Environmental Monitoring QA/QC and Data Interpretation
QA/QC and Data Interpretation
Quality assurance defines how a monitoring programme is designed and managed; quality control checks whether individual sampling and measurement activities performed as intended.
1. Plan quality before collecting data
Define data-quality objectives, required accuracy or precision where applicable, detection/reporting limits, completeness, representativeness and comparability. Select laboratories and methods capable of meeting those objectives.
2. Field quality controls
Depending on the method and objective, controls may include field blanks, trip blanks, equipment blanks, field duplicates, temperature checks, calibration verification and documented equipment-cleaning procedures. Not every control applies to every parameter; specify the rationale in the sampling plan.
3. Laboratory quality controls
Review method blanks, calibration checks, laboratory control samples, matrix spikes, duplicates, surrogate recoveries and control-chart status where relevant. Check accreditation scope for the specific method and analyte rather than relying only on a general accreditation statement.
4. Data validation sequence
- Confirm sample identity, location, date/time and custody.
- Check method, container, preservation and holding time.
- Review instrument calibration and field notes.
- Examine laboratory qualifiers, detection limits and QC acceptance.
- Verify units, dilution factors, significant figures and transcription.
- Flag deviations and determine whether data are usable for the stated objective.
- Preserve the original result and document any corrected or qualified value.
5. Interpret results responsibly
Compare data with the correct criterion and basis. Confirm whether the criterion applies to a grab or composite sample, a particular averaging period, flow condition, water category, air reference condition or noise metric. A result below a reporting limit is not zero; report it using the laboratory’s qualifier and reporting convention.
6. Trend analysis
Plot results against time and annotate production rate, rainfall, process changes, maintenance, incidents and monitoring-method changes. Look for persistent shifts, seasonal patterns, outliers and relationships between operating variables. Do not infer causation from correlation alone.
7. Useful summary statistics
Depending on the data distribution and objective, report count, completeness, minimum, maximum, mean, median, range and relevant percentiles. Explain treatment of non-detects and missing values. Do not average incompatible sample types or results collected under materially different conditions without justification.
8. Data review register
| Record ID | Issue / qualifier | Potential impact | Review decision | Reviewer / date |
|---|---|---|---|---|
Acceptance criteria for QC, validation flags and statistical methods must follow the approved method, laboratory quality system and monitoring objective.