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Environmental Health Scotland
Figure 5. Predicted NO 2
concentrations in Glasgow, 1998-99 (μg/m 3 ).
Other useful predictor variables included postcodes
within 300m and junctions within 250m. Figure 5
shows the surface of predicted pollution concentrations
for NO 2
in Glasgow, 1998-99.
The variables that predicted the highest amount of
variation in pollution concentrations for the BS model
were the road length within 250m of the monitoring
site, the distance to the edge of the urban area and the
altitude (table 2). Other useful predictor variables
were the road length within 50m, junctions within
250m and households within 300m. Figure 6 shows
the surface of predicted pollution concentrations for
BS in Central Scotland, 1970-79.
The best model predicted 38% of the variation in
BS concentrations, which means that the BS model
was not very reliable. The main problems with this
model were that contemporary road and urban edge
data were used to predict historical BS concentrations
and that the black smoke measurements from the
1970s were incomplete with many extended periods
of missing data. Using contemporary surrogate
emission and advection data was not ideal, but
historical road and urban area data were not available.
Statisticians working on an epidemiological study
on long-term black smoke exposure and mortality
(Beverland et al, 2007) predicted the missing
pollution data using sophisticated imputation methods
(Yap, 2005), which improved the reliability of
the pollution concentrations. However, bivariate
correlations between the BS measurements and each
predictor variable were weak, which in turn led to a
model with poor predictive power.
Ideal conditions for using land use regression modelling
would be when good quality data for pollution
concentrations and predictor variables are measured
during the same time period.
Table 2. Multiple regression model for BS in Central Scotland, 1970-79.
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