Geographically weighted methods and their use in network re-designs for environmental monitoring

• Published in: Stochastic environmental research and risk assessment 2014-10, Vol.28 (7), p.1869-1887
• Main Authors: Harris, Paul, Clarke, Annemarie, Juggins, Steve, Brunsdon, Chris, Charlton, Martin
• Format: Article
• Language: English
• Subjects: Acidification; Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Design; Earth and Environmental Science; Earth Sciences; Environment; Environmental monitoring; Fresh water; Geostatistics; Math. Appl. in Environmental Science; Optimization algorithms; Original Paper; Physics; Probability Theory and Stochastic Processes; Statistics for Engineering; Waste Water Technology; Water chemistry; Water Management; Water Pollution Control
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-014-0851-1

Given an initial spatial sampling campaign, it is often of importance to conduct a second, more targeted campaign based on the properties of the first. Here a network re-design modifies the first one by adding and/or removing sites so that maximum information is preserved. Commonly, this optimisation is constrained by limited sampling funds and a reduced sample network is sought. To this extent, we demonstrate the use of geographically weighted methods combined with a location-allocation algorithm, as a means to design a second-phase sampling campaign in univariate, bivariate and multivariate contexts. As a case study, we use a freshwater chemistry data set covering much of Great Britain. Applying the two-stage procedure enables the optimal identification of a pre-specified number of sites, providing maximum spatial and univariate/bivariate/multivariate water chemistry information for the second campaign. Network re-designs that account for the buffering capacity of a freshwater site to acidification are also conducted. To complement the use of basic methods, robust alternatives are used to reduce the effect of anomalous observations on the re-designs. Our non-stationary re-design framework is general and provides a relatively simple and a viable alternative to geostatistical re-design procedures that are commonly adopted. Particularly in the multivariate case, it represents an important methodological advance.