Use of Association Algorithms in Air Quality Monitoring

Abstract

Over the years, there has been a gradual increase in the emission of pollutants, and it is imperative to establish mechanisms to monitor air quality. In addition to carbon dioxide (CO2), particulate matter (PM) is considered one of the main types of air pollution. However, there is a wide variety of pollutants, and high investment is required to carry out detailed air quality monitoring. We present the third version of a previously proposed air quality monitoring platform based on CO2 concentration measurements. In this new version, a specific sensor for PM measurements and an artificial intelligence algorithm were added. The added algorithm traced associations between measurements of CO2 and PM concentrations. Thus, the measurement of a pollutant can be used for estimating the concentration of another. This can contribute to the development of a simpler and cheaper monitoring system. The acquisition of CO2 and PM concentrations was carried out daily over a period of one month. Pollutant measurements were taken in three strategic locations in a Brazilian city. It was possible to determine a correlation between pollutant concentrations for the monitored locations. Thus, it would be possible to efficiently estimate the PM concentration based on the measured CO2 concentration.

Keywords

• data association
• air quality monitoring
• environmental sensors
• particulate matter
• carbon dioxide