Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: a combined experimental and neural network modeling study

• Published in: Environmental science and pollution research international Vol. 25; no. 4; pp. 3510 - 3517
• Main Authors: Liu, Zhijian, Cheng, Kewei, Li, Hao, Cao, Guoqing, Wu, Di, Shi, Yunjie
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018; Springer Nature B.V
• Subjects: Air quality; Airborne microorganisms; Airborne sensing; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon dioxide; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Estimation; Fungi; Indoor air pollution; Indoor air quality; Indoor environments; Learning algorithms; Learning theory; Machine learning; Mathematical models; Model accuracy; Neural networks; Particulate matter; Relative humidity; Research Article; Residential areas; Residential buildings; Statistical analysis; Support vector machines; Waste Water Technology; Water Management; Water Pollution Control; and PM; Indoor airborne culturable fungi; Prediction; Machine learning; Artificial neural network (ANN); PM; Indoor air quality; PM2.5 and PM10
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-017-0708-5

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 PM 2.5 and PM 10 concentrations, indoor temperature, indoor relative humidity, and indoor CO 2 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.