Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000–2015 using quantile and multiple line regression models

Wei Zhao, Shaojia Fan, Hai Guo, Bo Gao, Jiaren Sun, Laiguo Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)


In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000–2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.
Original languageEnglish
Pages (from-to)182-193
Number of pages12
JournalAtmospheric Environment
Publication statusPublished - 1 Nov 2016


  • Dominance analysis
  • Meteorological variables
  • Multiple linear regression
  • Ozone
  • Quantile regression

ASJC Scopus subject areas

  • Environmental Science(all)
  • Atmospheric Science

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