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Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China.
Cheng, Xin; Yu, Jie; Su, Die; Gao, Shuang; Chen, Li; Sun, Yanling; Kong, Shaofei; Wang, Hui.
Afiliação
  • Cheng X; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.
  • Yu J; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.
  • Su D; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.
  • Gao S; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China. Electronic address: shuang1gao@tjnu.edu.cn.
  • Chen L; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.
  • Sun Y; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.
  • Kong S; Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China. Electronic address: kongshaofei@cug.edu.cn.
  • Wang H; Tianjin Changhai Environmental Monitoring Service Corporation, Tianjin, China.
Environ Pollut ; : 124738, 2024 Aug 13.
Article em En | MEDLINE | ID: mdl-39147223
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 PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 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 PM2.5 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 PM2.5 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 PM2.5, 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 PM2.5, thereby ensuring public health.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Pollut Assunto da revista: SAUDE AMBIENTAL Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Pollut Assunto da revista: SAUDE AMBIENTAL Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China