Intra-urban spatial variability of surface ozone and carbon dioxide in Riverside, CA: viability and validation of low-cost sensors

Abstract

Abstract. Sensor networks are being more widely used to characterize and understand compounds in the atmosphere such as ozone and carbon dioxide. 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 and CO2 in a 314 km 2 area of Riverside County near Los Angeles, 10California. This tool provides 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 and CO2 measurements to minute resolution reference observations resulted in R-squared and root mean squared errors (RMSE) of 0.95 -0.97 and 4.4 -15 7.2 ppbv for O3 and 0.79 and 15 ppmv CO2, respectively. Using the deployment data, ozone and carbon dioxide concentrations were observed to vary on this small spatial scale. In the analysis based on hourly binned data, the median R-squared 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 ppb for the ozone deployment. For carbon dioxide, distributions 20 of all measurements vary from 413 -425 ppm during the calibration (collocation) and 406 -472 during the deployment. Since median differences between U-Pod concentrations during deployment are larger than the respective root mean square error values for ozone and carbon dioxide, we can conclude that there is spatial variability in these pollutants across the study area. This is important because it means that citizens may be exposed to more ozone than they would assume based on current regulatory monitoring. 25Atmos. Meas. Tech. Discuss., https://doi

Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments

“…For context, co-location studies of different electrochemical sensors targeting more abundant pollutants have found correlations with reference instruments (r 2 ) to range between 0.7 and 0.96 for O 3 , NO 2 , NO, and CO (Cross et al, 2017;Jiao et al, 2016;Mead et al, 2013;Popoola et al, 2016;Zimmerman et al, 2017), with estimates of RMSE spanning 4-60 ppb for O 3 (Cross et al, 2017;Sadighi et al, 2017;Spinelle et al, 2015), 4-22 ppb for NO (Cross et al, 2017;Masson et al, 2015), 39 ppb for CO (Cross et al, 2017), and 4.5 ppb for NO 2 (Cross et al, 2017) and estimates of MAE of 38 ppb for CO, 3.5 ppb for NO 2 , and 3.4 ppb for O 3 (Zimmerman et al, 2017). However, it is difficult to directly compare performance metrics (r 2 , RMSE) obtained from the different calibration algorithms taken in these different studies, given the differences not only in sensor types but in also environmental conditions (T , RH, range of pollutant concentrations, and interferences by other pollutants).…”

“…There are multiple advantages to this approach: the reference instruments are regularly calibrated, the reference measurement data are generally made publicly available (e.g., EPA AirNow, US EPA, 2017;OpenAQ, Hasenkopf, 2017), and the calibrations are carried out under ambient conditions that are (at least partially) representative of the sensor measurements to be made. Indeed, the effectiveness of co-location has been demonstrated in several recent studies, with sensor outputs (voltages) and other environmental parameters (e.g., temperature) related to the true concentration values (from the reference instruments) via some form of regression from either parametric models (Jiao et al, 2016;Lewis et al, 2015;Masson et al, 2015;Mueller et al, 2017;Popoola et al, 2016;Sadighi et al, 2017;Smith et al, 2017) or machine-learning/nonparametric methods (Cross et al, 2017;Spinelle et al, 2015;Zimmerman et al, 2017).…”

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

“…As configured, these Pods draw roughly 11 Watts. These systems have been used in several other indoor and outdoor air quality studies (Casey et al, 2017;Sadighi et al, 2017, Cheadle et al, 2017. …”

“…Field normalization provides one approach to correcting for the cross-sensitivities low-cost sensors tend to exhibit with respect to temperature, humidity, and other trace gases (Spinelle et al, 2015(Spinelle et al, , 2017Sadighi et al, 2017, Wang et al, 2010 and this method is implemented by co-locating 15 low-cost sensor systems with high-quality reference instruments (typically regulatory-grade monitors) for a given period and then generating a calibration model using an approach such as linear regression. These calibration models predict the methane concentration (in ppm) based on the sensor signal (Rs/R0) and other predictors.…”

The BErkeley Atmospheric CO<sub>2</sub> Observation Network: Field Calibration and Evaluation of Low-cost Air Quality Sensors

“…Devices and networks of devices are emerging that are low cost, report at high time resolution, and are capable of long-term deployment, providing potential for improvement over the two major weaknesses of the passive sampling. Examples include metal oxide sensors used to measure O 3 , CO, NO 2 , and total VOCs (Williams et al, 2013;Bart et al, 2014;Piedrahita et al, 15 2014;Moltchanov et al, 2015;Sadighi et al, 2017), and electrochemical sensors used to measure CO, NO, NO 2 , O 3 , and SO 2 (Mead et al, 2013;Sun et al, 2015;Jiao et al, 2016;Hagan et al, 2017;Jerrett et al, 2017;Michael et al, 2017). These different low-cost sensor systems were compared during the 1 st EuNetAir Air Quality Joint Intercomparison Exercise in 2014 (Borrego et al, 2016).…”