Intra-urban spatial variability of surface ozone in Riverside, CA: viability and validation of low-cost sensors
- 1Department of Mechanical Engineering, University of Colorado Boulder, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USA
- 2South Coast Air Quality Management District, Air Quality Sensor Performance Evaluation Center, 21865 Copley Drive, Diamond Bar, CA 91765-4178, USA
- 3Department of Computer Science, University of Colorado Boulder, 430 UCB, 1111 Engineering Drive, Boulder, CO 80309, USA
Abstract. Sensor networks are being more widely used to characterize and understand compounds in the atmosphere like ozone (O3). This study employs a measurement tool, called the U-Pod, constructed at the University of Colorado Boulder, to investigate spatial and temporal variability of O3 in a 200 km2 area of Riverside County near Los Angeles, California. This tool contains low-cost sensors to collect ambient data at non-permanent locations. The U-Pods were calibrated using a pre-deployment field calibration technique; all the U-Pods were collocated with regulatory monitors. After collocation, the U-Pods were deployed in the area mentioned. A subset of pods was deployed at two local regulatory air quality monitoring stations providing validation for the collocation calibration method. Field validation of sensor O3 measurements to minute-resolution reference observations resulted in R2 and root mean squared errors (RMSEs) of 0.95–0.97 and 4.4–5.9 ppbv, respectively. Using the deployment data, ozone concentrations were observed to vary on this small spatial scale. In the analysis based on hourly binned data, the median R2 values between all possible U-Pod pairs varied from 0.52 to 0.86 for ozone during the deployment. The medians of absolute differences were calculated between all possible pod pairs, 21 pairs total. The median values of those median absolute differences for each hour of the day varied between 2.2 and 9.3 ppbv for the ozone deployment. Since median differences between U-Pod concentrations during deployment are larger than the respective root mean square error values, we can conclude that there is spatial variability in this criteria pollutant across the study area. This is important because it means that citizens may be exposed to more, or less, ozone than they would assume based on current regulatory monitoring.