Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

被引:4
作者
Labib, S. M. [1 ,2 ]
机构
[1] Univ Utrecht, Fac Geosci, Dept Human Geog & Spatial Planning, Utrecht, Netherlands
[2] Vening Meineszgebouw A,Princetonlaan 8A, NL-3584 CB Utrecht, Netherlands
关键词
Air pollution; Temperature; Exposure Assessment; Machine Learning; Greenness exposure; Public Health; SURROUNDING GREENNESS; SOCIAL DETERMINANTS; GREATER MANCHESTER; TIME-SERIES; LIFE LOST; HEALTH; GREENSPACE; POPULATION; ASSOCIATION; BURDEN;
D O I
10.1016/j.scitotenv.2024.172387
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Background: Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework. Methods: Years of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008-2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation. Results: Spatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions. Conclusion: Air pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.
引用
收藏
页数:13
相关论文
共 94 条
[71]   Explanation of machine learning models using shapley additive explanation and application for real data in hospital [J].
Nohara, Yasunobu ;
Matsumoto, Koutarou ;
Soejima, Hidehisa ;
Nakashima, Naoki .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 214
[72]   Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models [J].
Ohanyan, Haykanush ;
Portengen, Lutzen ;
Kaplani, Oriana ;
Huss, Anke ;
Hoek, Gerard ;
Beulens, Joline W. J. ;
Lakerveld, Jeroen ;
Vermeulen, Roel .
ENVIRONMENT INTERNATIONAL, 2022, 170
[73]   Machine learning approaches to characterize the obesogenic urban exposome [J].
Ohanyan, Haykanush ;
Portengen, Lutzen ;
Huss, Anke ;
Traini, Eugenio ;
Beulens, Joline W. J. ;
Hoek, Gerard ;
Lakerveld, Jeroen ;
Vermeulen, Roel .
ENVIRONMENT INTERNATIONAL, 2022, 158
[74]  
Barboza EP, 2021, LANCET PLANET HEALTH, V5, pE718, DOI 10.1016/S2542-5196(21)00229-1
[75]   Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology [J].
Petch, Jeremy ;
Di, Shuang ;
Nelson, Walter .
CANADIAN JOURNAL OF CARDIOLOGY, 2022, 38 (02) :204-213
[76]   Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches [J].
Ren, Xiang ;
Mi, Zhongyuan ;
Georgopoulos, Panos G. .
JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY, 2024, 34 (02) :197-207
[77]   Green spaces and mortality: a systematic review and meta-analysis of cohort studies [J].
Rojas-Rueda, David ;
Nieuwenhuijsen, Mark J. ;
Gascon, Mireia ;
Perez-Leon, Daniela ;
Mudu, Pierpaolo .
LANCET PLANETARY HEALTH, 2019, 3 (11) :E469-E477
[78]  
Rouse J., 1974, NASA SPEC PUBL, V351, P309, DOI DOI 10.1021/JF60203A024
[79]   Machine learning approaches to the social determinants of health in the health and retirement study [J].
Seligman, Benjamin ;
Tuljapurkar, Shripad ;
Rehkopf, David .
SSM-POPULATION HEALTH, 2018, 4 :95-99
[80]   Walkability and Greenness Do Not Walk Together: Investigating Associations between Greenness and Walkability in a Large Metropolitan City Context [J].
Shuvo, Faysal Kabir ;
Mazumdar, Soumya ;
Labib, S. M. .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (09)