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Villanueva E, Espezua S, Castelar G, Diaz K, Ingaroca E. Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors. SENSORS (BASEL, SWITZERLAND) 2023; 23:3776. [PMID: 37050836 PMCID: PMC10099154 DOI: 10.3390/s23073776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/21/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.
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Affiliation(s)
- Edwin Villanueva
- Engineering Department, Pontificia Universidad Católica del Perú, 1801 Universitaria Av., San Miguel, Lima 15088, Peru
| | - Soledad Espezua
- Departamento Académico de Ingeniería, Universidad del Pacífico, 2020 Salaverry Av., Jesús María, Lima 15072, Peru;
| | - George Castelar
- Subgerencia de Gestión Ambiental, Municipalidad Metropolitana de Lima, Palacio Municipal, Lima 15001, Peru
| | - Kyara Diaz
- Subgerencia de Gestión Ambiental, Municipalidad Metropolitana de Lima, Palacio Municipal, Lima 15001, Peru
| | - Erick Ingaroca
- Subgerencia de Gestión Ambiental, Municipalidad Metropolitana de Lima, Palacio Municipal, Lima 15001, Peru
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Iyer SR, Balashankar A, Aeberhard WH, Bhattacharyya S, Rusconi G, Jose L, Soans N, Sudarshan A, Pande R, Subramanian L. Modeling fine-grained spatio-temporal pollution maps with low-cost sensors. NPJ CLIMATE AND ATMOSPHERIC SCIENCE 2022; 5:76. [PMID: 36254321 PMCID: PMC9555706 DOI: 10.1038/s41612-022-00293-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.
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Affiliation(s)
- Shiva R. Iyer
- Department of Computer Science, New York University, New York, NY USA
| | | | | | - Sujoy Bhattacharyya
- Columbia University, New York, NY USA
- Evidence for Policy Design (EPoD) at the Institute for Financial Management and Research (IFMR), New Delhi, New Delhi India
| | - Giuditta Rusconi
- Evidence for Policy Design (EPoD) at the Institute for Financial Management and Research (IFMR), New Delhi, New Delhi India
- State Secretariat for Education, Research and Innovation (SERI), Bern, Switzerland
| | - Lejo Jose
- Kai Air Monitoring Pvt Ltd, Gautam Buddha Nagar, UP India
| | - Nita Soans
- Kai Air Monitoring Pvt Ltd, Gautam Buddha Nagar, UP India
| | - Anant Sudarshan
- Department of Economics, University of Chicago, Chicago, IL USA
| | - Rohini Pande
- Department of Economics, Yale University, New Haven, CT USA
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Russell HS, Frederickson LB, Kwiatkowski S, Emygdio APM, Kumar P, Schmidt JA, Hertel O, Johnson MS. Enhanced Ambient Sensing Environment-A New Method for Calibrating Low-Cost Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:7238. [PMID: 36236337 PMCID: PMC9571921 DOI: 10.3390/s22197238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/13/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0-120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time.
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Affiliation(s)
- Hugo Savill Russell
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
| | - Louise Bøge Frederickson
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
| | | | - Ana Paula Mendes Emygdio
- Global Center for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK
| | - Prashant Kumar
- Global Center for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK
| | | | - Ole Hertel
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
- Department of Ecoscience, Aarhus University, DK-4000 Roskilde, Denmark
| | - Matthew Stanley Johnson
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
- Department of Chemistry, University of Copenhagen, DK-2100 Copenhagen Ø, Denmark
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Relevance of Drift Components and Unit-to-Unit Variability in the Predictive Maintenance of Low-Cost Electrochemical Sensor Systems in Air Quality Monitoring. SENSORS 2021; 21:s21093298. [PMID: 34068777 PMCID: PMC8126229 DOI: 10.3390/s21093298] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 01/20/2023]
Abstract
As key components of low-cost sensor systems in air quality monitoring, electrochemical gas sensors have recently received a lot of interest but suffer from unit-to-unit variability and different drift components such as aging and concept drift, depending on the calibration approach. Magnitudes of drift can vary across sensors of the same type, and uniform recalibration intervals might lead to insufficient performance for some sensors. This publication evaluates the opportunity to perform predictive maintenance solely by the use of calibration data, thereby detecting the optimal moment for recalibration and improving recalibration intervals and measurement results. Specifically, the idea is to define confidence regions around the calibration data and to monitor the relative position of incoming sensor signals during operation. The emphasis lies on four algorithms from unsupervised anomaly detection-namely, robust covariance, local outlier factor, one-class support vector machine, and isolation forest. Moreover, the behavior of unit-to-unit variability and various drift components on the performance of the algorithms is discussed by analyzing published field experiments and by performing Monte Carlo simulations based on sensing and aging models. Although unsupervised anomaly detection on calibration data can disclose the reliability of measurement results, simulation results suggest that this does not translate to every sensor system due to unfavorable arrangements of baseline drifts paired with sensitivity drift.
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