Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: a combined experimental and neural network modeling study
- 1.3k Downloads
Indoor airborne culturable fungi exposure has been closely linked to occupants’ health. However, conventional measurement of indoor airborne fungal concentration is complicated and usually requires around one week for fungi incubation in laboratory. To provide an ultra-fast solution, here, for the first time, a knowledge-based machine learning model is developed with the inputs of indoor air quality data for estimating the concentration of indoor airborne culturable fungi. To construct a database for statistical analysis and model training, 249 data groups of air quality indicators (concentration of indoor airborne culturable fungi, indoor/outdoor PM2.5 and PM10 concentrations, indoor temperature, indoor relative humidity, and indoor CO2 concentration) were measured from 85 residential buildings of Baoding (China) during the period of 2016.11.15–2017.03.15. Our results show that artificial neural network (ANN) with one hidden layer has good prediction performances, compared to a support vector machine (SVM). With the tolerance of ± 30%, the prediction accuracy of the ANN model with ten hidden nodes can at highest reach 83.33% in the testing set. Most importantly, we here provide a quick method for estimating the concentration of indoor airborne fungi that can be applied to real-time evaluation.
KeywordsIndoor airborne culturable fungi Indoor air quality PM2.5 and PM10 Prediction Machine learning Artificial neural network (ANN)
We are grateful to all the other participants who assisted the data collections.
This work was supported by the National Key R&D Program of China-Source identification, monitoring and integrated control of indoor microbial contamination (No. 2017YFC0702800), National Science Foundation of China (No. 51708211), and Natural Science Foundation of Hebei (No. E2017502051).
- Li H, Chen F, Cheng K et al (2015a) Prediction of zeta potential of decomposed peat via machine learning: comparative study of support vector machine and artificial. Neural Netw 10:6044–6056Google Scholar
- Li H, Tang X, Wang R, Lin F, Liu Z, Cheng K (2016) Comparative study on theoretical and machine learning methods for acquiring compressed liquid densities of 1,1,1,2,3,3,3-heptafluoropropane (R227ea) via Song and Mason equation, support vector machine, and artificial neural networks. Appl Sci 6(1):25. https://doi.org/10.3390/app6010025 CrossRefGoogle Scholar
- Liu Z, Li H, Zhang X, Jin G, Cheng K (2015a) Novel method for measuring the heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters based on artificial neural networks and support vector machine. Energies 8(8):8814–8834. https://doi.org/10.3390/en8088814 CrossRefGoogle Scholar
- Liu Z, Liu K, Li H, Zhang X, Jin G, Cheng K (2015b) Artificial neural networks-based software for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters. PLoS One 10(12):e0143624. https://doi.org/10.1371/journal.pone.0143624 CrossRefGoogle Scholar
- Liu Z, Li H, Liu K, Yu H, Cheng K (2017b) Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: a combined modeling and experimental study. Sol Energy 142:61–67. https://doi.org/10.1016/j.solener.2016.12.015 CrossRefGoogle Scholar
- Parat S, Perdrix A, Fricker-Hidalgo H, Saude I, Grillot R, Baconnier P (1997) Multivariate analysis comparing microbial air content of an air-conditioned building and a naturally ventilated building over one year. Atmos Environ 31(3):441–449. https://doi.org/10.1016/S1352-2310(96)00212-9 CrossRefGoogle Scholar
- Spilak MP, Madsen AM, Knudsen SM, Kolarik B, Hansen EW, Frederiksen M, Gunnarsen L (2015) Impact of dwelling characteristics on concentrations of bacteria, fungi, endotoxin and total inflammatory potential in settled dust. Build Environ 93:64–71. https://doi.org/10.1016/j.buildenv.2015.03.031 CrossRefGoogle Scholar
- Wang X, Liu W, Huang C, Cai J, Shen L, Zou Z, Lu R, Chang J, Wei X, Sun C, Zhao Z, Sun Y, Sundell J (2016) Associations of dwelling characteristics, home dampness, and lifestyle behaviors with indoor airborne culturable fungi: on-site inspection in 454 Shanghai residences. Build Environ 102:159–166. https://doi.org/10.1016/j.buildenv.2016.03.010 CrossRefGoogle Scholar