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Chinnappan B, Hakim K, Kumar NS, Elumalai V. Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:39372-39387. [PMID: 38819512 DOI: 10.1007/s11356-024-33717-9] [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: 01/27/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024]
Abstract
Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model's air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques.
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Affiliation(s)
- Balasubramanian Chinnappan
- Electronics and Instrumentation Engineering, B.S.A Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, Tamil Nadu, India
| | - Kareemullah Hakim
- Electronics and Instrumentation Engineering, B.S.A Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, Tamil Nadu, India.
| | - Neelam Sanjeev Kumar
- Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, 600026, Tamil Nadu, India
| | - Vijayalakshmi Elumalai
- Electronics and Instrumentation Engineering, B.S.A Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, Tamil Nadu, India
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Ali S, Alam F, Potgieter J, Arif KM. Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:2930. [PMID: 38733036 PMCID: PMC11086096 DOI: 10.3390/s24092930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/26/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024]
Abstract
Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.
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Affiliation(s)
- Sharafat Ali
- Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand; (S.A.); (K.M.A.)
| | - Fakhrul Alam
- Department of Electrical & Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand
| | - Johan Potgieter
- Manawatu Agrifood Digital Lab, Palmerston North 4410, New Zealand;
| | - Khalid Mahmood Arif
- Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand; (S.A.); (K.M.A.)
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3
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Koziel S, Pietrenko-Dabrowska A, Wojcikowski M, Pankiewicz B. Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO 2 sensor using machine learning. Sci Rep 2024; 14:9152. [PMID: 38644408 PMCID: PMC11033258 DOI: 10.1038/s41598-024-59993-6] [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] [Received: 02/06/2024] [Accepted: 04/17/2024] [Indexed: 04/23/2024] Open
Abstract
Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.
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Affiliation(s)
- Slawomir Koziel
- Engineering Optimization and Modeling Center, Reykjavik University, 102, Reykjavik, Iceland.
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland.
| | - Anna Pietrenko-Dabrowska
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
| | - Marek Wojcikowski
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
| | - Bogdan Pankiewicz
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
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Godja NC, Munteanu FD. Hybrid Nanomaterials: A Brief Overview of Versatile Solutions for Sensor Technology in Healthcare and Environmental Applications. BIOSENSORS 2024; 14:67. [PMID: 38391986 PMCID: PMC10887000 DOI: 10.3390/bios14020067] [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: 12/26/2023] [Revised: 01/18/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024]
Abstract
The integration of nanomaterials into sensor technologies not only poses challenges but also opens up promising prospects for future research. These challenges include assessing the toxicity of nanomaterials, scalability issues, and the seamless integration of these materials into existing infrastructures. Future development opportunities lie in creating multifunctional nanocomposites and environmentally friendly nanomaterials. Crucial to this process is collaboration between universities, industry, and regulatory authorities to establish standardization in this evolving field. Our perspective favours using screen-printed sensors that employ nanocomposites with high electrochemical conductivity. This approach not only offers cost-effective production methods but also allows for customizable designs. Furthermore, incorporating hybrids based on carbon-based nanomaterials and functionalized Mxene significantly enhances sensor performance. These high electrochemical conductivity sensors are portable, rapid, and well-suited for on-site environmental monitoring, seamlessly aligning with Internet of Things (IoT) platforms for developing intelligent systems. Simultaneously, advances in electrochemical sensor technology are actively working to elevate sensitivity through integrating nanotechnology, miniaturization, and innovative electrode designs. This comprehensive approach aims to unlock the full potential of sensor technologies, catering to diverse applications ranging from healthcare to environmental monitoring. This review aims to summarise the latest trends in using hybrid nanomaterial-based sensors, explicitly focusing on their application in detecting environmental contaminants.
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Affiliation(s)
| | - Florentina-Daniela Munteanu
- Faculty of Food Engineering, Tourism and Environmental Protection, “Aurel Vlaicu” University of Arad, 2–4 E. Drăgoi Str., 310330 Arad, Romania;
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Katoto PDMC, Bihehe D, Brand A, Mushi R, Kusinza A, Alwood BW, van Zyl-Smit RN, Tamuzi JL, Sam-Agudu NA, Yotebieng M, Metcalfe J, Theron G, Godri Pollitt KJ, Lesosky M, Vanoirbeek J, Mortimer K, Nawrot T, Nemery B, Nachega JB. Household air pollution and risk of pulmonary tuberculosis in HIV-Infected adults. Environ Health 2024; 23:6. [PMID: 38233832 PMCID: PMC10792790 DOI: 10.1186/s12940-023-01044-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/19/2023] [Indexed: 01/19/2024]
Abstract
BACKGROUND In low- and middle-income countries countries, millions of deaths occur annually from household air pollution (HAP), pulmonary tuberculosis (PTB), and HIV-infection. However, it is unknown whether HAP influences PTB risk among people living with HIV-infection. METHODS We conducted a case-control study among 1,277 HIV-infected adults in Bukavu, eastern Democratic Republic of Congo (February 2018 - March 2019). Cases had current or recent (<5y) PTB (positive sputum smear or Xpert MTB/RIF), controls had no PTB. Daily and lifetime HAP exposure were assessed by questionnaire and, in a random sub-sample (n=270), by 24-hour measurements of personal carbon monoxide (CO) at home. We used multivariable logistic regression to examine the associations between HAP and PTB. RESULTS We recruited 435 cases and 842 controls (median age 41 years, [IQR] 33-50; 76% female). Cases were more likely to be female than male (63% vs 37%). Participants reporting cooking for >3h/day and ≥2 times/day and ≥5 days/week were more likely to have PTB (aOR 1·36; 95%CI 1·06-1·75) than those spending less time in the kitchen. Time-weighted average 24h personal CO exposure was related dose-dependently with the likelihood of having PTB, with aOR 4·64 (95%CI 1·1-20·7) for the highest quintile [12·3-76·2 ppm] compared to the lowest quintile [0·1-1·9 ppm]. CONCLUSION Time spent cooking and personal CO exposure were independently associated with increased risk of PTB among people living with HIV. Considering the high burden of TB-HIV coinfection in the region, effective interventions are required to decrease HAP exposure caused by cooking with biomass among people living with HIV, especially women.
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Affiliation(s)
- Patrick D M C Katoto
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
- Office of the President and CEO, South African Medical Research Council, Cape Town, South Africa.
- Centre for Tropical Diseases and Global Health, Catholic University of Bukavu, Bukavu, Democratic Republic of the Congo.
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
| | - Dieudonné Bihehe
- Department of Internal Medicine, Université Evangélique en Afrique, Bukavu, DR, Congo
| | - Amanda Brand
- Centre for Evidence-Based Health Care, Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, South Africa
| | - Raymond Mushi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Aline Kusinza
- Department of Medicine, Division of Pulmonology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Brian W Alwood
- Department of Medicine, Division of Pulmonology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Richard N van Zyl-Smit
- Division of Pulmonology & UCT Lung Institute, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Jacques L Tamuzi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Nadia A Sam-Agudu
- International Research Center of Excellence, Institute of Human Virology Nigeria, Abuja, Nigeria
- Division of Epidemiology and Prevention, Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Marcel Yotebieng
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - John Metcalfe
- Division of Pulmonary and Critical Care Medicine, Trauma Center, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA
| | - Grant Theron
- South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, NRF-DST Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, USA
| | - Maia Lesosky
- Division of epidemiology and Biostatistics, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Jeroen Vanoirbeek
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Kevin Mortimer
- Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | - Tim Nawrot
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Centre of Environmental Health, University of Hasselt, Hasselt, Belgium
| | - Benoit Nemery
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Jean B Nachega
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA.
- Department of Medicine, Center for Infectious Diseases, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
- Department of Epidemiology and Center for Global Health, Infectious Diseases and Microbiology, University of Pittsburgh Graduate School of Public Health, 130 DeSoto St., Room A522 Crabtree Hall, Pittsburgh, 15260, PA, USA.
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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.
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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
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Hernández-Rodríguez E, González-Rivero RA, Schalm O, Martínez A, Hernández L, Alejo-Sánchez D, Janssens T, Jacobs W. Reliability Testing of a Low-Cost, Multi-Purpose Arduino-Based Data Logger Deployed in Several Applications Such as Outdoor Air Quality, Human Activity, Motion, and Exhaust Gas Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:7412. [PMID: 37687868 PMCID: PMC10490711 DOI: 10.3390/s23177412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023]
Abstract
This contribution shows the possibilities of applying a low-cost, multi-purpose data logger built around an Arduino Mega 2560 single-board computer. Most projects use this kind of hardware to develop single-purpose data loggers. In this work, a data logger with a more general hardware and software architecture was built to perform measurement campaigns in very different domains. The wide applicability of this data logger was demonstrated with short-term monitoring campaigns in relation to outdoor air quality, human activity in an office, motion of a journey on a bike, and exhaust gas monitoring of a diesel generator. In addition, an assessment process and corresponding evaluation framework are proposed to assess the credibility of low-cost scientific devices built in-house. The experiences acquired during the development of the system and the short measurement campaigns were used as inputs in the assessment process. The assessment showed that the system scores positively on most product-related targets. However, unexpected events affect the assessment over the longer term. This makes the development of low-cost scientific devices harder than expected. To assure stability and long-term performance of this type of design, continuous evaluation and regular engineering corrections are needed throughout longer testing periods.
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Affiliation(s)
- Erik Hernández-Rodríguez
- Faculty of Electrical Engineering, Universidad Central “Marta Abreu” de Las Villas, Road to Camajuaní Km 5.5, Santa Clara 54830, Cuba; (E.H.-R.); (A.M.); (L.H.)
| | - Rosa Amalia González-Rivero
- Faculty of Chemistry, Universidad Central “Marta Abreu” de Las Villas, Road to Camajuaní Km 5.5, Santa Clara 54830, Cuba; (R.A.G.-R.); (D.A.-S.)
| | - Olivier Schalm
- Antwerp Maritime Academy, Noordkasteel Oost 6, 2030 Antwerpen, Belgium; (T.J.); (W.J.)
| | - Alain Martínez
- Faculty of Electrical Engineering, Universidad Central “Marta Abreu” de Las Villas, Road to Camajuaní Km 5.5, Santa Clara 54830, Cuba; (E.H.-R.); (A.M.); (L.H.)
| | - Luis Hernández
- Faculty of Electrical Engineering, Universidad Central “Marta Abreu” de Las Villas, Road to Camajuaní Km 5.5, Santa Clara 54830, Cuba; (E.H.-R.); (A.M.); (L.H.)
| | - Daniellys Alejo-Sánchez
- Faculty of Chemistry, Universidad Central “Marta Abreu” de Las Villas, Road to Camajuaní Km 5.5, Santa Clara 54830, Cuba; (R.A.G.-R.); (D.A.-S.)
| | - Tim Janssens
- Antwerp Maritime Academy, Noordkasteel Oost 6, 2030 Antwerpen, Belgium; (T.J.); (W.J.)
| | - Werner Jacobs
- Antwerp Maritime Academy, Noordkasteel Oost 6, 2030 Antwerpen, Belgium; (T.J.); (W.J.)
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8
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Hasan MH, Yu H, Ivey C, Pillarisetti A, Yuan Z, Do K, Li Y. Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal. ACS OMEGA 2023; 8:5917-5924. [PMID: 36816698 PMCID: PMC9933490 DOI: 10.1021/acsomega.2c07734] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/16/2023] [Indexed: 05/31/2023]
Abstract
Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO2) and ozone (O3) were deployed in six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different climatic and geographical conditions on their performance and calibration. Three calibration methods were applied, including regression via linear and polynomial models and random forest methods. When signals from carbon monoxide (CO) sensors were included in the calibration models for NO2 and O3 sensors, model performance generally increased, with pronounced improvements in selected cities such as Riverside and New York City. Such improvements may be due to (1) temporal co-variation between concentrations of CO and NO2 and/or between CO and O3; (2) different performance levels of low-cost CO, NO2, and O3 sensors; and (3) different impacts of environmental conditions on sensor performance. The results showed an innovative approach for improving the calibration of NO2 and O3 sensors by including CO sensor signals into the calibration models. Community users of LCAQ sensors may be able to apply these findings further to enhance the data quality of their deployed NO2 and O3 monitors.
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Affiliation(s)
- Md Hasibul Hasan
- Department
of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States
| | - Haofei Yu
- Department
of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States
| | - Cesunica Ivey
- Department
of Civil and Environmental Engineering, The University of California, Berkeley, Berkeley, California94720, United States
| | - Ajay Pillarisetti
- Environmental
Health Sciences, School of Public Health, University of California, Berkeley, California94720, United States
| | - Ziyang Yuan
- Sailbri
Cooper, Inc., Tigard, Oregon97223, United States
| | - Khanh Do
- Department
of Chemical and Environmental Engineering, University of California, Riverside, California92521, United States
| | - Yi Li
- Sailbri
Cooper, Inc., Tigard, Oregon97223, United States
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9
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Chong JL, Chew KW, Peter AP, Ting HY, Show PL. Internet of Things (IoT)-Based Environmental Monitoring and Control System for Home-Based Mushroom Cultivation. BIOSENSORS 2023; 13:98. [PMID: 36671933 PMCID: PMC9856179 DOI: 10.3390/bios13010098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
The control and monitoring of the environmental conditions in mushroom cultivation has been a challenge in the mushroom industry. Currently, research has been conducted to implement successful remote environmental monitoring, or, in some cases, remote environmental control, yet there is not yet a combination of both these systems providing live stream images or video. As a result, this research aimed to design and develop an Internet of things (IoT)-based environmental control and monitoring system for mushroom cultivation, whereby the growth conditions of the mushrooms, such as temperature, humidity, light intensity, and soil moisture level, are remotely monitored and controlled through a mobile and web application. Users would be able to visualize the growth of the mushroom remotely by video and images through the Internet. The respective sensors are implemented into the mushroom cultivation process and connected to the NodeMCU microcontroller, which collects and transfers the data to the cloud server, enabling remote access at any time through the end device with internet connection. The control algorithm regulates the equipment within the cultivational chamber autonomously, based on feedback from the sensors, in order to retain the optimum environment for the cultivation of mushrooms. The sensors were tested and compared with manual readings to ensure their accuracy. The implementation of IoT toward mushroom cultivation would greatly contribute to the advancement of the current mushroom industry which still applies the traditional cultivation approach.
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Affiliation(s)
- Jiu Li Chong
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham, Jalan Broga, Semenyih 43500, Selangor, Malaysia
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Angela Paul Peter
- Postgraduate Studies Unit, Research and Postgraduate Centre, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, Sepang 43900, Selangor, Malaysia
| | - Huong Yong Ting
- Drone Research and Application Centre, University of Technology Sarawak, No. 1, Jalan Universiti, Sibu 96000, Sarawak, Malaysia
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham, Jalan Broga, Semenyih 43500, Selangor, Malaysia
- Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India
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10
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Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2023. [DOI: 10.1108/ijpcc-07-2022-0271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Purpose
Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.
Design/methodology/approach
This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.
Findings
The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.
Originality/value
This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.
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Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors. SENSORS 2022; 22:s22103964. [PMID: 35632373 PMCID: PMC9146777 DOI: 10.3390/s22103964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023]
Abstract
The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.
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12
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Gäbel P, Koller C, Hertig E. Development of Air Quality Boxes Based on Low-Cost Sensor Technology for Ambient Air Quality Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22103830. [PMID: 35632239 PMCID: PMC9144299 DOI: 10.3390/s22103830] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 06/12/2023]
Abstract
Analyses of the relationships between climate, air substances and health usually concentrate on urban environments because of increased urban temperatures, high levels of air pollution and the exposure of a large number of people compared to rural environments. Ongoing urbanization, demographic ageing and climate change lead to an increased vulnerability with respect to climate-related extremes and air pollution. However, systematic analyses of the specific local-scale characteristics of health-relevant atmospheric conditions and compositions in urban environments are still scarce because of the lack of high-resolution monitoring networks. In recent years, low-cost sensors (LCS) became available, which potentially provide the opportunity to monitor atmospheric conditions with a high spatial resolution and which allow monitoring directly at vulnerable people. In this study, we present the atmospheric exposure low-cost monitoring (AELCM) system for several air substances like ozone, nitrogen dioxide, carbon monoxide and particulate matter, as well as meteorological variables developed by our research group. The measurement equipment is calibrated using multiple linear regression and extensively tested based on a field evaluation approach at an urban background site using the high-quality measurement unit, the atmospheric exposure monitoring station (AEMS) for meteorology and air substances, of our research group. The field evaluation took place over a time span of 4 to 8 months. The electrochemical ozone sensors (SPEC DGS-O3: R2: 0.71-0.95, RMSE: 3.31-7.79 ppb) and particulate matter sensors (SPS30 PM1/PM2.5: R2: 0.96-0.97/0.90-0.94, RMSE: 0.77-1.07 µg/m3/1.27-1.96 µg/m3) showed the best performances at the urban background site, while the other sensors underperformed tremendously (SPEC DGS-NO2, SPEC DGS-CO, MQ131, MiCS-2714 and MiCS-4514). The results of our study show that meaningful local-scale measurements are possible with the former sensors deployed in an AELCM unit.
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Affiliation(s)
- Paul Gäbel
- Regional Climate Change and Health, Faculty of Medicine, University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany;
| | - Christian Koller
- Faculty of Design, Hochschule München, Lothstraße 34, 80335 Munich, Germany;
| | - Elke Hertig
- Regional Climate Change and Health, Faculty of Medicine, University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany;
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Rogulski M, Badyda A, Gayer A, Reis J. Improving the Quality of Measurements Made by Alphasense NO 2 Non-Reference Sensors Using the Mathematical Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22103619. [PMID: 35632025 PMCID: PMC9144097 DOI: 10.3390/s22103619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 06/02/2023]
Abstract
Conventional NO2 monitoring devices are relatively cumbersome, expensive, and have a relatively high-power consumption that limits their use to fixed sites. On the other hand, they offer high-quality measurements. In contrast, the low-cost NO2 sensors offer greater flexibility, are smaller, and allow greater coverage of the area with the measuring devices. However, their disadvantage is much lower accuracy. The main goal of this study was to investigate the measurement data quality of NO2-B43F Alphasense sensors. The measurement performance analysis of Alphasense NO2-B43F sensors was conducted in two research areas in Poland. Sensors were placed near fixed, professional air quality monitoring stations, carrying out measurements based on reference methods, in the following periods: July-November, and December-May. Results of the study show that without using sophisticated correction methods, the range of measured air pollution concentrations may be greater than their actual values in ambient air-measured in the field by fixed stations. In the case of summer months (with air temperature over 30 °C), the long-term mean absolute percentage error was over 150% and the sensors, using the methods recommended by the manufacturer, in the case of high temperatures could even show negative values. After applying the mathematical correction functions proposed in this article, it was possible to significantly reduce long-term errors (to 40-70% per month, regardless of the location of the measurements) and eliminate negative measurement values. The proposed method is based on the recalculation of the raw measurement, air temperature, and air RH and does not require the use of extensive analytical tools.
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Affiliation(s)
- Mariusz Rogulski
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland; (A.B.); (A.G.)
| | - Artur Badyda
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland; (A.B.); (A.G.)
| | - Anna Gayer
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland; (A.B.); (A.G.)
| | - Johnny Reis
- CESAM—Center for Environmental and Marine Studies & Department Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal;
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A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists. SENSORS 2022; 22:s22031272. [PMID: 35162017 PMCID: PMC8838550 DOI: 10.3390/s22031272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 01/17/2022] [Accepted: 02/05/2022] [Indexed: 12/22/2022]
Abstract
We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO2 and O3 and an optical particle-matter (PM) sensor for PM2.5 and PM10 concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R2 > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user's understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.
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Calibration of Low-Cost NO 2 Sensors through Environmental Factor Correction. TOXICS 2021; 9:toxics9110281. [PMID: 34822672 PMCID: PMC8624883 DOI: 10.3390/toxics9110281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/12/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022]
Abstract
Low-cost air quality sensors (LCSs) have become more widespread due to their low cost and increased capabilities; however, to supplement more traditional air quality networks, the performance of these LCSs needs to be validated. This study focused on NO2 measurements from eight Clarity Node-S sensors and used various environmental factors to calibrate the LCSs. To validate the calibration performance, we calculated the root-mean-square error (RMSE), mean absolute error (MAE), R2, and slope compared to reference measurements. Raw results from six of these sensors were comparable to those reported for other NO2 LCSs; however, two of the evaluated LCSs had RMSE values ~20 ppb higher than the other six LCSs. By applying a sensor-specific calibration that corrects for relative humidity, temperature, and ozone, this discrepancy was mitigated. In addition, this calibration improved the RMSE, MAE, R2, and slope of all eight LCS compared to the raw data. It should be noted that relatively stable environmental conditions over the course of the LCS deployment period benefited calibration performance over time. These results demonstrate the importance of developing LCS calibration models for individual sensors that consider pertinent environmental factors.
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Zuidema C, Schumacher CS, Austin E, Carvlin G, Larson TV, Spalt EW, Zusman M, Gassett AJ, Seto E, Kaufman JD, Sheppard L. Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study. SENSORS 2021; 21:s21124214. [PMID: 34205429 PMCID: PMC8234435 DOI: 10.3390/s21124214] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 11/30/2022]
Abstract
We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.
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Affiliation(s)
- Christopher Zuidema
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Cooper S. Schumacher
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Graeme Carvlin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Timothy V. Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 18195, USA
| | - Elizabeth W. Spalt
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Marina Zusman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Amanda J. Gassett
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Joel D. Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
- Department of Medicine, University of Washington, Seattle, WA 18195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 18195, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
- Department of Biostatistics, University of Washington, Seattle, WA 18795, USA
- Correspondence:
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From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development. SENSORS 2021; 21:s21093190. [PMID: 34062961 PMCID: PMC8124547 DOI: 10.3390/s21093190] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 04/27/2021] [Accepted: 04/30/2021] [Indexed: 11/21/2022]
Abstract
Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.
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