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SAMPLE HANDLING STRATEGIES FOR ACCURATE LEAD-IN-SOIL MEASUREMENTS IN THE FIELD AND LABORATORY

The inhomogenous lead-in-soil matrix can present serious obstacles to accurate sample collection and handling. In typical lead-in-soil measurement, particle size related errors in sampling and sample handling often exceed all other sources of error. The magnitude of error can vary widely depending on the particulate nature of the lead contaminant and the effectiveness of control measures. Large particle contaminants, such as lead bearing paint chips, pose a much greater challenge to accurate sample handling than do small particle contaminants, such as air dispersed industrial emissions. A sample handling protocol demonstrated to give reliable, valid data in small particle situations may prove entirely inadequate for large particle cases. This paper focuses on the importance of fundamental error, a statistical consequence of particulate sampling. We discuss in quantitative terms the significance of fundamental error on the measurement of paint chip contaminated soils near a 400 ppm action level. On the basis of error estimates, we recommend that sample handling protocols control particle related errors by ensuring adequate sample size and sample definition, and by accomplishing sufficient particle size reduction and homogenization before subsampling. We discuss particle related errors and their effect on laboratory, field, and in-situ analytical methods. We recommend that quality assurance protocols aim to determine the overall measurement quality by evaluating error at all stages from sampling and sample handling through analysis. SAMPLING DESIGN AND GEOSTATISTICS The prerequisite of a well-designed study is a clear statement of the study's objectives for data quantity, quality, reliability, speed, and cost. The planner develops objectives with careful attention to the data's ultimate utility and the available resources of people, technology, and money. The objectives should be stated in quantitative terms, with exact figures to indicate the necessary precision and accuracy at and around the action or decision concentrations. In large projects with multimillion dollar budgets, such as some Superfund cleanups, statisticians develop a sampling design with the aid of geostatistical theory and preliminary data indicating the spatial variability of site contamination. The sampling design defines the number points to sample and the pattern. The statistician attempts to design a sampling effort that achieves the lowest possible cost of the total sampling, analysis, and remediation effort. [1,2,3] Geostatistics is an art of some subtlety; its effectiveness varies widely with the skill of the statistician. [4] Although, the application of geostatistics to sampling design is well beyond the scope of this paper, some of its principles (e.g. sample support) can be used to improve the effectiveness of small sampling projects. SAMPLE HANDLING OPERATIONS A typical soil sample experiences a number of physical manipulations at the sampling site and in the laboratory. According to the sampling plan, the field technician extracts soil from the ground, often by means of a core sampling device. The technician may combine soil collected from several points to form a composite sample. To avoid transporting unnecessarily large quantities of sample material, the technician may thoroughly mix, then split the sample, taking only the minimum necessary for the lab.