Research on improving the quality of groundwater self-monitoring via Blockchain technology

Abstract

There is a serious issue of data distortion in groundwater self-monitoring (GSM) on the basis of key soil pollution regulatory units (KSPRUs). In the absence of widespread online multiparameter water quality monitoring devices, traditional blockchain technology (BT) can ensure only the secure transmission and storage of data; however, it cannot effectively improve data quality. Therefore, by categorizing and analysing four types of data distortion—execution deviations, data conflicts, outliers, and data inaccuracies—three main factors contributing to data distortion are identified in this study: operational errors, nonstandard operations, and human falsification. Each factor is further broken down into specific behaviours. On this basis, a BT-based GSM model is proposed. The framework of this model includes four modules: task management and distribution, monitoring process control, intelligent data analysis and sharing, and pollution emergency plan activation. The goal is to gain confirmation from all participants and effectively prevent data disputes. During the monitoring process, BT ensures that all step-by-step information is recorded and stored through methods such as auto-filling, barcode scanning, photo input, and full-process video recording. Subsequent reviews by the system and quality control units can prevent operational errors, non-compliance, and human falsification. Through encryption storage and traceability features, the system can restore modified data without authorization and identify the individuals responsible, thereby reducing the likelihood of data falsification. Finally, data auditing and verification on the basis of Benford's law (BL) demonstrate that the BT-based GSM can effectively improve the quality of groundwater monitoring data in practical applications. After applying this model, issues related to data conflicts, outliers, inaccuracies, and human execution deviations are effectively resolved, and the possibility of human data falsification is significantly reduced.