Coupling Physical Measurement with Machine Learning for Holistic Environmental Sensing

The interest in characterizing the abundance and nature of airborne particulates has been increasing over the last decade, driven in large part by the rising awareness of the manifold health impacts of airborne particulates. Since regulatory observations of airborne particulates are usually made with expensive instruments, the number of sensors that can be deployed is naturally limited by the costs involved. This dissertation describes the substantial progress we have made in the physical sensing of airborne particulates by providing low-cost, high-quality observations of airborne particulates by utilizing advances in low-cost laser-based sensors, that can be deployed at scale, coupled with machine learning used for accurate calibration of these low-cost sensors. The abundance of airborne particulates is usually quantified by an integrated mass density in µg/m3 over the airborne aerosol size distribution (e.g. PM2.5, the integrated mass density of all airborne particulates with a diameter of up to 2.5 microns). A persistent feature of all airborne observations of particulates is the variability over small temporal and spatial scales. This persistent and ubiquitous variability underscores the value of being able to deploy a large number of low-cost sensors that can make accurate measurements every few seconds, 24/7. Taking this into account, I have built, calibrated, and deployed a large number of sensors across the Dallas-Fort Worth (DFW) Metroplex in Texas as a part of my dissertation work. Other physical measurements can also be utilized in accurate assessment of airborne particulates. Just as weather RADARs are used to examine the spatial and temporal distribution of atmospheric precipitation, we show that if we use machine learning, we can also employ the weather RADARs to examine the spatial distribution of airborne particulates. CO2 has gained a lot of attention in recent years due to global warming. It is considered the principal anthropogenic greenhouse gas driving global warming. As a result, CO2 levels must be monitored and controlled. The present study describes how machine learning can be used to calibrate a low-cost CO2 sensor which is already part of the sensor systems that I have built and deployed. This dissertation provides an overview of how low-cost physical sensing can be combined with machine learning to provide environmental sensing systems at scale, thus using physics in service of society.