Articles | Volume 12, issue 8
https://doi.org/10.5194/amt-12-4211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-4211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring
Sharad Vikram
CORRESPONDING AUTHOR
Department of Computer Science and Engineering, University of California, San Diego, California, USA
Ashley Collier-Oxandale
Environmental Engineering Program, University of Colorado, Boulder, Colorado, USA
Michael H. Ostertag
Department of Computer Science and Engineering, University of California, San Diego, California, USA
Massimiliano Menarini
Department of Computer Science and Engineering, University of California, San Diego, California, USA
Camron Chermak
Department of Computer Science and Engineering, University of California, San Diego, California, USA
Sanjoy Dasgupta
Department of Computer Science and Engineering, University of California, San Diego, California, USA
Tajana Rosing
Department of Computer Science and Engineering, University of California, San Diego, California, USA
Michael Hannigan
Environmental Engineering Program, University of Colorado, Boulder, Colorado, USA
William G. Griswold
Department of Computer Science and Engineering, University of California, San Diego, California, USA
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19 citations as recorded by crossref.
- Enhanced Ambient Sensing Environment—A New Method for Calibrating Low-Cost Gas Sensors H. Russell et al. 10.3390/s22197238
- A Novel Method for Removing Baseline Drifts in Multivariate Chemical Sensor A. Grover & B. Lall 10.1109/TIM.2020.2976224
- Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights J. Hofman et al. 10.3389/fenvh.2023.1232867
- Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction S. Koziel et al. 10.1016/j.measurement.2024.114529
- A Global Multiunit Calibration as a Method for Large-Scale IoT Particulate Matter Monitoring Systems Deployments S. De Vito et al. 10.1109/TIM.2023.3331428
- Characterizing methane and total non-methane hydrocarbon levels in Los Angeles communities with oil and gas facilities using air quality monitors K. Okorn et al. 10.1016/j.scitotenv.2021.146194
- Towards the Development of a Sensor Educational Toolkit to Support Community and Citizen Science A. Collier-Oxandale et al. 10.3390/s22072543
- Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. 10.1145/3512889
- Long-term field calibration of low-cost metal oxide VOC sensor: Meteorological and interference gas effects G. Hong et al. 10.1016/j.atmosenv.2023.119955
- Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights J. Hofman et al. 10.3390/atmos13060944
- Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program A. Collier-Oxandale et al. 10.1016/j.atmosenv.2019.117092
- A lightweight low-cost and multipollutant sensor package for aerial observations of air pollutants in atmospheric boundary layer X. Pang et al. 10.1016/j.scitotenv.2020.142828
- Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science J. Hofman et al. 10.3390/s24175653
- Machine learning calibration of low-cost NO<sub>2</sub> and PM<sub>10</sub> sensors: non-linear algorithms and their impact on site transferability P. Nowack et al. 10.5194/amt-14-5637-2021
- Field Calibration of Low-Cost Mos Voc Sensors and Application for Source Characterization G. Hong et al. 10.2139/ssrn.4198986
- Applications and Limitations of Quantifying Speciated and Source-Apportioned VOCs with Metal Oxide Sensors K. Okorn & M. Hannigan 10.3390/atmos12111383
- Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors A. Abu-Hani et al. 10.5194/amt-17-3917-2024
- Innovative Characterization and Comparative Analysis of Water Level Sensors for Enhanced Early Detection and Warning of Floods R. Tawalbeh et al. 10.3390/jlpea13020026
- Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
1 citations as recorded by crossref.
Latest update: 01 Nov 2024
Short summary
Low-cost air quality sensors are enabling people to collect data to better understand their local environment and potential exposures. However, there is some concern regarding how reliable the calibrations of these sensors are in new and different environments. To explore this issue, our team colocated sensors at three different sites with high-quality monitoring instruments to compare to. We explored the transferability of calibration models as well as approaches to improve reliability.
Low-cost air quality sensors are enabling people to collect data to better understand their...