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

作者：Yannick Robin, Johannes Amann, Tobias Baur, Payman Goodarzi, Caroline Schultealbert, Tizian Schneider, Andreas Schütze · 发表于：Atmosphere · 年份：2021 · DOI：10.3390/atmos12111487 · 被引用次数：53 · 研究领域：Advanced Chemical Sensor Technologies、Air Quality Monitoring and Forecasting、Gas Sensing Nanomaterials and Sensors

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 testin...