Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning
Abstract In recent years, with the repeated occurrence of extreme weather and the continuous increase of air pollution, the incidence of weather-related diseases has increased yearly. Air pollution and extreme temperature threaten sensitive groups’ lives, among which air pollution is most closely related to respiratory diseases. Owing to the skewed attention, timely intervention is necessary to better predict and warn the occurrence of death from respiratory diseases. In this paper, according to the existing research, based on a number of environmental monitoring data, the regression model is established by integrating the machine learning methods XGBoost, support vector machine (SVM), and generalized additive model (GAM) model. The distributed lag nonlinear model (DLNM) is used to set the warning threshold to transform the data and establish the warning model. According to the DLNM model, the cumulative lag effect of meteorological factors is explored. There is a cumulative lag effect between air temperature and PM2.5, which reaches the maximum when the lag is 3 days and 5 days, respectively. If the low temperature and high environmental pollutants (PM2.5) continue to influence for a long time, the death risk of respiratory diseases will continue to rise, and the early warning model based on DLNM has better performance..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:30 |
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| Enthalten in: |
Environmental science and pollution research - 30(2023), 18 vom: 03. März, Seite 53754-53766 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sun, HongYing [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
DLNM |
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| Anmerkungen: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s11356-023-26017-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract In recent years, with the repeated occurrence of extreme weather and the continuous increase of air pollution, the incidence of weather-related diseases has increased yearly. Air pollution and extreme temperature threaten sensitive groups’ lives, among which air pollution is most closely related to respiratory diseases. Owing to the skewed attention, timely intervention is necessary to better predict and warn the occurrence of death from respiratory diseases. In this paper, according to the existing research, based on a number of environmental monitoring data, the regression model is established by integrating the machine learning methods XGBoost, support vector machine (SVM), and generalized additive model (GAM) model. The distributed lag nonlinear model (DLNM) is used to set the warning threshold to transform the data and establish the warning model. According to the DLNM model, the cumulative lag effect of meteorological factors is explored. There is a cumulative lag effect between air temperature and PM2.5, which reaches the maximum when the lag is 3 days and 5 days, respectively. If the low temperature and high environmental pollutants (PM2.5) continue to influence for a long time, the death risk of respiratory diseases will continue to rise, and the early warning model based on DLNM has better performance. | ||
| 650 | 4 | |a Respiratory diseases |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Forecast and early warning model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning methods |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a XGBoost |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a DLNM |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SVM |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Chen, SiYi |4 aut | |
| 700 | 1 | |a Li, XinYi |4 aut | |
| 700 | 1 | |a Cheng, LiPing |0 (orcid)0000-0001-5320-7142 |4 aut | |
| 700 | 1 | |a Luo, YiPei |4 aut | |
| 700 | 1 | |a Xie, LingLi |4 aut | |
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