Dataset Information

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO₂, O₃, and SO₂.

ABSTRACT: Pollutant gases, such as CO, NO₂, O₃, and SO₂ affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O₃ > NO₂ > SO₂ for the coefficient of determination (R²) and root mean square error (RMSE). The MLR did not increase the R² after considering the temperature and relative humidity influences compared with the SLR (with R² remaining at approximately 0.6 for O₃ and 0.4 for NO₂). However, the RFR and LSTM models significantly increased the O₃, NO₂, and SO₂ performances, with the R² increasing from 0.3-0.5 to >0.7 for O₃ and NO₂, and the RMSE decreasing from 20.4 to 13.2 ppb for NO₂. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O₃ and NO₂), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

SUBMITTER: Han P

PROVIDER: S-EPMC7795951 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2.

Han Pengfei P Mei Han H Liu Di D Zeng Ning N Tang Xiao X Wang Yinghong Y Pan Yuepeng Y

Sensors (Basel, Switzerland) 20210102 1

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regres ...[more]

PMID: 33401737

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Project description:The use of air sensor technology is increasing worldwide for a variety of applications, however, with significant variability in data quality. The United States Environmental Protection Agency held a workshop in July 2019 to deliberate possible performance targets for air sensors measuring particles with aerodynamic diameters of 10 μm or less (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). These performance targets were discussed from the perspective of non-regulatory applications and with the sensors operating primarily in a stationary mode in outdoor environments. Attendees included representatives from multiple levels of government organizations, sensor developers, environmental nonprofits, international organizations, and academia. The workshop addressed the current lack of sensor technology requirements, discussed fit-for-purpose data quality needs, and debated transparency issues. This paper highlights the purpose and key outcomes of the workshop. While more information on performance and applications of sensors is available than in past years, the performance metrics, or parameters used to describe data quality, vary among the studies reports and there is a need for more clear and consistent approaches for evaluating sensor performance. Organizations worldwide are increasingly considering, or are in the process of developing, sensor performance targets and testing protocols. Workshop participants suggested that these new guidelines are highly desirable, would help improve data quality, and would give users more confidence in their data. Given the wide variety of uses for sensors and user backgrounds, as well as varied sensor design features (e.g., communication approaches, data tools, processing/adjustment algorithms and calibration procedures), the need for transparency was a key workshop theme. Suggestions for increasing transparency included documenting and sharing testing and performance data, detailing best practices, and sharing data processing and correction approaches.

Project description:BackgroundExposure to fine particulate matter (PM2.5) increases the risk of asthma exacerbations, and thus, monitoring personal exposure to PM2.5 may aid in disease self-management. Low-cost, portable air pollution sensors offer a convenient way to measure personal pollution exposure directly and may improve personalized monitoring compared with traditional methods that rely on stationary monitoring stations. We aimed to understand whether adults with asthma would be willing to use personal sensors to monitor their exposure to air pollution and to assess the feasibility of using sensors to measure real-time PM2.5 exposure.MethodsWe conducted semi-structured interviews with 15 adults with asthma to understand their willingness to use a personal pollution sensor and their privacy preferences with regard to sensor data. Student research assistants used HabitatMap AirBeam devices to take PM2.5 measurements at 1-s intervals while walking in Philadelphia neighborhoods in May-August 2018. AirBeam PM2.5 measurements were compared to concurrent measurements taken by three nearby regulatory monitors.ResultsAll interview participants stated that they would use a personal air pollution sensor, though the consensus was that devices should be small (watch- or palm-sized) and light. Patients were generally unconcerned about privacy or sharing their GPS location, with only two stating they would not share their GPS location under any circumstances. PM2.5 measurements were taken using AirBeam sensors on 34 walks that extended through five Philadelphia neighborhoods. The range of sensor PM2.5 measurements was 0.6-97.6 μg/mL (mean 6.8 μg/mL), compared to 0-22.6 μg/mL (mean 9.0 μg/mL) measured by nearby regulatory monitors. Compared to stationary measurements, which were only available as 1-h integrated averages at discrete monitoring sites, sensor measurements permitted characterization of fine-scale fluctuations in PM2.5 levels over time and space.ConclusionsPatients were generally interested in using sensors to monitor their personal exposure to PM2.5 and willing to share personal sensor data with health care providers and researchers. Compared to traditional methods of personal exposure assessment, sensors captured personalized air quality information at higher spatiotemporal resolution. Improvements to currently available sensors, including more reliable Bluetooth connectivity, increased portability, and longer battery life would facilitate their use in a general patient population.

Dataset Information

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO₂, O₃, and SO₂.

Publications

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO<sub>2</sub>, O<sub>3</sub>, and SO<sub>2</sub>.

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