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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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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.
Keyes et al. (Fri,) studied this question.