Enhancing Low-Cost Air Quality Sensors with AI for Smart Green Routing

Abstract

Urban air pollution poses severe health risks, demanding accurate monitoring and exposure-reducing solutions. Low-Cost air quality sensors (LCS) provide high spatial resolution but suffer from accuracy limitations that hinder their reliability. This paper presents the AIQS project (AI-enhanced air quality sensor for optimizing green routes), an ongoing initiative that combines artificial intelligence, sensor hardware optimization, and pedestrian routing innovation to address these challenges. AIQS applies machine learning techniques, including Multilayer Perceptrons and fuzzy logic, to correct sensor readings. In parallel, hardware-level optimizations, such as fluid dynamics simulations and pre-treatment modules, are explored to enhance sensor performance. The corrected AQ data is then incorporated into a configurable routing tool capable of estimating pollutant exposure and computing low-exposure pedestrian paths in urban environments. First evaluations, shows that our correction models achieve up to 0.92 R2 against reference data across diverse urban environments. The corrected data drives a configurable routing tool that computes paths minimizing cumulative pollution exposure while balancing user preferences (e.g., proximity to green spaces). Preliminary validation in Modena, Italy demonstrates viable "green routes".