Spatio-Temporal River Contamination Measurements with Electrochemical Probes and Mobile Sensor Networks

Iván Vizcaíno,Enrique Carrera,Luis Cumbal,Sergio Muñoz-Romero,José Rojo-Álvarez

doi:10.3390/su10051449

Abstract

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.

Highlights

The continuous increase in population, urbanization, industrialization, and energy demand have generated global climatic changes that have affected the quality of water from natural sources, especially the rivers
We restricted ourselves to benchmark well-known conventional interpolation algorithms with a machine learning algorithm known as k-Nearest Neighbors (k-NN), and it was shown therein that the use of basic machine learning based algorithm provided us with the best estimation of spatio-temporal dynamics. These conclusions were obtained by working with measurement campaigns at Machángara River. After this [15], we proposed the use of several enhanced statistical interpolation methods, we included a priori available information of the quality parameter measurements through the use of Support Vector Regression (SVR) algorithms
We want to stress that each algorithm could yield better performance than the others in different conditions, so it is highly recommendable to visualize the results for the three algorithms, in order not to trust only in a single one and to be able to determine if the dynamics were well captured according to the consistency across the algorithms

Summary

Introduction

The continuous increase in population, urbanization, industrialization, and energy demand have generated global climatic changes that have affected the quality of water from natural sources, especially the rivers. There has been a decrease in the water level of streams, rivers, and lakes, while watersheds and surface water deposits have been contaminated. According to data from the World Health Organization, up to 844 million people lacked a basic service of drinking water supply in 2015, a figure that includes 159 million people who depend on the untreated surface waters of lakes, ponds, rivers, or streams. Health prevention and environmental care require an efficient intervention of the authorities to manage water resources in a responsible manner. In this setting, water quality monitoring becomes a permanent activity within water management actions, especially in modern times where there are new technologies available that could help to improve the water vigilance

Methods

Results

Conclusion