An Air Quality Monitoring and Forecasting System for Lima City With Low-Cost Sensors and Artificial Intelligence Models

Abstract

Monitoring air quality is very important in urban areas to alert the citizens about the risks posed by the air they breathe. However, implementing conventional monitoring networks may be unfeasible in developing countries due to its high costs. In addition, it is important for the citizen to have current and future air information in the place where he is, to avoid overexposure. In the present work, we describe a low-cost solution deployed in Lima city that is composed of low-cost IoT stations, Artificial Intelligence models, and a web application that can deliver predicted air quality information in a graphical way (pollution maps). In a series of experiments, we assessed the quality of the temporal and spatial prediction. The error levels were satisfactory when compared to reference methods. Our proposal is a cost-effective solution that can help identify high-risk areas of exposure to airborne pollutants and can be replicated in places where there are no resources to implement reference networks.

Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors

“…In response to this, a variety of low-cost sensor (LCS) technologies for assessing air quality have recently emerged. These solutions enable the deployment of large sensor networks, facing the challenge of monitoring air quality in extensive metropolises in real time and with high spatial resolution [ 5 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ].…”