Optimization of a model for evaluation of the air quality monitoring network: the case of EMEP stations

The aim of this work was to optimize a model which gives a possibility of evaluating the localization of EMEP monitoring stations. To estimate if the location of air monitoring stations was correct, a single-layer artificial neural network (ANN) was used. In the procedure of unsupervised network training, a standard competitive training based on the k-means algorithm with a modification called the "neural gas" was used. The goal of modeling was to indicate the best locations of the hypothetical monitoring stations. The parameters, varying during model optimization, covered the value of subareas, the value of iterations and the value of hypothetical stations. As a result, an optimal model, useful for the EMEP monitoring network evaluation, was derived.