| Autor: |
Yanjun Du, Yingying Zhang, Yaoling Li, Qiang Huang, Yanwen Wang, Qing Wang, Runmei Ma, Qinghua Sun, Qin Wang, Tiantian Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Ecotoxicology and Environmental Safety, Vol 287, Iss , Pp 117285- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
0147-6513 |
| DOI: |
10.1016/j.ecoenv.2024.117285 |
| Popis: |
Many studies have confirmed that PM2.5 exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM2.5 in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM2.5 data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM2.5 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R2 value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM2.5 concentrations of the cities ranged from 54.6 μg/m3 to 82.7 μg/m3, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM2.5 and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM2.5 and contribute to research on the indoor air quality and human health in China. Synopsis: This study established a machine learning model and predicted indoor PM2.5 big data, which could support the research of indoor PM2.5 and health. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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