Assessing The Pollution Potential Of Non Point Mine Wastes On Surface Water Using A Geo Spatial Modeling Approach

Abandoned mine lands (or inactive and abandoned mines) have received increasing concerns because they may cause severe environmental and public health problems. Most of previous studies to characterize mine waste pollution potential were focused on screening-level investigations. The issues related to pollution potential of mine waste were poorly addressed from the perspective of non-point source pollution, and few efforts have been made to study the effect of spatial characteristics of mine wastes on water quality using spatial technology such as GIS, remote sensing and spatial modeling. This research develops a geo-spatial approach to assessing mine waste pollution on surface water, which integrates GIS, remote sensing and watershed modeling techniques in order to effectively address the effects of spatial characteristics of pollutants. The study area is Tri-State Mining District which is located in the conjunction of Missouri, Kansas and Oklahoma. This district was the most important lead and zinc mining area in U.S. The historic mining left behind a huge area of mine wastes. Satellite remote sensing data (Landsat MSS and TM) were acquired, processed and classified in a decadal interval to generate land use/land cover (LULC) data for the entire district. Watersheds within the district were delineated by using USGS DEM data and a newly-developed GIS tool. Water quality indicators were selected and relevant water quality data between 1970 and 2002 was retrieved from USGS and USEPA databases. With the classified LULC data as a data source, landscape metrics (composition and spatial configuration indices) for each water quality station in mine waste-located watersheds were calculated. Statistical analyses were performed to quantify the relationship between landscape and surface water quality and to evaluate the impacts of landscape characteristics on surface water quality. Related GIS data layers were then created and a cell-based watershed modeling was conducted. The study shows that mine waste area in the district decreased evidently over last three decades. Landscape characteristics could account for as much as 77% of the variation of surface water quality. The proportion indices of LULC types were shown to be more important than spatial configuration indices in characterizing surface water quality. The modeling results characterized the pollution potential and detected the pollution severity in the watersheds within the study area. The results of this research would help develop management strategies and prioritize future remediation to reduce pollution potential.