Accurate and efficient prediction of atmospheric PM1, PM2.5, PM10, and O3 concentrations using a customized software package based on a machine-learning algorithm DOI
Le Xie, Jiawei He,

R. T. Lei

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 368, P. 143752 - 143752

Published: Nov. 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

31
Combined PMF modelling and machine learning to identify sources and meteorological influencers of volatile organic compound pollution in an industrial city in eastern China DOI

Wei Chen,

Xuezhe Xu,

Wenqing Liu

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 334, P. 120714 - 120714

Published: July 23, 2024

Language: Английский

Citations

4
Additive-feature-attribution methods: A review on explainable artificial intelligence for fluid dynamics and heat transfer DOI Creative Commons
A. Cremades, Sergio Hoyas, Ricardo Vinuesa

et al.

International Journal of Heat and Fluid Flow, Journal Year: 2024, Volume and Issue: 112, P. 109662 - 109662

Published: Dec. 9, 2024

Language: Английский

Citations

4
Spatiotemporal correlations of PM2.5 and O3 variations: A street-scale perspective on synergistic regulation DOI

Xinyuan Lin,

Yangbin Dong,

Z.K. Teng

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 965, P. 178578 - 178578

Published: Jan. 31, 2025

Language: Английский

Citations

0
Comprehensive 24-Hour Ground-Level Ozone Monitoring: Leveraging Machine Learning for Full-Coverage Estimation in East Asia DOI
Yejin Kim, Seohui Park, Hyunyoung Choi

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 488, P. 137369 - 137369

Published: Feb. 1, 2025

Language: Английский

Citations

0
A Visibility-Based Historical PM2.5 Estimation for Four Decades (1981–2022) Using Machine Learning in Thailand: Trends, Meteorological Normalization, and Influencing Factors Using SHAP Analysis DOI Creative Commons
Nishit Aman, Sirima Panyametheekul,

Ittipol Pawarmart

et al.

Aerosol and Air Quality Research, Journal Year: 2025, Volume and Issue: 25(1-4)

Published: March 27, 2025

Abstract Introduction PM 2.5 pollution is a significant environmental and health concern in Thailand, with levels intensifying during the dry season. However, lack of long-term PM2.5 data limits understanding historical trends meteorological influences. Objective This study aims to reconstruct from 1981 2022 analyze influence various contributing factors across six key provinces Thailand: Chiang Mai (CM), Lampang (LP), Khon Kaen (KK), Bangkok (BK), Chonburi (CB), Songkhla (SK). Methods A Light Gradient Boosting Machine (LightGBM) model was developed using aerosol-related variables Thai Meteorological Department MERRA-2. The trained on spanning 2012–2022, depending availability for each province. Model performance evaluated diurnal, monthly, annual scales then used reconstruction data. SHAP analysis determine important predictor affecting prediction. Results LightGBM accurately predicted all provinces, showing better daily prediction than hourly accuracy higher clean hours haze hours. Good agreement between observed found different time (diurnal, annually). CM shows non-significant trend, limiting insights into effects, while LP exhibits decreases PM2.5_emis, indicating positive weather impacts air quality. In contrast, regions like KK, BK, CB display worsening influences, or increasing despite declines _emis. SK, removing effects reveals decreasing underscoring critical role meteorology. identified visibility, gridded , specific humidity at 2 m as common over along additional that were not consistent provinces. Conclusion effectively reconstructs provides insight influences Based findings study, some policy implications have also been provided. Graphical abstract

Language: Английский

Citations

0
Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China DOI Creative Commons
Yan Liu, Tingting Hu, Yusen Duan

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 457 - 457

Published: April 15, 2025

Elevated O3 concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses investigating the key factors affecting formation in ecologically sensitive Dongtan Wetland (Chongming District, Shanghai, China) area. By comparing performance of concentration prediction multiple machine learning models, this found that random forest model achieved highest accuracy (R2 = 0.9, RMSE 11.5). Feature importance structure mining showed peroxyacetyl nitrate (PAN), nitrogen oxides (NOx), temperature, wind direction, relative humidity were main drivers formation. Specifically, PAN exceeding 0.1 ppb temperatures above 3 °C have impact levels, especially spring, summer, autumn. Trajectory analysis westward urban pollution emissions transported from ocean highlights need for targeted emission control strategies, precursors generated by ships NOx industries, providing important insights improving air quality areas.

Language: Английский

Citations

0
Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method DOI
Zizheng Li, Weihang Wang, Qingqing He

et al.

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(6), P. 102114 - 102114

Published: March 13, 2024

Language: Английский

Citations

3
A spatiotemporal deep learning ensemble for multi-step PM2.5 prediction: A case study of Bangkok metropolitan region in Thailand DOI
Veerasit Kaewbundit,

Chaiyo Churngam,

Papis Wongchaisuwat

et al.

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102406 - 102406

Published: Jan. 1, 2025

Language: Английский

Citations

0
Estimation of Near-Surface High Spatiotemporal Resolution Ozone Concentration in China Using Himawari-8 AOD DOI Creative Commons

Yixuan Wang,

Chongshui Gong, Li Dong

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 528 - 528

Published: Feb. 4, 2025

Near-surface ozone is a secondary pollutant, and its high concentrations pose significant risks to human plant health. Based on an Extra Tree (ET) model, this study estimated near-surface with the spatiotemporal resolution based Himawari-8 aerosol optical depth (AOD) data meteorological variables from 1 January 2016 31 December 2020. The SHapley Additive exPlanation (SHAP) method was employed evaluate contribution of AOD factors concentration. results indicate that (1) ET model achieves sample-based cross-validation R2 0.75–0.87 RMSE (μg/m3) 17.96–20.30. coefficient determination (R2) values in spring, summer, autumn, winter are 0.81, 0.80, 0.87, 0.75, respectively. (2) Higher temperature boundary layer heights were found positively contribute concentration, whereas higher relative humidity exerted negative influence. (3) From 11:00 15:00 (Beijing time, UTC+08:00), concentration increases gradually, highest occurring followed by spring. This has obtained spatial temporal data, offering valuable insights for development fine-scale pollution prevention control strategies.

Language: Английский

Citations

0