Interpreting Mobile and Handheld Air Sensor Readings in Relation to Air Quality Standards and Health Effect Reference Values: Tackling the Challenges

Abstract

The US Environmental Protection Agency (EPA) and other federal agencies face a number of challenges in interpreting and reconciling short-duration (seconds to minutes) readings from mobile and handheld air sensors with the longer duration averages (hours to days) associated with the National Ambient Air Quality Standards (NAAQS) for the criteria pollutants-particulate matter (PM), ozone, carbon monoxide, lead, nitrogen oxides, and sulfur oxides. Similar issues are equally relevant to the hazardous air pollutants (HAPs) where chemical-specific health effect reference values are the best indicators of exposure limits; values which are often based on a lifetime of continuous exposure. A multi-agency, staff-level Air Sensors Health Group (ASHG) was convened in 2013. ASHG represents a multi-institutional collaboration of Federal agencies devoted to discovery and discussion of sensor technologies, interpretation of sensor data, defining the state of sensor-related science across each institution, and provides consultation on how sensors might effectively be used to meet a wide range of research and decision support needs. ASHG focuses on several fronts: improving the understanding of what hand-held sensor technologies may be able to deliver; communicating what hand-held sensor readings can provide to a number of audiences; the challenges of how to integrate data generated by multiple entities using new and unproven technologies; and defining best practices in communicating health-related messages to various audiences. This review summarizes the challenges, successes, and promising tools of those initial ASHG efforts and Federal agency progress on crafting similar products for use with other NAAQS pollutants and the HAPs. NOTE: The opinions expressed are those of the authors and do not necessary represent the opinions of their Federal Agencies or the US Government. Mention of product names does not constitute endorsement.

Measurements of PM<sub>2.5</sub> with PurpleAir under atmospheric conditions

“…Each PMS5003 sensor has an effective measurement range for PM 2.5 concentration of 0-500 µg m −3 with a resolution of 1 µg m −3 , and the maximum standard PM 2.5 concentration is above 1000 µg m −3 . According to the manufacturer, each PMS5003 sensor will work effectively in a T range of −10 to 60 • C and RH range of 0 %-99 % (Yong, 2018).…”

End-User Feedback on a Low-Cost Portable Air Quality Sensor System—Are We There Yet?

“…Even if low-cost portable sensors can bring new opportunities for observing air pollution at higher spatial resolutions while providing personalized air quality data [ 25 ], there are many challenges preventing the wide-scale implementation of such devices. Some of these challenges relate to low-quality data deriving from the use of low-cost sensors and the lack of meaningful information that they provide to the public [ 3 , 8 , 21 , 26 , 27 , 28 , 29 ]. It is also recognized that although the involvement of citizens increases spatiotemporal coverage of an area [ 30 ], data collected by non-experts might be considered less reliable [ 31 ].…”

Authors' response to RC1

“…and health messaging in megacities with significant variability in microenvironments (Mead et al, 2013;Kumar et al, 2015;Ramaswami et al, 2016). Sensors also enable new techniques for mobile monitoring (McKercher and Vanos, 2018;Woodall et al, 2017). However, without a proper understanding of sensor data quality and calibration, low-cost sensors have the potential to mislead interested community and research groups (Rai et al, 2017).…”

“…Correlations of low-cost sensors have been found to vary from study to study, spanning from negligible to high correlations. Recent studies have shown the correlation between sensors and reference measurements can be improved by the application of correction factors for environmental conditions such as relative humidity (Crilley et al, 2018) or multivariate models and machine learning (Cross et al, 2017;Zimmerman et al, 2018;Hagan et al, 2018).…”