Background

Environmental assessments rely on measured concentrations in a variety of matrices to characterize the chemical exposure of aquatic and terrestrial organisms or humans. Reliability refers to the inherent quality of the dataset, and it is crucial to report and evaluate exposure data systematically using specific criteria to promote consistent and transparent assessments. These criteria can help data users understand and characterize uncertainties when existing data are used and can serve as guidance on best practices for the reporting of data by generators (to maximize utility of their datasets). Although most reliability criteria are universal, some practices may need to be evaluated considering the purpose of the assessment. This can include a relevance evaluation (completed separately) to determine the extent to which environmental monitoring datasets are “fit for purpose,” i.e., suitable for use in various types of assessments.

Producing reliable environmental data depends on use of coordinated quality practices across all stages from planning and sampling to analysis. Recent guidance from the Criteria for Reporting and Evaluating Exposure Datasets (CREED) provides a framework to support data transparency and usability. CREED defines six essential “gateway criteria” required for any environmental dataset so that they can be interpreted, compared, or integrated with other datasets.

§  sampling medium

§  analyte measured

§  spatial location

§  sample collection year

§  measurement units

§  original data source

After the gateway step, CREED involves evaluating the dataset relative to detailed criteria pertaining to both field and analytical information. Key field considerations include documenting collection techniques, identifying the sample medium, recording precise locations, and collection times. Laboratory data quality depends on validated analytical method performance including accuracy, precision, appropriate detection limits, and control for interferences (accredited laboratory methods inherently address many of these requirements).

When collecting environmental samples, quality assurance and quality control (QA/QC) measures are central to evaluating data reliability. QA is about prevention while QC is about verification. In the laboratory, QA/QC components include: the addition of chemical surrogates and internal standards, matrix spikes of targeted chemicals, replicates/duplicates, laboratory procedural blanks, and information about holding times and sample storage. Comparable QA/QC elements in the field include equipment blanks, matrix spikes of targeted chemicals in the field, split samples, and replicate sampling.

Additionally, there may be supporting information needed for interpretations (e.g., hardness‑adjusted cadmium water concentrations or carbon‑normalized sediment concentrations for comparison to toxicity values). Proper data interpretation requires an understanding of statistics such as normalization procedures, treatment of outliers, and handling of censored results.

Once a dataset is generated, CREED calls for evaluating the dataset’s relevance in the context of its intended application; for example, regulatory risk assessments may require less data uncertainty than would be allowed for general detection/surveillance. Furthermore, some high-quality datasets may be suitable for some purposes but not others; a groundwater dataset would not be appropriate for assessing effects on stream aquatic life or estuarine bed sediment sampling would not be appropriate for assessing effects on human health.

Transparent, openly shared environmental datasets that adhere to the FAIR principles (findable, accessible, interoperable, reusable) and work on limiting barriers, such as proprietary constraints or withheld location information, increase their usefulness to future data users.

Key reading

CREED

Merrington, G., Nowell, L. H., Peck, C., 2024, An introduction to Criteria for Reporting and Evaluating Exposure Data (CREED). Integr. Environ. Assess. Manag., v. 20, pp. 975-980. https://doi.org/10.1002/ieam.4899

Hladik, M.L., Markus, A., Helsel, D., Nowell, L.H., Polesello, S., Rüdel, H., Szabo, D., Wilson, I., 2024, Evaluating the reliability of environmental concentration data to characterize exposure in environmental risk assessments, Integr. Environ. Assess. Manag., v. 20, pp. 981-1003. https://doi.org/10.1002/ieam.4893

Peters, A., Beking, M., Oste, L. Hamer, M., Vuaille, J., Harford, A.J., Backhaus, T., Lofts, S., Svendsen, C., Peck, C., 2024, Assessing the relevance of environmental exposure data sets, Integr. Environ. Assess. Manag., v. 20, pp. 1004-1018. https://doi.org/10.1002/ieam.4881

Di Paolo, C., Bramke, I., Stauber, J., Whalley, C., Otter, R., Verhaegen, Y., Nowell, L.H., Ryan, A. C., 2024, Implementation of the CREED approach for environmental assessments. Integr. Environ. Assess. Manag., v. 20, pp. 1019-1034. https://doi.org/10.1002/ieam.4909

Other

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.‐W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B., 2016, The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

OECD Guidance Document on the Generation, Reporting and Use of Research Data for Regulatory Assessments: https://one.oecd.org/document/ENV/CBC/MONO(2025)18/en/pdf

Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3.

Practical Stats: https://practicalstats.com/