Automatic sensor arrangement system for building energy and environmental management

Kim, Jong Won; Jeong, Youn Kwae; Lee, Il Woo

doi:10.1016/j.egypro.2011.12.928

Cited by 11 publications

(4 citation statements)

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“…Air quality management is based on an adopted monitoring paradigm (Kim et al 2012), which is subject to continuous evolution due to technological progress and the development of portable, low-cost (~£100s) air pollution monitoring devices (i.e. sensors) and wireless communication systems.…”

Section: Introductionmentioning

confidence: 99%

“…Demonstration studies have applied mobile sensor networks in some cities, such as Cambridge (UK), Valencia (Spain) and Lagos (Nigeria) (Mead et al 2013), but their widespread long-term application is yet to find a legislative purpose. (Kim et al 2012), but our focus here remains on the application of low-cost sensing for air pollution management in urban outdoor environments. A comprehensive overview of these questions, highlighting operational challenges and a way forward, is therefore presented.…”

Section: Introductionmentioning

confidence: 99%

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The rise of low-cost sensing for managing air pollution in cities

Kumar

Morawska

Martani

et al. 2015

Environment International

757

447

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The rise of low-cost sensing for managing air pollution in cities

Kumar

Morawska

Martani

et al. 2015

Environment International

757

447

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“…Data reliability and accuracy is of paramount importance in making use of sensor data for predicting and modelling indoor exposure, and there are many questions in this regard, which are yet to be addressed (Kim et al, 2012;White et al, 2012). In order to address these challenges, performance evaluation of emerging technologies in general and indoor sensors in particular, are being carried out by research groups across the world.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings

Kumar

Skouloudis

Bell

et al. 2016

Science of The Total Environment

144

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Household air pollution is ranked the 9(th) largest Global Burden of Disease risk (Forouzanfar et al., The Lancet 2015). People, particularly urban dwellers, typically spend over 90% of their daily time indoors, where levels of air pollution often surpass those of outdoor environments. Indoor air quality (IAQ) standards and approaches for assessment and control of indoor air require measurements of pollutant concentrations and thermal comfort using conventional instruments. However, the outcomes of such measurements are usually averages over long integrated time periods, which become available after the exposure has already occurred. Moreover, conventional monitoring is generally incapable of addressing temporal and spatial heterogeneity of indoor air pollution, or providing information on peak exposures that occur when specific indoor sources are in operation. This article provides a review of new air pollution sensing methods to determine IAQ and discusses how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide. We also show that besides the opportunities, challenges still remain in terms of maturing technologies, or data mining and their interpretation. Moreover, we discuss further research and essential development needed to close gaps between what is available today and needed tomorrow. In particular, we demonstrate that awareness of IAQ risks and availability of appropriate regulation are lagging behind the technologies.

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“…Fine-scale air quality measurement and analysis have been powered by recent advances in sensor technologies, which allow for the use of mobile network platform with low-cost sensors for the purpose of general monitoring (Snyder et al, 2013;Morawska et al, 2013;Castellini et al, 2014;Sarto et al, 2016) and personal exposure assessment (Holstius et al, 2014;Kim et al, 2012;White et al, 2012). There is a significant difference in temporal resolution between mobile measurements and fixed point networks.…”

Section: Introductionmentioning

confidence: 99%

Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles

Guan

Johnson

Katzfuß

et al. 2019

Journal of the American Statistical Association

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People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their dayto-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens' personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.

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Automatic sensor arrangement system for building energy and environmental management

Cited by 11 publications

References 0 publications

The rise of low-cost sensing for managing air pollution in cities

The rise of low-cost sensing for managing air pollution in cities

Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings

Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles

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