Smart Sensor Networks for Urban Air Quality Monitoring and Management

Thesis Abstract

Urban air pollution poses a significant threat to public health and environmental sustainability, necessitating effective real-time monitoring and management strategies. This study aims to develop and evaluate a decentralized smart sensor network system for continuous air quality assessment in metropolitan areas, with the overarching objective of enhancing pollution detection accuracy, data dissemination, and policy responsiveness. Specific objectives include designing an optimized sensor deployment framework, analyzing spatial and temporal pollution patterns, assessing the feasibility of IoT-enabled data integration, and proposing actionable management protocols based on real-time data insights. Employing a mixed-methods research design, the study integrates quantitative spatial-temporal data analysis with qualitative evaluations of system usability and stakeholder engagement. The population of interest comprises air quality monitoring stations within a large metropolitan city, supplemented by sensor nodes deployed across diverse urban zones, totaling 250 units chosen through stratified random sampling to ensure representativeness across traffic, industrial, and residential areas. Data collection instruments include low-cost, high-precision sensor modules capable of measuring parameters such as PM2.5, NO2, SO2, CO, and O3, integrated with IoT communication modules. These sensors transmit data via a cloud-based platform over an 18-month period, enabling continuous monitoring. To validate the sensor readings, calibration exercises using reference-grade monitors are conducted, establishing reliability and validity. Data analysis employs regression analysis to investigate relationships between pollution levels and potential predictors such as traffic density and meteorological variables. Geospatial analysis using Geographic Information Systems (GIS) facilitates the mapping of pollution hotspots and temporal trends. Machine learning algorithms, specifically Random Forest classifiers, are utilized to predict pollution episodes based on historical data. Theoretical frameworks incorporating the Technology Acceptance Model (TAM) and Environmental Information System (EIS) theories underpin system usability and stakeholder engagement evaluations. Expected findings indicate that the smart sensor network significantly improves the spatial and temporal resolution of air quality data, enabling prompt detection of pollution spikes and facilitating targeted interventions. The spatial analysis is anticipated to reveal critical pollution hotspots influenced by traffic congestion and industrial activities, while predictive models are expected to demonstrate high accuracy (above 85%) in forecasting pollution episodes. The study also anticipates identifying key factors influencing stakeholder acceptance of the IoT-based system, leading to practical recommendations for deployment and public communication strategies. This research contributes to the body of knowledge by demonstrating the technical feasibility, effectiveness, and social acceptance of integrated sensor networks in urban air quality management. It advances understanding of how IoT technologies can be optimized for environmental monitoring and policy formulation, especially in resource-constrained settings. The findings provide a foundation for policymakers and urban planners to adopt data-driven approaches that improve air quality standards and public health outcomes. The main conclusion underscores that smart sensor networks are a viable and impactful solution for real-time urban air quality management. Recommendations include scaling sensor deployment in collaboration with local government agencies, integrating the system with existing environmental policies, and fostering community participation through transparent data dissemination. Future research should explore the integration of additional data sources, such as satellite imagery and citizen science contributions, to enhance system robustness and comprehensiveness. Ultimately, this study aims to foster smarter cities equipped to respond proactively to air pollution challenges through innovative ICT-driven solutions.