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中国重点城市群突变事件中高 PM 暴露的空间来源、模拟改善和短期健康影响。

Spatial source, simulating improvement, and short-term health effect of high PM exposure during mutation event in the key urban agglomeration regions in China.

机构信息

School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.

School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.

出版信息

Environ Pollut. 2024 Oct 15;359:124738. doi: 10.1016/j.envpol.2024.124738. Epub 2024 Aug 13.

Abstract

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM, thereby ensuring public health.

摘要

由于污染控制措施的有效实施,中国的空气质量得到了显著改善。然而,在城市群地区,颗粒物短期激增导致的突变事件仍频繁发生。识别空间来源和影响因素,提高突变事件中高浓度颗粒物的预测精度,对公众健康至关重要。在这项研究中,我们首先引入离散小波变换(DWT)来识别四个关键城市群中高浓度颗粒物的突变事件,并使用混合单粒子拉格朗日综合轨迹(HYSPLIT)模型评估污染情景的空间来源。此外,DWT 与广泛使用的人工神经网络(ANN)相结合,提高了 PM 浓度七天提前(七天预测)的预测精度。结果表明,突变事件通常发生在冬季的北部地区,这是受短期脏空气团传输和不利气象条件的共同控制。与单独使用 ANN 相比,当使用 DWT-ANN 模型时,平均波段误差降低了 9%。与单独使用 ANN 相比,使用 DWT-ANN 获得的平均相关系数(R)和均方根误差(RMSE)提高了 10%和 12%,表明 DWT 和 ANN 的结合在模拟 PM 方面的效率和准确性。然后计算突变事件期间的短期死亡率,四个地区的总避免全因、心血管和呼吸道死亡人数分别为 4751、2554 和 582 人。2018 年至 2020 年避免死亡人数的下降趋势表明,由于实施了《打赢蓝天保卫战三年行动计划》,突变事件期间的污染强度逐渐降低。本研究提出的方法可以为决策者提供参考,以便在 PM 突然增加时采取预防措施,从而保障公众健康。

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