Assessing relocation strategies of urban air quality monitoring stations by GA-based compromise programming

This paper presents a GA-based compromise programming technique for assessing the relocation strategy of urban air quality monitoring network with respect to the multi-objective and multi-pollutant design criteria. While the impact of conservative, quasi-stable, and reactive pollutants are considered in the design principles via a simulation analysis, cost, effectiveness, and efficiency characteristics are postulated in the optimization process. Therefore, technical coverage for illustrating the needs of siting air quality monitoring stations (AQMS) includes both the air quality simulation and optimization modeling analyses in a two-stage analytical framework simultaneously. It starts from determining the spatial interrelationship among those candidate sites using various types of air quality simulation models as an integrated means. And the outputs drawn from the simulation models can then be used as the required inputs in the compromise programming model in order to screen all those siting alternatives that may satisfy the planning goals subject to the essential constraints throughout the multi-objective optimization process. For the illustrating purposes, a series of technical settings for finding the optimal relocation scenarios of AQMS were examined in the case study for the city of Kaohsiung in South Taiwan where the long-term violations of official standards of ozone and particulates turn out to be critical. It not only expresses the ideas of relocation strategy but also indicates how to utilize those alternatives in the decision-making process for improving the functionality of air quality monitoring in the urban environment. Experience gained in this study clearly indicates that the more the number of pollutants and objectives considered simultaneously, the higher the number of candidate sites to be selected in the relocation strategy.