Abstract
This review examines the current state of ambient water quality monitoring systems (AWQMS) in relation to
Sustainable Development Goal (SDG) indicator 6.3.2, which focuses on assessing water quality in natural water
bodies, independent of specific human usage. This approach underscores the significance of evaluating water
quality in rivers, lakes, and groundwater concerning their natural state. On a global scale, poor ambient water
quality is primarily driven by weak regulatory oversight of industrial discharges, agricultural runoff, unsustainable
farming practices, and inadequate wastewater treatment infrastructure. Real-time monitoring enabled
by machine learning (ML) models and Internet of Things (IoT) technologies offers a promising solution to these
challenges. In alignment with SDG 6.3.2, this review analyzes the capabilities of ambient water quality monitoring
systems (AWQMS), focusing on SDG 6.3.2 Level 1 parameters, model types, performance evaluations using
the REFORMS checklist, monitored water body categories, IoT-based AWQMS comparisons, and prototyping
insights drawn from 42 studies published between 2000 and 2024. Key findings reveal (1) the need for further
refinement of ML models, (2) limited monitoring of nitrogen, phosphorus, and total oxidized nitrogen within
Level 1 parameters, (3) insufficient application of the REFORMS checklist for model evaluations, (4) minimal
focus on groundwater monitoring, (5) inadequate model prototyping, (6) heavy reliance on battery-powered
sensors with limited investigation into power-harvesting technologies, and (7) restricted open access to
ambient water quality data. This review aims to guide future research and policy initiatives, driving meaningful
progress towards achieving SDG 6.3.2