Abstract
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.
Highlights
With indoor air quality (IAQ) being one of the most common and unavoidable threats to human health and one of the most difficult to determine accurately, it is more important than ever to be able to make accurate measurements of IAQ [1]
As deep learning has proven to be very successful for the interpretation of complex patterns [14], this study provides a first test of deep-learning-based methods utilizing advanced ML techniques such as convolutional neural networks (CNNs) [15] in combination with neural architecture search (NAS) [16] for improved IAQ monitoring
The Feature Extraction Selection Regression (FESR) model indicated an increase of approximately 600 ppb, which is in accordance with the amount released, cf. (Table 2), while the TCOCNN indicated a slightly smaller increase of 500 ppb
Summary
With indoor air quality (IAQ) being one of the most common and unavoidable threats to human health and one of the most difficult to determine accurately, it is more important than ever to be able to make accurate measurements of IAQ [1]. Dangerous are volatile organic compounds (VOCs), which can lead to serious health problems. Indoor air contains hundreds or even thousands of compounds, some of them benign, others toxic, even at very low concentrations, making accurate quantification of each of them impossible, at least for routine and continuous measurements [4]. We concentrated on VOCs with a high-to-medium vapor pressure including the carcinogens formaldehyde and benzene, which are considered as two of the most toxic substances in indoor air with guideline threshold values in the low ppb range according to the WHO [11]. Comprehensive VOC monitoring is required to provide a universal indicator for IAQ, e.g., as a basis for demand-controlled ventilation to reduce the overall burden on people [12]
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