The mismatch between PM2.5 exposure and related health burden during 2000–2023 in the Sichuan Basin, China

IF 9.7 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environment International Pub Date : 2025-09-02 DOI:10.1016/j.envint.2025.109766

Hua Lu , Min Xie , Bojun Liu , Junyao Zhou , Shitong Chen , Jinyue Jiang , Bingliang Zhuang , Danyang Ma , Yangzhihao Zhan

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引用次数: 0

Abstract

Sichuan Basin (SCB) is a critical region in China facing the dual pressures of air pollution and population aging. This study constructed high resolution (1 km) PM_2.5 datasets for SCB using advanced machine learning approaches − Super Resolution Generative Adversarial Networks (SRGAN) and Convolutional Neural Network − Long Short-Term Memory (CNN-LSTM). Evaluation results demonstrate good performance of the machine learning model (SRGAN: R² = 0.93, RMSE = 0.08; CNN-LSTM: R² = 0.93, RMSE = 9.79 µg/m³). Our analysis reveals distinct temporal patterns in PM_2.5 exposure and related public health burdens (PHB), closely tied to major policy interventions including the 11th Five-Year Plan (2006), Clean Air Action (2013), and COVID-19 lockdown (2020). While PM_2.5 exposure followed a “rise-slight fall-rapid fall-stable” pattern, PHB exhibited a mismatched dynamics as “rise-slight rise-fall-rebound” with the variation rates of +28.2, +7.1 %, –23.9 % and 27.9 %. PHB in SCB grew by 33.9 % from 2000 to 2023, with Chengdu contributing 49.8 % to the total burden. Population aging led to 62.2 % increase in PHB and substantially offsetting gains from pollution control measures. Before the COVID-19 breakout, stable decrease baseline mortality rate (BMR) helped alleviated PHB with −3.1 %, −4.8 % and −9.4 % in three stages. But during the post-COVID period, increase BMR begun to contribute to the rise in PHB. These findings underscore the urgent need for integrated policy approaches that simultaneously address air quality management and aging-related health vulnerabilities, particularly in urban centers where pollution and population density converge. Effective mitigation will require sustained, coordinated efforts across environmental and public health sectors to counteract the compounding effects of demographic change and environmental degradation.

Synopsis

Population aging and mortality rate variations caused diverging trends in PM2.5 exposure and health burden during 2000–2023 in Sichuan Basin

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2000-2023年四川盆地PM2.5暴露与健康负担的失配

四川盆地是中国面临大气污染和人口老龄化双重压力的关键地区。本研究使用先进的机器学习方法-超分辨率生成对抗网络（SRGAN）和卷积神经网络-长短期记忆（CNN-LSTM）为SCB构建了高分辨率（1 km） PM2.5数据集。机器学习模型的评价结果表明性能良好(SRGAN: R2 = 0.93,RMSE = 0.08;CNN-LSTM: R2 = 0.93,RMSE = 9.79 µg / m3)。我们的分析揭示了PM2.5暴露和相关公共卫生负担（PHB）的不同时间模式，这与“十一五”规划（2006年）、“清洁空气行动”（2013年）和COVID-19封锁（2020年）等重大政策干预密切相关。PM2.5暴露表现为“上升-小幅下降-快速下降-稳定”的模式，PHB表现为“上升-小幅上升-下降-反弹”的不匹配动态，变化率为+28.2,+7.1 %,-23.9 %和27.9 %。从2000年到2023年，SCB的PHB增长了33.9 %，其中成都占总负担的49.8 %。人口老龄化导致PHB增加62.2 %，大大抵消了污染控制措施带来的收益。在COVID-19爆发前，基线死亡率（BMR）稳定下降，3个阶段分别为- 3.1 %、- 4.8 %和- 9.4 %，有助于缓解PHB。但在后疫情时期，BMR的增加开始导致ph的上升。这些调查结果强调，迫切需要采取综合政策办法，同时解决空气质量管理和与老龄化有关的健康脆弱性问题，特别是在污染和人口密度集中的城市中心。有效的缓解将需要环境和公共卫生部门持续、协调的努力，以抵消人口变化和环境退化的复合影响。人口老龄化和死亡率变化导致2000-2023年四川盆地PM2.5暴露和健康负担呈现不同趋势

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来源期刊

Environment International 环境科学-环境科学

CiteScore

21.90

自引率

3.40%

发文量

734

审稿时长

2.8 months

期刊介绍： Environmental Health publishes manuscripts focusing on critical aspects of environmental and occupational medicine, including studies in toxicology and epidemiology, to illuminate the human health implications of exposure to environmental hazards. The journal adopts an open-access model and practices open peer review. It caters to scientists and practitioners across all environmental science domains, directly or indirectly impacting human health and well-being. With a commitment to enhancing the prevention of environmentally-related health risks, Environmental Health serves as a public health journal for the community and scientists engaged in matters of public health significance concerning the environment.