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Xuping Song

Biographic Data

ID6695996
NAMEXuping Song
GIVEN NAMESXuping
FAMILY NAMESong
SIGNATURESONG X
AFFILIATIONSLanzhou University
ORCID0000-0002-4518-4945
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS2
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2020
LATEST PUBLICATION YEAR2023
H-INDEX1
  • Long-term exposure to particulate matter on cardiovascular and respiratory diseases in low- and middle-income countries

    Open Access•Juanmei Guo, Guorong Chai et al.•ARTICLE•Frontiers in Public Health•2023

  • Methodological and reporting quality of systematic reviews on health effects of air pollutants were higher than extreme temperatures

    Open Access•Xuping Song, Qiyin Luo et al.•ARTICLE•BMC Public Health•2023

    Methodological and reporting quality of SRs on the health effect of air pollutants were higher than those of temperatures. However, deficiencies in protocol registration and the assessment of risk of bias remain an issue for both pollutants and temperatures. In addition, developing a risk-of-bias assessment tool applicable to the temperature field may improve the quality of SRs

  • Applications of artificial intelligence in the field of air pollution

    Open Access•Qiangqiang Guo, Mengjuan Ren et al.•ARTICLE•Frontiers in Public Health•2022

    Background Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution. Methods All literature on the application of AI to air pollu…

  • How does social learning facilitate urban disaster resilience? A systematic review

    Qingxia Zhang, Junyan Hu et al.•ARTICLE•Environmental Hazards•2020

  • The influence of media consumption on public risk perception

    Chunhua Niu, Zhixin Jiang et al.•ARTICLE•Journal of Risk Research•2020•Cited by: 2•References: 8

    Media is an important explanatory variable in the research on the social amplification of risk; the public often perceives risks in social life through media consumption such as by relying on, using, and trusting the media dialectically. Currently, researchers have not reached a consistent conclusion about the relationship between media consumption and public risk perception (PRP). This study uses meta-analysis to integrate empirical literature a…

  • The influence of media consumption on public risk perception

    Chunhua Niu, Zhixin Jiang et al.•ARTICLE•Journal of Risk Research•2020•Cited by: 2•References: 8

    Media is an important explanatory variable in the research on the social amplification of risk; the public often perceives risks in social life through media consumption such as by relying on, using, and trusting the media dialectically. Currently, researchers have not reached a consistent conclusion about the relationship between media consumption and public risk perception (PRP). This study uses meta-analysis to integrate empirical literature a…

  • How does social learning facilitate urban disaster resilience? A systematic review

    Qingxia Zhang, Junyan Hu et al.•ARTICLE•Environmental Hazards•2020

  • The influence of media consumption on public risk perception

    Chunhua Niu, Zhixin Jiang et al.•ARTICLE•Journal of Risk Research•2020•Cited by: 2•References: 8

    Media is an important explanatory variable in the research on the social amplification of risk; the public often perceives risks in social life through media consumption such as by relying on, using, and trusting the media dialectically. Currently, researchers have not reached a consistent conclusion about the relationship between media consumption and public risk perception (PRP). This study uses meta-analysis to integrate empirical literature a…

  • Applications of artificial intelligence in the field of air pollution

    Open Access•Qiangqiang Guo, Mengjuan Ren et al.•ARTICLE•Frontiers in Public Health•2022

    Background Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution. Methods All literature on the application of AI to air pollu…

  • Long-term exposure to particulate matter on cardiovascular and respiratory diseases in low- and middle-income countries

    Open Access•Juanmei Guo, Guorong Chai et al.•ARTICLE•Frontiers in Public Health•2023

  • Methodological and reporting quality of systematic reviews on health effects of air pollutants were higher than extreme temperatures

    Open Access•Xuping Song, Qiyin Luo et al.•ARTICLE•BMC Public Health•2023

    Methodological and reporting quality of SRs on the health effect of air pollutants were higher than those of temperatures. However, deficiencies in protocol registration and the assessment of risk of bias remain an issue for both pollutants and temperatures. In addition, developing a risk-of-bias assessment tool applicable to the temperature field may improve the quality of SRs

Air pollutants (2 works) · Air pollution (2 works) · Air quality index (2 works) · Computer Science (2 works) · Medicine (2 works) · Meta-analysis (2 works) · Meteorology (2 works) · Advanced Technologies in Various Fields (1 work) · Advertising (1 work) · Air Quality and Health Impacts (1 work)

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