Analysis of short-term ozone and PM2.5measurements: Characteristics and relationships for air sensor messaging.
Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.



Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
Elizabeth
E
Mannshardt
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
Kristen
K
Benedict
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
Scott
S
Jenkins
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
Martha
M
Keating
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
David
D
Mintz
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
Susan
S
Stone
a U.S. Environmental Protection Agency , Research Triangle Park , NC , USA.
Richard
R
Wayland
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D010126:Ozone / Q000032:analysis*; D052638:Particulate Matter / Q000032:analysis*; D011634:Public Health / Q000592:standards; D014481:United States; D014484:United States Environmental Protection Agency / Q000592:standards
D000393:Air Pollutants; D052638:Particulate Matter; D010126:Ozone