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A central composite design was used to optimize the sampling condition.
This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting.
Due to the limited resources that are typically available for forest management, including the forest inventory phase, it is necessary to optimize the sampling approaches.
Response surface methodology (RSM) with central composite design (CCD) was also employed in two separate consecutive steps to optimize the sampling and device parameters.
The Austre Brøggerbreen and Edithbreen were considered as test sites to setup the experiment, to optimize the sampling strategy and to identify some basic experimental artefacts.
In addition, to further optimize the sampling, we describe a preconditioning scheme, which permits to treat each field degree of freedom individually with regard to the evolution through the auxiliary field configuration space.
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Additionally, a fractional factorial design was used to shorten and optimize the sample extraction.
After choosing preliminary conditions, an experimental design was carried out with the variables C18, PSA, NaOH and CH3COONa to optimize the sample preparation step.
Since this method is based upon an underlying dynamical system, we can utilize existing work to develop, implement, and optimize the sampler's performance.
To optimize the sample preparation different precipitation methods have been tested.
In order to optimize the sample size, missing values were estimated using Expectation Maximization.
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