A novel automated framework to design optimal networks of atmospheric greenhouse gas stations

This research proposes an optimal design framework for a mesoscale atmospheric greenhouse gas network dedicated to inverse flux monitoring at urban, regional, or national scales.

The framework’s design is based on data processing of atmospheric concentrations using multiple machine learning techniques such as image processing and pattern recognition, among others, all of them powered by optimization algorithms, giving the solution process explorative and exploitative features over the problem search space. 

Besides, the data processing uses graph representation as it considers a discrete search space, which in turn allows for speeding up the information access in each stage, especially during the inverse analysis procedure.

All of the above is framed with a learning system whose purpose is automatizing the processing when combining diverse data sources by mixing the supervised and unsupervised learning types in pre- and post-processing, respectively. 

On the one hand, the problem is related to the design of a monitoring network of greenhouse gases, in which it is required to decide the locations of a specific number of towers according to their measurement influence region, hence minimizing the number of towers while guaranteeing the appropriate parameter estimation.

On the other hand, the solution strategy conducts a data analysis, where observed and fitted data are treated as spatial-temporal images. During the batch processing, these images are filtered, contrasted, binarized, classified, and clustered, among other operations to maximize the data analysis.

Performance tests were based on reference datasets from the Weather Research and Forecasting model (here hourly simulated concentrations at 3 kilometers resolution over eastern France) as well as other synthetically and randomly generated concentration fields, which allowed for comparison of the proposed algorithm processing.

According to the parametric and non-parametric tests used to evaluate the scheme, our framework is competitively capable of designing optimal monitoring networks by using data processing and high-performance computing.