Open Access
The rise of low-cost sensing for managing air pollution in cities
Prashant Kumar,Lidia Morawska,Claudio Martani,George Biskos,George Biskos,George Biskos,Marina Neophytou,Silvana Di Sabatino,Margaret Bell,Leslie Norford,Rex Britter +10 more
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In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.Abstract:
Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.read more
Citations
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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 Article
Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings
Prashant Kumar,Andreas N. Skouloudis,Margaret Bell,Mar Viana,Maria-Cristina Carotta,George Biskos,Lidia Morawska +6 more
TL;DR: Forouzanfar et al. as discussed by the authors provide a review of the new air pollution sensing methods to determine indoor air quality and discuss how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide.
Journal ArticleDOI
Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art
TL;DR: This article summarizes the existing studies on the state-of-the-art of LCS for AQM, and conceptualizes a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data.
Ultrafine particles in cities
Prashant Kumar,Lidia Morawska,Wolfram Birmili,Pauli Paasonen,Min Hu,Markku Kulmala,Roy M. Harrison,Leslie Norford,Rex Britter +8 more
TL;DR: In this paper, the authors reviewed some fundamental drivers of UFP emissions and dispersion, and highlighted unresolved challenges, as well as recommendations to ensure sustainable urban development whilst minimising any possible adverse health impacts.
Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model
Marguerite Nyhan,Stanislav Sobolevsky,Chaogui Kang,Prudence Robinson,Andrea Corti,Michael Szell,David G. Streets,Zifeng Lu,Rex Britter,Steven R. H. Barrett,Carlo Ratti +10 more
TL;DR: In this paper, a taxi fleet of over 15,000 vehicles was analyzed with the aim of predicting air pollution emissions for Singapore, and the results showed that highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions.
References
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Journal ArticleDOI
Seasonal variations and the influence of ventilation rates on IAQ: A case study of five low-energy London apartments:
TL;DR: The indoor air quality of five low-energy London apartments has been assessed through the measurement of 16 key pollutants, using continuous and diffusive methods across heating and non-heating as mentioned in this paper.
Proceedings ArticleDOI
Energy-Efficient Air Pollution Monitoring with Optimum Duty-Cycling on a Sensor Hub
TL;DR: It is demonstrated that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information.
Journal ArticleDOI
Determination of total and lung-deposited particle surface area concentrations, in central Athens, Greece
TL;DR: Estimation methods indicate that a trustworthy assessment of the temporal variability of SA and LDSA concentration metrics can be provided in real time, on the basis of relatively lower-cost instrumentation, especially in view of recent advances in particle sensing technologies.
Proceedings ArticleDOI
MapTransfer : Urban Air Quality Map Generation for Downscaled Sensor Deployments
TL;DR: MapTransfer is designed, an air quality map generation scheme which augments the current sensor measurements from the downscaled sparse deployment with appropriate historical data from the initial dense deployment, and integrates the best historical data with the current measurements via a multi-output Gaussian process model at sub-region levels.
Journal ArticleDOI
Fine-Grained Air Pollution Inference with Mobile Sensing Systems: A Weather-Related Deep Autoencoder Model
TL;DR: This work proposes a deep autoencoder framework based inference algorithm that better captures the spatiotemporal dependencies in the pollution map with unevenly distributed samples than other real-time approaches, and presents a weather-related ConvLSTM to enable quasi real- time applications.
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