Optimization of ambient air quality monitoring networks : (Part I).

A method has been developed to obtain a joint solution to the problem of optimum number and configuration of ambient air quality monitors, on the principles of spatial correlation analysis and the minimum spanning tree. The interest in this case is to represent the patterns of regional air quality, at a minimum of an overlap of information. This methodology is extended to account for the uncertainties in air quality simulations and also to incorporate the probabilities of occurrence. As an illustration to these methodologies, an example of Taipei City, Taiwan has been considered.

Optimization of ambient air quality monitoring networks : (Part II).

Minimum Spanning Tree (MST) algorithm developed by Modak and Lohani (1984a) has been extended to consider multiple objectives for the optimum siting of ambient air monitors. Two approaches have been proposed, namely one based on the utility function and another based on the principles of sequential interactive compromise. The sequential interactive approach is heuristic but perhaps best suited to consider several objectives at a time, and particularly when professional judgements are also involved. The utility function approach may be normally restricted to two objectives at a time, but could be extended to consider a number of pollutants in the optimum design. For the purpose of illustration, the case of Taipei City, Taiwan has been considered.

Optimization of ambient Air Quality Monitoring Networks : (Part III).

The methodologies presented in Parts I and II (refer Modak and Lohani, 1984a and b) are essentially for deciding the best number and configuration for a single pollutant monitor. In practical situations however, Air Quality Monitoring Networks (AQMN) are expected to measure more than one pollutant and therefore simultaneous consideration of different types of pollutants must be made.In this paper, two new approaches have been developed for the multipollutant AQMN design. The first method makes use of the index theory and the other makes use of the principles of Pareto optimality. As an illustration of these methodologies, an example from Taipei City, Taiwan is considered.