An Investigation Into The Association Of Ozone
With Traffic-related Air Pollutants Using A
Quantile Regression Approach
Author(s)
S. Munir, H. Chen & K. Ropkins
Abstract
Ground-level ozone (O3) is one of the most harmful air pollutants due to its
adverse effects on human health, agricultural crops, biodiversity and materials.
Ozone is a secondary air pollutant and interacts with meteorological variables as
well as with many other air pollutants such as nitric oxide (NO), nitrogen dioxide
(NO2), particles (PM2.5), and carbon monoxide (CO). This paper intends to
investigate the relationship of ozone with these air pollutants and lagged ozone
(previous day ozone) at a roadside monitoring site in Leeds UK. A quantile
regression approach has been applied, which is suitable for the non-normal
ozone distribution and capable of handling nonlinearities in the associations of
ozone with its predictors; as it examines the entire distribution of the variables
rather than a single measure of central tendency (mean or median). Our results
show that lagged ozone has positive, whereas NO, NO2 and CO have negative
associations with ozone. PM2.5 is negatively correlated with ozone at lower
quantiles (below 0.6) and the relationship becomes positive at upper quantiles
(0.6 and above), perhaps indicating more complex interactions. Also, it is shown
that the effect of explanatory variables on ozone concentrations is a function of
quantiles and hence the behaviour and interaction of the covariates with ozone
change at different regimes of ozone concentrations, information which is
normally hidden in the traditional regression models. Further statistical analysis
demonstrates that for some air pollutants the nature of relationship (negative or
positive) between ozone and its predictors remains unchanged and only the
strength changes, for others nature and strength both change at different
quantiles. The study explores the impacts of traffic-related air pollutants on
ground level ozone concentrations and suggests the use of quantile regression
Keywords
quantile regressions, ozone, air pollutants, NOx, CO, PM2.5, lagged ozone
Related Book
Other papers in this volume
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