Using Machine Learning to Develop a Calibration Model for Low-Cost Air Quality Sensors Deployed during a Dust Event
Description:
Low-cost sensors have the potential to create dense air monitoring networks that help enhance our understanding of pollution exposure and variability at the individual and neighborhood-level; however, sensors can be easily influenced by environmental conditions, resulting in performance inconsistencies across monitoring settings. During summer 2020, 20 low-cost particulate sensors were deployed with a reference PM2.5 monitor in Denton, Texas in preparation for calibration. However, from mid to …
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Date:
May 2021
Creator:
Hickey, Sean
Partner:
UNT Libraries