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基于云模型的北京空气质量评估

Air quality assessment in Beijing based on cloud model.

作者信息

Rao Weidong, Li Jialu, Guo Hankun

机构信息

School of Mathematical Sciences, Jiangxi Science and Technology Normal University, Nanchang, 330038, China.

School of Economics, Management and Law, Jiangxi Science and Technology Normal University, Nanchang, 330038, China.

出版信息

Sci Rep. 2025 Jul 1;15(1):21994. doi: 10.1038/s41598-025-05751-1.

Abstract

This research employs the cloud model method to assess the outdoor air quality in Beijing over the past ten years. The study establishes standard and evaluation clouds for major air pollutants and constructs a comprehensive evaluation cloud model for the Air Quality Index (AQI) using aggregation functions and cloud model attributes. A novel normal cloud similarity measurement method based on the second-order Fréchet distance is adopted to conduct annual assessments. The core findings indicate a significant improvement in Beijing's air quality, with the AQI showing a continuous downward trend from moderate pollution in 2014 to mild pollution in 2023. Specific pollutants such as [Formula: see text], [Formula: see text], and [Formula: see text] have shown marked reductions, transitioning from good or light pollution levels to excellent ratings. The cloud model method effectively captures the probabilistic nature of pollutant concentrations, providing a more nuanced and rigorous assessment compared to traditional methods. These results validate the effectiveness and precision of the cloud model approach, offering actionable insights for environmental management and policy development.

摘要

本研究采用云模型方法评估北京过去十年的室外空气质量。该研究为主要空气污染物建立了标准云和评价云,并利用聚合函数和云模型属性构建了空气质量指数(AQI)综合评价云模型。采用一种基于二阶弗雷歇距离的新型正态云相似度测量方法进行年度评估。核心研究结果表明,北京的空气质量有显著改善,AQI呈现出从2014年的中度污染持续下降到2023年的轻度污染的趋势。诸如[公式:见正文]、[公式:见正文]和[公式:见正文]等特定污染物已显著减少,从良好或轻度污染水平转变为优级。云模型方法有效地捕捉了污染物浓度的概率性质,与传统方法相比提供了更细致和严格的评估。这些结果验证了云模型方法的有效性和精确性,为环境管理和政策制定提供了可操作的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4432/12219394/5bdfb1e24db6/41598_2025_5751_Fig1_HTML.jpg

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