<p>Indoor air quality in office buildings can impact the health, well-being, and productivity of occupants. In most buildings, occupants exhaled breath is the primary source of carbon dioxide (CO₂). Concentrations of indoor CO₂ are also strongly associated with the operational mode of the mechanical ventilation system. While CO2 is routinely monitored in indoor environments, there are few spatially-resolved real-time measurements of CO₂ throughout mechanical ventilation systems. Such measurements can provide insight into indoor- and outdoor-generated CO₂ dispersion throughout a building and between the building and the outdoor atmosphere. This thesis aims to investigate spatiotemporal variations in CO₂ concentrations and mass fluxes throughout a mechanical ventilation system of a living laboratory office in a LEED-certified building. The impact of human occupancy patterns and ventilation conditions of CO₂ concentrations and fluxes was evaluated. </p> <p>A four-month measurement campaign was conducted in one of the four living laboratory offices at the Ray W. Herrick Laboratories. The living laboratory offices feature precise control and monitoring of the mechanical ventilation system via an advanced building automation system. Various mechanical ventilation modes were implemented, such as variable outdoor air exchange rates (AERs) and recirculation ratios. A novel multi-location sampling manifold was used to measure CO₂ at eight locations throughout the ventilation system, such as across the outdoor, supply, and return air ducts. Office occupancy was measured via a chair-based temperature sensor array. Volumetric airflow rate data and CO₂ concentration data were used to estimate CO₂ mass fluxes through the ventilation system. The CO₂ mass flux for the outdoor and exhaust air was used to evaluate the net CO₂ transport from the office to the outdoor atmosphere. </p> <p>The measurements demonstrate that there exist significant spatiotemporal variations in CO₂ concentrations across the outdoor, supply, and return air ducts. CO₂ concentrations varied with human occupancy in the office and the outdoor AER of the mechanical ventilation system. Due to human-associated CO₂ emissions, the net CO₂ mass flux from the office to the outdoor environment was approximately 700 kg of CO₂ per year. Thus, occupied offices may represent an important, yet unrecognized, source of CO₂ to the urban atmosphere.</p>

<p dir="ltr">The goal of this thesis is to develop a method of evaluating long-term IAQ performance of an industrial facility and use machine-learning to model the relationship between critical air pollutants and the facility’s HVAC systems and processes. The facility under study for this thesis is an electroplating manufacturer. The air pollutants at this facility that were studied were particulate matter, total-volatile organic compounds, and carbon-dioxide. Upon sensor installation, seven “zones” were identified to isolate areas of the plant for measurement and analysis. A statistical review of the long-term data highlighted how this facility performed in terms of compliance. Their gaseous pollutants were well within regulation. Particulate matter, however, was found to be a pressing issue. PM10 was outside of compliance more than 15% of the time in five out of seven of the zones of study. Some zones were out of compliance up to 80% of the total collection period. The six pollutants that met these criteria were deemed critical and moved on to machine learning modeling. Our model of best fit for each pollutant used a gaussian process regression model, which fits best for non-linear rightly skewed datasets. The performance of each of our models was deemed significant. Every model had at least a regression coefficient of 0.935 and above for both validation and testing. The maximum average error was 12.64 ug.m^3, which is less than 10% of the average PM10 concentration. Through our modeling, we were able to study how HVAC and production played a role in particulate matter presence for each zone. Exhaust systems of the west side of the plant were found to be insufficient at removing particulates from their facility. Overall, the methods developed in this thesis project were able to meet the goal of analyzing IAQ compliance, modeling critical pollutants using machine learning, and identifying a relationship between these pollutants and an industrial facility’s HVAC and production systems.</p>

<p>This study is focused on the design, production, and operation of a controlled environmental olfaction chamber to evaluate human physiological and emotional response to volatile chemical emissions (VCPs) from scented household products in addition to careful characterization of the volatile organic compounds (VOCs) present in these product emissions. Utilizing proton transfer reaction time-of-flight mass spectrometry, the chamber can collect VCP emissions and identify VOCs present to complete an accurate chemical profile of household and common product emissions not previously known. This instrument is one of the first of its kind and will serve as a key element in understanding the relationship between human physical and cognitive health and the built environment.</p>