Sustainable development reduces particulate matter emissions and mitigates aging’s cognitive impact

IF 8.5 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

npj Climate and Atmospheric Science Pub Date : 2025-05-09 DOI:10.1038/s41612-025-01052-6

Guiming Zhu, Yanchao Wen, Rule Du, Kexin Cao, Rong Zhang, Xiangfeng Lu, Jie Liang, Qian Gao, Tong Wang

{"title":"Sustainable development reduces particulate matter emissions and mitigates aging’s cognitive impact","authors":"Guiming Zhu, Yanchao Wen, Rule Du, Kexin Cao, Rong Zhang, Xiangfeng Lu, Jie Liang, Qian Gao, Tong Wang","doi":"10.1038/s41612-025-01052-6","DOIUrl":null,"url":null,"abstract":"China’s aging population and the rising public health burden from cognitive impairment are pressing concerns. Using mixed-effects models, we analyzed the association between particulate matter and its components with cognitive function using 20,115 observations from 123 Chinese cities and assessed economic costs under various socioeconomic scenarios. The single-pollutant model showed cognitive scores decrease with higher pollutant concentrations: PM1 (−0.53 points/0.1 µg/m3), PM2.5 (−0.30), PM10 (−0.14), organic matter (−1.44), ammonium (−1.55), sulfate (−1.70), and black carbon (−7.23). Nitrate showed no statistical association. In the multi-pollutant model, PM₁, PM₂.₅, organic matter, sulfate, and black carbon exhibited a statistically negative association with cognitive scores. Sustainable strategies reducing particulate matter levels could mitigate aging impacts and lower economic costs by $19.35 billion by 2050, offering significant health and financial benefits.<figure></figure>","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"31 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1038/s41612-025-01052-6","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

China’s aging population and the rising public health burden from cognitive impairment are pressing concerns. Using mixed-effects models, we analyzed the association between particulate matter and its components with cognitive function using 20,115 observations from 123 Chinese cities and assessed economic costs under various socioeconomic scenarios. The single-pollutant model showed cognitive scores decrease with higher pollutant concentrations: PM₁ (−0.53 points/0.1 µg/m³), PM_2.5 (−0.30), PM₁₀ (−0.14), organic matter (−1.44), ammonium (−1.55), sulfate (−1.70), and black carbon (−7.23). Nitrate showed no statistical association. In the multi-pollutant model, PM₁, PM₂.₅, organic matter, sulfate, and black carbon exhibited a statistically negative association with cognitive scores. Sustainable strategies reducing particulate matter levels could mitigate aging impacts and lower economic costs by $19.35 billion by 2050, offering significant health and financial benefits.

Abstract Image

查看原文本刊更多论文

可持续发展减少了颗粒物排放，减轻了老龄化对认知的影响

中国的人口老龄化和认知障碍带来的公共卫生负担日益加重是迫切需要关注的问题。利用混合效应模型分析了中国123个城市的20115个观测数据，分析了颗粒物及其成分与认知功能之间的关系，并评估了不同社会经济情景下的经济成本。单污染物模型显示，随着污染物浓度的增加，认知得分下降：PM1（- 0.53分/0.1µg/m3）、PM2.5（- 0.30分）、PM10（- 0.14分）、有机物（- 1.44分）、铵（- 1.55分）、硫酸盐（- 1.70分）和黑碳（- 7.23分）。硝酸盐含量无统计学相关性。在多污染物模型中，PM 1、PM 2。5、有机物、硫酸盐和黑碳在统计上与认知得分呈负相关。减少颗粒物水平的可持续战略可以缓解老龄化影响，到2050年将经济成本降低193.5亿美元，带来巨大的健康和经济效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

npj Climate and Atmospheric Science Earth and Planetary Sciences-Atmospheric Science

CiteScore

8.80

自引率

3.30%

发文量

审稿时长

21 weeks

期刊介绍： npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols. The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.