Use of electronic cigarettes (e-cigarettes) is rapidly growing around the world. E-cigarettes are commonly used as an alternative nicotine delivery system, and have been advocated as generating lower levels of harmful chemicals compared to conventional cigarettes. Cigarette smoke-like aerosols are generated when e-cigarettes heat e-liquids. The main components of e-cigarette liquids are propylene glycol (PG) and glycerol (GL) in a varying ratio, plus nicotine and flavor chemicals. Both PG and GL are considered safe to ingest in foods and beverages, but the toxicity of these chemicals in aerosols is unknown. Current studies of e-cigarettes have mainly focused on dehydration and oxidation products of PG and GL. In this study, the other degradation products that can be generated during the vaping process are discussed. In addition, the gas/particle partitioning of chemicals in vaping aerosols is determined. This work finds that the formation of benzene in electronic cigarettes depends on the wattage, types of coils, and devices. To simulate commerical e-cigarette liquids, mixtures containing equal parts of PG and GL by volume were made with the following added components: benzoic acid (BA), benzoic acid with nicotine (Nic), benzaldehyde (BZ), band enzaldehyde with nicotine. PG only, GL only, and PG and GL mixtures were also made for comparison. The data presented here demonstrate that more benzene is generated as the wattage of a device increases. The results also seem to support the importance of ventilation in the generation of benzene. More benzene is generated from the mixtures containing benzoic acid when using the EVOD device with a smaller vent. However, benzaldehyde yields more benzene when using the Subtank Nano device with a larger vent. Findings also indicate that more benzene is produced from GL rather than PG. This thesis also addresses the chemical formation pathways of degradation compounds found in the aerosols formed from isotopically labeled e-cigarette liquids. Mixtures of both 13C-labeled and unlabeled PG as well as GL were made. The mixtures were vaped and gas-phase samples were collected to determine which chemicals were in the gas-phase portion of the aerosols. With the use of GC/MS methods, these isotopic labeling experiments provided evidence that the majority of the benzene, acetaldehyde, 2,3-butanedione, toluene, xylene, acrolein, and furan found in e-cigarette aerosols originates from GL in the PG plus GL mixtures. It was also shown that the majority of propanal is derived from PG: while hydroxyacetone can be formed from both PG and GL. Possible mechanisms for the formation of acetaldehyde, benzene, 2,3-butanedione, toluene, and xylene formation are proposed. Last, this study investigated the gas/particle partitioning of nicotine and flavor-related chemicals in e-cigarette fluids. The gas/particle partitioning behavior of chemicals in e-cigarettes fluids is highly dependent on the chemical volatility. A total of 37 compounds were examined. The target compounds were divided into 3 groups based on their vapor pressures: high, medium, and low. Headspace gas samples were collected and analyzed to determine the concentration of a compound in equilibrium with the liquid phase. The gas and liquid concentrations were used to calculate the gas/particle partitioning constant (Kp) for each compound. In an e-cigarette aerosol, volatile compounds have smaller Kp values and tend to be found in greater proportion in the gas-phase, whereas the less volatile compounds are likely to stay in the particle phase. General agreement with theory was found for compounds with known activity coefficients in PG and GL, indicating that theory can be used to predict Kp values for other compounds.

Flame and smoke image processing and analysis could improve performance to detect smoke or fire and identify many complicated fire hazards, eventually to help firefighters to fight fires safely. Deep Learning applied to image processing has been prevailing in recent years among image-related research fields. Fire safety researchers also brought it into their studies due to its leading performance in image-related tasks and statistical analysis. From the perspective of input data type, traditional fire research is based on simple mathematical regressions or empirical correlations relying on sensor data, such as temperature. However, data from advanced vision devices or sensors can be analyzed by applying deep learning beyond auxiliary methods in data processing and analysis. Deep Learning has a bigger capacity in non-linear problems, especially in high-dimensional spaces, such as flame and smoke image processing. We propose a video-based real-time smoke and flame analysis system with deep learning networks and fire safety knowledge. It takes videos of fire as input and produces analysis and prediction for flashover of fire. Our system consists of four modules. The Color2IR Conversion module is made by deep neural networks to convert RGB video frames into InfraRed (IR) frames, which could provide important thermal information of fire. Thermal information is critically important for fire hazard detection. For example, 600 °C marks the start of a flashover. As RGB cameras cannot capture thermal information, we propose an image conversion module from RGB to IR images. The core of this conversion is a new network that we innovatively proposed: Dual-Attention Generative Adversarial Network (DAGAN), and it is trained using a pair of RGB and IR images. Next, Video Semantic Segmentation Module helps extract flame and smoke areas from the scene in the RGB video frames. We innovated to use synthetic RGB video data generated and captured from 3D modeling software for data augmentation. After that, a Video Prediction Module takes the RGB video frames and IR frames as input and produces predictions of the subsequent frames of their scenes. Finally, a Fire Knowledge Analysis Module predicts if flashover is coming or not, based on fire knowledge criteria such as thermal information extracted from IR images, temperature increase rate, the flashover occurrence temperature, and increase rate of lowest temperature. For our contributions and innovations, we introduce a novel network, DAGAN, by applying foreground and background attention mechanisms in the image conversion module to help reduce the hardware device requirement for flashover prediction. Besides, we also make use of combination of thermal information from IR images and segmentation information from RGB images in our system for flame and smoke analysis. We also apply a hybrid design of deep neural networks and a knowledge-based system to achieve high accuracy. Moreover, data augmentation is also applied on the Video Semantic Segmentation Module by introducing synthetic video data for training. The test results of flashover prediction show that our system has leading places quantitative and qualitative in terms of various metrics compared with other existing approaches. It can give a flashover prediction as early as 51 seconds with 94.5% accuracy before it happens.

Tobacco smoke particulate matter (PM) is a complex mixture of condensed organic compounds, with about 5 to 10% water. Its general properties are similar in some respects to that of atmospheric organic aerosol PM and thus provides a useful surrogate when studying atmospheric PM. Due to its ability to undergo acid-base chemistry, nicotine is of particular interest in the tobacco smoke system. The gas/particle partitioning of nicotine depends on the protonation state of nicotine in the particles, so the distribution of nicotine between these phases provides a means of understanding the acid-base balance in the tobacco smoke system. The goal of this work is to develop an acid-base balance for mainstream tobacco smoke that accounts for the extent of protonation of nicotine. Samples of extracted smoke particulate matter from seven brands of cigarettes were analyzed by ion chromatography (IC) and titration by both acid (HCl) and base (lithium phenoxide) for comparison with nicotine data collected by colleagues. IC analysis was used to quantify tracers of known acidic and basic species in tobacco smoke. Anion tracers for acids included: glycolate, acetate, formate, lactate, chloride, nitrite, sulfate, and nitrate. The cation tracers for base were ammonium, sodium, and potassium. The tobacco smoke extracts were also analyzed after acidification by the HCl titrant for changes in ammonia and organic acid concentrations to determine whether "bound" forms of these compounds were present in the PM. The titration data provided total concentrations of weak acid and bases in the samples. This titration data was compared with the concentrations of the tracers for weak acids and bases (along with the quantification of total nicotine by colleagues) to determine whether the IC analyses were accounting for all of the important species. The results of this comparison show that these analyses missed relevant species in the tobacco smoke system. As tobacco smoke PM is a complex organic mixture, the ability of acid species to protonate nicotine will be different than in aqueous media. The acidic species of interest were assumed to be either strong or weak, with the strong species assumed to be fully ionized after protonation of nicotine. Some portion of the weak acid species could then protonate any available nicotine. An electroneutrality equation (ENE) was developed for the tobacco smoke PM and populated using the IC data and the nicotine data obtained by colleagues. Using this ENE, the extent ionization of the weak acids species (α1A) and the net reaction constant for the protonation of nicotine by these weak acids (K*) was estimated. However, interpretation of the results were complicated by the underrepresentation of the pertinent weak acid species in our IC analyses. This study concluded that further work is needed to identify the missing weak acid and base species to obtain a better representation of the acid-base balance in tobacco smoke PM and to understand the ability of these weak acid species to protonate nicotine.

Some metals and metalloids make significant contributions to the harmful effects of tobacco consumption although understanding the mechanisms involved in toxicity is hampered by the lack of information on their chemical and valence species, both in tobacco and in smoke. This research addresses the speciation of the metals and metalloids most frequently implicated, particularly those elements that exist in nature in multiple valence states, namely arsenic (As) and chromium (Cr), there being considerable differences in toxicity with oxidation state. A strategy was devised to overcome some of the problems that have thwarted earlier attempts at speciation. Firstly tobacco plants were cultivated under controlled conditions in compost burdened with high levels of metals and metalloids resulting in leaf with up to 250 µg g⁻¹ As, although Cr uptake was less successful. Secondly valence speciation even at the exceptionally low concentrations of As and Cr in smoke from unburdened tobacco was achieved with XANES analysis using the exceptionally bright Diamond synchrotron source. This revealed that combustion of tobacco has a marked effect on valence speciation with As(III), the reduced form of As, dominating (and persisting) in condensate of tobacco smoke while ash is dominated by the oxidised form, As(V). Chromium also appears to be present in smoke mainly as reduced Cr(III) species. HPLC-ICPMS analysis of arsenic indicates the dominance of inorganic over organic species (~4:1). Other metals were investigated in less detail. These findings establish that arsenic is present in smoke in its most toxic form and represents a significant risk to health. Conversely smokers appear to be exposed to the less harmful species of chromium. These results support a recent WHO report that includes As but not Cr in a list of four metals and metalloids recommended for regulation in crops and commercial products in the interests of public health.

Acid-base equilibria in organic particulate matter (PM) are poorly understood, but have important implications for air quality and public health. First, acid-base reactions in organic particulate matter affect the gas/particle partitioning of organic compounds in the atmosphere, and these processes are not currently represented in atmospheric and climate change models. Second, the acid-base balance of tobacco smoke affects the amount of nicotine absorbed by the smoker, and a greater understanding of this balance would help to relate cigarette smoke composition to the addictive properties of cigarettes. This work presents data related to both air quality and tobacco smoke modeling. The gas/particle partitioning behavior of organic acids and bases is highly dependent on acid-base equilibria and speciation between neutral and ionic forms, because ionic compounds do not volatilize. Descriptions of acid dissociation behavior in atmospheric PM have, to date, focused primarily on phases in which the solvent is water; however, atmospheric PM may include up to 90% organic matter. Data is presented here describing the acid dissociation behavior of organic acids and protonated amines in organic/aqueous mixtures (chosen to approximate the characteristics of organic PM) with varying levels of water content. In such mixtures, the preferential solvation of ions and neutral molecules (by the aqueous portion or the organic portion, respectively) affects the acid-base equilibria of the solutes. It is demonstrated that neutralization reactions between acids and bases that create ions are likely to have non-negligible effects on gas/particle partitioning under certain atmospheric conditions. Thus, including acid-base reactions in organic gas/particle partitioning models could result in a greater proportion of acidic and basic compounds partitioning to the particulate phase. In addition, the acid dissociation constants (pKa values) of atmospherically-relevant acids and bases vary with water content. Specifically, as water content increases, the pKa values of organic acids decrease dramatically, while the pKa values of protonated amines changes only slightly. This situation can result in drastically different speciations and partitioning behavior depending on water content. This second part of this work reports some of the data needed to develop an acid-base balance for tobacco smoke PM using electroneutrality as a governing principle. Five brands of cigarettes were sampled and the smoke PM extracted. Cations (sodium, potassium, and ammonia) and anions (organic acids, nitrate, nitrite, and chloride) were measured using ion chromatography. Ammonia and organic acids were also re-measured after the acidification of the sample in order to determine whether "bound" forms of these compounds exist in cigarette PM. Weak acids were determined by acid-base titration to determine whether or not all of the weak acids (including organic acids) had been accounted for by the ion chromatography. Weak bases were also determined by acid-base titration, and the majority of weak base is expected to be accounted for by total nicotine (to be measured in a separate analysis). In terms of total acidic species and total basic species, two of the five cigarette brands measured were relatively basic, and three were relatively acidic. Between 50% and 89% of the titrated acids were accounted for by the anionic species measured in ion chromatography. Based on samples tested after sample acidification, about half of the potential ammonia in tobacco smoke PM exists in "bound" form. The speciation of weak acids and bases in tobacco smoke PM cannot be determined from this data alone, because the equilibrium constants of acid-base reactions are not understood in complex organic media. The data presented here, when combined with data from free-base and total nicotine analyses, represent a first step toward a predictive model of acid-base behavior in tobacco smoke PM.