to be 9.3%. In general terms, the physical retrieval improved on the a priori, and sensitivity studies were performed to demonstrate the accuracy and stability of the retrieval scheme under a numberof perturbations. A full system characterization and error analysis is additionally preformed to elicidate the nature of this complex problem. The hyperspectral IASI measurements introduce a significant correlation error in the retrieval. The Absorption Line Cluster (ALC) channel selection method was developed in this thesis, to address the correlation error explicitly. When a first neighbour correlation factor of 0.71 is assumed in the measurement error covariance for the clusters of ALC channels, then most of the correlation error is removed in the retrieval. In conclusion, the total column trace gas retrieval scheme developed here is fast, simple, intuitive, transparent and robust. These characteristics together make it highly suitable for implementation in an operational environment intended for air quality monitoring on a regional scale.

M.Sc. (Environmental Management)
The use of atmospheric dispersion models to predict ground level pollutants concentrations has been on an increase in South Africa in the last decade. At this stage National Department of Environmental Affairs has published a draft document to provide guidelines on the type or use of models. Most Air Quality Specialists in the country make use of the United States Environmental Protection Agency approved atmospheric dispersion models to conduct air quality investigations. These models were developed in the United States of America after having considered the environmental set up and monitoring capabilities. In light of the above, much of the required input data are not readily available and calculations have been conducted to make up for the shortfall. For domestic emissions, quantifying the emissions factors is proving to be a challenge for modellers. They calculate emissions factors using different data sets from variable sources – sometimes the data are not up to date. This variability could potentially compromise the output of the model. This study aim was to model domestic emissions from an isolated rural township, Leandra, in the Mpumalanga Province – located within a nationally declared Highveld air quality management priority area – for two one month periods – in both the winter – July 2008 – and the summer – October 2008. This was achieved by using a United States Environmental Protection Agency approved AERMOD atmospheric dispersion model. Hourly surface measured meteorology data were obtained from the Langverwacht ambient air quality monitoring station and upper air data from the Irene monitoring station. The data were screened for any suspect values, formatted and then pre-processed by AERMET to be used by AERMOD. The study also investigated and compared the modelled time-series and monitored time-series data. This study calculated the effective emissions rate of 0.3 g PM10 s-1 m-2 by using a combination of monitored hourly PM10 concentrations and dispersion modelling time series data, for a typical Highveld township. Furthermore, the study revealed that, during winter when air is stagnant, Leandra was demonstrably isolated from other emissions sources of strength in the region – i.e. power station and domestic emissions were the dominant emissions sources. Under these circumstances, indoor and outdoor emissions were above the acceptable standards – i.e. they constituted unhealthy ambient air conditions. During summer – with the higher average wind speeds – Leandra was under the influence of industrial sources and the argument of isolation was not valid.

Ph.D. (Geography)
The focus of Aeolian research has mainly been on wind-blown dust from desert and arid areas. Numerous dust emission schemes have been developed over the years aimed at accurately estimating dust emission rates from various soil types and land use surfaces. Limited research has been done on wind-blown dust from smaller area sources – such as mine tailings and ash storage facilities. Lately, the concern about the environmental and health impacts, caused by dust from mine tailings storage facilities and ash disposal sites, has become more prominent, calling for better methods in determining dust emissions and their related impacts. This thesis established a practical approach for wind-blown dust emissions estimation and dispersion modelling from mine waste and ash storage facilities for the purpose of legal compliance assessment. Extensive research on the physics of wind erosion has been done over the past decade, compelling the re-evaluation of previously applied techniques. The latest and most widely applied dust emission schemes are evaluated to determine, through systematic testing of parameterisation and validation, using empirical mine waste and coal ash data, a best-practice prescription for quantifying wind-blown dust emissions and determining effects on a local scale using commercially available dispersion models. The applicability of two dust-flux schemes, (one developed by Marticorena and Bergametti (1995) and the simplified Shao 2004 scheme, as reported in 2011) for the quantification of wind-blown dust emissions, were tested using site specific particle size distribution data, bulk density and moisture content from six gold- and one platinum- tailings storage facilities and from two ash storage facilities. The availability of the required input parameters and the uncertainty associated with these parameters, were tested. The dependency of the Shao et al. (2011) model on plastic pressure (P) and the coefficient cy, both of which are not easily determined, added to the uncertainty of the emission rates. In this study, P and cy were both interpolated using the range limits provided by Shao (2004) for natural soils. By calculating P, using the salt and calcium carbonate content, similar values were obtained. The minimally disturbed dust fraction, as required by the Shao et al. (2011) scheme were derived from particle size distribution analysis but found to be more representative of the fully disturbed particle size faction (𝜂fi) and therefore needed to be corrected to represent the minimally disturbed particle size faction (𝜂mi) through the application of a correction factor, CF𝜂mi. Specific attention was given to the quantification of the threshold friction velocity (u*t) and the threshold velocities (u*), and how these two parameters relate to each under variable wind speed and time durations. This was tested using sub-hourly averaged meteorological data, one set reflected 5-minute intervals and the other 10-minute intervals. Dependent on the frequency and strength of the sub-hourly wind gusts, the resulting dust-flux rates were found to vary significantly when based on hourly averaged wind data in comparison with 5- and 10-minute wind data. Dispersion models are useful tools in air quality management. Whereas ambient monitoring provides actual ambient concentrations for specific pollutants at set locations, atmospheric dispersion models can be used to simulate any number of pollutants and determine the impacts at any location within the modelling domain. These dust-flux schemes of Marticorena and Bergametti (1995) and Shao et al. (2011) have been coupled with the US EPA regulatory Gaussian plume AERMOD dispersion model for the simulation of ground level concentrations resulting from wind-blown dust from mine tailings facilities. For this study, two Case Studies were evaluated; one included two of the gold mine tailings and the second focused on the platinum tailings. Simulated ambient near surface concentrations were validated with ambient monitored data for the same period as used in the model. For the Marticorena and Bergametti (1995) dust-flux scheme, only z0 had to be adjusted to provide a good fit with measured data – whereas the Shao et al. (2011) scheme resulted in significantly higher concentrations, resulting in an over-prediction of the measured data. By applying the correction factor, CF𝜂mi, to the minimally disturbed dust fraction, the predicted concentrations improved considerably. The coupling of the dust-flux schemes with a regulatory Gaussian plume model provided simulated ground level PM10 concentrations in good agreement with measured data. The best correlation was found under conditions of high wind speeds when the prevailing wind was from the direction of the tailings storage facility. This thesis demonstrates that simulated impacts from complex source groups can be performed, within an acceptable range of certainty, using widely applied dust-flux schemes. These dust-flux schemes, developed primarily for large-scale desert and arid areas, have been demonstrated to be applicable also to small-scale sources, of the order of 1 km2, and can be coupled to regularly available dispersion models for impact evaluations of wind-blown dust. The value of this improved approach to the mining and mineral processing industries are substantial, allowing for more accurate health risks and adverse environmental assessments from wind-blown dust from large material storage piles, a source category that has hitherto been difficult to quantify.

Ambient observations have indicated that ozone formation in the Houston area is frequently faster and more efficient, with respect to NOx consumed, than other urban areas in the country. It is believed that these unique characteristics of ozone formation in the Houston area are associated with the plumes of reactive hydrocarbons, emanating from the industrial Houston Ship Channel area. Thus, accurate quantification of industrial emissions, particularly of reactive hydrocarbons, is critical to effectively address the rapid ozone formation and the consequent high levels of ozone in the area. Industrial emissions of hydrocarbons have significant temporal variability as evidenced by various measurements, but they have been assumed to be continuous at constant levels for air quality regulation and photochemical modeling studies. This thesis examines the effect of emission variability from industrial sources on ozone formation in the HoustonGalveston area. Both discrete emission events and variability in continuous emissions are examined; new air quality modeling tools have been developed to perform these analyses. Also, this thesis evaluates the impact of emission variability on the effectiveness of emission control strategies in the Houston-Galveston area. Overall, the results indicate that industrial emission variability plays a substantial role in ozone formation and that controlling emission variability can be effective in ozone reduction. These results suggest that a quantitative treatment of emission variability should be included in the development of air quality plans for regions with extensive industrial activity, such as Houston.