Abstract
This paper 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.
Highlights
It is well known that the air pollution causes adverse effects on human health in addition to the impact on environment
Taking the cue on these advantages, this paper proposes a simple and innovative process to optimize Air Quality Monitoring Network (AQMN) by using Geographical Information System (GIS), interpolation methods and the historical data
The interpolated data are compared with the observed data
Summary
It is well known that the air pollution causes adverse effects on human health in addition to the impact on environment. Due to rapid urbanization and industrialization, air pollution assumes high significance in large cities. Continuous monitoring of the air pollution with a well-designed air quality monitoring network. (2016) Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques. (AQMN) is the first step in addressing this issue. Obtaining the continuously monitored data to ensure the safe levels of air quality is one of the primary objectives of AQMN, in addition to evaluating exposure hazards and implementing effective control strategies. Environmental protection agencies would be looking for an optimal design of AQMN meeting these objectives with an obvious focus on minimizing cost
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