A laboratory result is only as good as the sample behind it. Most challenges to environmental data are not about the analysis — they are about everything that happened before it.
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Environmental data arrives looking authoritative: a number, a method reference, a laboratory letterhead. Whether it can support the conclusion drawn from it depends on a chain of decisions upstream of the instrument — where samples were taken and why, whether the container and preservative were right, whether holding times were met, whether blanks demonstrate that nothing was introduced during collection, and whether detection limits were low enough that a non-detect means anything. A dataset assembled for regulatory compliance often fails to answer litigation questions not because it is unreliable but because it was designed to characterise a site rather than to establish whether contamination reached a particular receptor.
Each of these has been the basis for excluding or discounting an entire sampling programme.
Documented control from collection through analysis. Gaps undermine every result they touch.
Analyte-specific limits after which results no longer reliably reflect the sample as collected.
Field, trip and equipment blanks showing whether the sampling itself introduced the analyte. Critical for PFAS.
Whether limits were low enough that a non-detect excludes the concentration of interest.
Whether locations and timing were chosen to answer the question being asked of the data.
Whether the analytical method suits the matrix and the analyte — compliance methods are not always fit for forensic use.
How data is assessed and how programmes are designed.
Data defects are unglamorous and frequently fatal — they can remove the factual basis for every expert opinion at once.
PFAS occur in ordinary field materials — some waterproof clothing, tubing, containers and personal care products — at levels that matter when the concentrations of interest are extremely low. Field and equipment blanks are not a formality here; a programme without them has no answer to the argument that the samplers brought the contamination with them.
Often yes, with limitations that should be identified early rather than discovered at deposition. Compliance data is generally well documented and was collected under obligation, which helps. But it was designed to answer a regulator's question — is this facility within its limits, is this site characterised — not a litigant's. Monitoring locations may not sit on the pathway to the claimed receptor, sampling frequency may miss episodic releases, the analyte list may exclude the compound at issue, and detection limits may be above the level that matters for exposure.
Only that the analyte was not present above the reporting limit, which is a much weaker statement than "not present" — and the difference is frequently decisive. If the reporting limit sits above the concentration relevant to exposure or to a screening value, the non-detect excludes nothing that matters. This is a recurring problem where older data is offered to show contamination was absent: analytical capability has improved substantially, and a historical non-detect at a high reporting limit is consistent with concentrations that would be actionable today.
Disproportionately, relative to its cost. Providing parallel samples to the opposing party or an independent laboratory removes an entire category of dispute — the other side cannot argue the data is unreliable when they hold a split showing the same result, and where splits diverge that is itself important information discovered early rather than at trial. It also signals confidence. Where sampling is happening at a site both parties are aware of, splits are usually worth offering even when not required.
Establish exactly what the defects are and what the data can still support, rather than either defending or abandoning it wholesale. Some defects are fatal to particular uses and irrelevant to others — an exceeded holding time for a volatile compound may invalidate that result while leaving metals data unaffected. Where the site still exists, resampling is usually better than litigating over a flawed dataset. Where it does not, the analysis has to be candid about the limitations, and conclusions drawn have to be ones the compromised data genuinely supports.
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