PM10 prediction has attracted special legislative and scientific attention due
to its harmful effects on human health. Statistical techniques have the
potential for high-accuracy PM10 prediction and accordingly, previous studies
on statistical methods for temporal, spatial and spatio-temporal prediction of
PM10 are reviewed and discussed in this paper. A review of previous studies
demonstrates that Support Vector Machines, Artificial Neural Networks and
hybrid techniques show promise for suitable temporal PM10 prediction. A review
of the spatial predictions of PM10 shows that the LUR (Land Use Regression)
approach has been successfully utilized for spatial prediction of PM10 in
urban areas. Of the six introduced approaches for spatio-temporal prediction
of PM10, only one approach is suitable for high-resolved prediction (Spatial
resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon
the LUR modeling method, short-term dynamic input variables are employed as
explanatory variables alongside typical non-dynamic input variables in a non-
linear modeling procedure