Smart Sensor Networks for Real-Time Pollution Monitoring and Response

Thesis Abstract

This study addresses the growing need for timely, scalable pollution monitoring through an integrated smart sensor network capable of real-time data collection, processing, and automated response to environmental hazards. The core problem is the fragmentation of ambient air and water quality monitoring, which often relies on sparse stations and delayed reporting, limiting early warning and mitigation actions. The aim is to design, implement, and evaluate a deployable sensor-network platform that delivers continuous, high-resolution measurements of key pollutants (PM2.5, NO2, SO2, O3, and turbidity) and coordinates rapid response mechanisms. Specific objectives include (1) developing a multi-modal sensor node architecture with low-power, wireless communication, edge computing, and secure data transmission; (2) implementing data fusion and anomaly detection algorithms to produce reliable pollutant concentration estimates under variable environmental conditions; (3) establishing a real-time alert and response framework that interfaces with municipal authorities and public advisory systems; (4) assessing system performance across urban, industrial, and peri-urban contexts; and (5) evaluating the socio-technical impacts of the platform on decision-making and public health outcomes. Methodologically, the study adopts a mixed-methods research design combining engineering prototyping with empirical field testing and qualitative stakeholder analysis. The population comprises urban air and water environments within a metropolitan region and a coastal industrial corridor. A purposive pilot deployment of 60 sensor nodes is planned across five neighborhoods, complemented by 10 reference-grade monitors for validation. Data collection will utilize commercially available electrochemical and optical sensors, calibrated against standardized reference instruments, and supported by high-resolution meteorological data. The instruments will capture pollutant concentrations, temperature, humidity, wind speed and direction, and turbidity at one-minute intervals, with historical data archived in a secure cloud platform. For data analysis, quantitative techniques include time-series analysis, Kalman filtering for sensor fusion, regression-based calibration models, and machine learning classifiers (Random Forest, Gradient Boosting) for anomaly detection and fault diagnosis. A social science component will apply semi-structured interviews and surveys with city officials and residents to explore perceived usefulness, accessibility, and trust in the system, analyzed via thematic analysis guided by the Technology Acceptance Model and the Diffusion of Innovations theory. The expected findings include (i) a validated sensor network architecture with demonstrated energy efficiency, reliability, and resilience to interference; (ii) robust data fusion outputs with quantified uncertainty bounds and real-time alert capabilities achieving false-positive and false-negative rates within 5% and 10%, respectively; (iii) a decision-support workflow that reduces mean time-to-respond by municipal agencies by an average of 40% during episodic pollution events; and (iv) evidence on public health benefits and community acceptance derived from stakeholder analyses. The study anticipates establishing a scalable deployment blueprint with performance metrics such as spatial coverage, data latency, and system uptime, and contributes methodological innovations in edge computing for on-node anomaly detection and secure, privacy-preserving data sharing. Theoretical contributions include integration of a sensor-network-centric framework with the Environmental Justice and Risk Communication literatures to examine how real-time data shapes equitable decision-making and risk communication. Practical implications encompass guidelines for city-scale sensor deployment, interoperability standards with existing environmental monitoring infrastructure, and policy-ready recommendations for emergency response protocols. Limitations are acknowledged in terms of calibration drift over time, potential sensor degradation, and variable city infrastructure that may affect network reliability. The study concludes that an ICT-driven, real-time sensor network can substantially enhance pollution surveillance and rapid response, provided that calibration, data governance, and stakeholder engagement are maintained. Recommendations for future work include expanding the sensor suite to capture emerging contaminants, integrating mobile sensing platforms to augment spatial resolution, and conducting longitudinal health impact assessments to quantify the long-term benefits of real-time environmental monitoring.