Abstract
Abstract. Surface water quality monitoring (SWQM) provides essential information for water environmental protection. However, SWQM is costly and limited in terms of equipment and sites. The global popularity of social media and intelligent mobile devices with GPS and photography functions allows citizens to monitor surface water quality. This study aims to propose a method for SWQM using social media platforms. Specifically, a WeChat-based application platform is built to collect water quality reports from volunteers, which have been proven valuable for water quality monitoring. The methods for data screening and volunteer recruitment are discussed based on the collected reports. The proposed methods provide a framework for collecting water quality data from citizens and offer a primary foundation for big data analysis in future research.
Highlights
Surface freshwater is a finite resource that is necessary to the survival of mankind and the ecosystem
This study aims to develop a method for surface water quality monitoring (SWQM) through volunteer citizens using a social media application
This study proposes a methodological framework for SWQM using social media
Summary
Surface freshwater is a finite resource that is necessary to the survival of mankind and the ecosystem. Collecting a single sample from this site costs between USD 4000 and 6000, while analyzing various physical/chemical parameters costs an additional USD 1500 to 2000 per sample (Horowitz, 2013) These costs reduce the number of samples and sites that can be monitored, thereby necessitating the installation of several monitors on various sites and samples at regular temporal intervals. These limitations hinder the monitoring program from detecting illegal polluting activities, such as hidden sewage dumping, which tend to occur in areas that are located far from the monitoring sites or at a time when no sampling has been conducted. Many Chinese industrial facilities dump their sewage water discharge in rivers in the middle of the night to avoid detection (Wei, 2013)
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