Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques-Reference-Cited by-同舟云学术

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Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques

Published:2024-04-01 Issue: Volume: Page:
ISSN:1559-0631
Container-title:Journal of Exposure Science & Environmental Epidemiology
language:en
Short-container-title:J Expo Sci Environ Epidemiol

Author:

Zhang Kai^ORCID,Lin Jeffrey,Li Yuanfei,Sun Yue,Tong Weitian,Li Fangyu,Chien Lung-Chang,Yang Yiping,Su Wei-Chung,Tian Hezhong,Fu Peng,Qiao Fengxiang,Romeiko Xiaobo Xue,Lin Shao,Luo Sheng,Craft Elena

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41370-024-00659-w.pdf

Reference32 articles.

1. EPA. Integrated Science Assessment (ISA) for Particulate Matter. 2019. Available from: https://www.epa.gov/isa/integrated-science-assessment-isa-particulate-matter.

2. Luong C, Zhang K. An assessment of emission event trends within the Greater Houston area during 2003–2013. Air Qual Atmosphere Health. 2017;10:543–54.

3. Liu S, Zhang K. Fine particulate matter components and mortality in Greater Houston: Did the risk reduce from 2000 to 2011? Sci Total Environ. 2015;538:162–8.

4. Liu S, Ganduglia CM, Li X, Delclos GL, Franzini L, Zhang K. Fine particulate matter components and emergency department visits among a privately insured population in Greater Houston. Sci Total Environ. 2016;566:521–7.

5. Liu S, Ganduglia CM, Li X, Delclos GL, Franzini L, Zhang K. Short-term associations of fine particulate matter components and emergency hospital admissions among a privately insured population in Greater Houston. Atmos Environ. 2016;147:369–75.

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