Indoor Air Quality

The monitoring of indoor air pollutants in a spatio-temporal basis is challenging. A key element is the access to local (i.e., indoor residential, workplace, or public building) exposure measurements. Unfortunately, the high cost and complexity of most current air pollutant monitors result in a lack...

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Bibliographic Details
Other Authors: Saraga, Dikaia E. (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
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DOAB: description of the publication
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520 |a The monitoring of indoor air pollutants in a spatio-temporal basis is challenging. A key element is the access to local (i.e., indoor residential, workplace, or public building) exposure measurements. Unfortunately, the high cost and complexity of most current air pollutant monitors result in a lack of detailed spatial and temporal resolution. As a result, individuals in vulnerable groups (children, pregnant, elderly, and sick people) have little insight into their personal exposure levels. This becomes significant in cases of hyper-local variations and short-term pollution events such as instant indoor activity (e.g., cooking, smoking, and dust resuspension). Advances in sensor miniaturization have encouraged the development of small, inexpensive devices capable of estimating pollutant concentrations. This new class of sensors presents new possibilities for indoor exposure monitoring. This Special Issue invites research in the areas of the triptych: indoor air pollution monitoring, indoor air modeling, and exposure to indoor air pollution. Topics of interest for the Special Issue include, but are not limited to, the following: low-cost sensors for indoor air monitoring; indoor particulate matter and volatile organic compounds; ozone-terpene chemistry; biological agents indoors; source apportionment; exposure assessment; health effects of indoor air pollutants; occupant perception; climate change impacts on indoor air quality. 
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653 |a electroencephalography 
653 |a electrocardiography 
653 |a airborne microorganisms 
653 |a bacteria 
653 |a fungi 
653 |a gyms 
653 |a indoor air quality 
653 |a libraries 
653 |a offices 
653 |a contactless measurements 
653 |a skin sensitivity index 
653 |a subtleness magnification 
653 |a deep learning 
653 |a piecewise stationary time series 
653 |a PM2.5 
653 |a sensor 
653 |a correction 
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653 |a secondhand smoke 
653 |a urban traffic 
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653 |a Toluene degradation  
653 |a n/a 
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