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. These M-Pods house two types of sensors – commercially available metal oxide semiconductor (MOx) sensors used to measure CO, O3, NO2, and total VOCs, and NDIR sensors used to measure CO2. The 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. We conducted two types of validation studies – first, deployments at a regulatory monitoring station in Denver, Colorado, and second, a user study. In the two deployments (at the regulatory monitoring station), M-Pod concentrations were determined using collocation calibrations and laboratory calibration techniques. M-Pods were placed near regulatory monitors to 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. The deployments revealed that collocation calibrations provide more accurate concentration estimates than laboratory calibrations. During collocation 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 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. By contrast, lab calibrations added bias and made it difficult to cover the necessary range of environmental conditions to obtain a good calibration. A separate user study was also conducted to assess uncertainty estimates and sensor variability. In this study, 9 M-Pods were calibrated via collocation multiple times over 4 weeks, and sensor drift was analyzed, with the result being a calibration function that included baseline drift. Three pairs of M-Pods were deployed, while users individually carried the other three. The user study suggested that inter-M-Pod variability between paired units was on the same order as calibration uncertainty; however, it is difficult to make conclusions about the actual personal exposure levels due to the level of user engagement. The user study provided real-world sensor drift data, showing limited CO drift (under −0.05 ppm day−1), and higher for O3 (−2.6 to 2.0 ppb day−1), NO2 (−1.56 to 0.51 ppb day−1), and CO2 (−4.2 to 3.1 ppm day−1). Overall, the user study confirmed the utility of the M-Pod as a low-cost tool to assess personal exposure.

Household cooking using solid biomass fuels is a major global health and environmental concern. As part of the Research on Emissions Air quality Climate and Cooking Technologies in Northern Ghana study, we conducted 75 in-field uncontrolled cooking tests designed to assess emissions and efficiency of the Gyapa woodstove, Philips HD4012, threestone fire and coalpot (local charcoal stove). Emission factors (EFs) were calculated for carbon monoxide (CO), carbon dioxide (CO), and particulate matter (PM). Moreover, modified combustion (MCE), heat transfer (HTE) and overall thermal efficiencies (OTE) were calculated across a variety of fuel, stove and meal type combinations. Mixed effect models suggest that compared to traditional stove/fuel combinations, the Philips burning wood or charcoal showed significant fuel and energy based EF differences for CO, but no significant PM changes with wood fuel. MCEs were significantly higher for Philips wood and charcoal-burning stoves compared to the threestone fire and coalpot. The Gyapa emitted significantly higher ratios of elemental to organic carbon. Fuel moisture, firepower and MCE fluctuation effects on stove performance were investigated with mixed findings. Results show agreement with other in-field findings and discrepancies with some lab-based findings, with important implications for estimated health and air quality impacts.

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.

Traditional air quality monitoring relies on point measurements from a small number of high-end devices. The recent growth in low-cost air sensing technology stands to revolutionize the way in which air quality data are collected and utilized. While several technologies have emerged in the field of low-cost monitoring, all suffer from similar challenges in data quality. One technology that shows particular promise is that of electrolytic (also known as amperometric) sensors. These sensors produce an electric current in response to target pollutants. This work addresses the development of practical models for understanding and quantifying the signal response of electrolytic sensors. Such models compensate for confounding effects on the sensor response, such as ambient temperature and humidity, and address other issues that affect the usability of low-cost sensors, such as sensor drift and inter-sensor variability.

Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a 1-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesburg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing and explore cross sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area -highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

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Biomass burning for home energy use is a major health and environmental concern. While transitioning to cleaner cooking technologies has the potential to generate significant health and environmental benefits, prior efforts to introduce improved cookstoves have encountered many hurdles. Here, we focus on the increased stove use hurdle; households tend to use improved stoves alongside their traditional stoves rather than replacing them entirely, a phenomenon called cookstove "stacking." This work provides a systematic, multi-method assessment of households' cooking behaviors and cookstove stacking in the context of a 200-home randomized cookstove intervention study in Northern Ghana. Two stoves were selected for the intervention, a locally made rocket stove (Gyapa) and the Philips HD4012 LS gasifier stove. There were four intervention groups: a control group, a group given two Gyapa stoves, a group given two Philips stoves, and a group given one of each. Two stoves were distributed to each home in an attempt to induce more substitution away from traditional stoves. Adoption and usage patterns were quantified using temperature loggers at a subset of homes, as well as quarterly surveying in all households. We find that using multiple stoves each day is common practice within each intervention group, and that the two groups given at least one Gyapa had the largest reductions in traditional stove use relative to the control group, though use of traditional stoves remained high in all groups.

REACCTING (Research on Emissions Air Quality, Climate, and Cooking Technologies in Northern Ghana) was a 200-home cookstove intervention study from 2013 to 2015. Study households were divided into four groups: a control group, a group given two locally made rocket stoves, a group given two Philips forced draft stoves, and a group given a locally made rocket stove and a Philips stove. In a subset of study households, 48-hour PM exposure samples were collected for adults and children, as well as in the primary cooking area. Further, weekly ambient background PM samples were collected for the first nine months of the study. All PM samples were analyzed for elemental and organic carbon (EC/OC), and a subset was also analyzed for organics. Mixed effects modeling was applied to quantify differences in PM exposures between the groups and to assess relationships between exposures and cooking area measurements. Results showed that personal OC exposure for the intervention groups was 56.6% lower than the control group (p≤0.01). Both intervention groups given Philips stoves had significantly lower EC exposure than the control group (60.6% reduction, p≤0.02). Only weak relationships were found between personal and cooking area EC or OC. Source apportionment modeling was performed on both the personal/microenvironment and the ambient organics PM data sets to assess the sources of the observed PM. We identified six PM sources. The identified source factors were similar among the data sets, as well as with previous work in Navrongo. Two sources, one characterized by the presence of methoxyphenols, and one by the presence of polyaromatic hydrocarbons and EC, were associated with biomass burning, and accounted for a median of 9.2% of OC and 15.3% of EC personal exposure. Here, we demonstrate the utility of using the cooking-related source apportionment factors within a mixed effects model for more precise estimation of exposures due to cooking, rather than other combustion sources unrelated to the intervention.