Measuring ambient air quality using low-cost sensors

Exposure to ambient air pollution increases morbidity and mortality - it is the leading environmental risk factor for the global burden of disease. Accurate and reliable air quality measurements can help identify sources of pollution, enabling mitigation strategies that lead to longer, healthier lives and reduce the economic impact caused by air pollution. Low-cost sensors (LCS) offer the opportunity to vastly increase our ability to make air quality measurements with finer spatiotemporal resolution than ever before; however, the use of such sensors has outpaced our understanding of their abilities, limitations, and overall utility. This thesis describes several detailed characterization experiments and modeling exercises that were carried out to better understand the strengths and limitations of using LCS for measuring air pollution and investigates new ways in which they can be used to identify types and sources of particulate pollution. First, we explore the use of electrochemical sensors for measuring sulfur dioxide in Hawaii. We introduce a new algorithm for reducing the uncertainty while still capturing high concentration data points and we demonstrate that electrochemical sensor performance does not degrade over the course of several months. Second, we investigate the long-term viability of electrochemical sensors with a three-year deployment in Boston. We show that CO and NO electrochemical sensors show little-to-no degradation over the course of at least two years. Third, we designed and built a Mie Theory-based model for exploring the ability of low-cost particle sensors to accurately measure fine particles. We show the expected differences between two commonly used technologies under a variety of ambient conditions. Last, we demonstrate how multipollutant LCS, despite their inherent limitations, can be used to provide insight into sources of fine particulate matter in complex urban environments. The results from this work demonstrate that LCS can be viable tools that can be integrated into the greater air quality measurement ecosystem and can be used to obtain reliable data at scales previously unseen.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Hagan, David Henry.

Advisor dc:contributor.advisor

• Jesse H. Kroll.

• Civil and Environmental Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/138522

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/138522