Accurate Quantification of Formaldehyde at ppb Level for Indoor Air Quality Monitoring
Authors: Baur Tobias , Schultealbert Caroline , Robin Yannick , Goodarzi Payman , Schneider Tizian , Schütze Andreas 
Published: July 2021

Continuous accurate quantification of hazardous VOCs for monitoring Indoor Air Quality (IAQ) with low-cost sensor systems would allow demand-controlled ventilation to significantly reduce health effects but is still an elusive goal. Especially monitoring of formaldehyde is in high demand due to its extensive emission from a wide range of sources, especially building materials, furniture, textiles and cleaning products [1]. While detection of formaldehyde is possible at concentrations well below the WHO recommended short-term (30-minute) guideline value of 0.1 mg/m3 (81 ppb) [1] using typical MOS sensors [2] achieving accurate quantification in complex indoor environments also requires a high level of selectivity. In this contribution we demonstrate formaldehyde quantification with an uncertainty of 11.3 ppb, corresponding to 15% of the WHO recommended guideline value, in a complex mixture of VOCs and other interfering gases typical for indoor environments. Our approach is based on a MOS sensor with four gas sensitive layers integrated on one micro hotplate (SGP30, Sensirion, CH) [3]. With extended access (possible with a non-disclosure agreement) it is possible to set the temperature of the heater and read out the resistance of the individual layers. Therefore, we can combine physical and virtual multisensor methods for the generation of multiple signals by operating the four different gas sensitive layers in temperature cycled operation (TCO). For selective measurement of VOCs in indoor environments, we have designed a complex temperature cycle comprising 12 different low temperatures ranging from 100 to 375°C interlaced with high temperature phases of 425°C. The total duration of the temperature cycle (T-cycle) is 120 s, which is suitable for IAQ applications where the gas composition changes slowly. Calibration is based on an automatic gas mixing apparatus (GMA) [4] using random mixtures [5]. The calibration scheme is based on mixtures of four VOCs (formaldehyde, acetone, benzene and toluene) as well as carbon monoxide (CO) and hydrogen (H2) as typical interfering gases. In addition, relative humidity is also varied. RH and all six gas concentrations are randomly selected from predefined distributions for each variable to reflect typical variations of these gases in indoor environments. Each specific mixture is then offered in the GMA for 20 min, i.e. 10 T-cycles, and the calibration model is built using only those cycles with stable signal patterns, i.e. cycles are excluded when the gas concentrations are not in steady state. The results presented here are based on a calibration run with a total of 495 gas exposures. The resistance values of the four gas sensitive layers are recorded every 25 ms resulting in 4 x 4800 raw data samples for every T-cycle. Features are extracted using adaptive linear approximation (ALA), i.e. the individual cycles are approximated by linear segments and the mean and slope of each segment are calculated for further evaluation. The achieve high performance quantification for formaldehyde we use a 10-layer deep modified ResNet [6]. The performance of the prediction is validated thoroughly using 10-fold cross validation. Specifically, the overall dataset is split into 10 subsets based on the gas exposures and each subset is used a test data for a model trained with the remaining 9 subsets. The prediction performance is therefore given as Root Mean Square Error for Validation (RMSEV) and the variation of the RMSEV for the different splits is also checked to ensure stable predictions. Note that this approach allows determination of prediction models for each of the gases included in the test.

For formaldehyde, tested over a range from 0 to 700 ppb we achieve an RMSEV of 11.3 ppb, which is fairly stable over the full range, see Fig. 1. This corresponds to an uncertainty of less than 15% of the WHO guideline value independent of the concentrations of acetone (range 0 .. 1.850 ppb), benzene (0 .. 1180 ppb), toluene (0 .. 250 ppb), CO (100 .. 2000 ppb), H2 (300 .. 2500 ppb) and rel. humidity (25% .. 75% RH). The variance of the RMSEV for the different splits is 3 ppb, indicating that a reliable prediction model is achieved. The overall duration of the calibration run can be reduced considerably by ML-based optimized selection of the T-cycles with stable patterns while still achieving an RMSEV for formaldehyde of 20 ppb, corresponding to an uncertainty of 25% of the WHO guideline value [7].

The resulting prediction models for different VOCs were further validated in realistic indoor field tests using standard VOC analysis (e.g. sampling on Tenax followed by GC/MS analysis) and performing release tests, i.e. evaporation of a certain amount of VOC in a room and comparing the measurement predictions with the theoretical values expected for uniform distribution.

[1] D.A. Kaden et al.: Formaldehyde, in: WHO Guidelines for Indoor Air Quality: Selected Pollutants, Geneva: World Health Organization; 2010, ISBN: 978-92-890-0213-4.

[2] M. Leidinger et al., J. Sens. Sens. Syst., 3 (2014), pp. 253-263, doi:10.5194/jsss-3-253-2014.

[3] D. Rüffer, F. Hoehne, J. Bühler, Sensors, 18/4 (2018), 1052, doi:10.3390/s18041052.

[4] N. Helwig et al., Meas. Sci. Technol. 25 (2014) 055903, doi:10.1088/0957-0233/25/5/055903

[5] T. Baur et al., J. Sens. Sens. Syst. (2020) 9, 411-424, doi:10.5194/jsss-9-411-2020.

[6] K. He et al.: Deep residual learning for image recognition, 2015, [Online] last access: 30 November 2020, available: http //arxiv.org/pdf/1512.03385v1:PDF

[7] Y. Robin et al.: Machine Learning based calibration time reduction for Gas Sensors in Temperature Cycled Operation, IEEE I2MTC – International Instrumentation and Measurement technology Conference 2021, submitted.

Figure 1