Low-cost PM2.5 sensors can help identify driving factors of poor air quality and benefit communities

Tim Keyes; Rea Domingo; Samantha Dynowski; Royal Graves; Martha Klein; Melissa Leonard; John Pilgrim; Alison Sanchirico; Kate Trinkaus

doi:10.1016/j.heliyon.2023.e19876

Low-cost PM_2.5 sensors can help identify driving factors of poor air quality and benefit communities

Heliyon. 2023 Sep 6;9(9):e19876. doi: 10.1016/j.heliyon.2023.e19876. eCollection 2023 Sep.

Authors

Tim Keyes^{1

2}, Rea Domingo², Samantha Dynowski³, Royal Graves³, Martha Klein³, Melissa Leonard³, John Pilgrim³, Alison Sanchirico³, Kate Trinkaus³

Affiliations

¹ Evergreen Business Analytics, LLC, USA.
² Sacred Heart University, USA.
³ Sierra Club Connecticut, USA.

Abstract

Air quality is critical for public health. Residents rely chiefly on government agencies such as the Environmental Protection Agency (EPA) in the United States to establish standards for the measurement of harmful contaminants including ozone, sulfur dioxide, carbon monoxide, volatile organic chemicals (VOCs), and fine particulate matter at or below 2.5 μm. According to the California Air Resources Board [1], "short-term PM_2.5 exposure (up to 24-h duration) has been associated with premature mortality, increased hospital admissions for heart or lung causes, acute and chronic bronchitis, asthma attacks, emergency room visits, respiratory symptoms, and restricted activity days". While public agency resources may provide guidance, it is often inadequate relative to the widespread need for effective local measurement and management of air quality risks. To that end, this paper explores the use of low-cost PM_2.5 sensors for measuring air quality through micro-scale (local) analytical comparisons with reference grade monitors and identification of potential causal factors of elevated sensor readings. We find that a) there is high correlation between the PM_2.5 measurements of low-cost sensors and reference grade monitors, assessed through calibration models, b) low-cost sensors are more prevalent and provide more frequent measurements, and c) low-cost sensor data enables exploratory and explanatory analytics to identify potential causes of elevated PM_2.5 readings. This understanding should encourage community scientists to place more low-cost sensors in their neighborhoods, which can empower communities to demand policy changes that are necessary to reduce particle pollution, and provide a basis for subsequent research.

Keywords: Air quality; Attribution modeling; Calibration modeling; Low-cost sensors; PM2.5; Particulate matter; Public health; Reference grade monitors; Statistical regression.