<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>