Accurate and precise measurements of marine macronutrient concentrations are fundamental to our understanding of biogeochemical cycles in the ocean. Quantifying the measurement uncertainty associated with macronutrient measurements remains a challenge. Large systematic biases (up to 10%) have been identified between datasets, restricting the ability of marine biogeochemists to distinguish between the effects of environmental processes and analytical uncertainty. In this study we combine the routine analyses of certified reference materials (CRMs) with the application of a simple statistical technique to quantify the combined (random + systematic) measurement uncertainty associated with marine macronutrient measurements using gas segmented flow techniques. We demonstrate that it is realistic to achieve combined uncertainties of ~1–4% for nitrate + nitrite (ΣNOx), phosphate (PO4 3-) and silicic acid (Si(OH)4) measurements. This approach requires only the routine analyses of CRMs (i.e. it does not require inter-comparison exercises). As CRMs for marine macronutrients are now commercially available, it is advocated that this simple approach can improve the comparability of marine macronutrient datasets and therefore should be adopted as ‘best practice’. Novel autonomous Lab-on-Chip (LoC) technology is currently maturing to a point where it will soon become part of the marine chemist's standard analytical toolkit used to determine marine macronutrient concentrations. Therefore, it is critical that a complete understanding of the measurement uncertainty of data produced by LoC analysers is achieved. In this study we analysed CRMs using 7 different LoC ΣNOx analysers to estimate a combined measurement uncertainty of < 5%. This demonstrates that with high quality manufacturing and laboratory practices, LoC analysers routinely produce high quality measurements of marine macronutrient concentrations.
|Number of pages||8|
|Early online date||8 Mar 2019|
|Publication status||Published - 1 Aug 2019|
- measurement uncertainty
- segmented flow