Ruhui Cao, Yaxi Xiao, Yangbin Dong, Fuwang Zhang, Kai Shi, Zhanyong Wang
{"title":"运用复杂系统理论理解中国长三角工业基地 PM2.5 和 O3 的协调控制效应","authors":"Ruhui Cao, Yaxi Xiao, Yangbin Dong, Fuwang Zhang, Kai Shi, Zhanyong Wang","doi":"10.1007/s00477-024-02791-3","DOIUrl":null,"url":null,"abstract":"<p>Regional air pollution represents a multifaceted and dynamic system, rendering linear statistical approaches insufficient for capturing its inherent variability, particularly the intricate fluctuations of multiple pollution indicators. Therefore, this study investigates the synergistic evolution mechanisms of PM<sub>2.5</sub> and O<sub>3</sub> in four cities within China’s Yangtze River Delta industrial base from 2013 to 2022, employing complex systems theory. Initially, the presence of multifractality and long-term persistence between PM<sub>2.5</sub> and O<sub>3</sub> is confirmed in each city using the multifractal detrended cross-correlation analysis. Quantitative indicators are then established to evaluate the synergistic control effects of PM<sub>2.5</sub> and O<sub>3</sub>. Furthermore, factors influencing coordinated control are analyzed using the ensemble empirical mode decomposition. Finally, the self-organized criticality (SOC) theory is introduced to elucidate dynamic pollution patterns. The results indicate the following: (1) Multifractality and long-term persistence exist between PM<sub>2.5</sub> and O<sub>3</sub> in the four cities, with persistence strengthening alongside the implementation of atmospheric pollution prevention and control policies. The application of complex systems theory facilitates the explanation and quantification of the synergistic control effectiveness of PM<sub>2.5</sub> and O<sub>3</sub>. (2) Since 2013, with the exception of Nanjing, the coordinated control effects of PM<sub>2.5</sub> and O<sub>3</sub> in Shanghai, Hangzhou, and Suzhou have been unsatisfactory and have shown little improvement. (3) Compared to short-term pollution emissions from human activities, annual atmospheric control measures, periodic meteorological variations, and long-range transport of regional pollutants exert a greater influence on the synergistic regulation effects of PM<sub>2.5</sub> and O<sub>3</sub>. (4) SOC may serve as the primary mechanism influencing the effectiveness of the synergistic regulation of PM<sub>2.5</sub> and O<sub>3</sub>. Sudden events, such as epidemic control measures, can disrupt the existing balance between PM<sub>2.5</sub> and O<sub>3</sub>, thereby diminishing the coordinated control effects.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"12 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using complex systems theory to comprehend the coordinated control effects of PM2.5 and O3 in Yangtze River Delta industrial base in China\",\"authors\":\"Ruhui Cao, Yaxi Xiao, Yangbin Dong, Fuwang Zhang, Kai Shi, Zhanyong Wang\",\"doi\":\"10.1007/s00477-024-02791-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Regional air pollution represents a multifaceted and dynamic system, rendering linear statistical approaches insufficient for capturing its inherent variability, particularly the intricate fluctuations of multiple pollution indicators. Therefore, this study investigates the synergistic evolution mechanisms of PM<sub>2.5</sub> and O<sub>3</sub> in four cities within China’s Yangtze River Delta industrial base from 2013 to 2022, employing complex systems theory. Initially, the presence of multifractality and long-term persistence between PM<sub>2.5</sub> and O<sub>3</sub> is confirmed in each city using the multifractal detrended cross-correlation analysis. Quantitative indicators are then established to evaluate the synergistic control effects of PM<sub>2.5</sub> and O<sub>3</sub>. Furthermore, factors influencing coordinated control are analyzed using the ensemble empirical mode decomposition. Finally, the self-organized criticality (SOC) theory is introduced to elucidate dynamic pollution patterns. The results indicate the following: (1) Multifractality and long-term persistence exist between PM<sub>2.5</sub> and O<sub>3</sub> in the four cities, with persistence strengthening alongside the implementation of atmospheric pollution prevention and control policies. The application of complex systems theory facilitates the explanation and quantification of the synergistic control effectiveness of PM<sub>2.5</sub> and O<sub>3</sub>. (2) Since 2013, with the exception of Nanjing, the coordinated control effects of PM<sub>2.5</sub> and O<sub>3</sub> in Shanghai, Hangzhou, and Suzhou have been unsatisfactory and have shown little improvement. (3) Compared to short-term pollution emissions from human activities, annual atmospheric control measures, periodic meteorological variations, and long-range transport of regional pollutants exert a greater influence on the synergistic regulation effects of PM<sub>2.5</sub> and O<sub>3</sub>. (4) SOC may serve as the primary mechanism influencing the effectiveness of the synergistic regulation of PM<sub>2.5</sub> and O<sub>3</sub>. Sudden events, such as epidemic control measures, can disrupt the existing balance between PM<sub>2.5</sub> and O<sub>3</sub>, thereby diminishing the coordinated control effects.</p>\",\"PeriodicalId\":21987,\"journal\":{\"name\":\"Stochastic Environmental Research and Risk Assessment\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastic Environmental Research and Risk Assessment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s00477-024-02791-3\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Environmental Research and Risk Assessment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s00477-024-02791-3","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Using complex systems theory to comprehend the coordinated control effects of PM2.5 and O3 in Yangtze River Delta industrial base in China
Regional air pollution represents a multifaceted and dynamic system, rendering linear statistical approaches insufficient for capturing its inherent variability, particularly the intricate fluctuations of multiple pollution indicators. Therefore, this study investigates the synergistic evolution mechanisms of PM2.5 and O3 in four cities within China’s Yangtze River Delta industrial base from 2013 to 2022, employing complex systems theory. Initially, the presence of multifractality and long-term persistence between PM2.5 and O3 is confirmed in each city using the multifractal detrended cross-correlation analysis. Quantitative indicators are then established to evaluate the synergistic control effects of PM2.5 and O3. Furthermore, factors influencing coordinated control are analyzed using the ensemble empirical mode decomposition. Finally, the self-organized criticality (SOC) theory is introduced to elucidate dynamic pollution patterns. The results indicate the following: (1) Multifractality and long-term persistence exist between PM2.5 and O3 in the four cities, with persistence strengthening alongside the implementation of atmospheric pollution prevention and control policies. The application of complex systems theory facilitates the explanation and quantification of the synergistic control effectiveness of PM2.5 and O3. (2) Since 2013, with the exception of Nanjing, the coordinated control effects of PM2.5 and O3 in Shanghai, Hangzhou, and Suzhou have been unsatisfactory and have shown little improvement. (3) Compared to short-term pollution emissions from human activities, annual atmospheric control measures, periodic meteorological variations, and long-range transport of regional pollutants exert a greater influence on the synergistic regulation effects of PM2.5 and O3. (4) SOC may serve as the primary mechanism influencing the effectiveness of the synergistic regulation of PM2.5 and O3. Sudden events, such as epidemic control measures, can disrupt the existing balance between PM2.5 and O3, thereby diminishing the coordinated control effects.
期刊介绍:
Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas:
- Spatiotemporal analysis and mapping of natural processes.
- Enviroinformatics.
- Environmental risk assessment, reliability analysis and decision making.
- Surface and subsurface hydrology and hydraulics.
- Multiphase porous media domains and contaminant transport modelling.
- Hazardous waste site characterization.
- Stochastic turbulence and random hydrodynamic fields.
- Chaotic and fractal systems.
- Random waves and seafloor morphology.
- Stochastic atmospheric and climate processes.
- Air pollution and quality assessment research.
- Modern geostatistics.
- Mechanisms of pollutant formation, emission, exposure and absorption.
- Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection.
- Bioinformatics.
- Probabilistic methods in ecology and population biology.
- Epidemiological investigations.
- Models using stochastic differential equations stochastic or partial differential equations.
- Hazardous waste site characterization.