GREEN CHEMISTRY: USING MORE SUSTAINABLE TECHNIQUES TO ESTIMATE THE WATER QUALITY OF THE UBERABA RIVER USING ORBITAL OPTICAL SENSORS

Abstract

Green Chemistry aims to prevent pollution resulting from activities in the chemical sector. Generally, laboratory activities generate pollution because toxic substances are used to perform analyses, which produce waste once the analyses are completed. This study aimed to monitor the quality of the Uberaba River using more sustainable and non-destructive techniques through orbital remote sensing and to develop methods for estimating water quality at reduced costs using orbital sensors and machine learning. The values of water quality parameters, such as iron, nitrate, chloride, total phosphorus, and total nitrogen, were studied between 2018 and 2023. Orbital sensing data (spectral bands and vegetation indices) were taken from the exact geographic coordinates of the collection points. Data were analyzed using Pearson’s correlation coefficient and random forest regression analysis. The study concludes that it is possible to perform orbital remote sensing by estimating water quality through random forest regression, correlating the information obtained from Sentinel-2 images with the values of these parameters. This approach is a sustainable technique that does not generate waste and represents a 100% saving compared to conventional chemical analyses.

Keywords

• machine learning
• remote sensing
• sustainability
• water quality.