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 IoT Based Automatic Air Pollution Monitoring and Purification System

2021 ◽  
Vol 9 (9) ◽  
pp. 1378-1383
Author(s):  
Manoj Gurung
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Premature Mortality ◽  
Cost Effective ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Air Purifier

Abstract: Degradation of air quality, like climate change and global warming, has become an all-encompassing existential hazard to humanity and natural life. Exposure to severely polluted air on a regular basis causes pulmonary disorders and contributes to severe allergies and asthma. According to studies, more than 10 million people die each year as a result of irregularities produced directly or indirectly by air pollution. The work of Lelieveld et al. [1] sheds light on the gravity of the problem. It is estimated that by 2050, the worldwide premature mortality from air pollution will exceed 6.6 million fatalities per year (358000 from ozone, the rest from PM 2.5) [1]. As a result, we decided to focus our study on improving indoor air quality. Despite the fact that there are numerous indoor air purifiers on the market, their cost belies their effectiveness, and the effective ones are far too expensive for working-class people to afford [2]. In order to address this issue, we created an automated Internet of Things (IoT) based air filtration system that uses an automated air purifier which is triggered when air quality falls below WHO criteria. Our initiative intends to improve indoor air quality by utilizing the most cost-effective and efficient modules available. Keywords: Indoor Air Pollution, Air Purifier, IAQ, Sharp Dust Sensor GP2Y1010AU0F, IoT, Particulate Matter (PM), HEPA Filter

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.

Calibration Transfer Among Sensor Arrays Designed for Monitoring Volatile Organic Compounds in Indoor Air Quality

2006 ◽  
Vol 6 (6) ◽  
pp. 1638-1643 ◽  
Author(s):  
Edward J. Wolfrum ◽  
Robert M. Meglen ◽  
Darren Peterson ◽  
Justin Sluiter
Keyword(s):  
Air Quality ◽  
Volatile Organic Compounds ◽  
Indoor Air Quality ◽  
Organic Compounds ◽  
Indoor Air ◽  
Sensor Arrays ◽  
Calibration Transfer ◽  
Volatile Organic
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