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 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 . • Eclat model revealed multi-scale relations among traffic, meteorology, pollutants. • Traffic & meteorology drove inconsistent P M 2 . 5 and O 3 synergies across timescales. • Cross-regional pollution transmission was considered to model site correlations. • 5-min, non-roadside P M 2 . 5 or O 3 association rules outperformed hourly, roadside.
Authors
- Yuzhuang Pian (ORCID: https://orcid.org/0000-0001-6201-7762)
- Shikun Liu (ORCID: https://orcid.org/0000-0001-5942-0841)
- Kui Liu (ORCID: https://orcid.org/0000-0003-1454-2567)
- Liu Y (ORCID: https://orcid.org/0000-0002-4339-6389)
- Rui Xu (ORCID: https://orcid.org/0009-0002-3955-6025)
- Xinru Yang
Institutions
- Sun Yat-sen University (CN)
- Guangdong Province Environmental Monitoring Center (CN)
- Guangdong Institute of Intelligent Manufacturing (CN)
Publication Details
- Journal
- Atmospheric Pollution Research
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.apr.2024.102305
- Citations
- 1
- Primary Topic
- Statistical and Computational Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Natural Science Foundation of China