Yonghong Liu, Xinru Yang, Kui Liu, Rui Xu, Yuzhuang Pian, Shikun Liu
{"title":"基于 Eclat 方法的动态交通-气象-大气污染物关联规则挖掘","authors":"Yonghong Liu, Xinru Yang, Kui Liu, Rui Xu, Yuzhuang Pian, Shikun Liu","doi":"10.1016/j.apr.2024.102305","DOIUrl":null,"url":null,"abstract":"<div><div>With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM<span><math><msub><mrow></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> and O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, have significantly increased and seriously affected public health. Traffic and meteorological conditions are the primary influencing factors of pollutant concentrations, and their spatial and temporal changes affect the dispersion of pollutants. Increasing use of high-resolution big data offers opportunities to explore these correlations. More extensive quantitative studies are essential for effective air pollution control. This study presents an Eclat algorithm to quantitatively reveal the relationship between traffic, meteorology and pollutants with hourly and 5-minute scale data in the urban area of Guangzhou. We establish a research framework covering temporal pollution analysis, multifactor rule mining, and spatial effects. The results show that <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> and O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> exhibit coordinated trends on the daily scale influenced by traffic flow and meteorology conditions, but on the hourly scale, they are negatively correlated. At the 5-minute scale, synchronized variations occur only during specific periods. This finer scale better identifies association rules for high-concentration pollutant scenarios, and non-roadside sites outperform roadside sites in mining these associations. For example, when humidity is below 37%, atmospheric pressure is 1016.2–1020.3 Pa, wind speed is 1.7–2.6 m/s, and the traffic volume on Jiefang North Road exceeds 635 vehicles every 5 min, there is a 92.86% probability that the <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> concentration at GYQ (a non-roadside monitoring site) will exceed 127 <span><math><mi>μ</mi></math></span>g/m<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>. These findings enhance our understanding of how dynamic traffic and meteorological conditions impact atmospheric pollutants and provide a scientific basis for regional collaborative pollution prevention.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 12","pages":"Article 102305"},"PeriodicalIF":3.9000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method\",\"authors\":\"Yonghong Liu, Xinru Yang, Kui Liu, Rui Xu, Yuzhuang Pian, Shikun Liu\",\"doi\":\"10.1016/j.apr.2024.102305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM<span><math><msub><mrow></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> and O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, have significantly increased and seriously affected public health. Traffic and meteorological conditions are the primary influencing factors of pollutant concentrations, and their spatial and temporal changes affect the dispersion of pollutants. Increasing use of high-resolution big data offers opportunities to explore these correlations. More extensive quantitative studies are essential for effective air pollution control. This study presents an Eclat algorithm to quantitatively reveal the relationship between traffic, meteorology and pollutants with hourly and 5-minute scale data in the urban area of Guangzhou. We establish a research framework covering temporal pollution analysis, multifactor rule mining, and spatial effects. The results show that <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> and O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> exhibit coordinated trends on the daily scale influenced by traffic flow and meteorology conditions, but on the hourly scale, they are negatively correlated. At the 5-minute scale, synchronized variations occur only during specific periods. This finer scale better identifies association rules for high-concentration pollutant scenarios, and non-roadside sites outperform roadside sites in mining these associations. For example, when humidity is below 37%, atmospheric pressure is 1016.2–1020.3 Pa, wind speed is 1.7–2.6 m/s, and the traffic volume on Jiefang North Road exceeds 635 vehicles every 5 min, there is a 92.86% probability that the <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></mrow></math></span> concentration at GYQ (a non-roadside monitoring site) will exceed 127 <span><math><mi>μ</mi></math></span>g/m<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>. These findings enhance our understanding of how dynamic traffic and meteorological conditions impact atmospheric pollutants and provide a scientific basis for regional collaborative pollution prevention.</div></div>\",\"PeriodicalId\":8604,\"journal\":{\"name\":\"Atmospheric Pollution Research\",\"volume\":\"15 12\",\"pages\":\"Article 102305\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Pollution Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1309104224002708\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Pollution Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1309104224002708","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method
With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM and O, have significantly increased and seriously affected public health. Traffic and meteorological conditions are the primary influencing factors of pollutant concentrations, and their spatial and temporal changes affect the dispersion of pollutants. Increasing use of high-resolution big data offers opportunities to explore these correlations. More extensive quantitative studies are essential for effective air pollution control. This study presents an Eclat algorithm to quantitatively reveal the relationship between traffic, meteorology and pollutants with hourly and 5-minute scale data in the urban area of Guangzhou. We establish a research framework covering temporal pollution analysis, multifactor rule mining, and spatial effects. The results show that and O exhibit coordinated trends on the daily scale influenced by traffic flow and meteorology conditions, but on the hourly scale, they are negatively correlated. At the 5-minute scale, synchronized variations occur only during specific periods. This finer scale better identifies association rules for high-concentration pollutant scenarios, and non-roadside sites outperform roadside sites in mining these associations. For example, when humidity is below 37%, atmospheric pressure is 1016.2–1020.3 Pa, wind speed is 1.7–2.6 m/s, and the traffic volume on Jiefang North Road exceeds 635 vehicles every 5 min, there is a 92.86% probability that the concentration at GYQ (a non-roadside monitoring site) will exceed 127 g/m. These findings enhance our understanding of how dynamic traffic and meteorological conditions impact atmospheric pollutants and provide a scientific basis for regional collaborative pollution prevention.
期刊介绍:
Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.