Portable mass spectrometry has become an important analytical tool for chemical detection and identification outside of a lab setting. Many variations and applications have been developed to benefit various fields of science. Membrane inlet mass spectrometry is used to allow certain analytes to pass into the mass spectrometer without breaking vacuum or letting in large particulate matter. These two important analytical tools have been applied to the detection of harmful chemicals in the air. Earth-based separations and reverse gas stack modelling are useful mathematical tools that can be used to locate the source of a chemical release by back calculation. Earth-based separations studies the way different molecules will diffuse and separate through the air. Reverse gas stack modelling refers to the concentration differences of a chemical in relation to its distance from its source. These four analytical techniques can be combined to quickly and accurately locate various harmful chemical releases. The same system can be used for many applications and has been tested to detect harmful chemicals within and air-handling system. The monitoring of air-handling systems can greatly reduce the threat of harm to the building occupants by detecting hazardous chemicals and shutting off the air flow to minimize human exposure.

Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1977 / Includes bibliographical references. / by Michael K. Martin. / M.S. / M.S. Massachusetts Institute of Technology, Sloan School of Management

Many applied statistical applications face the potential problem of model contamination and measurement error. The form and degree of contamination as well as the measurement error are usually unknown and sample-specific, which brings additional challenges for researchers. In this thesis, we have proposed several Bayesian inference models to address these issues, with the application to one type of special data for allergen concentration measurement, which is called serial dilution data and is self-calibrated. In our first chapter, we address the problem of model contamination by using a multilevel model to simultaneously flag problematic observations and estimate unknown concentrations in serial dilution data, a problem where the current approach can lead to noisy estimates and difficulty in estimating very low or high concentrations. In our second chapter, we propose the Bayesian joint contamination model for modeling multiple measurement units at the same time while adjusting for differences between experiments using the idea of global calibration, and it could account for uncertainty in both predictors and response variables in Bayesian regression. We are able to get efficacy gain by analyzing multiple experiments together while maintaining robustness with the use of hierarchical models. In our third chapter, we develop a Bayesian two-step inference model to account for measurement uncertainty propagation in regression analysis when the joint inference model is infeasible. We aim to increase model inference reliability while providing flexibility to users by not restricting the type of inference model used in the first step. For each of the proposed methods, We also demonstrate how to integrate multiple model building blocks through the idea of Bayesian workflow. In extensive simulation studies, we show that our proposed methods outperform other commonly used approaches. For the data applications, we apply the proposed new methods to the New York City Neighborhood Asthma and Allergy Study (NYC NAAS) data to estimate indoor allergen concentrations more accurately as well as reveal the underlying associations between dust mite allergen concentrations and the exhaled nitric oxide (NO) measurement for asthmatic children. The methods and tools developed here have a wide range of applications and can be used to improve lab analyses, which are crucial for quantifying exposures to assess disease risk and evaluating interventions.

Atmospheric pollution in some degree has been around since time began. In recent times it has reached levels in some areas which proved to be harmful to man's health. A brief history review of these occurrences is presented. Laws have been enacted to combat this threat. A brief review of these laws is presented. Instruments for remote sensing of polluting sources are being developed to monitor ambient air quality and aid in enforcing these laws. A review of the techniques employed and the present state of the art is explored. Available instruments are presented in section III-5.

The efficacy of indoor environment evaluation is, in part, a function of the reliability and validity of the different measures used. This thesis presents results of a study, conducted in a building without known problems, which compares the reliability and validity of sensory comfort components from three well-known survey questionnaires. A review of literature reveals that sensory comfort theory draws upon many disciplines including, hedonics, psychometrics, and olfaction theory. The fundamental domains thermal, air quality, lighting, and acoustics and their dimensions are identified. The conceptual model integrates these theories underlying human response to sensory comfort. The research questions involved in the selection of survey questionnaires are explored by examining sixteen indoor environment survey questionnaires. A meta-evaluation reveals that these questionnaires have three major functions, proactive, reactive, and re-evaluative studies. Finally, the methods used to analyze survey questionnaires for reliability and validity are examined. An analysis of variance shows that the order in which questions were presented did not affect responses. The reliability of the measures tested ranged from poor to good. Examination of content and face validity by expert and untrained judges demonstrates inconsistencies in common or accepted meanings of the measures considered in evaluating the indoor environment. Analysis of construct validity indicates that not all survey questionnaire variables were categorized under their expected dimensions. Contrary to advice found in the literature, this thesis suggests that the practice of combining items from different questionnaires is problematic. Finally, in buildings with known problems we can expect a relatively high degree of reliability and validity. However, the utility of such questionnaires in inventorying and assessing buildings without known problems will prove to be questionable. / Master of Urban and Regional Planning

An online, semi-continuous instrument to measure both total and gas phase atmospheric reactive oxygen species (ROS) and determine the concentration of ROS in the particle phase (ROS(p)) was developed. This instrument was based on a fluorescent probe for quantifying ambient ROS, specifically 2'7'-dichlorodihydrofluorescin, or DCFH probe. This probe was analyzed for sensitivity to a variety of offline and online parameters for efficient use in a field instrument. The ROS(p) instrument measures the peak light intensity at 530 nm to determine ambient ROS concentrations. ROS particles and gases are collected in a mist chamber in a nebulized mist. The instrument alternates measurements of ROS(p+g), or ROS(tot) by means of an inline filter. Fine (PM₂.₅) (ROS(p) is determined by subtraction of the ROS(g) concentration from the ROS(tot), as the ROS(g) signal could not be excluded. This instrument was tested during the summer (May-July) of 2012 at urban and rural sites in the metropolitan Atlanta and surrounding region. Concentrations of ROS(p) determined from this instrument were often below limit of detection. Average concentrations of ROS(p) were found to be 0.25 nmol/m³ in urban Atlanta (Jefferson St. and Georgia Tech), and 0.15 nmol/m³ in Yorkville, a rural site. A side by side comparison of this method with a filter collection method was made in July. The average ROS(p) offline concentrations were 0.15 nmol/m³. These concentrations were comparable to the online average concentrations of 0.21 nmol/m³ for the same period of time. This average and the majority of the measurements comprising it is dominated by the high limit of detection. The ROS instrument as constructed and operated is an efficient way to conduct ROS(p) measurements at the level of a filter study while reducing the labor intensive filter collection and extraction. In order for this instrument to be successful at measuring ambient ROS in the particle phase, the removal of the gas phase from the current sampling scheme is critical as the ROS(g) concentrations are over 90% of the measured ROS. The system as currently operable is best suited for source measurements, including biomass burning plumes or fresh exhaust to capture immediate formation.

No abstract available. / Thesis (Ph.D.)-University of Natal, Durban, 1986.

M.Sc. (Environmental Management) / With the rapid expansion of the industries in South Africa, environmental problems including air pollution have been increasing. Among industries that cause air pollution is the iron and steel industry. Air pollution impacts negatively on the environment and therefore the measures implemented to improve air quality by this industry were investigated. The purpose of this thesis was to critically analyse the air pollution control measures implemented by the iron and steel industry in South Africa and to find out what they are doing to address the problem of air pollution, as well as their processes in involving and encouraging community involvement with regard to environmental issues. The key findings from this study were that the South African iron and steel companies are doing their best in trying to control the problem of air pollution. Some of them do not only rely on the South African legislation, they also do self-regulation by monitoring and controlling the air pollution problems even if not strictly required to by legislation. The iron and steel industry does also involve communities, through participation in public environmental forums. Air pollution has always been an issue in South Africa, but due to a lack of enabling legislation in the country, many people were not aware of their environmental rights. Now that the South African Constitution highlights the rights of people to an environment that is not harmful to their health or wellbeing, people are becoming more aware and have started taking the issue of air pollution in a very serious light. With the new environmental legislation including the National Environmental Management Act 108 of 1009 and the National Environmental Management: Air Quality Bill (Draft 1, April 2003), most of the issues relating to air pollution will be dealt with in a better and more enforceable way.

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.