Quantifying Neighborhood-Scale Spatial Variations of Ozone at Open Space and Urban Sites in Boulder, Colorado Using Low-Cost Sensor Technology

Abstract

Recent advances in air pollution sensors have led to a new wave of low-cost measurement systems that can be deployed in dense networks to capture small-scale spatio-temporal variations in ozone, a pollutant known to cause negative human health impacts. This study deployed a network of seven low-cost ozone metal oxide sensor systems (UPods) in both an open space and an urban location in Boulder, Colorado during June and July of 2015, to quantify ozone variations on spatial scales ranging from 12 m between UPods to 6.7 km between open space and urban measurement sites with a measurement uncertainty of ~5 ppb. The results showed spatial variability of ozone at both deployment sites, with the largest differences between UPod measurements occurring during the afternoons. The peak median hourly difference between UPods was 6 ppb at 1:00 p.m. at the open space site, and 11 ppb at 4:00 p.m. at the urban site. Overall, the urban ozone measurements were higher than in the open space measurements. This study evaluates the effectiveness of using low-cost sensors to capture microscale spatial and temporal variation of ozone; additionally, it highlights the importance of field calibrations and measurement uncertainty quantification when deploying low-cost sensors.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region

“…The correlation observed between Atmos and reference instruments for measured PM 2.5 was better than that for PM 10 (Supplementary Figure S3). Some of the recent papers from other countries have also shown that low-cost sensors' PM 2.5 matches more in comparison to PM 10 [8]. In the case of PM 2.5 as well, we observed p < 0.001 for all four cases.…”

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

“…In another example, researchers demonstrated the feasibility of collecting personal CO, NO 2 , O 3 , and CO 2 exposure data with uncertainty estimations using a portable, wearable system (Piedrahita et al, 2014). Several studies have also made use of sensors to study the spatial variability of O 3 on various scales (Sadighi et al, 2018;Cheadle et al, 2017;Moltchanov et al, 2015). Connected to this effort on sensor applications, there has been much work evaluating the performance of individual sensors (Masson et al, 2015a, b;Spinelle et al, 2015Spinelle et al, , 2017Lewis at al., 2016) and demonstrating the performance of different calibration approaches (Zimmerman et al, 2018;Kim et al, 2018;Cross et al, 2017).…”

“…All data are logged to an onboard micro-SD card. As configured, these pods draw roughly 11 W. These systems have been used in several other indoor and outdoor air quality studies (Casey et al, 2018;Sadighi et al, 2018;Cheadle et al, 2017). Figure 1 includes a labeled photo of a Y-Pod interior and a photo of two Y-Pods deployed.…”

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. …”

“…estimations using a portable, wearable system (Piedrahita et al, 2014). Several studies have also made use of sensors to study the spatial variability of O3 on various scales Cheadle et al, 2017;Moltchanov et al, 2015).…”