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Are your environmental data defensible?

As international environmental standards grow in complexity and corporations become more aware of their responsibilities, regulators and special interest groups are closely scrutinising sample selection and analytical methods, and quality assurance results for data used to predict environmental impacts.

SRK recommends careful design of data collection to ensure that the end product is representative and can be defended. Consider the following key aspects needed to produce a defensible dataset.

Involve regulators in program design: Before data collection begins, you should consider the final end use of the data and the audience to which it will be distributed. Usually, regulators will make management decisions based on your data so it is important to involve them in the planning process. That way they can also have ownership of the dataset obtained.

Consider site specific conditions: You should determine site specific conditions and the data collection goals before any data are collected because these will affect sampling requirements. For example, the site geology and mineralogy will dictate the number, type and spacing of waste rock samples.

Consistently apply sampling methods: Sampling methods should be site-specific and consistently applied. Sampling and analysis methods applied may be standard methods or may be specified by the regulators, but you might require additional or modified methods to meet the needs of the project. Normally, published methods are most defensible, but modifications may be appropriate for certain site conditions.

Validate your data: You should carefully validate your data before you use it. Otherwise, the analysis may be flawed, costing you both time and money. Each dataset should be reviewed for internal consistency and evaluated to ensure accuracy and reproducibility. You should check that the results make sense with respect to field observations.

Did you meet your objectives? Finally, you should evaluate your data in the context of the sampling plan, field observations, sampling procedures and analytical methods to ensure that the data collected have achieved the project goals in a defensible manner. If you have anomalous data, additional samples may be needed.

… and one last thing – avoid shortcuts: The temptation to take shortcuts when designing and implementing environmental data collection programs in an attempt to accelerate project schedules can have exactly the opposite effect. The result may be a dataset that cannot provide scientifically defensible conclusions.

Jeffrey Parshley:

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