Search USGSSearch

USGS · 70012825

Statistical evaluation of an inductively coupled plasma atomic emission spectrometric method for routine water quality testing

Abstract

In an interlaboratory test, inductively coupled plasma atomic emission spectrometry (ICP-AES) was compared with flame atomic absorption spectrometry and molecular absorption spectrophotometry for the determination of 17 major and trace elements in 100 filtered natural water samples. No unacceptable biases were detected. The analysis precision of ICP-AES was found to be equal to or better than alternative methods. Known-addition recovery experiments demonstrated that the ICP-AES determinations are accurate to between plus or minus 2 and plus or minus 10 percent; four-fifths of the tests yielded average recoveries of 95-105 percent, with an average relative standard deviation of about 5 percent.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J.R. Garbarino, B. E. Jones, G.P. Stein. 1985-05-01. Statistical evaluation of an inductively coupled plasma atomic emission spectrometric method for routine water quality testing. https://doi.org/10.1366/0003702854248458

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Stacked machine learning for timber identification with laser-induced breakdown spectroscopy (LIBS)

This study presents a new approach to wood species identification using laser-induced breakdown spectroscopy (LIBS) combined with stacked machine learning techniques. The research analyzed 700 samples comprising nine Dalbergia species and nine additional tropical timber species, utilizing a handheld LIBS analyzer. A stacking methodology was developed by integrating three support vector machine (SVM) models with different kernel functions (linear, polynomial, and radial) in a one-versus-all (OVA) configuration. These SVM outputs were then combined using a partial least squares discriminant analysis (PLS-DA) meta-learner. Through PCA-based variable selection, the dimensionality was reduced from 23 401 to wavelengths while maintaining classification accuracy. The stacking approach achieved a Cohen's kappa value of 0.8671 in the validation set, significantly outperforming traditional flat classifiers. Variable importance analysis revealed calcium, magnesium, and barium as crucial elements for species differentiation, with their concentrations reflecting environmental conditions and geographical origins. This research demonstrates the potential of combining LIBS spectroscopy with advanced machine learning techniques for rapid, non-invasive timber identification, which can support efforts against illegal logging and enforcement of international trade regulations.

Applied Spectroscopy

Determination of provenance soil type using inductively coupled plasma mass spectrometry (ICP-MS) analyses of Pinus ponderosa ash

This study demonstrates the feasibility of determining soil provenance from tree ash composition using elemental analysis and chemometric techniques. To date, no published studies have applied chemometric approaches to classify ash for provenance determination following forest fires. In this work, Pinus ponderosa ash was analyzed to distinguish samples based on soil type and geographic location. Pinus ponderosa , a widely distributed pine species in the western United States where wildfires are prevalent, was selected as a model system. Needles were collected from trees grown in five distinct soil types across northern Arizona and Colorado, then dry-ashed under controlled conditions. Classification was performed using three preprocessing techniques and five machine learning algorithms, including hierarchical modeling structures to optimize separation. Partial least squares discriminant analysis (PLS-DA) following a Box-Cox transformation yielded the highest classification accuracy, achieving a prediction kappa value of 0.98 for soil type identification. However, classification performance decreased when distinguishing both soil type and geographic location, indicating that additional variability may influence predictive accuracy in broader applications. These findings highlight the potential of inductively coupled plasma mass spectrometry (ICP-MS) and machine learning for post-wildfire forensic analysis and environmental monitoring.

Applied Spectroscopy

In situ study of mass transfer in aqueous solutions under high pressures via Raman spectroscopy: A new method for the determination of diffusion coefficients of methane in water near hydrate formation conditions

A new method was developed for in situ study of the diffusive transfer of methane in aqueous solution under high pressures near hydrate formation conditions within an optical capillary cell. Time-dependent Raman spectra of the solution at several different spots along the one-dimensional diffusion path were collected and thus the varying composition profile of the solution was monitored. Diffusion coefficients were estimated by the least squares method based on the variations in methane concentration data in space and time in the cell. The measured diffusion coefficients of methane in water at the liquid (L)-vapor (V) stable region and L-V metastable region are close to previously reported values determined at lower pressure and similar temperature. This in situ monitoring method was demonstrated to be suitable for the study of mass transfer in aqueous solution under high pressure and at various temperature conditions and will be applied to the study of nucleation and dissolution kinetics of methane hydrate in a hydrate-water system where the interaction of methane and water would be more complicated than that presented here for the L-V metastable condition. ?? 2006 Society for Applied Spectroscopy.

Applied Spectroscopy