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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method

Yuzhuang Pian, Shikun Liu, Kui Liu, Liu Y et al.
1 citations
Atmospheric Pollution Research
Statistical and Computational Modeling
0.35
article

Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method

Yuzhuang Pian, Shikun Liu, Kui Liu, Liu Y, Rui Xu, Xinru Yang
article en
1 citations

Abstract

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.

Atmospheric Pollution ResearchVol. 15(12)
Sun Yat-sen University (CN), Guangdong Province Environmental Monitoring Center (CN), Guangdong Institute of Intelligent Manufacturing (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 34%
Statistical and Computational Modeling
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.