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Top1. Introduction
Delicately balancing the needs of natural aquatic ecosystems is difficult with pressures from encroaching urbanisation and population growth (McGrane (2016)). Aquatic environmental monitoring programs are an essential strategy in providing timely and accurate information to decision makers to aid in planning and management (Danielsen et al. (2010), Laut et al. (2013)). There are two main approaches to aquatic environmental monitoring: 1) Manual human-based field sampling; or 2) Remote monitoring using Internet of Things (IoT) technologies.
Traditionally, water quality parameters are physically measured via field instruments and water samples gathered for laboratory analysis (Abowei (2010)). However, this process is costly, time consuming, hazardous and can present multiple sources of error. Water samples typically require prompt analysis to guarantee accurate results as quality deteriorates over time (i.e., changes occur in the sample container’s mini ecosystem, which no longer reflect the system the sample was extracted from). Field-testing equipment is often expensive to maintain/handle due the harsh conditions they are exposed to in various environments. Retrieving samples at night can be difficult without adequate lighting (and disturbing the natural peace). Areas with rough terrain and concealed shrubbery may restrict movement and could be inhabited by dangerous/venomous animals. Field technicians must be highly skilled and aware of the health risks involved with undertaking regular field work.
An alternative approach is to use IoT technologies to undertake remote monitoring (Gholizadeh, Melesse and Reddi (2016), Glasgow et al. (2004), Lee et al. (2018)). Networked sensors (or wireless sensor networks (Madakam et al. (2015))) are deployed in the environment and provide remote telemetry at periodic intervals on water quality/condition. This approach acts as an early warning/indicator of common issues affecting water quality, rather than just a measurement tool. Water management agencies can proactively prepare for and react to potential disasters, thereby mitigating environmental damage and/or loss of life and property. There exist numerous commercial systems for undertaking remote aquatic environmental monitoring (Fondriest (2021), Hach (2021), Analytical Solutions (2021)), but these tend to be expensive and technologically restrictive (i.e., proprietary). Some proposals from the scientific literature include (Hongpin et al. (2015), Simbeye and Yang (2014), Li et al. (2013), Sung, Chen and Wang (2014), Ragai et al. (2017)), but these systems are now either defunct, do not capture sufficient quality data across a broad range of environmental parameters, do not scale commercially (i.e., are for experimental research purposes only), or are also cost-prohibitive.
Trevathan et al. (2021) presented a remote aquatic near real-time monitoring IoT platform that can be deployed for up to 12 months between maintenance cycles. However, the platform was restricted to measuring underwater temperature, lux (light) and turbidity (Trevathan, Read and Schmidtke (2020), Trevathan et al. (2020)). While this platform is robust, the limited types of sensor parameters (i.e., temperature, lux and turbidity) are insufficient for undertaking holistic environmental assessments. Factors such as salt content, oxygenation and water acidity are often critical factors that influence fish and plant species survivability (Bartram and Ballance (1996)).