This thesis explores the use of spatiotemporal data mining in the air quality domain to understand causes of PM2.5 air pollution. PM2.5 refers to fine particulate matter less than 2.5 microns in diameter and is a major threat to human and environmental health. A review of air quality modeling methods is provided, emphasizing data-driven modeling techniques. While data mining methods have been applied to air quality data, including temporal sequence mining algorithms, spatiotemporal sequence mining methods have not been broadly applied to study air pollution. However, air pollution is highly spatial in nature, so such methods can offer new insights into air quality. This thesis applies one such method, the Spatiotemporal Sequence Miner (STS Miner) algorithm, to air quality data from a low-cost sensor network to explore causes and trends related to PM2.5. To facilitate the use of this method, an open-source library called OpenSTSMiner is developed to implement this algorithm. Various domain results are found; for instance, low temperature and low relative humidity are strongly associated with worsening levels of air quality. Lastly, to highlight the utility of the STS Miner algorithm, a comparison is presented between STS Miner and spatial Markov chains, another spatiotemporal modeling method used in the air quality domain.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2332660 |
| Date | 05 1900 |
| Creators | Biancardi, Michael Anthony |
| Contributors | Huang, Yan, Liang, Lu, Yuan, Xiaohui |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Biancardi, Michael Anthony, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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