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

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 O 3 and CO 2 in a 314 km 2 area of Riverside County near Los Angeles, California. 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 O 3 and CO 2 measurements to minute resolution reference observations resulted in R-squared and root mean squared errors (RMSE) of 0.95–0.97 and 4.4–7.2 ppbv for O 3 and 0.79 and 15 ppmv CO 2 , 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 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.

[1]  Debbie A. Niemeier,et al.  The impact of rush hour traffic and mix on the ozone weekend effect in southern California , 2007 .

[2]  David E Williams,et al.  High density ozone monitoring using gas sensitive semi-conductor sensors in the Lower Fraser Valley, British Columbia. , 2014, Environmental science & technology.

[3]  E. Kort,et al.  Surface observations for monitoring urban fossil fuel CO2 emissions: Minimum site location requirements for the Los Angeles megacity , 2013 .

[4]  L. Morawska,et al.  The rise of low-cost sensing for managing air pollution in cities. , 2015, Environment international.

[5]  G. Hagler,et al.  Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. , 2016, Atmospheric measurement techniques.

[6]  L. Shang,et al.  The next generation of low-cost personal air quality sensors for quantitative exposure monitoring , 2014 .

[7]  Gb Stewart,et al.  The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks , 2013 .

[8]  B. Hubbell,et al.  Assessing Temporal and Spatial Patterns of Observed and Predicted Ozone in Multiple Urban Areas , 2016, Environmental health perspectives.

[9]  Kevin R. Gurney,et al.  Urbanization and the carbon cycle: Current capabilities and research outlook from the natural sciences perspective , 2014 .

[10]  R. Weiss,et al.  Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project – Part 1: calibration, urban enhancements, and uncertainty estimates , 2016, Atmospheric chemistry and physics.

[11]  E. Kort,et al.  Diurnal tracking of anthropogenic CO 2 emissions in the Los Angeles basin megacity during spring 2010 , 2013 .

[12]  U. Lerner,et al.  On the feasibility of measuring urban air pollution by wireless distributed sensor networks. , 2015, The Science of the total environment.

[13]  J. Salmond,et al.  Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network , 2013 .

[14]  D. Jacob.  Heterogeneous chemistry and tropospheric ozone , 2000 .

[15]  Robert Bogue,et al.  Recent developments in MEMS sensors: a review of applications, markets and technologies , 2013 .

[16]  G. Korotcenkov.  Metal oxides for solid-state gas sensors: What determines our choice? , 2007 .

[17]  Chun Lin,et al.  Evaluation and calibration of Aeroqual Series 500 portable gas sensors for accurate measurement of ambient ozone and nitrogen dioxide , 2015 .