IoT Sensor Technologies for Monitoring Trace Elements in Water Quality Systems

Internet of Things (IoT) sensors are increasingly applied to real-time water quality monitoring, with implications for environmental supervision and public health. This systematic review synthesised findings from 52 studies identified through Google Scholar, Web of Science, and Scopus (7,606 records; 1,054 screened after duplicates). Publications grew steadily from 2014, peaking in 2018–2019. Ion-selective sensors and multi-parameter systems each accounted for 18.2%, followed by optical, electrochemical, and biosensors at 9.1% each, with other types at 4.5%. Target parameters included nutrients/eutrophication indicators (30%), heavy metals/trace elements (29%), microbiological contaminants (6%), emerging pollutants (4%), industrial pollutants (3%), salinity/marine conditions (8%), general indicators (8%), physical–chemical parameters (8%), infrastructure monitoring (2%), recreational water risks (2%), and mixed parameters (8%). Sensitivity (27.3%) and combined detection limits/sensitivity (18.2%) dominated performance metrics. Low-cost devices (USD 50–500) comprised 46% of systems; medium-cost 14%; very low-cost and high-cost 8% each; research-stage 14%; mixed cost 8%. Industrial protocols led interfaces (32%), followed by analog/digital mixed IoT-enabled (14%), analog-only (8%), digital-only (4%), and mixed architectures (8%). Arduino platforms were most common (29%), followed by Raspberry Pi (20%). Field deployment dominated (44%), with field–remote IoT-enabled at 19%, lab–field hybrids at 9%, and others less frequent. Calibration was regular/periodic in 34%, implied periodic in 20%, general in 23%, detailed in 14%, specialist in 6%, and not required in 4%. Key challenges were biofouling (32%), sensor drift (22%), signal interference (14%), power limitations (10%), and environmental or data issues (8% each). IoT-based water quality monitoring is dominated by low-cost, field-ready solutions, but standardised calibration, improved fouling resistance, and integration of advanced analytics are needed for scalable, reliable deployment.