Abstract
The escalating concerns regarding urban air pollution and its detrimental effects on public health necessitate advanced real-time air quality monitoring solutions. This paper introduces a novel integration of the Internet of Things (IoT) and machine learning (ML) technologies to develop sophisticated chemoresistive sensors capable of detecting key urban pollutants with high sensitivity and selectivity. We detail the synthesis of innovative nanocomposites comprising polyaniline, metal oxide nanoparticles, and carbon nanostructures, tailored to enhance the performance characteristics of traditional chemoresistive sensors. By incorporating IoT elements, our system facilitates continuous, real-time monitoring of environmental parameters, while the embedded ML algorithms analyze sensor data to predict air quality levels dynamically. The smart sensor system has been deployed and validated in an urban setting, providing critical insights into pollutant dispersion and concentration patterns. The results underscore the potential of this integrated approach to significantly improve urban health diagnostics and offer a scalable model for cities aiming to enhance air quality monitoring infrastructures. This research not only paves the way for next-generation air quality sensors but also contributes to the broader discourse on sustainable urban living and public health safety.