Date of Award
Doctor of Philosophy
Monitoring and maintaining air quality in a built environment is essential for occupants health and safety. An indoor environment is subjected to various particulate, gaseous matter etc. Exposure to these contaminants can result in various health problems such as asthma, skin diseases and in some case cancer. Therefore indoor air quality monitoring sensors are important for early detection of these contaminants. An indoor contaminant is transported via the airflow. Various building uncertainties affect the airflow. Therefore it is important to account these uncertainties for designing optimal sensor network. Further, in case of an accidental or intentional release of hazardous contaminants, the network should also assist for risk assessments such as after release contaminant source distribution and identifying source location. The purpose of this research is to develop a unified framework for designing an optimal contaminant monitoring sensor network accounting building uncertainties and develop a methodology for carrying risk assessment under hazardous contaminant release. The framework uses the discrete form of Perron-Frobenius (P-F) transfer operator to carry fast, accurate contaminant transport analysis. The work develops a methodology for accounting occupancy and weather uncertainties to designing the sensor network. Once constructed the P-F operator is also used with an Ensemble Kalman Filter (EnKF) estimator to estimate contaminant distribution using sensor measurement. Further, for identifying the release location a Bayesian inference method is developed using the constructed P-F operator. The developed framework can be used in developing strategies for people evacuation during toxic contaminant release containment of airborne infectious disease. It can also be integrated with it with the buildings to make smart HVAC systems.
Sharma, Himanshu, "A framework for indoor air quality sensor placement accounting for uncertainties and performing risk assessments" (2019). Graduate Theses and Dissertations. 17314.