Stepwise genetic algorithm for adaptive management: Application to air quality monitoring network optimization

A novel algorithm named the stepwise genetic algorithm (SGA) is proposed to optimize the air quality monitoring network of mainland China under the framework of adaptive management. SGA is adapted from the genetic algorithm by modifying the operators of “mutation” and “crossover” to increase the number of removed sites by one at each step. Approximately half of the sites are adequate to achieve the same mean kriging variance (MKV) as that from all the sites, and the PM2.5 maps interpolated from these two site sets are very similar. Based on the site array proposed by SGA, the MKV shows a U-shaped trend with the number of removed sites, where the initial decrease of MKV (indicating improvement of interpolation accuracy by removing some sites) has only rarely been reported before. Mathematical proof demonstrates that the clustered sites tend to cause collinearity in the covariance matrix and hence result in MKV inflation. [Display omitted] •Topological indices quantify clustering of air quality monitoring network of China.•A novel Stepwise Genetic Algorithm is proposed to facilitate adaptive management.•Clustered monitoring sites tend to cause higher kriging interpolation uncertainty.•This is due to negative values in inverse of covariance matrix for clustered sites.•The PM2.5 map derived from 50% selective sites approximates that from all sites.