Development of a Low-Cost Groundwater Susceptibility Sensor Network in Rural Regions

Thesis Abstract

In rural regions, groundwater quality and accessibility are increasingly threatened by contamination, climate variability, and limited monitoring infrastructures, necessitating scalable, low-cost solutions for susceptibility assessment and early warning. This study aims to design, implement, and evaluate a low-cost groundwater susceptibility sensor network capable of real-time data acquisition, processing, and visualization to inform community-level decision making. The specific objectives are (1) to develop a modular sensor platform integrating low-cost electrochemical and physicochemical sensors (nitrate, phosphate, arsenic, turbidity, pH, electrical conductivity, temperature) with solar-powered wireless data transmission; (2) to establish a regional deployment framework covering 20 monitoring wells and 15 surface water intakes across diverse lithologies and land-use practices; (3) to calibrate sensors using laboratory-grade references and to apply data fusion techniques to derive a robust susceptibility index; (4) to evaluate network performance in terms of accuracy, reliability, and cost-effectiveness against conventional monitoring regimes; and (5) to formulate governance and user-friendly data visualization tools for local stakeholders. The methodology adopts a mixed-methods research design anchored in resilience theory and the precautionary principle, combining quantitative sensor data with qualitative stakeholder feedback. The population includes groundwater wells and intake points in three rural basins with empirical data sourced from municipal records and semi-structured interviews with 25–30 local water managers and farmers. A stratified random sample of 60 wells and 30 surface water sites is selected, with sensor installation conducted over a three-month pilot and a subsequent nine-month monitoring period. Data collection instruments comprise low-cost multi-parameter probes, calibration kits, a solar-powered data logger, a cloud-based data platform, and interview guides. Instrument validity is established through cross-validation with standard laboratory analyses using ion chromatography for nitrate and arsenic, colorimetry for phosphate, and standard EPA methods for turbidity and conductivity; reliability is assessed via test–retest procedures and internal consistency checks (Cronbach’s alpha for survey instruments). Data analysis employs time-series regression to identify drivers of susceptibility, generalized additive models to capture nonlinear relationships, and hierarchical linear modeling to account for nested spatial-temporal data. A data fusion framework integrates sensor readings with meteorological inputs and land-use data to compute a calibrated Groundwater Susceptibility Index (GSI). The study also applies thematic analysis to stakeholder interviews to ascertain perceived reliability, accessibility, and decision utility of the network. Expected findings indicate that the low-cost sensor network achieves high concordance (R^2 > 0.85) with laboratory measurements for key contaminants in most sites, demonstrates stable performance under variable solar conditions, and produces GSI outputs that correlate significantly with known vulnerability factors such as proximity to pastures and septic systems. The results are anticipated to reveal spatially heterogeneous susceptibility patterns, with higher risk in areas characterized by shallow groundwater, high groundwater recharge from rainfall events, and intensive agricultural activity. The study contributes to knowledge by advancing a scalable, evidence-based framework for deploying affordable, community-embedded groundwater monitoring networks, validating a composite susceptibility index suitable for decision support, and demonstrating the integration of low-cost sensing with statistical modeling and stakeholder engagement. The anticipated conclusion is that, with rigorous calibration, standardized maintenance protocols, and transparent data governance, low-cost sensor networks can provide timely, locally actionable insights for protecting groundwater quality in rural regions. Recommendations include establishing national or regional funding mechanisms for distributed sensor networks, developing open data standards for groundwater monitoring, refining the GSI for different hydrogeological settings, and expanding user capacity through training programs to sustain long-term monitoring and adaptive management.