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proposes a simple method of optimizing Air Quality Monitoring Network (AQMN)
using Geographical Information System (GIS), interpolation techniques and
historical data. Existing air quality stations are systematically eliminated
and the missing data are filled in using the most appropriate interpolation
technique. The interpolated data are then compared with the observed data.
Pre-defined performance measures root mean square error (RMSE), mean absolute
percentage error (MAPE) and correlation coefficient (r) were used to check the
accuracy of the interpolated data. An algorithm was developed in GIS
environment and the process was simulated for several sets of measurements
conducted in different locations in Riyadh, Saudi Arabia. This methodology
proves to be useful to the decision makers to find optimal numbers of stations
that are needed without compromising the coverage of the concentrations across
the study area.