Date of Award

Winter 12-2016

Embargo Period

6-7-2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Robotics Institute

Advisor(s)

Illah R. Nourbakhsh

Abstract

Air quality has long been a major health concern for citizens around the world, and increased levels of exposure to fine particulate matter (PM2:5) has been definitively linked to serious health effects such as cardiovascular disease, respiratory illness, and increased mortality. PM2:5 is one of six attainment criteria pollutants used by the EPA, and is similarly regulated by many other governments worldwide. Unfortunately, the high cost and complexity of most current PM2:5 monitors results in a lack of detailed spatial and temporal resolution, which means that concerned individuals have little insight into their personal exposure levels. This is especially true regarding hyper-local variations and short-term pollution events associated with industrial activity, heavy fossil fuel use, or indoor activity such as cooking. Advances in sensor miniaturization, decreased fabrication costs, and rapidly expanding data connectivity have encouraged the development of small, inexpensive devices capable of estimating PM2:5 concentrations. This new class of sensors opens up new possibilities for personal exposure monitoring. It also creates new challenges related to calibrating and characterizing inexpensively manufactured sensors to provide the level of precision and accuracy needed to yield actionable information without significantly increasing device cost. This thesis addresses the following two primary questions: 1. Can an inexpensive air quality monitor based on mass-manufactured dust sensors be calibrated efficiently in order to achieve inter-device agreement in addition to agreement with professional and federally-endorsed particle monitors? 2. Can an inexpensive air quality monitor increase the confidence and capacity of individuals to understand and control their indoor air quality? In the following thesis, we describe the development of the Speck fine particulate monitor. The Speck processes data from a low-cost dust sensor using a Kalman filter with a piecewise sensing model. We have optimized the parameters for the algorithm through short-term co-location tests with professional HHPC-6 particle counters, and verified typical correlations between the Speck and HHPC-6 units of r2 > 0:90. To account for variations in sensitivity, we have developed a calibration procedure whereby fine particles are aerosolized within an open room or closed calibration chamber. This allows us to produce Specks for commercial distribution as well as the experiments presented herein. Drawing from previous pilot studies, we have distributed low-cost monitors through local library systems and community groups. Pre-deployment and post-deployment surveys characterize user perception of personal exposure and the effect of a low-cost fine particulate monitor on empowerment.

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