Modelling and assessing trends in traffic-related emissions using a generalised additive modelling approach

Generalised additive models are applied to roadside air quality monitoring data to separate traffic-related emission trends from meteorological and background influences, providing a robust framework for assessing progress in reducing vehicle emissions.

Modelling and assessing trends in traffic-related emissions using a generalised additive modelling approach

D.C. Carslaw, S.D. Beevers, J.E. Tate

Atmospheric Environment, 2007

A generalised additive modelling (GAM) approach is used to model daily concentrations of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), carbon monoxide (CO), benzene and 1,3-butadiene at a busy street canyon location in central London. The models were developed for the period July 1998–June 2005 using appropriate meteorological and road traffic covariates. For all models, the complex and localised wind-flow patterns resulting from the street canyon location of the monitoring site, which can be difficult to model deterministically, have a large influence on the model predictions. GAMs built using simple covariates explain a large amount of the daily variation for these pollutants (mean r² = 0.86). Although measured concentrations of NO_x have declined from 1998 to 2005, much of the decline appears to be associated with reductions in overall traffic and meteorological factors rather than reduced emissions of NO_x. Unadjusted NO_x trends show a 28.6% reduction from 1998 to 2005, whereas meteorologically adjusted trends show a 19.3% decline over this period. Benzene and 1,3-butadiene concentrations have declined in a manner consistent with emission calculations.

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