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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.

Titel:	Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning.
Autor/in / Beteiligte Person:	Sun, H ; Chen, S ; Li, X ; Cheng, L ; Luo, Y ; Xie, L
Link:	Zum Volltext
Zeitschrift:	Environmental science and pollution research international, Jg. 30 (2023-04-01), Heft 18, S. 53754-53766
Veröffentlichung:	<2013->: Berlin : Springer ; <i>Original Publication</i>: Landsberg, Germany : Ecomed, 2023
Medientyp:	academicJournal
ISSN:	1614-7499 (electronic)
DOI:	10.1007/s11356-023-26017-1
Schlagwort:	Humans Meteorological Concepts Particulate Matter analysis China epidemiology Air Pollutants analysis Air Pollution analysis Respiration Disorders Respiratory Tract Diseases epidemiology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Environ Sci Pollut Res Int] 2023 Apr; Vol. 30 (18), pp. 53754-53766. <i>Date of Electronic Publication: </i>2023 Mar 03. MeSH Terms: Air Pollutants* / analysis ; Air Pollution* / analysis ; Respiration Disorders* ; Respiratory Tract Diseases* / epidemiology ; Humans ; Meteorological Concepts ; Particulate Matter / analysis ; China / epidemiology References: Analitis A, De ’Donato F, Scotichini M (2018) Synergistic effects of ambient temperature and air concentration on health in Europe: results from the PHASE Project. Int J Environ Res Public Health 15(9):1856. (PMID: 10.3390/ijerph15091856) ; Anenberg SC, Henze DK, Tinney V (2018) Estimates of the global burden of ambient PM25, ozone, NO2 on asthma incidence and emergence room visits. Environ Health Perspect 126(10):107004. 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Grant Information: Grant No. GD22XGL24 2022 Guangdong Social Science Project Contributed Indexing: Keywords: DLNM; Forecast and early warning model; Machine learning methods; Respiratory diseases; SVM; XGBoost Substance Nomenclature: 0 (Air Pollutants) ; 0 (Particulate Matter) Entry Date(s): Date Created: 20230302 Date Completed: 20230424 Latest Revision: 20230424 Update Code: 20240513

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Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning.