Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-37-2021
© Author(s) 2021. 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-14-37-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jan 2021
Research article |
| 04 Jan 2021
| 04 Jan 2021
Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O3 and NO2 concentrations in diverse environments
Ravi Sahu
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Ayush Nagal
Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Kuldeep Kumar Dixit
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Harshavardhan Unnibhavi
Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
Srikanth Mantravadi
Department of Electrical Communication Engineering, Indian Institute of Science, Bengaluru, India
Srijith Nair
Department of Electrical Communication Engineering, Indian Institute of Science, Bengaluru, India
Yogesh Simmhan
Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
Brijesh Mishra
Department of Electrical Engineering, Indian Institute of Technology, Mumbai, India
Rajesh Zele
Department of Electrical Engineering, Indian Institute of Technology, Mumbai, India
Ronak Sutaria
Centre for Urban Science and Engineering, Indian Institute of Technology, Mumbai, India
Vidyanand Motiram Motghare
Maharashtra Pollution Control Board, Mumbai, India
Purushottam Kar
Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Sachchida Nand Tripathi
CORRESPONDING AUTHOR
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India
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Cited
21 citations as recorded by crossref.
- Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring M. Ionascu et al. 10.1016/j.atmosenv.2024.120841
- A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction Z. Song et al. 10.3390/electronics10040373
- RETRACTED ARTICLE: IoT devices and data availability optimization by ANN and KNN Z. Chen et al. 10.1186/s13635-023-00145-0
- Rapid rise in premature mortality due to anthropogenic air pollution in fast-growing tropical cities from 2005 to 2018 K. Vohra et al. 10.1126/sciadv.abm4435
- Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
- Air Quality Estimation Using Dendritic Neural Regression with Scale-Free Network-Based Differential Evolution Z. Song et al. 10.3390/atmos12121647
- Design and Implementation of an Air Quality Testing System Based on STC12C5A F. Wu et al. 10.46300/9106.2021.15.110
- Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges M. Tarazona Alvarado et al. 10.1080/01431161.2024.2373338
- SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities A. Kaginalkar et al. 10.3389/fenvs.2022.785129
- Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. 10.5194/amt-16-4723-2023
- Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi A. Bittner et al. 10.5194/amt-15-3353-2022
- Calibration of Low-Cost NO2 Sensors through Environmental Factor Correction J. Miech et al. 10.3390/toxics9110281
- Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors S. Jha et al. 10.1109/JSEN.2021.3118454
- 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
- Electrochemical sensors on board a Zeppelin NT: in-flight evaluation of low-cost trace gas measurements T. Schuldt et al. 10.5194/amt-16-373-2023
- Evaluating uncertainty in sensor networks for urban air pollution insights D. Peters et al. 10.5194/amt-15-321-2022
- Modular Air Quality Calibration and Forecasting Method for Low-Cost Sensor Nodes Y. Hashmy et al. 10.1109/JSEN.2023.3233982
- Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India S. V et al. 10.1021/acsearthspacechem.2c00257
- Few-Shot Calibration of Low-Cost Air Pollution (PM$_{2.5}$) Sensors Using Meta Learning K. Yadav et al. 10.1109/LSENS.2022.3168291
- Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. 10.1145/3512889
- Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
21 citations as recorded by crossref.
- Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring M. Ionascu et al. 10.1016/j.atmosenv.2024.120841
- A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction Z. Song et al. 10.3390/electronics10040373
- RETRACTED ARTICLE: IoT devices and data availability optimization by ANN and KNN Z. Chen et al. 10.1186/s13635-023-00145-0
- Rapid rise in premature mortality due to anthropogenic air pollution in fast-growing tropical cities from 2005 to 2018 K. Vohra et al. 10.1126/sciadv.abm4435
- Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
- Air Quality Estimation Using Dendritic Neural Regression with Scale-Free Network-Based Differential Evolution Z. Song et al. 10.3390/atmos12121647
- Design and Implementation of an Air Quality Testing System Based on STC12C5A F. Wu et al. 10.46300/9106.2021.15.110
- Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges M. Tarazona Alvarado et al. 10.1080/01431161.2024.2373338
- SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities A. Kaginalkar et al. 10.3389/fenvs.2022.785129
- Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. 10.5194/amt-16-4723-2023
- Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi A. Bittner et al. 10.5194/amt-15-3353-2022
- Calibration of Low-Cost NO2 Sensors through Environmental Factor Correction J. Miech et al. 10.3390/toxics9110281
- Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors S. Jha et al. 10.1109/JSEN.2021.3118454
- 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
- Electrochemical sensors on board a Zeppelin NT: in-flight evaluation of low-cost trace gas measurements T. Schuldt et al. 10.5194/amt-16-373-2023
- Evaluating uncertainty in sensor networks for urban air pollution insights D. Peters et al. 10.5194/amt-15-321-2022
- Modular Air Quality Calibration and Forecasting Method for Low-Cost Sensor Nodes Y. Hashmy et al. 10.1109/JSEN.2023.3233982
- Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India S. V et al. 10.1021/acsearthspacechem.2c00257
- Few-Shot Calibration of Low-Cost Air Pollution (PM$_{2.5}$) Sensors Using Meta Learning K. Yadav et al. 10.1109/LSENS.2022.3168291
- Evaluation of Low-cost Air Quality Sensor Calibration Models K. Aula et al. 10.1145/3512889
- Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors I. Christakis et al. 10.3390/signals5010004
Latest update: 13 Nov 2024
Short summary
A unique feature of our low-cost sensor deployment is a swap-out experiment wherein four of the six sensors were relocated to different sites in the two phases. The swap-out experiment is crucial in investigating the efficacy of calibration models when applied to weather and air quality conditions vastly different from those present during calibration. We developed a novel local calibration algorithm based on metric learning that offers stable and accurate calibration performance.
A unique feature of our low-cost sensor deployment is a swap-out experiment wherein four of the...
