Articles | Volume 12, issue 9
https://doi.org/10.5194/amt-12-5161-2019
© 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-12-5161-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2019
Research article |
| 26 Sep 2019
| 26 Sep 2019
Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi
Tongshu Zheng
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Duke University,
Durham, NC 27708, USA
Michael H. Bergin
Department of Civil and Environmental Engineering, Duke University,
Durham, NC 27708, USA
Ronak Sutaria
Respirer Living Sciences Pvt. Ltd, 7, Maheshwar Nivas, Tilak Road,
Santacruz (W), Mumbai 400054, India
Sachchida N. Tripathi
Department of Civil Engineering, Indian Institute of Technology
Kanpur, Kanpur, Uttar Pradesh 208016, India
Robert Caldow
TSI Inc., 500 Cardigan Road, Shoreview, MN 55126, USA
David E. Carlson
Department of Civil and Environmental Engineering, Duke University,
Durham, NC 27708, USA
Department of Biostatistics and Bioinformatics, Duke University,
Durham, NC 27708, USA
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Cited
21 citations as recorded by crossref.
- Detecting Inaccurate Sensors on a Large-Scale Sensor Network Using Centralized and Localized Graph Neural Networks D. Wu et al. 10.1109/JSEN.2023.3287270
- Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution A. Masic et al. 10.5194/amt-13-6427-2020
- Learning-Based Adaptive Sensor Selection Framework for Multi-Sensing WSN S. Ghosh et al. 10.1109/JSEN.2021.3069264
- Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California Y. Liang et al. 10.1073/pnas.2106478118
- Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
- A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives B. Alfano et al. 10.3390/s20236819
- Enhancing the Applicability of Satellite Remote Sensing for PM 2.5 Estimation Using Machine Learning Models in China J. Chai et al. 10.1155/2022/7148682
- Few-Shot Calibration of Low-Cost Air Pollution (PM$_{2.5}$) Sensors Using Meta Learning K. Yadav et al. 10.1109/LSENS.2022.3168291
- Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective A. Kaginalkar et al. 10.1016/j.uclim.2021.100972
- Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors S. Jha et al. 10.1109/JSEN.2021.3118454
- A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring P. Wang et al. 10.3390/atmos12101344
- A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data C. Heffernan et al. 10.1214/23-AOAS1751
- Learning to Identify Malfunctioning Sensors in a Large-Scale Sensor Network T. Lin et al. 10.1109/JSEN.2021.3138250
- Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology T. Zheng et al. 10.3390/rs13071356
- Long-Term Analysis of Aerosol Concentrations Using a Low-Cost Sensor: Monitoring African Dust Outbreaks in a Suburban Environment in the Canary Islands S. Alonso-Pérez & J. López-Solano 10.3390/s23187768
- Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor V. Kumar & M. Sahu 10.1016/j.jaerosci.2021.105809
- Air Quality Enhancement Districts: democratizing data to improve respiratory health K. Stevens et al. 10.1007/s13412-021-00670-9
- Edge Intelligence Framework for Data-Driven Dynamic Priority Sensing and Transmission S. Ghosh et al. 10.1109/TGCN.2021.3136139
- Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
- Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments R. Sahu et al. 10.5194/amt-14-37-2021
- Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
21 citations as recorded by crossref.
- Detecting Inaccurate Sensors on a Large-Scale Sensor Network Using Centralized and Localized Graph Neural Networks D. Wu et al. 10.1109/JSEN.2023.3287270
- Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution A. Masic et al. 10.5194/amt-13-6427-2020
- Learning-Based Adaptive Sensor Selection Framework for Multi-Sensing WSN S. Ghosh et al. 10.1109/JSEN.2021.3069264
- Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California Y. Liang et al. 10.1073/pnas.2106478118
- Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. 10.1016/j.atmosenv.2020.117451
- A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives B. Alfano et al. 10.3390/s20236819
- Enhancing the Applicability of Satellite Remote Sensing for PM 2.5 Estimation Using Machine Learning Models in China J. Chai et al. 10.1155/2022/7148682
- Few-Shot Calibration of Low-Cost Air Pollution (PM$_{2.5}$) Sensors Using Meta Learning K. Yadav et al. 10.1109/LSENS.2022.3168291
- Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective A. Kaginalkar et al. 10.1016/j.uclim.2021.100972
- Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors S. Jha et al. 10.1109/JSEN.2021.3118454
- A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring P. Wang et al. 10.3390/atmos12101344
- A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data C. Heffernan et al. 10.1214/23-AOAS1751
- Learning to Identify Malfunctioning Sensors in a Large-Scale Sensor Network T. Lin et al. 10.1109/JSEN.2021.3138250
- Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology T. Zheng et al. 10.3390/rs13071356
- Long-Term Analysis of Aerosol Concentrations Using a Low-Cost Sensor: Monitoring African Dust Outbreaks in a Suburban Environment in the Canary Islands S. Alonso-Pérez & J. López-Solano 10.3390/s23187768
- Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor V. Kumar & M. Sahu 10.1016/j.jaerosci.2021.105809
- Air Quality Enhancement Districts: democratizing data to improve respiratory health K. Stevens et al. 10.1007/s13412-021-00670-9
- Edge Intelligence Framework for Data-Driven Dynamic Priority Sensing and Transmission S. Ghosh et al. 10.1109/TGCN.2021.3136139
- Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
- Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments R. Sahu et al. 10.5194/amt-14-37-2021
- Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
Discussed (preprint)
Latest update: 23 Apr 2024
Short summary
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline to calibrate and monitor dense wireless low-cost particulate matter sensor networks (WLPMSNs) on the fly by using all available reference monitors across an area. Our approach can achieve an overall 30 % prediction error at a 24 h scale, can differentiate malfunctioning nodes, and track drift. Our solution can substantially reduce manual labor for managing WLPMSNs and prolong their lifetimes.
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline...
