Environ. Sci. Technol. 1997, 31, 3461-3467
Use of Near-Infrared Reflectance Spectroscopy in Prediction of Heavy Metals in Freshwater Sediment by Their Association with Organic Matter D. F. MALLEY* Freshwater Institute, Department of Fisheries and Oceans, Central and Arctic Region, 501 University Crescent, Winnipeg, Manitoba R3T 2N6, Canada P. C. WILLIAMS Grain Research Laboratory Division, Canadian Grain Commission, Room 1404, 303 Main Street, Winnipeg, Manitoba R3C 3G8, Canada
The application of the rapid, non-destructive, cost-effective technique of near-infrared reflectance spectroscopy (NIRS) for the prediction of heavy metal concentrations in freshwater sediment was explored. Sediments were sandy and highly organic littoral samples from a Precambrian Shield lake (37-ha surface area) in northwestern Ontario. Sediment metals were at background concentrations except for Cd, which was augmented by 6 yr of experimental addition of Cd to the lake. Samples were scanned between 1100 and 2500 nm using an NIRSystems Near-Infrared Model 6500 spectrophotometer. R 2 between NIR-predicted and chemically-analyzed metal concentrations were Cd, 0.63; Cu, 0.91; Zn, 0.93; Pb, 0.81; Ni, 0.88; Mn, 0.93; and Fe, 0.86. Wavelengths from partial least squares regression analysis associated with most of the variance in heavy metal concentrations were attributed to protein, cellulose, and oil. The first PLS factor accounted for 75-95% of the variability in the data for each metal, except for Cd (50%). The different behavior for Cd was attributed to its much shorter geological time in the lake and to its proportionately greater association with inorganic ligands as compared with the other metals. This study demonstrates that the prediction of heavy metal concentrations in freshwater sediment by NIRS is feasible.
Introduction Contamination of marine and freshwater sediments by heavy metals is a problem of global concern (1) in large part because the metals can be accumulated by benthic organisms to toxic levels. Bioavailability and subsequent toxicity are dependent upon the geochemical partitioning of the metals to sediment components (2, 3), referred to as metal speciation. In oxic lake sediments, Mn, Fe, and organic matter have been identified as the three most important sediment components for metal partitioning (4-6). A number of studies have demonstrated that organic matter content in oxic sediment is important not only in controlling the binding of metals to sediments but also in the bioavailability and toxicity of metals (7-13). * Author to whom to address correspondence should be addressed. Telephone: 204-983-5173; fax: 204-984-2404; e-mail: Diane_Malley@ fwi.dfo.ca.
S0013-936X(97)00214-9 CCC: $14.00
Published 1997 by the Am. Chem. Soc.
Because of its importance in binding metals and hydrophobic organic contaminants in sediments, the organic matter content of sediments is routinely determined as total organic carbon content (14, 15) or percent organic matter as loss on ignition. Despite the recognized importance of organic matter content in influencing contaminant behaviour in sediments, rarely is the organic matter qualitatively analyzed in contaminant studies. Nevertheless, a variety of chemical, geochemical, microbiological, and ecological studies show that not only the organic matter content of sediments but also the composition of organic matter varies widely. Most attempts to study the speciation of metals in sediments utilize a method of geochemical fractionation to separate and identify metals associated with operationallydefined fractions in the sediment (8), including a fraction “metals bound to organics”. Methods of fractionation involve sequential (3, 16-18) or simultaneous extraction (19). Often these fractions are related to bioavailability of the metals to organisms. A drawback of operational extractions is that they provide no information on the nature of the organic ligands in the metals bound to organic fraction. Moreover it is not necessarily clear that this operational fraction contains all the metals bound to organics and only the metals bound to organics. Thus, these methods provide little information on the organic ligands that is generalizable from one sampling location to another. This paper explores the application of a rapid, nondestructive technology that is widely used for the quantification and qualitative analysis of organic matter in commercial and industrial applications to the analysis of organic ligands in sediment. Near-infrared spectroscopy (NIRS) has been extensively used for the quantititive and qualitative analysis of organic matter in the fields of agriculture, food, textiles, petrochemicals, and pharmaceuticals (20) and is the subject of more than 6500 publications. Recently, NIRS is beginning to be applied to the analysis of environmental samples (21), including predicting C, N, and P in sediments (22) and suspended matter (23) and inferring lake water chemistry from the analysis of surface sediments (24). Theory and practice of NIRS are discussed by Osborne et al. (25), Williams and Norris (20), Williams (26), and Burns and Cziuczak (27). Organic matter has distinctive spectra in the NIR region due to C-H, O-H, N-H, and C-O covalent bonds in protein, starch, cellulose, carboxyl, amide, amino acids, and other molecules (25). Pure metals do not absorb in the NIR region but may be detectable because they are complexed with organic matter, associated with moieties such as hydroxides, sulfides, carbonates, or oxides that are detectable (28); or adsorbed to clays that absorb light in this wavelength range (29). The first objective of this study was to examine whether NIRS can “predict” metal concentrations in sediments widely varying in organic matter content and metal levels. Following prediction of the metals in the sediment samples, the next objective was to employ partial least squares regression analysis in an attempt to characterize the organic ligands. This was done by identifying wavelengths of the spectra statistically associated with variability in the heavy metal concentrations and attributing these wavelengths to known organic absorbers.
Methods Sampling Site. The experimental addition of Cd as CdCl2 to 36.9 ha, 13.1 m deep Precambrian Shield lake 382 in northwestern Ontario, Canada (30), provided the opportunity to study sediments with anthropogenically-elevated Cd concentrations and background concentrations of other heavy
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metals. Lake 382 lies at 49°37′ N and 93°41′ W in the Experimental Lakes Area, a dedicated ecological research area (31, 32). Lake 382 is representative of the oligotrophic, softwater lakes in the ELA (33) and thousands of small Shield lakes; representative water chemistry is given by Malley et al. (34). During the ice-free seasons of 1987-1992, lake 382 received 6.7 kg of Cd (35), more than 95% of which was retained in the sediments (36). Sediment Sampling and Processing. Four to six cores were taken at each of three sites in lake 382 on 7-8 September 1994. The first site was sandy bottom in the metalimnion at 4.6 m water depth, termed the meta-sandy site. The second site was sandy bottom in the epilimnion in the middle bay at 1.5 m water depth, termed the epi-sandy site. The third site was in the southwest bay on highly organic bottom, at 1.5 m water depth, termed the epi-organic site. See Figure 1 in Stephenson et al. (36). Cores were taken by a SCUBA diver using a 5 cm i.d. Plexiglass tube inserted by hand into the sediment. At the lake, cores (6-14 cm long) were sliced into 1-cm-thick sections. Each slice (i.e., sample) was placed into a Whirlpak bag and frozen on dry ice within 3-4 h of sampling. Samples were stored frozen until processing. Samples were freeze-dried from 48 to 140 h on a Lab Con Co. Freeze Dry 5 (Fisher Scientific Co., Winnipeg, MB) at -68 to -75 °C and a pressure of 0.5-1 Pa. Samples were sieved through a no. 10 sieve (2.00 mm or 0.0787 in. opening) to remove large organic and inorganic particles. The material 10. Statistical Analysis: Partial Least Squares Regression. Using the NSAS software, factors from partial least squares regression analysis were calculated from the second derivative of the raw optical data for the 169 averaged spectra. The second derivative transformation was used because it more clearly displays the absorbance bands than does the raw optical data or the first derivative. The weights for the first several factors were plotted against wavelength. The weights indicate wavelength regions where variance was used in computing the PLS calibration equations. Using the second derivative, the “valleys” of the spectrum indicate positive influences of absorbers on the development of the equations, and the “peaks” indicate inverse influences. Constituents or chemical structures absorbing at the wavelengths associated with the valleys were identified where possible using tables of chemical assignments to wavelengths, such as in Osborne et al. (25). The cumulative percent of the total variance in the reference metal concentration for each metal explained by the first 10 factors was plotted against the number of factors.
Results Concentrations of C, N, P, and Heavy Metals in Sediment Cores. The sites ranged widely in organic matter content determined by loss on ignition from 0.90 (Table 1). The RDPs for these metals were above 3.0, and RERs were above 11.0, indicative of good calibrations (Table 1). The calibrations for Ni (Figure 1F), Fe (Figure1A), and Pb (Figure 1E) gave r 2 >0.8 but 1.5 µg g-1 Cd were underpredicted by NIRS and samples >0.2 µg g-1 were poorly predicted (Figure 1G). Possibly at the higher Cd concentrations proportionately less of the Cd was on the ligands being detected by NIRS. Of the metals in this study, Fe has been previously predicted by NIRS in a set of 91 soil samples by Ben-Dor and Banin (38). In that study, r2 for Fe2O3 (by X-ray florescence)
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FIGURE 3. Cumulative % of the variance in metal concentrations explained by increasing numbers of partial least squares factors for the seven metals. was 0.55 and for free iron oxides (by dithionite-citrate buffered with sodium bicarbonate) was 0.62, both lower than the r 2 value obtained here for sediment. It may be that the predictive capability of NIRS for these metals can be improved in the future by separately calibrating for highly organic and highly inorganic samples. In our experience, better NIR results are obtained for predicting C, N, and P (unpublished data) when all the samples are of a similar type rather than widely-varying. In the inorganic, sandy sediments, the organic component is primarily a coating of humic material on sand grains. In contrast, the organic matter at the organic site in lake 382 was more particulate and much less degraded. The two types of sediments in this study are reflected in Figure 1 as a grouping of low metal concentrations in sandy sediments and a range of higher concentrations in the organic sediments, with the exception of Fe. The samples were best suited for the development of a calibration for Fe, since the reference concentrations are fairly evenly distributed throughout the range. The results of this study are particularly striking in that the first PLS factors for all seven of the metals were virtually identical (Figure 2A) and accounted for the vast majority of the variance in the metal concentrations. This is interpreted as indicating that, for the background metals, the organic matter responsible for the variability in the metal levels is of the same quality for all metals. The second and subsequent factors deviated among the metals in weights when plotted against wavelength (Figure 2B) but were much less important in explaining the variance in metal levels except for Cd, where the first component explained only 50% of the variance in metal concentrations. As indicated above, possibly binding to inorganic ligands is more important for Cd than for the other metals. Wavelengths weighted in the first PLS factor in the present study were indicative of protein, cellulose, and oil, i.e., generally undegraded organic matter. There is still considerable unidentified information (valleys and peaks) in the first PLS factor that may be indicative of other components of naturally-occurring organic matter (NOM), such as humic and fulvic acids that are the refractory degradation products of material of biological origin. The assignment of wavelengths to constituents is most advanced for agricultural products, particularly grains and oil seeds, due to the large number of empirical NIR studies in this field (e.g., ref 25). Assignment of wavelength for constituents of NOM in sediments and soils is rare, although some information exists for the clay mineral lattice (28, 39). The results here are interpreted as indicating that the composition or quality of organic matter ligand(s), composed of protein, cellulose, and lipid, is associated with differences in the concentrations of the seven metals in the sample set. The ligands are in high concentration at the epi-organic site
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and presumably in very low concentrations at the sandy sites, where organic matter is expected to be largely humic in nature and inorganic ligands are more important. In summary, the prediction of background levels of Fe, Mn, Zn, Cu, Pb, and Ni and possibly experimentally-elevated levels of Cd in freshwater sediments was possible because the sample set contained a wide range of organic matter and a resulting wide range in metal concentrations. Information from partial least squares regression demonstrated that the metal levels were correlated with NIR-detectable absorbances indicative of organic matter containing protein, cellulose, and oil. It is interpreted that this information primarily describes the ligands in the highly organic samples. These ligands may be less important in the sandy samples where considerable published evidence suggests that inorganic iron and manganese hydroxides and humic coatings are the major metal ligands. Cadmium behaved differently from the other six metals in that the variance in its concentrations were less attributable to the absorbances by the organic ligands. Possibly, the difference is explained by the shorter residence time of most of the Cd in the lake as compared with the other metals.
Acknowledgments W. Hunter and A. R. Stewart conducted the field sampling with support from B. Townsend and the Divers for Science program. L. Wesson provided technical assistance. S. Friesen and B. Hauser provided reference analyses. A. Riedel assisted with data analysis. J. Delaronde performed routine NIR calibrations. This work was made possible by the support of NIRSystems, Inc. (Silver Spring, MD). Reviews were provided by C. Baron, R. V. Hunt, and several anonymous reviewers.
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Received for review March 11, 1997. Revised manuscript received July 14, 1997. Accepted September 4, 1997.X ES970214P X
Abstract published in Advance ACS Abstracts, October 15, 1997.
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