Long-memory characteristics of urban roadside air quality

Jason C. Lau, W. T. Hung, David D. Yuen, Chun Shun Cheung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

Carbon monoxide is a major contributor to air pollution in urban cities, particularly at the roadside. Hourly, monthly and seasonal mean carbon monoxide concentration data are collected from a roadside air monitoring station in Hong Kong over 7-years. The station is a few metres from a major intersection surrounded by tall buildings. In particular, hourly patterns of concentrations on different days of the week are investigated. The data show that hourly carbon monoxide concentrations resemble the traffic pattern of the area and tend to be lower in the summer. Using a seasonal autoregressive integrated moving average models shows that the daily traffic cycle strongly influences concentrations. Further, it is found that urban roadside carbon monoxide monitoring data exhibits a long-memory process, suggesting that a model incorporating long memory and seasonality effects is needed simulate urban roadside air quality.
Original languageEnglish
Pages (from-to)353-359
Number of pages7
JournalTransportation Research Part D: Transport and Environment
Volume14
Issue number5
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • ARIMA model
  • Carbon monoxide
  • Long memory
  • Roadside air quality

ASJC Scopus subject areas

  • Transportation
  • Environmental Science(all)

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