Abstract

Urban air pollution has emerged as a critical environmental and public health challenge worldwide, exacerbated by rapid urbanization, vehicular emissions, and industrial activities. Traditional monitoring approaches often struggle to provide real-time, spatially granular data necessary for effective urban planning and mitigation. Integrating smart sensor networks with advanced computational models can enable proactive management of air quality, supporting climate-resilient urban infrastructure. Despite the availability of various air quality monitoring systems, challenges remain in handling the inherent uncertainties, nonlinearities, and dynamic variations of urban pollutant levels. Conventional statistical models often fail to capture complex relationships between pollutant sources, meteorological factors, and urban morphology. There is a critical need for modeling approaches that accommodate ambiguity and provide actionable insights for decision-makers in urban planning. This study presents a fuzzy logic-based framework for modeling urban air pollution using data collected from a distributed network of low-cost sensors. Fuzzy logic enables the incorporation of expert knowledge and real-time sensor measurements to handle uncertainty and nonlinearity in pollutant dynamics. The framework integrates multi-source environmental data, including traffic density, meteorological variables, and green infrastructure coverage, to predict air quality indices across urban zones. Model validation is conducted using historical pollution records and real-time sensor data to assess predictive accuracy and robustness. The proposed fuzzy logic model demonstrates significant improvement in capturing spatiotemporal variations of key pollutants, such as PM2.5, NO2, and O3, compared to traditional linear regression methods. The results reveal that zones with optimized green infrastructure and traffic management strategies experience a measurable reduction in pollutant concentrations, highlighting the model’s utility for urban planning. The approach offers actionable insights for deploying climate-resilient green infrastructure and optimizing urban air quality interventions in real time.

Authors

Prem Kumar Dara¹, G. Raghavendra²
Gambella University, Ethiopia¹, Andhra Pradesh Capital Region Development Authority, India²

Keywords

Urban Air Pollution, Fuzzy Logic Modeling, Sensor Networks, Climate-Resilient Infrastructure, Green Urban Planning