| Title |
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, November 2017
|
| DOI | 10.1007/s11356-017-0708-5 |
| Pubmed ID | |
| Authors |
Zhijian Liu, Kewei Cheng, Hao Li, Guoqing Cao, Di Wu, Yunjie Shi |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Ireland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 92 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 13% |
| Student > Ph. D. Student | 12 | 13% |
| Student > Master | 10 | 11% |
| Student > Doctoral Student | 9 | 10% |
| Student > Bachelor | 4 | 4% |
| Other | 11 | 12% |
| Unknown | 34 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 15 | 16% |
| Computer Science | 6 | 7% |
| Nursing and Health Professions | 5 | 5% |
| Environmental Science | 5 | 5% |
| Medicine and Dentistry | 4 | 4% |
| Other | 19 | 21% |
| Unknown | 38 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 November 2017.
All research outputs
#21,420,714
of 23,911,072 outputs
Outputs from Environmental Science and Pollution Research
#7,000
of 9,883 outputs
Outputs of similar age
#379,977
of 443,986 outputs
Outputs of similar age from Environmental Science and Pollution Research
#195
of 269 outputs
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