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?

Thank you



--
View this message in context: http://r-sig-geo.2731867.n2.nabble.com/interpolation-with-missing-values-tp7583335.html
Sent from the R-sig-geo mailing list archive at Nabble.com.