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

Exposure to ground-level ozone (O₃), which forms secondarily in the atmosphere, intensifies the risk of respiratory and cardiovascular diseases. Effective mitigation strategies require understanding the spatiotemporal variability of O₃ precursors, including nitrogen oxides (NOx) and volatile organic compounds (VOCs), as well as O₃ formation photochemistry. This thesis examines the concentrations of trace gases closely related to O₃ production, specifically nitrogen dioxide (NO₂, the dominant component of NOx) and formaldehyde (HCHO, a proxy for VOC reactivity), as well as photochemical conditions. I investigate how these factors differ on high-O₃ days, change diurnally, and respond to the temporal resolution of anthropogenic emissions. The focus is on the summer of 2018 due to the availability of trace gas retrievals from the TROPOspheric Monitoring Instrument (TROPOMI) and in situ measurements from field campaigns. I first investigate New York City (NYC) and the Baltimore/Washington D.C. area, where high O₃ levels frequently occur in summer. On high-O₃ days (when the maximum daily 8-hour average (MDA8) O₃ exceeds 70 ppb), tropospheric vertical column densities (VCDTrop) of HCHO and NO₂ increase in urban centers. The HCHO/NO2 VCDTrop ratio, proposed as an indicator of local surface O₃ production sensitivity to its precursors, generally rises due to a more pronounced increase in HCHO VCDTrop. This suggests a shift toward a more NOx-sensitive O₃ production regime that could enhance the effectiveness of NOx controls on the highest O₃ days. As retrievals of tropospheric trace gases from Low Earth Orbit (LEO) satellites like TROPOMI are limited to one overpass per day (early afternoon), I then analyze spatial variability in HCHO and NO₂ concentration diurnal patterns and connect changes in column densities with surface concentrations. Diurnal HCHO patterns indicate the impact of temperature-dependent VOC emissions, while a bimodal surface NO₂ pattern reflects diurnal patterns of local anthropogenic NOx emissions and boundary layer dynamics. Column concentration peaks generally occur about four hours after surface concentration peaks (morning for NO2 and midday for HCHO), highlighting the challenge of relating column densities to health-related surface concentrations. I also explore how the temporal resolution of anthropogenic emissions influences air pollution levels and diurnal variations. Surface NOx and O3 concentrations show different spatial patterns of change when switching from daily mean to hourly varying nitric oxide emissions. In urban areas of both the western and eastern CONUS, adding hourly NO emissions increases daytime emissions, leading to O₃ decreases, indicating NOx-saturated O₃ chemistry. In the western CONUS, monthly mean surface NO₂ increases, while in the eastern CONUS, characterized by shorter NO₂ lifetimes, NO₂ decreases. These sensitivities highlight the importance of accounting for diurnal changes when inferring emissions from concentrations. This thesis advances our understanding of O₃-NOx-VOC air pollution by exploring variations in both surface and column conditions across urban-rural gradients. It integrates in situ measurements, space-based observations, and modeling techniques and assesses advanced modeling tools for future applications. These findings support the future applications of geostationary satellite retrievals for continuous trace gas observation throughout daylight hours, supplementing the once-a-day LEO satellite data used in this thesis, with implications such as aiding source attribution and targeting cost-effective control measures for O₃ mitigation.

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.