National Water Quality Program

Considerations for Incorporating Quality Control Into Water 

Quality Sampling Strategies for the U.S. Geological Survey

Open-File Report 2020–1109

U.S. Department of the Interior

U.S. Geological Survey

Considerations for Incorporating Quality 

Control Into Water Quality Sampling 

Strategies for the U.S. Geological Survey

By Laura Medalie

National Water Quality Program

Open-File Report 2020–1109

U.S. Department of the Interior

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

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Suggested citation:

Medalie, L., 2020, Considerations for incorporating quality control into water quality sampling strategies for 

the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1109, 5 p., https://doi.org/10.3133/

ofr20201109.

ISSN 2331-1258 (online)
iii

Contents

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

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

Considerations for Incorporating QA/QC Into Discrete Water-Quality Sampling at the 

U.S. Geological Survey ....................................................................................................................2

Robustness.............................................................................................................................................2

External Reproducibility .......................................................................................................................3

Collaborative Large-Volume Data Analysis ......................................................................................3

Efficient Delivery of Water-Quality Data ...........................................................................................3

Summary..........................................................................................................................................................3

References Cited............................................................................................................................................4

Abbreviations

NWIS National Water Information System

NWQL National Water Quality Laboratory

QA/QC quality assessment and quality control

QC quality control

QSB Quality Systems Branch

USGS U.S. Geological Survey

Considerations for Incorporating Quality Control 

Into Water Quality Sampling Strategies for the 

U.S. Geological Survey

By Laura Medalie

Abstract

This report describes considerations for incorporating 

routine quality-assessment and quality-control evaluations into 

U.S. Geological Survey discrete water-sampling programs 

and projects. U.S. Geological Survey water-data science in 

2020 is characterized by robustness, external reproducibility, 

collaborative large-volume data analysis, and efficient delivery of water-quality data. Confidence in data, or robustness, 

can be increased by supplementing traditional field-based 

quality-control data with laboratory quality control (QC) data, 

such as third-party blind spikes and blind blanks, laboratory 

blanks, and laboratory-reagent spikes. Laboratory qualitycontrol data can provide additional information about bias 

and variability, method performance, and false-positive and 

false-negative rates that are not available from field QC data 

alone. Reproducibility is supported by means of standardizing metadata and documentation. Collaborative analysis 

brings together disparate elements of various types of qualitycontrol review and communicates persistent data quality 

issues for compounds to data users internal and external to the 

U.S. Geological Survey. Efficient delivery of water-quality 

data is achieved when quality-control review is accomplished 

in the same expedited (near real-time) time frame as distribution of environmental results to the public and might be 

improved with consideration given to data versioning or to a 

system of alerting data users to data interpretation that might 

differ from originally published data.

Introduction

The Water Science Strategy for the U.S. Geological 

Survey (USGS) explains that acknowledgment of uncertainty 

in water science goes along with proficiency in the work that 

USGS does (Evenson and others, 2013). The Science Strategy 

specifies that the USGS will make a strategic effort to estimate or quantify uncertainty associated with its information 

products. Since the inception of the National Water-Quality 

Assessment (NAWQA) Project in 1991, the USGS has developed, applied, and documented a number of approaches to 

quantify uncertainty in water-quality results. Standard protocols for assessment of quality of chemical results of water 

samples reported by the National Water Quality Network 

include timely review of all environmental and field quality control (QC) results and metadata for accuracy, including 

assessment of outliers and contextual reasonableness (Riskin 

and others, 2018). Examples of additional QC interpretations that have been done on an irregular basis, usually by 

large and long-term USGS projects like the NAWQA project 

or the Groundwater Ambient Monitoring and Assessment 

(GAMA) Program Priority Basin Project, include evaluations 

of long-term and collective (1) field-blank results to determine 

potential bias from nonlaboratory-derived contamination over 

time (Medalie and Martin, 2016; Mueller and Titus, 2005), 

(2) field replicate results to quantify variability over time 

(Martin, 2002; Mueller and Titus, 2005) and (3) field matrix 

spike and laboratory reagent spike results (Shoda and others, 

2018; Martin and others, 2009). Most of these approaches are 

described in detail by Mueller and others (2015).

Historic approaches to QC water-quality data assessments 

were based solely on field QC samples; recent approaches 

supplement field QC with laboratory QC samples (Medalie 

and others, 2019; Bexfield and others, 2020). As approaches 

toward conducting science and delivering products at the 

USGS evolve, it is essential that the concomitant approaches 

to assessing and reporting on the quality of the data also 

evolve. A systematic strategy for review of long-term QC datasets needs to be incorporated into future USGS water-quality 

programs and projects. The purpose of this report is to discuss 

generalized considerations for incorporating quality assurance/quality control (QA/QC) into long-term discrete water 

sampling programs and projects at the USGS.
2 Considerations for Incorporating Quality Control Into Water Quality Sampling Strategies for the U.S. Geological Survey

Considerations for Incorporating 

QA/QC Into Discrete Water-Quality 

Sampling at the U.S. Geological Survey

QA/QC science for discrete water-quality sampling 

continues to evolve with concomitant changing goals and tools 

identified by the USGS for the generation of scientific data. 

Important needs that increasingly characterize USGS waterdata science include (1) robustness, (2) external reproducibility, (3) collaborative large-volume data analysis, and (4) efficient delivery of water-quality data (U.S. Geological Survey, 

2019). The successful utilization of these data-science tools 

requires that traditional QC tools and practices that support 

and document environmental data quality need to be updated, 

documented, communicated, and automated.

Robustness

Robustness means that QA/QC instills confidence that 

water-quality data are used appropriately in interpretive 

studies. Confidence is generated by provision of quantitative measures of uncertainty: confidence intervals for a mean 

or median, confidence limits for a percentile or proportion, 

false-positive and false-negative rates, or uncertainty or variability expressed as imprecision or error (Mueller and others, 

2015). Periodic review of long-term field QC information 

to assess bias and variability has been a hallmark of longterm USGS programs such as the National Water Quality 

Assessment Project and the Groundwater Ambient Monitoring 

and Assessment Program Priority Basin Project, but review of 

long-term laboratory QC information did not become part of 

some USGS project protocols (as distinguished from internal 

reviews routinely conducted by the National Water Quality 

Laboratory [NWQL] and Quality Systems Branch [QSB]) 

until recent years (Bexfield and others, 2019, 2020; Medalie 

and others, 2019; Medalie and Bexfield, 2020).

Information about bias and variability in field QC 

samples reflects laboratory (as well as sample collection, 

storage, and shipment) processes; however, it is not possible 

to separate laboratory-related bias and variability from fieldrelated bias and variability without QC data from the NWQL 

and QSB. For example, laboratory QC data from QSB thirdparty samples (such as blind spikes and blind blanks from the 

Inorganic Blind Sample Project and the Organic Blind Sample 

Project) provide information about recovery bias and variability, false-positive and false-negative rates, and method performance over time. Recovery data from blind spikes can reveal 

low or high biases or high variability in reported water-quality 

results. Recovery is the measured amount of the compound 

in a spiked sample expressed as a percentage of the amount 

spiked, ideally 100 percent, and a recovery bias is identified 

when the median recovery percentage for a given compound 

over a period of time is outside of a target range (Martin and 

others, 2009; Shoda and others, 2018). False-positive and 

false-negative rates from QSB samples can be compared with 

rates deemed acceptable for project-specific objectives and can 

provide important metadata for interpretations of environmental data. Results from blind samples contribute information 

about method performance because these samples are subject 

to the same laboratory processes as environmental samples.

NWQL laboratory blank and laboratory reagent spike 

datasets also provide important information for a comprehensive assessment of the quality of environmental water-quality 

data. Laboratory blanks can be used to identify episodic and 

random laboratory contamination not addressed by set censoring (Medalie and others, 2019; Bexfield and others, 2020). 

Laboratory reagent spikes supplement information from field 

QC and QSB samples about recovery bias and overall method 

performance and can help identify specific quality issues that 

occasionally arise, such as the degradation of target compounds in spike solutions (Shoda and others, 2018).

Reviews of laboratory QC datasets should be structured 

to assess environmental relevance in time and space, basically the “inference space” as described by Mueller and others 

(2015). An inference space for QC includes the spatial and 

temporal conditions under which QC samples can be related 

to environmental samples because the QC results reflect the 

same conditions, in terms of potential bias and variability, 

under which the environmental samples were collected. 

Biased or highly variable results often occur in discrete 

episodes of time that do not correspond to calendar constructs 

(that is, months or years), such as from inadvertent use of 

contaminated laboratory vials or spike solutions. A relevant 

way to address discrete episodes of issues with data quality 

is to target qualifications or corrective action to environmental data that coincide with the episode, that is, not to extend 

those actions beyond the need. For instance, in Medalie and 

Bexfield (2020), periods of bias and variability are identified 

on the basis of water years1 for expediency, whereas in many 

other cases, the periods with uncertain quality are less than 

complete years. In those cases, another approach might be to 

calculate a moving average that identifies time periods during 

which a given condition indicative of contamination bias (such 

as detection frequency greater than 10 percent in laboratory 

blanks) prevails (Fram and Belitz, 2011; Medalie and others, 

2019). Similarly, method-performance issues illustrated by 

recovery bias can exhibit patterns in time that span multiple 

years or that frequently alternate between high and low biases 

(Shoda and others, 2017).

1A water year is defined as the 12-month period extending from October 1 

of any given year through September 30 of the following year and is designated by the calendar year in which it ends.
Summary 3

External Reproducibility

Reproducibility of findings is largely addressed by 

adequate documentation of methods and interpretations, along 

with accessible information. Documentation is the foundation 

of QA/QC. Tools and techniques that ensure robust and efficient production of environmental data are of little use without 

adequate documentation. The fundamental science practices 

at the USGS for scientific-data management require scientists 

to place all reviewed and interpreted datasets, along with a 

complete metadata record, in a permanent, trusted repository 

(U.S. Geological Survey, 2017).

Collaborative Large-Volume Data Analysis

Collaborative analysis in a QC context means designing a system to bring together and provide access to disparate 

elements of data review. For example, currently [2020], the 

primary mechanism for distributing results or interpretations 

from QC analyses after data publication is to publish the 

description in a report or paper and the data used for the interpretation in a data release. If different data analysts are interested in using that same dataset for other studies, they might 

not be aware that the data published in the public National 

Water Information System (NWIS) portal (U.S. Geological 

Survey, 2020) have been interpreted and that a modified 

dataset is available (which dataset is preferable is another 

question). There is no mechanism or system to routinely communicate data-quality issues identified from interpretations of 

published data.

Medalie and Bexfield (2020) describe several triggers for 

flagging individual results in NWIS or for flagging compounds 

from the analysis of this report. For example, individual 

results for 19 compounds were marked with a permanent 

“E” remark code for quantified results because data-quality 

objects were not met during validation. Data users, especially non-USGS users who are less likely to be familiar 

with NWIS water-quality metadata, might not be aware that 

various qualifier codes or interpretations described in reports 

such as Medalie and Bexfield (2020) could be important for 

data analysis. More accessible metadata that communicates 

QC interpretations as a standard accompaniment to published 

water-quality results is an important way to move USGS data 

science forward.

Efficient Delivery of Water-Quality Data

The identification of datasets and techniques for 

improving robustness needs to be accompanied by efficient 

implementation of data review and vetting techniques. The 

importance of providing water-quality results to the public 

in relevant timeframes is emphasized on the national level 

(U.S. Geological Survey, 2019), and a corollary is needed to 

produce QC review in the same expedited timeframe. Data 

review would benefit from routine publication of QC datasets 

from the QSB and NWQL rather than made available by 

request. To the extent feasible, review procedures for waterquality QC data that adhere to USGS policies and meet the 

needs of data users can be automated. For example, versioning 

of water-quality data is one approach that could allow for the 

competing needs of providing quick delivery of data to the 

public as well as providing data that are more thoroughly vetted after review of long-term QC datasets. If data versioning is 

not possible, best practices might include provision of relevant 

citations to products that interpret or analyze related datasets 

through the same process that serves water-quality results.

QC data can be evaluated at various stages of reporting results. Although it would be preferable in some respects 

to schedule all QC reviews before the publication of waterquality data, either at the NWQL before preliminary results 

are sent to water science centers for review or during preliminary review by data analysts at water science centers, current 

[2020] protocols allow environmental results to be published 

after water science center review and before review of longterm datasets (Medalie and others, 2019, fig. 1). The reasons 

for this discrepancy in review and publication include the 

expectations of data users to get analytical results as quickly 

as possible and the intrinsic retroactive nature of reviewing 

long-term datasets. With the goal of increasing efficiency, 

opportunities for implementing review in a real-time or near 

real-time environment should be identified. One action that 

could be taken at the NWQL before production of preliminary 

results is to develop an automated protocol to flag detections 

in different types of laboratory blanks (blind blanks or laboratory blanks in sets in the same batch or in other sets analyzed 

close in time), which might help to address random laboratory 

contamination before results are published. Other opportunities for developing systematic (integrated and automated) 

routines for review of long-term datasets are numerous.

Summary

Characteristics of U.S. Geological Survey (USGS) water 

data science include (1) robustness, (2) external reproducibility, (3) collaborative large-volume data analysis, and 

(4) efficient delivery of water-quality data. Robustness means 

that a suite of quality control and quality assurance (QA/QC) 

samples provides confidence that environmental water-quality 

data meet data-quality objectives for specific uses. Laboratory 

quality control (QC) results, historically not evaluated as part 

of standard USGS protocols, can supplement field QC results 

by providing information about more targeted types of biases 

and variability. Blind spikes and blind blank samples from the 

USGS Quality Systems Branch (QSB), for example, provide 

information about recovery bias and variability for specific 

methods of data analysis, as well as false-positive and falsenegative rates, and method performance over time. Laboratory 

blanks from the National Water Quality Laboratory identify 

episodic and random laboratory contamination not addressed 
4 Considerations for Incorporating Quality Control Into Water Quality Sampling Strategies for the U.S. Geological Survey

by set censoring, and laboratory reagent spikes supplement 

information from field QC and QSB samples about recovery 

bias and overall method performance.

Reproducibility involves documentation and accessible information. Collaborative science requires coordinated 

communication of all aspects of QA/QC interpretations as a 

standard accompaniment to published water-quality results. 

Automation of QA/QC review processes would help ensure 

that integrated QA/QC documentation is available within the 

same expedited time frame as the distribution of environmental results to the public. Data versioning or a system to 

alert data users to results of data interpretation (qualification, 

removal, or changes to the data) that might affect various uses 

of that data is an important consideration. Systems to bring 

together disparate elements of various types of QA/QC review 

and to communicate persistent data-quality issues for compounds are warranted.

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For more information about this report, contact:

Director, New England Water Science Center

U.S. Geological Survey

10 Bearfoot Road

Northborough, MA 01532

dc_nweng@usgs.gov

or visit our website at

https://www.usgs.gov/centers/new-england-water

Publishing support provided by the 

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Medalie—Considerations for Incorporating Quality Control Into Water Quality Sampling Strategies for the U.S. Geological Survey—OFR 2020–1109

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20201109