Abstract
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.
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Acknowledgments
We are grateful to all the other participants who assisted the data collections.
Funding
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).
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Liu, Z., Cheng, K., Li, H. et al. Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: a combined experimental and neural network modeling study. Environ Sci Pollut Res 25, 3510–3517 (2018). https://doi.org/10.1007/s11356-017-0708-5
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DOI: https://doi.org/10.1007/s11356-017-0708-5