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Flow injection analysis as a tool for enhancing oceanographic nutrient measurements: A review
Worsfold, P.; Clough, R; Lohan, C; Monbet, P; Ellis, S; Quétel, C.R.; Floor, G.H.; McKelvie, D (2013). Flow injection analysis as a tool for enhancing oceanographic nutrient measurements: A review. Anal. Chim. Acta 803: 15-40. dx.doi.org/10.1016/j.aca.2013.06.015
In: Analytica Chimica Acta. Elsevier: New York; Amsterdam. ISSN 0003-2670, more
Peer reviewed article  

Available in  Authors 
    VLIZ: Open Repository 274944 [ OMA ]

Keyword
    Marine
Author keywords
    Marine waters; Estuarine waters; Macronutrients; Micronutrients; Flowinjection analysis; Data quality

Authors  Top 
  • Worsfold, P.
  • Clough, R
  • Lohan, C
  • Monbet, P
  • Ellis, S
  • Quétel, C.R., more
  • Floor, G.H., more
  • McKelvie, D

Abstract
    Macronutrient elements (C, N and P) and micronutrient elements (Fe, Co, Cu, Zn and Mn) are widely measured in their various physico-chemical forms in open ocean, shelf sea, coastal and estuarine waters. These measurements help to elucidate the biogeochemical cycling of these elements in marine waters and highlight the ecological and socio-economic importance of the oceans. Due to the dynamic nature of marine waters in terms of chemical, biological and physical processes, it is advantageous to make these measurements in situ and in this regard flow injection analysis (FIA) provides a suitable shipboard platform. This review, therefore, discusses the role of FIA in the determination of macro-and micro-nutrient elements, with an emphasis on manifold design and detection strategies for the reliable shipboard determination of specific nutrient species. The application of various FIA manifolds to oceanographic nutrient determinations is discussed, with an emphasis on sensitivity, selectivity, high throughput analysis and suitability for underway analysis and depth profiles. Strategies for enhancing sensitivity and minimizing matrix effects, e.g. refractive index (schlieren) effects and the important role of uncertainty budgets in underpinning method validation and data quality are discussed in some detail.

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