Blockchain-Enabled Water Quality Monitoring: A Comprehensive Review of Digital Innovations and Challenges

Abstract

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems.

1. Introduction

Rapid economic development and increasing societal demands have intensified the release of complex micropollutants into natural ecosystems. Water contamination is now recognized as a critical global issue and a primary driver of environmental degradation. Therefore, water quality monitoring (WQM) has become an indispensable component of environmental management, providing essential data for regulatory compliance, pollution control, and mitigation strategies [1,2,3,4]. Effective monitoring enables the identification of contamination sources, assessment of ecological risks, and timely implementation of remediation measures. Despite its importance, WQM faces persistent challenges. The heterogeneous nature of water systems, coupled with variability in structural, operational, and environmental parameters, complicates the design of efficient monitoring programs [4,5]. Existing systems often require extensive manual calibration, consume significant energy, incur high operational costs, and may suffer from limited precision and resilience [2,6]. While approaches, such as data mining-based parameter optimization, have been proposed [5]. These limitations underscore the need for more robust, automated, and cost-effective solutions.

Blockchain technology (BCT) offers promising capabilities in this context. As a decentralized, cryptographically secured ledger, blockchain supports immutable, transparent, and verifiable data storage without reliance on central authorities [7,8,9,10]. Each block contains a header—olding metadata, such as timestamps, previous block hashes, and the Merkle root—and a body storing the transaction or measurement data. Consensus algorithms and peer-to-peer (P2P) networks enable distributed verification and communication among nodes, ensuring trust in recorded information. The integration of blockchain with emerging tools, such as the Internet of Things (IoT), smart contracts, big data analytics, and artificial intelligence, has already shown potential in environmental monitoring and agricultural applications [11,12,13]. In water management, IoT-enabled sensor networks can provide continuous measurements of key parameters (e.g, pH, temperature, turbidity, and dissolved oxygen), transmitting them in real time to blockchain-secured databases. This integration enhances data integrity, prevents unauthorized modification, and enables traceability of pollution events from origin to impact zones. For example, blockchain can support the issuance and monitoring of water abstraction permits, record compliance data, and track contamination pathways with high accuracy [14,15,16]. The framework proposed in this study combines BTC with a wireless sensor network (WSN) arranged in a directed acyclic graph (DAG) topology and Geographic Information System (GIS) mapping. Contamination pathways are traced from irrigation water intake points, mapped spatially, and analyzed using the Water Quality Analysis Simulation Program (WASP) to identify probable pollution sources, including illegal discharges. Comparable systems in have wastewater treatment and recycling have demonstrated notable efficiency gains, achieving recycling rates above 90% and operational efficiencies exceeding 85% [17,18,19]. Other implementations, such as IoT-based real-time monitoring platforms for rivers and lakes, have integrated big data analytics for anomaly detection, predictive modelling, and automated alerts [15,20]. The combined application of big data addresses several limitations of traditional WQM, including manual data handling, high verification costs, and delayed response time [18,21,22]. By ensuring secure data sharing and reliable real-time access, these technologies can improve decision making, operational efficiency, and stakeholder trust. However, practical deployments of blockchain in water resource management remain limited, necessitating further research into optimal integration strategies, cost-benefit analysis, and scalability considerations.

Alighted with Sustainable Development Goals (particularly SDG 6, which calls for improved water quality, reduced pollution and sustainable water use [23]), this work synthesizes current knowledge on blockchain application in WQM and introduces a novel integrated framework. The approach aims to enhance monitoring reliability, enable transparent data governance, and contribute to sustainable water resource management.

2. Blockchain Technology for Water Resources Protection and Irrigation

2.1. Hydrology and Water Resources for Our Human Life

Water resources are crucial for sustaining human life in multiple dimensions. Safe and clean water is essential for drinking, domestic use, and sanitation—needs that become even more critical during disasters and pandemics, when the risk of waterborne diseases is high. Maintaining a sustainable water supply system is, therefore, vital. Beyond domestic needs, water is indispensable for agriculture and aquaculture, which together consume the largest share of global water resources, primarily through irrigation and water supply systems maintained by investors. Water also underpins hydropower generation—the largest renewable energy source—offering clean operation, low greenhouse gas emissions, and long-term sustainability [24]. Over 47,000 hydropower dams exist worldwide [25], including 64 large dams in the Mekong River Basin [26], making hydropower a critical element of sustainable energy [27]. Furthermore, water supports aquatic ecosystems and fisheries and is essential to industry development; nearly all manufacturing processes require water.

Natural variations in the hydrological system, intensified by climate change and human interventions [28,29]. Climate change is projected to alter precipitation patterns, increase the magnitude and frequency of floods and droughts [30,31,32], and exacerbate regional extremes, i.e., wetter conditions in wet areas and drier conditions in dry areas. Such shifts threaten crop yield, food security, human safety, and infrastructure [33].

Examples include projected flood increases in the Mekong [34], Upper Yellow River [35], and Upper Yangtze River [36], and altered drought and flood frequencies in Thailand under various scenarios [37]. In contrast, some South American basins may see reduced flooding [38] while Europe shows mixed trends [39].

Human activities, notably dam construction and land use change, also strongly influence hydrology. While dams regulate seasonal flows [40,41] and can moderate climate-driven extremes [42], they cannot fully offset climate change impacts and may, in some cases, worsen flooding [29,43]. Dams also trap sediment, leading to downstream erosion and salinity intrusion, as in the Vietnamese Mekong Delta [41,44]. Deforestation and urbanization increase flood risk by raising runoff coefficients and can exacerbate droughts by reducing infiltration and soil moisture [45]. Collectively, such human interventions often outweigh climate impacts in altering flow regimes [26]. The complexity of these combined pressures underscores the need for robust, secure, and transparent monitoring and management tools, roles for which BCT has shown strong potential.

2.2. Blockchain Applications in Water Resource Protection and Irrigation

BCT provides a decentralized, tamper-proof ledger that can securely record, store, and share hydrological and environmental data [46]. When integrated with the IoT—which supplies real-time data via in situ sensors and wireless transmission—BCT ensures that, once collected, data cannot be altered, while remaining transparently accessible to authorized stakeholders. This integration supports accurate forecasting, disaster risk management, and collaborative decision-making by combining continuous monitoring with secure, verifiable storage [47,48,49,50].

A typical blockchain-based water management architecture has the following four layers, as presented in Figure 1 below [10]: (i) data collection via in situ IoT sensors; (ii) transmission over secure networks; (iii) blockchain-based storage and smart contract execution; and (iv) application and interaction layers for management functions and stakeholder interfaces. Such systems can enhance drought risk management [48], bridge local and global governance systems [49], and increase public participation in water stewardship [50]. They have been applied in downstream flood prediction [10], hydropower operations [51], groundwater management [52], and consumption tracking [53].

In agriculture, secure and reliable data platforms are especially important in developing regions, where vulnerabilities to cyberattacks and data loss can undermine irrigation management [54]. Blockchain facilitates autonomous collection of farm-level data on irrigation and environmental parameters [55], supporting smart, and secure water management.

Smart irrigation systems built on BCT can optimize water, fertilizer, and pesticide use, improving yields and reducing pollution [56,57]. They integrate data from soil moisture, meteorology [58], water quality, and agricultural supply–demand monitoring [59], triggering authomated actions based on threshold comparisons.

For example, Munir, M.S et al. [60] developed a blockchain–IoT–fuzzy logic watering system for small to medium farms in Pakistan, where real-time plants and environmental data inform automated irrigation schedules. Figure 2 shows the structure of the smart watering system using a blockchain-based IoT model. This framework allows communication between smart devices (e.g., smartphones), sensors (e.g., for temperature and soil moisture), actuators, and cloud storage, users, and networks. Farmers can easily operate this system with a mobile app on their Android smartphones. Such systems reduce costs, conserve resources, and enhance farmers’ revenues.

3. Application of Blockchain Technology in Water Quality Monitoring

Water pollution is a growing global concern, making continuous, reliable quality monitoring essential [61]. Building on the blockchain–IoT framework described in Section 2.2, applications in WQM focus on securely collecting, validating, and sharing measurements to support transparency, compliance, and rapid response.

3.1. Overview of WQM

Water monitoring programs systematically collect, analyze, and report data on parameters across surface water, groundwater, and coastal systems [62,63,64,65]. These programs inform policymakers, safeguard ecosystem health, and protect drinking water supplies [64,66,67,68]. Examples include the WHO-UNICEF Joint Monitoring Program [69], the Canada-B.C. WQM Program [70] and the Mekong River Commission Network [71].

3.1.1. Blockchain in WQM

Blockchain’s immutability, decentralization, and encryption make it well-suited for securing large-scale water databases [72,73,74,75]. Public, alliance, and private chains offer varying levels of accessibility [74], and smart contracts can automate validation and alerts [75].

3.1.2. Blockchain-Enabled Monitoring Approaches

Cyber–physical systems (CPS) using blockchain and IoT can continuously track water quality parameters, detects anomalies, and issue alerts [76]. Case studies include Etherum-based crowdsensing with token incentives [77], LPWAN-linked low-cost sensor networks for traceability in Puerto Rico [78], and smart contract-driven markets for “quality credits” [79].

Crowdsensing platforms enhanced with blockchain validation can promote citizen engagement [80,81], while integrated system can address climate-related challenges and stormwater management [47].

Modular blockchain frameworks for water management can integrate quality monitoring with supply planning in real time [10]. Decision-support models using consensus mechanisms [82] encourage fair, decentralized governance, while systems, like the NEM symbol-based monitoring platform [83] and Hyperledger Fabric deployments [84] demonstrate secure, transparent, and multi-user access.

3.1.3. Key Water Quality Parameters

Blockchain–IoT systems can monitor physical, chemical, and biological indicators, ensuring secure data storage and traceability [10,85,86,87,88,89] (Figure 3).

Typical parameters include temperature, pH, dissolved oxygen, salinity, turbidity, conductivity, oxidation–reduction potential, and chemical oxygen demand, each offering insight into ecosystem health and potential pollution sources [90].

4. Synthesis and Critical Analysis

Blockchain technology could be helpful for WQM; however, several gaps between the “current state” and the “desired state” in implementing BCT in this field remain. A gap analysis compares the “current state” of blockchain WQM systems to the “desired state”; the gaps identified are provided in Table 1.

Table 1. Gap analysis comparing the “current state” of blockchain WQM systems to the “desired state”, with gaps identified.

Area	Current State	Desired State	Gaps	Reference
Data integrity and trust	Conventional systems are prone to data fraud and tampering	Unchangeable records to increase trust and accountability	Blockchain implementation remains small-scale, with pilots, and is not fully implemented at a large scale	[23,85,91]
Integration with IoT	IoT sensors are widely used, but data systems are isolated	Continuous integration of sensors and blockchain for real-time data logging	Technical standards and middleware for smooth integration are still lacking	[23,92]
Scalability	Blockchains are being applied mostly in small-scale projects	Large-scale WQM projects apply BCT	Requires high storage	[23]
Cost efficiency	Centralized systems are initially often less expensive	Cost-effective blockchain implementation with long-term savings	High costs and a lack of clear return on investment in many cases	[10]
Policy and regulation	Regulations do not specifically require blockchain for WQM	Supportive policies for using blockchain in WQM	Lack of regulation prevents BCT adoption	[46]
Technical skills and cross-departmental collaboration	Lack of IT or blockchain expertise in the water sector, and vice versa, the IT and blockchain sector lacks water monitoring knowledge	Trained workforce capable of operating BCT for WQM activities	Training, educational programs, and collaboration are inadequate	[46]

Table 1. Gap analysis comparing the “current state” of blockchain WQM systems to the “desired state”, with gaps identified.

Area	Current State	Desired State	Gaps	Reference
Data integrity and trust	Conventional systems are prone to data fraud and tampering	Unchangeable records to increase trust and accountability	Blockchain implementation remains small-scale, with pilots, and is not fully implemented at a large scale	[23,85,91]
Integration with IoT	IoT sensors are widely used, but data systems are isolated	Continuous integration of sensors and blockchain for real-time data logging	Technical standards and middleware for smooth integration are still lacking	[23,92]
Scalability	Blockchains are being applied mostly in small-scale projects	Large-scale WQM projects apply BCT	Requires high storage	[23]
Cost efficiency	Centralized systems are initially often less expensive	Cost-effective blockchain implementation with long-term savings	High costs and a lack of clear return on investment in many cases	[10]
Policy and regulation	Regulations do not specifically require blockchain for WQM	Supportive policies for using blockchain in WQM	Lack of regulation prevents BCT adoption	[46]
Technical skills and cross-departmental collaboration	Lack of IT or blockchain expertise in the water sector, and vice versa, the IT and blockchain sector lacks water monitoring knowledge	Trained workforce capable of operating BCT for WQM activities	Training, educational programs, and collaboration are inadequate	[46]

A new framework using BCT for WQM is proposed in Figure 4 below. In this framework, the integration of various technological layers required for real-time, trustworthy, and decentralized water quality monitoring and data management is showcased.

In this framework, the data will flow from the physical layer (water quality sensors, e.g., pH, flow rate, temperature, TSS, COD sensors, and distributed IoT devices) through the oracle layer into the blockchain layer. Simultaneously, it is processed by the analytics system, which supports high-level applications in the application layer. Blockchain ensures trust, traceability, and automation (via smart contracts), creating secure tools for water quality governance.

At the physical layer, devices serve as primary data sources. The oracles and validation layer are critical additions to the architecture, bridging the gap between raw, real-world sensor readings and the immutable blockchain ledger. Oracles perform multiple verification functions, such as sensor authentication, anomaly detection, and cross-referencing with trusted datasets [93]. Only validated data will proceed to the blockchain later, where the consensus mechanism ensures agreement among network nodes before recoding the transaction. The application layer leverages the secure, validated blockchain records for downstream functions, such as visualization, analytics and reporting.

In decentralized WQM systems, this blockchain-based architecture mitigates Sybil attacks through a combination of identity management, consensus protocols [94,95,96], cryptographic safeguards, and incentive mechanisms. The oracle layers enforce strict sensors/node identity verification. The blockchain layer uses PBFT (Practical Byzantine Fault Tolerance) with a vetted validator list. Data validation includes multi-sensor or cross-referenced checks.

The proposed framework incorporates multiple safeguards at the data acquisition and oracle validation stages to minimize the risk of “garbage in, garbage out” or erroneous/malicious sensor readings being recorded [97,98]. Sensors with built-in security modules that prevent firmware tampering and ensure that data are digitally signed at source will be deployed. Before committing data to the blockchain, the oracle layers apply predefined physical plausibility checks (e.g., pH range, conductivity limits) and anomaly detection models to filter out implausible readings.

Blockchains, such as Ethereum, have limited throughput (<20 transactions per second), which makes direct ingestion of high-frequency sensor data impractical. To address this limitation, the proposed framework adopts a hybrid on-chain/off-chain architecture to enable near real-time monitoring while avoiding blockchain bottlenecks [99,100]. Off-chain data preprocessing ensures that only validated and significant events or aggregated metrics are forwarded to the blockchain layer. Bach commitment via Oracle submits data to the blockchain in batch transactions at defined intervals (e.g., every minute or hour), reducing on-chain transaction load. The optional Layer-2/sidechain will handle data validation at higher speeds. By combining off-chain preprocessing, batch commitments, and optional Layer-2/sidechain integration, the proposed framework can support high-frequency WQM sensor networks while ensuring that the blockchain remains a tamper-proof record of validated, relevant, and verifiable data.

Case Studies in Using BCT for Water Governance

Frikha et al. [101] developed an integrated platform based on AI and smart contract to monitor and track water consumption in Tunisia. Lin, Mukhtar, Huang, Petway, Lin, Chou, and Liao [85] used a combined IoT and BCT framework for real-time monitoring and identifying polluted sources and pathways of electrical conductivity (EC) and copper (Cu²⁺) in the irrigation system in Taoyuan District, Taiwan. The framework comprised an IoT-based wireless sensor network for real-time WQM, a blockchain-based platform for tracing real-time water quality data, a GIS spatial detecting tool, and a model of water quality analysis simulation program (WASP). The Gcoin blockchain was applied in the traceability system. They also suggested that their proposed framework can be used in complex WQM networks and to identify sources and pathways of water pollution. In their study, Lin, Mukhtar, Huang, Petway, Lin, Chou, and Liao [85] employed BCT to enhance real-time water monitoring and swiftly identify sources and pathways of water pollution, showcasing its effectiveness in this context

A blockchain-based framework for intelligent water management was also developed by [10]. The framework includes four parts, namely a data collection and transmission part, a blockchain network part, an application part, and an interaction part. The data collection and transmission part is based on the WQM system. It provides various services in collecting, transmitting, and integrating real-time data with existing databases. Each node in the blockchain network part stores and evaluates data. The data are uploaded and exchanged among nodes within the block. The blockchain network part can also be used to perform data queries and develop smart contract rules. Four application modules were set up in the application part to manage water sources, supply, utilization, and discharge. The interaction part provides an integrated platform and information portal. The proposed system showed that BCT has advantages in efficient, reliable, and secure collecting and transmitting water quality data, as well as tracing water quality problems.

Agriculture consumes approximately 70% of Taiwan’s total water, with rice paddies being the primary user through traditional, water-intensive continuous flooding methods. As reported by Zeng et al. [102], a representative agricultural area in Chia-Nan District of Tainan City was chosen to apply the intelligent irrigation system, which is IoT-based and structured into perception, network, and application layers. The study tested three irrigation strategies: conventional practice (CP), which represents continuous flooding, and two modified CP methods (MCP1 and MCP2). The modified methods reduced irrigation water levels by 60–80% and incorporated alternating wetting and drying (AWD) principles to enhance farmer acceptance. The results consistently showed the system’s efficacy. During the dry season, water-saving rates ranged from ~2.9–6.5% (214–1150 m³/ha saved) while in the wet season, rates were higher at ~8.8–19.3% (493–1181 m³/ha saved). Crucially, these water reductions had no significant negative impact on crop yield or other agronomic traits. In some instances, MCP strategies even led to slightly higher crop production compared to CP, particularly in the first crop season. Several lessons learned highlight the system’s value. The IoT-based approach proved highly effective in alleviating the burden of complex, labor-intensive irrigation methods, directly benefiting Taiwan’s aging agricultural workforce. The system’s customization to user needs fostered greater farmer acceptance. It successfully protected farmers’ existing rights by maintaining rice quality and yield while reducing labor costs and enhancing irrigation efficiency.

In Colombia, several IoT projects focused on smart irrigation are already underway, with plans for future 5G integration. A key area of focus is the optimization of water use in irrigation systems. For instance, in the Department of Valle del Cauca, IoT networks have been successfully applied to sugarcane crops for monitoring stations to manage irrigation effectively, leading to optimized water resources. Similarly, in the Department of Sucre, IoT is utilized to optimize water resources in squash crops. While these projects have predominantly leveraged existing communication technologies for data transmission, like Zigbee, GPRS, UMTS, and HSDPA, their foundational design allows for seamless future integration with 5G, indicating a clear trajectory towards more advanced smart farming capabilities. These pilots demonstrate the tangible benefits of applying smart irrigation principles to enhance efficiency and minimize water wastage within specific Colombian agricultural contexts [103]. Despite these promising applications, several significant challenges must be addressed for the widespread deployment and massification of 5G/IoT in Colombian agriculture. A major hurdle is the persistent digital divide in rural areas, where a considerable portion of mobile Internet access still relies on older 2G and 3G technologies, thereby limiting access to the benefits of higher speeds offered by 4G and 5G. The high initial infrastructure cost of 5G implies that its initial deployment will likely concentrate in densely populated urban areas, making its extension to remote agricultural zones contingent upon specific national and regional development plans and supportive public policies. Furthermore, a significant number of Colombian farmers face economic barriers, lacking the financial means to invest in sophisticated technological tools, underscoring the necessity for economic policies from the state and mobile operators to promote the acquisition of essential equipment. Lastly, there is a critical need for technology training programs to bridge the knowledge gap often present in remote rural areas with lower educational levels, ensuring that farmers can effectively understand and utilize complex SF applications. Technical challenges, such as ensuring reliable data transmission amidst noise, fading, and interference, and managing the power consumption of user terminal equipment in remote locations to extend battery life, also remain crucial considerations for efficient 5G deployment in smart farms. Overcoming these multifaceted challenges through collaborative efforts between government, operators, and educational institutions is essential for realizing the full transformative potential of smart irrigation and precision agriculture in Colombia. Differences in results or challenges by those regions are provided in

Table 2. Differences in results or challenges by regions.

Region and Study	Key Issues/Challenges	Implemented Solutions/Projects	Results/Impact	Ref.
Tunisia AI and smart contract-based platform for water consumption monitoring in the context of Industry 4.0	- Need to modernize urban water infrastructure. - Lack of real-time leak detection capabilities. - Energy constraints for IoT devices.	- Developed an AI and smart contract-based platform to monitor and track water consumption.	- Improved the reliability and efficiency of urban water systems.	[104]
Taiwan Blockchain-based water pollution monitoring system	- Risk of water pollution from industrial discharge. - Need for tamper-proof data to identify the source of pollution.	- Utilized a technological framework combining WSN, blockchain, GIS, and WASP simulation.	- Effectively monitored copper (Cu2+) and electrical conductivity (EC) in the irrigation system in Taoyuan County.	[23,85]
Bangladesh IoT-based water quality monitoring system	- Shortage of drinking water in coastal areas due to complex hydrogeological forms and natural disasters. - Toxins from industrial sources pose a threat to safe drinking water. - Traditional monitoring methods are expensive, time-consuming, and inefficient.	- Developed a low-cost, sustainable IoT-based water quality measurement system. - Used sensors (pH, turbidity, temperature, dissolved oxygen, and salinity) connected to Arduino and NodeMCU to transmit real-time data to a web interface. - A QR code is used for easy access to water quality data for end users.	- The system helps authorities take necessary steps to provide solutions for affected areas. - Users can easily check if the water is safe to drink by scanning a QR code.	[101]
Taiwan IoT-based smart irrigation system for rice paddies	- Seasonal water scarcity and an aging agricultural workforce. - Farmland is fragmented into small-scale farms.	- Implemented an IoT-based smart irrigation system for rice paddies.	- Saved 2.9–19.3% of water and reduced the labor burden in Chiayi County.	[102,105],
Colombia Application of 5G/IoT in smart agriculture	- Smart agriculture is not widely adopted. - Rural digital divide and lack of network coverage.	- Proposed the use of 5G and IoT networks for implementing precision and smart agriculture.	- 5G can create many benefits for Colombian agriculture, improving production and efficiency.	[103]
Netherlands Digital platform for irrigation management	- Need for efficient water management in agriculture. - Need for an easy-to-use tool for irrigation decision making.	- Designed a data-driven digital platform for irrigation management.	- The platform helps farmers optimize water usage based on real-time data.	[106]

.

5. Challenges, Limitations, and Future Research Directions

Despite the promising capabilities of BCT for WQM [104], its widespread and practical implementation is impeded by several significant challenges and limitations [13]. These obstacles span economic, technical, regulatory, and operational domains and must be systematically addressed to realize the full potential of the technology.

Economic viability and implementation costs: A primary barrier to adoption is the substantial financial outlay required for deployment [13]. Establishing a blockchain-based WQM system necessitates significant capital investment in infrastructure, including hardware, software, security protocols, and distributed networking capabilities [23]. These costs increase due to the requirement for highly skilled human resources. Furthermore, integrating IoT devices for data acquisition increases operational and maintenance expenditures [107]. The “oracle issue”, which pertains to the secure exchange of data between the blockchain and physical systems, introduces additional complexity and cost. Consequently, implementing BCT, particularly in rural areas with infrastructural and financial constraints, remains a formidable challenge [108]. Future research must focus on developing low-cost, scalable solutions and open-source frameworks that reduce the initial capital expenditure and demonstrate tangible long-term savings through enhanced efficiency and reduced fraud.

Scalability and transaction throughput: One of the most significant technical limitations is the scalability of current blockchain networks. Public blockchains, such as Ethereum, before it transitions to proof-of-stake (PoS), are limited by a low transaction processing speed (e.g., <20 transactions per second), which is inadequate for handling the vast, high-frequency data streams generated by real-time WQM systems [105,109]. This constraint creates a fundamental bottleneck, making direct data ingestion from thousands of sensors impractical. To overcome this, innovative architectures are being explored. A hybrid on-chain/off-chain architecture is a promising solution, where only validated, aggregated data or critical events are recorded on the blockchain, while the bulk of the raw sensor data are processed off-chain [99,100,110]. This approach, combined with batch commitments and Layer-2 solutions or sidechains, can significantly improve throughput and reduce latency, enabling a more practical and responsive monitoring system. For instance, a Layer-2 scaling solution could handle high-frequency sensor data validation at a higher speed before periodically submitting a summarized and cryptographically-secured record to the main blockchain. The use of alternative, lightweight consensus algorithms, like delegated proof-of-stake (PoS), has also been proposed to cater to the performance needs of resource-constrained IoT networks, demonstrating low latency [111].

Data integrity and input security: While blockchain ensures data immutability post-record, the ultimate reliability of the ledger is contingent upon the fidelity of the initial data input [91,104]. This vulnerability, often referred to as the “garbage in, garbage out” problem, means that if a sensor is compromised—whether through malfunction, disturbance, or malicious attack—the erroneous data will be permanently and irrevocably recorded on the blockchain [105]. This fundamentally undermines the trust and transparency the technology is intended to provide.

Regulatory, governance, and standardization gaps: The absence of standardized protocols for data formats, communication, and interoperability makes it challenging to implement BCT across different agencies, organizations, or international borders in a synchronized manner [13,108]. Furthermore, the governance of such decentralized systems introduces complex questions regarding data ownership, access rights, and responsibility [23]. Sensitive environmental data, such as pollution indicators or resource security metrics, require careful control over public sharing, necessitating a clear regulatory policy that defines who has the right to write, read, and verify data on the blockchain. A lack of supportive policies and clear regulations is a major impediment to the widespread adoption of BCT in the water sector [108].

Human capital and interdisciplinary expertise: The effective application of BCT in WQM demands a workforce possessing interdisciplinary knowledge across blockchain technology, IoT, and environmental engineering. A persistent gap between technology specialists and environmental experts often complicates collaboration and impedes successful project implementation and operation.

Environmental and energy concerns: A significant, though often overlooked, challenge is the environmental footprint of certain blockchain consensus mechanisms. Energy-intensive protocols, like proof-of-work (PoW), used by early blockchains, like Bitcoin, consume vast amounts of energy, potentially undermining the sustainability goals that WQM systems are designed to support [105]. Studies have shown that PoW-based networks can consume energy equivalent to that of small countries [110]. While more energy-efficient alternatives, such as PoS exist [98,101], their adoption alone does not fully address the environmental responsibility of the technology. The transition of Ethereum from PoW to PoS in 2022 (“The Merge”) serves as a key example, reducing its energy consumption by over 99% [110]. Future research must prioritize the careful evaluation and selection of blockchain protocols that balance performance and security with ecological impact. A systematic literature review found that PoW emissions can amount to 380,000 g of CO₂ per transaction, while PoS generates only 0.8 g-CO₂ per transaction, highlighting the need for energy-efficient consensus mechanisms for sustainable applications [112].

Integration with legacy systems: The existing water monitoring infrastructure often relies on traditional technologies, like supervisory control and data acquisition (SCADA) systems, which are not inherently compatible with blockchain architectures [77]. Integrating new BCT solutions with these legacy systems is a complex, time-consuming, and costly process that can disrupt existing operations. A study on blockchain-based SCADA architecture notes that recent trends show a need for a secure hardware-software solution to ensure continuous authentication of sensors and data integrity [110]. Future research should investigate standardized middleware and API development to create seamless bridges between these different technological layers, enabling a gradual and more cost-effective transition to blockchain-enabled water governance. The use of blockchain in SCADA systems has been referred to as an “evolutionary proposal” that contributes to the transition towards the Industrial IoT [77].

Future research should address these challenges through the development of cost-effective solutions, scalable blockchain architectures, robust standards, and interdisciplinary collaboration frameworks. Additionally, exploring lightweight consensus mechanisms and hybrid approaches may help balance the benefits of blockchain with environmental sustainability and practical deployment requirements in water quality monitoring systems.

6. Conclusions

This review demonstrates that the BCT, when integrated with IoT-enabled sensing networks, offers a transformative pathway for addressing limitations in conventional WQM systems. Through case studies in Tunisia, Taiwan, and Colombia, blockchain-based frameworks have shown advantages in enhancing data integrity, transparency, traceability, and automation of compliance monitoring. The proposed hybrid on-chain/off-chain architecture with oracle-based validation addresses critical operational constraints such s mitigating “garbage in, garbage out” risk, preventing Sybil attacks, and accommodating high-frequency sensor data without network bottlenecks. By securing validated, real-time measurements in a tamper-proof ledger, blockchain enables more responsive and accountable water governance, particularly in contexts requiring rapid detection of contamination events and cross-sector collaboration.

However, the synthesis of evidence also highlights barriers, such as high initial implementation costs, scalability constraints of public blockchain, interoperability gaps with legacy systems, insufficient regulatory frameworks, and the need for interdisciplinary technical capacity. These challenges indicate that BCT for WQM remains in an early, experimental stage, with most deployments confined to pilot scales.

To move toward practical, large-scale applications, future research should prioritize the following:

(a) Technical optimization: It will be necessary to develop lightweight consensus mechanisms, scalable storage solutions, and standardized middleware for seamless IoT-blockchain integration. (b) Cost and energy efficiency: It will be necessary to design low-cost deployment models and assess the environmental footprint of blockchain protocols, favoring energy-efficient consensus algorithms, such as proof-of-stake or Layer-2 solutions. (c) Data quality and security: It will be necessary to advance multi-layer oracle validation, secure sensor authentication, and anomaly detection models to ensure reliable on-chain records. (d). Regulatory and governance frameworks: It will be necessary to establish clear policies for data ownership, assess rights, and ensure interoperability to encourage institutional adoption and cross-border collaboration. (e) Social–technical capacity building: It will be necessary to implement training programs and participatory governance models to bridge expertise gaps between blockchain engineers and WQM engineers. (f) Integration with broader sustainability goals: It will be necessary to align blockchain-based WQM with SGD 6 targets by embedding social equity, environmental safeguards, and resilience planning into system design.

In conclusion, blockchain’s value for WQM lies not merely in digitizing existing processes but in enabling a paradigm shift toward decentralized, verifiable, and participatory water governance. Realizing this potential will require coordinated advances in technology, policy, and capacity building, ensuring that blockchain serves as both a technological and institutional enabler of sustainable and equitable water management.