Status of near-road air quality monitoring stations and data application DOI Creative Commons

Xie Pei-yuan,

C. Zhang,

Yangbing Wei

et al.

Atmospheric Environment X, Journal Year: 2024, Volume and Issue: 23, P. 100292 - 100292

Published: Aug. 1, 2024

Language: Английский

Application of machine learning in atmospheric pollution research: A state-of-art review DOI

Zezhi Peng,

Bin Zhang,

Diwei Wang

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 910, P. 168588 - 168588

Published: Nov. 18, 2023

Language: Английский

Citations

32
Prediction of respiratory diseases based on random forest model DOI Creative Commons

Xiaotong Yang,

Yi Li,

Lang Liu

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 14, 2025

In recent years, the random forest model has been widely applied to analyze relationships among air pollution, meteorological factors, and human health. To investigate patterns influencing factors of respiratory disease-related medical visits, this study utilized data on visits from urban areas Tianjin, observations, pollution data. First, temporal variation characteristics 2013 2019 were analyzed. Subsequently, was employed identify dominant construct a statistical forecasting that relates these number visits. Additionally, predictive analysis in Tianjin for year conducted. The results indicate following: (1) From 2019, exhibited seasonal fluctuations, with significant decline observed 2017, which may be directly related adjustments hospital policies. (2) Among average temperature, relative humidity, precipitation, ozone concentration significantly influenced while wind speed, precipitation amount, boundary layer height lesser importance. Furthermore, different linear exist factors; specifically, show negative correlation pollutant elements, there is strong factors. (3) When ranged 50 200, predictions made by closely matched actual values, demonstrating performance ability effectively forecast daily variations over extended periods, thus exhibiting good stability generalization capability. (4) However, since relies large amount validation, it limitations capturing extreme visit numbers. Future research could address issue integrating models enhance capabilities.

Language: Английский

Citations

0
Quantifying regional transport contributions to PM2.5-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach DOI

Naihua Chen,

Jianyong You,

Lin Qing

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: 377, P. 126448 - 126448

Published: May 13, 2025

Language: Английский

Citations

0
Evaluating Long-Term Reductions in Trace Metal Emissions from Shipping in Shanghai DOI
Meng Wang, Yusen Duan, Juntao Huo

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 480, P. 136367 - 136367

Published: Oct. 31, 2024

Language: Английский

Citations

1
Status of near-road air quality monitoring stations and data application DOI Creative Commons

Xie Pei-yuan,

C. Zhang,

Yangbing Wei

et al.

Atmospheric Environment X, Journal Year: 2024, Volume and Issue: 23, P. 100292 - 100292

Published: Aug. 1, 2024

Language: Английский

Citations

0