PM2.5-mediated cardiovascular disease in aging: Cardiometabolic risks, molecular mechanisms and potential interventions

Chanda, Francis; Lin, Kai-xuan; Chaurembo, Abdallah Iddy; Huang, Jian-yuan; Zhang, Hui-juan; Deng, Wen-hui; Xu, Yun-jing; Li, Yuan; Fu, Li-dan; Cui, Hao-dong; Shu, Chi; Chen, Yang; Xing, Na; Lin, Han-bin

doi:10.1016/j.scitotenv.2024.176255

Science of The Total Environment

2024

DOI: 10.1016/j.scitotenv.2024.176255

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PM2.5-mediated cardiovascular disease in aging: Cardiometabolic risks, molecular mechanisms and potential interventions

Francis Chanda,

Kai-xuan Lin,

Abdallah Iddy Chaurembo

et al.

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Cited by 2 publications

References 211 publications

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Association between wildfire-related PM2.5 and epigenetic aging: A twin and family study in Australia

Wu,

Xu,

et al. 2025

Journal of Hazardous Materials

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Association between wildfire-related PM2.5 and epigenetic aging: A twin and family study in Australia

Wu,

Xu,

et al. 2025

Journal of Hazardous Materials

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Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Wu,

Lu,

Tian

et al. 2024

Atmosphere

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Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.

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PM2.5-mediated cardiovascular disease in aging: Cardiometabolic risks, molecular mechanisms and potential interventions

Cited by 2 publications

References 211 publications

Association between wildfire-related PM2.5 and epigenetic aging: A twin and family study in Australia

Association between wildfire-related PM2.5 and epigenetic aging: A twin and family study in Australia

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

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