气候变化领域集成服务门户(CCPortal): Modeling air pollution-related hospital admissions employing remote sensing and geographical information systems

CCPortal

DOI	10.1016/j.atmosenv.2021.118502
	Modeling air pollution-related hospital admissions employing remote sensing and geographical information systems
	Tavera Busso I.; Rodríguez Núñez M.; Amarillo A.C.; Mettan F.; Carreras H.A.
发表日期	2021
ISSN	1352-2310
卷号	261
英文摘要	Land-use regression models and remote sensing data have been widely employed to forecast atmospheric aerosol levels. Recently, these methodologies have been combined to predict the influence of this pollutant on human health. However, traditional land-use regression models do not often consider the complex interactions between predictors, and most of these do not include socioeconomic variables. Thus, in the present study, we aimed to estimate suspended particle-related hospital admissions by employing remote sensing, meteorological, environmental, and demographic parameters. In this cohort study, we analyzed 1,612,049 hospital admissions from Córdoba city, Argentina, from 2005 to 2011, and developed several regression and machine learning land-use models to compare their predictive powers. We found that childhood was the age group with the highest number of hospital admissions related with upper respiratory tract diseases. When predicting population-normalized hospital admissions, the machine learning models, in particular the generalized boosted machine, revealed a better performance than regression models, exhibiting the lowest root mean square error (0.4264) in the test data set. This model also achieved the best R2adj (0.6088) when plotting predicted vs. reported normalized cases. The most important predictors were the meteorological variables, followed by the aerosol optical depth and the planet boundary layer height. Some other predictors, such as educational level, land value, and unsatisfied basic needs, showed less relevance but enhanced the model's prediction power. Furthermore, the predictive power increased after a 1-day lag in hospital admissions (RMSE = 0.4121), highlighting the importance of meteorological and environmental variables in the onset of respiratory diseases. © 2021 Elsevier Ltd
关键词	Land-use models Machine learning Respiratory diseases Suspended particles
语种	英语
scopus关键词	Air pollution; Atmospheric aerosols; Atmospheric thermodynamics; Boundary layer flow; Boundary layers; Forecasting; Hospitals; Learning algorithms; Machine learning; Mean square error; Regression analysis; Remote sensing; Satellite imagery; Statistical tests; Geographical information; Hospital admissions; Land use modelling; Land-use regression models; Machine-learning; Meteorological variables; Predictive power; Remote sensing data; Remote sensing information; Suspended particles; Land use; aerosol; atmospheric modeling; boundary layer; hospital sector; machine learning; optical depth; regression analysis; remote sensing; adult; aerosol; aged; air pollution; Argentina; Article; basic needs; boundary layer; child; childhood; city; cohort analysis; controlled study; demography; environmental factor; female; geographic information system; groups by age; hospital admission; human; land use; lower respiratory tract; machine learning; major clinical study; male; measurement accuracy; meteorology; model; optical depth; predictive value; remote sensing; respiratory tract disease; seasonal variation; suspended particulate matter; upper respiratory tract; Argentina
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248314
作者单位	Instituto Multidisciplinario de Biología Vegetal (IMBIV), Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina; Departamento de Química, Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina
推荐引用方式 GB/T 7714	Tavera Busso I.,Rodríguez Núñez M.,Amarillo A.C.,et al. Modeling air pollution-related hospital admissions employing remote sensing and geographical information systems[J],2021,261.
APA	Tavera Busso I.,Rodríguez Núñez M.,Amarillo A.C.,Mettan F.,&Carreras H.A..(2021).Modeling air pollution-related hospital admissions employing remote sensing and geographical information systems.ATMOSPHERIC ENVIRONMENT,261.
MLA	Tavera Busso I.,et al."Modeling air pollution-related hospital admissions employing remote sensing and geographical information systems".ATMOSPHERIC ENVIRONMENT 261(2021).