<![CDATA[The ambient air quality monitoring system in Surabaya has five fixed monitoring stations. Monitoring provides important information for public, but is expensive to purchase, utilize, and maintain. Based on result from spatial prediction of spatio-temporal additive model for air pollutant PM10, it is necessary to move the existing monitoring stations at other locations. In this study, we develop a methodology for reallocation of existing monitoring network to find an optimal configuration. The result of reallocation shows that new location of monitoring network can increase the accuracy of spatial prediction, especially at area with high concentration of PM10 Key words:: spatio-temporal data, spatio-temporal additive model, spatial prediction, reallocation monitoring network]]>
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