The GIS-Based Approach for Optimal Design of Air Quality Monitoring Network for Management of Chemical Clusters

An industrial district with chemical plants producing inside poses a great threat to the surrounding atmospheric environment and human health. Therefore, designing a proper and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution controlling strategies and facilities in dealing with reducing pollutants in the planning stage of emergency management. Whereas monitoring facilities located at inappropriate sites would affect data validity. Thus, in this paper, a geospatial technique-Bayesian Maximum Entropy (BME) in conjunction with a multi-objective optimization model was utilized to optimize the design of an AQMN of gas sensors. Our developed atmospheric dispersion simulation system was employed to generate 'real' historical data for the above method and an experiment was implemented to illustrate the feasibility of the proposed approach. This work is expected to facilitate a decision-making process for determining an appropriate AQMN and assist the management work of environmental protection authorities.

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