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Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021.

ABSTRACT: Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to < 1 at final calibration timepoint [Airbeam 1 Mean (SD) = 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N = 27 out of 56, failure rate 48.2%) than Airbeam 2 (N = 2 out of 24, failure rate 8.3%) due to electronics, battery, or data output issues. Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.

SUBMITTER: Anastasiou E

PROVIDER: S-EPMC9411839 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021.

Anastasiou Elle E Vilcassim M J Ruzmyn MJR Adragna John J Gill Emily E Tovar Albert A Thorpe Lorna E LE Gordon Terry T

Scientific reports 20220826 1

Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and ...[more]

PMID: 36028517

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Project description:BackgroundLow-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment.ObjectiveBuild direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level.MethodsUsing data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore.ResultsWe demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 μg/m3, and also on monitors not included in the training set.SignificanceWe provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD.Impact statementWe demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process.

Project description:Because most people spend the majority of their time in microenvironments, indoor air pollution (IAP) has gained more attention than outdoor air pollution recently. It is indeed crucial to understand IAP sources and the factors that influence human exposure. We synthesized evidence on IAP levels and contributing factors in Ethiopia from available literature, utilizing findings from 19 studies to retrieve 66 relevant values. Particulate matters (PM2.5, PM4, PM10, and TSP), as well as gaseous pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), polyaromatic hydrocarbons (PAHs), and total volatile organic compounds (TVOCs), were analyzed. The calculated mean concentrations for PM2.5, PM10, NO2, TVOCs, and CO were 477.47 μg/m3, 228.38 μg/m3, 63.84 μg/m3, 1361.79 μg/m3, and 18.82 ppm, respectively, all of which exceeded the annual WHO exposure guidelines. Geographical location, stove type, and household activities showed a variation in pollutants concentration. The higher levels of pollutants were attributed to emissions from biomass fuel used for baking injera, wot preparation, and conducting a coffee ceremony, as well as poor ventilation, season, cooking time, and tobacco smoke. The health risk assessments for exposure to various domestic activities were found to be acceptable, except for PM10, with the highest correlation with an acute respiratory infection. Although improved cookstove technology has been proposed as a sustainable energy source, investigations in Ethiopia have revealed that there is still room for public health protection. There is a paucity of research on the relationship between indoor and outdoor air pollution. Future research should prioritize these issues, with a focus on the link between IAP exposure and health effects. In conclusion, there is a higher IAP concentration in Ethiopia so the community should be made aware of it as well as related health effects, and immediate mitigation measures are needed to achieve a reduction in exposure.

Project description:BackgroundAir quality affects us all and is a rapidly growing concern in the 21st century. We spend the majority of our lives indoors and can be exposed to a number of pollutants smaller than 2.5 microns (particulate matter, PM2.5) resulting in detrimental health effects. Indoor air quality sensors have the potential to provide people with the information they need to understand their risk and take steps to reduce their exposure. One such sensor is the Speck sensor developed at the Community Robotics, Education and Technology Empowerment Lab at Carnegie Mellon University. This sensor provides users with continuous real-time and historical PM2.5 information, a Web-based platform where people can track their PM2.5 levels over time and learn about ways to reduce their exposure, and a venue (blog post) for the user community to exchange information. Little is known about how the use of such monitors affects people's knowledge, attitudes, and behaviors with respect to indoor air pollution.ObjectiveThe aim of this study was to assess whether using the sensor changes what people know and do about indoor air pollution.MethodsWe conducted 2 studies. In the first study, we recruited 276 Pittsburgh residents online and through local branches of the Carnegie Library of Pittsburgh, where the Speck sensor was made available by the researchers in the library catalog. Participants completed a 10- to 15-min survey on air pollution knowledge (its health impact, sources, and mitigation options), perceptions of indoor air quality, confidence in mitigation, current behaviors toward air quality, and personal empowerment and creativity in the spring and summer of 2016. In our second study, we surveyed 26 Pittsburgh residents in summer 2016 who checked out the Speck sensor for 3 weeks on the same measures assessed in the first study, with additional questions about the perception and use of the sensor. Follow-up interviews were conducted with a subset of those who used the Speck sensor.ResultsA series of paired t tests found participants were significantly more knowledgeable (t25=-2.61, P=.02), reported having significantly better indoor air quality (t25=-5.20, P<.001), and felt more confident about knowing how to mitigate their risk (t25=-1.87, P=.07) after using the Speck sensor than before. McNemar test showed participants tended to take more action to reduce indoor air pollution after using the sensor (χ225=2.7, P=.10). Qualitative analysis suggested possible ripple effects of use, including encouraging family and friends to learn about indoor air pollution.ConclusionsProviding people with low- or no-cost portable indoor air quality monitors, with a supporting Web-based platform that offers information about how to reduce risk, can help people better express perceptions and adopt behaviors commensurate with the risks they face. Thus, thoughtfully designed and deployed personal sensing devices can help empower people to take steps to reduce their risk.

Dataset Information

Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021.

Publications

Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021.

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