ASSESSMENT OF WATER QUALITY IN A MINING COMMUNITY USING REMOTE SENSING AND GIS TECHNIQUES

Turbidity of Surface Water bodies in the Prestea Huni-Valley Municipality

Abstract

Mining communities are faced with several environmental challenges associated with exploration, extraction and processing of minerals. The last few decades has witnessed massive activities of illegal small scale gold mining in or close to water bodies in most mining communities leading to pollution of the water resources. The traditional methods of monitoring water quality is a difficult task due to the extent of manual work required and the time involved. An alternative approach for assessing surface water quality is by using remote sensing data which has the advantage of broad coverage area and multi-temporal data availability for effective monitoring. This study used remote sensing techniques and field data to evaluate the water quality for the Prestea-Huni Valley Municipality. The normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and normalized difference turbidity index (NDTI) were estimated from remote sensing data obtained from Landsat 9 satellite. The field samples were analysed at the Laboratory to determine some water quality parameters that is Total Suspended Solids (TSS) and Turbidity. These respective quality indicators values were used to produce interpolated maps for the study area using Inverse Distance Weighting approach. The values of the water quality indicators obtained from the reflectance values of the satellite images were found to be highly correlated with the measured water quality parameters acquired from the laboratory analysis. Very high turbidity and TSS values were recorded for most of the rivers in the study area, particularly the Ankobra which exceed the standard for drinking water set by the WHO and the Ghana water Company Limited. The utilization of GIS techniques and water quality indices in the assessment of water quality in this framework proved to be a beneficial contribution statistically to the monitoring and management of water resources.