Article ID Journal Published Year Pages File Type
6342751 Atmospheric Environment 2011 12 Pages PDF
Abstract

In this paper, we compare the Generalised Linear Model (GLM) and Generalised Additive Model (GAM) for modelling the particle number concentration (PNC) of outdoor, airborne ultrafine particles in Helsinki, Finland. We examine temporal trends in PNC and examine the relationship between PNC and rainfall, wind speed and direction, humidity, temperature and solar insolation. Model choice is via the Akaike Information Criterion (AIC).We have shown that the Generalised Additive Model provides a better fit than the equivalent Generalised Linear Model (ELM) when fitting models with the same covariates with equivalent degrees of freedom (AIC and BIC for the GAM are 10266.52 and 10793.04, AIC and BIC for the ELM are 10297.19 and 10885.97, both have an R2 value of 0.836). We also present results that show that modelling both temporal trends and the effect of rainfall, wind speed and direction, humidity, temperature and solar insolation yields a better fitting model, according to the AIC, than either temporal trends or meteorological conditions by themselves.The model is applicable to any longitudinal monitoring-type measurement campaign where long time series are recorded. Use of this technique may be inappropriate for very short measurement campaigns. Attempting to fit a representative daily trend to one or two days' measurements may lead to a high degree of uncertainty; inclusion of a yearly trend requires having at least a year's worth of data with few gaps, particularly large gaps. In such a situation, the temporal trends may end up being penalised to zero and the model reverts to one largely influenced by meteorology.

► A Generalised Additive Model (GAM) was used to model outdoor ultrafine particle number concentration (PNC) in Helsinki. ► The GAM provided a better fit to measured PNC than the linear model (LM). ► The GAM also allowed for qualitatively and quantitatively different statistical inferences to be made. ► A GAM based on both meteorological and temporal data provided a better fit to measured PNC than only one of these.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Atmospheric Science
Authors
, , , , ,