Smartphone-based colorimetric detection of formaldehyde in the air

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外文摘要：Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the air.Conventional formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to civilians.This study introduced a novel approach utilizing smartphone-based colorimetric analysis.Changes of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde concentrations.It is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of 0.98.Detection limit of the smartphone-based method is 0.008 mg/m3.Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34％,17％,and 6％for concentration ranges of 0-0.06 mg/m3,0.06-0.12 mg/m3,and 0.12-0.35 mg/m3,respectively.This method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation bars.It would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

外文关键词：

indoor air qualityVOCRGBcolor identificationmachine vision

作者：

Meng Yang、Jin Ye、Tao Yu、Ying Song、Hua Qian、Tianyi Liu、Yang Chen、Junqi Wang、Shi-jie Cao、Cong Liu

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作者单位：

School of Energy and Environment,Southeast University,Nanjing 210096,China

School of Energy and Power,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212100,China

Wuhan Second Ship Design and Research Institute,Wuhan 430205,China

Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test,Hubei Provincial Institute for Food Supervision and Test,Wuhan 430075,China

Laboratory of Image Science and Technology,the School of Computer Science and Engineering,Southeast University,Nanjing,China

Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing,Southeast University,Nanjing,China

School of Cyber Science and Engineering Southeast University,Nanjing,China

Key Laboratory of Computer Network and Information Integration(Southeast University),Ministry of Education,Nanjing,China

School of Architecture,Southeast University,China

Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control,Southeast University,China

Global Centre for Clean Air Research(GCARE),University of Surrey,UK

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出版年：

2024

DOI：

10.1007/s12273-024-1172-z

建筑模拟(英文版)

ISSN：1996-3599

年,卷(期)：2024.17(11)