Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

Abstract

Adequate biogeochemical characterization and monitoring of aquatic ecosystems, both for scientific purposes and for water management, pose high demands on spatial and temporal replication of chemical analyses. Near-infrared reflectance spectroscopy (NIRS) may offer a rapid, low-cost and reproducible alternative to standard analytical sample processing (digestion or extraction) and measuring techniques used for the chemical characterization of aquatic sediments. We analyzed a total of 191 sediment samples for total and NaCl-extractable concentrations of Al, Ca, Fe, K, Mg, Mn, N, Na, P, S, Si, and Zn as well as oxalate- extractable concentrations of Al, Fe, Mn and P. Based on the NIR spectral data and the reference values, calibration models for the prediction of element concentrations in unknown samples were developed and tested with an external validation procedure. Except Mn, all prediction models of total element concentrations were found to be acceptable to excellent (ratio of performance deviation: RPD 1.8–3.1). For extractable element fractions, viable model precision could be achieved for NaCl-extractable Ca, K, Mg, NH4 +-N, S and Si (RPD 1.7–2.2) and oxalate-extractable Al, Fe and P (RPD 1.9–2.3). For those elements that showed maximum total values below 3 g kg−1 prediction models were found to become increasingly critical (RPD <2.0). Low concentrations also limited the performance of NIRS calibrations for extracted elements, with critical concentration thresholds <0.1 g kg−1 and 3.3 g kg−1 for NaCl and oxalate extractions, respectively. Thus, reliable NIRS measurements of trace metals are restricted to sediments with high metal content. Nevertheless, we demonstrated the suitability of NIRS measurements to determine a large array of chemical properties of aquatic sediments. The results indicate great potential of this fast technique as an analytical tool to better understand the large spatial and temporal variation of sediment characteristics in an economically viable way.

Synergistic Use of Vis‐NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry

“…The goodness of fit for the conventional pXrF calibration data set are given for comparison with and animal health (essential trace elements Co, Cu, Mn, and Zn) in a dataset of non-polluted soils. To give this result perspective, case studies in polluted environments have rarely been able to successfully deliver a full assessment of soil contamination by using either vis-NIR or pXRF methods (Bray et al, 2009;Hu et al, 2014;Kemper and Sommer, 2002;Kleinebecker et al, 2013;Malley and Williams, 1997;Siebielec et al, 2004;Weindorf et al, 2012;Wu et al, 2007;2005). In the case of pXRF, results are influenced by the digestion procedure before ICP analysis (as discussed above in Vis-NIR, MIR and pXRF prediction of soil properties section).…”

“…Low levels of heavy metals can be indirectly predicted from spectra due to their association with Fe oxides, clays, and organic matter (Wu et al, 2010). The pollutant metals As, Cd, Cr, Cu, Hg, Pb, Ni, Sb, and Zn have been successfully determined by vis-NIR spectroscopy in soils and sediments (Kemper and Sommer, 2002;Kleinebecker et al, 2013;Malley and Williams, 1997;Wu et al, 2005Wu et al, , 2007, however, not all pollutant metals are predicted satisfactorily for each study site. Assessment of a full suite of soil geochemistry from large geographical datasets has shown a high number of elements to be determined by MIR spectroscopy Al, B, Be, Bi, Ca, Ce, Co, Cr, Cu, Cs, Fe, Ga, In, K, Li, Mg, Mn, Na, Nb, Ni, P, Rb, S, Sc, Sr, Ti, Th, V, Y, Zn, and Zr (Soriano-Disla et al, 2013a;Reeves and Smith, 2009).…”

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

“…This effect is described and demonstrated in the study carried out by Putra et al; although some complexes might be similar in different samples, slight differences in spectral features such as shifts in peak wavelength may still be seen depending on the nature of the cation [30]. In addition, the electromagnetic radiation spectrum in the near-infrared region contains useful information about environmental sample constituents such as soil that can be used for prediction of metal concentrations [25,28,33]. For instance, the absorption features associated with electronic transitions of Fe 3+ and Fe 2+ ions in Fe-bearing minerals can be found in the near-infrared region at 780-1200 nm [33][34][35].…”

“…Typically, NIRS is primarily based on absorbance characteristics caused by vibrations of covalent bonds between H, C, O, S, and N, which are the main components of the organic matter [28]. Pure metals do not absorb in the NIR region [29].…”

“…This was achieved by quantitative evaluations of the spectral activity of sediment and soil properties [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. However, due to challenges such as the collinearity, band overlaps, and interactions for some soil properties, the spectra of soil, sediment, or suspended solids are often broad and nonspecific [25].…”