1
|
D’Elia G, Ferro M, Sommella P, Ferlito S, De Vito S, Di Francia G. Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:2786. [PMID: 38732892 PMCID: PMC11086340 DOI: 10.3390/s24092786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/16/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024]
Abstract
Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO2 sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.
Collapse
Affiliation(s)
- Gerardo D’Elia
- TERIN-SSI-EDS Laboratory, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy; (S.F.); (S.D.V.); (G.D.F.)
- Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy;
| | - Matteo Ferro
- Hippocratica Imaging S.r.l., Via Giulio Pastore, 32, 84131 Salerno, Italy;
| | - Paolo Sommella
- Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy;
| | - Sergio Ferlito
- TERIN-SSI-EDS Laboratory, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy; (S.F.); (S.D.V.); (G.D.F.)
| | - Saverio De Vito
- TERIN-SSI-EDS Laboratory, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy; (S.F.); (S.D.V.); (G.D.F.)
| | - Girolamo Di Francia
- TERIN-SSI-EDS Laboratory, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy; (S.F.); (S.D.V.); (G.D.F.)
| |
Collapse
|
2
|
Ma X, Zou B, Deng J, Gao J, Longley I, Xiao S, Guo B, Wu Y, Xu T, Xu X, Yang X, Wang X, Tan Z, Wang Y, Morawska L, Salmond J. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. ENVIRONMENT INTERNATIONAL 2024; 183:108430. [PMID: 38219544 DOI: 10.1016/j.envint.2024.108430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/26/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
Collapse
Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China.
| | - Jun Deng
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jay Gao
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland 1010, New Zealand
| | - Shun Xiao
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Tingting Xu
- School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Xiaosha Yang
- Shandong Nova Fitness Co., Ltd., Baoji, Shaanxi 722404, China
| | - Xiaoqi Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Zelei Tan
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yifan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Jennifer Salmond
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| |
Collapse
|
3
|
Heffernan C, PenG R, Gentner DR, Koehler K, Datta A. A DYNAMIC SPATIAL FILTERING APPROACH TO MITIGATE UNDERESTIMATION BIAS IN FIELD CALIBRATED LOW-COST SENSOR AIR POLLUTION DATA. Ann Appl Stat 2023; 17:3056-3087. [PMID: 38646662 PMCID: PMC11031266 DOI: 10.1214/23-aoas1751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by environmental conditions, and usually need to be field-calibrated by collocating low-cost sensors with reference-grade instruments. We show, theoretically and empirically, that the common procedure of regression-based calibration using collocated data systematically underestimates high air pollution concentrations, which are critical to diagnose from a health perspective. Current calibration practices also often fail to utilize the spatial correlation in pollutant concentrations. We propose a novel spatial filtering approach to collocation-based calibration of low-cost networks that mitigates the underestimation issue by using an inverse regression. The inverse-regression also allows for incorporating spatial correlations by a second-stage model for the true pollutant concentrations using a conditional Gaussian Process. Our approach works with one or more collocated sites in the network and is dynamic, leveraging spatial correlation with the latest available reference data. Through extensive simulations, we demonstrate how the spatial filtering substantially improves estimation of pollutant concentrations, and measures peak concentrations with greater accuracy. We apply the methodology for calibration of a low-cost PM2.5 network in Baltimore, Maryland, and diagnose air pollution peaks that are missed by the regression-calibration.
Collapse
Affiliation(s)
| | - Roger PenG
- Department of Statistics and Data Sciences, University of Texas, Austin
| | - Drew R. Gentner
- Department of Chemical & Environmental Engineering, Yale University
| | - Kirsten Koehler
- Department of Environmental Health and Engineering, Johns Hopkins University
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins University
| |
Collapse
|
4
|
Sá JP, Chojer H, Branco PTBS, Alvim-Ferraz MCM, Martins FG, Sousa SIV. Two step calibration method for ozone low-cost sensor: Field experiences with the UrbanSense DCUs. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 328:116910. [PMID: 36495826 DOI: 10.1016/j.jenvman.2022.116910] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 09/26/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Urban air pollution is a global concern impairing citizens' health, thus monitoring is a pressing need for city managers. City-wide networks for air pollution monitoring based on low-cost sensors are promising to provide real-time data with detail and scale never before possible. However, they still present limitations preventing their ubiquitous use. Thus, this study aimed to perform a post-deployment validation and calibration based on two step methods for ozone low-cost sensor of a city-wide network for air pollution and meteorology monitoring using low-cost sensors focusing on the main challenges. Four of the 23 data collection units (DCUs) of the UrbanSense network installed in Porto city (Portugal) with low-cost sensors for particulate matter (PM), carbon monoxide (CO), ozone (O3), and meteorological variables (temperature, relative humidity, luminosity, precipitation, and wind speed and direction) were evaluated. This study identified post-deployment challenges related to their validation and calibration. The preliminary validation showed that PM, CO and precipitation sensors recorded only unreliable data, and other sensors (wind speed and direction) very few data. A multi-step calibration strategy was implemented: inter-DCU calibration (1st step, for O3, temperature and relative humidity) and calibration with a reference-grade instrument (2nd step, for O3). In the 1st step, multivariate linear regression (MLR) resulted in models with better performance than non-linear models such as artificial neural networks (errors almost zero and R2 > 0.80). In the 2nd step, the calibration models using non-linear machine learning boosting algorithms, namely Stochastic Gradient Boosting Regressor (both with the default and post-tuning hyper-parameters), performed better than artificial neural networks and linear regression approaches. The calibrated O3 data resulted in a marginal improvement from the raw data, with error values close to zero, with low predictability (R2 ∼ 0.32). The lessons learned with the present study evidenced the need to redesign the calibration strategy. Thus, a novel multi-step calibration strategy is proposed, based on two steps (pre and post-deployment calibration). When performed cyclically and continuously, this strategy reduces the need for reference instruments, while probably minimising data drifts over time. More experimental campaigns are needed to collect more data and further improve calibration models.
Collapse
Affiliation(s)
- J P Sá
- LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - H Chojer
- LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - P T B S Branco
- LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - M C M Alvim-Ferraz
- LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - F G Martins
- LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - S I V Sousa
- LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal.
| |
Collapse
|
5
|
Li B, Wang Z, Zhao S, Hu C, Li L, Liu M, Zhu J, Zhou T, Zhang G, Jiang J, Zou C. Enhanced Pd/a-WO 3 /VO 2 Hydrogen Gas Sensor Based on VO 2 Phase Transition Layer. SMALL METHODS 2022; 6:e2200931. [PMID: 36287026 DOI: 10.1002/smtd.202200931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/12/2022] [Indexed: 06/16/2023]
Abstract
The utilization of clean hydrogen energy is becoming more feasible for the sustainable development of this society. Considering the safety issues in the hydrogen production, storage, and utilization, a sensitive hydrogen sensor for reliable detection is essential and highly important. Though various gas sensor devices are developed based on tin oxide, tungsten trioxide, or other oxides, the relatively high working temperature, unsatisfactory response time, and detection limitation still affect the extensive applications. In the current study, an amorphous tungsten trioxide (a-WO3 ) layer is deposited on a phase-change vanadium dioxide film to fabricate a phase transition controlled Pd/a-WO3 /VO2 hydrogen sensor for hydrogen detection. Results show that both the response time and sensitivity of the hydrogen sensor are improved greatly if increasing the working temperature over the transition temperature of VO2 . Theoretical calculations also reveal that the charge transfer at VO2 /a-WO3 interface becomes more pronounced once the VO2 layer transforms to the metal state, which will affect the migration barrier of H atoms in a-WO3 layer and thus improve the sensor performance. The current study not only realizes a hydrogen sensor with ultrahigh performance based on VO2 layer, but also provides some clues for designing other gas sensors with phase-change material.
Collapse
Affiliation(s)
- Bowen Li
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Zhaowu Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang, Henan, 471023, P. R. China
| | - Shanguang Zhao
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Changlong Hu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Liang Li
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Meiling Liu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Jinglin Zhu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Ting Zhou
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Guobin Zhang
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| | - Jun Jiang
- Hefei National Laboratory for Physical Sciences at the Microscale, Collaborative Innovation Center of Chemistry for Energy Materials, CAS Center for Excellence in Nanoscience, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, P. R. China
| | - Chongwen Zou
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230029, P. R. China
| |
Collapse
|
6
|
Yang LH, Hagan DH, Rivera-Rios JC, Kelp MM, Cross ES, Peng Y, Kaiser J, Williams LR, Croteau PL, Jayne JT, Ng NL. Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7063-7073. [PMID: 35357805 DOI: 10.1021/acs.est.1c07005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO2, and O3) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and non-normalized data sets. We identify four factors with different temporal trends and properties for both normalized and non-normalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.
Collapse
Affiliation(s)
- Laura Hyesung Yang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - David H Hagan
- QuantAQ, Inc., Somerville, Massachusetts 02143, United States
| | - Jean C Rivera-Rios
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Makoto M Kelp
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Eben S Cross
- QuantAQ, Inc., Somerville, Massachusetts 02143, United States
| | - Yuyang Peng
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jennifer Kaiser
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Leah R Williams
- Aerodyne Research, Inc., Billerica, Massachusetts 01821, United States
| | - Philip L Croteau
- Aerodyne Research, Inc., Billerica, Massachusetts 01821, United States
| | - John T Jayne
- Aerodyne Research, Inc., Billerica, Massachusetts 01821, United States
| | - Nga Lee Ng
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| |
Collapse
|
7
|
Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring. SENSORS 2022; 22:s22031093. [PMID: 35161837 PMCID: PMC8839978 DOI: 10.3390/s22031093] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/07/2022] [Accepted: 01/25/2022] [Indexed: 12/10/2022]
Abstract
With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.
Collapse
|
8
|
Ionascu ME, Castell N, Boncalo O, Schneider P, Darie M, Marcu M. Calibration of CO, NO 2, and O 3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring. SENSORS 2021; 21:s21237977. [PMID: 34883981 PMCID: PMC8659498 DOI: 10.3390/s21237977] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022]
Abstract
During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.
Collapse
Affiliation(s)
- Marian-Emanuel Ionascu
- Faculty of Automatics and Computers, Politehnica University of Timisoara, 300223 Timisoara, Romania; (O.B.); (M.M.)
- Correspondence: ; Tel.: +40-745-532-759
| | - Nuria Castell
- Norwegian Institute for Air Research (NILU), 2007 Kjeller, Norway; (N.C.); (P.S.)
| | - Oana Boncalo
- Faculty of Automatics and Computers, Politehnica University of Timisoara, 300223 Timisoara, Romania; (O.B.); (M.M.)
| | - Philipp Schneider
- Norwegian Institute for Air Research (NILU), 2007 Kjeller, Norway; (N.C.); (P.S.)
| | - Marius Darie
- National Institute for Research and Development in Mine Safety and Protection to Explosion–INSEMEX, 332047 Petrosani, Romania;
| | - Marius Marcu
- Faculty of Automatics and Computers, Politehnica University of Timisoara, 300223 Timisoara, Romania; (O.B.); (M.M.)
| |
Collapse
|
9
|
Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors. SENSORS 2021; 21:s21186162. [PMID: 34577369 PMCID: PMC8472837 DOI: 10.3390/s21186162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 11/24/2022]
Abstract
Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.
Collapse
|
10
|
Qiao X, Zhang Q, Wang D, Hao J, Jiang J. Improving data reliability: A quality control practice for low-cost PM 2.5 sensor network. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 779:146381. [PMID: 33743460 DOI: 10.1016/j.scitotenv.2021.146381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
Low-cost air quality sensor networks have been increasingly used for high spatial resolution air quality monitoring in recent years. Ensuring data reliability during continuous operation is critical for these sensor networks. Using particulate matter sensor as an example, this study reports a data quality control method, including sensor selection, pre-calibration, and online inspection. It was used in developing and operating the dense low-cost particle sensor networks in two Chinese cities. Firstly, seven mainstream sensors were tested and one model of particle sensor was selected due to its better linearity and stability. For a batch of sensors of the same model, although they were calibrated after manufactured, there are differences in response toward the same concentration of pollutants. The systematical variation of sensors was corrected and unified through pre-calibration. After deploying them in the field, a data analysis method is established for online inspecting their working status. Using data from these sensors, it evaluates parameters such as intraclass correlation coefficients and normalized root mean square error. These two metrics help to construct a two-dimensional coordinate system and to classify sensors into four status, including normal, fluctuation, hotspots, and malfunction. During a one-month operation in the two cities, 8 (out of 82) and 10 (out of 59) sensors with suspected malfunctions were screened out for further on-site inspection. Moreover, the sensor networks show potential in identifying illegal emission sources that cannot be typically detected by sparse regulatory air quality monitoring stations.
Collapse
Affiliation(s)
- Xiaohui Qiao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiang Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Dongbin Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
| |
Collapse
|
11
|
Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations. ATMOSPHERE 2021. [DOI: 10.3390/atmos12030300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.
Collapse
|
12
|
Datta A, Saha A, Zamora ML, Buehler C, Hao L, Xiong F, Gentner DR, Koehler K. Statistical field calibration of a low-cost PM 2.5 monitoring network in Baltimore. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2020; 242:117761. [PMID: 32922146 PMCID: PMC7480820 DOI: 10.1016/j.atmosenv.2020.117761] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory-grade (FEM or FRM) instruments, universal quality standards for low-cost sensors are yet to be established and their data quality varies widely. This mandates thorough evaluation and calibration before any responsible use of such data. This study presents evaluation and field-calibration of the PM2.5 data from a network of low-cost monitors currently operating in Baltimore, MD, which has only one regulatory PM2.5 monitoring site within city limits. Co-location analysis at this regulatory site in Oldtown, Baltimore revealed high variability and significant overestimation of PM2.5 levels by the raw data from these monitors. Universal laboratory corrections reduced the bias in the data, but only partially mitigated the high variability. Eight months of field co-location data at Oldtown were used to develop a gain-offset calibration model, recast as a multiple linear regression. The statistical model offered substantial improvement in prediction quality over the raw or lab-corrected data. The results were robust to the choice of the low-cost monitor used for field-calibration, as well as to different seasonal choices of training period. The raw, lab-corrected and statistically-calibrated data were evaluated for a period of two months following the training period. The statistical model had the highest agreement with the reference data, producing a 24-hour average root-mean-square-error (RMSE) of around 2 μg m -3. To assess transferability of the calibration equations to other monitors in the network, a cross-site evaluation was conducted at a second co-location site in suburban Essex, MD. The statistically calibrated data once again produced the lowest RMSE. The calibrated PM2.5 readings from the monitors in the low-cost network provided insights into the intra-urban spatiotemporal variations of PM2.5 in Baltimore.
Collapse
Affiliation(s)
- Abhirup Datta
- Department of Biostatistics, Johns Hopkins University
| | | | - Misti Levy Zamora
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, Maryland 21205
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT, USA
| | - Colby Buehler
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT, USA
- Department of Chemical & Environmental Engineering, Yale University, School of Engineering and Applied Science, New Haven, Connecticut 06511, USA
| | - Lei Hao
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, Maryland 21205
| | - Fulizi Xiong
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT, USA
- Department of Chemical & Environmental Engineering, Yale University, School of Engineering and Applied Science, New Haven, Connecticut 06511, USA
| | - Drew R Gentner
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT, USA
- Department of Chemical & Environmental Engineering, Yale University, School of Engineering and Applied Science, New Haven, Connecticut 06511, USA
| | - Kirsten Koehler
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, Maryland 21205
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT, USA
| |
Collapse
|
13
|
Abstract
The plausibility of data from networks of low-cost measurement devices is a growing and important contentious issue. Informal networks of low-cost devices have particularly come to prominence for air quality monitoring. The contentious point is the believability of data without regular on-site calibration, since this is a specialist task and the costs very quickly become much larger than the cost of installation in the first place. This Sensor Issues suggests that approaches to the problem that involve appropriate use of independent information have the potential to resolve the contention. Ideas are illustrated particularly with reference to low-cost sensor networks for air quality measurement.
Collapse
Affiliation(s)
- David E Williams
- School of Chemical Sciences and MacDiarmid Institute for Advanced Materials and Nanotechnology, University of Auckland, Private Bag
921019, Auckland 1142, New Zealand
| |
Collapse
|
14
|
Reece S, Williams R, Colón M, Southgate D, Huertas E, O'Shea M, Iglesias A, Sheridan P. Spatial-Temporal Analysis of PM 2.5 and NO₂ Concentrations Collected Using Low-Cost Sensors in Peñuelas, Puerto Rico. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4314. [PMID: 30544516 PMCID: PMC6308536 DOI: 10.3390/s18124314] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/28/2018] [Accepted: 12/04/2018] [Indexed: 01/09/2023]
Abstract
The U.S. Environmental Protection Agency (EPA) is involved in the discovery, evaluation, and application of low-cost air quality (AQ) sensors to support citizen scientists by directly engaging with them in the pursuit of community-based interests. The emergence of low-cost (<$2500) sensors have allowed a wide range of stakeholders to better understand local AQ conditions. Here we present results from the deployment of the EPA developed Citizen Science Air Monitor (CSAM) used to conduct approximately five months (October 2016⁻February 2017) of intensive AQ monitoring in an area of Puerto Rico (Tallaboa-Encarnación, Peñuelas) with little historical data on pollutant spatial variability. The CSAMs were constructed by combining low-cost particulate matter size fraction 2.5 micron (PM2.5) and nitrogen dioxide (NO₂) sensors and distributed across eight locations with four collocated weather stations to measure local meteorological parameters. During this deployment 1 h average concentrations of PM2.5 and NO₂ ranged between 0.3 to 33.6 µg/m³ and 1.3 to 50.6 ppb, respectively. Peak concentrations were observed for both PM2.5 and NO₂ when conditions were dominated by coastal-originated winds. These results advanced the community's understanding of pollutant concentrations and trends while improving our understanding of the limitations and necessary procedures to properly interpret measurements produced by low-cost sensors.
Collapse
Affiliation(s)
- Stephen Reece
- Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
| | - Ron Williams
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
| | - Maribel Colón
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
| | | | - Evelyn Huertas
- U.S. Environmental Protection Agency, Region 2, Caribbean Environmental Protection Division, Guaynabo, PR 00968-8069, USA.
| | - Marie O'Shea
- Region 2, U.S. Environmental Protection Agency, 290 Broadway, New York, NY 10007-1866, USA.
| | - Ariel Iglesias
- Region 2, U.S. Environmental Protection Agency, 290 Broadway, New York, NY 10007-1866, USA.
| | - Patricia Sheridan
- Region 2, U.S. Environmental Protection Agency, Edison, NJ 08837-3679, USA.
| |
Collapse
|