Department of Health Seal

TGM for the Implementation of the Hawai'i State Contingency Plan
Section 10.3
DATA QUALITY ASSURANCE PROCEDURES

10.3 DATA QUALITY ASSURANCE PROCEDURES

Implementing QA/QC procedures from start to finish in an investigation helps assure data that are usable and will meet and support the DQO. Procedures for Data Quality Assurance are presented within this subsection. Specifically, QA/QC parameters for precision, accuracy, representativeness, completeness, and comparability (commonly referred to as the "PARCC parameters") must be evaluated. The parameters of precision, accuracy, and completeness are quantitative measures, while representativeness and comparability are largely qualitative.

10.3.1 Precision and Accuracy

Precision and accuracy are evaluated quantitatively by collecting tPrecision and accuracy are evaluated quantitatively by collecting the types of QC samples listed in Table 10-1. While these QC samples are primarily intended for evaluation of precision and accuracy, the results are also used as necessary information for evaluating the other quality parameters.

Table 10-1
Recommended QC Sample Frequency
QC Type QC Sample Default Frequency 1
Field QC Soil replicates/ triplicates Depends on numbers of Decision Units (DU), COPCs, site characteristics. See Section 4.2.3 regarding field replicates (triplicates for MIS).
Groundwater duplicates 1 per day for every 10 samples
Equipment rinsate blank Not required routinely when effective decontamination protocols are documented in the SAP. When required (e.g., investigations for trace levels), 1 per day per type of non-disposable sampling equipment
Trip blanks 1 per shipping container containing volatile samples
Source blanks 1 per water source per investigation, if used to decontaminate equipment for re-use.
Laboratory QC Method blanks 1 per every 20 samples
Sub-sampling replicates 1 per every 20 samples for soil analyses of non-volatile contaminants (triplicates preferred)
MS/MSD percent recovery 1 per every 20 samples
LCS/LCSD or blank spikes percent recovery 1 per every 20 samples  
Surrogate standard percent recovery Every sample for organic analysis by gas chromatography
Notes:
      LCS/LCSD Laboratory Control Sample/Laboratory Control Sample Duplicate
      MS/MSD Matrix Spike/Matrix Spike Duplicate
      MIS Multi-Increment sample
      1 Based on HEER Office guidance and SW-846 Method 8000C
      Guidance (USEPA, 2003a) pertaining to laboratory QC.

The default, or preferred frequency, for these parameters is listed; however, different project-specific frequencies may be proposed to best meet project DQO. If proposing different QC sampling frequencies for a specific investigation, the proposed QC sampling program and the rationale should be presented in detail in the project-specific SAP or QAPP and should receive approval from the HEER Office prior to field investigation. More detailed descriptions of the individual types of QC samples and the modes of collection and handling are presented in Subsections 10.6 and 10.7.

Precision

Precision is the degree of mutual agreement between individual measurements of the same property under similar conditions. For soil samples, combined field and laboratory precision is typically evaluated by collecting and analyzing field triplicates and then calculating the variance between the samples as a Relative Standard Deviation (RSD) percent:

Figure 10.4

Groundwater field duplicates are evaluated by determining a RPD for the replicates, using RPD formula as noted below for laboratory MS/MSD precision determinations.

Laboratory analytical precision is evaluated by analyzing laboratory duplicates or MS and MSD, typically utilizing the following formula:

Figure 10.1
where:
A = First duplicate concentration
B = Second duplicate concentration

The results of the analysis of each MS/MSD and sample duplicate pairs will be used to calculate an RPD for evaluating precision (USEPA, 2003a). These are default values that laboratories may use until they develop in-house QC limits for each method, in accordance with the guidelines established in SW-846 (USEPA, 2008a).

Laboratory sub-sampling poses the greatest potential for error in soil sample analyses for non-volatile contaminants; therefore, the HEER Office recommends laboratories perform triplicate sub-sampling analyses from at least one in every 20 of these soil samples (original sub-sample plus two additional sub-sample replicates collected independently from the entire mass of soil in the sample). Laboratory sub-sampling precision is typically calculated as RSD percent (for triplicates or more). The lab sub-sampling precision measure is also helpful to compare the degree of lab sub-sampling and analysis error to the total error (i.e. the field replicate precision data representing total error from field sampling plus lab sub-sampling and analysis). Soil sub-sample replicates (as well as sub-samples for any other soil analyses for non-volatiles) are collected by the laboratory from the entire mass of available sample using a sectorial splitter or by hand Multi-Increment sampling, as described in Section 4.2.2. This laboratory sub-sampling QC guidance applies to soil samples collected by Multi-Increment or discrete sampling approaches.

Accuracy

Sample spiking will be conducted to evaluate laboratory accuracy. This includes analysis of the MS and MSD samples, laboratory control samples (LCS) and laboratory control sample duplicates (LCSD), or blank spikes, surrogate standards, and method blanks. MS and MSD samples will be prepared and analyzed at a frequency of 5 percent. LCS or blank spikes are also analyzed at a frequency of 5 percent. Surrogate standards, where available, are added to every sample analyzed for organic constituents. The results of the spiked samples are used to calculate the percent recovery for evaluating accuracy (USEPA, 2003a).

Figure 10.2
where:
S = Measured spike sample concentration
C = Sample concentration
T = True or actual concentration of the spike

Results that fall outside the project-specific accuracy goals will be further evaluated on the basis on the results of other QC samples. Table 10-1 summarizes recommended default frequencies for QC sample types. Example default precision and accuracy goals for laboratory analyses are described in Subsection 10.7.

10.3.2 Representativeness

Representativeness is a qualitative measure that expresses the degree to which field data accurately and precisely represents a characteristic of a population, parameter variations at a sampling point, process condition, or environmental condition. For purposes of environmental investigation, representativeness is how well the media (e.g., soil) sampled represents impact (i.e., contamination) at the site. In the initial planning stages of an investigation, representativeness of data collected is first ensured by proper sampling design. Project planners account for the difficulty in knowing when, where, and how to collect representative samples by developing a statistical or random sampling approach; collecting adequate numbers of increments or samples to determine a representative average COPC concentration in each decision unit; collecting samples at several different phases of natural or anthropogenic cycles; sampling at different locations within the project area; collecting Multi-Increment samples as opposed to grab samples; and verifying and validating the sampling techniques. The general strategies for ensuring representativeness are described in Section 3. The specific strategy used by the investigation team for each site is to be documented in detail in the project-specific QAPP or SAP.

One measurement of representativeness is the degree to which implementation of the sampling program has ensured that results reflect the site contaminant conditions and not outside impacts related to analytical preparation, field sampling, field decontamination, sample handling, sample shipping and other aspects of field investigation. The degree to which the sampling strategy has achieved representativeness can be measured as a qualitative parameter based on the proper implementation of the sampling program and laboratory analytical program (i.e., the QA/QC program set out in the QAPP). The results of field QC samples (i.e., replicates, trip blanks, field source blanks, or equipment blanks) may indicate that compounds have been introduced into the samples, possibly to an extent that would affect representativeness of the overall investigation.

Representativeness may also be measured by how well samples were delivered to the analytical laboratory within the described holding times and holding temperatures prescribed for individual analyses. Potential impacts to data quality measured by the QA/QC methods include (but are not limited to) the following:

  • Insufficiency or lack of cleanliness of sample collection containers, materials or preservatives provided by the analytical laboratory prior to field work, to ensure that outside contaminants are not introduced into the analytical process
  • Impurities detected in final decontamination rinse water that may not have originated from the site
  • Contaminants originating from exposure during transport of samples from the field to the analytical laboratory
  • Sample transport where delivery time to the laboratory exceeds holding time or sample temperature exceeds allowable temperature limits. Occurrence of either may indicate loss of contaminants during transport prior to extraction and analysis

Representativeness should be assessed for each matrix (media) and for each COPC. In addition to trip blanks for sites with volatile organics sampling (see Subsection 10.6.2.1) or equipment rinsate blanks and field source blanks (as described in Subsections 10.6.2.2 and 10.6.2.3), the following field QC procedures are used in evaluating representativeness:

  • Temperature measurement, usually of the samples themselves and sometimes via separate temperature blanks. These blanks are containers of analyte-free water included with field samples, handled and transported in the same manner and measure for temperature upon delivery to the analytical laboratory. Trip blanks sometimes double as temperature blanks
  • Chain-of-custody forms that document date and time of sampling and sample preservation for each sample

If analyses of field QC blank samples result in detected contaminants, the field procedures for decontamination, sample handling, and sample transport should be evaluated for how well procedures were followed, for any potential introduction of contaminants from outside sources, or for potential losses in the course of sample handling or transport.

10.3.3 Completeness

Completeness is a measure of the percentage of data that are valid. Data validation is performed by evaluating field and laboratory QC analyses combined with field QC logs, and chain-of-custody form information to determine how well field samples were collected and analyzed in accordance with QC procedures outlined in the QAPP. Field analytical data are acceptable if log and Chain-of-Custody (COC) information show that field QC procedures were properly followed, no significant level of analytes are detected in QC blank analyses, and when none of the QC objectives that affect data usability are exceeded. Data validation is also performed to determine when data should be rejected or declared unusable due to improper field QC, detection of analytes in blanks or laboratory QC limit exceedances. Completeness will also be evaluated as part of the data quality assessment process. This evaluation will help determine whether any limitations are associated with the decisions to be made based on the data collected.

Completeness is a percentage value, calculated to determine if an acceptable amount of usable data was obtained so that a valid scientific site assessment may be completed. The QAPP should present completeness goals (e.g., commonly 95%) to evaluate the degree of completeness. Percent completeness is calculated using the following equation:

Figure 10.3
where:
%C = percent completeness
T = total number of sample results
R = total number of rejected sample results

Completeness at a minimum should be determined for all field analytical results by method, but should also be determined by comparing the planned number of samples per method and specific matrix.

10.3.4 Comparability

Comparability is a qualitative parameter that expresses the confidence with which one data set can be compared with another. It is important that data sets be comparable if they are used in conjunction with other data sets. This type of comparison manifests itself most commonly (but not limited to) the following scenarios:

  • Data from the same site but collected during different investigations.
  • Data from the same site but collected during widely separated time-frames.
  • Comparison of data from the same site and investigation, but analyzed by different laboratories.

Comparability of data can be achieved by consistently following standard field and laboratory procedures and by using standard measurement units in reporting analytical data. The factors affecting comparability include sample collection and handling techniques, matrix type, and analytical method. If these aspects of sampling and analysis are carried out according to standard analytical procedures and the procedures implemented properly, the data may be considered comparable. Comparability is also dependent upon other quality criteria, because only when precision, accuracy, and representativeness are known may data sets be compared with confidence. In some cases, additional care must be taken to evaluate comparability. For instance, groundwater samples handled in the exact same fashion, collected within the same sampling event, and analyzed by the same analytical method may not be directly comparable if one sample was filtered and the other was not.