A better balance: Energy transition and industrial restructuring for a sustainable economy-climate-environment pathway

Journal of Environmental Management, Год журнала: 2025, Номер 381, С. 125265 - 125265

Опубликована: Апрель 9, 2025

Язык: Английский

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Long-Term (2015–2024) Daily PM2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition

Remote Sensing, Год журнала: 2025, Номер 17(9), С. 1632 - 1632

Опубликована: Май 4, 2025

Fine particulate matter (PM2.5) has garnered significant scientific and public health concern owing to its capacity for deep penetration into the human respiratory system, presenting risks. Despite implementation of strict environmental policies in China over past decade reduce PM2.5 levels, long-term concerns remain a serious issue. Our study aims provide high-quality, seamless daily dataset covering years 2015 2024. A two-step estimation model is established based on machine learning algorithm spatio-temporal decomposition method. First, we utilize XGBoost (EXtreme Gradient Boosting) address gaps MAIAC (Multi-Angle Implementation Atmospheric Correction) AOD (Aerosol Optical Depth), with R2/RMSE (coefficient determination/Root Mean Square Error) 0.67/0.2678 compared AERONET Robotic Network) AOD. Then, novel approach by integrating EOF (Empirical Orthogonal Function) introduced estimation. The integration allows incorporation entire meteorological field information model, significantly enhancing accuracy: spatial CV (cross-validation)-R2 improved from 0.8340 0.8935, CV-RMSE reduced 13.8177 11.0668. Leveraging newly produced dataset, analyze variations across decomposition, particularly noting that levels eastern anthropogenic intensive regions continuously declined 2020, fluctuated steadily during 2020–2024. This research underscores critical need sustained effective air quality management strategies China.

Язык: Английский

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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods

Atmosphere, Год журнала: 2025, Номер 16(6), С. 655 - 655

Опубликована: Май 28, 2025

The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps AOD from satellites like Fengyun significantly hinder continuous, seamless capabilities, posing challenges for the long-term analysis trends, responses to sudden ecological events, disaster management. This study aims develop a high-precision method fill spatial missing values generate daily full-coverage Beijing–Tianjin–Hebei region 2021 by integrating multi-dimensional data, including meteorological models, multi-source sensing, surface conditions, nighttime light parameters, applying machine learning methods. A comparison five models showed that random forest model performed optimally inversion, achieving root mean square error (RMSE) 0.11 coefficient determination (R2) 0.93. Seasonal evaluation further indicated model’s simulation was best winter. Variable importance identified relative humidity (RH) as most critical factor influencing results. reconstructed product exhibited distribution trend higher southern plain areas compared mountainous regions, consistent with actual aerosol patterns area. Moreover, demonstrated overall smoothness high accuracy. research lays foundation establishing long-term, 1 km resolution, spatially continuous beyond, providing more robust support addressing regional larger-scale challenges.

Язык: Английский

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Long-term monitoring reveals air pollution and its relationship to deposition in karstic suburb of one typical industrialized city, SW China

Environmental Pollution, Год журнала: 2025, Номер 381, С. 126621 - 126621

Опубликована: Июнь 4, 2025

Язык: Английский

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Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(17), С. 50067 - 50095

Опубликована: Фев. 15, 2023

Язык: Английский

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