Comparing Building and Neighborhood-Scale Variability of CO2 and O3 to Inform Deployment Considerations for Low-Cost Sensor System Use

Abstract

The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO2 (a primary pollutant) and O3 (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions.

Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

“…In order to detect a variety of gaseous compounds for inferring the presence of a local pollution source, it was necessary to develop an array of sensing elements that were sensitive to a varied mix of pollutants at relevant concentrations. This was accomplished by modifying the existing sensor platform, the Y-Pod [18,19]. The Y-Pod is an Arduino-based open-source platform that is typically configured to include nondispersive infrared (NDIR), photoionization detector (PID), metal oxide (MOx), and electrochemical types of low-cost sensors for gaseous compounds (see Figure 1).…”

Kitchen Area Air Quality Measurements in Northern Ghana: Evaluating the Performance of a Low-Cost Particulate Sensor within a Household Energy Study

“…The G-Pod is a modified U-Pod sensor platform designed and built by the University of Colorado, Boulder's Hannigan Air Quality and Technology Research Lab (mobilesensingtechnology.com; https: //www.colorado.edu/lab/hannigan), used extensively in air quality research applications [9,14,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. The G-Pod represents a version of a fleet of second-generation U-Pods with the capacity to measure carbon monoxide (electrochemical CO-B4; Alphasense, UK), nitrogen oxides (electrochemical NO 2 -B4 and NO-B4; Alphasense, UK), ozone (metal oxide MICS-2611, SGX, Switzerland, formerly MicroChemical Systems SA), carbon dioxide (nondispersive infrared S-300; ELT, Korea), volatile organic compounds (metal oxide 2600, 2601; Figaro, USA), temperature and humidity (capacitive and band gap SHT25; Sensirion AG, Switzerland), pressure (piezoresistive BMP-180; Bosch Sensortec, Germany), and location (global positioning system (GPS), 63530 Copernicus II; Trimble, USA) for less than 1500 USD.…”

Understanding the ability of low-cost MOx sensors to quantify ambient VOCs

“…Figure 1b includes a diagram of the interior or a Y-Pod, a newer version of the technology utilizing the same sensors. U-Pods and newer versions of the system (the Y-Pods) have been used in several indoor and outdoor air quality studies that included sensor quantification and an examination of spatial variability or air quality trends Cheadle et al, 2017;Sadighi et al, 2018;Collier-Oxandale et al, 2018a).…”

“…Low-cost sensing systems often cost orders of magnitude less than conventional instruments on a per-unit basis and are simpler to deploy and operate making them particularly well-suited to provide preliminary or supplementary data for community-based projects or projects in partnership with environmental justice communities where resources may be limited (Shamasunder et al, 2018). Deployments of these sensing systems have already demonstrated the capacity to provide information on pollutant variability at small scales (Cheadle et al, 2017;Sadighi et al, 2018;Collier-Oxandale et al, 2018a), to differentiate regional trends from local emissions (Heimann et al, 2015), and to support personal exposure monitoring (Piedrahita et al, 2014;Jerrett et al, 2017). However, sensor performance in regards of quantification is an ongoing challenge for this technology.…”