U.S. Department of the Interior

U.S. Geological Survey

Prepared by the USGS Office of Water Quality, National Water Quality Laboratory

Determination of Elements in Natural-Water, Biota, Sediment, and 

Soil Samples Using Collision/Reaction Cell Inductively Coupled 

Plasma–Mass Spectrometry

Chapter 1

Section B, Methods of the National Water Quality Laboratory

Book 5, Laboratory Analysis

Techniques and Methods Book 5–B1
Determination of Elements in Natural-Water, 

Biota, Sediment, and Soil Samples Using 

Collision/Reaction Cell Inductively Coupled

Plasma–Mass Spectrometry 

By John R. Garbarino, Leslie K. Kanagy, and Mark E. Cree

Chapter 1

Section B, Methods of the National Water Quality Laboratory 

Book 5, Laboratory Analysis

Techniques and Methods 5–B1

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2006

For sale by U.S. Geological Survey, Information Services

Box 25286, Denver Federal Center

Denver, CO 80225

For more information about the USGS and its products:

Telephone: 1-888-ASK-USGS 

World Wide Web: http://www.usgs.gov/

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Garbarino, J.R., Kanagy, L.K., and Cree, M.E., 2006, Determination of elements in natural-water, biota, sediment, and 

soil samples using collision/reaction cell inductively coupled plasma–mass spectrometry: U.S. Geological 

Survey Techniques and Methods, book 5, sec. B, chap. 1, 88 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

 Purpose and Scope ..............................................................................................................................2

Analytical Method..........................................................................................................................................2

Application and Method Detection Limits ........................................................................................3

Interferences.........................................................................................................................................4

Instrumentation...................................................................................................................................10

Sample Collection, Preservation, Shipment, and Holding Times................................................10

Calibration Standards and Other Solutions....................................................................................13

Quantitation..........................................................................................................................................13

Reporting of Analytical Data.............................................................................................................14

Laboratory Quality Assurance/Quality Control ..............................................................................14

Results and Discussion of Method Validation Data...............................................................................14

Summary and Conclusions.........................................................................................................................24

References Cited..........................................................................................................................................25

Appendix —Linear Regression Analyses of Elemental Results ..........................................................27

Glossary.........................................................................................................................................................87

Figures

1. The effect of increasing concentrations of chloride from two sources on the

determination of arsenic at mass-to-charge ratio 75.............................................................6

2. The effect of increasing concentrations of chloride from two sources on the

determination of vanadium at mass-to-charge ratio 51.........................................................6

3. The effect of increasing concentrations of dissolved organic carbon from two

natural-water sources on the determination of chromium at mass-to-charge 

ratio 52 ............................................................................................................................................7

4. The variability in percentage recovery for selected elements extending the

operational mass range as a function of increasing specific conductance......................7

5. Sample-flow paths used with new collision/reaction cell inductively coupled

plasma–mass spectrometric methods....................................................................................11

6. Linear regression analysis of experimental concentrations for all elements in 

biological standard reference material digestates analyzed by collision/reaction 

cell inductively coupled plasma–mass spectrometry in relation to the certified 

concentrations ............................................................................................................................24
iv

Appendix Figures

A1. Linear regression analysis of silver results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................29

A2. Linear regression analysis of silver results from whole-water recoverable 

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................30

A3. Linear regression analysis of aluminum results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................31

A4. Linear regression analysis of aluminum results from whole-water recoverable 

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP-MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................32

A5. Linear regression analysis of arsenic results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS.....................................................................................................................................33

A6. Linear regression analysis of arsenic results from whole-water recoverable

digestates analyzed by graphite furnace–atomic absorption spectrometry

(GF–AAS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................34

A7. Linear regression analysis of boron results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................35

A8. Linear regression analysis of boron results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................36

A9. Linear regression analysis of barium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................37

A10. Linear regression analysis of barium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................38

A11. Linear regression analysis of beryllium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................39
A12. Linear regression analysis of beryllium results from whole-water recoverable 

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................40

A13. Linear regression analysis of calcium results from filtered water samples 

analyzed by inductively coupled plasma–atomic emission spectrometry

(ICP–AES) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................41

A14. Linear regression analysis of calcium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–atomic emission 

spectrometry (ICP–AES) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS).................................................................................42

A15. Linear regression analysis of cadmium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................43

A16. Linear regression analysis of cadmium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................44

A17. Linear regression analysis of cobalt results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................45

A18. Linear regression analysis of cobalt results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................46

A19. Linear regression analysis of chromium results from filtered water samples 

analyzed by graphite furnace–atomic absorption spectrometry (GF–AAS) 

and collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................47

A20. Linear regression analysis of chromium results from whole-water recoverable

digestates analyzed by graphite furnace–atomic absorption spectrometry

(GF–AAS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................48

A21. Linear regression analysis of copper results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................49

A22. Linear regression analysis of copper results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................50
vi

A23. Linear regression analysis of iron results from filtered water samples 

analyzed by inductively coupled plasma–atomic emission spectrometry 

(ICP–AES) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................51

A24. Linear regression analysis of iron results from whole-water recoverable 

digestates analyzed by inductively coupled plasma–atomic emission 

spectrometry (ICP–AES) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS).................................................................................52

A25. Linear regression analysis of potassium results from filtered water samples

analyzed by inductively coupled plasma–atomic emission spectrometry 

(ICP–AES) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................53

A26. Linear regression analysis of potassium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–atomic emission 

spectrometry (ICP–AES) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS).................................................................................54

A27. Linear regression analysis of lithium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................55

A28. Linear regression analysis of lithium results from whole-water recoverable 

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................56

A29. Linear regression analysis of magnesium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–AES) and 

collision/reaction cell inductively coupled plasma–mass spectrometry 

(cICP–MS)....................................................................................................................................57

A30. Linear regression analysis of magnesium results from whole-water

recoverable digestates analyzed by inductively coupled plasma–atomic

emission spectrometry (ICP–AES) and collision/reaction cell inductively

coupled plasma–mass spectrometry (cICP–MS)..................................................................58

A31. Linear regression analysis of manganese results from filtered water

samples analyzed by inductively coupled plasma–mass spectrometry

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................59

A32. Linear regression analysis of manganese results from whole-water

recoverable digestates analyzed by inductively coupled plasma–mass

spectrometry (ICP–MS) and collision/reaction cell inductively coupled

plasma–mass spectrometry (cICP–MS).................................................................................60

A33. Linear regression analysis of molybdenum results from filtered water

samples analyzed by inductively coupled plasma–mass spectrometry

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................61

A34. Linear regression analysis of molybdenum results from whole-water

recoverable digestates analyzed by inductively coupled plasma–mass

spectrometry (ICP–MS) and collision/reaction cell inductively coupled

plasma–mass spectrometry (cICP–MS).................................................................................62
vii

A35. Linear regression analysis of sodium results from filtered water samples

analyzed by inductively coupled plasma–atomic emission spectrometry

(ICP–AES) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................63

A36. Linear regression analysis of sodium results from whole-water

recoverable digestates analyzed by inductively coupled plasma–atomic

emission spectrometry (ICP–AES) and collision/reaction cell inductively

coupled plasma–mass spectrometry (cICP–MS)..................................................................64

A37. Linear regression analysis of nickel results from filtered water samples

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS)

and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................65

A38. Linear regression analysis of nickel results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................66

A39. Linear regression analysis of lead results from filtered water samples

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS)

and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................67

A40. Linear regression analysis of lead results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................68

A41. Linear regression analysis of antimony results from filtered water

samples analyzed by inductively coupled plasma–mass spectrometry

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass

spectrometry (cICP–MS)...........................................................................................................69

A42. Linear regression analysis of antimony results from whole-water

recoverable digestates analyzed by inductively coupled plasma–mass

spectrometry (ICP–MS) and collision/reaction cell inductively coupled

plasma–mass spectrometry (cICP–MS).................................................................................70

A43. Linear regression analysis of selenium results from filtered water 

samples analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................71

A44. Linear regression analysis of selenium results from whole-water 

recoverable digestates analyzed by inductively coupled plasma–mass 

spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS).................................................................................72

A45. Linear regression analysis of silicon (as SiO2) results from filtered water 

samples analyzed by inductively coupled plasma–atomic emission 

spectrometry (ICP–AES) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS).................................................................................73

A46. Linear regression analysis of silicon (as SiO2) results from whole-water 

recoverable digestates analyzed by inductively coupled plasma–atomic 

emission spectrometry (ICP–AES) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS)..................................................................74
viii

A47. Linear regression analysis of strontium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................75

A48. Linear regression analysis of strontium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................76

A49. Linear regression analysis of thallium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................77

A50. Linear regression analysis of thallium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................78

A51. Linear regression analysis of uranium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................79

A52. Linear regression analysis of uranium results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................80

A53. Linear regression analysis of vanadium results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................81

A54. Linear regression analysis of vanadium results from whole-water 

recoverable digestates analyzed by inductively coupled plasma–atomic 

emission spectrometry (ICP–AES) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS)..................................................................82

A55. Linear regression analysis of tungsten results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................83

A56. Linear regression analysis of tungsten results from whole-water recoverable

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................84

A57. Linear regression analysis of zinc results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and 

collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................85

A58. Linear regression analysis of zinc results from whole-water recoverable 

digestates analyzed by inductively coupled plasma–mass spectrometry 

(ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS)...........................................................................................................86
ix

Tables

1. Codes for elements in water, biota, and sediment and for arsenic species 

in water samples determined by collision/reaction cell inductively 

coupled plasma–mass spectrometry. .......................................................................................3

2. Method detection limits for elements and species determined using

collision/reaction cell inductively coupled plasma–mass spectrometry............................4

3. Potential molecular ion interferences for elements determined using

collision/reaction cell inductively coupled plasma–mass spectrometry............................8

4. Effectiveness of using a collision/reaction cell gas for the elimination of 

molecular ion interferences........................................................................................................9

5. Typical operating characteristics for collision/reaction cell inductively 

coupled plasma–mass spectrometry ......................................................................................12

6. Long-term bias and variability of results from aqueous standard 

reference materials using collision/reaction cell inductively coupled 

plasma–mass spectrometry......................................................................................................15

7. Long-term bias and variability for the analysis of reagent water spiked 

with analyte concentrations in the lower one-third of the normal 

calibration range using collision/reaction cell inductively coupled 

plasma–mass spectrometry......................................................................................................17

8. Long-term bias and variability for the analysis of reagent water spiked 

with analyte concentrations at one-third and two-thirds of the normal 

calibration range using collision/reaction cell inductively coupled 

plasma–mass spectrometry......................................................................................................18

9. Long-term bias and variability for the analysis of surface water spiked 

with analyte concentrations at one-third and two-thirds of the normal 

calibration range using collision/reaction cell inductively coupled 

plasma–mass spectrometry......................................................................................................19

10. Long-term bias and variability for the analysis of ground water spiked 

with analyte concentrations at one-third and two-thirds of the normal 

calibration range using collision/reaction cell inductively coupled 

plasma–mass spectrometry......................................................................................................20

11. Linear regression results for filtered natural-water samples analyzed 

using collision/reaction cell inductively coupled plasma–mass spectrometry 

and other spectrometric methods. ..........................................................................................21

12. Linear regression results for unfiltered natural-water digestates analyzed 

using collision/reaction cell inductively coupled plasma–mass spectrometry 

and other spectrometric methods. ..........................................................................................22

13. Bias and variability of results for digested biological standard reference 

materials analyzed by collision/reaction cell inductively coupled 

plasma–mass spectrometry......................................................................................................23
CONVERSION FACTORS, ABBREVIATED WATER-QUALITY UNITS, 

ADDITIONAL ABBREVIATIONS, AND DEFINITIONS

Multiply By To obtain

centimeter (cm) 3.94 x 10-1 inch

millimeter (mm) 3.94 x 10-2 inch

micrometer (μm) 3.94 x 10-5 inch

Mass

gram (g) 3.53 x 10-2 ounce, avoirdupois

milligram (mg) 3.53 x 10-5 ounce, avoirdupois

microgram (μg) 3.53 x 10-8 ounce, avoirdupois

Volume

liter (L) 2.64 x 10-1 gallon

milliliter (mL) 2.64 x 10-4 gallon

microliter (μL) 2.64 x 10-7 gallon

Degree Celsius (°C) may be converted to degree Fahrenheit (°F) by using the following equation:

 °F = 9/5 (°C) + 32

Abbreviated water-quality units used in this report:

μg/g micrograms per gram

μg/L micrograms per liter

μL/min microliters per minute

μS/cm microsiemens per centimeter at 25oC

mg/g milligrams per gram

mg/L milligrams per liter

mL/min milliliters per minute

Other abbreviations used in this report:

ASTM American Society for Testing and Materials

cICP–MS collision/reaction cell inductively coupled plasma–mass spectrometry

GF–AAS graphite furnace–atomic absorption spectrometry

HPLC high-performance liquid chromatography

ICP–MS inductively coupled plasma–mass spectrometry

ICP–AES inductively coupled plasma–atomic emission spectrometry

LT–MDL long-term method detection level

MDL(s) method detection limit(s)

m/z mass-to-charge ratio

NIST National Institute of Standards and Technology
xi

Other abbreviations used in this report (Continued):

NWQL National Water Quality Laboratory

OctP octapole

P/A pulse/analog factor

QP quadrupole

SRM standard reference material

SRWS(s) U.S. Geological Survey standard reference water sample(s)

USGS U.S. Geological Survey

% Bias percent error between the expected concentration and the experimental 

mean concentration

% RSD percent relative standard deviation

≥ greater than or equal to

% percent

± plus or minus

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L). Concentrations of chemical constituents in solid digestates are 

given either in milligrams per gram (mg/g) or micrograms per gram (μg/g), dry weight.

DETERMINATION OF ELEMENTS IN NATURAL-WATER, 

BIOTA, SEDIMENT, AND SOIL SAMPLES USING 

COLLISION/REACTION CELL INDUCTIVELY COUPLED 

PLASMA–MASS SPECTROMETRY

By John R. Garbarino, Leslie K. Kanagy, and Mark E. Cree

Abstract

A new analytical method for the determination of elements in filtered aqueous matrices using inductively coupled 

plasma–mass spectrometry (ICP–MS) has been implemented 

at the U.S. Geological Survey National Water Quality Laboratory that uses collision/reaction cell technology to reduce 

molecular ion interferences. The updated method can be 

used to determine elements in filtered natural-water and 

other filtered aqueous matrices, including whole-water, biota, 

sediment, and soil digestates. Helium or hydrogen is used 

as the collision or reaction gas, respectively, to eliminate or 

substantially reduce interferences commonly resulting from 

sample-matrix composition. Helium is used for molecular ion 

interferences associated with the determination of As, Co, Cr, 

Cu, K, Mg, Na, Ni, V, W and Zn, whereas hydrogen is used 

for Ca, Fe, Se, and Si. Other elements that are not affected by 

molecular ion interference also can be determined simply by 

not introducing a collision/reaction gas into the cell. Analysis time is increased by about a factor of 2 over the previous 

method because of the additional data acquisition time in the 

hydrogen and helium modes.

Method detection limits for As, Ca, Co, Cr, Cu, Fe, K, 

Mg, Na, Ni, Se, Si (as SiO2), V, W, and Zn, all of which use a 

collision/reaction gas, are 0.06 microgram per liter (μg/L) As, 

0.04 milligram per liter (mg/L) Ca, 0.02 μg/L Co, 0.02 μg/L 

Cr, 0.04 μg/L Cu, 1 μg/L Fe, 0.007 mg/L K, 0.009 mg/L Mg, 

0.09 mg/L Na, 0.05 μg/L Ni, 0.04 μg/L Se, 0.03 mg/L SiO2, 

0.05 μg/L V, 0.03 μg/L W, and 0.04 μg/L Zn. Most method 

detection limits are lower or relatively unchanged compared to 

earlier methods except for Co, K, Mg, Ni, SiO2, and Tl, which 

are less than a factor of 2 higher.

Percentage bias for samples spiked at about one-third 

and two-thirds of the concentration of the highest calibration 

standard ranged from –8.1 to 7.9 percent for reagent water, 

–14 to 21 percent for surface water, and –16 to 16 percent for 

ground water. The percentage bias for reagent water spiked at 

trace-element concentrations of 0.5 to 3 μg/L averaged 4.4 

percent with a range of –6 to 16 percent, whereas the average 

percentage bias for Ca, K, Mg, Na, and SiO2 was 1.4 percent 

with a range of –4 to 10 percent for spikes of 0.5 to 3 mg/L. 

Elemental results for aqueous standard reference materials 

compared closely to the certified concentrations; all elements were within 1.5 F-pseudosigma of the most probable 

concentration. In addition, results from 25 filtered naturalwater samples and 25 unfiltered natural-water digestates were 

compared with results from previously used methods using 

linear regression analysis. Slopes from the regression analyses averaged 0.98 and ranged from 0.87 to 1.29 for filtered 

natural-water samples; for unfiltered natural-water digestates, 

the average slope was 1.0 and ranged from 0.83 to 1.22. Tests 

showed that accurate measurements can be made for samples 

having specific conductance less than 7,500 microsiemens per 

centimeter (μS/cm) without dilution; earlier ICP–MS methods required dilution for samples with specific conductance 

greater than 2,500 μS/cm.

Introduction

Inductively coupled plasma–mass spectrometry (ICP–

MS) has been used to determine elements in natural-water 

samples at the U.S. Geological Survey National Water Quality 

Laboratory since the early 1990s (Faires, 1993; Garbarino 

and Taylor, 1994; Garbarino and Struzeski, 1998; Garbarino, 

1999; Garbarino, 2000; Garbarino and others, 2002). Methods 

have been updated over the years to document changes in data 

acquisition modes, to expand the number of elements determined, and to expand the types of matrices analyzed. This 

new ICP–MS method was implemented to use recent advances 

in collision/reaction cell technology that reduce molecular ion 

interference resulting in improved accuracy and in expanded 

scope.

A molecular ion interferes when its mass cannot be 

resolved from the mass of the element of interest because of 

limitations on the resolution of the quadrupole mass 
spectrometer. The most prominent molecular-ion interferences are associated with oxide and hydroxide species arising 

from matrix elements in the sample (for details, see Horlick 

and Montaser, 1998). One example of such interference is 

35Cl16O+

 on the determination of 51V+. Other molecular-ion 

interferences, such as 40Ar35Cl+ on 75As+ and 40Ar23Na+ on 63Cu+

, are associated with elements in the plasma and sample 

matrix. Before the introduction of collision/reaction cell technology, such interference corrections were commonly made 

by measuring the signal at an associated interference-free isotope and subtracting the fraction corresponding to the interference from the signal of the quantifying isotope. Although 

this approach was fairly successful, analyte concentrations in 

the micrograms-per-liter range likely were to become more 

negatively biased with increasing concentrations of matrix 

interferents. Alternatively, the formation of oxide species 

could be reduced by using cold plasma conditions or by using 

a different elemental isotope whenever possible. However, 

either approach usually compromised the sensitivity of the 

analysis.

Using a collision/reaction cell system with an ICP–MS 

reduces molecular ion interferences through either collisional 

or reactional processes. Helium is used as the cell gas to 

promote collisional dissociation and energy discrimination 

of molecular ions that cause interference. A molecular ion 

at the same nominal mass-to-charge ratio (m/z) and kinetic 

energy as an analyte ion experiences more collisions because 

of its larger cross-sectional area. As a result of these collisions, the kinetic energy of the molecular ion is reduced, and 

it is rejected by the cell, whereas the analyte ion is transmitted through the quadrupole to the detector. When hydrogen 

is used as the cell gas, simple reactions with argon-based 

molecular ions take place through charge-, proton-, or atomtransfer processes. Analyte ions generally do not react with 

hydrogen; therefore, there is no loss in analyte signal. Reaction processes can produce new molecular ions, principally 

hydrides of the original molecular-ion interference. However, 

such species have low kinetic energies and usually do not 

interfere.

Purpose and Scope

This report describes a new method for the determination 

of As, Co, Cr, Cu, Ni, Se, V, and Zn in filtered water, unfiltered water digestates, biota digestates, sediment digestates, 

and soil digestates using collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). In addition, 

other elements, such as Ca, Fe, K, Mg, Na, Si, and W that 

were not determined routinely by earlier ICP–MS methods, 

have been added to the method. Furthermore, the method can 

be used to determine arsenic species using high-performance 

liquid chromatography (HPLC) for separation and cICP–MS 

for detection. Elements that were determined in earlier 

ICP–MS methods and that are not affected by molecular ion 

interference also are determined by cICP–MS by simply not 

introducing hydrogen or helium into the gas cell. New laboratory codes and parameter codes are not needed for Ag, Al, 

B, Ba, Be, Cd, Li, Mn, Mo, Pb, Sb, Sr, Tl, and U because the 

plasma conditions, acquisition characteristics, and analytical 

performance for such elements are not drastically different 

from earlier ICP–MS methods that did not use reaction/collision cell technology.

Information is provided in this report to:

Establish the method detection limits (MDLs) for all 

elements determined using the cICP–MS method.

Evaluate the capability of collision/reaction cell technology to reduce molecular ion interferences on the 

determination of Ag, Al, As, Ca, Cd, Co, Cr, Cu, Fe, 

K, Mg, Mn, Na, Ni, Se, Si, V, W, and Zn.

Review sample dilution guidelines using spiked field 

samples having specific conductance ≥2,000 μS/cm.

Determine the analytical variability of measurements 

in laboratory reagent water as a function of analyte 

concentration.

Determine the bias and variability of the cICP–MS 

method using results from standard reference 

materials.

Validate the cICP–MS method for the determination of 

elements in ground-water, surface-water, and 

laboratory reagent-water matrices.

Compare the bias and variability of cICP–MS with 

results from other analytical methods for 25 filtered 

natural-water samples and 25 whole-water digestates.

The method described was developed by the U.S. 

Geological Survey (USGS) for use at the National Water 

Quality Laboratory (NWQL). This method replaces ICP–MS 

method I-2477-92 for the determination of dissolved As, Co, 

Cr, Cu, Ni, Se, V, and Zn (Faires, 1993; Garbarino, 1999), 

methods I-4471-97 and I-4472-97 for the determination of 

whole-water recoverable As, Co, Cr, Cu, Ni, Se, V, and Zn 

(Garbarino, 2000; Garbarino and Struzeski, 1998), methods 

I-1190-02, I-2191-02, I-2193-02, and I-2192-02 for arsenic 

speciation (Garbarino and others, 2002). The new method 

was implemented at the NWQL in October 2005. 

Analytical Method

New laboratory codes, parameter codes, and method 

codes for collision/reaction cell inductively coupled plasma–

mass spectrometry (cICP–MS) are listed in table 1 for As 

(speciated and unspeciated), Co, Cr, Cu, Ni, Se, V, and Zn in 

different sample matrices. New codes also were established 

for Ca, Mg, Fe, K, Na, Si (as silica), and W for filtered and 

unfiltered water matrices. Laboratory codes, parameter codes, 

and method codes for Ag, Al, B, Ba, Be, Cd, Li, Mn, Mo, Pb,

•

•

•

•

•

•

•

 Determination of Elements Using Collision/Reaction Cell ICP–MS
Sb, Sr, Tl, and U, elements not affected by molecular ion 

interference, remain unchanged for each type of sample 

matrix. 

Application and Method Detection Limits

The method described in this report can be used to 

determine a wide range of elements in filtered water, unfiltered 

water digestates, biological digestates, sediment digestates, 

and soil digestates. The collision/reaction gas is used when 

determining As (speciated and unspeciated), Ca, Co, Cr, Cu, 

Fe, K, Mg, Na, Ni, Se, Si, V, W, and Zn. Method detection 

limits are 0.06 microgram per liter (μg/L) As, 0.04 milligram 

per liter (mg/L) Ca, 0.02 μg/L Co, 0.02 μg/L Cr, 0.04 μg/L Cu, 

1 μg/L Fe, 0.007 mg/L K, 0.009 mg/L Mg, 0.09 mg/L Na, 0.05 

μg/L Ni, 0.04 μg/L Se, 0.03 mg/L SiO2, 0.05 μg/L V, 0.03 μg/L 

W, and 0.04 μg/L Zn (see table 2). Most method detection 

limits are lower or relatively unchanged compared to earlier 

methods except for Co, K, Mg, Ni, SiO2, and Tl, which are 

less than a factor of 2 higher. Other elements (Ag, Al, B, Ba, 

Be, Cd, Li, Mn, Mo, Pb, Sb, Sr, Tl, and U) that have 

been determined using previous ICP–MS methods can be 

Element Lab code P code Lab code P code

Elements, water, filtered, 

cICP–MS, I-2020-05

Elements, water, unfiltered, 

cICP–MS, I-4020-05

As, μg/L 3122 01000 H 3123 01002 H

Ca, mg/L 2916 00915 I 2917 00916 D

Co, μg/L 3124 01035 I 3125 01037 I

Cr, μg/L 3126 01030 J 3127 01034 I

Cu, μg/L 3128 01040 I 3129 01042 I

Fe, μg/L 2974 01046 I 2975 01045 D

K, mg/L 3014 00935 E 3015 00937 D

Mg, mg/L 2984 00925 I 2985 00927 D

Na, mg/L 3048 00930 I 3049 00929 D

Ni, μg/L 3130 01065 I 3131 01067 J

Se, μg/L 3132 01145 G 3133 01147 H

SiO2, mg/L 3046 00955 J 3047 00956 B

V, μg/L 3134 01085 G 3135 01087 D

W, μg/L 3136 01155 A 3137 01154 A

Zn, μg/L 3138 01090 I 3139 01092 E

Elements, biota,

cICP–MS, 

I-9020-05

Elements, sediment or 

soil, recoverabe, cICP-MS, 

I-5020-05

As, μg/g 6055 49247 C 3144 64847 A

Co, μg/g 6056 49250 D 3145 01038 E

Cr, μg/g 6057 49240 C 3146 01029 E

Cu, μg/g 6058 49241 C 3147 01043 E

Ni, μg/g 6059 49253 D 3148 01068 D

Se, μg/g 6060 49254 C 3149 64848 A

V, μg/g 6061 49465 C 3150 64849 A

Zn, μg/g 6062 49245 C 3151 01093 C

Species Lab code P code

Arsenic species, water, filtered, field 

separation, cICP–MS, I-2197-05

Arsenite [As(III)], H3AsO3 3140 62452 E

Arsenate [As(V)], H2AsO4

- 3140 62453 E

Arsenic species, water, filtered, 

laboratory separation, 

malonate/acetate mobile phase,

 cICP–MS, I-2196-05

Arsenite [As(III)], H3AsO3 3141 62452F

Arsenate [As(V)], H2AsO4

- 3141 62453 F

Arsenic species, water, filtered, 

laboratory separation, phosphate

mobile phase with arsine 

generation, cICP–MS, I-2195-05

Arsenite [As(III)], H3AsO3 3142 62452 G

Arsenate [As(V)], H2AsO4

- 3142 62453 G

Monomethylarsonate (MMA),

(CH3)HAsO3

-

3142 62454 E

Dimethylarsinate (DMA),

(CH3)

2

HAsO2

3142 62455 E

Arsenic species, water, filtered, 

laboratory separation, nitric acid 

mobile phase, cICP–MS, I-2193-05

Arsenite [As(III)], H3AsO3 3143 62452 H

Arsenate [As(V)], H2AsO4

- 3143 62453 H

Monomethylarsonate (MMA),

(CH3)HAsO3

-

3143 62454 F

Dimethylarsinate (DMA),

(CH3)HAsO2

3143 62455 F

Analytical Method 

Table 1. Codes for elements in water, biota, and sediment and for arsenic species in water samples determined by collision/reaction 

cell inductively coupled plasma-mass spectrometry.

[cICP-MS, collision/reaction cell inductively coupled plasma-mass spectrometry; I-xxxx-xx, method number; Lab code, laboratory code; P code, parameter code 

and method code; μg/L, micrograms per liter; mg/L, milligrams per liter; μg/g, micrograms per gram dry weight]
determined with similar performance using this method. The 

typical linear calibration range extends from the method 

detection limit (MDL) to 100 μg/L for each trace element or 

species. Selected elements like Al, Ba, Mn, Sr, and Zn are 

calibrated up to 2,500 μg/L. Elements such as Ca, K, Mg, and 

Na normally are calibrated up to 100 mg/L, whereas Si and Fe 

are calibrated up to 50 mg/L and 1,000 μg/L, respectively.

The method detection limits for all elements and species 

determined in filtered-water matrices using cICP–MS and 

long-term method detection levels (LT–MDL) from earlier 

methods are listed in table 2. These limits were calculated 

using guidelines of the U.S. Environmental Protection Agency 

(2000). Most method detection limits are lower or relatively 

unchanged compared to earlier methods except for Co, K, 

Mg, Ni, SiO2, and Tl, which are less than a factor of 2 higher. 

Method detection limits for unfiltered water digestates are 

similar to those listed in table 2 except for vanadium and arsenic, which are about a factor of 10 lower than earlier methods. 

Method detection limits (in micrograms per gram, dry weight) 

for elements determined in biota, sediment, and soil digestates 

can be estimated by multiplying the limits in table 2 by the 

digestate volume (typically 0.100 L) and dividing it by the 

sample dry weight (typically 0.5 g). The linear calibration 

range can be extended when the analog stage of the detector 

is calibrated, otherwise samples that have elemental concentrations that exceed the upper calibration standard must be 

diluted or, in the case of arsenic speciation, a smaller sample 

volume can be injected. In addition, samples with specific 

conductance greater than 7,500 μS/cm need to be diluted prior 

to analysis.

Interferences

Spectral interferences associated with isobaric ions and 

molecular ions can affect the accuracy of elemental analysis 

using ICP–MS. Such interferences have been documented 

in the literature (Tan and Horlick, 1986; Horlick and Montaser, 1998). Molecular ion interferences evolve from the 

argon plasma and elements composing the sample matrix. 

Molecular ions that are potential isobaric interferences for the 

elements determined are listed in table 3. Such interferences 

are greatly reduced or eliminated for ICP–MS instruments 

that use a collision/reaction cell without using correction 

equations. By introducing a gas into the cell, either He or 

H2 in this method, an interfering molecular ion is isolated 

from the analyte ion through collisional or reactional interactions, thereby improving the accuracy of the determination of 

selected elements.

Test solutions containing analytes and elements 

associated with molecular ion interferences were analyzed

Table 2. Method detection limits for elements and species 

determined using collision/reaction cell inductively coupled 

plasma–mass spectrometry.

[Analyte, concentrations are in micrograms per liter unless otherwise noted; 

m/z, mass-to-charge ratio; Mode, either normal (none), He (helium), or H2

(hydrogen) cell gas; MDL, method detection limit; LT–MDL, long-term 

method detection level as of 2005; mg/L, milligrams per liter; cICP–MS, collision/reaction cell inductively coupled plasma–mass spectrometry; ICP–MS, 

inductively coupled plasma–mass spectrometry; ICP–AES, inductively 

coupled plasma–atomic emission spectrometry; GF–AAS, graphite furnace–

atomic absorption spectrometry; SiO2, determined as silicon (Si) but reported 

as silica (SiO2); na, not applicable]

Analyte m/z

cICP–MS Previous method

Mode MDL LT–MDL Technique

Ag 107 Normal 0.07 0.1 ICP–MS

Al 27 Normal 0.4 0.8 ICP–MS

As 75 He 0.06 0.1 ICP–MS

B 11 Normal 2 4 ICP–MS

Ba 137 Normal 0.06 0.1 ICP–MS

Be 9 Normal 0.02 0.03 ICP–MS

Ca, mg/L 40 H2 0.04 0.02 ICP–AES 

Cd 111 Normal 0.02 0.02 ICP–MS

Co 59 He 0.02 0.008 ICP–MS

Cr 52 He 0.02 0.4 GF–AAS

Cu 63 He 0.04 0.2 ICP–MS

Fe 56 H2 1 3 ICP–AES 

K, mg/L 39 He 0.007 0.005 ICP–AES 

Li 7 Normal 0.1 0.3 ICP–MS

Mg, mg/L 24 He 0.009 0.004 ICP–AES 

Mn 55 Normal 0.07 0.1 ICP–MS

Mo 95 Normal 0.06 0.2 ICP–MS

Na, mg/L 23 He 0.09 0.1 ICP–AES 

Ni 60 He 0.05 0.03 ICP–MS

Pb 206,

207,

208,

Normal 0.01 0.04 ICP–MS

Sb 121 Normal 0.02 0.1 ICP–MS

Se 78 H2 0.04 0.2 ICP–MS

SiO2, 

mg/L

28 H2 0.03 0.02 ICP–AES 

Sr 88 Normal 0.08 0.2 ICP–MS

Tl 205 Normal 0.03 0.02 ICP–MS

U 238 Normal 0.002 0.02 ICP–MS

V 51 He 0.05 0.07 ICP–MS

W 182 He 0.03 na na

Zn 66 He 0.04 0.3 ICP–MS

 Determination of Elements Using Collision/Reaction Cell ICP–MS
Analytical Method 

tions was eliminated for the two types of dissolved organic 

carbon tested. The average chromium concentrations measured in the Suwannee River and Big Soda Lake solutions 

were 22.8±0.2 and 22.6±0.5 μg/L, respectively.

Other potential molecular ion interferences that are listed 

in table 3 also were evaluated. The percentage spike recovery 

for each analyte in the presence of elements that are associated with molecular ion interference was determined. Spike 

concentrations ranged from 5 to 30 μg/L for trace elements 

and 0.5 mg/L for Ca, K, Mg, Na, and Si. Concentrations 

ranged from 2 mg/L for interfering trace elements like Mo to 

500 mg/L for major elements like Ca. Based on the percentage spike recovery results, only Co, Fe, Ni, and Zn required 

the use of a cell gas to eliminate molecular ion interference 

(see table 4). For example, if a cell gas was not used, 3 μg/L 

of Co would be measured in the presence of 500 mg/L Ca 

because of the 43Ca16O+ interference. Molecular ions 29Si27Al+

and 40Ca16O+ severely affected the determination of Fe when 

hydrogen was not used making it difficult to estimate the 

degree of interference. All the other interference solutions 

indicated negligible bias for the interferent concentration 

range tested. For some elements where negligible bias was 

indicated, no cell gas is used during routine analyses (table 2) 

because the interferent concentration tested is rarely exceeded 

(for example, the interferences on Ag and Cd, see table 3), or 

the element being measured is normally present at concentrations higher than trace levels (for example, Al and Mn). 

Nevertheless, the use of a cell gas might be required when 

interferent concentrations are greater than those tested.

Interferences also may be associated with aqueous 

sample introduction and ionization processes. Dissolved-solid 

concentrations affect nebulization efficiency and suppress ionization. Typical limitations for dissolved-solid concentration 

for most pneumatic nebulizers range from 0.1 to 0.3 percent; 

some high dissolved-solid nebulizers do not have this limitation but also have higher sample flow rates. High dissolvedsolid concentrations associated with easily ionized cations like 

sodium affect the ionization of analyte ions. Dissolved-solid 

concentrations of 0.1 to 0.3 percent translate into estimated 

specific conductances of between 4,000 and 6,000 μS/cm in 

natural water (Hem, 1986). Percentage recoveries in sample 

matrices with increasing specific conductance were used 

to establish the effect of specific conductance on cICP–MS 

analyses for various elements extending the mass range (see 

figure 4). The variability for elements heavier than 51V averaged less than 3 percent over the duration of the experiment 

(about 17 hours); the variability for lighter elements ranged 

from 5 to 8 percent. These test-sample results suggest that 

samples having specific conductances less than 7,500 μS/cm 

can be analyzed directly with acceptable bias and variability. 

In previous ICP–MS methods, samples with specific conductances greater than 2,500 μS/cm required dilution (Faires, 

1993; Garbarino and Taylor, 1994; Garbarino and Struzeski, 

1998; Garbarino, 1999, 2000). The median specific conductance for samples submitted to NWQL in 2004 was

Table 2. Method detection limits for elements and species 

determined using collision/reaction cell inductively coupled 

plasma–mass spectrometry.—Continued

[HPLC/cICP–MS; high-performance liquid chromatography/collision/reaction cell inductively coupled plasma–mass spectrometry using a phosphate 

mobile phase and hydride generation; Analyte, concentrations are in micrograms per liter; m/z, mass-to-charge ratio; Mode, He (helium), cell gas; MDL, 

estimated method detection limit based on non-chromatographic determination; LT–MDL, long-term method detection level as of 2005; HPLC/ICP–MS, 

high-performance liquid chromatography/inductively coupled plasma–mass 

spectrometry]

Analyte m/z HPLC/cICP–MS Previous method

Mode MDL LT–MDL Technique

As(III) 75 He 0.09 0.3 HPLC/ 

ICP–MS

As(V) 75 He 0.1 0.4 HPLC/ 

ICP–MS

MMA 75 He 0.2 0.6 HPLC/ 

ICP–MS

DMA 75 He 0.09 0.3 HPLC/ 

ICP–MS

to evaluate the performance of cICP–MS. The effects of 

increasing concentrations of chloride on the recovery of 5 μg/

L arsenic and vanadium are shown in figures 1 and 2, respectively. Two different sources of chloride, calcium chloride 

(CaCl2), and hydrochloric acid (HCl) were investigated. The 

data corresponding to measurements made without using the 

He cell gas shows an apparent increase in 75As+ concentration 

as a result of 40Ar35Cl+. Previous ICP–MS methods corrected for such interference by using equations based on other 

measurements, whereas no correction equations are needed 

with the new method when He is used. The advantage of this 

approach is the elimination of errors resulting from measuring 

low analyte concentration in the presence of high interferent 

concentration. The average arsenic concentrations measured 

by the new method in the CaCl2 and HCl solutions having up 

to 5,000 mg/L chloride were 5.38±0.09 and 5.38±0.08 μg/L, 

respectively. Similar results are shown for vanadium (see fig. 

2). When He is used, the 35Cl16O+ ion is eliminated and does 

not contribute to the vanadium signal. The average vanadium 

concentration measured in the CaCl2 solution was 5.06±0.08 

μg/L, whereas the concentration in the HCl was 5.2±0.4 μg/L. 

The median chloride concentration for samples submitted to 

NWQL in 2004 was 16 mg/L; 95 percent of the samples had 

chloride concentration less than 370 mg/L.

The effect of increasing concentrations of carbon on the 

recovery of about 20 μg/L chromium is shown in 

figure 3. Solutions containing dissolved organic carbon from 

the Suwannee River (Florida) and Big Soda Lake (Nevada) 

were used to test the effectiveness of cICP–MS in eliminating 

the 40Ar12C+ interference on 52Cr+. The dissolved organic carbon concentration ranged from 0 to about 20 mg/L; the maximum concentration tested is at least a factor of 10 greater than 

most samples submitted to NWQL. The results show that 

carbon molecular ion interference on chromium determina-
0 1,000 2,000 3,000 4,000 5,000

0

5

10

15

20

25

30

0

5

10

15

20

25

30

From HCl

A

RSE

NIC

C

O

N

C

E

N

T

R

A

TIO

N, in g/L

CHLORIDE CONCENTRATION, in mg/L

With cell gas

Without cell gas

From CaCl

2

With cell gas

Without cell gas

Figure 1. The effect of increasing 

concentrations of chloride from 

two sources on the determination 

of arsenic at mass-to-charge 

ratio 75. The graphs show that by 

using helium as the cell gas the 

molecular ion interference from 

chloride [40Ar35Cl+] is minimized. 

The chloride concentration was 

about equal in each source. Error 

bars correspond to the standard 

deviation for three replicate 

determinations. Arsenic and 

chloride concentrations are 

in micrograms per liter (μg/L) 

and milligrams per liter (mg/L), 

respectively.

Figure 2. The effect of increasing 

concentrations of chloride from 

two sources on the determination 

of vanadium at mass-to-charge 

ratio 51. The graphs show that by 

using helium as the cell gas the 

molecular ion interference from 

chloride [35Cl16O+] is minimized. 

The chloride concentration was 

about equal in each source. Error 

bars correspond to the standard 

deviation for three replicate 

determinations. Vanadium and 

chloride concentrations are 

in micrograms per liter (μg/L) 

and milligrams per liter (mg/L), 

respectively.

0

5

10

15

20

25

30

0 1,000 2,000 3,000 4,000 5,000

0

5

10

15

20

25

30

CHLORIDE CONCENTRATION, in mg/L

With cell gas

Without cell gas

V

A

N

A

DIU

M

C

O

N

C

E

N

T

R

A

TIO

N, in g/L

From CaCl

2

From HCl

With cell gas

Without cell gas

 Determination of Elements Using Collision/Reaction Cell ICP–MS
Analytical Method 

Figure 4. The variability in 

percentage recovery for selected 

elements extending the operational 

mass range as a function of 

increasing specific conductance. 

Specific conductances are in 

microsiemens per centimeter 

(μS/cm) at 25 degrees Celsius. The 

variability for elements heavier than 

vanadium averaged less than 3 

percent over the range of specific 

conductance; the variability for 

lighter elements ranged from 5 to 8 

percent. The data points represent 

the average for spiked naturalwater samples analyzed in triplicate 

over a 17-hour period; the average 

standard deviation was 2.6 percent 

for elements with masses greater 

than vanadium and 16 percent for 

lighter elements.

Figure 3. The effect of increasing 

concentrations of dissolved organic 

carbon from two natural-water 

sources on the determination of 

chromium at mass-to-charge ratio 

52. The graphs show that by using 

helium as the cell gas the molecular 

ion interference from carbon 

[

40Ar12C+

] is minimized. Error bars 

correspond to the standard deviation 

for three replicate determinations. 

Chromium and dissolved organic 

carbon concentrations are 

in micrograms per liter (μg/L) 

and milligrams per liter (mg/L), 

respectively.

0 5 10 15 20

15

18

21

24

27

30

15

18

21

24

27

30

Suwannee River

C

H

R

O

MIU

M

C

O

N

C

E

N

T

R

A

TIO

N, in

μ

g/L

DISSOLVED ORGANIC CARBON, in mg/L

Big Soda Lake

V

2,000 3,000 4,000 5,000 6,000 7,000 8,000

A

RIA

BILIT

Y IN

E

L

E

M

E

N

T

A

L

P

E

R

C

E

N

T

A

G

E

R

E

C

O

V

E

R

Y, IN

A

R

BIT

R

A

R

Y

U

NIT

S

SPECIFIC CONDUCTANCE, in S/cm

U

Pb

Ba

Sb

Cd

Mo

As

Cu

V

Be
Table 3. Potential molecular ion interferences for elements determined using collision/reaction cell inductively 

coupled plasma–mass spectrometry.

[All superscripted numbers are nominal mass-to-charge ratios]

Ion Molecular ion Ion Molecular ion Ion Molecular ion

23Na+ 7

Li16O+ 52Cr+ 40Ar12C+

35Cl16OH+

36Ar16O+

51VH+

75As+ 40Ar35Cl+

48Ti27Al+

63Cu12C+

24Mg+ 12C2+

23NaH+

55Mn+ 40Ar14NH+

28Si27Al+

39K16O+

54FeH+

54CrH+

78Se+ 77SeH+

40Ar38Ar+

66Zn12C+

27Al+ 12C14NH+

26MgH+

11B16O+

13C14N+

12C15N+

56Fe+ 40Ar16O+

55MnH+

40Ca16O+

29Si27Al+

44Ca12C+

40K16O+

107Ag+ 91Zr16O+

28Si+ 14N2+

27AlH+

12C16O+

59Co+ 58FeH+

43Ca16O+

41K18O+

111Cd+ 95Mo16O+

39K+ 38ArH+

27Al12C+

23Na16O+

60Ni+ 44Ca16O+

48Ca12C+

23Na37Cl+

43Ca16OH+

182W+ 166Er16O+

40Ca+ 40Ar+

28Si12C+

24Mg16O+

40K+

63Cu+ 40Ar23Na+

31P16O2+

51V12C+

47V16O+

51V+ 35Cl16O+

39K12C+

66Zn+ 34S16O2+

33S2+

54Fe12C+

50Cr16O+

50Ti16O+

54Cr12C+

 Determination of Elements Using Collision/Reaction Cell ICP–MS
Analytical Method 

Table 4. Effectiveness of using a collision/reaction cell gas for the elimination of molecular ion interferences.

[Analyte, nominal elemental concentration; Interferent(s), elements associated with molecular ions that interfere with the analyte and the nominal 

concentration tested; Cell gas, N identifies an analyte that does not require the use of a cell gas for the interferent concentration tested and X identifies 

an analyte that requires a cell gas for accurate quantitation followed in parentheses by either the apparent analyte concentration, in micrograms per 

liter, associated with interferent concentration listed or an s indicating that the apparent analyte concentration could not be estimated because of severe 

molecular ion interference; mg/L, milligrams per liter; &, and]

Analyte Interferent(s) Cell gas Analyte Interferent(s) Cell gas

Ag Zr, 2 mg/L N Mg Na, 500 mg/L

C, 50 mg/L

N

N

Al Mg, 200 mg/L

C & N, 50 mg/L

N

N Mn K, 200 mg/L

Fe, 500 mg/L

N

N

Ca Mg, 200 mg/L

K, 200 mg/L

C & Si, 50 mg/L

N

N

N

Ni Ca, 500 mg/L

Ca & C, 100 mg/L

Na, 500 mg/L

Na & Cl, 500 mg/L

X(14)

X(13)

N

Cd Mo, 2 mg/L N N

Co Ca, 500 mg/L Se C & Zn, 50 mg/L N

Fe, 500 mg/L

K, 200 mg/L

X(3)

N

X(s) Si Al, 200 mg/L

C, 50 mg/L

N

N

Cu Na, 500 mg/L

P, 200 mg/L

N

N V K & C, 100 mg/L

C & K, 50 mg/L

N

N

Cr C, 50 mg/L N

Fe Al & Si, 200 mg/L

Ca, 500 mg/L

Ca & C, 100 mg/L

K, 200 mg/L

C & Ca, 50 mg/L

X(s)

X(s)

X(8)

X(s)

N

W Er, 2 mg/L N

Zn Fe & C, 100 mg/L

S, 200 mg/L

C & Fe, 50 mg/L

Ti, 2 mg/L

N

N

N

X(2)

K Al & C, 100 mg/L

Na, 500 mg/L

N

N
398 μS/cm; 95 percent of the samples had specific conductances of less than 3,000 μS/cm.

Instrumentation

The instrumental operating conditions for the cICP–MS 

method are listed in table 5. The inductively coupled plasma 

operating characteristics are typical of those used in earlier 

methods. However, there are substantial differences in the 

sample introduction and ion optics in conjunction with the 

use of the collision/reaction cell. A pneumatic concentric 

nebulizer is used to introduce samples at a flow rate of 0.2 to 

0.3 mL/min into a thermostatically controlled spray chamber. 

This introduction system minimizes sample volume requirements, increases sample introduction efficiency, reduces 

instrumental drift, and reduces oxide and hydroxide molecular 

ions. The internal standards are introduced automatically 

through a junction tee (see fig. 5A). Most of the lens potentials and pole biases are similar regardless of whether a collision gas is introduced; notable differences are the cell entrance 

and exit potentials and the quadrupole (QP) and octapole 

(OctP) biases (see table 5). Nominal hydrogen and helium 

flow rate in the gas cell is about 4 mL/min. Previous ICP–MS 

methods used substantially different sample-introduction systems (nebulizer and spray chamber) and ion optics (see Faires, 

1993; Garbarino and Taylor, 1994; Garbarino and Struzeski, 

1998; Garbarino, 1999 and 2000; Garbarino and others, 2002).

Minor changes were required for determining the distribution of arsenic species using high-performance liquid 

chromatography (HPLC) in combination with cICP–MS 

instrumentation. An Agilent 1100 HPLC was used to separate 

the arsenic species with various anion exchange columns and 

mobile phases as described in the original methods (Garbarino and others, 2002). No changes were needed for arsenic 

speciation methods that use hydride generation for sample 

introduction. However, there was a mismatch in flow rates 

between the HPLC (about 1 mL/min) and the microflow nebulizer (about 0.3 mL/min) for other arsenic speciation methods. 

The mismatch required using the junction tee that is usually 

used for introducing the internal standard to reduce flow into 

the nebulizer (see fig. 5B). Differences in data-acquisition 

characteristics, nebulizer flow rate, spray-chamber volume, 

and transfer-line lengths had negligible effects on the chromatographic resolution and retention times.

Refer to Agilent Technologies (2001, 2004a, 2004b) and 

NWQL Standard Operating Procedures INCM0458.0 (M.E. 

Cree, U.S. Geological Survey, written commun., 2005) and 

ID0359.1 (J.R. Garbarino, U.S. Geological Survey, written 

commun., 2005) for details of the operation and maintenance 

of the instrumentation used in this report.

Sample Collection, Preservation, Shipment, and 

Holding Times

Samples must be collected and preserved using the 

protocols outlined in the USGS National Field Manual for the 

Collection of Water-Quality Data (U.S. Geological Survey, 

variously dated). Samples used for the determination of dissolved elements in natural-water samples need to be filtered 

using either a 0.45-μm membrane capsule filter or in-line filter, and the filtrate must be preserved using contaminant-free 

nitric acid. Samples used for the determination of recoverable elements in unfiltered water also must be preserved with 

contaminant-free nitric acid and digested using the in-bottle 

procedure (Hoffman and others, 1996). The prescribed holding times for acid-preserved filtered and unfiltered samples 

stored in tightly capped polyethylene bottles for elemental 

determinations in this method is 6 months (U.S. Environmental Protection Agency, 2005).

Biota samples, including mollusks, fish, aquatic insects, 

crayfish, and aquatic plants, need to be collected and processed as outlined by Crawford and Luoma (1993). Samples 

are stored frozen in glass or polyethylene containers at 

–20°C. Frozen samples need to be shipped overnight on dry 

ice to the laboratory. The holding time for frozen samples 

analyzed for the elements determined in this method is 6 

months from the time of collection (Crawford and Luoma, 

1993). Biota samples are digested with nitric acid using a 

closed-vessel microwave digestion procedure (U.S. Environmental Protection Agency, 1996). The holding time for biota 

digestates has not been established but is expected to be the 

same as acid-preserved aqueous samples or sediment and soil 

digestates (6 months).

Sediment and soil samples are collected using coring or 

other suitable techniques, stored in glass or polyethylene containers, and shipped overnight on ice to the laboratory (U.S. 

Geological Survey, variously dated). In the laboratory, the 

samples are air-dried and sieved using a 2-mm plastic sieve 

(Fishman and Friedman, 1989). Sieved samples are stored 

in glass or polyethylene containers. The holding times for 

unprocessed and processed sediment and soil samples have 

not been established because total elemental concentrations 

will most likely not change. Various subsampling techniques, 

such as cone-and-quartering or riffle splitting, are used to 

obtain a representative subsample of the sieved material prior 

to digestion. Samples are digested with nitric acid using a 

closed-vessel microwave digestion procedure (U.S. Environmental Protection Agency, 1998); the digestion is typically 

not a total dissolution, therefore the results represent recoverable concentrations. The recommended holding time for 

sediment and soil digestates is 6 months (U.S. Environmental 

Protection Agency, 2005).

10 Determination of Elements Using Collision/Reaction Cell ICP–MS
Analytical Method 11

Figure 5. Sample-flow paths used with new collision/reaction cell inductively coupled plasma–mass spectrometric 

methods. Panel A represents the sample-flow path used for the introduction of internal standard solution with filtered 

samples and filtered biota, sediment, and soil digestates. Panel B represents the sample-flow path used for arsenic 

speciation that reduces the flow rate from the high-performance liquid chromatograph (HPLC) to the level that is 

required by the microflow nebulizer. The pump speed is increased over that used in panel A so that the flow rate at the 

nebulizer is reduced from 1 milliliter per minute (mL/min) to about 0.3 mL/min.

To microflow

nebulizer

Internal standard

Orange/red tubing

From autosampler

White/white tubing

To microflow

nebulizer

Peristaltic pump

To waste white/white

tubing

~1 mL/min

from HPLC

Peristaltic pump

A

B

Junction tee

Junction tee
Table 5. Typical operating characteristics for collision/reaction cell inductively coupled plasma–mass spectrometry.

[cICP–MS, collision/reaction cell inductively coupled plasma–mass spectrometer; Pa, pascal; °C, degrees Celsius; %, percent; L/min, 

liters per minute; kPa, kilopascal; W, watts; mL/min, milliliters per minute; rpm, revolutions per minute; mm, millimeters; id, inside 

diameter; V, volts; <, less than; OctP, octapole; QP, quadrupole; RF, radio frequency]

cICP–MS Typical setting (range)

Instrument Agilent 7500ce

Forward power 1550 W (700–1600)

Reflected power 4 W (<20)

Analyzer pressure 3.33 x 10-3 Pa (3 x 10-4 to 2 x 10-3)

Carrier argon 1.00 L/min (0.8 to 1.3)

Makeup argon None (0 to 1.0 L/min)

Plasma argon 15 L/min (about 15)

Auxiliary argon 0.9 L/min (0 to 1.0)

Sampling depth 8 mm (7 to 10)

Sample introduction

Concentric nebulizer 0.3 mL/min (0.2 to 0.3)

Quartz spray chamber 2°C

Peristaltic pump 0.1 rpm, during data acquisition

Sample tube white/white, 1.02 mm id 

Internal standard tube orange/red, 0.19 mm id

Waste tube yellow/blue, 1.52 mm id

Ion optics and collision/reaction cell

Extract 1 0 V

Extract 2 –133 V (–120 to –150)

Omega bias –16 V (–15 to –40)

OctP RF 161 V (160 to 200)

Normal mode He mode H2 mode

Cell gas 0 mL/min 4.0 (2.5 to 5.5) 4.0 (3 to 6)

Cell entrance –30 V (–30 to –10) –25 –25

QP focus 3 V (1 to 5) –8 –8

Cell exit –30 V (–30 to –8) –45 –45

OctP bias –6 V (–10 to –3) –18 –18

QP bias –3 V (–7 to 0.5) –13 –5

12  Determination of Elements Using Collision/Reaction Cell ICP–MS
Analytical Method 13

Filtered arsenic speciation samples are collected using 

the procedures outlined in the USGS National Field Manual 

for the Collection of Water-Quality Data (U.S. Geological 

Survey, variously dated). Samples are preserved with ethylenediaminetetraacetic acid and stored in opaque polyethylene 

bottles (U.S. Geological Survey, variously dated, specifically 

chap. A5.6.4). Samples that are submitted for speciation in 

the laboratory need to be shipped on ice and analyzed within 

90 days of collection to ensure the accuracy of the results. 

The solid phase extraction cartridge and eluate for samples 

that are speciated in the field need to be shipped on ice to the 

laboratory within 30 days of collection. The cartridge needs 

to be extracted in the laboratory within 49 days of sample 

collection. The extract and eluate need to be analyzed within 

6 months.

Calibration Standards and Other Solutions

ASTM Type I reagent water (American Society for Testing and Materials, 2000, p. 10), acids, and reagents used in 

this method must be verified to have analyte concentrations 

less than the method detection limits. Calibration standards 

for elemental determinations are prepared from either commercially available single-element stock solutions or multielement stock solutions. The concentrations in all solutions 

used need to be verified by using National Institute of Standards and Technology (NIST) standards or standards that are 

traceable to NIST as a reference. Multielement calibration 

standards containing Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, 

Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sb, Se, Si, Sr, Tl, U, 

V, W, and Zn are prepared by making appropriate dilutions of 

commercial stock solutions with reagent water acidified with 

nitric acid (0.4 percent by volume). Calibration standards for 

whole-water digestates are prepared in 0.4 percent nitric acid 

and 2 percent hydrochloric acid. The multielement standard 

containing Sb also must contain 0.1 percent hydrofluoric acid 

by volume as a preservative. Calibration concentrations are 

typically 10 and 100 μg/L for all trace elements, whereas Al, 

Ba, Mn, Sr, and Zn use an additional standard at 2,500 μg/L 

to extend the calibration range. Multielement calibration 

standards for Ca and Fe were prepared at 25 and 250 mg/L 

and 100 and 1,000 μg/L, respectively, whereas K, Mg, Na, and 

SiO2 were prepared at 10 and 100 mg/L.

Several internal standard elements are introduced automatically into the sample stream to minimize sample matrix 

effects and instrumental drift (see fig. 5A). A multielement 

internal standard solution containing Li (highly enriched in 

lithium-6), Bi, Ce, Cs, Ge, In, Rh, Sc, Th, and Y at 1 mg/L is 

prepared by making appropriate dilutions of each stock solution with reagent water acidified with nitric acid (0.4 percent 

by volume).

The cICP–MS operating conditions are optimized daily 

by using a tuning solution composed of Ce, Co, Li, Mg, Th, 

and Y at 10 μg/L each. The tuning solution was prepared 

by making appropriate dilutions of each stock solution with 

reagent water and acidifying with nitric and hydrochloric acid 

at 0.4 and 2 percent (by volume), respectively.

The electron multiplier of the cICP–MS is operated in 

the pulse-counting mode whenever an elemental concentration produces less than about 2 million counts per second. 

Higher count rates saturate the multiplier, thereby requiring 

that the multiplier be used in the analog mode. The linear 

transition between the two modes requires that a pulse/analog 

(P/A) calibration factor be established daily for each element 

determined. A solution containing all the elements is prepared 

by making the appropriate dilution of the stock solutions with 

reagent water acidified with nitric acid (0.4 percent by volume). Elemental concentrations range from 5 to 25 μg/L.

The performance of cICP–MS in interference reduction 

is tested periodically by analyzing solutions containing major 

interfering constituents and affected analytes. A solution 

having interfering constituents contains 1,000 mg/L Cl (from 

HCl); 100 mg/L each of Al, K, Mg, Mn, P, S, and Zn; 50 mg/L 

Si; 250 mg/L each of C (from potassium hydrogen phthalate), 

Ca, Fe, and Na; and 2 mg/L each of Mo, Ti, and Er. Another 

solution that also contains selected analytes is prepared as 

above with the addition of Ag, As, Cd, Co, Cr, Cu, Ni, Se, V, 

and W, each at 20 μg/L. Potential interferences associated 

with these elements are listed in table 3. The solutions are 

prepared by making suitable dilutions of contaminant-free 

commercial solutions (except for C and Cl) with reagent water 

acidified to 1 percent by volume with nitric acid.

Quantitation

Analyte concentrations are calculated automatically from 

the linear regression equations by the operating system. The 

operating system monitors results for quality-control and 

quality-assurance samples in real-time and updates calibration whenever acceptance criteria fail. Furthermore, sample 

carryover is minimized by automatically increasing the length 

of the rinse-cycle period until an analyte signal reaches an 

acceptable level. Such automatic quantitation and control features are not unlike those provided by older ICP–MS instruments in methods for routine elemental analyses. However, 

He or H2 must be introduced into the cell during data acquisition for elements that are affected by molecular ion interferences and purged from the cell for other elements. There is 

also a notable difference in the procedures used to calculate 

arsenic speciation results when using the cICP–MS. Previous 

arsenic speciation methods were unable to calculate results 

in real time. The chromatographic peaks were integrated and 

the regression equations were calculated offline and required 

substantial manual data processing. The new operating system 

software makes it possible to process the results and calculate 

concentrations in real time.
Reporting of Analytical Data

Elemental concentrations presented in USGS data reports 

are reported so that the rightmost digit (called the least significant digit) represents the uncertainty in the result (Novak, 

1985; Hansen, 1991; U.S. Geological Survey, 2002). The least 

significant digit is determined using guidance outlined by the 

American Society for Testing and Materials (1999). NWQL 

reports results in the national database to the least significant 

digit plus one additional digit. Results for water samples 

will be reported in either micrograms per liter or milligrams 

per liter, depending on the element (see table 1). Elemental 

results for biota, sediment, and soil digestates will be reported 

in micrograms per gram or milligrams per gram, dry weight, 

depending on the element. All arsenic species are reported in 

micrograms–arsenic per liter.

Laboratory Quality Assurance/Quality Control

Laboratory samples are prepared in sample sets consisting of the calibration standards, the interference check solution, laboratory reagent blanks (LRB), and laboratory control 

samples (LCS); the required frequency of analyzing LRB 

and LCS samples is outlined below. Furthermore, standard 

reference materials, laboratory reagent spike samples, and 

laboratory duplicate samples can be included to meet specific 

data-quality objectives. The NWQL uses the Quality Management System Document for essential Quality-Assurance/Quality-Control requirements (Maloney, 2005).

The LRB is prepared to match the matrix of the samples 

and calibration standards. A minimum of one LRB is analyzed for every 10 analytical samples. The LRB cannot have 

any detectable levels of contaminants. The control limits for 

the LRB are plus or minus the LT–MDL for each element. For 

LRB that have a detectable concentration of a certain element, 

the source of contamination must be determined and either 

eliminated or minimized before the environmental samples 

are reanalyzed. In cases where the source of contamination 

cannot be identified, the element is assigned a data-qualifying 

code. The concentration of the element in the environmental 

sample, however, can be reported without a qualifying code 

if it is 10 times greater than the concentration in the LRB. 

Conversely, if concentrations are less than the LT–MDL, the 

samples need to be reanalyzed.

The LCS is prepared from either a solution having a different lot number than the calibration standards or a solution 

from a different vendor than the calibration standards. In 

either case, the solution used to prepare the LCS needs to be 

traceable to NIST. A minimum of one LCS is analyzed for 

every 10 analytical samples. The control limits for the LCS 

are plus or minus two standard deviations of the expected 

concentration of the element of interest. Any elemental results 

that fall outside of the control limits are not reported. All 

environmental samples associated with the failed LCS must be 

reanalyzed until the specified acceptance criterion is met.

All material used to make calibration standards need to 

be NIST traceable. Calibration standards are verified by using 

a NIST traceable LCS. Determined concentrations need to be 

within two standard deviations of the specified concentration.

Results and Discussion of Method 

Validation Data

The analytical bias and variability across the normal 

calibration range of the analytes were determined by using 

results for two USGS standard reference water samples 

(SRWS), and results for reagent-water, ground-water, and 

surface-water samples spiked at several concentrations. The 

SRWSs are based on spiked natural-water matrices whose 

most probable elemental concentrations have been established 

through interlaboratory analyses using multiple analytical 

methods. SRWS T145 was made with water collected from 

the Yampa River near Steamboat Springs, Colorado (Farrar 

and Long, 1997), and SRWS T169 was made with water collected from the South Platte River west of Bailey, Colorado 

(Woodworth and Conner, 2002); water samples from both 

sources were subsequently filtered, spiked, and preserved with 

nitric acid. SRWS T145 and T169 were analyzed between 

21 and 68 times over several days and multiple instrument 

calibrations. A statistical summary of these results is listed in 

table 6. The average results for T145 are within one F-pseudosigma of the most probable concentration except for Na and 

Mg. Nevertheless, the averages for Na and Mg are within 1.5 

F-pseudosigma of the most probable concentration, which is 

considered to be acceptable. The average results for T169 are 

within one F-pseudosigma of the most probable concentration 

except for Ba, Ni, and U, which are within 1.5 F-pseudosigma. 

The concentration of W in the SRWSs was not established; 

therefore, a NIST W standard was used to prepare solutions in 

reagent water. The resulting experimental W concentrations 

were within 4 and 18 percent of the expected concentrations 

of 1 and 50 μg/L, respectively. The corresponding variability 

ranged from 3 to 7 percent for 30 measurements. Results 

for reagent blank samples analyzed with the SRWSs showed 

acceptably low concentration levels with slight variability (see 

table 6).

The new cICP–MS method was validated by determining 

the percentage bias and variability at 2 to 4 spike-concentration levels in reagent-water, ground-water, and surface-water 

matrices. Reagent water was spiked at 4 concentrations; 2 

levels were in the lower one-third of the normal calibration 

range, 1 at roughly one-third and 1 at roughly two-thirds of 

the normal calibration range. Ground-water and surface-water 

matrices were spiked at roughly one-third and two-thirds of 

the normal calibration range. The number of measurements 

at each concentration was between 16 and 33 for each matrix 

determined over several days.

14  Determination of Elements Using Collision/Reaction Cell ICP–MS
Results and Discussion of Method Validation Data 15

Table 6. Long-term bias and variability of results from aqueous standard reference materials using collision/

reaction cell inductively coupled plasma–mass spectrometry.

[Analyte, concentrations are in micrograms per liter unless otherwise noted; SRWS, U.S. Geological Survey standard reference 

water sample; Exp, mean concentration plus or minus one standard deviation for experimental results based on the number of 

measurements, in italics, measured over several days for SRWS T145 and T169; MPV, most probable mean value plus or minus 

one F-pseudosigma for interlaboratory results using multiple methods except for tungsten (W), which are nominal concentrations 

from a diluted National Institute of Standards and Technology reference standard; Blank, mean concentration plus or minus one 

standard deviation for 35 reagent blank measurements taken over several days; mg/L, milligrams per liter]

Analyte

SRWS T145 SRWS T169 Blank

Exp MPV Exp MPV

Ag 7.8±0.3, 68 7.6±0.9 3.9±0.1, 49 3.9±0.2 –0.01±0.01

Al 64±6, 52 68±11 32.9±0.6, 21 34±4 –0.3±0.2

As 10.4±0.6, 52 10±1 8.6±0.7, 49 8.6±0.7 –0.02±0.03

B 46±2, 40 45±6 25±1, 49 24±2 0.3±0.6

Ba 36±2, 68 37±2 41±1, 49 43±2 0.05±0.1

Be 9.0±0.3, 40 9.0±0.7 9.4±0.4, 49 9.6±0.6 –0.009±0.01

Ca, mg/L 30±2, 28 31±1 37±1, 21 37.9±0.9 –0.02±0.02

Cd 9.8±0.3, 68 9.3±0.8 3.60±0.08, 49 3.4±0.2 –0.007±0.01

Co 10.0±0.6, 68 10.0±0.9 1.91±0.05, 49 1.9±0.2 –0.002±0.04

Cr 13.9±0.5, 48 15±1 7.8±0.3, 49 8.2±0.4 –0.024±0.02

Cu 11.6±0.6, 68 11±1 14±1, 49 14.3±0.8 0.008±0.1

Fe 94±4, 38 101±8 12.1±0.5, 21 11±4 –0.03±0.2

K, mg/L 2.0±0.1, 28 2.1±0.2 2.50±0.07, 21 2.6±0.1 –0.014±0.02

Li 26.6±0.9, 40 27±2 9.1±0.3, 49 9.6±0.6 0.003±0.04

Mg, mg/L 8.1±0.5, 28 8.7±0.4 4.2±0.1, 21 4.3±0.1 –0.02±0.03

Mn 20±1, 68 21±2 27±1, 49 27.0±0.9 0.02±0.08

Mo 8.6±0.4, 68 9±1 71.8±0.6, 25 71±2 –0.005±0.02

Na, mg/L 39±2, 21 41±2 10.3±0.4, 21 10.6±0.3 –0.02±0.02

Ni 11±1, 68 11±1 11.1±0.6, 49 10.3±0.7 –0.11±0.03

Pb 12.4±0.6, 68 13±1 10.7±0.3, 49 11.4±0.7 –0.01±0.02

Sb 8.3±0.4, 68 8.8±0.9 2.92±0.09, 49 3.3±0.4 –0.008±0.01

SiO2, mg/L 11.5±0.8, 28 11.3±0.7 6.2±0.2, 21 6.0±0.2 –0.03±0.05

Se 10.7±0.8, 52 10±1 2.7±0.2, 49 2.9±0.4 –0.07±0.08

Sr 208±7, 68 203±19 176±6, 49 174±6 0.2±0.2

Tl 15.1±0.8, 68 15±3 4.3±0.1, 49 4.8±0.5 –0.04±0.02

U 1.02±0.06, 68 1.1±0.1 1.55±0.02, 25 1.7±0.1 –0.008±0.009

V 11.2±0.7, 68 12±2 7.4±0.3, 49 7.4±0.4 –0.03±0.02

W 0.96±0.07, 30 1 59±2, 30 50 –0.1±0.3

Zn 9.6±0.8, 68 10±2 18±1, 49 19±1 –0.2±0.4
The bias and variability for reagent water spiked with 

analyte concentrations in the lower one-third of the normal 

calibration range are listed in table 7. The percentage bias for 

reagent water spiked at trace-element concentrations of 0.5 

to 3 μg/L (column 3 of table 7) averaged 4.4 percent with a 

range of –6 to 16 percent, whereas the average percentage bias 

for Ca, K, Mg, Na, and SiO2 was 1.4 percent with a range of 

–4 to 10 percent for spikes of 0.5 to 3 mg/L. For the other 3 

spike levels (column 6 of table 7 and columns 3 and 6 of table 

8), the average bias for trace elements was 0.9 percent with 

percentages ranging from –8.1 to 13 percent, whereas for Ca, 

K, Mg, Na, and SiO2, the average bias was 1.0 percent with 

percentages ranging from –7.7 to 10 percent. The corresponding variability ranged from 0.7 to 10 percent relative standard 

deviation (percent RSD) for trace elements and from 1.0 to 10 

percent RSD for Ca, K, Mg, Na, and SiO2.

Validation data for surface-water and ground-water matrices are listed in tables 9 and 10, respectively. Surface water 

was spiked with between 17 and 1,754 μg/L for trace elements 

and 8 and 95 mg/L for Ca, K, Mg, Na, and SiO2. The average bias for trace elements was 1.8 percent with percentages 

ranging from –14 to 21 percent; for Ca, K, Mg, Na, and SiO2, 

the average bias was 0.96 percent with a range of –4.2 to 7.8 

percent. The associated variability ranged from 2.6 to 22 percent RSD for trace elements and from 1.0 to 5.8 percent RSD 

for Ca, K, Mg, Na, and SiO2. Ground water was spiked with 

between 17 and 1,769 μg/L for trace elements and 9 and 95 

mg/L for Ca, K, Mg, Na, and SiO2. The average bias for trace 

elements was –0.59 percent with percentages ranging from 

–16 to 16 percent; for Ca, K, Mg, Na, and SiO2, the average 

bias was 1.5 percent with a range of –7.5 to 11 percent. The 

associated variability ranged from 1 to 18 percent RSD for 

trace elements and from 0.8 to 5.0 percent RSD for Ca, K, Mg, 

Na, and SiO2.

Linear regression analysis was used to evaluate whether 

cICP–MS results for 25 filtered natural-water samples and 25 

unfiltered natural-water digestates were notably different than 

results from established analytical methods. The elemental 

concentration range for the test samples typically extended 3 

to 4 orders of magnitude (see column 6 in tables 11 and 12, in 

addition, see graphs of regression analyses in the Appendix). 

The slopes of the regression equations were used to identify 

systematic or random errors in the analytical results; a slope 

of unity indicates there is no difference between instrumental methods. A nonzero y-intercept indicates possible faulty 

background correction for one of the methods.

Eighty-three percent (24 of 29) of the elements had slopes 

within ±10 percent of unity in the 95 percent confidence interval for filtered natural-water samples. Four of the remaining 

five elements were within ±15 to 30 percent; cICP–MS results 

for Li tended to be lower than the previous method and Tl and 

W tended to be higher. The slope for B was about 30 percent 

greater than unity possibly from carryover from samples with 

high B concentrations. The slope for Be was notably different 

than unity because of the low concentrations present in the test 

samples. For unfiltered digestate samples, 79 percent (23 of 

29) of the elements had slopes within ±10 percent of unity in 

the 95 percent confidence interval. Four of the remaining six 

elements were within ±15 to 20 percent; cICP–MS results for 

Al, Cu, and W tended to be lower than the other methods and 

Mo tended to be higher. Slopes for Ag and Sb were substantially different than unity because of the low concentrations 

present in the test samples.

The median y-intercept for trace elements corresponding 

to the linear regression analysis of filtered results was 0.01 

μg/L with a range from –1.7 to 69 μg/L; the median for Ca, K, 

Mg, Na, and SiO2 was 0.05 mg/L with a range of –0.8 to 0.3 

mg/L. B, Ba, Li, and Sr had the largest y-intercepts ranging from –2 to 69 μg/L and corresponded to sample results 

extending from 100 to 1,000 μg/L. For trace elements in 

whole-water digestates, the median y-intercept was 0.04 μg/L 

with a range from –26 to 1.7 μg/L; the medium for Ca, K, 

Mg, Na, and SiO2 was –0.2 mg/L with a range of –0.6 to 1.1 

mg/L. The y-intercepts for Al and Fe were –22 and –26 μg/L, 

respectively, most likely an effect of sample concentrations 

extending to 10,000 μg/L.

The bias and variability associated with the analysis of 

biological standard reference materials also was determined. 

The analysis of sediment or soil standard reference material 

was not tested because the microwave digestion procedure 

used by NWQL does not completely solubilize such solids, 

thereby making the comparison of results to certified concentrations problematic. The results of the analysis of National 

Research Council Canada Dolt-2 and Dorm-2 standard reference materials (SRM) and NIST SRM 1577b using cICP–MS 

are listed in table 13. Linear regression analysis comparing 

the experimental elemental concentrations from cICP–MS to 

the certified concentrations in the standard reference materials 

is shown in figure 6. The regression coefficients indicate an 

acceptable linear relation. Nevertheless, on the average, the 

experimental results were about 17 percent higher for elemental concentrations ranging five orders of magnitude.

16  Determination of Elements Using Collision/Reaction Cell ICP–MS
Results and Discussion of Method Validation Data 17

Table 7. Long-term bias and variability for the analysis of reagent water spiked with analyte concentrations 

in the lower one-third of the normal calibration range using collision/reaction cell inductively coupled 

plasma–mass spectrometry.

[Spiked reagent-water samples were analyzed randomly with spiked surface- and ground-water samples over several days; 

Analyte, concentrations are in micrograms per liter unless otherwise noted; Exp, mean concentration plus or minus standard 

deviation for experimental results based on 16 to 22 replicate determinations at each spike concentration; Percent bias, percentage error between the expected concentration and the mean experimental concentration; Percent RSD, percent relative standard 

deviation; mg/L, milligrams per liter]

Analyte Exp Percent 

bias

Percent 

RSD Exp Percent bias

Percent 

RSD

Ag 1.05±0.03 5.0 2.9 5.17±0.04 3.4 0.8

Al 2.91±0.09 16 3.1 13.9±0.6 11 4.2

As 1.04±0.05 4.0 4.8 5.21±0.09 4.2 1.7

B 2.4±0.6 2.8 25 12.1±0.9 –3.0 7.5

Ba 2.6±0.2 4.8 5.7 13.2±0.3 5.6 2.3

Be 0.50±0.01 0 2.0 2.51±0.04 0.4 1.6

Ca, mg/L 2.19±0.09 9.5 4.0 16.6±0.2 10 1.0

Cd 1.05±0.02 5.0 1.9 5.20±0.08 4.0 1.5

Co 1.06±0.03 6.0 2.8 5.26±0.04 5.2 0.8

Cr 1.00±0.02 0 2.2 4.7±0.3 –5.8 6.2

Cu 2.7±0.3 7.2 9.7 13.0±0.3 4.2 2.0

Fe 2.4±0.3 9.1 11 11.5±0.1 0 0.7

K, mg/L 0.54±0.01 8.0 2.2 2.20±0.08 10 3.5

Li 2.53±0.08 1.2 3.2 12.6±0.3 0.4 2.3

Mg, mg/L 0.48±0.01 –4.0 2.7 2.09±0.07 4.5 3.2

Mn 2.68±0.07 7.2 2.6 13.4±0.2 7.2 1.6

Mo 1.00±0.02 0 2.0 4.90±0.05 –2.0 1.0

Na, mg/L 0.48±0.01 –4.0 2.7 5.1±0.2 1.8 4.5

Ni 1.07±0.05 7.0 4.7 5.2±0.1 3.0 2.1

Pb 1.05±0.05 5.0 4.8 5.1±0.2 2.8 3.7

Sb 1.00±0.03 0 3.0 4.86±0.04 –2.9 0.8

Se 1.12±0.06 12 5.4 5.6±0.2 12 3.0

SiO2, mg/L 2.9±0.2 –2.3 6.5 16.0±0.3 6.7 1.8

Sr 1.89±0.07 –5.5 3.7 13.0±0.7 3.8 5.2

Tl 0.51±0.02 2.0 3.9 2.65±0.07 6.0 2.6

U 1.05±0.04 5.0 3.8 5.2±0.2 4.6 4.0

V 1.01±0.04 1.0 4.0 5.1±0.1 1.2 2.0

W 0.79±0.02 –1.3 2.7 8.9±0.1 11 1.0

Zn 2.87±0.09 13 3.1 14.1±0.3 13 2.3
Table 8. Long-term bias and variability for the analysis of reagent water spiked with analyte concentrations at onethird and two-thirds of the normal calibration range using collision/reaction cell inductively coupled plasma–mass 

spectrometry.

[Spiked reagent-water samples were analyzed randomly with spiked surface- and ground-water samples over several days; Analyte, concentrations are in micrograms per liter unless otherwise noted; Exp, mean concentration plus or minus standard deviation for experimental 

results based on 20 to 33 replicate determinations at each spike concentration; Percent bias, percentage error between the expected concentration and the experimental mean concentration; Percent RSD, percent relative standard deviation; mg/L, milligrams per liter]

Analyte Exp Percent bias Percent RSD Exp Percent bias Percent RSD

Ag 34±2 0.6 4.8 72±4 7.7 6.1

Al 310±20 –7.3 5.6 640±20 –4.0 2.6

As 35±3 5.7 7.6 66±3 –1.5 4.8

B 82±8 –1.8 10 160±20 –4.3 10

Ba 86±3 3.0 3.0 173±4 3.7 2.3

Be 16.5±0.8 –1.2 4.8 35±1 5.1 3.6

Ca, mg/L 48.6±0.9 –2.9 1.8 83±2 –2.6 3.1

Cd 33.8±0.8 1.2 2.4 65±1 –2.1 2.2

Co 34±1 1.5 3.2 66±2 –0.6 2.5

Cr 182±2 5.8 1.2 340±30 2.2 8.8

Cu 85±7 2.2 8.7 170±10 1.2 7.1

Fe 371±3 3.1 0.7 717±7 –0.4 1.0

K, mg/L 17.6±0.2 –2.0 1.2 35±2 –2.0 5.2

Li 80±3 –4.2 3.9 170±5 1.9 2.7

Mg, mg/L 19.4±0.6 2.0 3.3 38±2 0.7 5.5

Mn 320±10 –3.2 3.1 640±20 –3.5 2.9

Mo 31±1 –6.3 3.5 62±2 –7.5 3.8

Na, mg/L 20±1 1.8 6.5 39±3 –3.0 7.8

Ni 33±2 –0.9 5.4 66±3 –0.9 4.2

Pb 34±2 2.3 4.4 65±2 –1.8 2.8

Sb 32±2 –3.4 7.7 61±2 –8.1 3.2

Se 35±1 5.6 4.0 69±2 3.2 2.6

SiO2, mg/L 22±2 –1.7 10 37±3 –7.7 7.3

Sr 800±40 –4.1 4.3 1,620±80 –3.2 4.7

Tl 17.3±0.7 3.5 4.0 34±1 2.1 2.8

U 32±2 –2.8 5.5 63±3 –5.7 5.3

V 32±2 –5.0 7.2 63±1 –4.8 2.3

W 41±2 7.9 6.0 73±5 –4.0 6.3

Zn 340±20 2.9 5.0 660±30 –1.2 4.7

18  Determination of Elements Using Collision/Reaction Cell ICP–MS
Results and Discussion of Method Validation Data 19

Table 9. Long-term bias and variability for the analysis of surface water spiked with analyte concentrations at one-third 

and two-thirds of the normal calibration range using collision/reaction cell inductively coupled plasma–mass spectrometry.

[Spiked surface-water samples were analyzed randomly with spiked reagent- and ground-water samples over several days; Analyte, concentrations are in micrograms per liter unless otherwise noted; Exp, mean concentration plus or minus standard deviation for experimental results 

based on 20 to 30 replicate determinations at each spike concentration; Percent bias, percentage error between the expected concentration and the 

experimental mean concentration; Percent RSD, percent relative standard deviation; mg/L, milligrams per liter

Analyte Exp Percent bias Percent RSD Exp Percent bias Percent RSD

Ag 34±2 3.0 6.5 57±2 –14 3.7

Al 350±60 4.7 18 770±170 16 22

As 33±2 –1.4 6.5 66±4 –0.8 6.4

B 130±10 21 7.8 230±20 19 9.9

Ba 122±6 8.8 4.6 201±8 3.1 4.0

Be 18±2 8.4 11 34±4 3.5 11

Ca, mg/L 59±2 7.5 4.0 91±5 –3.9 5.8

Cd 33±2 –1.5 6.0 66±4 0 5.9

Co 34±2 0.7 5.3 66±4 –0.3 5.6

Cr 160±10 –5.1 6.6 310±20 –6.9 7.9

Cu 93±8 8.5 8.9 170±20 –0.6 11

Fe 440±12 8.0 2.6 640±20 –3.4 3.4

K, mg/L 18.3±0.8 –3.8 4.3 36±1 7.8 3.7

Li 90±9 5.2 9.5 170±20 –0.9 9.8

Mg, mg/L 20.2±0.8 –4.0 4.2 38.8±0.7 2.1 1.8

Mn 320±20 –2.5 5.3 630±30 –6.0 4.1

Mo 34±2 1.7 7.2 73±5 9.5 7.5

Na, mg/L 14.5±0.7 3.6 5.0 32.3±0.3 –3.0 1.0

Ni 33±2 –0.1 6.3 67±4 0.1 5.9

Pb 33±2 –1.0 6.4 67±4 0.6 6.4

Sb 32±2 –3.0 5.1 63±4 –5.3 5.7

Se 36±2 9.2 5.3 66±4 –1.2 5.4

SiO2, mg/L 8.6±0.2 7.5 2.8 32±1 –4.2 4.1

Sr 940±50 2.1 5.4 1,680±80 –4.4 4.9

Tl 16±1 –4.2 6.4 34±2 0.6 6.6

U 38±2 –9.4 4.7 68±3 2.2 4.4

V 33±2 0 5.9 69±4 3.6 5.8

W 43±1 14 3.3 76±3 1.2 3.5

Zn 320±20 –5.8 5.0 640±30 –6.1 4.9
Table 10. Long-term bias and variability for the analysis of ground water spiked with analyte concentrations at 

one-third and two-thirds of the normal calibration range using collision/reaction cell inductively coupled plasma–

mass spectrometry.

[Spiked ground-water samples were analyzed randomly with spiked reagent- and surface-water samples over several days; Analyte, 

concentrations are in micrograms per liter unless otherwise noted; Exp, mean concentration plus or minus standard deviation for 

experimental results based on 20 to 32 replicate determinations at each spike concentration; Percent bias, percentage error between the 

expected concentration and the experimental mean concentration; Percent RSD, percent relative standard deviation; mg/L, milligrams 

per liter]

Analyte Exp Percent bias Percent RSD Exp Percent bias Percent RSD

Ag 33±2 –0.9 5.7 64±4 –4.1 6.3

Al 320±60 –5.7 18 700±100 9.1 18

As 32±2 –5.1 7.0 66±5 –1.5 7.0

B 106±8 16 7.8 200±20 14 7.6

Ba 134±6 5.3 4.4 214±9 1.6 4.4

Be 17±2 1.2 9.2 33±3 –1.5 8.8

Ca, mg/L 57±2 3.2 3.2 88±4 –7.5 5.0

Cd 32±2 –4.2 5.6 66±4 –1.2 5.5

Co 32±2 –3.1 5.3 65±4 –1.8 5.4

Cr 162±2 –3.3 1.0 300±20 –8.7 8.1

Cu 93±7 2.7 7.7 170±10 –4.0 8.5

Fe 420±40 7.7 8.8 550±30 –16 4.8

K, mg/L 18.9±0.8 –0.5 4.2 35±1 5.7 3.1

Li 85±7 2.0 8.2 160±10 –2.8 7.7

Mg, mg/L 15.3±0.4 2.0 2.4 37±1 11.0 3.5

Mn 310±20 –6.7 4.8 630±30 –5.2 5.1

Mo 34±2 1.2 6.8 74±5 10 7.2

Na, mg/L 17.5±0.6 2.9 3.3 32.6±0.3 –2.4 1.0

Ni 32±2 –3.1 7.3 66±4 –0.8 6.5

Pb 32±2 –4.2 6.0 66±4 –0.9 6.0

Sb 32±2 –3.1 5.1 63±4 –5.5 5.7

Se 34±1 3.2 4.3 63±3 –5.0 4.6

SiO2, mg/L 9.40±0.08 4.3 0.8 32.0±0.5 –3.9 1.6

Sr 920±50 –1.7 5.3 1,800±100 1.9 5.8

Tl 16±1 –4.2 6.4 34±2 0.6 6.6

U 44±2 7.0 4.8 75±1 –0.1 1.6

V 33±2 0 5.9 69±4 3.5 5.8

W 39.9±0.8 5.0 2.0 64±2 –3.9 3.2

Zn 320±20 –5.8 5.0 640±30 –6.1 4.9

20 Determination of Elements Using Collision/Reaction Cell ICP–MS
Results and Discussion of Method Validation Data  21

Table 11. Linear regression results for filtered natural-water samples analyzed using collision/reaction cell 

inductively coupled plasma–mass spectrometry and other spectrometric methods.

[Natural-water samples filtered using a 0.45-micrometer pore-size membrane and acidified to a pH of less than 2; Analyte, 

concentrations are in micrograms per liter unless otherwise noted; Slope, see Appendix for other spectrometric method used in 

comparison; 95% CI, 95-percent confidence interval for the slope or y-intercept; y-inter, y intercept; Conc. range, concentration 

range of the 25 samples tested; MDL, method detection limit (see table 2); mg/L, milligrams per liter]

Analyte Slope 95% CI 

for slope y-inter 95% CI for y-inter Conc. range

Ag 0.933 0.929 to 0.936 –0.006 –0.022 to 0.009 MDL to 19

Al 0.965 0.961 to 0.970 –0.090 –0.153 to –0.026 MDL to 40

As 0.954 0.935 to 0.972 0.107 –0.091 to 0.305 0.3 to 45

B 1.294 1.287 to 1.301 –1.735 –6.905 to 3.434 3 to 2,000

Ba 0.891 0.832 to 0.949 –1.117 –5.782 to 3.548 31 to 203

Be 0.138 –0.102 to 0.378 –0.005 0.001 to 0.008 MDL to 0.06

Ca, mg/L 0.915 0.878 to 0.952 0.052 –2.047 to 2.151 13 to 150

Cd 0.926 0.924 to 0.928 0.002 –0.020 to 0.023 MDL to 47

Co 0.986 0.981 to 0.990 0.018 –0.049 to 0.085 MDL to 50

Cr 1.050 1.029 to 1.071 –0.003 –0.489 to 0.482 0.09 to 7,400

Cu 0.964 0.953 to 0.975 0.299 0.098 to 0.499 0.06 to 60

Fe 0.934 0.913 to 0.955 –0.035 –0.549 to 0.480 MDL to 110

K, mg/L 1.048 1.025 to 1.070 –0.112 –0.183 to –0.040 0.7 to 8.0

Li 1.208 1.160 to 1.255 5.659 1.708 to 9.610 0.2 to 230

Mg, mg/L 0.982 0.967 to 0.997 –0.779 –1.299 to –0.260 1.6 to 110

Mn 1.026 0.983 to 1.069 0.082 –1.256 to 1.420 MDL to 600

Mo 1.045 1.038 to 1.053 –0.112 –0.239 to 0.016 0.3 to 57

Na, mg/L 0.906 0.898 to 0.915 0.273 –0.263 to 0.810 1.6 to 220

Ni 1.045 1.035 to 1.056 0.494 0.285 to 0.704 0.1 to 70

Pb 0.922 0.917 to 0.928 0.024 –0.047 to 0.095 MDL to 42

Sb 0.950 0.949 to 0.952 0.016 0.006 to 0.026 MDL to 44

Se 0.929 0.913 to 0.944 0.134 –0.035 to 0.303 MDL to 52

SiO2, mg/L 0.971 0.937 to 1.005 0.339 –0.890 to 1.567 3.9 to 74

Sr 0.870 0.829 to 0.911 68.799 9.040 to 128.559 30 to 5,000

Tl 0.873 0.870 to 0.875 0.011 –0.035 to 0.057 MDL to 58

U 0.920 0.905 to 0.935 0.246 0.089 to 0.403 0.08 to 45

V 1.010 1.005 to 1.014 –0.162 –0.803 to 0.478 0.2 to 750

W 0.874 0.873 to 0.876 0.031 –0.013 to 0.076 MDL to 132

Zn 1.020 1.013 to 1.028 –0.052 –0.692 to 0.587 0.4 to 280
Table 12. Linear regression results for unfiltered natural-water digestates analyzed using collision/reaction cell 

inductively coupled plasma–mass spectrometry and other spectrometric methods.

[Unfiltered natural-water digestates prepared using in-bottle digestion procedure (Hoffman and others, 1996) and filtered using 20-

micrometer pore-size membrane; Analyte, concentrations are in micrograms per liter unless otherwise noted; Slope, see Appendix for 

other spectrometric method used in comparison; 95% CI, 95-percent confidence interval for the slope or y-intercept; y-inter, y intercept; 

Conc. range, concentration range of the 25 samples tested; MDL, method detection limit (see table 2); mg/L, milligrams per liter]

Analyte Slope 95% CI 

for slope y-inter 95% CI for y-inter Conc. range

Ag 1.854 1.615 to 2.092 –0.011 –0.020 to –0.001 MDL to 0.12

Al 1.167 1.153 to 1.181 –21.799 –52.662 to 9.064 1.7 to 8,200

As 1.021 0.956 to 1.085 –0.413 –0.95 to 0.125 1.4 to 28

B 1.071 1.047 to 1.094 0.076 –1.160 to 1.312 3.8 to 140

Ba 0.970 0.951 to 0.990 –1.493 –3.517 to 0.531 4.6 to 365

Be 0.918 0.897 to 0.939 0.035 –0.009 to 0.080 MDL to 10

Ca, mg/L 1.000 0.954 to 1.046 1.193 –1.097 to 3.483 12 to 140

Cd 0.925 0.904 to 0.946 –0.002 –0.036 to 0.032 MDL to 7.9

Co 1.137 1.065 to 1.208 –0.127 –0.382 to 0.127 0.06 to 10

Cr 0.921 0.874 to 0.969 0.232 –0.274 to 0.738 0.4 to 44

Cu 1.220 1.147 to 1.294 –0.034 –0.463 to 0.396 0.2 to 15

Fe 1.078 1.069 to 1.087 –26.485 –44.006 to –8.963 5.6 to 10,000

K, mg/L 1.026 1.005 to 1.047 –0.210 –0.276 to –0.145 0.7 to 7.7

Li 0.863 0.821 to 0.906 1.472 0.018 to 2.926 MDL to 110

Mg, mg/L 0.993 0.975 to 1.011 –0.594 –1.206 to 0.019 1.5 to 104

Mn 0.938 0.907 to 0.969 1.682 –0.026 to 3.39 1.1 to 200

Mo 0.847 0.815 to 0.879 0.202 0.119 to 0.286 0.2 to 8.9

Na, mg/L 0.942 0.932 to 0.953 0.213 –0.382 to 0.807 1.6 to 196

Ni 1.128 1.080 to 1.176 0.188 0.016 to 0.360 0.3 to 17

Pb 1.089 1.043 to 1.135 0.039 –0.167 to 0.244 0.04 to 17

Sb 1.846 1.484 to 2.207 –0.161 –0.249 to –0.073 0.06 to 0.63

Se 0.831 0.695 to 0.967 0.255 0.133 to 0.378 0.08 to 2.1

SiO2, mg/L 1.136 1.014 to 1.259 0.238 –1.999 to 2.475 3.3 to 37

Sr 1.004 0.987 to 1.020 –2.933 –6.698 to 0.831 27 to 720

Tl 0.942 0.874 to 1.009 0.072 0.031 to 0.113 MDL to 3.0

U 1.024 0.951 to 1.097 0.046 –0.313 to 0.405 0.2 to 14

V 0.900 0.862 to 0.939 0.716 –0.035 to 1.466 1.4 to 75

W 1.122 0.654 to 1.589 –0.016 –0.039 to 0.008 MDL to 0.13

Zn 0.995 0.983 to 1.008 0.538 –0.560 to 1.637 0.7 to 340

22  Determination of Elements Using Collision/Reaction Cell ICP–MS
Results and Discussion of Method Validation Data  23

Analyte

Dolt-2 Dorm-2 Bovine

Exp Target Exp Target Exp Target

Ag 0.655±0.005 0.608±0.032 0.035±0.007 0.041±0.013 0.043±0.009 0.039

Al 36±1 25.2±2.4 12.4±0.5 10.9±1.7 1.06±0.05 (3)

As 18.31±0.06 16.6±1.1 20.7±0.1 18.0±1.1 0.071±0.008 (0.05)

Ca, mg/g na na na na 0.13±0.01 0.116

Cd 24.4±0.3 20.8±0.5 0.07±0.2 0.043±0.008 0.6±0.2 0.50

Co 0.24±0.02 0.24±0.05 0.22±0.02 0.182±0.031 0.27±0.02 (0.25)

Cr 0.30±0.01 0.37±0.08 37.4±0.6 34.7±5.5 na na

Cu 29±2 25.8±1.1 2.1±0.1 2.34±0.16 160±10 160

Fe 1,290±5 1,103±47 183.6±0.8 142±10 217±2 184

K, mg/g na na na na 10.54±0.04 9.94

Mg, mg/g na na na na 0.67±0.01 0.601

Mn 7.2±0.3 6.88±0.56 4.3±0.2 3.66±0.34 11.7±0.5 10.5

Mo na na na na 4.1±0.2 3.5

Na, mg/g na na na na 2.49±0.09 2.42

Ni 0.17±0.02 0.20±0.02 19.6±0.4 19.4±3.1 na na

Pb 0.25±0.02 0.22±0.02 0.045±0.004 0.065±0.007 0.124±0.008 0.129

Se 7.7±0.1 6.06±0.49 1.77±0.06 1.4±0.09 0.940±0.009 0.73

Sr na na na na 0.143±0.003 0.136

V na na na na 0.109±0.001 (0.123)

Zn 104±3 85.8±2.5 26.7±0.6 25.6±2.3 140±4 127

Table 13. Bias and variability of results for digested biological standard reference materials analyzed by collision/

reaction cell inductively coupled plasma–mass spectrometry.

[Analyte, concentrations are in micrograms per gram unless otherwise noted; Dolt-2, National Research Council Canada (NRC) dogfish liver 

standard reference material; Dorm-2, NRC dogfish muscle standard reference material; Bovine, National Institute of Standards and Technology bovine liver standard reference material 1577b; Exp, mean concentration plus or minus one standard deviation for experimental results 

based on three replicate measurements; Target, certified concentration (plus or minus one standard deviation when available), numbers in 

parentheses are uncertified; mg/g, milligrams per gram; na, concentration not available]
Figure 6. Linear regression analysis of experimental concentrations for all elements in biological standard reference 

material digestates analyzed by collision/reaction cell inductively coupled plasma–mass spectrometry in relation to 

the certified concentrations. Error bars in each axis represent one standard deviation. The regression coefficients 

are the slope (m), y-intercept (b), correlation coefficient (R), and the number of data points (N). Concentrations are 

in micrograms per gram or milligrams per gram, dry weight. The dataset is listed in table 13.

Summary and Conclusions

A new analytical method using collision/reaction cell 

inductively coupled plasma mass spectrometry (cICP–MS) 

has been shown to eliminate molecular ion interferences associated with selected elements. Helium or hydrogen was used 

to eliminate or substantially reduce such interferences. The 

scope of the updated method was expanded by including elements that were not determined by earlier inductively coupled 

plasma–mass spectrometric (ICP–MS) methods. The updated 

method can be used to determine elements in filtered natural water and in a wide variety of filtered aqueous matrices 

including whole water, biota, sediment, and soil digestates. 

Elements that are not affected by molecular ion interference 

also can be determined with bias and variability comparable 

to earlier ICP–MS methods. 

The findings of this report are summarized as follows to 

address previously cited objectives:

Method detection limits were established for all 

elements determined using the new method. Most 

method detection limits are lower or unchanged 

compared to earlier methods except for Co, K, Mg, Ni, 

SiO2, and Tl, which are less than a factor of 2 higher. 

The results presented have shown that As, Ca, Co, 

Cr,Cu, Fe, K, Mg, Na, Ni, Se, Si, V, W, and Zn can be 

determined accurately when using a collision/reaction 

gas without mathematical corrections in the presence 

of interferent concentrations substantially greater than 

those found in routinely submitted samples. A collision/reaction gas was not needed when determining 

Ag, Al, Cd, and Mn because the interferences were 

insignificant at the interferent concentrations tested.

•

•

0.01 0.1 1 10 100 1,000

0.01

0.1

1

10

100

1,000

Linear regression line

E

X

P

E

RIM

E

N

T

A

L

C

O

N

C

E

N

T

R

A

TIO

N

CERTIFIED CONCENTRATION

Regression coefficients

m = 1.17

b = 0.45

R = 0.9999

N = 26

24  Determination of Elements Using Collision/Reaction Cell ICP–MS
References Cited  25

Samples having specific conductances less than 7,500 

μS/cm can be analyzed directly by cICP–MS with 

acceptable bias and variability. Previous ICP–MS 

methods required that samples with specific conductances greater than 2,500 μS/cm be diluted. Ninetyfive percent of the samples submitted to NWQL in 

2004 had specific conductances less than 3,000 μS/cm.

The bias and variability of the cICP–MS method 

were determined using results from aqueous standard 

reference materials. The average results for the two 

standard reference materials tested were within one 

F-pseudosigma of the most probable concentration 

except for Ba, Na, Mg, Ni, and U. Nevertheless, the 

averages for these elements were within an acceptable 

1.5 F-pseudosigma of the most probable concentration. Elemental results for biological standard reference materials also compared closely to the certified 

concentrations.

The new cICP–MS method was validated by using 

analytical results from reagent-water, ground-water, 

and surface-water matrices that were spiked at 2 to 4 

concentration levels. The percentage biases for reagent 

water, ground water, and surface water ranged from 

–14 to 21 percent for trace elements and –7.7 to 10 

percent for Ca, K, Mg, Na, and SiO2.

The cICP–MS results from 25 filtered natural-water 

samples and 25 whole-water digestates were compared 

with results from previously used methods. Furthermore, experimental results for three biological standard reference material digestates were compared to 

certified concentrations. Linear regression analysis of 

these results indicated that the bias and variability of 

cICP–MS is acceptable for a wide variety of sample 

matrices.

References Cited

Agilent Technologies, 2001, Agilent 1100 series reference 

manuals for micro autosampler, quaternary pump, thermostatted column compartment, and thermostatted autosampler: Part numbers G1313-90004, G1316-90003, G1311-

90003, and G1330-90002, Waldbronn, Germany.

Agilent Technologies, 2004a, Agilent 7500 ICP–MS ChemStation operator’s manual: Part number G3270-90110010, 

Rev. A 2004/04, Tokyo, Japan.

Agilent Technologies, 2004b, Agilent 7500 ICP–MS hardware manual: Part number G3270-90100, Rev. A 2004/04, 

Tokyo, Japan.

•

•

•

•

American Society for Testing and Materials, 1999, E29-93a 

Standard practice for using significant digits in test data 

to determine conformance to specifications: Annual book 

of ASTM standards, Reporting test results, section 7.4, v. 

14.02.

American Society for Testing and Materials, 2000, Annual 

book of ASTM standards, Section 11, Water: 

Philadelphia, American Society for Testing and Materials, 

v. 11.01, D1193, p. 10.

Childress, C.J.O., Foreman, W.T., Connor, B.F., and 

Maloney, T.J., 1999, New reporting procedures based on 

long-term method detection levels and some considerations 

for interpretations of water-quality data provided by the 

U.S. Geological Survey National Water Quality Laboratory: 

U.S. Geological Survey Open-File Report 99-193, 19 p. 

Crawford, J.K., and Luoma, S.N., 1993, Guidelines for studies 

of contaminants in biological tissues for the National WaterQuality Assessment Program: U.S. Geological Survey 

Open-File Report 92-494, 69 p.

Faires, L.M., 1993, Methods of analysis by the U.S. 

Geological Survey National Water Quality Laboratory—

Determination of metals in water by inductively coupled 

plasma–mass spectrometry: U.S. Geological Survey OpenFile Report 92-634, 28 p.

Farrar, J.W., and Long, H.K., 1997, Report on the U.S. 

Geological Survey’s evaluation program for standard reference samples distributed in September 1996: U.S. 

Geological Survey Open-File Report 97-20, 145 p.

Fishman, M.J., and Friedman, L.C., eds., 1989, Methods of 

determination of inorganic substances in water and fluvial 

sediments: U.S. Geological Survey Techniques of WaterResources Investigations, book 5, chap. A1, 545 p.

Garbarino, J.R., and Taylor, H.E., 1994, Inductively coupled 

plasma–mass spectrometric method for the determination of 

dissolved trace elements in natural water: U.S. Geological 

Survey Open-File Report 94-358, 28 p. 

Garbarino, J.R., and Struzeski, T.M., 1998, Methods of analysis by the U.S. Geological Survey National Water Quality 

Laboratory—Determination of elements in whole-water 

digests using inductively coupled plasma–optical emission 

spectrometry and inductively coupled plasma–mass spectrometry: U.S. Geological Survey Open-File Report 98-165, 

101 p.

Garbarino, J.R. 1999, Methods of analysis by the U.S. 

Geological Survey National Water Quality Laboratory—Determination of dissolved arsenic, boron, lithium, 

selenium, strontium, thallium, and vanadium using inductively coupled plasma–mass spectrometry: U.S. Geological 

Survey Open-File Report 99-093, 31 p.
Garbarino, J.R., 2000, Methods of analysis by the U.S. 

Geological Survey National Water Quality Laboratory—

Determination of whole-water recoverable arsenic, boron, 

and vanadium using inductively coupled plasma–mass 

spectrometry: U.S. Geological Survey Open-File Report 

99-464, 15 p.

Garbarino, J.R., Bednar, A.J., and Burkhardt, M.R., 2002, 

Methods of analysis by the U.S. Geological Survey National 

Water Quality Laboratory—Arsenic speciation in naturalwater samples using laboratory and field methods: U.S. 

Geological Survey Water-Resources Investigations Report 

02-4144, 40 p.

Hansen, W.R., 1991, Suggestions to authors of the reports of 

the United States Geological Survey (7th ed.): U.S. Government Printing Office, p. 119–121.

Hem, J.D., 1986, Study and interpretation of the chemical 

characteristics of natural water: U.S. Geological Survey 

Water-Supply Paper 2254, p. 67.

Hoaglin, D.C., 1983, Letter values: A set of selected order 

statistics, in Hoaglin, D.C., Mosteller, Frederick, and Tukey, 

J.W., eds., Understanding robust and exploratory data analysis: New York, John Wiley & Sons, Inc., chap. 2, p. 35–55.

Hoffman, G.L., Fishman, M.J., and Garbarino, J.R., 1996, 

Methods of analysis by the U.S. Geological Survey National 

Water Quality Laboratory—In-bottle acid digestion of 

whole-water samples: U.S. Geological Survey Open-File 

Report 96-225, 28 p.

Horlick, Gary, and Montaser, Akbar, 1998, Analytical 

characteristics of ICPMS, in Montaser, Akbar, ed., Inductively coupled plasma mass spectrometry: New York, 

Wiley-VCH, p. 503–613.

Maloney, T.J., 2005, Quality management system, U.S. 

Geological Survey National Water Quality Laboratory: U.S. 

Geological Survey Open-File Report 2005-1263, version 

1.3, November 9, 2005, accessed January 25, 2006, at http://

pubs.water.usgs.gov/ofr2005-1263/

National Environmental Laboratory Accreditation Conference, 2003, Glossary for NELAC: 2003 NELAC Standard, 

EPA/600/R-04/003, accessed September 8, 2005, at URL 

http://www.epa.gov/nelac/2003standards.html.

Novak, C.E., 1985, Preparation of water-resources data 

reports: U.S. Geological Survey Open-File Report 85-480, 

331 p.

Tan, S.H., and Horlick, G., 1986, Background spectral features 

in inductively coupled plasma/mass spectrometry: Applied 

Spectroscopy, v. 40, p. 445–460.

Taylor, J.K., 1990, Statistical techniques for data analysis: 

Lewis Publishers, Inc., CRC Press, Boca Raton, Florida, 

ISBN 0-87371-250-1.

U.S. Environmental Protection Agency, 1996, Microwave 

assisted acid digestion of siliceous and organically based 

matrices: U.S. Environmental Protection Agency Method 

3052, Revision 0, December 1996, 20 p.

U.S. Environmental Protection Agency, 1998, Microwave 

assisted acid digestion of sediments, sludges, soils, and oils: 

U.S. Environmental Protection Agency Method 3051A, 

Revision 1, January 1998, 25 p.

U.S. Environmental Protection Agency, 2000, Guideline establishing test procedures for the analysis of pollutants (Part 

136, Appendix B. Definition and procedure for the determination of the method detection limit—Revision 1.11): U.S. 

Code of Federal Regulations, Title 40, revised as of July 1, 

2000.

U.S. Environmental Protection Agency, 2005, Test methods 

for evaluating solid waste, physical/chemical methods: 

U.S. Environmental Protection Agency SW-846 manual 

(3rd ed.), publication number 955-001-00000-1, accessed 

September 6, 2005, at URL http://www.epa.gov/epaoswer/

hazwaste/test/main.htm.

U.S. Geological Survey, 2002, Policy for storing and reporting significant figures for chemical data: Office of Water 

Quality Technical Memorandum No. 2002.11, accessed 

September 6, 2005, at URL http://water.usgs.gov/admin/

memo/QW/qw02.11.html.

U.S. Geological Survey, variously dated, National field manual 

for the collection of water-quality data: U.S. Geological 

Survey Techniques of Water-Resources Investigations, book 

9, chaps. A1 (Rev. 1/2005), A2 (Rev. 3/2003), A3 (Rev. 

4/2004), A4 (Rev. 9/1999), A5 (Rev. 4/2004), and A5.6.4 

(Rev. 1/2005), accessed September 6, 2005, at URL http://

water.usgs.gov/owq/FieldManual/.

Woodworth, M.T., and Conner, B.F., 2002, Results of the 

U.S. Geological Survey’s analytical evaluation program for 

standard reference samples distributed in March 2002: U.S. 

Geological Survey Open-File Report 02-243, 113 p.

26  Determination of Elements Using Collision/Reaction Cell ICP–MS
Appendix

Linear Regression Analyses of Elemental Results

Figure A1. Linear regression analysis of silver results from filtered water samples analyzed by inductively 

coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled plasma–

mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals table, 

the Intercept and Ag cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).

-4

0

4

8

12

16

20

Ag ICP-MS, in μg/L

-4 0 4 8 12 16 20

Ag cICP-MS, in μg/L

Ag ICP-MS, in μg/L = -.006 + .933 * Ag cICP-MS, in μg/L; R^2 = 1

-.006 -.022 .009

.933 .929 .936

Coefficient 95% Lower 95% Upper

Intercept

Ag cICP-MS, in μg/L

Confidence Intervals

Ag ICP-MS, in μg/L vs. Ag cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Appendix  29
30 Determination of Elements Using Collision/Reaction Cell ICP–MS

-.1

0

.1

.2

.3

.4

.5

Ag ICP-MS, in μg/L

-.1 0 .1 .2 .3 .4 .5

Ag cICP-MS, in μg/L

Ag ICP-MS, in μg/L = -.011 + 1.854 * Ag cICP-MS, in μg/L; R^2 = .918

-.011 -.020 -.001

1.854 1.615 2.092

Coefficient 95% Lower 95% Upper

Intercept

Ag cICP-MS, in μg/L

Confidence Intervals

Ag ICP-MS, in μg/L vs. Ag cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A2. Linear regression analysis of silver results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Ag cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  31

-5

0

5

10

15

20

25

30

35

40

45

Al ICP-MS, in μg/L

-5 0 5 10 15 20 25 30 35 40 45

Al cICP-MS, in μg/L

Al ICP-MS, in μg/L = -.09 + .965 * Al cICP-MS, in μg/L; R^2 = 1

-.090 -.153 -.026

.965 .961 .970

Coefficient 95% Lower 95% Upper

Intercept

Al cICP-MS, in μg/L

Confidence Intervals

Al ICP-MS, in μg/L vs. Al cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A3. Linear regression analysis of aluminum results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Al cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
32  Determination of Elements Using Collision/Reaction Cell ICP–MS

1

1E1

1E2

1E3

1E4

1E5

Al ICP-MS, in μg/L

1 1E1 1E2 1E3 1E4 1E5

Al cICP-MS, in μg/L

Al ICP-MS, in μg/L = -21.799 + 1.167 * Al cICP-MS, in μg/L; R^2 = .999

-21.799 -52.662 9.064

1.167 1.153 1.181

Coefficient 95% Lower 95% Upper

Intercept

Al cICP-MS, in μg/L

Confidence Intervals

Al ICP-MS, in μg/L vs. Al cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A4. Linear regression analysis of aluminum results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Al cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
Appendix  33

Figure A5. Linear regression analysis of arsenic results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the 

confidence intervals table, the Intercept and As cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).

-10

0

10

20

30

40

50

As ICP-MS, in μg/L

-10 0 10 20 30 40 50

As cICP-MS, in μg/L

As ICP-MS, in μg/L = .107 + .954 * As cICP-MS, in μg/L; R^2 = .998

.107 -.091 .305

.954 .935 .972

Coefficient 95% Lower 95% Upper

Intercept

As cICP-MS, in μg/L

Confidence Intervals

As ICP-MS, in μg/L vs. As cICP-MS, in μg/L

LINEAR REGRESSION EQUATION
34  Determination of Elements Using Collision/Reaction Cell ICP–MS

0

5

10

15

20

25

30

As GF-AAS in μg/L

0 5 10 15 20 25 30

As cICP-MS, in μg/L

As GF-AAS in μg/L = -.413 + 1.021 * As cICP-MS, in μg/L; R^2 = .979

-.413 -.950 .125

1.021 .956 1.085

Coefficient 95% Lower 95% Upper

Intercept

As cICP-MS, in μg/L

Confidence Intervals

As GF-AAS in μg/L vs. As cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A6. Linear regression analysis of arsenic results from whole-water recoverable digestates 

analyzed by graphite furnace–atomic absorption spectrometry (GF–AAS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and As cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  35

1

1E1

1E2

1E3

1E4

B ICP-MS, in μg/L

1 1E1 1E2 1E3 1E4

B cICP-MS, in μg/L

B ICP-MS, in μg/L = -1.735 + 1.294 * B cICP-MS, in μg/L; R^2 = 1

-1.735 -6.905 3.434

1.294 1.287 1.301

Coefficient 95% Lower 95% Upper

Intercept

B cICP-MS, in μg/L

Confidence Intervals

B ICP-MS, in μg/L vs. B cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A7. Linear regression analysis of boron results from filtered water samples analyzed by inductively 

coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled plasma–mass 

spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals table, the 

Intercept and B cICP–MS coefficients are the y-intercept and slope, respectively. Results are in micrograms 

per liter (μg/L).
36  Determination of Elements Using Collision/Reaction Cell ICP–MS

Figure A8. Linear regression analysis of boron results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and B cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).

-20

0

20

40

60

80

100

120

140

160

B ICP-MS, in μg/L

-20 0 20 40 60 80 100 120 140 160

B cICP-MS, in μg/L

B ICP-MS, in μg/L = .076 + 1.071 * B cICP-MS, in μg/L; R^2 = .997

.076 -1.160 1.312

1.071 1.047 1.094

Coefficient 95% Lower 95% Upper

Intercept

B cICP-MS, in μg/L

Confidence Intervals

B ICP-MS, in μg/L vs. B cICP-MS, in μg/L

LINEAR REGRESSION EQUATION
Appendix  37

0

50

100

150

200

Ba ICP-MS, in μg/L

0 50 100 150 200

Ba cICP-MS μg/L

Ba ICP-MS, in μg/L = -1.117 + .891 * Ba cICP-MS μg/L; R^2 = .977

-1.117 -5.782 3.548

.891 .832 .949

Coefficient 95% Lower 95% Upper

Intercept

Ba cICP-MS μg/L

Confidence Intervals

Ba ICP-MS, in μg/L vs. Ba cICP-MS μg/L

LINEAR REGRESSION EQUATION

Figure A9. Linear regression analysis of barium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Ba cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
38  Determination of Elements Using Collision/Reaction Cell ICP–MS

-50

0

50

100

150

200

250

300

350

400

Ba ICP-MS, in μg/L

-50 0 50 100 150 200 250 300 350 400

Ba cICP-MS μg/L

Ba ICP-MS, in μg/L = -1.493 + .97 * Ba cICP-MS μg/L; R^2 = .998

-1.493 -3.517 .531

.970 .951 .990

Coefficient 95% Lower 95% Upper

Intercept

Ba cICP-MS μg/L

Confidence Intervals

Ba ICP-MS, in μg/L vs. Ba cICP-MS μg/L

LINEAR REGRESSION EQUATION

Figure A10. Linear regression analysis of barium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Ba cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  39

Figure A11. Linear regression analysis of beryllium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Be cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).

-.02

0

.02

.04

.06

.08

.1

Be ICP-MS, in μg/L

-.02 0 .02 .04 .06 .08 .1

Be cICP-MS, in μg/L

Be ICP-MS, in μg/L = .005 + .138 * Be cICP-MS, in μg/L; R^2 = .058

.005 .001 .008

.138 -.102 .378

Coefficient 95% Lower 95% Upper

Intercept

Be cICP-MS, in μg/L

Confidence Intervals

Be ICP-MS, in μg/L vs. Be cICP-MS, in μg/L

LINEAR REGRESSION EQUATION
40 Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

Be ICP-MS, in μg/L

-2 0 2 4 6 8 10

Be cICP-MS, in μg/L

Be ICP-MS, in μg/L = .035 + .918 * Be cICP-MS, in μg/L; R^2 = .997

.035 -.009 .080

.918 .897 .939

Coefficient 95% Lower 95% Upper

Intercept

Be cICP-MS, in μg/L

Confidence Intervals

Be ICP-MS, in μg/L vs. Be cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A12. Linear regression analysis of beryllium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Be cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  41

0

10

20

30

40

50

60

70

80

90

100

Ca ICP-AES, in mg/L

0 10 20 30 40 50 60 70 80 90 100

Ca cICPMS, in mg/L

Ca ICP-AES, in mg/L = .052 + .915 * Ca cICPMS, in mg/L; R^2 = .991

.052 -2.047 2.151

.915 .878 .952

Coefficient 95% Lower 95% Upper

Intercept

Ca cICPMS, in mg/L

Confidence Intervals

Ca ICP-AES, in mg/L vs. Ca cICPMS, in mg/L

LINEAR REGRESSION EQUATION

Figure A13. Linear regression analysis of calcium results from filtered water samples analyzed by 

inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Ca cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).
42  Determination of Elements Using Collision/Reaction Cell ICP–MS

10

20

30

40

50

60

70

80

90

Ca ICP-AES, in mg/L

10 20 30 40 50 60 70 80 90

Ca cICP-MS, in mg/L

Ca ICP-AES, in mg/L = 1.193 + 1 * Ca cICP-MS, in mg/L; R^2 = .989

1.193 -1.097 3.483

1.000 .954 1.046

Coefficient 95% Lower 95% Upper

Intercept

Ca cICP-MS, in mg/L

Confidence Intervals

Ca ICP-AES, in mg/L vs. Ca cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A14. Linear regression analysis of calcium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction 

cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Ca cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).
Appendix  43

-10

0

10

20

30

40

50

Cd ICP-MS, in μg/L

-10 0 10 20 30 40 50

Cd cICP-MS, in μg/L

Cd ICP-MS, in μg/L = .002 + .926 * Cd cICP-MS, in μg/L; R^2 = 1

.002 -.020 .023

.926 .924 .928

Coefficient 95% Lower 95% Upper

Intercept

Cd cICP-MS, in μg/L

Confidence Intervals

Cd ICP-MS, in μg/L vs. Cd cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A15. Linear regression analysis of cadmium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Cd cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).

e
44  Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

Cd ICP-MS, in μg/L

-2 0 2 4 6 8 10

Cd cICP-MS, in μg/L

Cd ICP-MS, in μg/L = -.002 + .925 * Cd cICP-MS, in μg/L; R^2 = .997

-.002 -.036 .032

.925 .904 .946

Coefficient 95% Lower 95% Upper

Intercept

Cd cICP-MS, in μg/L

Confidence Intervals

Cd ICP-MS, in μg/L vs. Cd cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A16. Linear regression analysis of cadmium results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Cd cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
Appendix  45

-10

0

10

20

30

40

50

Co ICP-MS, in μg/L

-10 0 10 20 30 40 50

Co cICP-MS, in μg/L

Co ICP-MS, in μg/L = .018 + .986 * Co cICP-MS, in μg/L; R^2 = 1

.018 -.049 .085

.986 .981 .990

Coefficient 95% Lower 95% Upper

Intercept

Co cICP-MS, in μg/L

Confidence Intervals

Co ICP-MS, in μg/L vs. Co cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A17. Linear regression analysis of cobalt results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Co cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
46  Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

12

Co ICP-MS, in μg/L

-2 0 2 4 6 8 10 12

Co cICP-MS, in μg/L

Co ICP-MS, in μg/L = -.127 + 1.137 * Co cICP-MS, in μg/L; R^2 = .979

-.127 -.382 .127

1.137 1.065 1.208

Coefficient 95% Lower 95% Upper

Intercept

Co cICP-MS, in μg/L

Confidence Intervals

Co ICP-MS, in μg/L vs. Co cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A18. Linear regression analysis of cobalt results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Co cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
Appendix  47

-20

0

20

40

60

80

100

Cr GF-AAS, in μg/L

-20 0 20 40 60 80 100

Cr cICP-MS, in μg/L

Cr GF-AAS, in μg/L = -.003 + 1.05 * Cr cICP-MS, in μg/L; R^2 = .998

-.003 -.489 .482

1.050 1.029 1.071

Coefficient 95% Lower 95% Upper

Intercept

Cr cICP-MS, in μg/L

Confidence Intervals

Cr GF-AAS, in μg/L vs. Cr cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A19. Linear regression analysis of chromium results from filtered water samples analyzed by graphite 

furnace–atomic absorption spectrometry (GF–AAS) and collision/reaction cell inductively coupled plasma–

mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals table, the 

Intercept and Cr cICP–MS coefficients are the y-intercept and slope, respectively. Results are in micrograms 

per liter (μg/L).
48  Determination of Elements Using Collision/Reaction Cell ICP–MS

-5

0

5

10

15

20

25

30

35

40

45

Cr GF-AAS, in μg/L

-5 0 5 10 15 20 25 30 35 40 45

Cr cICP-MS, in μg/L

Cr GF-AAS, in μg/L = .232 + .921 * Cr cICP-MS, in μg/L; R^2 = .986

.232 -.274 .738

.921 .874 .969

Coefficient 95% Lower 95% Upper

Intercept

Cr cICP-MS, in μg/L

Confidence Intervals

Cr GF-AAS, in μg/L vs. Cr cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A20. Linear regression analysis of chromium results from whole-water recoverable digestates 

analyzed by graphite furnace–atomic absorption spectrometry (GF–AAS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Cr cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  49

-20

0

20

40

60

80

100

Cu ICP-MS, in μg/L

-20 0 20 40 60 80 100

Cu cICP-MS, in μg/L

Cu ICP-MS, in μg/L = .299 + .964 * Cu cICP-MS, in μg/L; R^2 = .999

.299 .098 .499

.964 .953 .975

Coefficient 95% Lower 95% Upper

Intercept

Cu cICP-MS, in μg/L

Confidence Intervals

Cu ICP-MS, in μg/L vs. Cu cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A21. Linear regression analysis of copper results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Cu cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
50 Determination of Elements Using Collision/Reaction Cell ICP–MS

-5

0

5

10

15

20

25

Cu ICP-MS, in μg/L

-5 0 5 10 15 20 25

Cu cICP-MS, in μg/L

Cu ICP-MS, in μg/L = -.034 + 1.22 * Cu cICP-MS, in μg/L; R^2 = .981

-.034 -.463 .396

1.220 1.147 1.294

Coefficient 95% Lower 95% Upper

Intercept

Cu cICP-MS, in μg/L

Confidence Intervals

Cu ICP-MS, in μg/L vs. Cu cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A22. Linear regression analysis of copper results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Cu cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  51

-20

0

20

40

60

80

100

120

Fe ICP-AES, in μg/L

-20 0 20 40 60 80 100 120

Fe cICP-MS, in μg/L

Fe ICP-AES, in μg/L = -.035 + .934 * Fe cICP-MS, in μg/L; R^2 = .997

-.035 -.549 .480

.934 .913 .955

Coefficient 95% Lower 95% Upper

Intercept

Fe cICP-MS, in μg/L

Confidence Intervals

Fe ICP-AES, in μg/L vs. Fe cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A23. Linear regression analysis of iron results from filtered water samples analyzed by 

inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. 

In the confidence intervals table, the Intercept and Fe cICP–MS coefficients are the y-intercept and 

slope, respectively. Results are in micrograms per liter (μg/L).
1

1E1

1E2

1E3

1E4

1E5

Fe ICP-AES, in μg/L

1 1E1 1E2 1E3 1E4 1E5

Fe cICP-MS, in μg/L

Fe ICP-AES, in μg/L = -26.485 + 1.078 * Fe cICP-MS, in μg/L; R^2 = 1

-26.485 -44.006 -8.963

1.078 1.069 1.087

Coefficient 95% Lower 95% Upper

Intercept

Fe cICP-MS, in μg/L

Confidence Intervals

Fe ICP-AES, in μg/L vs. Fe cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A24. Linear regression analysis of iron results from whole-water recoverable digestates analyzed by 

inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Fe cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).

52  Determination of Elements Using Collision/Reaction Cell ICP–MS
-2

0

2

4

6

8

10

K ICP-AES, in mg/L

-2 0 2 4 6 8 10

K cICP-MS, in mg/L

K ICP-AES, in mg/L = -.112 + 1.048 * K cICP-MS, in mg/L; R^2 = .998

-.112 -.183 -.040

1.048 1.025 1.070

Coefficient 95% Lower 95% Upper

Intercept

K cICP-MS, in mg/L

Confidence Intervals

K ICP-AES, in mg/L vs. K cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A25. Linear regression analysis of potassium results from filtered water samples analyzed 

by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and K cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).

Appendix  53
54  Determination of Elements Using Collision/Reaction Cell ICP–MS

0

1

2

3

4

5

6

7

8

K ICP-AES, in mg/L

0 1 2 3 4 5 6 7 8

K cICP-MS, in mg/L

K ICP-AES, in mg/L = -.21 + 1.026 * K cICP-MS, in mg/L; R^2 = .998

-.210 -.276 -.145

1.026 1.005 1.047

Coefficient 95% Lower 95% Upper

Intercept

K cICP-MS, in mg/L

Confidence Intervals

K ICP-AES, in mg/L vs. K cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A26. Linear regression analysis of potassium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction 

cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. 

In the confidence intervals table, the Intercept and K cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).
Appendix  55

-50

0

50

100

150

200

250

300

Li ICP-MS, in μg/L

-50 0 50 100 150 200 250 300

Li cICP-MS, in μg/L

Li ICP-MS, in μg/L = 5.659 + 1.208 * Li cICP-MS, in μg/L; R^2 = .992

5.659 1.708 9.610

1.208 1.160 1.255

Coefficient 95% Lower 95% Upper

Intercept

Li cICP-MS, in μg/L

Confidence Intervals

Li ICP-MS, in μg/L vs. Li cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A27. Linear regression analysis of lithium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Li cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
56  Determination of Elements Using Collision/Reaction Cell ICP–MS

Figure A28. Linear regression analysis of lithium results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Li cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).

-20

0

20

40

60

80

100

120

Li ICP-MS, in μg/L

-20 0 20 40 60 80 100 120

Li cICP-MS, in μg/L

Li ICP-MS, in μg/L = 1.472 + .863 * Li cICP-MS, in μg/L; R^2 = .987

1.472 .018 2.926

.863 .821 .906

Coefficient 95% Lower 95% Upper

Intercept

Li cICP-MS, in μg/L

Confidence Intervals

Li ICP-MS, in μg/L vs. Li cICP-MS, in μg/L

LINEAR REGRESSION EQUATION
Appendix  57

-20

0

20

40

60

80

100

120

Mg ICP-AES, in mg/L

-20 0 20 40 60 80 100 120

Mg cICP-MS, in mg/L

Mg ICP-AES, in mg/L = -.779 + .982 * Mg cICP-MS, in mg/L; R^2 = .999

-.779 -1.299 -.260

.982 .967 .997

Coefficient 95% Lower 95% Upper

Intercept

Mg cICP-MS, in mg/L

Confidence Intervals

Mg ICP-AES, in mg/L vs. Mg cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A29. Linear regression analysis of magnesium results from filtered water samples analyzed 

by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. 

In the confidence intervals table, the Intercept and Mg cICP–MS coefficients are the y-intercept and 

slope, respectively. Results are in milligrams per liter (mg/L).
58  Determination of Elements Using Collision/Reaction Cell ICP–MS

-20

0

20

40

60

80

100

120

Mg ICP-AES, in mg/L

-20 0 20 40 60 80 100 120

Mg cICP-MS, in mg/L

Mg ICP-AES, in mg/L = -.594 + .993 * Mg cICP-MS, in mg/L; R^2 = .998

-.594 -1.206 .019

.993 .975 1.011

Coefficient 95% Lower 95% Upper

Intercept

Mg cICP-MS, in mg/L

Confidence Intervals

Mg ICP-AES, in mg/L vs. Mg cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A30. Linear regression analysis of magnesium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/

reaction cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of 

determination. In the confidence intervals table, the Intercept and Mg cICP–MS coefficients are the 

y-intercept and slope, respectively. Results are in milligrams per liter (mg/L).
Appendix  59

-25

25

75

125

175

Mn ICP-MS, in μg/L

-25 25 75 125 175

Mn cICP-MS, in μg/L

Mn ICP-MS, in μg/L = .082 + 1.026 * Mn cICP-MS, in μg/L; R^2 = .991

.082 -1.256 1.420

1.026 .983 1.069

Coefficient 95% Lower 95% Upper

Intercept

Mn cICP-MS, in μg/L

Confidence Intervals

Mn ICP-MS, in μg/L vs. Mn cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A31. Linear regression analysis of manganese results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Mn cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
60 Determination of Elements Using Collision/Reaction Cell ICP–MS

-25

0

25

50

75

100

125

150

175

200

225

Mn ICP-MS, in μg/L

-25 0 25 50 75 100 125 150 175 200 225

Mn cICP-MS, in μg/L

Mn ICP-MS, in μg/L = 1.682 + .938 * Mn cICP-MS, in μg/L; R^2 = .994

1.682 -.026 3.390

.938 .907 .969

Coefficient 95% Lower 95% Upper

Intercept

Mn cICP-MS, in μg/L

Confidence Intervals

Mn ICP-MS, in μg/L vs. Mn cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A32. Linear regression analysis of manganese results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. 

In the confidence intervals table, the Intercept and Mn cICP–MS coefficients are the y-intercept and 

slope, respectively. Results are in micrograms per liter (μg/L).
Appendix  61

-20

0

20

40

60

80

100

Mo ICP-MS, in μg/L

-20 0 20 40 60 80 100

Mo cICP-MS, in μg/L

Mo ICP-MS, in μg/L = -.112 + 1.045 * Mo cICP-MS, in μg/L; R^2 = 1

-.112 -.239 .016

1.045 1.038 1.053

Coefficient 95% Lower 95% Upper

Intercept

Mo cICP-MS, in μg/L

Confidence Intervals

Mo ICP-MS, in μg/L vs. Mo cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A33. Linear regression analysis of molybdenum results from filtered water samples analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the 

confidence intervals table, the Intercept and Mo cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
62  Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

Mo ICP-MS, in μg/L

-2 0 2 4 6 8 10

Mo cICP-MS, in μg/L

Mo ICP-MS, in μg/L = .202 + .847 * Mo cICP-MS, in μg/L; R^2 = .993

.202 .119 .286

.847 .815 .879

Coefficient 95% Lower 95% Upper

Intercept

Mo cICP-MS, in μg/L

Confidence Intervals

Mo ICP-MS, in μg/L vs. Mo cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A34. Linear regression analysis of molybdenum results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Mo cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
Appendix  63

1

1E1

1E2

1E3

Na ICP-AES, in mg/L

1 1E1 1E2 1E3

Na cICP-MS, in mg/L

Na ICP-AES, in mg/L = .273 + .906 * Na cICP-MS, in mg/L; R^2 = 1

.273 -.263 .810

.906 .898 .915

Coefficient 95% Lower 95% Upper

Intercept

Na cICP-MS, in mg/L

Confidence Intervals

Na ICP-AES, in mg/L vs. Na cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A35. Linear regression analysis of sodium results from filtered water samples analyzed by 

inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Na cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).
64  Determination of Elements Using Collision/Reaction Cell ICP–MS

1

1E1

1E2

1E3

Na ICP-AES, in mg/L

1 1E1 1E2 1E3

Na cICP-MS, in mg/L

Na ICP-AES, in mg/L = .213 + .942 * Na cICP-MS, in mg/L; R^2 = .999

.213 -.382 .807

.942 .932 .953

Coefficient 95% Lower 95% Upper

Intercept

Na cICP-MS, in mg/L

Confidence Intervals

Na ICP-AES, in mg/L vs. Na cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A36. Linear regression analysis of sodium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction 

cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Na cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).
Appendix  65

-20

0

20

40

60

80

100

Ni ICP-MS, in μg/L

-20 0 20 40 60 80 100

Ni cICP-MS, in μg/L

Ni ICP-MS, in μg/L = .494 + 1.045 * Ni cICP-MS, in μg/L; R^2 = .999

.494 .285 .704

1.045 1.035 1.056

Coefficient 95% Lower 95% Upper

Intercept

Ni cICP-MS, in μg/L

Confidence Intervals

Ni ICP-MS, in μg/L vs. Ni cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A37. Linear regression analysis of nickel results from filtered water samples analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of 

determination. In the confidence intervals table, the Intercept and Ni cICP–MS coefficients 

are the y-intercept and slope, respectively. Results are in micrograms per liter (μg/L).
66  Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

12

14

16

18

20

Ni ICP-MS, in μg/L

-2 0 2 4 6 8 10 12 14 16 18 20

Ni cICP-MS, in μg/L

Ni ICP-MS, in μg/L = .188 + 1.128 * Ni cICP-MS, in μg/L; R^2 = .99

.188 .016 .360

1.128 1.080 1.176

Coefficient 95% Lower 95% Upper

Intercept

Ni cICP-MS, in μg/L

Confidence Intervals

Ni ICP-MS, in μg/L vs. Ni cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A38. Linear regression analysis of nickel results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Ni cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  67

-10

0

10

20

30

40

50

Pb ICP-MS, in μg/L

-10 0 10 20 30 40 50

Pb cICP-MS, in μg/L

Pb ICP-MS, in μg/L = .024 + .922 * Pb cICP-MS, in μg/L; R^2 = 1

.024 -.047 .095

.922 .917 .928

Coefficient 95% Lower 95% Upper

Intercept

Pb cICP-MS, in μg/L

Confidence Intervals

Pb ICP-MS, in μg/L vs. Pb cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A39. Linear regression analysis of lead results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the 

confidence intervals table, the Intercept and Pb cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
68  Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

12

14

16

18

20

Pb ICP-MS, in μg/L

-2 0 2 4 6 8 10 12 14 16 18 20

Pb cICP-MS, in μg/L

Pb ICP-MS, in μg/L = .039 + 1.089 * Pb cICP-MS, in μg/L; R^2 = .99

.039 -.167 .244

1.089 1.043 1.135

Coefficient 95% Lower 95% Upper

Intercept

Pb cICP-MS, in μg/L

Confidence Intervals

Pb ICP-MS, in μg/L vs. Pb cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A40. Linear regression analysis of lead results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Pb cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  69

-10

0

10

20

30

40

50

Sb ICP-MS, in μg/L

-10 0 10 20 30 40 50

Sb cICP-MS, in μg/L

Sb ICP-MS, in μg/L = .016 + .95 * Sb cICP-MS, in μg/L; R^2 = 1

.016 .006 .026

.950 .949 .952

Coefficient 95% Lower 95% Upper

Intercept

Sb cICP-MS, in μg/L

Confidence Intervals

Sb ICP-MS, in μg/L vs. Sb cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A41. Linear regression analysis of antimony results from filtered water samples 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction 

cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of 

determination. In the confidence intervals table, the Intercept and Sb cICP–MS coefficients are 

the y-intercept and slope, respectively. Results are in micrograms per liter (μg/L).
70 Determination of Elements Using Collision/Reaction Cell ICP–MS

-.2

0

.2

.4

.6

.8

1

1.2

Sb ICP-MS, in μg/L

-.2 0 .2 .4 .6 .8 1 1.2

Sb cICP-MS, in μg/L

Sb ICP-MS, in μg/L = -.161 + 1.846 * Sb cICP-MS, in μg/L; R^2 = .829

-.161 -.249 -.073

1.846 1.484 2.207

Coefficient 95% Lower 95% Upper

Intercept

Sb cICP-MS, in μg/L

Confidence Intervals

Sb ICP-MS, in μg/L vs. Sb cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A42. Linear regression analysis of antimony results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Sb cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  71

-10

0

10

20

30

40

50

Se ICP-MS, in μg/L

-10 0 10 20 30 40 50

Se cICP-MS, in μg/L

Se ICP-MS, in μg/L = .134 + .929 * Se cICP-MS, in μg/L; R^2 = .998

.134 -.035 .303

.929 .913 .944

Coefficient 95% Lower 95% Upper

Intercept

Se cICP-MS, in μg/L

Confidence Intervals

Se ICP-MS, in μg/L vs. Se cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A43. Linear regression analysis of selenium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Se cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
72  Determination of Elements Using Collision/Reaction Cell ICP–MS

-.5

0

.5

1

1.5

2

2.5

3

Se ICP-MS, in μg/L

-.5 0 .5 1 1.5 2 2.5 3

Se cICP-MS, in μg/L

Se ICP-MS, in μg/L = .255 + .831 * Se cICP-MS, in μg/L; R^2 = .875

.255 .133 .378

.831 .695 .967

Coefficient 95% Lower 95% Upper

Intercept

Se cICP-MS, in μg/L

Confidence Intervals

Se ICP-MS, in μg/L vs. Se cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A44. Linear regression analysis of selenium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Se cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  73

-20

0

20

40

60

80

Si ICP-AES, in mg/L

-20 0 20 40 60 80

Si cICP-MS, in mg/L

Si ICP-AES, in mg/L = .339 + .971 * Si cICP-MS, in mg/L; R^2 = .993

.339 -.890 1.567

.971 .937 1.005

Coefficient 95% Lower 95% Upper

Intercept

Si cICP-MS, in mg/L

Confidence Intervals

Si ICP-AES, in mg/L vs. Si cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A45. Linear regression analysis of silicon (as SiO2) results from filtered water samples analyzed by 

inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and Si cICP–MS coefficients are the y-intercept and slope, respectively. Results 

are in milligrams per liter (mg/L).
74  Determination of Elements Using Collision/Reaction Cell ICP–MS

0

5

10

15

20

25

30

35

40

Si ICP-AES, in mg/L

0 5 10 15 20 25 30 35 40

Si cICP-MS, in mg/L

Si ICP-AES, in mg/L = .238 + 1.136 * Si cICP-MS, in mg/L; R^2 = .946

.238 -1.999 2.475

1.136 1.014 1.259

Coefficient 95% Lower 95% Upper

Intercept

Si cICP-MS, in mg/L

Confidence Intervals

Si ICP-AES, in mg/L vs. Si cICP-MS, in mg/L

LINEAR REGRESSION EQUATION

Figure A46. Linear regression analysis of silicon (as SiO2) results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction 

cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. 

In the confidence intervals table, the Intercept and Si cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in milligrams per liter (mg/L).
Appendix  75

-1000

0

1000

2000

3000

4000

5000

6000

Sr ICP-MS, in μg/L

-1000 0 1000 2000 3000 4000 5000 6000

Sr cICP-MS, in μg/L

Sr ICP-MS, in μg/L = 68.799 + .87 * Sr cICP-MS, in μg/L; R^2 = .988

68.799 9.040 128.559

.870 .829 .911

Coefficient 95% Lower 95% Upper

Intercept

Sr cICP-MS, in μg/L

Confidence Intervals

Sr ICP-MS, in μg/L vs. Sr cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A47. Linear regression analysis of strontium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Sr cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
76  Determination of Elements Using Collision/Reaction Cell ICP–MS

20

200

2000

Sr ICP-MS, in μg/L

20 200 2000

Sr cICP-MS, in μg/L

Sr ICP-MS, in μg/L = -2.933 + 1.004 * Sr cICP-MS, in μg/L; R^2 = .999

-2.933 -6.698 .831

1.004 .987 1.020

Coefficient 95% Lower 95% Upper

Intercept

Sr cICP-MS, in μg/L

Confidence Intervals

Sr ICP-MS, in μg/L vs. Sr cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A48. Linear regression analysis of strontium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Sr cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  77

-10

0

10

20

30

40

50

60

Tl ICP-MS, in μg/L

-10 0 10 20 30 40 50 60

Tl cICP-MS, in μg/L

Tl ICP-MS, in μg/L = .011 + .873 * Tl cICP-MS, in μg/L; R^2 = 1

.011 -.035 .057

.873 .870 .875

Coefficient 95% Lower 95% Upper

Intercept

Tl cICP-MS, in μg/L

Confidence Intervals

Tl ICP-MS, in μg/L vs. Tl cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A49. Linear regression analysis of thallium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively 

coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the 

confidence intervals table, the Intercept and Tl cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
78  Determination of Elements Using Collision/Reaction Cell ICP–MS

Figure A50. Linear regression analysis of thallium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and Tl cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).

-.5

0

.5

1

1.5

2

2.5

3

3.5 Tl ICP-MS, in μg/L

-.5 0 .5 1 1.5 2 2.5 3 3.5

Tl cICP-MS, in μg/L

Tl ICP-MS, in μg/L = .072 + .942 * Tl cICP-MS, in μg/L; R^2 = .973

.072 .031 .113

.942 .874 1.009

Coefficient 95% Lower 95% Upper

Intercept

Tl cICP-MS, in μg/L

Confidence Intervals

Tl ICP-MS, in μg/L vs. Tl cICP-MS, in μg/L

LINEAR REGRESSION EQUATION
Appendix  79

-10

0

10

20

30

40

50

U ICPMS, in μg/L

-10 0 10 20 30 40 50

U cICP-MS, in μg/L

U ICPMS, in μg/L = .246 + .92 * U cICP-MS, in μg/L; R^2 = .999

.246 .089 .403

.920 .905 .935

Coefficient 95% Lower 95% Upper

Intercept

U cICP-MS, in μg/L

Confidence Intervals

U ICPMS, in μg/L vs. U cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A51. Linear regression analysis of uranium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and U cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
80 Determination of Elements Using Collision/Reaction Cell ICP–MS

-2

0

2

4

6

8

10

12

14

16

18

20

U ICP-MS, in μg/L

-2 0 2 4 6 8 10 12 14 16 18 20

U cICP-MS, in μg/L

U ICP-MS, in μg/L = .046 + 1.024 * U cICP-MS, in μg/L; R^2 = .973

.046 -.313 .405

1.024 .951 1.097

Coefficient 95% Lower 95% Upper

Intercept

U cICP-MS, in μg/L

Confidence Intervals

U ICP-MS, in μg/L vs. U cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

 Figure A52. Linear regression analysis of uranium results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals table, 

the Intercept and U cICP–MS coefficients are the y-intercept and slope, respectively. Results are in micrograms 

per liter (μg/L).
Appendix  81

1E-2

1E-1

1

1E1

1E2

1E3

V ICP-MS, in μg/L

1E-2 1E-1 1 1E1 1E2 1E3

V cICP-MS, in μg/L

V ICP-MS, in μg/L = -.162 + 1.01 * V cICP-MS, in μg/L; R^2 = 1

-.162 -.803 .478

1.010 1.005 1.014

Coefficient 95% Lower 95% Upper

Intercept

V cICP-MS, in μg/L

Confidence Intervals

V ICP-MS, in μg/L vs. V cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A53. Linear regression analysis of vanadium results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and V cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
82  Determination of Elements Using Collision/Reaction Cell ICP–MS

-10

0

10

20

30

40

50

60

70

80

V ICP-AES, in μg/L

-10 0 10 20 30 40 50 60 70 80

V cICP-MS, in μg/L

V ICP-AES, in μg/L = .716 + .9 * V cICP-MS, in μg/L; R^2 = .99

.716 -.035 1.466

.900 .862 .939

Coefficient 95% Lower 95% Upper

Intercept

V cICP-MS, in μg/L

Confidence Intervals

V ICP-AES, in μg/L vs. V cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A54. Linear regression analysis of vanadium results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–atomic emission spectrometry (ICP–AES) and collision/reaction 

cell inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. 

In the confidence intervals table, the Intercept and V cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  83

1E-1

1

1E1

1E2

W ICP-MS, in μg/L

1E-1 1 1E1 1E2

W cICP-MS, in μg/L

W ICP-MS, in μg/L = .031 + .874 * W cICP-MS, in μg/L; R^2 = 1

.031 -.013 .076

.874 .873 .876

Coefficient 95% Lower 95% Upper

Intercept

W cICP-MS, in μg/L

Confidence Intervals

W ICP-MS, in μg/L vs. W cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A55. Linear regression analysis of tungsten results from filtered water samples analyzed by 

inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence 

intervals table, the Intercept and W cICP–MS coefficients are the y-intercept and slope, respectively. 

Results are in micrograms per liter (μg/L).
84  Determination of Elements Using Collision/Reaction Cell ICP–MS

-.05

0

.05

.1

.15

.2

W ICP-MS, in μg/L

-.05 0 .05 .1 .15 .2

W cICP-MS, in μg/L

W ICP-MS, in μg/L = -.016 + 1.122 * W cICP-MS, in μg/L; R^2 = .617

-.016 -.039 .008

1.122 .654 1.589

Coefficient 95% Lower 95% Upper

Intercept

W cICP-MS, in μg/L

Confidence Intervals

W ICP-MS, in μg/L vs. W cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A56. Linear regression analysis of tungsten results from whole-water recoverable digestates 

analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell 

inductively coupled plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In 

the confidence intervals table, the Intercept and W cICP–MS coefficients are the y-intercept and slope, 

respectively. Results are in micrograms per liter (μg/L).
Appendix  85

1E-2

1E-1

1

1E1

1E2

Zn ICP-MS, in μg/L

1E-2 1E-1 1 1E1 1E2

Zn cICP-MS, in μg/L

Zn ICP-MS, in μg/L = -.052 + 1.02 * Zn cICP-MS, in μg/L; R^2 = 1

-.052 -.692 .587

1.020 1.013 1.028

Coefficient 95% Lower 95% Upper

Intercept

Zn cICP-MS, in μg/L

Confidence Intervals

Zn ICP-MS, in μg/L vs. Zn cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A57. Linear regression analysis of zinc results from filtered water samples analyzed by inductively 

coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled plasma–

mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals table, 

the Intercept and Zn cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
86  Determination of Elements Using Collision/Reaction Cell ICP–MS

-50

0

50

100

150

200

250

300

350

400

Zn ICP-MS, in μg/L

-50 0 50 100 150 200 250 300 350 400

Zn cICP-MS, in μg/L

Zn ICP-MS, in μg/L = .538 + .995 * Zn cICP-MS, in μg/L; R^2 = .999

.538 -.560 1.637

.995 .983 1.008

Coefficient 95% Lower 95% Upper

Intercept

Zn cICP-MS, in μg/L

Confidence Intervals

Zn ICP-MS, in μg/L vs. Zn cICP-MS, in μg/L

LINEAR REGRESSION EQUATION

Figure A58. Linear regression analysis of zinc results from whole-water recoverable digestates analyzed 

by inductively coupled plasma–mass spectrometry (ICP–MS) and collision/reaction cell inductively coupled 

plasma–mass spectrometry (cICP–MS). R^2 is the coefficient of determination. In the confidence intervals 

table, the Intercept and Zn cICP–MS coefficients are the y-intercept and slope, respectively. Results are in 

micrograms per liter (μg/L).
Glossary  87

Glossary

Bias Systematic error that is manifested as a 

consistent positive or negative deviation from 

the known or true value (Taylor, 1990). The 

error can be positive or negative. Positive 

error can result from sample contamination 

or spectral interference, whereas negative 

error can result from analyte loss or signal 

suppression. Bias differs from random error 

which shows no such consistent or systematic 

deviation.

F-pseudosigma A nonparametric, resistant 

measure of data spread defined as the interquartile range of the data divided by 1.349 

(Hoaglin and others, 1983). F-pseudosigma 

and the standard deviation of the data will be 

nearly equivalent if the data have a nearnormal distribution.

Long-term method detection level (LT–MDL)

The minimum concentration of an element 

that can be measured and reported with 99-

percent confidence that the elemental concentration is greater than zero. The LT–MDL is 

calculated from replicate analyses of samples 

fortified with all the method elements, and 

includes variability introduced by multiple 

instruments, multiple analysts, and multiple 

calibrations over 6 to 12 months (Childress 

and others, 1999).

Matrix The substrate of a test sample 

(National Environmental Laboratory Accreditation Conference, 2003).

Matrix spike A sample prepared by adding 

a known mass of target analyte to a specified amount of matrix sample for which an 

independent estimate of target analyte 

concentration is available. Matrix spike is 

used to determine the effect of the matrix on a 

method’s recovery efficiency (National Environmental Laboratory Accreditation Conference, 2003).

Method detection limit (MDL) The minimum 

concentration of an element that can be measured and reported with 99-percent confidence 

that the elemental concentration is greater 

than zero and is determined from analysis of 

a sample in a given matrix containing the element of interest (U.S. Environmental Protection Agency, 2000).

Spike A known mass of target analyte added 

to a blank sample or subsample; used to determine the recovery efficiency or for other quality-control purposes (National Environmental 

Laboratory Accreditation Conference, 2003).

Variability Random error in independent 

measurements as the result of repeated application of the process under specific conditions. Precision is a measure of variability 

in experimental data that can be statistically 

characterized by the standard deviation 

(Taylor, 1990).
Manuscript approved for publication, November 18, 2005

Prepared by the Publishing Group, U.S. Geological Survey,

National Water Quality Laboratory, Denver, Colorado

USGS Publishing Staff

Editorial Assistance by Jon W. Raese and Keith L. Lucey

Cover Designed by Suzanne C. Roberts

Report Preparation by Barbara L. Kemp

Technical Reviews

Anthony Bednar, U.S. Army Corps of Engineers

David A. Roth, USGS, National Research Program 

Jeffrey W. Pritt, USGS, National Water Quality Laboratory

Alfred J. Driscoll, USGS, National Water Quality Laboratory

Peter F. Rogerson, USGS, Office of Water Quality

For more information concerning the research in this report, contact the 

National Water Quality Laboratory, Laboratory Chief

U.S. Geological Survey, P.O. Box 25046, MS 407

Denver Federal Center

Denver, Colorado 80225-0046

http://nwql.cr.usgs.gov
Garbarino and others—Determination of Elements Using Collision/Reaction Cell ICP–MS —Techniques and Methods Book 5–B1

Printed on recycled paper