Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method
Liu Y, Xinru Yang, Kui Liu, Rui Fang Xu, Yuzhuang Pian, Shikun Liu · Atmospheric Pollution Research · 2024
With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM 2 . 5 and O 3 , 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 P M 2 . 5 and O 3 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 P M 2 . 5 concentration at GYQ (a non-roadside monitoring site) will exceed 127 μ g/m 3 . 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 .