High-Performance VOC Quantification for IAQ Monitoring Using Advanced Sensor Systems and Deep Learning
نویسندگان
چکیده
With air quality being one target in the sustainable development goals set by United Nations, accurate monitoring also of indoor is more important than ever. Chemiresistive gas sensors are an inexpensive and promising solution for volatile organic compounds, which high concern indoors. To fully exploit potential these sensors, advanced operating modes, calibration, data evaluation methods required. This contribution outlines a systematic approach based on dynamic operation (temperature-cycled operation), randomized calibration (Latin hypercube sampling), use advances deep neural networks originally developed natural language processing computer vision, applying this to compound measurements monitoring. paper discusses pros cons laboratory environment comparing quantification accuracy state-of-the-art with 10-layer convolutional network (TCOCNN). The overall performance both was compared complex mixtures several as well interfering gases changing ambient humidity comprehensive lab evaluation. Furthermore, were tested under realistic conditions field additional release tests compounds. results obtained during testing analytical measurements, namely gold standard chromatography mass spectrometry analysis Tenax sampling, two mobile systems, chromatograph photo-ionization detection reducing photometer hydrogen. showed that TCOCNN outperforms methods, example critical pollutants such formaldehyde, achieving uncertainty around 11 ppb even mixtures, offers robust environment, real most targets.
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ژورنال
عنوان ژورنال: Atmosphere
سال: 2021
ISSN: ['2073-4433']
DOI: https://doi.org/10.3390/atmos12111487