U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2012–5281

Assessing Total Nitrogen in Surface-Water 

Samples—Precision and Bias of Analytical 

and Computational Methods

Assessing Total Nitrogen in Surface-Water 

Samples—Precision and Bias of Analytical 

and Computational Methods

By David L. Rus, Charles J. Patton, David K. Mueller, and Charles G. Crawford

Scientific Investigations Report 2012–5281

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Rus, D.L., Patton, C.J., Mueller, D.K., and Crawford, C.G., 2012, Assessing total nitrogen in surface-water samples—

Precision and bias of analytical and computational methods: U.S. Geological Survey Scientific Investigations 

Report 2012–5281, 38 p.
iii

Contents

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

Introduction.....................................................................................................................................................2

The Challenges of Quantifying Total Nitrogen .................................................................................3

Kjeldahl Digestion........................................................................................................................3

Alkaline-Persulfate Digestion....................................................................................................3

High-Temperature-Combustion Oxidation...............................................................................4

Initial Problem Identification...............................................................................................................4

Follow-Up Studies.................................................................................................................................4

Purpose and Scope ..............................................................................................................................4

Methods...........................................................................................................................................................4

Laboratory-Experiment Design and Sample Analysis ....................................................................6

Synoptic-Field-Study Design and Sample Analysis........................................................................8

Dissolved-Nitrogen Procedures................................................................................................8

Particulate-Nitrogen Procedures .............................................................................................9

Selection of a Total-Nitrogen Reference for Evaluation of Bias in Synoptic-Field 

Samples .........................................................................................................................12

Bias and Precision Metrics...............................................................................................................12

Assessment of Precision for Total-Nitrogen Methods ..........................................................................12

Precision of Nitrogen Analyses........................................................................................................12

Factors That Could Affect the Variability of Particulate-Nitrogen Measurements .................14

Precision of Computed Total Nitrogen ............................................................................................17

Assessment of Bias in Total-Nitrogen Methods.....................................................................................17

Assessment of Bias Determined by the Laboratory Experiment ................................................17

Bias in the Alkaline-Persulfate and Kjeldahl Methods........................................................17

Source of Bias in the Alkaline-Persulfate Method ..............................................................18

Source of Bias in the Kjeldahl-Digestion Method................................................................20

Assessment of Bias Determined by the Synoptic-field Study.....................................................21

Bias in the Alkaline-Persulfate-Digestion Method ..............................................................22

Spatial Patterns of Bias in Total Nitrogen Determined by Alkaline-Persulfate 

Digestion...............................................................................................................24

Bias in the Kjeldahl Method.....................................................................................................25

Evaluation of Alternatives for Determining Total Nitrogen ...................................................................29

Factors Considered.............................................................................................................................29

Anticipated Precision and Bias...............................................................................................29

Impact on Field and Laboratory Procedures.........................................................................31

Impact on Data Analyses .........................................................................................................31

Replace TN-A with TN-C ...................................................................................................................31

Replace TN-K with TN-C ...................................................................................................................32

Add TN-C Analyses to Existing Analyses of TN-A or TN-K .........................................................32

Summary........................................................................................................................................................32

Acknowledgments..............................................................................................................................35

References Cited..........................................................................................................................................35

Appendixes ...................................................................................................................................................37

Table A1–A5 .........................................................................................................................................38
iv

Figures

1. Graph showing nitrogen content measured for various amounts of three reference 

materials at the U.S. Geological Survey National Water Quality Laboratory, Denver, 

Colorado, in comparison to reference values determined at the U.S. Geological 

Survey Sediment Chemistry Laboratory, Atlanta, Georgia ....................................................7

2. Map showing distribution of sites sampled for the synoptic-field study of bias in 

total nitrogen by alkaline-persulfate digestion (TN-A) and as the sum of total 

Kjeldahl nitrogen and nitrate plus nitrite (TN-K), 2009–10 .....................................................9

3. Graphs showing relations of standard deviation (A, C, E, and G) and relative standard 

deviation (B, D, F, and H) to mean concentration for selected nitrogen species in 

replicate samples collected for the synoptic-field study, 2009–10.....................................14

4. Graphs showing relations of (A) suspended sediment concentration and (B) water 

volume filtered to relative standard deviation of particulate-nitrogen measurements 

in replicate samples collected for the synoptic-field study, 2009–10 ................................16

5. Graph showing bias in total nitrogen determined by alkaline-persulfate digestion 

(TN-A) and total Kjeldahl nitrogen (TKN) in laboratory tests using synthetic samples 

made from nutrient-fortified water and various concentrations of two reference 

materials: Buffalo River sediment or Montana soil...............................................................18

6. Graph showing comparison of bias in total-nitrogen concentration in synthetic 

samples analyzed using the standard alkaline-persulfate method and an alternate, 

fortified-reagent test using four times the reagent relative to the original sample 

volume ..........................................................................................................................................19

7. Graph showing apparent recovery of particulate nitrogen (assuming complete 

and accurate recovery of dissolved nitrogen species) in synthetic samples analyzed 

for total nitrogen by alkaline-persulfate digestion................................................................20

8. Graph showing apparent recovery of nitrate during Kjeldahl digestion (determined as the difference between measured and known concentrations of total Kjeldahl 

nitrogen in comparison to the known concentration of nitrate nitrogen) in synthetic 

samples amended with Buffalo River sediment or Montana soil.......................................21

9. Boxplots showing distribution of (A) bias and (B) relative bias in total nitrogen 

determined by alkaline-persulfate digestion (TN-A) and as the sum of total 

Kjeldahl nitrogen and nitrate plus nitrite (TN-K) observed in the synoptic-field 

study, 2009–10..............................................................................................................................22

10. Graphs showing relations of bias in total nitrogen determined by alkaline-persulfate 

digestion (TN-A) in samples collected as part of the synoptic-field study to (A and B) 

suspended-sediment concentration and (C) particulate-nitrogen concentration ..........23

11. Boxplots showing distribution of (A) bias and (B) relative bias in total nitrogen 

determined by alkaline-persulfate digestion (TN-A) in samples collected for the 

synoptic-field study and categorized by suspended-sediment concentration range ....24

12. Graphs showing general trends (as represented by locally weighted regression) of 

(A) bias and (B) relative bias in total nitrogen determined by alkaline-persulfate 

digestion (TN-A) in sediment and soil samples from the laboratory experiment 

and synoptic-field study ............................................................................................................25

13. Maps showing (A) mean bias and (B) mean relative bias in total nitrogen determined 

by alkaline-persulfate digestion (TN-A) for sampling sites with 10 or more measurements, synoptic-field study, 2009–10 .......................................................................................26

14. Boxplots showing comparisons of relative bias in total nitrogen determined by 

alkaline-persulfate digestion (TN-A) to drainage-basin characteristics for selected 

sampling sites in synoptic-field study, 2009–10, for (A) percent silt and clay in 

topsoil, (B) dominant land use, and (C) dominant physiographic division ........................27
v

15. Graphs showing relations of bias and relative bias in total nitrogen computed as 

the sum of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K) to nitrate plus nitrite 

concentration in samples collected as part of the synoptic-field study, 2009–10...........28

16. Boxplots showing distribution of (A) bias and (B) relative bias in total nitrogen determined as the sum of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K) observed 

in samples collected for the synoptic-field study, 2009–10, among ranges of nitrate 

plus nitrite concentration ..........................................................................................................29

17. A graphical depiction of the relative effect of precision and bias on three methods 

for determining total nitrogen...................................................................................................30

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

micrometer (μm) 0.00003937 inch (in.)

Area

square kilometer (km2) 0.3861 square mile (mi2) 

Volume

liter (L) 1.057 quart (qt)

milliliter (mL) 0.06102 cubic inch (in3)

Mass

milligram (mg) 0.00003527 ounce, avoirdupois (oz)

Pressure

kilopascal (kPa) 0.1450 pound-force per square inch 

(lbf/in2) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32

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

or percent.

Tables

1. Nitrogen analyses of samples collected from sites in the Mississippi River Basin 

during April and May, 2005..........................................................................................................5

2. Nitrogen content of reference materials determined by analyses at the U.S. Geological Survey Sediment Chemistry Laboratory, Atlanta, Georgia.............................................6

3. Nitrogen content of reference materials determined by analyses at the U.S. Geological Survey National Water Quality Laboratory in Denver, Colorado...................................8

4. Sampling sites and number of samples collected for the synoptic-field study of bias 

in total nitrogen as determined by alkaline-persulfate digestion (TN-A) or the sum 

of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K), 2009–10......................................10

5. Estimates of variability in replicate measurements for nitrogen species.........................13

6. Summary of advantages and disadvantages associated with three alternatives to 

current U.S. Geological Survey practices for determining total-nitrogen concentration in surface-water samples..................................................................................................33
vi

Abbreviations and Acronyms

BQS U.S. Geological Survey Branch of Quality Systems

DKN dissolved Kjeldahl nitrogen

DOC dissolved organic carbon

DON dissolved organic nitrogen

HTCO high-temperature, combustion oxidation

NASQAN National Stream-Quality Assessment Network

NAWQA National Water-Quality Assessment Program

NIST National Institute of Standards and Technology

NMN National Monitoring Network

NWQL U.S. Geological Survey National Water Quality Laboratory, Lakewood, Colorado

OWQ U.S. Geological Survey Office of Water Quality, Reston, Virginia

RSD relative standard deviation

TN total nitrogen

SCL U.S. Geological Survey Sediment Chemistry Laboratory, Atlanta, Georgia

SSC suspended-sediment concentration

USGS U.S. Geological Survey

Glossary of Total-Nitrogen Methods

DN dissolved nitrogen, measured by the use of alkaline-persulfate digestion of a filtered 

sample

NOX dissolved nitrate (NO3) plus nitrite (NO2), measured colorimetrically

PN particulate nitrogen, measured by high-temperature combustion oxidation of a filter 

pad

TN-A total nitrogen measured by the use of alkaline-persulfate digestion of a whole-water 

sample

TN-C total nitrogen, computed from DN + PN

TN-K total nitrogen, computed from TKN + NOX

TKN total Kjeldahl nitrogen, measured by the use of Kjeldahl digestion of a whole-water 

sample. TKN represents the sum of ammonia, dissolved-organic nitrogen, and 

particulate nitrogen in the sample
Abstract

The characterization of total-nitrogen (TN) concentrations is an important component of many surface-waterquality programs. However, three widely used methods for 

the determination of total nitrogen—(1) derived from the 

alkaline-persulfate digestion of whole-water samples (TN-A); 

(2) calculated as the sum of total Kjeldahl nitrogen and dissolved nitrate plus nitrite (TN-K); and (3) calculated as the 

sum of dissolved nitrogen and particulate nitrogen (TN-C)—

all include inherent limitations. A digestion process is intended 

to convert multiple species of nitrogen that are present in the 

sample into one measureable species, but this process may 

introduce bias. TN-A results can be negatively biased in the 

presence of suspended sediment, and TN-K data can be positively biased in the presence of elevated nitrate because some 

nitrate is reduced to ammonia and is therefore counted twice in 

the computation of total nitrogen. Furthermore, TN-C may not 

be subject to bias but is comparatively imprecise. In this study, 

the effects of suspended-sediment and nitrate concentrations 

on the performance of these TN methods were assessed using 

synthetic samples developed in a laboratory as well as a series 

of stream samples. A 2007 laboratory experiment measured 

TN-A and TN-K in nutrient-fortified solutions that had been 

mixed with varying amounts of sediment-reference materials. 

This experiment identified a connection between suspended 

sediment and negative bias in TN-A and detected positive bias 

in TN-K in the presence of elevated nitrate. A 2009–10 synoptic-field study used samples from 77 stream-sampling sites to 

confirm that these biases were present in the field samples and 

evaluated the precision and bias of TN methods. 

The precision of TN-C and TN-K depended on the precision and relative amounts of the TN-component species used 

in their respective TN computations. Particulate nitrogen had 

an average variability (as determined by the relative standard deviation) of 13 percent. However, because particulate 

nitrogen constituted only 14 percent, on average, of TN-C, the 

precision of the TN-C method approached that of the method 

for dissolved nitrogen (2.3 percent). On the other hand, total 

Kjeldahl nitrogen (having a variability of 7.6 percent) constituted an average of 40 percent of TN-K, suggesting that the 

reduced precision of the Kjeldahl digestion may affect precision of the TN-K estimates. For most samples, the precision of 

TN computed as TN-C would be better (lower variability) than 

the precision of TN-K. In general, TN-A precision (having a 

variability of 2.1 percent) was superior to TN-C and TN-K 

methods. 

The laboratory experiment indicated that negative bias in 

TN-A was present across the entire range of sediment concentration and increased as sediment concentration increased. 

This suggested that reagent limitation was not the predominant 

cause of observed bias in TN-A. Furthermore, analyses of particulate nitrogen present in digest residues provided an almost 

complete accounting for the nitrogen that was underestimated 

by alkaline-persulfate digestion. This experiment established 

that, for the reference materials at least, negative bias in TN-A 

was caused primarily by the sequestration of some particulate 

nitrogen that was refractory to the digestion process. TN-K 

biases varied between positive and negative values in the laboratory experiment. Positive bias in TN-K is likely the result 

of the unintended reduction of a small and variable amount 

of nitrate to ammonia during the Kjeldahl digestion process. 

Negative TN-K bias may be the result of the sequestration of a 

portion of particulate nitrogen during the digestion process.

Negative bias in TN-A was present across the entire range 

of suspended-sediment concentration (1 to 14,700 milligrams 

per liter [mg/L]) in the synoptic-field study, with relative bias 

being nearly as great at sediment concentrations below 10 

mg/L (median of -3.5 percent) as that observed at sediment 

concentrations up to 750 mg/L (median of -4.4 percent). This 

lent support to the laboratory-experiment finding that some 

particulate nitrogen is sequestered during the digestion process, and demonstrated that negative TN-A bias was present in 

samples with very low suspended-sediment concentrations. At 

sediment concentrations above 750 mg/L, the negative 

TN-A bias became more likely and larger (median of 

-13.2 percent), suggesting a secondary mechanism of bias, 

such as reagent limitation. From a geospatial perspective, 

Assessing Total Nitrogen in Surface-Water Samples—

Precision and Bias of Analytical and Computational 

Methods

By David L. Rus, Charles J. Patton, David K. Mueller, and Charles G. Crawford
2 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

trends in TN-A bias were not explained by selected basin characteristics. Though variable, TN-K bias generally was positive 

in the synoptic-field study (median of 3.1 percent), probably as 

a result of the reduction of nitrate. 

Three alternative approaches for assessing TN in surface water were evaluated for their impacts on existing and 

future sampling programs. Replacing TN-A with TN-C would 

remove the bias from subsequent data, but this approach also 

would introduce discontinuity in historical records. Replacing 

TN-K with TN-C would lead to the removal of positive bias in 

TN-K in the presence of elevated nitrate. However, in addition 

to the issues that may arise from a discontinuity in the data 

record, this approach may not be applicable to regulatory programs that require the use of total Kjeldahl nitrogen for stream 

assessment. By adding TN-C to existing TN-A or TN-K analyses, historical-data continuity would be preserved and the transitional period could be used to minimize the impact of bias on 

data analyses. This approach, however, imposes the greatest 

burdens on field operations and in terms of analytical costs. 

The variation in these impacts on different sampling programs 

will challenge U.S. Geological Survey scientists attempting 

to establish uniform standards for TN sample collection and 

analytical determinations. 

Introduction

Dissolved and particulate inorganic and organic nitrogen occurs in a variety of forms (species) in environmental 

waters. The diversity of nitrogen species and their distributions between dissolved and particulate phases complicate 

sample collection and laboratory analyses required to measure 

their concentrations, transport, and cycling in environmental 

aquatic systems. Additional complications arise because a 

generic water-quality indicator, such as total nitrogen, includes 

multiple species that might be determined at different levels 

of detection, accuracy, and precision. Digestion processes are 

often used to convert multiple, co-occurring species of nitrogen into one measureable species. If the digestion process is 

insufficient in converting all of the targeted species, a negative 

bias might result. If the digestion process converts nontargeted 

species, a positive bias might result. This report focuses on the 

challenges and limitations of three widely used methods for 

routine determination of total nitrogen in filtered-water and 

whole-water samples. Specifically, it explores how suspendedsediment and nitrate concentrations affect the performance of 

these methods, both in synthetic media—laboratory-formulated, nitrogen-fortified deionized-water suspensions of reference materials (river sediment and soil)—and in a set of more 

than 900 geographically and compositionally diverse water 

samples collected and processed at 77 U.S. Geological Survey 

(USGS) stream-sampling sites during June 2009 through 

September 2010.

Nitrogenous nutrients that collectively compose total 

nitrogen in environmental water include:

•	 Ammonium ion (NH4

+

) and ammonia (NH3),

•	 Nitrate (NO3

-

),

•	 Nitrite (NO2

-

),

•	 Dissolved organic nitrogen (DON), and

•	 Particulate nitrogen (PN).

Constituents of DON include urea, methylamines, amino 

acids, and proteins (Berman and Bronk, 2003; Pehlivanoglu 

and Sedlak, 2006; Maie and others, 2006). PN includes particulate organic nitrogen and any NH4

+

 that might be associated with mineral sediments.

A variety of nutrient-analysis methods are utilized by the 

USGS National Water Quality Laboratory (NWQL). In environmental waters, dissolved NH4

+

 and NH3 are in pH-dependent equilibrium. Under ambient pH conditions (about 6.0–

8.5 standard units), NH4

+

 predominates. Colorimetric methods 

are equally reactive with both species, which by convention 

are reported collectively as “ammonia” (Fishman, 1993). 

Nitrate is determined colorimetrically as nitrite after chemical 

reduction to that species (Fishman, 1993); therefore, these two 

species are reported collectively as NO2

-

 + NO3

-

 (henceforth 

referred to as NOX in this report). In principle, Kjeldahl digestion (Patton and Truitt, 2000; American Public Health Association, 2012) reduces organic and particulate nitrogen species, 

but not NOX, to ammonia. Kjeldahl nitrogen is by definition, 

then, the sum of ammonia, DON, and PN. Determining ammonia in filtered-water and whole-water Kjeldahl digests, therefore, yields the operationally defined parameters of dissolved 

Kjeldahl nitrogen (DKN) and total Kjeldahl nitrogen (TKN), 

respectively. Until 2003, USGS monitoring programs typically 

estimated total nitrogen (TN) as the sum of TKN and wholewater NOX. Since then, a method for direct determination of 

TN by an alkaline-persulfate digestion method (Patton and 

Kryskalla, 2003) has been available at the NWQL. Operationally, alkaline-persulfate digestion oxidizes all forms of nitrogen to nitrate. Therefore, determining nitrate in filtered-water 

and whole-water alkaline-persulfate digests provides a direct 

measure of dissolved nitrogen (DN) and TN, respectively. PN 

can be calculated as the difference between TN and DN or as 

the difference between TKN and DKN. Direct determination 

of PN also is possible by high-temperature, combustion oxidation (HTCO) elemental analysis (U.S. Environmental Protection Agency (USEPA) method 440.0, Zimmerman and others, 

1997). Sample preparation for PN analysis occurs at collection 

sites and involves separating suspended sediment from a wellmixed, measured volume of whole water by vacuum or pressure filtration through a 25-millimeter (mm)-diameter glassfiber filter with a nominal pore size of 0.7 micrometer (μm). 

Summing PN and DN provides a third way to estimate TN. 
Introduction 3

The three approaches for TN estimation described above 

are summarized in the Glossary in the front of this report as 

well as the in the list below: 

1. TN-A is TN measured in alkaline-persulfate digests of 

whole-water samples, 

2. TN-K is TKN + NOX, and

3. TN-C is DN + PN.

TN-K estimates of TN predominate in the USGS National 

Water Information System database (http://waterdata.usgs.gov/

nwis) and reports prior to 2003. Since then, direct determination of TN (TN-A) has become more common, particularly 

for samples collected for the USGS National Water-Quality 

Assessment (NAWQA) Program (http://water.usgs.gov/

nawqa/). Over the past few years, estimating TN by the TN-C 

approach has increased as a consequence of a growing awareness of apparent bias in TN-K and TN-A values. 

The Challenges of Quantifying Total Nitrogen

These three approaches for determining or calculating 

total nitrogen are affected by field- and laboratory-sampling 

operations and by analytical limitations—some well-known 

and understood, others less so. TN data quality depends on 

the nontrivial exercise of collecting representative samples of 

environmental waters and the suspended sediment entrained in 

them. Especially important is maintaining a uniform distribution of suspended sediment when composited whole-water 

samples are split into various containers for shipment and storage prior to laboratory analyses. A USGS study by Capel and 

Larson (1996) documents the precision and accuracy resulting 

from use of cone and churn devices to split water samples.

Kjeldahl Digestion

In the USGS Kjeldahl method, water samples are 

digested at 370 degrees Celsius (°C) with concentrated sulfuric acid containing potassium sulfate and catalytic amounts of 

mercuric sulfate (Patton and Truitt, 2000). Organic nitrogen 

compounds in sample digests are reduced to ammonia, which 

along with any ammonia originally present in samples prior to 

digestion, are measured by colorimetry. In practice, the Kjeldahl-nitrogen method has three deficiencies. First, its method 

detection limit for ammonia is higher than those of methods 

for other nitrogen species, which is problematic for studies 

in low-concentration nutrient regimes. During 1994–2000, 

NWQL reporting levels for DKN and TKN were 0.05–

0.2 milligrams per liter (mg/L) as nitrogen (N), whereas the 

reporting levels for dissolved ammonia and NOX were as low 

as 0.008 and 0.002 mg/L as N, respectively. Second, variability of the TKN analyte is high relative to other nitrogen species. In replicate samples collected for the NAWQA Program 

during 1992–2001, TKN variability was 7.6 percent, compared 

to 1.9 percent and 2.2 percent for dissolved ammonia and 

NOX, respectively (Mueller and Titus, 2005). Third, when 

samples contain substantial concentrations of nitrate, some 

fraction of it can be reduced to ammonia during Kjeldahl 

digestion (American Public Health Association, 2012; Patton 

and Truitt, 2000; Patton and Kryskalla, 2003, p. 23–25 and 

fig. 8). If this occurs, calculated concentrations of TN-K are 

positively biased because of some double counting of nitrate. 

Although well-known to crop and soil analysts (Pace and others, 1982; Wyland and Jackson, 1994), the potential for nitrate 

interference in Kjeldahl-nitrogen analysis is seldom addressed 

in environmental-water testing literature. Kjeldahl-nitrogen 

methods also pose safety, health, and waste-stream handling 

concerns to analysts and waste-disposal personnel. These 

analytical and operational drawbacks notwithstanding, USEPA 

National Pollutant Discharge Elimination System and Safe 

Drinking Water Act regulations (40 CFR Part 136.3) stipulate TKN rather than TN for wastewater and drinking-water 

assessment. 

Alkaline-Persulfate Digestion

The NWQL developed an alkaline-persulfate digestion 

method for analysis of TN (Patton and Kryskalla, 2003) to 

achieve lower detection limits, produce more precise analytical results, improve sample-collection efficiency, and avoid 

the health and safety concerns associated with Kjeldahl-nitrogen methods. The USGS method was adapted from a widely 

applied method for analysis of TN in estuarine and marine 

waters (D’Elia and others, 1977). Additional references and 

discussion of analytical considerations for alkaline-persulfate 

digestion are available in the USGS method documentation 

(Patton and Kryskalla, 2003). Alkaline-persulfate digestion 

methods are attractive because, in principle, they provide a 

direct measure of DN and TN in filtered and whole-water 

samples. In this method, water samples are dosed with alkaline 

persulfate reagent, sealed, and heated for an hour at elevated 

temperature and pressure in an autoclave. During the digestion, all forms of nitrogen are oxidized to nitrate, which is then 

determined by colorimetry. A potential for negative bias when 

other oxidizable substances, such as organic carbon or certain 

particulate minerals, are present in samples had been noted 

for this method. The cause of this bias was hypothesized to be 

that the persulfate reagent might not be sufficient to oxidize all 

nitrogen species to nitrate. In order to assess persulfate reagent 

limitation by dissolved organic carbon (DOC), Patton and 

Kryskalla added increasing concentrations of glucose to 

deionized water containing ammonium at a concentration 

of 2.5 mg/L as N. The onset of reagent limitation by DOC 

occurred at a carbon concentration of 150 mg/L (Patton and 

Kryskalla, 2003, p. 21–22), which is much greater than DOC 

concentrations normally found in environmental waters. Their 

study also explored the effects of nitrate on observed differences between TN-K and TN-A but did not consider suspended sediment in whole-water samples as a potential source 

of negative bias in the TN-A method.
4 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

High-Temperature-Combustion Oxidation

In June 2000, the NWQL validated an HTCO method 

for routine, simultaneous determination of total particulate 

carbon and PN (Zimmerman and others, 1997; USGS Office 

of Water Quality [OWQ] Technical Memorandum 2000.08). 

In this method, glass-fiber filter pads used to collect suspended 

sediment from measured volumes of whole water are dried 

and then combusted at 980 °C in a pure oxygen atmosphere. 

Nitrogen oxides produced from particulate nitrogen are subsequently reduced to molecular nitrogen and detected by thermal 

conductivity. Unlike the Kjeldahl and alkaline-persulfate 

methods, HTCO PN determinations do not require wet-chemical digestion and do not generate hazardous waste; however, 

filtering water with suspended-sediment concentrations (SSC) 

higher than about 1,000 mg/L at collection sites can be time 

consuming.

Initial Problem Identification

During April and May 2005, 10 large-volume (40–

80 liter [L]) water samples were collected at sites that were 

part of the USGS National Stream-Quality Accounting 

Network (NASQAN) (http://water.usgs.gov/nasqan/) in the 

Mississippi River Basin. These samples were sent to the 

USGS Sediment Chemistry Laboratory (SCL) in Atlanta, Ga., 

for processing and analysis. When the SCL processed these 

samples, small-volume (1 L) splits of whole water and filtered 

water were prepared that were subsequently shipped to the 

NWQL for analysis of TKN, NOX, TN-A, DN, and PN. The 

SCL provided an alternative analysis of PN using dried and 

sieved sediment samples (50 to 75 mg, typically), which were 

separated from the large-volume water samples by continuousflow centrifugation. Two estimates of TN-C were calculated: 

one using PN from the NWQL and the other using PN from 

the SCL. In general, TN-K was slightly lower than TN-C, 

and TN-A also was lower than TN-C but by a greater amount 

(table 1). Reagent limitation from carbon or mineral particulates in the whole-water samples was suspected to be the cause 

of the negative bias in TN-A. 

Follow-Up Studies

By the end of 2006, it had become apparent that some 

form of negative bias was present in TN-A data. However, 

the cause and extent of this bias, which was most apparent 

in samples with high SSCs, remained unknown. Therefore, 

a controlled-laboratory experiment, the results of which are 

described in this report, was undertaken to identify the cause 

and extent of the bias. Based on the results of these experiments, the Office of Water Quality issued a statement alerting USGS data users about potential negative bias in TN 

results from the alkaline-persulfate digestion method (U.S. 

Geological Survey, 2009). In response, a 2-year synopticfield study was conducted using data collected at 77 USGS 

stream-sampling sites to assess the magnitude of bias in 

environmental samples and to evaluate mitigation alternatives. 

This field study is described in this report and was considered 

“synoptic” in that it utilized data from a broad area.

Purpose and Scope

This report describes the performance of alkaline-persulfate and Kjeldahl digestion methods for assessment of TN by 

TN-A and TN-K approaches. These methods are compared to 

a third approach that computes TN as the sum of dissolved and 

particulate nitrogen analyses or TN-C. Methods and results 

are described for two complementary comparative studies 

that began with a series of laboratory experiments performed 

in 2007 and concluded with a comprehensive synoptic-field 

study conducted between June 2009 and September 2010. The 

synoptic-field study provided 905 samples from established 

NAWQA, NASQAN, and National Monitoring Network 

(NMN) (http://water.usgs.gov/nasqan/objectives_subnets.html) 

sites for which TN-A and TN-K values were evaluated in relation to TN-C. Bias and precision associated with each of the 

three approaches are characterized. The report concludes with 

a discussion of some advantages and disadvantages associated 

with three different scenarios for reducing potential bias in 

future data-collection efforts. 

Methods

Bias and precision in estimates of total nitrogen were 

evaluated through laboratory experimentation as well as a 

synoptic-field study. Though bias was evaluated for TN-A and 

TN-K, the primary focus of both the laboratory experiment 

and the field study was to characterize the effect of sediment 

on TN-A bias. 

Standards of known TN and sediment concentrations 

were used in laboratory experiments to verify the presence 

of bias in TN-A estimates of total nitrogen. In these experiments, increasing amounts of sediment reference materials 

were added to a deionized water matrix containing constant 

amounts of dissolved inorganic and organic nitrogen. Under 

these controlled conditions, it was possible to assess the extent 

of negative bias in TN-A in relation to the added mass of 

reference material and to distinguish among possible mechanisms for observed negative bias —alkaline-persulfate reagent 

limitation, reference-material constituents that were refractory 

(resistant to alkaline-persulfate digestion), or post-digestion 

interference in colorimetric determinative methods. Because 

TKN also was measured on the synthetic samples, it was possible to assess the magnitude of bias in TN-K over a range of 

sediment concentrations. 

The subsequent synoptic-field study was designed to 

provide insights into the extent of bias expected for different 

TN-estimation approaches and how reduction in bias might 

affect the precision of reported TN. The designs and methods 
Methods 

5

Table 1. Nitrogen analyses of samples collected from sites in the Mississippi River Basin during April and May, 2005.[mg/L, milligrams per liter; NOX, nitrite plus nitrate as nitrogen; TKN, total Kjeldahl nitrogen; TN-K, total nitrogen by summation of NOX and TKN; TN-A, total nitrogen by alkaline-persulfate digestion; DN, dissolved nitrogen by alkaline-persulfate digestion; PN, particulate nitrogen; TN-C, total nitrogen computed as DN plus PN; NWQL, U.S. Geological Survey National Water Quality Laboratory in Lakewood, Colorado; SCL, U.S. Geological Survey Sediment Chemistry Laboratory in Atlanta, Georgia]

Site name Station number Sample date Suspended sediment, in mg/L

Nitrogen species, in mg/L1 Percent difference Nitrogen 

content of 

suspended 

sediment 

(percent)2

NOX TKN TN-K TN-A DN PN (NWQL) PN (SCL) TN-C (NWQL) TN-C (SCL)

in TN-K 

compared to:

in TN-A 

compared to:

TN-C

(NWQL)

TN-C 

(SCL)

TN-C 

(NWQL)

TN-C 

(SCL)

Ohio River at 

Dam 53 near Grand Chain, Ill.

03612500 5/25/2005 81 1.43 0.48 1.91 1.82 1.80 0.19 0.20 1.99 2.00 -4.1 -4.7 -8.3 -8.9 0.24

Mississippi 

River at Clinton, Iowa

05420500 4/11/2005 146 1.68 0.88 2.56 2.44 2.20 0.313 0.53 2.51 2.73 2.1 -6.1 -2.9 -10.6 0.29

Mississippi 

River at Clinton, Iowa

05420500 5/24/2005 47 1.86 0.73 2.59 2.55 2.45 0.20 0.20 2.65 2.65 -2.2 -2.2 -3.1 -3.1 0.43

Missouri River 

at Hermann, Mo.

06934500 5/11/2005 162 2.39 0.63 3.02 2.71 2.80 0.213 0.49 3.01 3.29 0.4 -8.0 -13.8 -21.0 0.22

Missouri River 

at Hermann, Mo.

06934500 5/16/2005 2,414 2.92 3.87 6.79 4.72 3.45 3.143 2.90 6.59 6.35 3.0 6.9 -27.3 -24.5 0.13

Mississippi 

River at Thebes, Ill.

07022000 5/4/2005 225 3.41 0.87 4.28 4.17 4.00 0.39 0.72 4.39 4.72 -2.5 -9.3 -5.6 -12.1 0.25

Mississippi 

River at Thebes, Ill.

07022000 5/23/2005 569 3.32 1.87 5.19 4.85 3.75 1.293 1.48 5.04 5.23 3.0 -0.7 -1.2 -4.8 0.24

Mississippi 

River at St. Francisville, La.

07373420 4/27/2005 140 1.19 0.55 1.74 1.65 1.504 0.24 0.45 1.74 1.95 -0.1 -10.5 -2.6 -12.7 0.25

Mississippi 

River at Baton Rouge, La.

07374000 4/28/2005 149 1.22 0.55 1.77 1.70 1.75 0.23 0.43 1.98 2.18 -10.5 -18.8 -13.9 -21.9 0.22

Atchafalaya 

River at Melville, La.

07381495 4/26/2005 121 0.71 0.59 1.30 1.22 1.30 0.303 0.35 1.60 1.65 -18.8 -21.2 -21.0 -23.3 0.27

Median -1.1 -7.1 -6.9 -12.4 0.25

1Mean value of two replicate subsamples analyzed at NWQL for NOX, TKN, TN-A, DN-A, and PN (unless noted).2Based on mean PN from NWQL and SCL.3Only one replicate was analyzed.4The analytic result of 2.4 for one of the replicate subsamples was considered erroneous and was not used to compute the mean value.
6 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

used for the laboratory experiment and the field study are 

described in subsections that follow. The three TN-estimation 

approaches also are summarized in the Glossary at the front of 

this report.

Laboratory-Experiment Design and Sample 

Analysis

In 2007, a controlled experiment explored potential 

causes of method bias in NWQL TN results. This laboratory 

experimentation addressed the following questions:

1. Is TN-A negatively biased in the presence of suspended 

sediment?

2. If so, does negative bias increase as SSC increases?

3. Is there a SSC threshold below which TN-A values are 

unbiased?

4. If the negative bias in TN-A values are caused by reagent 

limitation, could the bias be resolved by diluting wholewater samples prior to alkaline-persulfate digestion, 

increasing the concentration of persulfate in the digestion 

reagent, or both? 

A secondary objective of the laboratory experiment was to 

determine whether TKN was positively biased, possibly 

because of conversion of nitrate to ammonia during the Kjeldahl digestion process. If this did happen, some nitrate would 

be counted twice when summing TKN and NOX to calculate 

TN-K.

The laboratory experiment was designed and coordinated 

by personnel in the USGS Branch of Quality Systems (BQS). 

All analyses were performed at the NWQL. The BQS prepared 

sediment-water samples by adding specific masses of standard 

reference materials to a fixed volume of nutrient-fortified, 

deionized water. A large batch of water matrix was prepared 

by adding nitrate and glycine to deionized water at nominal 

concentrations of 2.0 and 0.3 mg/L as N, respectively. The 

former approximates the median nitrate concentration of the 

10 samples analyzed in the 2005 field test (table 1). Glycine 

was used as a surrogate for the DON measured in samples 

from the 2005 field test. Sulfuric acid (having strength of 

4.5 normality) was added to match field preservation of 

samples collected for total-nitrogen analysis. Ten milliliters 

(mL) of this acidified, nitrogen-fortified, deionized-water 

matrix were pipetted into alkaline-persulfate or Kjeldahl-nitrogen digestion tubes, and then reference material was added in 

amounts corresponding to sediment concentrations ranging 

from 100 mg/L to 3,000 mg/L. Replicate samples were prepared for each digestion method at each concentration.

Three reference materials were used for sediment amendments: Buffalo River sediment (standard reference material 

2704) and Montana soil (standard reference material 2710), 

both of which are available from the National Institute of 

Standards and Technology (NIST), and Green River Shale 

(reference material SGR–1), which is a USGS geochemical 

reference material collected from the Mahogany zone of the 

Green River Formation. Buffalo River sediment and Montana 

soil were selected as reasonable surrogates for suspended 

sediment in riverine systems; the mass percentage of nitrogen 

content of these materials is in the range observed for suspended sediment at NASQAN sites in the 2005 field test (table 

1). Green River Shale, which contains greater concentrations of nitrogen, was selected to test for reagent limitation in 

alkaline-persulfate and Kjeldahl digestion procedures. These 

three materials lack NIST-certified values for nitrogen, so 

reference values were determined by multiple analyses of each 

material at the SCL (table 2). Each analysis was done on about 

200–250 mg of material. Analyses of Montana soil and Green 

River Shale were done routinely over the time period 2000–07 

and analyses of Buffalo River sediment were done in 2007 

in preparation for the laboratory experiment. Mean nitrogen 

content was established as 0.20 percent by weight (0.20 mg N 

per 100 mg sediment) in Buffalo River sediment, 0.13 percent 

in Montana soil, and 0.90 percent in Green River Shale. The 

value determined for Buffalo River sediment is close to 

the nitrogen content (0.191 percent) recently reported by 

the Maryland Geological Survey (Wells and Ortt, 2011, 

p. 30), and the value determined for Montana soil is identical 

to the nitrogen content reported by the Keck Laboratory at Arizona State University (http://kfleb.asu.edu/Analytical/gIRMS/

Instrumentandanalysis/Analytical/CandNonEA.html).

Prior to the laboratory experiment, the three reference 

materials were analyzed at the NWQL (table 3) to compare 

nitrogen contents to the reference values determined by the 

SCL and to evaluate whether results were consistent across 

a range of material mass analyzed. As shown in figure 1, the 

Table 2. Nitrogen content of reference materials determined by analyses at the U.S. Geological Survey Sediment Chemistry 

Laboratory, Atlanta, Georgia.

[NIST, National Institue of Standards and Technology; SRM, Standard Reference Material; USGS, U.S. Geological Survey; SGR-1, USGS Green River Shale]

Reference material Reference identifier Number of 

analyses

Mean nitrogen content 

(percent)

Standard deviation 

(percent)

Relative standard deviation 

(percent of mean)

Buffalo River sediment NIST SRM 2704 10 0.203 0.004 2.2

Montana soil NIST SRM 2710 61 0.130 0.023 17.5

Green River Shale USGS SGR-1 50 0.901 0.039 4.4
Methods 7

NWQL results for Buffalo River sediment and Montana soil 

agree closely with the reference values over a range of mass 

that covers the planned reference-material additions to laboratory samples. For these two reference materials, nitrogencontent results from the NWQL generally were unbiased with 

only a small variability over most of the range. Results for 

Green River Shale had a positive bias of about 6 percent and 

a slightly higher variability than for the other two materials; however, this was not a major concern for the laboratory 

experiment because this shale material had been selected 

to represent extreme conditions not expected in riverine 

sediments. 

The expected or “true” concentration of nitrogen in each 

synthetic sample of fortified water plus sediment was the sum 

of the known amount of dissolved nitrate and organic nitrogen 

(glycine) in the aqueous matrix plus PN known from the mass 

of reference material added to each digestion tube. PN concentration was determined as:

 (1)

where

Npct is the mass percentage of nitrogen in the 

reference material, 

Sed is the mass, in milligrams, of reference 

material added to each digestion tube, and 

Vol is the volume, in milliliters, of nutrientfortified matrix into which the reference 

material was added. 

TN-A results were compared to the known concentrations 

of total nitrogen. TKN results were compared to the known 

concentrations of organic nitrogen plus PN.

To test the effect of sample dilution and reagent enhancement using the alkaline-persulfate method, some digests for 

TN-A analysis were prepared with half the aqueous-matrix 

volume and double the persulfate reagent volume. This 

0 5 10 15 20 25 30

Mass of reference material analyzed, in milligrams

0

0.2

0.4

0.6

0.8

1.0

1.2

Nitrogen content, in percent

Buffalo River sediment

Buffalo River sediment reference value (0.20 percent nitrogen)

Montana soil

Montana soil reference value (0.13 percent nitrogen)

Green River Shale

Green River Shale reference value (0.90 percent nitrogen)

EXPLANATION

Figure 1. Nitrogen content measured for various amounts of three reference materials at the U.S. Geological Survey 

National Water Quality Laboratory, Denver, Colorado, in comparison to reference values determined at the U.S. Geological 

Survey Sediment Chemistry Laboratory, Atlanta, Georgia.

/100

/

1,000

Vol

( )Sed Npct

PN =
8 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

increased the reagent concentration by a factor of four relative 

to sample volume, which theoretically would decrease the 

effect of reagent limitation. 

For each synthetic sample, the expected concentrations 

of selected nitrogen species and measured concentrations of 

TN-A and TKN are listed in the appendix (tables A1 and A2, 

respectively). Bias, relative bias (in percent), and apparent 

recoveries are also listed in the appendix. 

Synoptic-Field-Study Design and Sample 

Analysis

The synoptic-field study was designed not only to assess 

the extent of bias in TN-A and TN-K but also to evaluate the 

efficacy of using the sum of DN and PN (TN-C) as a more 

accurate alternative to direct measurement of total nitrogen in 

water samples. Data were collected to assess how variability 

in analytical results for PN might affect bias and precision of 

TN-C values. To this end, replicate filter pads for PN determinations were prepared for each sample to permit assessment 

of PN variability over the full range of SSC. Besides data 

collected during the synoptic-field study, additional data are 

stored in the NWIS database that could be used to make TN-A 

and TN-K comparisons; however, because sampling protocols 

and analytical methods associated with these data were not 

explicitly defined, they are not evaluated in this report.

From June 2009 through September 2010, analytical 

schedules of three national monitoring programs—NASQAN, 

NAWQA, and NMN—were modified to include alkalinepersulfate digestion for determining DN and HTCO for PN, 

which allowed for computation of TN-C values. Samples 

included in the synoptic-field study were collected at 77 sites 

(fig. 2, table 4) using standard procedures (U.S. Geological 

Survey, variously dated). TN-A data were being collected 

already or were added to the samples at all 77 sites. TN-K data 

also were available at 19 of these 77 sites and were typically 

associated with sites of the NASQAN and NMN programs. 

Subsamples for particular analyses or suites of analyses were 

prepared using churn splitters or cone splitters (U.S. Geological Survey, variously dated, section 5.1.1). The purpose of the 

synoptic-field study was twofold: (1) to evaluate the extent 

of TN-A and TN-K bias in environmental samples; and 

(2) to evaluate potential alternatives to the existing methods 

for estimating TN concentrations.

Dissolved-Nitrogen Procedures

DN subsamples were processed at collection sites by filtering water through a polysulfone, pleated membrane having 

0.45-μm pore size. Filtrates were bottled, chilled, and sent to 

the NWQL for analysis by USGS alkaline-persulfate digestion 

method I–2650–03 (Patton and Kryskalla, 2003). 

Table 3. Nitrogen content of reference materials determined by analyses at the U.S. Geological Survey National Water Quality 

Laboratory in Denver, Colorado.

[mg, milligrams; --, not applicable]

Targeted mass of 

reference material 

(mg)

Buffalo River sediment Montana soil Green River Shale

Measured mass 

of material 

(mg)

Nitrogen content 

(percent by 

weight)

Measured mass 

of material 

(mg)

Nitrogen content 

(percent by 

weight)

Measured mass 

of material 

(mg)

Nitrogen content 

(percent by 

weight)

2 2.26 0.217 2.07 0.070 2.14 1.008

2 1.87 0.202 1.86 0.081 1.86 0.893

2 2.09 0.180 1.97 0.128 2.00 0.955

4 4.05 0.175 3.97 0.130 4.05 0.997

4 3.69 0.174 4.22 0.143 4.09 0.886

4 4.29 0.167 3.70 0.122 4.04 0.936

6 6.64 0.204 6.33 0.144 5.75 0.959

8 7.96 0.200 7.60 0.144 8.31 0.954

10 9.52 0.198 9.75 0.129 9.84 0.952

12 12.18 0.197 11.80 0.129 12.44 0.965

15 15.91 0.198 14.02 0.134 15.62 0.958

20 20.54 0.189 20.56 0.141 21.37 0.945

25 23.93 0.189 25.28 0.140 27.58 0.967

Mean of all 

analyses

-- 0.192 -- 0.126 -- 0.952
Methods 9

Particulate-Nitrogen Procedures

PN subsamples were collected on 25-mm diameter, 

0.7-μm pore size glass-fiber filter pads by vacuum filtration 

using procedures specified for total particulate carbon in the 

USGS National Field Manual (U.S. Geological Survey, variously dated, section 5.2.2.C). The filtration apparatus consisted 

of a 25-mm polysulfone filter funnel with a 200-mL reservoir 

and stainless steel filter support. It was connected to a 500-

mL polypropylene filtering flask and a metered, hand-vacuum 

pump was used to induce a vacuum of -68.9 kilopascals (-10 

pound-force per square inch) or less. A subsample was split 

from the sample into a baked, glass bottle. The subsample was 

shaken vigorously to suspend the particulate matter before 

pouring an aliquot into the filter funnel, equipped with a filter 

pad, and applying vacuum to the filtering funnel. The filtrate 

was collected and measured in a polypropylene graduated 

cylinder. In some cases, the filtrate volume was measured 

using the graduated cylinder (or a graduated, polystyrene 

pipette for heavily sediment-laden samples) prior to being 

added to the filter funnel. After sample volume was recorded, 

inorganic-free water was used to rinse any residual sediments 

adhering to the walls of the cylinder, pipette, or filter funnel onto the filter. After filtration was complete, as indicated 

by a visibly dry filter, the system was depressurized and the 

top of the filter funnel was carefully removed to expose the 

filter. Using two metal forceps, the filter was gently removed 

from the support, placed on a clean aluminum foil square, and 

folded in half with the suspended material on the inside. The 

filters were enclosed within aluminum foil envelopes, chilled, 

and sent to the NWQL for analysis. 

Because clogging increases as more whole water is filtered, PN sample volumes necessarily decrease as particulate 

concentrations increase. Although 50-mL PN aliquots were 

typical, 2-mL aliquots were used for some sediment-laden 

samples. Precision of PN values resulting from 2-mL aliquots 

was expected to be inferior to that derived from 50-mL (or 

greater) aliquots. Therefore, extra efforts were made to quantify PN variability. Specifically, up to four replicate PN filters 

were prepared for each sample. In most cases, the filtered 

volumes were the same for each replicate. Regardless, filtered 

volumes for each replicate filter were recorded. 

Figure 2. Distribution of sites sampled for the synoptic-field study of bias in total nitrogen by alkaline-persulfate digestion (TNA) and as the sum of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K), 2009–10.

100° 90° 80°

70°

120°

110°

40°

30°

50°

0 200 400 600 800 KILOMETERS

0 200 400 600 800 MILES

TN–A sampling site

TN–K sampling site

EXPLANATION

Base from U.S. Geological Survey digital data, 2005, 1:2,000,000

Albers Equal-area Conic projection

Standard parallels 29°30’N and 45°30’N, central meridian 96°W

North American Datum of 1983
10 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

Table 4. Sampling sites and number of samples collected for the synoptic-field study of bias in total nitrogen as determined by 

alkaline-persulfate digestion (TN-A) or the sum of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K), 2009–10.

[Station ID, U.S. Geological Survey station identification number; TN-A, total nitrogen measured from alkaline-persulfate digestion; TN-K, total nitrogen computed as the sum of total Kjeldahl nitrogen and nitrate plus nitrite; --, not available]

Station ID Station name Drainage area

(square kilometers)

Number of samples

TN-A TN-K

01170100 Green River near Colrain, Mass. 107 1 0

01209710 Norwalk River at Winnipauk, Conn. 85.5 11 2

01356190 Lisha Kill northwest of Niskayuna, N.Y. 40.4 12 0

01357500 Mohawk River at Cohoes, N.Y. 8,940 7 0

01403300 Raritan River at Queens Bridge at Bound Brook, N.J. 2,080 23 0

01463500 Delaware River at Trenton, N.J. 17,600 13 13

01464907 Little Neshaminy Creek at Valley Road near Neshaminy, Pa. 69.4 13 0

01472157 French Creek near Phoenixville, Pa. 153 8 0

01621050 Muddy Creek at Mount Clinton, Va. 37 13 0

01646580 Potomac River at Chain Bridge, at Washington, D.C. 30,000 9 7

01654000 Accotink Creek near Annandale, Va. 61.9 15 0

02084160 Chicod Creek at SR1760 near Simpson, N.C. 117 18 0

02087580 Swift Creek near Apex, N.C. 54.4 2 0

02089500 Neuse River at Kinston, N.C. 6,970 16 0

02169570 Gills Creek at Columbia, S.C. 154 13 0

02226160 Altamaha River near Everett City, Ga. 36,300 12 12

02306774 Rocky Creek at State Highway 587 at Citrus Park, Fla. 45.3 5 0

02317797 Little River at Upper Ty Ty Road, near Tifton, Ga. 334 10 0

02335870 Sope Creek near Marietta, Ga. 79.5 18 0

02338000 Chattahoochee River near Whitesburg, Ga. 6,290 18 0

02338523 Hillabahatchee Creek at Thaxton Road, near Franklin, Ga. 43.5 2 0

0242354750 Cahaba Valley Creek at Cross Creek Road at Pelham, Ala. 66.3 7 0

02424000 Cahaba River at Centreville, Ala. 2,660 18 0

02470500 Mobile River at Mt. Vernon, Ala. 111,000 13 13

03267900 Mad River at St. Paris Pike at Eagle City, Ohio 803 13 0

03374100 White River at Hazleton, Ind. 29,300 22 14

03467609 Nolichucky River near Lowland, Tenn. 4,370 17 0

03612500 Ohio River at Dam 53 near Grand Chain, Ill. 526,000 12 12

04063700 Popple River near Fence, Wis. 360 4 0

040869415 Lincoln Creek at 47th Street at Milwaukee, Wis. 24.8 10 0

04161820 Clinton River at Sterling Heights, Mich. 800 14 0

04175600 River Raisin near Manchester, Mich. 342 16 0

05288705 Shingle Creek at Queen Ave In Minneapolis, Minn. 73 11 0

05331580 Mississippi River below L&D #2 at Hastings, Minn. 96,100 5 0

05420500 Mississippi River at Clinton, Iowa 222,000 13 13

05451210 South Fork Iowa River northeast of New Providence, Iowa 580 12 0

05465500 Iowa River at Wapello, Iowa 32,400 10 10

05490500 Des Moines River at Keosauqua, Iowa 36,400 10 10

05532500 Des Plaines River at Riverside, Ill. 1,630 14 0

05586100 Illinois River at Valley City, Ill. 69,300 16 14
Methods 11

Table 4. Sampling sites and number of samples collected for the synoptic-field study of bias in total nitrogen as determined by 

alkaline-persulfate digestion (TN-A) or the sum of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K), 2009–10.—Continued

[Station ID, U.S. Geological Survey station identification number; TN-A, total nitrogen measured from alkaline-persulfate digestion; TN-K, total nitrogen computed as the sum of total Kjeldahl nitrogen and nitrate plus nitrite; --, not available]

Station ID Station name Drainage area

(square kilometers)

Number of samples

TN-A TN-K

06295000 Yellowstone River at Forsyth, Mont. 104,000 2 0

06324970 Little Powder River above Dry Creek, near Weston, Wyo. 3,200 4 0

06610000 Missouri River at Omaha, Nebr. 836,000 10 10

06713500 Cherry Creek at Denver, Colo. 1,060 11 0

06754000 South Platte River near Kersey, Colo. 25,000 14 0

06795500 Shell Creek near Columbus, Nebr. 761 13 0

06800000 Maple Creek near Nickerson, Nebr. 953 16 0

06805500 Platte River at Louisville, Nebr. 221,000 18 14

07288955 Yazoo River below Steele Bayou near Long Lake, Miss. 34,600 23 11

07373420 Mississippi River near St. Francisville, La. 2,910,000 14 14

07375050 Tchefuncte River near Covington, La. 376 14 0

07379960 Dawson Creek at Bluebonnet Blvd near Baton Rouge, La. 39.1 10 0

08051500 Clear Creek near Sanger, Tex. 764 13 0

08057200 White Rock Creek at Greenville Ave, Dallas, Tex. 172 6 0

08057410 Trinity River below Dallas, Tex. 16,300 17 0

08116650 Brazos River near Rosharon, Tex. 117,000 13 13

08364000 Rio Grande at El Paso, Tex. 83,400 12 0

094196783 Las Vegas Wash below Flamingo Wash confluence near 

Las Vegas, Nev.

3,550 3 0

09481740 Santa Cruz River at Tubac, Ariz. 3,130 11 0

09517000 Hassayampa River near Arlington, Ariz. 3,810 7 0

10168000 Little Cottonwood Creek at Jordan River near Salt Lake City, Utah 119 5 0

10171000 Jordan River at 1700 South at Salt Lake City, Utah 8,900 11 0

10311400 Carson River at Deer Run Road near Carson City, Nev. 2,480 14 0

11074000 Santa Ana River below Prado Dam, Calif. 5,850 19 0

11273500 Merced River at River Road Bridge near Newman, Calif. 3,300 7 0

11274538 Orestimba Creek at River Road near Crows Landing, Calif. -- 11 0

11303500 San Joaquin River near Vernalis, Calif. 35,100 23 12

11447650 Sacramento River at Freeport, Calif. -- 13 13

12464770 Crab Creek at Rocky Ford Road near Ritzville, Wash. 995 10 0

12505450 Granger Drain at Granger, Wash. -- 13 0

13055000 Teton River near St. Anthony, Idaho 2,270 5 0

13056500 Henrys Fork near Rexburg, Idaho 7,560 11 0

14205400 East Fork Dairy Creek near Meacham Corner, Oreg. 87.5 4 0

14206950 Fanno Creek at Durham, Oreg. 81.6 6 0

14211720 Willamette River at Portland, Oreg. 29,000 19 0

14246900 Columbia River at Beaver Army Terminal near Quincy, Oreg. 665,000 12 11

394340085524601 Sugar Creek at County Road 400S at New Palestine, Ind. 240 15 0
12 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

The NWQL analyzed each filter for particulate nitrogen 

using HTCO methods. The PN result from the first filter in 

each set was used to compute TN-C. For cases involving censored PN values, half of the method-detection level—or onefourth of the laboratory reporting level (Childress and others, 

1999)—was used in the computation of TN-C. The additional 

replicate filters allowed characterization of the variability 

that might be introduced by the use of TN-C as an alternative 

method to TN-A or TN-K. 

Selection of a Total-Nitrogen Reference for 

Evaluation of Bias in Synoptic-Field Samples

The rationale for using TN-C, the sum of DN and PN, as 

the least biased estimator of total nitrogen in synoptic study 

samples is threefold. First, in the absence of suspended sediment, DN method bias—from reagent limitation caused by 

dissolved inorganic and organic nitrogen, dissolved organic 

carbon, or both—is expected to be minor. Previous research at 

the NWQL reported recoveries for 2.5 mg/L as N of inorganic 

and organic nitrogen species—ammonia, urea, nicotinic acid, 

and glycine—in the range of 97.9 to 100.5 percent (Patton and 

Kryskalla, 2003, p. 21–22, table 12). These DN recoveries 

agree favorably with those reported for similar methods developed elsewhere (Hosomi and Sudo, 1986; Cabrera and Beare, 

1993). Second, the onset of reagent limitation from dissolvedorganic carbon in the USGS DN method occurred at 150 mg/L 

as carbon, much greater than typically found in environmental 

water (Patton and Kryskalla, 2003, p. 21–22, fig. 5). And 

third, PN results from HTCO analyses at the NWQL had low 

variability and were nearly unbiased in comparison to reference values (fig. 1). Researchers concerned with total nitrogen 

determinations in estuarine and marine waters adopted this 

approach over 20 years ago on the basis of similar experiments 

and reasoning (Zimmerman and others, 1997). 

Bias and Precision Metrics

Bias in the total-nitrogen methods was evaluated by comparing measured values with a reference value. As described 

earlier, the laboratory experiment used samples having known 

total-nitrogen concentration. Because the true TN concentration was unknown in the synoptic-field study samples, results 

from the TN-C method were used as reference values. Bias 

was computed as:

Bias = TNmeas – TNref (2)

where 

Bias is the deviation of the measured value from 

the reference value, in milligrams per liter 

as N;

	 TNmeas is the measured value of TN (either TN-A or 

TN-K), in milligrams per liter as N; and

TNref is the reference value of TN (known TN for 

the laboratory experiment or TN-C for the 

synoptic-field study), in milligrams per 

liter as N.

Bias is positive if measured TN exceeds the expected concentration (reference value); bias is negative if measured TN is 

less than the expected concentration. Because bias could vary 

over several orders of magnitude, “relative bias” was computed to normalize Bias relative to the reference value:

Relative Bias = 100 x Bias/TNref (3)

where

Relative Bias is the difference between the measured and 

reference TN, in percent.

The precision of an analytical result is affected by variability introduced during the collection of field samples as 

well as by variability that is inherent in all analytical methods. 

Variability is determined by repeated, independent measurements of analyte concentrations in replicate samples. Measures of variability include the standard deviation and relative 

standard deviation (RSD) of these measurements. Standard 

deviation of nutrient analytical results, based on field replicates, generally is uniform near the analytical detection limit, 

but at concentrations higher than about 5–10 times the detection limit, standard deviation tends to increase in proportion 

to concentration (Mueller and Titus, 2005). Within this higher 

range of concentrations, RSD (defined as the standard deviation of the set of replicates divided by the mean concentration) 

generally is uniform.

Assessment of Precision for TotalNitrogen Methods

For each measured-nitrogen constituent, precision was 

characterized by determining variability over low and high 

concentration ranges. Variability was compared among constituents, and the effect of variability on calculated values of 

TN was estimated.

Precision of Nitrogen Analyses

During the synoptic-field study, 67 replicate water 

samples were collected and analyzed for at least one of the 

constituents NOX, DN, or TN-A. One or more replicate filter 

samples for PN analysis were collected at 896 of the 905 

sampling events. 

For this report, unrounded and uncensored data were 

obtained from the NWQL for analysis of replicate variability. 

This was done to avoid problems that could be introduced 

when comparing small differences between two rounded 

values or when comparing censored values to each other or 

to uncensored values. These data are listed in the appendix 
Assessment of Precision for Total-Nitrogen Methods 13

(table A3). Because data have not been quality assured using 

laboratory rounding and censoring rules, readers are cautioned 

these data should not be used for purposes other than precision 

analyses such as done for this report. Uncensored results could 

include negative values because of uncertainty in the calibration curve at very low concentrations. This explains the two 

replicate data values for PN that have concentrations of -0.002 

and -0.020 mg/L as N in table A3.

Data plotted in figure 3 illustrate the low-range and 

high-range variability for NOX, DN, TN, and PN. These plots 

include a locally weighted scatterplot-smoothing (LOWESS) 

curve (Helsel and Hirsch, 2002) through the data points to 

show the general relation between concentration and standard 

deviation or RSD. Where this curve is horizontal, there is no 

relation between standard deviation or RSD and concentration, so variability could be considered constant. The boundary 

concentration was determined visually by selecting a point 

on the mean-concentration axis below which the curve for 

standard deviation was essentially horizontal and above which 

the curve for RSD was essentially horizontal. The division 

between low- and high-concentration ranges is represented 

with a vertical dashed line. Variability could then be estimated 

as the mean standard deviation for the low range or the mean 

RSD for the high range.

Variability for NOX (figs. 3A and 3B) has a division at a 

concentration of 0.3 mg/L as N (table 5). Over the low range, 

variability is estimated as the mean standard deviation (plus 

or minus (±) 0.001mg/L as N), and over the high range, as the 

mean RSD (±1.0 percent of the measured concentration). Both 

these estimates are lower than those reported by Mueller and 

Titus (2005) using a dataset with many more samples and a 

larger overall range of NOX concentrations. The true variability of NOX might be slightly underestimated using replicates 

from the 2009–10 field study, but there is no indication that 

variability is larger than the previous estimate from Mueller 

and Titus.

Plots of standard deviation and RSD for DN and TN 

show no obvious division between low- and high-range variability (figs. 3C–F). The trend in RSD is about constant 

for both of these analytes; therefore, variability could be estimated by the average RSD for all concentrations greater than 

0.2 mg/L as N. In general, estimated variability is low (typically no more than 2 percent) for measured values of DN, TN, 

and NOX in excess of 1 mg/L as N.

Many more replicates were collected for PN than for 

the other analytes during the synoptic-field study. This was 

done because there was little previous information about the 

variability of the PN method. The plots showing the relations 

of standard deviation and RSD with mean PN concentration 

were slightly truncated in order to more clearly show patterns 

in variability (figs. 3G and 3H). Four high standard-deviation 

values (0.5–2.0 mg/L as N) and one high RSD value (about 

300 percent) are not shown, as are three sets of replicates with 

mean concentrations less than 0.01 mg/L as N (the lowest 

detection limit for PN during the study period). All values are 

retained in determining the LOWESS lines shown on the plots. 

There is no obvious threshold dividing low and high ranges 

of variability. Standard deviation is fairly constant up 

to a concentration of 0.05 mg/L as N but does not increase 

substantially until concentrations become greater than about 

0.3 mg/L as N. There is an inflection point in the RSD smoothing line at about 0.05 mg/L as N, but it is not horizontal over 

any of the range. For mean PN concentrations no greater than 

0.05 mg/L as N (approximately 5 times the lowest detection limits), variability averaged ±0.007 mg/L as N, which 

is between the two variability estimates for the low range of 

NOX concentrations (table 5). Over the high range (mean PN 

greater than 0.05 mg/L as N), mean RSD is ±13 percent of 

Table 5. Estimates of variability in replicate measurements for nitrogen species.

[mg/L, milligrams per liter; N, nitrogen]

Analyte Source

Low concentrations High concentrations

Concentration range

(mg/L)

Number of 

replicate 

sets

Variability 

(mean standard 

deviation, 

in mg/L)

Concentration range

(mg/L)

Number of 

replicate 

sets

Variability 

(mean relative 

standard deviation, 

in percent)

Nitrite plus nitrate, 

as N

Synoptic-field study, 

2009–10

0.01–0.3 14 0.001 0.3–15 49 1.0

Mueller and Titus, 

2005

0.004–1.0 691 0.012 1.0–41 541 2.2

Dissolved nitrogen Synoptic-field study, 

2009–10

-- -- -- 0.2–15 51 2.3

Total nitrogen Synoptic-field study, 

2009–10

-- -- -- 0.2–15 62 2.1

Particulate nitrogen Synoptic-field study, 

2009–10

0.005–0.05 171 0.007 0.051–20 725 13

Total Kjeldahl nitrogen Mueller and Titus, 

2005

0.05–0.5 523 0.027 0.5–40 591 7.6
14 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

the measured concentration, larger than the high-range variability for other nitrogen species (table 5). RSD of individual 

replicate sets was much larger in many cases, though RSD 

was less than 20 percent for almost 80 percent of these sets, 

and RSD exceeded 30 percent in fewer than 8 percent of sets. 

For consistency with other constituents, variability for highconcentration PN was estimated as mean RSD, but this might 

be somewhat low under certain conditions. 

Because TKN sampling occurred at only 16 of the 

77 sites in the synoptic-field study, only 12 replicate samples 

were collected, and variability of TKN could not be estimated 

using those data. The estimates provided by Mueller and Titus 

(2005) are reproduced in table 5. Average RSD over the high 

range of concentrations is larger than the corresponding estimates for NOX, DN, and TN and is smaller than the estimate 

for PN.

Factors That Could Affect the Variability of 

Particulate-Nitrogen Measurements

Particulate nitrogen is derived from, and thus strongly 

correlated to, concentrations of suspended sediment. Thus, the 

variability of PN might be related to the variability of suspended sediment, especially if sample filtration is affected by 

high or low SSCs. This sampling issue is associated with the 

amount of water that is passed through the filter during collection of the particulates. At high SSCs, it is not possible to filter 

much water before clogging occurs. In this study, although 

most PN samples were collected from at least 50 mL of water, 

some were collected from as little as 2 mL. The relation of 

SSC and filtered volume to the RSD of PN measurements in 

the synoptic-field-study dataset is plotted in figure 4. RSD is 

slightly higher at low SSCs and slightly lower at high SSCs, 

Figure 3. Relations of standard deviation (A, C, E, and G) and relative standard deviation (B, D, F, and H) to mean concentration for 

selected nitrogen species in replicate samples collected for the synoptic-field study, 2009–10. (LOWESS trend line is locally weighted 

scatterplot smooth.)

0.01 0.10 1.00 10.00

Dissolved nitrite plus nitrate concentration, in milligrams per liter as N

0

0.02

0.04

0.06

0.08

0.01 0.10 1.00 10.00

Dissolved nitrite plus nitrate concentration, in milligrams per liter as N

0

5

10

15

0.1 1.0 10.0

Dissolved nitrogen concentration, in milligrams per liter

0

0.1

0.2

0.3

0.4

0.5

Standard deviation, in milligrams per liter

0.1 1.0 10.0

Dissolved nitrogen concentration, in milligrams per liter

0

5

10

15

Relative standard deviation, in percent

Relative standard deviation, in percent

Standard deviation, in milligrams per liter

A

B

C

D

Low/high-concentrationrange threshold

LOWESS trend of data
Assessment of Precision for Total-Nitrogen Methods 15

but the locally smoothed values (indicative of central tendency) do not vary substantially over the range of four orders 

of magnitude in SSC. RSD is even more constant with respect 

to mean filtered volume, thus indicating no increase in PN 

variability in samples requiring low filter volumes because of 

high SSC.

Obtaining representative subsamples for preparation of 

filter pads is another factor that might affect PN variability. 

Subsamples can be extracted by drawing from a churn splitter 

or by pouring through a cone splitter. Some variability among 

replicate PN samples might be a result of using a separate subsample for each filter. The sampling protocol for the synopticfield study did not specify a method for subsample extraction, 

so there is no certainty that separate subsamples were used 

for each filter. If replicate PN samples were filtered from the 

same subsample, variability estimated from replicate standard 

deviations and RSD values might be low. The greatest impact 

would be for samples with SSC greater than 1,000 mg/L, the 

upper limit for extraction of representative subsamples from 

a churn splitter (U.S. Geological Survey, variously dated, section 5.1.1). In this study, subsamples for all waters with SSC 

greater than 3,000 mg/L were extracted using a cone splitter, 

so these subsamples are more likely to be representative. Only 

13 of 896 replicate sets had SSCs in the 1,000–3,000 mg/L 

range, for which representative subsamples might be questionable. An additional 42 samples had no reported SSC, but the 

PN concentrations in these samples were less than the range 

of PN measured in high SSC samples. It appears that sampling 

protocols were appropriate for obtaining representative subsamples, although deviation from standard protocols might be 

one explanation for large RSDs in some of the replicate sets. 

Other field procedures might also have contributed to large 

RSDs. For example, imprecise measurement of the volume of 

water passed through the filter could lead to large differences 

Figure 3. Relations of standard deviation (A, C, E, and G) and relative standard deviation (B, D, F, and H) to mean concentration for 

selected nitrogen species in replicate samples collected for the synoptic-field study, 2009–10. (LOWESS trend line is locally weighted 

scatterplot smooth.)—Continued

Total nitrogen concentration, in milligrams per liter

Mean particulate nitrogen concentration, in milligrams per liter

0

0.1

0.2

0.3

0.4

0.5

Standard deviation, in milligrams per liter

Mean particulate nitrogen concentration, in milligrams per liter

Relative standard deviation, in percent

Relative standard deviation, in percent

Standard deviation, in milligrams per liter

E

F

G

H

0.1 1.0 10.0

Total nitrogen concentration, in milligrams per liter

0

0.1

0.2

0.3

0.4

0.5

0.1 1.0 10.0

0

5

10

15

0.01 0.10 1.00 10.00

0.01 0.10 1.00 10.00

0

20

40

60

80

100

120

140

LOWESS trend of data
16 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

1 10 100 1,000 10,000

Suspended-sediment concentration, in milligrams per liter

0

20

40

60

80

100

120

140

Relative standard deviation of particulate nitrogen, in percent

1 10 100

Mean volume of water filtered, in milliliters

0

20

40

60

80

100

120

140

Relative standard deviation of particulate nitrogen, in percent

A

B

LOWESS trend of data

Figure 4. Relations of (A) suspended sediment concentration and (B) water volume filtered to relative 

standard deviation of particulate-nitrogen measurements in replicate samples collected for the synopticfield study, 2009–10.
Assessment of Bias in Total-Nitrogen Methods 17

in computed PN concentration in replicate samples. Overall, 

the field component of variability seems likely to be more 

important for PN than for other nitrogen species.

Precision of Computed Total Nitrogen

The precision of computed total nitrogen, whether the 

sum of DN and PN (TN-C) or TKN and NOX (TN-K), depends 

on the proportion of each analyte in the sample. For TN-C, if 

DN is predominant, precision would be affected primarily by 

the variability of DN (estimated in this study at 2.3 percent of 

the analytical result). If PN is predominant, precision would 

decrease because of the higher variability of PN results (about 

13 percent). If the concentrations of DN and PN in a sample 

are approximately equal, the expected variability of TN-C 

would be intermediate, perhaps about 7–8 percent. For the 

905 samples from the synoptic-field study, the average proportion of PN to TN was about 14 percent, and only 34 samples 

had proportions of PN to TN greater than 50 percent. Therefore, for most samples variability of TN-C likely approximates 

that of DN.

Similarly for TN-K, precision depends on the proportion 

of TKN and NOX. Based on table 5, for samples with concentrations greater than 1, if NOX is predominant, the expected 

variability of TN-K would be smaller (RSD about 2 percent) 

than if TKN is predominant (RSD about 8 percent). The mean 

proportion of TKN to TN-K in 212 synoptic-field samples was 

about 40 percent, and 70 samples had proportions greater than 

50 percent. In this case, the higher variability of TKN has a 

substantial effect on the precision of total nitrogen computed 

as TN-K. Thus the expected variability of TN-K is intermediate between 2 and 8 percent. For most samples, the precision 

of total nitrogen computed as TN-C would be better (lower 

variability) than the precision of TN-K.

Assessment of Bias in Total-Nitrogen 

Methods

Bias in measured TN-A and TN-K was computed by 

comparison to known TN in synthetic samples prepared for 

the laboratory experiment or in comparison to measured TN-C 

for samples collected during the synoptic-field study. Sources 

and mechanisms of bias were investigated to the extent possible for each of the analytical methods: alkaline persulfate 

digestion for TN-A and Kjeldahl digestion for TN-K.

Assessment of Bias Determined by the 

Laboratory Experiment

The nutrient-fortified, deionized-water matrix for the 

laboratory experiment contained nitrate (2.0 mg/L as N) 

and glycine (0.3 mg/L as N) as the sole components of DN. 

Standard reference materials provided uniform sources of 

PN. Interpretation of results obtained for various additions 

of reference materials was more straightforward than would 

have been possible for complex environmental water matrices. 

Whereas the Buffalo River sediment and Montana soil reference materials were selected to approximate characteristics of 

suspended sediment in environmental river samples, the Green 

River Shale reference material, with its very high nitrogen and 

carbon content, was selected to challenge the alkaline-persulfate and Kjeldahl digestion methods. 

In both methods, nitrogen bias was extremely negative 

for the samples amended with Green River Shale (tables A1 

and A2 in the appendix), a clear indication that either the 

reagent concentration was insufficient to completely digest 

the PN or that some fraction of PN was refractory (resistant 

to digestion), even under the extreme conditions of heat and 

acidity of the Kjeldahl digestion. As expected, these results 

pointed out the limitations of both methods, but because Green 

River Shale represents nutrient-enrichment conditions that are 

not expected in environmental streams, these results were not 

included in further assessment of the bias in nitrogen analytical methods.

Bias in the Alkaline-Persulfate and Kjeldahl 

Methods

As stated earlier, reagent limitation caused by high 

concentrations of organic materials in sediment-laden samples 

was suspected as the primary mechanism for negative bias 

in TN-A. However, results from laboratory experiments with 

Buffalo River sediment and Montana soil (fig. 5) revealed 

negative bias in TN-A results, even at the lowest sediment 

concentrations tested (100 and 200 mg/L, respectively). Note, 

however, that minimum sediment concentrations in the synthetic samples were substantially greater than many SSCs typically encountered at field sites. In general, the negative bias 

in TN-A was greater at higher sediment concentrations but to 

different degrees for each of the reference materials. Negative 

bias was smaller for samples prepared with Montana soil at 

all levels of sediment addition. For both reference materials, 

variability of results among sample replicates was small in the 

laboratory experiment. 

In addition to the comparisons of TN-A at various sediment concentrations, 14 TN-A analyses were performed on the 

nutrient-fortified water with no sediment reference material 

added. The TN-A bias of these samples, listed in table A1 in 

the appendix, varied by 0.05 mg/L and averaged 0.00 mg/L, 

indicating essentially no bias in the alkaline-persulfate method 

when no sediment is present.

TKN bias showed a less pronounced trend in relation to 

sediment concentration than did bias in TN-A (fig. 5). Bias 

in TKN ranged from about 5 percent negative to more than 

10 percent positive, and in general, there was a wide range 

in results for each reference material at all sediment concentrations. Measured TKN values were positively biased 

for the Montana soil samples, but the LOWESS smooth for 

the Buffalo River sediment samples indicates a slight shift 
18 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

from generally positive to negative bias in TKN for sediment 

concentrations above 1,000 mg/L. This suggests that an abundance of sediment might affect the extent of nitrate reduction 

during Kjeldahl digestion, and that bias in TN results is likely 

to vary among field sites. The presence of negatively biased 

TKN may be the result of PN sequestration analogous to that 

observed for TN-A. In this regard, it is noteworthy that a small 

and variable fraction of PN resistant to Kjeldahl digestion in 

bed sediments from the tidal Potomac River and estuary was 

documented in an earlier USGS study (Simon and Kennedy, 

1987). Simon and Kennedy recovered this sequestered PN 

fraction by dissolving post Kjeldahl-digestion particulate 

residues in hydrofluoric acid. In this study, HTCO analysis 

revealed a sequestered PN fraction in wet chemical-digest 

residues. Considering the near 100 percent recoveries of PN 

from reference sediments by HTCO (fig. 1), it is reasonable to 

assume that HTCO would be at least as effective as hydrofluoric acid dissolution with respect to releasing any PN fraction 

not recovered by commonly used wet-chemical digestion 

procedures.

Source of Bias in the Alkaline-Persulfate Method

The potential for preventing reagent limitation in the 

alkaline-persulfate method was tested by analyzing digests 

prepared with half the volume of aqueous, nutrient-fortified 

matrix and twice the volume of persulfate reagent. In relation 

to the standard alkaline-persulfate method, this modification 

doubled the persulfate concentration and halved the dissolved 

nitrogen concentration in the aqueous matrix. Results of TN-A 

analysis using this fortified-reagent test are shown in figure 6. 

Over the range of sediment concentration tested, relative bias 

in TN-A was generally 10 percent smaller using the fortified 

reagent but was still negative, indicating that reagent limitation was not the predominant factor.

After alkaline-persulfate digestion reagent limitation was 

ruled out as the primary mechanism of low nitrogen recovery, 

additional testing was done to determine whether the bias in 

TN-A was caused by sequestration or suppression. Sequestration would occur if some of the nitrogen in the reference 

material was refractory (resistant to digestion by the persulfate 

EXPLANATION

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Sediment concentration in water mixture, in milligrams per liter

-40

-30

-20

-10

0

10

20

Nitrogen bias, in percent of known concentration

Buffalo River sediment TN-A

LOWESS trend of data

Montana soil TN-A

Buffalo River sediment TKN

Montana soil TKN

Figure 5. Bias in total nitrogen determined by alkaline-persulfate digestion (TN-A) and total Kjeldahl nitrogen (TKN) in 

laboratory tests using synthetic samples made from nutrient-fortified water and various concentrations of two reference 

materials: Buffalo River sediment or Montana soil.
Assessment of Bias in Total-Nitrogen Methods 19

reagent). Suppression would occur if some substance in the 

reference material interfered with colorimetric determination 

of nitrogen following digestion. The possibility of sequestration was qualitatively investigated using the HTCO PN 

method to analyze the residue remaining in alkaline-persulfate 

digest tubes. The PN measured in digest residues approximated the amount of nitrogen not recovered from alkalinepersulfate digests. This established, for the reference materials 

at least, that negative bias in TN-A resulted from sequestration 

rather than the suppression of a determinative step through 

reagent limitation.

To demonstrate the degree of sequestration occurring in 

the reference materials, apparent PN recovery by the alkalinepersulfate digestion method was computed as the ratio of 

the measured TN-A minus the known DN (2.3 mg/L) to the 

known PN concentration in the synthetic sample (known from 

reference material composition). The apparent PN recovery 

computation assumes dissolved nitrogen species were completely and accurately recovered, and reference material composition is known with accuracy. Apparent recoveries of PN 

averaged 58 percent for samples with Buffalo River sediment 

and 78 percent for samples with Montana soil 

(fig. 7). The apparent recovery of PN from Buffalo River 

sediment was substantially less overall and more variable for 

the smallest mass additions, perhaps because weighing errors 

or sample heterogeneity could cause proportionately greater 

differences among samples when mass additions are small. 

The lowest sediment concentrations in samples prepared with 

Montana soil samples were twice those of samples prepared 

with Buffalo River sediment, which might account for the 

comparatively better recoveries at smaller mass additions. 

The slight decrease in apparent PN recovery at SSCs above 

2,000 mg/L suggests the onset of reagent limitation. These 

experimental results further support the hypothesis that some 

fraction of PN in each reference material is not recoverable by 

alkaline-persulfate digestion. The results also indicate that this 

fraction is relatively constant between sediment concentrations 

of 500 and 2,000 mg/L, differs among reference materials, and 

likely would differ among environmental sediments.

Figure 6. Comparison of bias in total-nitrogen concentration in synthetic samples analyzed using the standard alkalinepersulfate method and an alternate, fortified-reagent test using four times the reagent relative to the original sample volume.

LOWESS trend of data

EXPLANATION

Nitrogen bias, in percent of known concentration

0 500 1,000 1,500 2,000 2,500 3,000 3,500

-40

-30

-20

-10

0

10

Buffalo River sediment (standard method)

Buffalo River sediment (fortified-reagent test)

Montana soil (standard method)

Montana soil (fortified-reagent test)

Sediment concentration in water mixture, in milligrams per liter
20 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

Source of Bias in the Kjeldahl-Digestion Method

The positive bias in TKN results for synthetic samples 

containing Montana soil indicates that nitrogen recovery is 

greater than 100 percent (fig. 5). This is also the case for most, 

but not all, of the samples containing Buffalo River sediment. 

Given that the nutrient-fortified matrix for these samples 

contained only glycine and nitrate, recoveries greater than 

100 percent are likely the result of some nitrate being reduced 

to ammonia. Soil and crop scientists have used 15N isotopes to 

confirm this mechanism (Pace and others, 1982; Wyland and 

Jackson, 1994). For the laboratory experiment, the amount of 

nitrate reduction that occurred during Kjeldahl digestion was 

estimated by subtracting the known concentrations of organic 

nitrogen (0.3 mg/L from glycine) and PN from the measured 

TKN. Because no ammonia was present in the synthetic 

samples, the expected value of this difference—considered 

the apparent recovery of nitrate—would be zero if no nitrate 

reduction was occurring. The apparent recovery was normalized against the known concentration of nitrate (2.0 mg/L as 

N) (fig. 8). 

The discussion of nitrate interference in Standard Methods (American Public Health Association, 2012) indicates that 

nitrate interference in the Kjeldahl digestion is highly variable 

and can be positive or negative. This variability in TKN bias 

was reflected in the apparent recovery of nitrate in the synthetic samples amended with Buffalo River sediment, where 

the standard deviation of apparent-nitrate recovery at a given 

sediment concentration averaged 2.8 percent, and the trend 

of the recovery shifted from a positive to a negative bias. In 

contrast, for samples amended with Montana soil, the apparent 

recovery of nitrate was less variable (the standard deviation 

of apparent nitrate recovery at a given sediment concentration 

averaged 1.2 percent), consistently positive, and increased 

with greater additions of reference material. This pattern 

might be the result of increasing amounts of organic matter 

facilitating the reduction of more nitrate to ammonia. The 

greater variability for Buffalo River sediment might be related 

to higher actual TKN concentrations at each level of mass 

addition caused by the greater nitrogen content in this reference material (0.20 percent in comparison to 0.13 percent for 

Montana soil). Likewise, as sediment concentration increases, 

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Sediment concentration in water mixture, in milligrams per liter

20

30

40

50

60

70

80

90

100

Apparent recovery of particulate nitrogen, in percent of known concentration

Buffalo River sediment

Montana soil

LOWESS trend of data

EXPLANATION

Figure 7. Apparent recovery of particulate nitrogen (assuming complete and accurate recovery of dissolved nitrogen species) 

in synthetic samples analyzed for total nitrogen by alkaline-persulfate digestion.
Assessment of Bias in Total-Nitrogen Methods 21

the relative impact of sequestered PN would increase, possibly 

resulting in the observed minor shift from positive to negative 

bias for Buffalo River sediment at concentrations greater than 

1,000 mg/L (figs. 5 and 8).

Several conclusions were drawn from the laboratory 

experiment:

• The Kjeldahl method is substantially less precise (more 

variable) than the alkaline-persulfate method.

• TN-A has a negative bias, primarily because some 

particulate nitrogen is refractory (resistant to alkalinepersulfate digestion), leading to sequestration of that 

nitrogen.

• The negative bias associated with the alkaline-persulfate method does not decrease substantially when 

sample volumes are halved and reagent volumes are 

doubled.

• For sediment-water mixtures typical of conditions in 

environmental streams, the Kjeldahl method could 

have a positive bias that may be a result of NOX

reduction to ammonia or a negative bias as a result 

of particulate nitrogen that is refractory (resistant to 

digestion).

The overall conclusion from this laboratory experiment was 

that total-nitrogen concentrations determined by alkalinepersulfate digestion (in the presence of suspended sediment) 

or by summation of TKN plus NOX are likely to be biased. An 

alternative method is needed to accurately quantify the total 

concentration of nitrogen in whole-water samples.

Assessment of Bias Determined by the 

Synoptic-Field Study

The 905 geographically and temporally diverse samples 

from 77 sites in the synoptic-field study had TN-C concentrations ranging from 0.15 to about 26 mg/L as N and SSCs ranging from 1 to 14,700 mg/L (appendix table A4). All samples 

were analyzed for TN-A bias and 218 also were analyzed 

Figure 8. Apparent recovery of nitrate during Kjeldahl digestion (determined as the difference between measured and known 

concentrations of total Kjeldahl nitrogen in comparison to the known concentration of nitrate nitrogen) in synthetic samples 

amended with Buffalo River sediment or Montana soil.

0 500 1,000 1,500 2,000 2,500 3,000

Sediment concentration in water mixture, in milligrams per liter

-10

-5

0

5

10

15

20

Apparent recovery of nitrate, in percent of known concentration

Buffalo River sediment

Montana soil

LOWESS trend of data

EXPLANATION
22 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

for TN-K bias using the associated TN-C value as reference. 

Results from the synoptic-field study confirmed the biases 

revealed by previous laboratory and field data—generally 

negative biases in TN-A and positive biases in TN-K (fig. 9). 

The median TN-A bias was -0.05 mg/L or -3.2 percent. The 

median TN-K bias was 0.04 mg/L or 3.1 percent.

Bias in the Alkaline-Persulfate-Digestion Method

Negative bias in TN-A was observed in the laboratoryexperiment results at the lowest SSCs evaluated (100 mg/L). 

The majority of samples (74 percent) in the synoptic-field 

study had SSCs less than 100 mg/L, ranging down to 1 mg/L. 

The negative bias appeared to be present over the entire range 

of observed SSCs (figs. 10A and 10B). 

These data were categorized into six ranges of SSC to 

evaluate the distribution of bias in each concentration range 

(fig. 11). In all six cases, the median TN-A bias was less than 

zero, suggesting that some degree of negative bias existed in 

the presence of even the lowest SSCs. In fact, the distribution 

of the relative bias of samples having SSCs from 1 to 10 mg/L 

was nearly as negative as the distribution of samples having 

SSCs from 101 to 750 mg/L (fig. 11B). Because the bias is 

present across the entire range of SSC, these results support 

the laboratory-experiment conclusion that negative bias is 

primarily from sequestration rather than digest-reagent limitation. It also demonstrates that on average, TN-A results are 

negatively biased even for samples with low SSCs.

Though still present, the pattern of increasingly negative 

TN-A bias with respect to SSC was not as well-defined in the 

synoptic-field study data as that for the laboratory experiment. 

Given the differences inherent between a controlled laboratory experiment and a field-sampling effort conducted at a 

national scale, an increase in variability around the general 

trend of the relation was not unexpected. One potential source 

of variability may have been related to the heterogeneity of 

sediments in the field samples. Not only would the composition of those sediments vary from site to site based on the soil 

and streambed characteristics of the contributing watershed, 

but they would also vary from sample to sample based on 

the dynamic nature of the erosional and sediment-transport 

processes that delivered the sediment to the stream for a given 

sample. However, a comprehensive determination of the sedimentary characteristics of each sample was outside the scope 

of this study.

With the exception of samples having SSCs greater than 

750 mg/L, the TN-A relative biases for the other five concentration ranges were similar (fig. 11B), with an overall median 

value of -3.2 percent. The abrupt expansion of negative 

bias in TN-A for samples with SSCs greater than 750 mg/L 

(fig. 11) indicates that another mechanism introducing bias, 

such as reagent limitation, might have operated at these higher 

SSCs. 

Though in general the TN-A bias is more negative at 

SSCs greater than 750 mg/L (figs. 10A and 11), there are still 

instances of only moderately negative and even positive TN-A 

bias at these higher SSCs. Likewise, there are instances of 

exceptionally negative TN-A bias at lower SSCs. These may 

suggest a dependence on the sediment composition or size 

distribution as well as concentration. Sediments with greater 

amounts of nitrogen bound to the particles (PN) would be 

more prone to negative TN-A bias than sediments with low 

PN, even if the overall SSC is held constant. This concept is 

illustrated when the TN-A biases in the synoptic-field study 

data are compared with PN concentration (fig. 10C) and suggests that SSC serves as a useful, though imperfect, predictor 

of PN in a given sample. 

Locally weighted regressions (Helsel and Hirsch, 2002) 

were used to compare the trend of TN-A bias observed in 

the synoptic-field data with that of the earlier laboratory 

experiment (fig. 12). Although variability in the field data 

TN-A TN-K

-0.30

-0.15

0.00

0.15

0.30 218

, n iot art necnoc det cepxe ot der ap moc sa s ia B

r etil r ep negorti n s mar iglli m in

905

TN-A

-20

-15

-10

-5

0

5

10

15

n iot art necnoc det cepxef ot necr ep in , s ia B

TN-K

905 218 EXPLANATION

218 Number of values

90th percentile

75th percentile

50th percentile (median)

25th percentile

10th percentile

A B

Figure 9. Distribution of (A) bias and (B) relative bias in total nitrogen determined by alkaline-persulfate digestion (TN-A) and as the 

sum of total Kjeldahl nitrogen and nitrate plus nitrite (TN-K) observed in the synoptic-field study, 2009–10.
Assessment of Bias in Total-Nitrogen Methods 23

EXPLANATION

0.01 0.1 1 10

Particulate-nitrogen concentration, in milligrams per liter

-12

-10

-8

-6

-4

-2

0

2

LOWESS trend of data

Suspended-sediment concentration 

less than 750 milligrams per liter

Suspended-sediment concentration 

greater than 750 milligrams per liter

-75

-50

-25

0

25

50

75

1 10 100 1,000 10,000 100,000

Suspended-sediment concentration, in milligrams per liter

1 10 100 1,000 10,000 100,000

Suspended-sediment concentration, in milligrams per liter

-12

-10

-8

-6

-4

-2

0

2

B

750 milligrams per liter

A

C

750 milligrams per liter

LOWESS trend of data

LOWESS trend of data

TN-A bias, in percent of expected concentration TN-A bias as compared to expected concentration, in milligrams nitrogen per liter TN-A bias as compared to expected concen- tration, in milligrams nitrogen per liter

Figure 10. Relations of bias in total nitrogen determined by alkaline-persulfate digestion (TN-A) in 

samples collected as part of the synoptic-field study to (A and B) suspended-sediment concentration 

and (C) particulate-nitrogen concentration
24 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

was greater than in laboratory experiment data, the LOWESS 

trends are similar. This indicates that the findings of the laboratory experiment bracket the observations from the field—the 

reference materials used for the laboratory experiment were 

found to be representative of the broad range of sediments, 

and PN content thereof, included in suspended-sediment 

samples from the nationwide field study. At lower SSCs, the 

regression line for the synoptic-field data indicated a constant 

negative relative bias that averages approximately 5 percent 

of the actual concentration (figs. 11B and 12B), confirming the 

presence of negative bias in samples with low SSC. At SSCs 

above 750 mg/L, relative bias became more negative in the 

synoptic-field data as well as in the synthetic samples (figs. 7, 

11B, and 12B).

Spatial Patterns of Bias in Total Nitrogen Determined by 

Alkaline-Persulfate Digestion 

Results from the synoptic-field study were evaluated for 

evidence of regional or land-use effects on the magnitude of 

TN-A bias. Mean bias was calculated for 57 sites with TN-A 

results from at least 10 samples (fig. 13). Though certain areas, 

particularly sites draining the central Plains and intermountain 

basins, tended to have more negative bias (fig. 13A), these distinctions became less clear when viewed as relative bias (fig. 

13B). This suggests that the negative bias in TN-A was largest 

when nitrogen concentrations generally were higher, rather 

than simply as a function of geographical location. 

Four basin characteristics were used to comparatively 

assess the mean relative bias in TN-A for selected sites in the 

synoptic-field study. These characteristics were computed 

using areal overlay techniques of Nakagaki and others (in 

press) for all sites having at least 10 measurements of relative 

bias in TN-A (appendix table A5). The versions of the digital basin delineations used in the analyses were current as of 

September 21, 2012. Results from the laboratory experiment 

indicated that TN-A bias might be affected by sediment type. 

Therefore, the percentage of silt and clay in the topsoil of the 

watershed (Natural Resources Conservation Service, 2010) 

was evaluated as a proxy for sediment type. However, the 

distribution of the mean relative bias in TN-A for the 

57 synoptic-field sites did not covary with the amount of silt 

and clay in topsoil (fig. 14A). The amount of PN present in 

the soil might also affect the magnitude of TN-A bias, and the 

predominant land use (Fry and others, 2011) was presumed 

to represent soils that may be enriched with nitrogen. Though 

agriculturally dominated basins were expected to have high 

nitrogen enrichment, only basins dominated by rangeland 

seemed to have stronger negative relative bias in TN-A 

(fig. 14B). Given the small number of sites in each landuse category, this finding is not conclusive. Predominant 

ecoregions (Omernik, 1987) and physiographic divisions 

(Fenneman and Johnson, 1946) provided general but potentially relevant information about the watersheds. Comparisons of relative bias in TN-A among ecoregions were not 

Figure 11. Distribution of (A) bias and (B) relative bias in total 

nitrogen determined by alkaline-persulfate digestion (TN-A) in 

samples collected for the synoptic-field study and categorized by 

suspended-sediment concentration range.

-4

-2

0

EXPLANATION

181 Number of values

90th percentile

75th percentile

50th percentile (median)

25th percentile

10th percentile

244 181 139 113 164 22

A

B

244 181 139 113 164 22

1–10

11–25

26–50

51–100

101–750

Greater than 750

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10

 Suspended-sediment concentration range, in milligrams per liter

n iot art necnoc det cepxef ot necr ep in , s ia b A- NT TN-A bias as compared to expected concentration,

in milligrams nitrogen per liter
Assessment of Bias in Total-Nitrogen Methods 25

meaningful because of the small number of sites in each 

ecoregion grouping. Of the six physiographic divisions 

represented among the sites, the Interior Plains, Intermontane 

Plateaus, and Pacific Mountain System may be regions with 

somewhat more negative TN-A bias (fig. 14C). As with land 

use, however, these differences may also be a coincidental 

artifact of the small number of sites in most groups. Given 

that the overall distribution of relative bias in TN-A did not 

vary greatly until SSC was greater than 750 mg/L (fig. 11B), 

a lack of relation between mean relative bias and these basin 

characteristics is not unexpected. 

Bias in the Kjeldahl Method

Bias was evaluated in TN-K concentrations measured in 

the synoptic-field samples, with a particular focus on the effect 

of NOX on that bias. For the 218 samples from 19 sites having 

sufficient data for TN-K bias analysis, the median TN-K bias 

was 0.04 mg/L as N (fig. 9A) or 3.1 percent (fig. 9B). 

The synoptic-field study supported the findings of the 

laboratory experiment in which the Kjeldahl-digestion method 

was shown generally to produce a positive bias in the presence of elevated NOX (fig. 15). Whereas NOX concentration in 

the laboratory samples did not vary, the synoptic-field study 

Figure 12. General trends (as represented by locally weighted regression) of (A) bias and (B) relative 

bias in total nitrogen determined by alkaline-persulfate digestion (TN-A) in sediment and soil samples 

from the laboratory experiment and synoptic-field study.

1 10 100 1,000 10,000

-40

-30

-20

-10

0

10

Suspended-sediment concentration, in milligrams per liter

B

-3

-4

-2

-1

0

1

A

Buffalo River sediment

Montana soil

Synoptic-field study

LOWESS trend of data

EXPLANATION

Buffalo River sediment

Montana soil

Synoptic-field study

LOWESS trend of data

EXPLANATION

TN-A bias, in percent of expected concentration TN-A bias as compared to expected concentration,

in milligrams nitrogen per liter
26 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

110°

100° 90°

80°

40°

30°

0 200 400 600 800 KILOMETERS

0 200 400 600 800 MILES

Base from U.S. Geological Survey digital data, 2005, 1:2,000,000

Albers Equal-area Conic projection

Standard parallels 29°30’N and 45°30’N, central meridian 96°W

North American Datum of 1983

100° 90° 80°

70°

120°

110°

40°

30°

50°

A

B

EXPLANATION

Mean TN-A bias as compared to 

expected concentration, in 

milligrams nitrogen per liter

Less than -0.4

Greater than -0.4 to -0.1

Greater than -0.1 to 0

Greater than 0 to 0.1

Greater than 0.1

EXPLANATION

Mean TN-A bias, in 

percent of expected 

concentration

Less than -10

Greater than -10 to -5

Greater than -5 to 0

Greater than 0 to 5

Greater than 5

Figure 13. (A) Mean bias and (B) mean relative bias in total nitrogen determined by alkaline-persulfate digestion (TN-A) for 

sampling sites with 10 or more measurements, synoptic-field study, 2009–10.
Assessment of Bias in Total-Nitrogen Methods 27

Figure 14. Comparisons of relative bias in total nitrogen determined by alkaline-persulfate digestion (TN-A) to 

drainage-basin characteristics for selected sampling sites in synoptic-field study, 2009–10, for (A) percent silt 

and clay in topsoil, (B) dominant land use, and (C) dominant physiographic division.

Dominant physiographic division

Appalachian Highlands

-15

-10

-5

0

5

10

12 9 22 6 6 2

Dominant land use

-20

-15

-10

-5

0

5

-20

-15

-10

-5

0

5

Percent silt and clay in the topsoil

TN-A bias, in percent of expected concentrationTN-A bias, in percent of expected concentration

19 15 13 10 A B 13 22 22

C

2 Number of values 

Individual observation 

90th percentile 

75th percentile 

50th percentile (median)

25th percentile 

10th percentile 

EXPLANATION

0–40 40–60 60–100 Agriculture

Forest

Rangeland

Urban

TN-A bias, in percent of expected concentration

Atlantic Plain

Interior Plains

Intermontane Plateaus

Pacific Mountain System

Rocky Mountain System
28 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

included samples with NOX ranging from less than the reporting level of 0.016 to about 7.6 mg/L as N (appendix table A4). 

In the same manner that bias in TN-A was further assessed 

by ranges of SSC, the bias in TN-K was grouped by ranges 

of NOX concentration (fig. 16). TN-K bias showed a slightly 

positive relation to concentration of NOX (figs. 15A and 16A) 

with variability increasing proportionally with NOX. Though 

it was generally positive, the relative bias in TN-K was 

more variable at lower concentrations of NOX and tended to 

become less positive with increasing NOX (figs. 9, 15B, and 

16B). The occurrence of positive TN-K bias in the presence of 

elevated NOX was expected given the documented potential 

for NOX to be reduced to ammonia during Kjeldahl digestion. 

This would produce an artificially high TKN value, thereby 

causing a positive bias in the computation of TN-K (American 

Public Health Association, 2012; Patton and Truitt, 2000; Patton and Kryskalla, 2003, p. 23–25 and fig. 8). This mechanism 

of bias may be causing the positive bias in TN-K seen in the 

results from the 2009–10 synoptic-field study. However, a 

conclusively upward trend in relative bias as concentrations of 

NOX increased was not apparent.

Four major conclusions were drawn from the synopticfield study:

• The patterns in bias observed in field samples are supported by findings of the laboratory experiment.

• Negative bias in TN-A results was present regardless 

of SSC, but it becomes more likely and larger at SSCs 

greater than 750 mg/L.

• Relative bias in TN-A does not appear to be explained 

by variations in the characteristics selected to describe 

the upstream drainage basin.

• Though variable, positive bias in TN-K is typical in the 

presence of elevated NOX.

LOWESS trend of data

0.001 0.01 0.1 1 10

Nitrate plus nitrite, in milligrams per liter as nitrogen

-40

-20

0

20

40

60

TN-K bias, in percent of expected concentration

B

-1.0

-0.5

0

0.5

1.0

TN-K bias as compared to expected concentration,

in milligrams nitrogen per liter

A

Figure 15. Relations of bias and relative bias in total nitrogen computed as the sum of total Kjeldahl nitrogen 

and nitrate plus nitrite (TN-K) to nitrate plus nitrite concentration in samples collected as part of the synopticfield study, 2009–10.
Evaluation of Alternatives for Determining Total Nitrogen 29

Evaluation of Alternatives for 

Determining Total Nitrogen

Data from the laboratory experiment and the synopticfield study yielded estimates of precision for TN concentrations determined using methods TN-A, TN-K, and TN-C as 

well as bias in the TN-A and TN-K methods. The evaluation 

of the advantages and disadvantages of each approach for 

determining TN is less straightforward, but of equal importance. Such an evaluation must consider the impacts on 

field and laboratory procedures, including costs, and on data 

interpretation, including any discontinuity in the historical 

record that might result from a methodological change. These 

impacts, which will likely vary considerably for different 

sampling programs, will challenge USGS scientists trying 

to establish uniform standards for TN sample collection and 

analytical determinations. In the paragraphs that follow, three 

alternatives were considered for programs currently collecting 

TN-A or TN-K data:

1. Replace TN-A with TN-C,

2. Replace TN-K with TN-C, and 

3. Addition of TN-C, perhaps only temporarily, to existing 

analyses of TN-A or TN-K.

Factors Considered

Each analytical approach was evaluated in relation to: (1) 

anticipated precision and bias of analytical results, (2) impacts 

on existing field and laboratory practices, and (3) impacts on 

data interpretation and the historical record. There might be 

additional considerations for a particular sampling program, 

but in this report the focus is on the above criteria that are 

common concerns across all programs.

Anticipated Precision and Bias

Precision and bias of TN values estimated by each 

approach will differ by individual sample and protocols in 

place for sampling programs. A generalized graphical summary of expected precision and bias for TN values resulting 

from the TN-A, TN-K, and TN-C approaches is shown in 

figure 17. More detailed discussions of precision and bias for 

each approach are included in earlier sections of this report. 

Figure 17 characterizes the precision of calculated values of 

TN (TN-C and TN-K) as a range associated with the precision of each analyte used in the calculation. However, the 

actual precision is dependent on the relative proportions of 

the nitrogen analytes within the sample. This study found that 

DN is typically predominant; therefore, TN-A precision is 

probably similar to that of the more precise DN method. In 

contrast, TKN often composed 40 percent or more of TN-K, 

Figure 16. Distribution of (A) bias and (B) relative bias in total 

nitrogen determined as the sum of total Kjeldahl nitrogen and 

nitrate plus nitrite (TN-K) observed in samples collected for the 

synoptic-field study, 2009–10, among ranges of nitrate plus nitrite 

concentration. Values outside the 10th and 90th percentiles are not 

shown.

0.00

0.15

0.30

T , n iot art necnoc det cepxe ot der ap moc sa s ia b K- N

r etil r ep negorti n s mar iglli m in

27 40 40 58 53

0.0–0.2 0.201–0.5 0.501–1.0 1.01–2.0 Greater

than 2.0

-15

-0.15

0

15

30

 Nitrate plus nitrite concentration range, 

in milligrams nitrogen per liter

n iot art necnoc det cepxef ot necr ep in , s ia b K- NT

27 40 40 58 53

EXPLANATION

40 Number of values

90th percentile

75th percentile

50th percentile (median)

25th percentile

10th percentile

A

B
30 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

TN-A TN-K TN-C

Mean RSD of 

dissolved-nitrogen 

analyses 

Median bias 

measured in 

synoptic-field 

samples

Mean RSD of 

particulate-nitrogen 

analyses

Mean RSD of 

total-Kjeldahl 

analyses

Mean RSD of nitrate 

plus nitrite analyses

Mean RSD of 

total-nitrogen

analyses

No bias detected in 

laboratory experiments

15

10

5

0

-5

-10

-15

Percent deviation from expected concentration of total nitrogen

Suspended-sediment concentrationgreater than 750 milligrams per liter

Suspended-sediment concentrationless than 750 milligrams per liter

Median bias 

measured in 

synoptic-field 

samples

Median bias 

measured in 

synoptic-field 

samples

TN-A

TN-K

TN-C

RSD

Total nitrogen by alkaline-persulfate digestion

Total nitrogen measured as the sum of total Kjeldahl nitrogen 

and nitrogen in dissolved nitrate plus nitrite

Total nitrogen computed as the sum of dissolved and 

particulate nitrogen

Relative standard deviation

Precision metrics for each method

Mean RSD of the first analyte (or the only analyte in the 

 case of TN-A) used in the computation of total nitrogen

Mean RSD of the second analyte used in the computation 

 of total nitrogen

EXPLANATION

Median bias in the total nitrogen estimate

Bias metrics for each method

Figure 17. A graphical depiction of the relative effect of precision and bias on three methods for determining total nitrogen 

(ranges in precision for computational methods reflect the corresponding precision of the methods for individual analytes 

used in the computation).
Evaluation of Alternatives for Determining Total Nitrogen 31

suggesting that TN-K precision is likely intermediate between 

the precision of its two analytes. Within this report, precision 

is characterized for both high and low concentrations of total 

nitrogen. Furthermore, with reference to figure 17, negative 

bias in TN-A seems to be dependent on SSC, particularly at 

concentrations greater than 750 mg/L, and positive bias in 

TN-K results is most pronounced for samples containing high 

concentrations of NOX.

Impact on Field and Laboratory Procedures

Each alternative considered for determining TN includes 

the addition of TN-C. Collection of whole-water (unfiltered) 

samples is fundamental to USGS sampling programs, most 

of which currently submit whole water for analysis by the 

alkaline-persulfate or Kjeldahl methods and filtered water for 

analysis of dissolved species such as NOX and ammonia. Adding analysis of DN in these filtered-water samples is unlikely 

to affect sample-collection costs, and likewise, replacing one 

whole-water method with another is unlikely to affect sampleanalysis costs. On the other hand, processing whole-water 

aliquots to prepare filter pads for PN analyses would increase 

sampling costs because preparing filter pads for PN analyses 

is an additional step in sample processing. Some sampling 

programs might incur additional costs of buying PN filtration 

equipment. Time spent cleaning equipment and preparing PN 

filter pads would vary; experience gained during the 2009–10 

synoptic-field study suggests that 0.5 person-hour per site is 

typical, but the additional time could be considerably more for 

sediment-laden samples.

With the inclusion of TN-C in each approach, laboratory 

impacts are primarily dependent on potential sample loads 

for DN and PN determinations. Analytical capacities at the 

NWQL for DN are likely sufficient to handle increased analytical loads that might result from any of the three approaches 

under discussion. However, additional equipment and personnel might be required to satisfy the increased demand that 

might come with the inclusion of HTCO analyses for nutrient 

samples.

Because the suite of nutrients collected varies by 

sampling program, the relative financial impacts of these 

approaches would differ among sampling programs. The 

relative cost comparison provided in this report pertains to a 

typical nutrient analysis consisting of nitrate, nitrite, ammonia, phosphorus, orthophosphate, and one of the methods 

of obtaining total nitrogen. The cost impact for programs 

utilizing different sets of constituents will vary accordingly. 

Estimated costs are based on published USGS NWQL pricing 

for 2012. For the alternatives considered in the report, the 

prices are presented as a percentage of the cost of the existing 

analyses (TN-A or TN-K). 

Impact on Data Analyses

There are many uses of total nitrogen data—ambient 

stream assessment, trend analyses, and load calculation to 

name a few—and limitations of any of the approaches considered might affect these interpretations. Reduced precision 

of data would correspondingly reduce the power of statistical 

analyses and might affect conclusions reached from those 

analyses. Biased data might affect the stream classification or 

loading rates from a given stream. Additional complications in 

selecting a TN assessment approach might arise if a regulatory 

program does not recognize a particular method. Analyticalmethod changes implemented during ongoing multiyear studies might introduce artificial trends in TN as a result of methodological rather than environmental changes. Alternatively, 

maintaining use of a biased method might suggest artificial 

trends if the mechanism driving the bias (such as the presence 

of sediment) varies from year to year. Spatial analyses could 

be affected if different sampling programs opted for different approaches to TN assessment. Furthermore, an individual 

data point could be used for many different purposes, and an 

approach that satisfies one set of data-quality objectives might 

be detrimental to another. 

Replace TN-A with TN-C

As described in earlier sections of this report, TN-A 

results from laboratory experiments with reference materials and from synoptic field samples of environmental water 

confirmed that some fractions of PN resist oxidation to nitrate 

by TN-A methods, thereby introducing a negative bias. The 

median bias in TN-A measured from the synoptic-field study 

was -0.05 mg/L or -3.2 percent. By comparison, recoveries of 

PN by HTCO elemental-analysis methods appeared unbiased 

based on close agreement with reference values across a range 

of sediment masses. Alkaline-persulfate digestion of filtered 

samples generally is accepted as the reference method for 

determining DN, and other studies have found unbiased recovery of DN by alkaline-persulfate digestion (Vandenbruwane 

and others, 2007, section 3.1 and references cited therein). 

Because TN-C is the sum of PN and DN, TN-C estimates of 

TN are expected to be unbiased (table 6). The effect of removing negative bias in TN data by using the TN-C approach 

should be more marked in waters with SSC greater than about 

750 mg/L, and in such regimes might have a greater impact on 

conclusions (such as nitrogen loads or stream classifications) 

drawn from those data. Although bias also was detected in 

samples with low SSCs, it was of lesser magnitude. 

However, there are several drawbacks to replacing TN-A 

with the TN-C method (table 6). More field time would be 

spent preparing samples for analysis, and analytical costs for 

a typical nutrient sample would increase by approximately 

56 percent. The PN method is less precise than the TN-A 

method. The imprecision of PN transfers loss of precision 

to the TN-C estimate in increasing magnitude as the relative 

contribution of PN to TN increases, though the majority of 
32 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

samples in the synoptic-field study was dominated by DN 

rather than PN. Although samples with very high SSC (and a 

correspondingly large contribution of PN) are atypical of the 

majority in most USGS sampling programs, they also may 

represent conditions during which a large proportion of nitrogen transport by a stream occurs. 

The replacement of one analytical method with another 

would complicate time-series analyses that span that replacement. The expected removal of bias from subsequent samples 

may lead to step trends that are explained by the method 

change rather than an environmental condition. Also, the 

potential for reduced precision may prevent otherwise significant trends from being detected. It is important to note, 

though, that trends computed from existing and future TN-A 

data also may be affected by negative bias that covaries with 

SSC.

Replace TN-K with TN-C

As described in previous studies and earlier sections of 

this report, the presence of high levels of NOX in a sample 

may result in the conversion of some NOX to ammonia during the digestion process, thereby introducing a positive bias 

into TN-K estimates. For the synoptic-field study, the median 

TN-K bias was 0.04 mg/L or 3.1 percent. Alternatively, the 

TN-C method does not appear to be affected by this bias. 

Therefore, sampling programs currently utilizing the TN-K 

method may consider its replacement with the TN-C method. 

The primary benefit of this approach is the apparent 

removal of positive bias from TN-K estimates (table 6). The 

removal of bias may lead to improved assessments of nitrogen 

load or stream classifications.

However, a switch from TN-K to TN-C methods 

may affect time-series analysis such as trend detection 

(table 6). Because the TN-K method has been in use for a 

 longer period of time than the TN-A method, there are likely 

to be more historical data associated with the TN-K method, 

and a switch to TN-C from TN-K would have a greater impact 

than a switch to TN-C from TN-A. This alternative likely 

would not be adopted by regulatory programs that might continue to require the use of TN-K for stream assessment. Operationally, this approach would require added field processing 

time and an analytical cost increase of around 50 percent for a 

typical nutrient sample. 

Add TN-C Analyses to Existing Analyses of TN-A 

or TN-K

Whereas the replacement of TN-A and TN-K analyses 

with TN-C is expected to alleviate bias in TN estimates, it also 

introduces difficulties, including loss of precision, and USGS 

lacks a period of concurrent measurements from which the 

impact of a method change can be evaluated. Therefore, an 

approach that adds TN-C analyses to TN-A or TN-K analyses 

is worthy of consideration. Operationally, this approach would 

follow the basic techniques of the synoptic field study in 

which two estimates of TN would be quantified using different 

methods. For the majority of samples, the TN-A or TN-K data 

would be considered the primary estimate of TN. However, the 

magnitude of bias in those data would be known from comparison to the TN-C value. If that bias exceeded a predefined 

threshold, the TN-C value would be considered the primary 

estimate of TN. In the context of figure 17, this approach recognizes that for most samples, the amount of bias in TN-A or 

TN-K may be bracketed by imprecision of the TN-C method. 

Only when bias exceeds the threshold would the alternative 

TN-C analyses be preferred for their lack of bias.

This approach would maintain consistency with 

historical data for the majority of samples and would prevent

a minority of samples from being affected by excessive bias 

(table 6). This approach would likely improve the accuracy 

of load estimates or stream classifications in sediment-laden 

streams. The overlapping data could be used to evaluate the 

impact that bias or a method change might have on temporal 

trends. In addition, the data set for comparing TN-A with 

TN-C would expand both in sample number and spatial coverage, thereby allowing for future evaluations of this approach.

The primary drawback to this approach is that it presents 

the largest burden on sampling programs (table 6). Sampling 

crews would be required to collect whole-water, filtered-water, 

and filter-pad samples. The additional analytical costs associated with DN and PN would not be offset by the removal 

of TN-A or TN-K, thereby resulting in an 83- or 78-percent 

cost increase for typical nutrient samples that currently use 

TN-A or TN-K, respectively. Data management also would 

be complicated by the inclusion of two estimates for the same 

constituent and the resulting determination of which value 

should be considered primary for use in interpretive analyses. 

In addition to these operational burdens, the continued use of 

TN-A or TN-K for the majority of samples may perpetuate the 

presence of bias of lesser magnitude in subsequent datasets.

Summary

The characterization of total-nitrogen concentrations is 

an important component of many surface-water-quality programs. This report focuses on the challenges and limitations 

of three widely used methods for routine determination of 

total nitrogen in water: (1) total nitrogen (TN-A) derived from 

the alkaline-persulfate digestion of whole-water samples 

(2) total nitrogen (TN-K) calculated as the sum of total Kjeldahl nitrogen (TKN) and dissolved nitrate plus nitrite (NOX); 

and (3) total nitrogen (TN-C) calculated as the sum of dissolved nitrogen (DN) and particulate nitrogen (PN) (See Glossary for total-nitrogen definitions). Specifically, it explores 

how suspended-sediment and nitrate concentrations affect the 

performance of these methods, both in synthetic media—laboratory-formulated, nutrient-fortified deionized-water suspensions of reference materials (river sediment and soil)—and in 
Summary 33

a set of over 900 geographically and compositionally diverse 

environmental water samples collected from 77 U.S. Geological Survey (USGS) stream-sampling sites during June 2009 

through September 2010.

A comparison of 10 large-volume water samples in 2005 

suggested a negative bias in TN-A when compared to TN-C 

data. A series of follow-up studies were undertaken to identify 

the cause and extent of this negative bias and to explore some 

remedial measures. The first was a 2007 laboratory experiment 

designed to identify a connection between suspended sediment 

and negative bias in TN-A, to explore potential remedies, and 

to detect positive bias in TN-K in the presence of elevated 

NOX. A synoptic-field study was undertaken in 2009–10 

to evaluate the efficacy of using TN-C as a more accurate 

alternative to TN-A for water samples. In addition, data were 

collected to address whether variability in analytical results for 

PN might introduce excessive error in TN-C, thereby creating 

one problem while alleviating another. 

Analytical variability was assessed primarily using data 

from the synoptic field study. At low concentrations, the 

Table 6. Summary of advantages and disadvantages associated with three alternatives to current U.S. Geological Survey practices for 

determining total-nitrogen concentration in surface-water samples.

[TN-A, total nitrogen measured by alkaline-persulfate digestion of a whole-water sample; TN-C, total nitrogen computed as the sum of dissolved and particulate nitrogen; DN, dissolved nitrogen measured by alkaline-persulfate digestion of a filtered sample; NOx

, nitrogen in dissolved nitrate plus nitrite; TKN, total 

Kjeldahl nitrogen; TN-K, total nitrogen measured as the sum of TKN and NOx.]

Advantages Disadvantages Analytical cost change 

relative to existing practice1

Alternative: Replace TN-A with TN-C

+Negative TN-A bias removed from subsequent 

data, especially in sediment-laden streams

+Improvement of subsequent load estimates or 

stream classifications

-Potential for less precision than TN-A (though 

most samples will approach the precision of 

DN, see fig. 17)

-Method replacement may introduce an artificial 

step-trend into time-series analysis of historical data

-Increased field processing time 

(0.5 person-hours)

56 percent increase.

Alternative: Replace TN-K with TN-C

+Positive TN-K bias removed from subsequent data, 

especially in NOx-enriched streams

+Improvement of subsequent load estimates or 

stream classifications

-Potential for less precision than TN-K (though 

most samples will approach the precision of 

DN, which exceeds the precision of TN-K, 

see fig. 17)

-Method replacement may introduce an artificial 

step-trend into time-series analysis of historical data

-Regulatory programs may stipulate the continued use of TKN, thereby adding to the 

financial burden of this alternative

-Increased field processing time 

(0.5 person-hours)

50 percent increase.

Alternative: Add TN-C to existing TN-A or TN-K analyses

+Negative TN-A bias removed from subsequent data 

in which bias exceeds a predefined threshold

+Maintains the higher precision of the TN-A method 

for the majority of samples

+Improvement of subsequent load estimates in 

sediment-laden streams

+Maintains consistency with historical data

+Expands the number of samples and sites by which 

negative TN-A bias can be determined

+Transition period allows for the impact of bias and 

method changes on interpretations of the data to 

be characterized 

-Of the three alternatives, this presents the 

largest financial burden specific to sample 

collection and analysis

-Increased field processing time 

(0.5 person-hours)

-The inclusion of a second estimate of TN 

would complicate data management and may 

confuse data users

-Bias of lesser magnitude will be maintained for 

the majority of subsequent samples

83 percent increase for TN-A 

analyses. 

78 percent increase for TN-K 

analyses.

1

Estimated relative to the cost of a typical nutrient sample that includes total nitrogen, ammonia, nitrate, nitrite, orthophosphate, and phosphorus analyses.
34 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

analytical methods tended to perform comparably with respect 

to variability. At higher concentrations, the relative variability 

was about 2 percent for methods that utilized colorimetric 

determinations of NOX following alkaline-persulfate digestion 

(TN-A, DN), but about 13 percent for PN, and 7.6 percent for 

the Kjeldahl-digestion (TKN) method. Comparisons of PN 

precision with the corresponding filtered-water volume indicated that filtered volume had little relation to relative variability over a range of four orders of magnitude in PN. 

TN-A precision outperformed that of the analytes in the 

TN-K and TN-C methods. The computed concentrations of 

total nitrogen with the TN-K or TN-C methods are less precise 

than direct measurements of TN-A. However, the loss of 

precision depends on the relative amount of TKN or PN in the 

total nitrogen computation. For the synoptic field study, PN 

(having a precision of 13 percent) constituted on average only 

14 percent of TN-C, suggesting that the precision of the TN-C 

method approaches that of DN (2.3 percent). On the other 

hand, TKN (having a precision of 7.6 percent) constituted on 

average 40 percent of TN-K, suggesting that the imprecision 

of the Kjeldahl digestion likely affects the TN-K estimates 

adversely. For most samples, the precision of total nitrogen 

computed as TN-C would be better (lower variability) than 

the precision of TN-K. 

Results from the laboratory experiment indicated that the 

negative bias in TN-A increased as sediment concentration 

increased but to different degrees for each of the reference 

materials. Negative bias was smallest for samples prepared 

with Montana soil and increased for samples prepared with 

Buffalo River sediment. The potential for reagent limitation in 

the alkaline-persulfate method was tested by analyzing digests 

prepared with half the volume of aqueous, nutrient-fortified 

matrix and twice the volume of persulfate reagent. Nitrogen 

recovery improved using the enhanced method but was still 

negatively biased. Supplemental analysis of digest residues 

provided an almost complete accounting for the missing nitrogen presumed from the negative bias. Therefore, the laboratory experiment established that TN-A has a negative bias, 

primarily because some PN is refractory (resistant to alkalinepersulfate digestion), leading to sequestration of that nitrogen.

Observed TKN values of the laboratory experiment were 

positively biased for the Montana soil samples but exhibited 

both positive and negative biases over a range of Buffalo 

River sediment concentrations. The positive biases are likely 

the result of some nitrate being reduced to ammonia and 

double counted in the calculation of TN-K. This mechanism is 

well documented during Kjeldahl digestion of soil and plant 

extracts. The negative TKN biases in Buffalo River sediments were associated with higher concentrations of sediment 

and may be the result of sequestration of particulate nitrogen 

within that sediment. In regard to TN-K, the laboratory experiment showed that:

• The Kjeldahl method is substantially less precise (more 

variable) than the alkaline-persulfate method.

• For sediment-water mixtures typical of conditions in 

environmental streams, the Kjeldahl method could 

have a positive bias that may be a result of NOX

reduction to ammonia or a negative bias as a result 

of particulate nitrogen that is refractory (resistant to 

digestion). 

Though the variability in TN-A and TN-K biases was 

considerably greater in the synoptic-field data than that seen in 

the laboratory experiment, similar trends in those biases were 

seen in both studies. TN-A bias was evaluated for 905 samples 

at 77 sites, encompassing total-nitrogen concentrations from 

0.15 to about 26 mg/L and suspended-sediment concentrations from 1 to 14,700 mg/L. Negative TN-A bias was present 

across the entire range of suspended-sediment concentrations, with relative bias being nearly as great at sediment 

concentrations below 10 mg/L as was observed at sediment 

concentrations up to 750 mg/L. This lends support to the idea 

that the mechanism of TN-A bias is primarily the sequestration of some portion of PN that prevents complete digestion 

of nitrogen in the sample. The field study demonstrated that 

TN-A bias was present in samples with very low suspendedsediment concentrations. At sediment concentrations above 

750 mg/L, the negative TN-A bias became more likely and 

larger, suggesting a secondary mechanism of bias, such as 

reagent limitation, may be contributing to the negative TN-A 

bias. Spatial analyses of relative bias in TN-A showed no 

strong relations to geographical location, topsoil composition, 

land use, or ecoregion.

The synoptic-field study data indicated both positive and 

negative biases in TN by the Kjeldahl method. In general, 

these biases were positive and may have been the result of the 

reduction of NOX during the digestion process. 

Four major conclusions were drawn from the synopticfield study:

• The observations from the field are supported by findings of the laboratory experiment.

• Negative bias in TN-A results could occur regardless 

of suspended-sediment concentration, but it becomes 

more likely and larger at suspended-sediment concentrations greater than 750 mg/L.

• Relative bias in TN-A does not appear to be explained 

by variations in the characteristics selected to describe 

the upstream drainage basin.

• Though variable, positive bias in TN-K could occur in 

the presence of elevated NOX.

Understanding the degree of precision and bias in the 

various total-nitrogen methods is important, but assessing the 

impacts of that precision and bias on existing and future sampling programs is equally important, especially in the context 

of a change in analytical method. The anticipated precision 

and bias of each method need to be considered to avoid creating a new data-quality issue (such as reduced precision) in the 
References Cited 35

process of solving another (such as bias). The potential addition of PN analyses to nutrient monitoring programs may incur 

additional sample-processing time in the field and additional 

analytical costs. All of the methods have some limitations that 

affect the quality of the total-nitrogen data and the interpretations that can be drawn from them. Whereas the presence of 

bias may have an adverse impact on one monitoring program, 

a discontinuity in the historical record resulting from a methodological change may be considered more adverse to another 

program. These considerations extend to potential interpretations made by any data user.

This report evaluated three alternative approaches for 

assessing total nitrogen in surface water: (1) the replacement of TN-A with TN-C; (2) the replacement of TN-K 

with TN-C; and (3) the addition of TN-C to existing TN-A 

or TN-K analyses. The primary benefit of replacing TN-A 

with TN-C is the expected removal of bias from subsequent 

data, but this approach would also introduce discontinuity in 

historical records. The concept of replacing TN-K with TN-C 

would typically apply to a different set of monitoring programs than those currently using TN-A. The primary benefit 

of this approach is the removal of positive bias in TN-K 

in the presence of NOX. However, in addition to the issues 

that may arise from a discontinuity in the data record, this 

approach may not be applicable to regulatory programs that 

require the use of TN-K for stream assessment. If TN-C data 

were available alongside TN-A or TN-K, biases exceeding a 

predetermined threshold would be removed while preserving, 

for the most part, historical data continuity and allowing for 

a transitional assessment of the impact resulting from bias or 

a method change. This approach, however, would perpetuate 

the production of biased TN data, places the greatest strain on 

field operations, and increases analytical costs more than the 

other alternatives. The variation in these impacts on different 

sampling programs will challenge USGS scientists trying to 

establish uniform standards for total nitrogen sample collection and analytical determinations.

Acknowledgments

This report is the compilation of several comparative 

analyses that began with a 10-sample dataset originating from 

the USGS Georgia Water Science Center and grew to extensive laboratory and field-sampling efforts. The authors wish 

to thank the following USGS personnel: Terry Schertz, Art 

Horowitz, and Colleen Rostad for their input on the design 

of the laboratory experiment and synoptic-field study; Mark 

Woodworth and the NWQL Nutrients Unit for technical assistance with laboratory experiments; field-sampling crews for 

collection of additional samples associated with the synopticfield tests; Naomi Nakagaki for ancillary data compilation; and 

Joel Blomquist, Jeff Martin, Nancy Simon, and Lori Sprague 

for helpful technical reviews.

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Appendixes A1–A5
38 Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methods

Publishing support provided by: 

Rolla and Lafayette Publishing Service Centers 

For more information concerning this publication, contact:

Director, USGS Nebraska Water Science Center

5231 South 19th Street

Lincoln, Nebraska 68512

(402) 328–4100

Or visit the Nebraska Water Science Center Web site at:

http://ne.water.usgs.gov/

Table A1. Results of the laboratory experiment for bias of total nitrogen in prepared water-sediment mixtures analyzed by the 

alkaline-persulfate method. (link to table in Excel format.)

Table A2. Results of the laboratory experiment for bias of ammonia plus organic nitrogen (total Kjeldahl nitrogen) in prepared 

water-sediment mixtures analyzed by the Kjeldahl method. (link to table in Excel format.)

Table A3. Unrounded, uncensored replicate data associated with synoptic-field study, 2009–10. (link to table in Excel format.)

Table A4. Sample data associated with synoptic-field study, 2009–10. (link to table in Excel format.)

Table A5. Basin characteristics of selected 2009–10 synoptic-field-study sites where bias in total nitrogen was measured in 

at least 10 samples by alkaline-persulfate digestion (method TN-A). (link to table in Excel format.)

Rus and others—Assessing Total Nitrogen in Surface-Water Samples—Precision and Bias of Analytical and Computational Methodse—Scientific Investigations Report 2012–5281