Applying a deployment strategy and data analysis model for water quality continuous monitoring and management

Abstract

In Taiwan, where residential and industrial areas are in close proximity, finding ways to effectively continuous monitor and manage water quality is an essential issue. This study established a total solution for an Internet of things water quality monitoring network that integrates domestic miniaturized water quality monitoring sensors for real-time transport data of pH, temperature, conductivity, chemical oxygen demand, and copper ions. The data will be used to establish an analysis model based on continuous monitoring of the nation’s background concentration. We designed an automatic continuous monitoring and early warning analysis module for automatic analysis of environmental and instrumental anomalies for decision makers, a “pollution source analysis module” utilizing static and dynamic cross-environment data to swiftly trace upstream pollution sources, and a “pollution hotspot analysis module” to evaluate the impact area of pollutants, and immediate response measures to achieve early warning and swift evaluation for the prevention of water pollution. To do this, we installed 100 domestic miniaturized water monitoring devices in Taoyuan City for testing the solution. We found that the establishment of an Internet of things environment analysis and response model integrated with cross-environment analysis can be applied in water quality monitoring and management to assure improved environmental quality.