PhD. (Energy Studies) / A review of health effects of emissions from solid fuel combustion shows clear links between morbidity and mortality, and residential combustion smoke exposure. On the interior plateau of the South African Highveld, use of coal fuel in informal domestic braziers – imbaulas – constitutes a major source of local ambient and household air pollution. This thesis aimed to develop an improved understanding of the complex processes of packed-bed combustion in small domestic devices studying smoke emissions from informal domestic stoves. A robust dilution sampling system for testing emissions from residential coal-burning appliances was developed and used in the emission studies. Systematic experiments were carried out to evaluate thermal performance and emissions of coal braziers, varying fire ignition method, ventilation rate, fuel moisture and fuel quality. Three field-collected and three laboratory constructed braziers were tested, with a range of ventilation hole-densities. The variables measured are particle mass (PM2.5 and PM10), gases (CO, CO2, NOx), and particle composition and morphology. Emission factors, referenced to zero excess oxygen are reported. Two fire-ignition methods are evaluated namely: the conventional bottom-lit updraft (BLUD) method, and the top-lit updraft (TLUD)–the so-called Basa njengo Magogo method. PM2.5 and PM10 emissions reduced by 80% on average when using the TLUD in contrast to the business-as-usual BLUD method. High smoke emissions from the BLUD method during pyrolysis are found to be associated with an oxygen deficit, allowing products of incomplete combustion to be emitted. Influences of ventilation rates on the stove emissions are reported – products of incomplete combustion (PM2.5 and CO) are higher for low ventilation rates. For a given device, PM2.5 and PM10 emission factors reduce by ~50% from low to high ventilation rates (an advantage offset by firepower too high for convenient cooking).

Die ab dem 1.1.2010 geltenden erweiterten Luftqualitätsgrenzwerte stellen die Kom-munen vor allem in verkehrlich belasteten Gebieten vor Probleme. Zum einen haben die Kommunen sicherzustellen, dass die Immissionsgrenzwerte eingehalten werden, zum anderen stehen ihnen aber nur eine Reihe beschränkt wirkungsvoller Maßnahmen zur Verfügung. Wie können die (Groß-) Städte darauf reagieren? Zunächst kann festgehalten werden, dass der Verkehrsbereich zukünftig den Schwerpunkt von Maßnahmen zu Klimaschutz und Luftreinhaltung bilden muss. Die wesentlichen urbanen Problemfelder werden durch den Verkehr bestimmt; bei den relevanten Luftschadstoffen stellen Fahrzeuge mit Dieselmotoren die Hauptemittenten dar und zur Reduktion der CO2-Emissionen müssen alle Fahrzeuge deutlich mehr beitragen als bisher. In der Vergangenheit war die Reduktion von Verkehrsemissionen vorrangig als Frage der Weiterentwicklung der Fahrzeugtechnik interpretiert worden. Da die technischen Weiterentwicklungen allein für die Problemlösung nicht ausreichen, sind grundsätzliche Änderungen von Verkehrsverhalten und Verkehrssystemen unumgänglich. Eine Verbesserung der Raumordnung, weniger Zersiedelung, eine multifunktionale Stadt der kurzen Wege und ein anderes Mobilitätsverhalten der Bevölkerung weisen die höchsten Reduktionspotentiale auf, wirken aber vor allem langfristig. Eine Übersicht mit denkbaren Maßnahmengruppen zur Erreichung der Luftqualitäts- und Klimaschutzziele wurde erarbeitet, die zur Entwicklung spezifischer Pakete von Kommunen genutzt werden kann.

Die ab dem 1.1.2010 geltenden erweiterten Luftqualitätsgrenzwerte stellen die Kom-munen vor allem in verkehrlich belasteten Gebieten vor Probleme. Zum einen haben die Kommunen sicherzustellen, dass die Immissionsgrenzwerte eingehalten werden, zum anderen stehen ihnen aber nur eine Reihe beschränkt wirkungsvoller Maßnahmen zur Verfügung. Wie können die (Groß-) Städte darauf reagieren? Zunächst kann festgehalten werden, dass der Verkehrsbereich zukünftig den Schwerpunkt von Maßnahmen zu Klimaschutz und Luftreinhaltung bilden muss. Die wesentlichen urbanen Problemfelder werden durch den Verkehr bestimmt; bei den relevanten Luftschadstoffen stellen Fahrzeuge mit Dieselmotoren die Hauptemittenten dar und zur Reduktion der CO2-Emissionen müssen alle Fahrzeuge deutlich mehr beitragen als bisher. In der Vergangenheit war die Reduktion von Verkehrsemissionen vorrangig als Frage der Weiterentwicklung der Fahrzeugtechnik interpretiert worden. Da die technischen Weiterentwicklungen allein für die Problemlösung nicht ausreichen, sind grundsätzliche Änderungen von Verkehrsverhalten und Verkehrssystemen unumgänglich. Eine Verbesserung der Raumordnung, weniger Zersiedelung, eine multifunktionale Stadt der kurzen Wege und ein anderes Mobilitätsverhalten der Bevölkerung weisen die höchsten Reduktionspotentiale auf, wirken aber vor allem langfristig. Eine Übersicht mit denkbaren Maßnahmengruppen zur Erreichung der Luftqualitäts- und Klimaschutzziele wurde erarbeitet, die zur Entwicklung spezifischer Pakete von Kommunen genutzt werden kann.

The very concept of sustainable development calls for a holistic understanding of socio-economic and natural systems in order to help achieve greater sustainability. The complexity characterizing such systems, however, makes it likely impossible for quantified approaches of even isolated problems to account for all relevant factors in a single, robust and deterministic representation of reality – an inherent feature which largely motivates the use of statistical models applied to empirical data. On three independent examples with significant socio-economic and environmental importance, the present dissertation illustrates how econometrical models applied to real-life environmental data can be fruitfully deployed to facilitate the identification and motivation of innovative policies to achieve greater sustainability. Specifically, the first chapter explores the extent to which large-scale irrigation affects local climate by inducing cooler temperatures in areas located downwind from irrigated land, an externality with positive economic consequences quantified in terms of improved crop yields and reduced human mortality. The second chapter illustrates the benefits offered by a family of new differencing estimators (as theoretically derived from a generalization of existing techniques found in the literature) on the example of the nonparametric estimation of error variance in streamflow measurements - a step that is critical for the accurate prediction by hydrological models of extreme flood events. The third chapter investigates the joint effect of traffic speed and acceleration on urban air quality in order to help anticipate the consequences of innovative traffic regulation on the concentration of key air pollutants with detrimental consequences on human health and the economy.

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.

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.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography: leaves 361-380. / by Andrew Schiffrin. / M.C.P.

Sponsored by Boston Edison Company, New England Power Service Company, Northeast Utilities Service Company under M.I.T. Energy Laboratory Electric Utility Program.