AIR QUALITY MONITORING NETWORK DESIGN USING INFORMATION THEORY
VINEET KUMAR JAIN
AbstractMost of the techniques currently available in literature for designing an Air Quality Monitoring Network (AQMN) are complex in nature and are suited to one or more specific objective(s) and particular conditions. The present study proposes a simple and generalized method for designing an optimum AQMN based on entropy concepts, which are central to the information theory. This study considers the AQMN as an environmental information system. The AQMN provides the information about the random events (pollution levels) occurring in the area of interest. Information observed at one station can be inferred partially from observations at other stations. This concept is used to form a network that conveys the maximum possible information about the environment of the area for a given number of stations. The optimum size of the network is determined when addition or a new station does not add significant information to the existing network. AQMN design based on multiple pollutant leads to different optimum AQMNs. A combined AQMN based on equal weightage to each pollutant is suggested. It is observed that design based on discrete random variables becomes computationally very intensive in large networks. As a possible solution, AQMN is designed based on continuous variables and a comparison is done with the discrete variables based design. This methodology is applied to the existing network of nine stations in Delhi being operated under the Indian National Ambient Air Quality Monitoring (NAAQM) Program.
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