{"title":"Dynamics of PM2.5 and network activity during extreme pollution events","authors":"Nail F. Bashan, Weiyu Li, Qi R. Wang","doi":"10.1038/s41612-024-00716-z","DOIUrl":null,"url":null,"abstract":"In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5 anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00716-z.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://www.nature.com/articles/s41612-024-00716-z","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
In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5 anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.
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.