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A Gap Analysis of Low-Cost Outdoor Air Quality Sensor In-Field Calibration
Francesco Concas,Julien Mineraud,Eemil Lagerspetz,Samu Varjonen,Kai Puolamäki,Petteri Nurmi,Sasu Tarkoma +6 more
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TLDR
This article presents low-cost sensor technologies, and it survey and assess machine learning-based calibration techniques for their calibration, and presents open questions and directions for future research.Abstract:
In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from crosssensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.read more
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Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?
Lidia Morawska,Phong K. Thai,Xiaoting Liu,Akwasi Bonsu Asumadu-Sakyi,Godwin A. Ayoko,Alena Bartonova,Andrea Bedini,Fahe Chai,Bryce Christensen,Matthew Dunbabin,Jian Gao,Gayle S.W. Hagler,Rohan Jayaratne,Prashant Kumar,Alexis K.H. Lau,Peter K.K. Louie,Mandana Mazaheri,Zhi Ning,Nunzio Motta,Ben Mullins,Mahmudur Rahman,Zoran Ristovski,Mahnaz Shafiei,Dian Tjondronegoro,Dane Westerdahl,Ronald Williams +25 more
TL;DR: In this article, the authors conducted a comprehensive literature search including both the scientific and grey literature, and concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use.
Journal ArticleDOI
From air quality sensors to sensor networks: Things we need to learn
Yi Li,Ziyang Yuan,L.-W. Antony Chen,Ajay Pillarisetti,Ajay Pillarisetti,Varun Yadav,Mengxian Wu,Houxin Cui,Chuanfeng Zhao +8 more
TL;DR: In this paper, low-cost air quality sensors (LCAQS) can be deployed in dense monitoring networks to provide timely and comprehensive snapshots of pollutant concentrations and their spatial and temporal variability at various scales with relatively less cost and labor.
References
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Proceedings ArticleDOI
Reducing multi-hop calibration errors in large-scale mobile sensor networks
TL;DR: This paper proposes a novel multi-hop calibration algorithm using geometric mean regression, which highly reduces error propagation in the network, distinctly outperforms ordinary least squares in themulti-hop scenario, and requires considerably fewer ground truth measurements compared to existing network calibration algorithms.
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Low Power Operation of Temperature-Modulated Metal Oxide Semiconductor Gas Sensors.
Javier Burgués,Santiago Marco +1 more
TL;DR: It is found that on-demand operation produced a deformation of the sensor conductance patterns, which led to an increase in the prediction error by almost a factor of 5 as compared to continuous operation, which may be advantageous for applications that do not require continuous and periodic measurements, and which can tolerate slightly higher prediction errors.
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Using statistical methods to carry out in field calibrations of low cost air quality sensors
TL;DR: In this paper, a two-step calibration method consisting of supervised statistical machine learning regression algorithms was devised in order to overcome the limitation of low-cost AQmesh sensors to accurately measure NO2 concentrations to be able to determine the effects of using photocatalytic pavements.
Journal ArticleDOI
Build a global Earth observatory
TL;DR: Markku Kulmala calls for continuous, comprehensive monitoring of interactions between the planet’s surface and atmosphere.
Journal ArticleDOI
An inexpensive light-scattering particle monitor: field validation.
Zohir Chowdhury,Rufus Edwards,Michael Johnson,Kyra Naumoff Shields,Tracy Allen,Eduardo Canuz,Kirk R. Smith +6 more
TL;DR: The UCB can consistently estimate PM(2.5) mass concentrations in wood-burning kitchens, and with appropriate cleaning of the sensing chamber, UCB mass sensitivity does not decrease with time when used intensively in open woodfire kitchens, demonstrating the significant potential of this monitor.