Development of Robust Indoor Air Quality Models for the Estimation of Volatile Organic Compound Concentrations in Buildings

1999 ◽  
Vol 8 (6) ◽  
pp. 345-360
Author(s):  
Michael V. Ellacott ◽  
Sue Reed
Keyword(s):  
Air Quality ◽  
Organic Compound ◽  
Indoor Air Quality ◽  
Volatile Organic Compound ◽  
Indoor Air ◽  
Quality Models ◽  
Volatile Organic

scholarly journals Visualising household air pollution: Colorimetric sensor arrays for monitoring volatile organic compounds indoors

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258281
Author(s):  
Emer Duffy ◽  
Kati Huttunen ◽  
Roosa Lahnavik ◽  
Alan F. Smeaton ◽  
Aoife Morrin
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Organic Compound ◽  
Indoor Air Quality ◽  
Volatile Organic Compound ◽  
Indoor Air ◽  
Sensor Arrays ◽  
Colorimetric Sensor ◽  
Pollution Monitoring ◽  
Volatile Organic

Indoor air quality monitoring as it relates to the domestic setting is an integral part of human exposure monitoring and health risk assessment. Hence there is a great need for easy to use, fast and economical indoor air quality sensors to monitor the volatile organic compound composition of the air which is known to be significantly perturbed by the various source emissions from activities in the home. To meet this need, paper-based colorimetric sensor arrays were deployed as volatile organic compound detectors in a field study aiming to understand which activities elicit responses from these sensor arrays in household settings. The sensor array itself is composed of pH indicators and aniline dyes that enable molecular recognition of carboxylic acids, amines and carbonyl-containing compounds. The sensor arrays were initially deployed in different rooms in a single household having different occupant activity types and levels. Sensor responses were shown to differ for different room settings on the basis of occupancy levels and the nature of the room emission sources. Sensor responses relating to specific activities such as cooking, cleaning, office work, etc were noted in the temporal response. Subsequently, the colorimetric sensor arrays were deployed in a broader study across 9 different households and, using multivariate analysis, the sensor responses were shown to correlate strongly with household occupant activity and year of house build. Overall, this study demonstrates the significant potential for this type of simple approach to indoor air pollution monitoring in residential environments.

scholarly journals High-Performance VOC Quantification for IAQ Monitoring Using Advanced Sensor Systems and Deep Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1487
Author(s):  
Yannick Robin ◽  
Johannes Amann ◽  
Tobias Baur ◽  
Payman Goodarzi ◽  
Caroline Schultealbert ◽  
...  
Keyword(s):  
Air Quality ◽  
Volatile Organic Compounds ◽  
Organic Compound ◽  
Organic Compounds ◽  
Volatile Organic Compound ◽  
Indoor Air ◽  
Deep Neural Networks ◽  
Evaluation Methods ◽  
Data Evaluation ◽  
Volatile Organic

With air quality being one target in the sustainable development goals set by the United Nations, accurate monitoring also of indoor air quality is more important than ever. Chemiresistive gas sensors are an inexpensive and promising solution for the monitoring of volatile organic compounds, which are of high concern indoors. To fully exploit the potential of these sensors, advanced operating modes, calibration, and data evaluation methods are required. This contribution outlines a systematic approach based on dynamic operation (temperature-cycled operation), randomized calibration (Latin hypercube sampling), and the use of advances in deep neural networks originally developed for natural language processing and computer vision, applying this approach to volatile organic compound measurements for indoor air quality monitoring. This paper discusses the pros and cons of deep neural networks for volatile organic compound monitoring in a laboratory environment by comparing the quantification accuracy of state-of-the-art data evaluation methods with a 10-layer deep convolutional neural network (TCOCNN). The overall performance of both methods was compared for complex gas mixtures with several volatile organic compounds, as well as interfering gases and changing ambient humidity in a comprehensive lab evaluation. Furthermore, both were tested under realistic conditions in the field with additional release tests of volatile organic compounds. The results obtained during field testing were compared with analytical measurements, namely the gold standard gas chromatography mass spectrometry analysis based on Tenax sampling, as well as two mobile systems, a gas chromatograph with photo-ionization detection for volatile organic compound monitoring and a gas chromatograph with a reducing compound photometer for the monitoring of hydrogen. The results showed that the TCOCNN outperforms state-of-the-art data evaluation methods, for example for critical pollutants such as formaldehyde, achieving an uncertainty of around 11 ppb even in complex mixtures, and offers a more robust volatile organic compound quantification in a laboratory environment, as well as in real ambient air for most targets.

scholarly journals The Improvement of Indoor Air Quality in Residential Buildings in Dubai, UAE

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 250
Author(s):  
Chuloh Jung ◽  
Jihad Awad
Keyword(s):  
Computer Simulation ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Building Materials ◽  
Field Survey ◽  
Residential Buildings ◽  
Radon Gas ◽  
Volatile Organic ◽  
Residential Projects

Due to unprecedented urbanization, UAE had built many new residential projects with poor choices of material and ventilation. This social phenomenon is leading UAE to Sick Building Syndrome (SBS) faster than any other countries. The Dubai Municipality regulates the indoor air quality with strict stipulation, but the detailed regulations are still insufficient. The objective of this paper is to measure the indoor air quality of new residential projects in Dubai to suggest the improvement of the regulations for indoor air quality. As a methodology, a field survey was conducted to investigate the status of indoor air pollution in residential buildings. Based on the field survey data, lab experiments for building materials were conducted and a computer simulation on radon gas was conducted. The result had shown that radon gas was mainly detected in new townhouses and labor camp houses, and its concentration was found to exceed the standard. Volatile organic solvents (VOCs) and formaldehyde (CH2O) were mainly detected in showhouses and new townhouses, and the concentration distribution was about 10 times higher than that of outdoors. It was proven that emission concentration of radon gas from various building materials were detected, and the order was red clay, gypsum board, and concrete. Volatile organic solvents (VOCs) are mainly detected in oil paints and PVC floor and the radiation amount of all pollutants increased with temperature increase. In computer simulation, it was found that a new townhouse needs a grace period from 20 days to 6 months to lower the radon gas concentration by 2 pCi/L. This study will serve as a basic data to establish more detailed regulation for the building materials and improve the IAQ standards in Dubai.

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