SenseAir: arquitetura de monitoramento de qualidade do ar com sensores de baixo custo e visão computacional para tráfego veicular

Abstract

The scarcity and regional concentration of reference air quality monitoring stations in Brazil limit reliable diagnostics and the design of evidence-based public policies, especially in undermonitored regions such as the Northeast. As a complementary alternative to official networks, this dissertation investigates a low-cost architecture composed of an embedded sensor node (SenseAir), a real-time mobile application, a computer vision model for vehicle traffic quantification, and an exploratory calibration module for PM2,5 anchored in data from a higher-performance portable instrument. The solution integrates continuous data acquisition, secure transmission and cloud storage, and visualization of the Air Quality Index (AQI) derived from PM2,5 with geolocation, targeting indicative monitoring in urban scenarios. Calibration is discussed in light of U.S. EPA guidelines for PM2,5 sensors, using metrics such as coefficient of determination (R2), bias (regression slope and intercept), and root mean square error (RMSE) to evaluate the correction applied to SenseAir. In parallel, a YOLO-based model embedded in the Raspberry Pi estimates the flow and composition of the vehicle fleet, enabling analysis of the role of traffic, together with other external factors, in the local degradation of air quality. The results discuss cost, performance, and limitations of the proposed architecture, as well as its potential use in dense indicative monitoring networks and in research and decision-support applications for public management.