Blockchain-Based Data Integrity and Provenance Tracking System for Environmental Electrochemical Sensor Network Analytics

The decisions made about pollution control are based on environmental electrochemical sensor networks, but their distributed acquisition pipelines are vulnerable to manipulation, measurement loss, and unverifiable processing. We propose an application-specific, blockchain-backed provenance system that ensures the entire analytics lifecycle, including sensor readout and calibration, quality control, transformation, and reporting. The architecture unites on-chain, role-regulated smart contracts with cryptographic commitments (Merkle roots, device attestations) with off-chain content-addressed stores of raw signals and metadata, reducing on-chain storage by more than 90% while maintaining end-to-end verifiability. An implemented lightweight edge agent performs piecewise polynomial with Lookup Table (PA-LUT) evaluation of calibration curves, hashes batched results and emits provenance events. The design is suitable for use with low-power gateways and can reduce registration latency to less than a second across consortium chains. After validation of the system in river monitoring by potentiostatic Pb/Cd/Cu sensing, we find: (i) 3.8 times faster calibration assessment vs. complete polynomial assessment, (ii) 45–78 mJ/100-reading batch energy, and (iii) >95% tamper detection at 10% challenge sampling. Our solution is superior to digitally signed databases, which introduce single points of failure; in contrast, immutable, queryable audit trails can be provided to regulators. Scalability analysis indicates that it is deployable with 10,000 nodes at five-minute sampling under a 400 tx/s consensus budget. The model realizes analytical traceability in environmental electrochemistry, enabling verification without centralized trust among regulators, utilities, and researchers.