Conventional and Advanced Geostatistics Mine Waste Monitoring and Management

INTRODUCTION Mine waste monitoring and management is essential when the industry is being required to accept the environmental liability for all generated, stored and disposed solid and hazardous waste. Environmental regulations mandate the operator to minimize waste production while requiring the operator to cleanup existing contamination. Since these requirements are being enforced, many operators do not have a clear under- standing, whether their existing operation will or could be required to adopt remedial plans to mitigate off-site contamination impacts. The following methods, using conventional and advanced geostatistical methods, provide a tool to make appropriate assessments whether existing or potential contaminant issues are present. BACKGROUND Generated solid wastes from mining operations are currently exempt from Solid Waste Disposal Act (SWDA) Sub- title C (RCRA), statutes and promulgated regulations, however, compliance with SWDA Subtitle D statutes and regulations is required. In order for the mining operator to be exempt from hazardous waste regulations, the operator must demonstrate that generated waste does not have hazardous waste characteristics. In many instances the operator will be able to use statistical tools and their existing data to determine whether their site could be a potential hazardous waste liability. METHODS The following statistical and geostatistical methods can be used to determine whether existing wastes could be a regulatory concern. The following presents a brief discussion of standard statistical methods and discusses, in greater detail, how advanced geostatistical tools can be used to define potential waste issues. The following will discuss how data can be screened, "Data Filtering and Reporting"; how data can be evaluated using "Conventional Statistics"; followed by a detailed discussion o f "Geostatistical and Estimation Techniques" and "Statistical Modeling". Data Filtering and Reporting Data filtering is the simplest, but potentially the most powerful statistical tool because data can be easily sorted to meet regulatory reporting requirements. Filtering includes unions and intersections of data such as date, maximum and minimum values of one or several variables. Using the data base presented in "Mine Waste Data Base Management Procedures", Robinson and Hagar, 1987, an example of a filter group is presented.