RCRA Waste Management: Planning, Implementation, and Assessment of Sampling Activities
|Publication Date:||1 January 2000|
EACH YEAR the EPA and the regulated community expend a significant amount of resources collecting waste management data for research, regulatory decision making, and regulatory compliance. While these investigations are required for accurate decision making and effective environmental protection, it is the goal of EPA and the regulated community to optimize these studies by eliminating unneeded, duplicative, or overly precise data [1,2]. At the same time, however, the data collected must be of sufficient quantity and quality to meet the objectives of the study.
There are numerous difficulties that can complicate efforts to meet this goal including: lack of definition of the data users objectives, inadequate identification of the decisions and alternate actions that may be taken based on the findings, lack of information on the sources of contamination, appropriate action levels or sampling/analytical approaches, undefined boundaries (spatial and temporal) including the types of media to be sampled, undefined scale of decision making, practical constraints to sample collection including equipment limitations, access to all areas of the target population, and extreme variability or heterogeneity associated with the media being sampled, undefined decision errors that are acceptable to the data users, inadequate optimization of the study design including resource limitations, lack of consideration of the study objectives, and insufficient incorporation of quality assurance into the sampling and analysis plan [1-3].
Specific difficulties associated with sampling a population can be classified into five general categories:
• population access problems making it difficult to sample all or portions of the population,
• sample collection difficulties due to physical properties of the population (for example, unwieldy large items or high viscosity),
• planning difficulties caused by insufficient knowledge regarding population size,
• heterogeneity of the contaminant of interest, or item size, or a combination thereof, and
• budget considerations that prevent implementation of a workable, but too costly, sampling design.
The most efficient way to accomplish the goal of optimizing waste management studies is to determine the type, quality, and quantity of data required to address the problem before the sampling study is initiated. In order to meet these requirements, EPA developed and refined the Data Quality Objectives (DQO) process, a systematic planning tool for determining the type, quantity, and quality of data that will be sufficient and appropriate for the data's intended use . ASTM has also developed a standard guide for the DQO process . Data generation efforts involve three phases: planning with DQO development and sampling design optimization [2,3], the implementation of sampling and analysis strategies, and the assessment of data quality [4, 5]. This manual uses a RCRA waste identification case history to illustrate the development of a sampling design and subsequent data assessment. This manual does not provide comprehensive sampling procedures, but references are given for locating guidance and standards where sampling procedures are discussed in more detail. It is the responsibility of the user to ensure appropriate procedures are used.