interpolation with missing values

I need to interpolate some data  about PM10 for some location (schools). I
have daily data and about 50 stations. I have to interpolate for every day
but my problems comes with the missing values of many stations in many days.
For example for one day I could have data for 10 stations while for other
day data from 50. When ignoring these missing data and interpolating using
ordinary kriging for each day, the results for each school varies a lot
depending of which stations have available data. For example a school near
one station changes a lot when that station have missing in one day. What
should be the best way to deal with this missing values, is there a method
for imputation that takes into account the temporal and the spatial
variability of the data?

Off the top of my head, do multiple imputations of the missing values
based on the mean and sd of the values at that site when not missing.
You'll then end up with a number (100, say) of kriged maps. You can
probably then take the mean over those as your map and for the
variance you'll have to combine the kriging variance with the
imputation variance...

 This is probably valid assuming the dropouts are random... Also, it
doesn't take into account any temporal correlation which might get you
a better estimate of your imputed values...

 What you do may also depend on what you are doing with the data. If
its just to produce pretty maps, then you might not need something so
sophisticated. If you are computing the number of days that PM10 in
some location exceeds some threshold, then you may have to give it
some more thought...

Barry