Enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System

Abstract

We present and approach for monitoring and built a dataset of regional historical air quality data in Mexico City. We design a hybrid air quality network prototype that combines mobile and stationary sensors to collect street-level data on particulate matter (PM2.5 and PM10). The network is composed of mobile monitoring modules, both stationary at street level and mounted on vehicles, to capture a comprehensive sample of particulate matter behavior in specific areas. Collected data is transmitted using IoT network and processed using machine learning techniques, to generate predictive models to forecast air quality at street level. This approach is an additional improvement to current monitoring capabilities in Mexico City by providing granular street-level data. The system provides a regional and periodic perspective on air quality, enhancing the understanding of pollution levels and supporting informed decision-making to enhance public health and well-being. This research represents a solution for environmental monitoring in urban environments to know how the behavior from pollution levels in air is. The experiments show the effectiveness, and the model of forecast has an overall performance around 81% that is acceptable for the small geographical area testing. As future work is required to include a major number of nodes to collect data from a big geographical coverage and test with other models and algorithms.