The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

Abstract

Abstract. Advances in embedded systems and low-cost gas sensors are enabling a new wave of low cost air quality monitoring tools. Our team has been engaged in the development of low-cost wearable air quality monitors (M-Pods) using the Arduino platform. The M-Pods use commercially available metal oxide semiconductor (MOx) sensors to measure CO, O3, NO2, and total VOCs, and NDIR sensors to measure CO2. MOx sensors are low in cost and show high sensitivity near ambient levels; however they display non-linear output signals and have cross sensitivity effects. Thus, a quantification system was developed to convert the MOx sensor signals into concentrations. Two deployments were conducted at a regulatory monitoring station in Denver, Colorado. M-Pod concentrations were determined using laboratory calibration techniques and co-location calibrations, in which we place the M-Pods near regulatory monitors to then derive calibration function coefficients using the regulatory monitors as the standard. The form of the calibration function was derived based on laboratory experiments. We discuss various techniques used to estimate measurement uncertainties. A separate user study was also conducted to assess personal exposure and M-Pod reliability. In this study, 10 M-Pods were calibrated via co-location multiple times over 4 weeks and sensor drift was analyzed with the result being a calibration function that included drift. We found that co-location calibrations perform better than laboratory calibrations. Lab calibrations suffer from bias and difficulty in covering the necessary parameter space. During co-location calibrations, median standard errors ranged between 4.0–6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for CO2. Median signal to noise (S/N) ratios for the M-Pod sensors were higher for M-Pods than the regulatory instruments: for NO2, 3.6 compared to 23.4; for O3, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for CO2, 42.2 compared to 300–500. The user study provided trends and location-specific information on pollutants, and affected change in user behavior. The study demonstrated the utility of the M-Pod as a tool to assess personal exposure.

Practical field calibration of electrochemical NO<sub>2</sub> sensors for urban air quality applications

“…temperature and humidity in a regression model or neural network, as shown by e.g. Piedrahita et al (2014), Spinelle et al (2015b), Masson et al (2015).…”

Traffic Maps and Smartphone Trajectories to Model Air Pollution, Exposure and Health Impact

“…Thus, the two exponential terms in Equation (19) are both approximately equal to unity and their sum is equal to two, so that:…”

“…However, low cost sensors are typically neither stable nor accurate [18] [19] [20] [21]. It is infeasible to scale up to population level due to the large cumulative cost.…”

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

“…CC BY 4.0 License. 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).…”