Spatio-temporal river contamination measurements with electrochemical probes and mobile sensor networks

The pollution of the rivers running through the cities or near to them is a current world-wide problem and requires actions and new technologically available approaches to control and restore those waters. In this work, we hypothesized that last-generation mobile sensor networks can be combined with emergent electrochemical probes and with recently proposed spatio-temporal analysis of the measurement dynamics using machine learning tools. With this purpose, we designed a mobile system to measure five variables: two environmental and three water quality variables in rivers: dissolved oxygen with an electrochemical probe, water temperature, electrical conductivity, air temperature and percentage of relative humidity using solid-state sensors, in each monitoring station. Our main contribution is a first mobile-sensor system that allows mobile campaigns for acquiring measurements with increased temporal and spatial resolution, which in turn allows for better capturing the spatio-temporal behavior of water quality parameters than conventional campaign measurements. Up to 23 monitoring campaigns were carried out, and the resulting measurements allowed the generation of spatio-temporal maps of first and second order statistics for the dynamics of the variables measured in the San Pedro River (Ecuador), by using previously proposed suitable machine learning algorithms. Significantly lower mean absolute interpolation errors were obtained for the set of mean values of the measurements interpolated with Support Vector Regression and Mahalanobis kernel distance, specifically 0.8 for water temperature, 0.4 for dissolved oxygen, 3.0 for air temperature, 11.6 for the percentage relative humidity, and 33.4 for the electrical conductivity of the water. The proposed system paves the way towards a new generation of contamination measurement systems, taking profit of information and communication technologies in several fields.