U.S. Department of the Interior

U.S. Geological Survey

Data Series 1124

National Water-Quality Assessment Project

Groundwater-Quality and Select Quality-Control Data from 

the National Water-Quality Assessment Project, January 

through December 2016, and Previously Unpublished Data 

from 2013 to 2015

WASHINGTON

IDAHO

MONTANA

OREGON

NORTH

DAKOTA MINNESOTA MAINE

WISCONSIN

CALIFORNIA

WYOMING

SOUTH

DAKOTA

NEW HAMPSHIRE

VERMONT

NEW YORK

NEVADA

UTAH

NEBRASKA IOWA PENNSYLVANIA

COLORADO

ILLINOIS

RHODE

ISLAND

OHIO

MICHIGAN

MI

NEW JERSERY

INDIANA

WEST VIRGINIA

MISSOURI KANSAS

MARYLAND

DELAWARE

VIRGINIA

ARIZONA

KENTUCKY

NEW

MEXICO

OKLAHOMA

TEXAS

NORTH

CAROLINA TENNESSEE

ARKANSAS

SOUTH

CAROLINA

GEORGIA

FLORIDA

ALABAMA

MISSISSIPPI

LOUISIANNA

CONNECTICUT

MASSACHUSETTS

C A N A D A

M E X I C O

GULF OF MEXICO

ATLANTIC OCEAN

PACIFIC OCEAN

125° 120° 115° 110° 105° 100° 95° 90° 85° 80° 75° 70° 65°

45°

40°

35°

30°

25°

EXPLANATION

Principal aquifer study boundary

Sampled well, color indicates network type

Enhanced trends network

Major aquifer study

Modeling support study

Land-use study

Principal aquifer study

Vertical flow-path study

Flow path study

Groundwater-Quality and Select 

Quality-Control Data from the National 

Water-Quality Assessment Project, 

January through December 2016, and 

Previously Unpublished Data from 

2013 to 2015

By Terri L. Arnold, Laura M. Bexfield, MaryLynn Musgrove, Melinda L. Erickson, 

James A. Kingsbury, James R. Degnan, Anthony J. Tesoriero, Justin T. Kulongoski 

and Kenneth Belitz

National Water-Quality Assessment Project

Data Series 1124

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

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, 

natural hazards, and the environment—visit https://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, visit 

https://store.usgs.gov/.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. 

Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, 

J.T., and Belitz, K., 2020, Groundwater-quality and select quality-control data from the National Water-Quality 

Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. 

Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124.

Associated data for this publication:

Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., Belitz, K., 

and Sharpe, J.B., 2020, Datasets from groundwater-quality and select quality-control data from the National WaterQuality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: 

U.S. Geological Survey data release, accessed February 2020 at https://doi.org/10.5066/P9W4RR74.

U.S. Geological Survey [USGS], 2018, USGS water data for the Nation: U.S. Geological Survey National Water 

Information System database, accessed December 18, 2018, at https://doi.org/10.5066/F7P55KJN.

ISSN 2327-638X (online)
iii

Foreword

Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems 

depends on the availability of sound water-resources data and information to develop effective, 

science-based policies. Effective management of water resources also brings more certainty and 

efficiency to important economic sectors. Taken together, these actions lead to immediate and 

long-term economic, social, and environmental benefits that make a difference to the lives of 

the almost 400 million people projected to live in the United States by 2050.

In 1991, Congress established the National Water-Quality Assessment (NAWQA) to address 

where, when, why, and how the Nation’s water quality has changed, or is likely to change in 

the future, in response to human activities and natural factors. Since then, NAWQA has been 

a leading source of scientific data and knowledge used by national, regional, state, and local 

agencies to develop science-based policies and management strategies to improve and protect 

water resources used for drinking water, recreation, irrigation, energy development, and ecosystem needs (https://water.usgs.gov/nawqa/applications/). Plans for the third decade of NAWQA 

(2013–23) address priority water-quality issues and science needs identified by NAWQA 

stakeholders, such as the Advisory Committee on Water Information and the National Research 

Council, and are designed to meet increasing challenges related to population growth, increasing needs for clean water, and changing land-use and weather patterns.

NAWQA is assessing the quality of groundwater used for public and domestic drinking-water 

supply. NAWQA obtains samples from public supply wells, domestic wells, and relatively shallow monitoring wells, and analyzes those samples for a large number of chemical constituents. 

These data are used to assess the suitability of the resource for human consumption, as well as 

to evaluate changes in groundwater quality over a variety of time scales. Groundwater quality 

also is assessed at multiple scales: locally, regionally, and nationally. Groundwater-quality data 

collected by the NAWQA Project during each year are published in annual data series reports. 

This report, the fourth in the series, combines groundwater-quality data collected at 648 sites 

to provide a summary of groundwater quality in selected aquifers across the Nation during the 

sampling period. All NAWQA reports are available online at https://water.usgs.gov/nawqa/bib/.

We hope this publication will provide you with insights and information to meet your waterresource needs and will foster increased citizen awareness and involvement in the protection 

and restoration of our Nation’s waters. The information in this report is intended primarily for 

those interested or involved in resource management and protection, conservation, regulation, 

and policymaking at the regional and national levels.

Dr. Donald W. Cline

Associate Director for Water

U.S. Geological Survey

v

Contents

Foreword ........................................................................................................................................................iii

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

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

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

Groundwater Study Design ..........................................................................................................................2

Principal Aquifer Study Networks .....................................................................................................2

Biscayne Aquifer Principal Aquifer Study Network (biscpas1) ...........................................8

Columbia Plateau Basaltic-Rock Aquifers Principal Aquifer Study Network 

(clptpas1) .........................................................................................................................8

High Plains Aquifer Principal Aquifer Study Network (hpaqpas1) ......................................9

Ozark Plateaus Aquifer System Principal Aquifer Study Network (ozrkpas1) ..................9

Decadal Trends Networks—Land-Use Study Networks ...............................................................9

Lake Erie-Lake St. Clair drainages urban land-use study network (lerilusrc1) .................9

Rio Grande Aquifer System Agricultural Land-Use Study Network (rioglusag1) ...........16

Rio Grande aquifer system urban land-use study network (rioglusrc1) ...........................16

Santee River Basin and Coastal Drainages Urban Land-Use Study Network 

(santusrc1) .....................................................................................................................16

Upper Mississippi River Basin Urban Land-Use Study Network (umislusrc1) ................16

Upper Snake River Basin Agricultural Land-Use Study Network (usnkluscr2) ..............17

Decadal Trends Networks—Major Aquifer Study Networks ......................................................17

Lake Erie-Lake St. Clair Drainages Major Aquifer Study Network (lerisus1) ..................17

Nevada Basin and Range Major Aquifer Study Network (nvbrsus2) ...............................17

Puget Sound Drainages Major Aquifer Study Network (pugtsus1) ..................................17

Enhanced Trends Networks ..............................................................................................................21

Central Valley Enhanced Trends Network (cvaletn1) ..........................................................21

Columbia Plateau Enhanced Trends Network (clptetn1) ....................................................21

Edwards-Trinity Aquifer System Enhanced Trends Network (edtretn1) ...........................24

Glacial Aquifer System Enhanced Trends Network (glacetn1) ..........................................24

Mississippi Embayment Aquifer System Enhanced Trends Network (metxetn1) ...........24

Northern Atlantic Coastal Plain Enhanced Trends Network (nacpetn1) ..........................24

New England Crystalline-Rock and Glacial Aquifer System Enhanced Trends 

Network (negxetn1) .....................................................................................................24

Rio Grande Aquifer System Enhanced Trends Network (rgaqetn1) ..................................25

Vertical Flow-Path Study Networks .................................................................................................25

Columbia Plateau Vertical Flow-Path Study Network (clptvfps1) .....................................25

Glacial Aquifer System Vertical Flow-Path Study Network (glacvfps1) ...........................25

Rio Grande Aquifer System Vertical Flow-Path Study Network (rgaqvfps1) ...................25

Flow-Path Study Networks ...............................................................................................................29

Central Valley Aquifer System Flow-Path Study Networks (cvalfps1, cvalfps2) .............29

Glacial Aquifer System Flow-Path Study Network (glacfps1) ............................................29

Mississippi Embayment Aquifer System Flow-Path Study Network (metxfps1) .............29
vi

Modeling Support Study Networks .................................................................................................29

Glacial Aquifer System Modeling Support Study Network (glacmss1) ............................37

Northern Atlantic Coastal Plain Modeling Support Study Networks (nacpmss1, 

nacpmss2) .....................................................................................................................37

Sample Collection and Analysis ................................................................................................................37

Data Reporting..............................................................................................................................................38

Quality-Assurance and Quality-Control Methods ..................................................................................39

Groundwater-Quality Data .........................................................................................................................40

Water-Quality Indicators ...................................................................................................................40

Inorganic Constituents .......................................................................................................................40

Organic Compounds ...........................................................................................................................40

Radiochemistry....................................................................................................................................41

Constituents of Special Interest .......................................................................................................41

Summary........................................................................................................................................................41

Acknowledgments.......................................................................................................................................42

References Cited..........................................................................................................................................42

Appendix 1 Information Contained in Previous Reports in This Series .........................................103

Appendix 2 Well Depth and Open Interval by Study Network ........................................................108

Appendix 3 High-Frequency Data from Enhanced Trends Networks ............................................110

Appendix 4 Quality-Control Samples and Data Analysis .................................................................112

Figures

1. Map showing groundwater study networks and wells sampled as part of the

U.S. Geological Survey National Water-Quality Assessment Project for which

water-quality data are included in this report .........................................................................3

2. Map showing study area and wells sampled as part of the Biscayne aquifer

principal aquifer study network .................................................................................................4

3. Map showing study area and wells sampled as part of the Columbia Plateau

Basaltic-Rock Aquifers principal aquifer study network .......................................................5

4. Map showing study area and wells sampled as part of the High Plains aquifer

system principal aquifer study network ....................................................................................6

5. Map showing study area and wells sampled as part of the Ozark Plateaus

aquifer system principal aquifer study network ......................................................................7

6. Map showing study area and wells sampled as part of the Lake Erie-Lake St.

Clair drainages urban land-use study network .....................................................................10

7. Map showing study area and wells sampled as part of the Rio Grande aquifer

system agricultural land-use study network ..........................................................................11

8. Map showing study area and wells sampled as part of the Rio Grande aquifer

system urban land-use study network ....................................................................................12

9. Map showing study area and wells sampled as part of the Santee River Basin

and Coastal Drainages urban land-use study network ........................................................13

10. Map showing study area and wells sampled as part of the Upper Mississippi

River Basin urban land-use study network ............................................................................14

11. Map showing study area and wells sampled as part of the Upper Snake River

Basin agricultural land-use study network ............................................................................15
vii

12. Map showing study area and wells sampled as part of the Lake Erie-Lake St. 

Clair drainages major aquifer study network .........................................................................18

13. Map showing study area and wells sampled as part of the Nevada Basin and 

Range major aquifer study network ........................................................................................19

14. Map showing study area and wells sampled as part of the Puget Sound 

drainages major aquifer study network ..................................................................................20

15. Maps showing study areas and wells sampled in the western United States as 

part of the enhanced trends networks ....................................................................................22

16. Maps showing study areas and wells sampled in the eastern United States as 

part of the enhanced trends networks ....................................................................................23

17. Map showing study and wells sampled as part of the Columbia Plateau vertical 

flow-path study network ............................................................................................................26

18. Map showing study and wells sampled as part of the Glacial aquifer system 

vertical flow-path study network .............................................................................................27

19. Map showing study and wells sampled as part of the Rio Grande aquifer system 

vertical flow-path study .............................................................................................................28

20. Map showing study and wells sampled as part of the Central Valley aquifer 

system flow-path study network ..............................................................................................30

21. Map showing study and wells sampled as part of the Central Valley aquifer 

system flow-path study network ..............................................................................................31

22. Map showing study and wells sampled as part of the Glacial aquifer system 

flow-path study network ............................................................................................................32

23. Map showing study and wells sampled as part of the Mississippi Embayment 

aquifer system flow-path study network ................................................................................33

24. Map showing study and wells sampled as part of the Northern Atlantic Coastal 

Plain modeling support study network ....................................................................................34

25. Map showing study and wells sampled as part of the Northern Atlantic Coastal 

Plain modeling support study network ....................................................................................35

26. Map showing study and wells sampled as part of the Glacial aquifer system 

modeling support study network ..............................................................................................36

Table

1. Information about wells that have environmental data included in this report ...............47
viii

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2) 2.590 square kilometer (km2)

Radioactivity

picocurie per liter (pCi/L) 0.037 becquerel per liter (Bq/L)

Datum

Vertical coordinate information is referenced to either the North American Vertical Datum of 

1988 (NAVD 88) or the National Geodetic Vertical Datum of 1929 (NGVD 29) and is specified in 

tables where vertical datum is reported.

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Supplemental Information

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm 

at 25 °C).

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

or micrograms per liter (μg/L).

Activities for radioactive constituents in water are given in picocuries per liter (pCi/L).
ix

Abbreviations

ASCII American Standard Code for Information Interchange

DL detection limit

EPA U.S. Environmental Protection Agency

ETN enhanced trends network

FPS flow-path study

HHB human-health benchmark

HHBP human-health benchmark for pesticides

ID identification

LUS land-use study

MAS major aquifer study

MRL minimum reporting level

MSS modeling support study

NAWQA National Water-Quality Assessment

NWQL National Water Quality Laboratory

PAS principal aquifer study

QC quality control

ssLc sample-specific critical level

ssMDC sample-specific minimum detectable concentration

USGS U.S. Geological Survey

VFPS vertical flow-path study

VOC volatile organic compound

Groundwater-Quality and Select Quality-Control Data 

from the National Water-Quality Assessment Project, 

January through December 2016, and Previously 

Unpublished Data from 2013 to 2015

By Terri L. Arnold, Laura M. Bexfield, MaryLynn Musgrove, Melinda L. Erickson, James A. Kingsbury, James 

R. Degnan, Anthony J. Tesoriero, Justin T. Kulongoski and Kenneth Belitz

Abstract

Environmental groundwater-quality data were collected from 648 wells as part of the National Water-Quality 

Assessment Project of the U.S. Geological Survey National 

Water-Quality Program and are included in this report. 

Most of the wells (514) were sampled from January through 

December 2016, and 60 of them were sampled in 2013 and 

74 in 2014. The data were collected from seven types of well 

networks: principal aquifer study networks, which are used 

to assess the quality of groundwater used for public-water 

supply; land-use study networks, which are used to assess 

land-use effects on shallow groundwater quality; major 

aquifer study networks, which are used to assess the quality 

of groundwater used for domestic supply; enhanced trends 

networks, which are used to evaluate the time scales during 

which groundwater quality changes; vertical flow-path study 

networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow-path study 

networks, which are used to evaluate changes in groundwater 

quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide 

data to support groundwater modeling. Groundwater samples 

were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile 

organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], 

and perchlorate). These groundwater-quality data, along with 

data from quality-control samples, are tabulated in this report 

and in an associated data release. Some data from environmental samples collected in 2013–14 and quality-control samples 

collected in 2012–15 also are included in the associated data 

release. Data from samples collected in 2016 are associated 

with networks described in this report and have not been published previously; data from samples collected between 2012 

and 2015 are associated with networks described in previous 

reports in this data series.

Introduction

The National Water-Quality Assessment (NAWQA) 

Project of the U.S. Geological Survey (USGS) National WaterQuality Project was fully implemented in 1991 and operates 

in about 10-year cycles. The NAWQA Project began its third 

cycle of studies in 2013. The NAWQA Project was designed 

to describe current water-quality conditions of the Nation’s 

freshwater streams, rivers, and aquifers; to describe how water 

quality is changing with time; to improve understanding of the 

natural and human factors that affect water quality; to forecast 

future water-quality conditions; and to assess effects of waterquality stressors on aquatic ecosystems (Rowe and others, 

2010, 2013).

The NAWQA Project groundwater assessments focus 

on the quality of groundwater used for public and domestic 

drinking-water supply; groundwater susceptibility to degradation; effects of natural and human factors on source, transport, 

and flux of contaminants to and within aquifers; groundwaterquality contributions to surface-water quality; and current and 

previous management practices relative to groundwater quality. Groundwater quality is studied at multiple scales: locally, 

regionally, and nationally. The primary regional scale at which 

groundwater data were collected during the third cycle of 

the NAWQA Project was the scale of the principal aquifers 

(Burow and Belitz, 2014). A principal aquifer is defined as 

a regionally extensive aquifer or aquifer system that has the 

potential to be used as a source of potable water. Principal 

aquifers were selected for assessment based on their national 

ranking as sources of water used for public supply (Arnold 

and others, 2016b).

Groundwater-quality data collected by the NAWQA 

Project during each year are published in data series reports. 

The first three reports and associated data releases in this 

series published available data from samples collected May 

2012 through December 2013 (Arnold and others, 2016a,b), 

January through December 2014 (Arnold and others, 2017a,b), 

and January through December 2015 (Arnold and others, 
2 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

2018a,b). Appendix 1, table 1.1 lists the networks that are 

described in this report and that are described in previous 

reports in this series (Arnold and others, 2016a,b, 2017a,b, 

2018a,b).

Purpose and Scope

The purpose of this report is to present the analytical results of the groundwater-quality samples collected in 

2016 as part of the third cycle of NAWQA Project studies 

and to provide brief descriptions of the groundwater-quality 

study networks for use in subsequent publications. Types of 

constituents analyzed include the following: water-quality 

indicators, major and minor ions, nutrients, volatile organic 

compounds (VOCs), pesticides, radionuclides, and select 

constituents of special interest (arsenic speciation, chromium 

[VI], and perchlorate). The water-quality data are presented 

in tables formatted as tab-delimited American Standard Code 

for Information Interchange (ASCII) text files, which may be 

imported into spreadsheet, database, or statistical software for 

manipulation and analysis. These water-quality data tables 

are available from a data release, Arnold and others (2020), at 

https://doi.org/10.5066/P9W4RR74. The data release includes 

data collected during 2016 and previously unpublished data 

from 2012 to 2015 sampling. These previously unpublished 

data are associated with networks described in previous 

reports (Arnold and others, 2016a,b, 2017a,b, 2018a,b).

Groundwater Study Design

Groundwater-quality samples were collected from wells 

that were organized into networks (fig. 1) for study purposes. 

A network is a group of wells that have been selected for sampling based on specific hydrogeologic conditions, land use, 

or other design criteria. Many networks have wells that were 

sampled in multiple decadal sampling periods; however, if a 

network well was damaged or destroyed, had too little water, 

or the current owner would not permit sampling, then that 

well was not resampled during 2016. Maps and tables in this 

report and in Arnold and others (2020) have well identification 

(ID) numbers assigned by the NAWQA Project to identify the 

wells; because some wells could not be resampled, some networks do not have consecutively numbered NAWQA Project 

IDs. As used on maps showing network-specific information 

(figs. 2–26), the ID numbers are shown either as numbers 

only or a combination of numbers and letters that indicate 

a particular well within the network. The NAWQA well ID 

number listed in table 1 of this report and table 1 of Arnold 

and others (2020) are a combination of the network name 

and the NAWQA Project ID. Data from seven primary types 

of groundwater study networks are presented in this report 

(fig. 1): principal aquifer study (PAS), land-use study (LUS), 

major aquifer study (MAS), enhanced trends networks (ETN), 

vertical flow-path study (VFPS), flow-path study (FPS), and 

modeling support study (MSS).

Wells in PAS, LUS, and MAS networks were selected 

randomly using an equal-area grid to divide the study area of 

each network (Scott, 1990) into equal-area cells. The equalarea grid method allows for evaluation of constituent concentrations at a regional scale (Belitz and others, 2010). For LUS 

networks, random potential sampling locations in each grid 

cell were generated by a software program (Scott, 1990), and 

monitoring wells were subsequently installed as near to the 

randomly selected locations as possible. Study areas for LUS 

networks included the areal extents of the primary aquifer 

and a specific land use (for example, orchard) of interest. For 

MAS and PAS networks, one well per grid cell was randomly 

selected from a population of existing domestic or publicsupply wells (Gilliom and others, 1995; Scott, 1990). For PAS 

networks, if no public-supply well was available within a grid 

cell (for example, because permission to sample could not be 

obtained), an additional well was selected within an adjacent 

grid cell, not to exceed four wells in two adjacent grid cells. 

Equal-area grids used for network design are shown only on 

figures relating to PAS networks because the grids are not 

available for LUS or MAS networks designed during the first 

two decades of sampling.

The ETN, VFPS, FPS, and MSS wells were selected 

from existing networks where possible. The ETN wells 

are in hydrogeologic settings where changes in hydrologic 

conditions, land use, or contaminant inputs are expected 

to be reflected quickly in groundwater (less than 10 years). 

The VFPS wells were “nested” wells with various depths 

collocated in a selected area to represent vertical gradients 

of groundwater flow to enhance the understanding of how 

contaminants move through aquifers over timespans of greater 

than a decade. The FPS wells were selected along a horizontal 

flow path. The MSS wells were selected to provide key data to 

support specific modeling goals.

Principal Aquifer Study Networks

The PAS networks consist of public-supply wells, and 

water is sampled from the part of the aquifer used for the public drinking-water supply (Burow and Belitz, 2014). Publicsupply wells are generally the deepest wells sampled. Wells in 

PAS networks are sampled once to assess groundwater-quality 

conditions in the study areas. The extents of PAS network 

areas are based on the USGS (2003) map of principal aquifers and may be modified in some areas, as described in this 

report. Data from the following PAS networks are included in 

this report: Biscayne aquifer PAS network (biscpas1; fig. 2), 

Columbia Plateau Basaltic-Rock Aquifers PAS network 

(clptpas1; fig. 3), High Plains aquifer system PAS network 

(hpaqpas1; fig. 4), and Ozark Plateaus aquifer system PAS 

network (ozrkpas1; fig. 5).
WASHINGTON

IDAHO

MONTANA

OREGON

NORTH

DAKOTA MINNESOTA 

WISCONSIN

CALIFORNIA

WYOMING

SOUTH

DAKOTA

HA

VERMON

NEW YOR

NEVADA

UTAH

NEBRASKA IOWA PENNSYLVANIA

COLORADO

ILLINOIS

OHIO

MICHIGAN

MI

INDIANA

WEST VIRGINIA MISSOURI KANSAS 

VIRGINIA

ARIZONA

KENTUCKY

NEW MEXICO

OKLAHOMA

TEXAS

NORTH CAROLINA TENNESSEE

ARKANSAS

SO

CA

GEORGIA

FLORIDA

ALABAMA MISSISSIPPI

LOUISIANNA

C A N A D A

M E X I C O

GULF OF MEXICO

PACIFIC OCEAN

0 100 200 300 400 500 MILES0 100 200 300 400 500 KILOMETERS

Base from U.S. Geological Survey digital data, 1:1,000,000 Albers Equal-Area Conic projectionStandard parallels 29°30' N. and 45°30' N.Central meridian 96°00' W.North American Datum of 1983

125° 120° 115° 110° 105° 100° 95° 90° 85° 80° 75° 

45°40°35°30°25°

EXPLANATION

Principal aquifer study boundary

Sampled well, color indicates network type

Enhanced trends networkMajor aquifer studyModeling support studyLand-use studyPrincipal aquifer studyVertical flow-path studyFlow path study

Figure 1. Groundwater study networks and wells sampled as part of the U.S. Geological Survey National Water-Quality Assessment Project for wdata are included in this report..
4 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

01

02 03

04 05

06

07 08

09

10

11

12 13

14

15

16 17

18 19

20

21

22

23

24

26 25

27

28

29 30

31

32

33

34

36 35

37 38

39

40

80°30' 80°20' 80°10' 80° 79°50' 79°40'

26°20'

26°10'

26°

25°50'

25°40'

25°30'

Coral

Springs

Davie

Fort

Lauderdale

Hialeah

Hollywood

Kendall

Miami

Miami

Beach

Pembroke 

Pines

Plantation

Pompano

Beach

Sunrise

FLORIDA

The Everglades

ATLANTIC OCEAN

Mowry

North Fork Miami River

Tarpon River

Canal

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

Base from U.S. Geological Survey digital data, 1:1,000,000, 2014, and Esri City Boundaries, 2010

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

Map Area of biscpas1 area

EXPLANATION

Equal-area grid cells used for

 randomized site selection

Biscayne aquifer principal 

 aquifer study network 

 (biscpas1) boundary

Sampled well and identification 

 number—See table 1 list for 

 network name biscpas1

01

Figure 2. Study area and wells sampled as part of the Biscayne aquifer principal aquifer study network (biscpas1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 5

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17 18

19

20

21

22

23 24

25

26

27

28

29

30, 31

32

33

34

55

56

57

58

59

60

35

36

37 38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

122° 121° 120° 119° 118° 117°

47°

46°

45°

44°

43°

Deschutes

Lake Chelan

Snake

Clearwater

Yakima

John

St. Joe

Crooked River

Spokane

River

River

Day River

River

River

Columbia

River

Columbia

River

River

River

Coeur d'Alene

Pasco

Lewiston

Seattle

Spokane

Walla Walla

Wenatchee

Yakima

WASHINGTON

OREGON

IDAHO

Kennewick

Base from U.S. Geological Survey digital data, 1:2,000,000, 2005, and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 20 40 60 80 100 MILES

0 20 40 60 80 100 KILOMETERS

North Fork Payette River

Map area

Area of clptpas1

EXPLANATION

Equal-area grid cells used for

 randomized site selection

Columbia Plateau basaltic-rock 

 aquifers principal aquifer study 

 network (clptpas1) boundary

Sampled well and identification 

 number—See table 1 list for 

 network name clptpas1

01

Figure 3. Study area and wells sampled as part of the Columbia Plateau Basaltic-Rock Aquifers principal aquifer study network 

(clptpas1) for the U.S. Geological Survey National Water-Quality Assessment Project.
6 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

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106° 104° 102° 100° 98° 96° 94°

42°

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32°

Abilene

Amarillo

Boulder

Casper

Cheyenne

Colorado

Springs

Denver

Fort Collins Grand

Island

Kansas City

Lawton

Lincoln

Lubbock

Manhattan

Midland

Norman

Odessa

Oklahoma City

Omaha

Pueblo

St. Joseph

Topeka

Tulsa

Wichita

Wichita Falls

OKLAHOMA

TEXAS

NEW MEXICO

COLORADO

WYOMING

MISSOURI

ARKANSAS

NEBRASKA

SOUTH DAKOTA

KANSAS

IOWA

Base from U.S. Geological Survey digital data, 1:2,000,000, 2005, and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 50 100 150 200 MILES

0 50 100 150 200 KILOMETERS

McKinney

Sherman

Map area

Area of hpaqpas1

EXPLANATION

Equal-area grid cells used for

 randomized site selection

High Plains aquifers principal 

 aquifer study network 

 (hpaqpas1) boundary

Sampled well and identification 

 number—See table 1 list for 

 network name hpaqpas1

64

Figure 4. Study area and wells sampled as part of the High Plains aquifer system principal aquifer study network (hpaqpas1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 7

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Arkansas

Missouri

Osage

Mississippi River

White

River

River

River

Missouri

River

River

Harry S.

Truman

Reservoir

Cape

Girardeau

Columbia

Fayetteville

Fort Smith

Jefferson

City

Jonesboro

Joplin

Kansas City Lawrence

Little Rock

St. Joseph

St. Louis

Springfield

KANSAS

ARKANSAS

ILLINOIS

MISSOURI

OKLAHOMA

Base from U.S. Geological Survey digital data, 1:2,000,000, 2005, and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 20 40 60 80 100 MILES

0 20 40 60 80 100 KILOMETERS

Memphis

Map area Area of ozrkpas1

EXPLANATION

Equal-area grid cells used for

 randomized site selection

Ozark Plateaus aquifer system

 principal aquifer study 

 network (ozrkpas1) boundary

Sampled well and identification 

 number—See table 1 list for 

 network name ozrkpas1

01

Figure 5. Study area and wells sampled as part of the Ozark Plateaus aquifer system principal aquifer study network (ozrkpas1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
8 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Biscayne Aquifer Principal Aquifer Study 

Network (biscpas1)

The Biscayne aquifer underlies an area of about 

4,000 square miles (mi2) (Miller, 1990), which has a population of about 4 million people in southeastern Florida 

(U.S. Census Bureau, 2010). About 700 million gallons per 

day (Mgal/d) were withdrawn for public supply in 2000 

(Maupin and Barber, 2005). The aquifer underlies parts of 

four (Broward, Miami-Dade, Palm Beach, Monroe) counties in southern Florida, which include the cities of Miami 

and Ft. Lauderdale. Most of the area overlying the aquifer is 

developed and consists of urban (63 percent) and agricultural 

(9 percent) land use (Homer and others, 2015).

The Biscayne aquifer is an unconfined aquifer made up of 

shallow, highly permeable limestone as well as some sandstone units (Miller, 1990). Because of the shallow depth of the 

units that make up this aquifer, the hydrologic connection to 

surface water is an important aspect of the hydrogeology of 

the Biscayne aquifer (Miller, 1990). A system of canals and 

levees are used to manage the freshwater resources of southern Florida. The system conserves freshwater, provides flood 

control, and minimizes saltwater encroachment in the aquifer 

along the coast (Miller, 1990). Recharge to the aquifer is from 

seepage of surface water from canals and from infiltration of 

rainfall (Miller, 1990). Groundwater generally flows from the 

west to the east, discharging in the Atlantic Ocean but where 

large withdrawals occur, flow directions have been locally 

modified (Miller, 1990).

The Biscayne aquifer PAS network (biscpas1; fig. 2) 

includes 40 public-supply wells distributed across the extent of 

the aquifer. Wells were selected using a 40-cell equal-area grid 

that extended across the extent of the aquifer system. Samples 

were collected between June and September 2016, and the 

data for these samples are presented in this report. Wells in 

the biscpas1 ranged from about 22 to 152 feet (ft) deep, with a 

median well depth of about 91 ft. (appendix 2, table 2.1).

Columbia Plateau Basaltic-Rock Aquifers 

Principal Aquifer Study Network (clptpas1)

The Columbia Plateau basaltic-rock aquifers underlie an 

area of 42,000 mi2, which includes about 1 million people in 

Washington, Oregon, and Idaho (U.S. Census Bureau, 2010). 

The aquifer system ranks 19th in the Nation as a source of 

groundwater for public supply, and about 223 Mgal/d were 

pumped for this use in 2000 (Maupin and Barber, 2005). Land 

use overlying the Columbia Plateau basaltic rock aquifers 

is primarily natural land cover (64 percent) and agriculture 

(33 percent) with small amounts of urban and other developed land (4 percent) (Homer and others, 2015). The urban 

areas of Spokane, Washington, and the Washington Tri-Cities 

(Richland, Kennewick, and Pasco); Moscow and Lewiston, 

Idaho; and Pendleton and Hermiston, Oregon, lie within the 

aquifer system’s overlying land along with dozens of smaller 

communities.

Most of the Columbia Plateau is semiarid (precipitation 

ranges from 7 to 15 inches per year in the central part of the 

Plateau), yet the region supports a $6-billion-per-year agricultural industry, which leads the Nation in production of apples, 

hops, and several other commodities (Vaccaro and others, 

2015). Water demand for agriculture, economic development, 

and ecological needs in the area is high (Kahle and others, 

2011). The Plateau accounts for approximately 5 percent of the 

Nation’s irrigated lands, which are supplied by a combination 

of groundwater pumping and surface-water diversions.

The Columbia Plateau is an intermontane basin lying 

between the Cascade Range and the Rocky Mountains that is 

filled with Cenozoic basalt and sediment. The primary aquifers 

in the region are the Miocene-aged continental flood basalts 

of the Columbia River Basalt Group and, in places, Neogene 

basin-fill sediments, which generally overlie the flood basalts 

(Vaccaro and others, 2015). Only the basalt-rock aquifers 

of the clptpas1 network were sampled for this study. The 

Columbia Plateau basin-fill aquifers rank 67th in the Nation 

as a source of groundwater for public supply with less than 

1 Mgal/day pumped for this use in 2000 (Maupin and Barber, 

2005). The Columbia River Basalt Group consists of a series 

of lava flows that erupted from 17 to 6 million years ago. 

Individual flows range from 10 to more than 300 ft in thickness, and the total thickness of the flows was estimated to be 

greater than 15,000 ft in some places (Kahle and others, 2011; 

Vaccaro and others, 2015). The basaltic-rock aquifer consists 

of a series of productive basalt units, the most extensive of 

which is the Grande Ronde Basalt, separated by less permeable interbeds that act as confining units in most places 

(Whitehead, 1994; Kahle and others, 2011). Groundwater flow 

is from topographically high margins of the plateau toward 

major surface drainages, including the Columbia and Snake 

Rivers. The Cascade Range in Oregon and Washington is an 

important recharge area for the western part of the regional 

aquifer system, whereas the western Rocky Mountains and 

Blue Mountains serve as important recharge areas in the 

eastern and southern extents of the Columbia Plateau, respectively. Locally, groundwater levels in the Columbia Plateau 

have changed by up to hundreds of feet because of irrigation 

practices, resulting in increases (recharge from irrigation) and 

decreases (groundwater pumping).

The Columbia Plateau basaltic-rock aquifers PAS network (clptpas1; fig. 3) includes 60 public-supply wells, which 

were selected using an equal-area grid of 60 cells. All 60 of 

the wells that were selected using the equal-area grid were 

sampled between June and September 2016 and have data 

presented in this report. Most of these wells ranged in depth 

from 91 to 2,434 ft, had a median depth of 484 ft (appendix 2, 

table 2.1), and were open to the aquifer across long intervals 

ranging up to approximately 1,297 ft (appendix 2, table 2.2). 
Groundwater Study Design 9

One well in this network also was sampled as part of the 

Columbia Plateau ETN (clptetn1) network (table 1) described 

later in this report and in Arnold and others (2017a,b).

High Plains Aquifer Principal Aquifer Study 

Network (hpaqpas1)

The High Plains aquifer underlies an area of 170,000 mi2, 

which has a population of about 2 million people in parts of 

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, Texas, 

South Dakota, and Wyoming (U.S. Census Bureau, 2010). 

The High Plains aquifer previously was described in Arnold 

and others (2018a,b), and the description is not repeated in 

this report.

The High Plains aquifer system PAS network (hpaqpas1, 

fig. 4) consists of 80 public-supply wells that were selected 

based on an equal-area grid. The area of each cell was about 

2,122 mi2. Data are presented in this report for 17 wells 

sampled between May and July 2016 in Texas. An additional 63 wells were sampled March and September 2015 

in Colorado, Kansas, Nebraska, New Mexico, Oklahoma, 

and Wyoming; data from the 2015 sampling are available in 

Arnold and others (2018a,b). Wells in the hpaqpas1 that were 

sampled in 2016 ranged from 180 to 812 ft deep with a median 

depth of about 321 ft (appendix 2, table 2.1); wells were open 

to the aquifer across a range of intervals, from 82 to 265 ft, 

with a median interval of about 161 ft (appendix 2, table 2.2).

Ozark Plateaus Aquifer System Principal Aquifer 

Study Network (ozrkpas1)

The Ozark Plateaus aquifer system underlies an area 

of about 49,000 mi2, including much of southern Missouri, 

northwestern Arkansas, and relatively small parts of Kansas 

and Oklahoma. About 4 million people reside in areas overlying this aquifer system (U.S. Census Bureau, 2010) and 

about 212 Mgal/d of groundwater was withdrawn for public 

and domestic supply in 2010 (Hays and others, 2016). Land 

use in the area overlying the Ozark Plateaus aquifer system 

is primarily agricultural (35 percent) and natural land cover 

(58 percent) with a small percentage (7 percent) of urban and 

other developed land (Homer and others, 2015); Springfield, 

Missouri, is the largest city overlying this aquifer system.

The Ozark Plateaus aquifers consists of three regional 

aquifers that are separated by confining units (Miller and 

Appel, 1997, Renken, 1998). The primary lithologies of the 

geologic formations making up the aquifers are limestone and 

dolomite. The uppermost Springfield aquifer and the intermediate Ozark aquifer are the primary water-bearing units in this 

aquifer system. The lowermost St. Francois aquifer has limited 

use in Arkansas because of its depth and the availability of 

water in the shallower aquifers and is used primarily where it 

is near the surface in Missouri (Miller and Appel, 1997). The 

carbonate rocks that make up these aquifers are susceptible to 

dissolution and, as a result, karst features such as sinkholes, 

springs, and caves are common across this aquifer system. 

Recharge to these aquifers is primarily from infiltration of 

rainfall, but sinkholes provide areas of focused recharge 

(Renken, 1998).

The Ozark Plateaus aquifer system PAS network (ozrkpas1; fig. 5) includes 60 public-supply wells distributed across 

the extent of the aquifer system. Wells were selected using a 

60-cell equal-area grid that extended across the extent of the 

aquifer system. Samples were collected between April and 

August 2016, and the data for these samples are presented in 

this report. Wells in the ozrkpas1 ranged from about 370 to 

3,420 ft deep and had a median well depth of about 1,115 ft. 

(appendix 2, table 2.1).

Decadal Trends Networks—Land-Use Study 

Networks

The LUS networks are designed to facilitate analysis 

of land-use effects on shallow groundwater quality. Wells in 

LUS networks are sampled once per decade to assess temporal 

trends in water quality. Wells in LUS networks typically are 

shallow and screened near the water table to allow sampling 

of recently recharged groundwater that may exhibit chemical characteristics indicative of the overlying land use. The 

LUS areas are determined by the areal extents of the primary 

aquifer and a targeted overlying land use (Lapham and others, 

1995). Data from the following LUS networks are included 

in this report: Lake Erie-Lake St. Clair drainages urban LUS 

network (lerilusrc1; fig. 6) near Detroit, Michigan; Rio Grande 

aquifer system agricultural LUS network (rioglusag1; fig. 7); 

Rio Grande aquifer system urban LUS network (rioglusrc1; 

fig. 8) near Albuquerque, New Mexico; Santee River Basin 

and Coastal Drainages urban LUS network (santusrc1; 

fig. 9) near Columbia, South Carolina; Upper Mississippi 

River Basin urban LUS network (umislusrc1; fig. 10) near 

Minneapolis/St. Paul, Minnesota; and Upper Snake River 

Basin agricultural LUS network (usnkluscr2; fig. 11).

Lake Erie-Lake St. Clair drainages urban 

land-use study network (lerilusrc1)

The Lake Erie-Lake St. Clair drainages urban LUS network (lerilusrc1; fig. 6) was established to assess how recent 

residential and commercial land use affects the quality of 

shallow groundwater in the developed area north and west of 

Detroit, Mich. The lerilusrc1 study area covers about 600 mi2

and coincides with the boundary of the lerisus1 network 

described later in this report. Discontinuous layers of sand and 

gravel are present at various depths within the glacial till, and 

collectively, these layers of sand and gravel are considered an 

aquifer system that is variably confined (Fleming, 1994). The 

hydrogeology is complex because of heterogeneity of glacial 

materials, fracturing in the surficial till, and the network of tile 

drains. (Thomas, 2000a).
10 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

01

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84° 83°30' 83°

43°

42°30'

42°

River Rouge

Huron River

Grand

Saline

River

River

Detroit

River

LAKE ERIE

Ann Arbor

Detroit

Flint

Jackson

Lansing

South Lyon--Howell

Toledo

MICHIGAN

CANADA

OHIO

Base from U.S. Geological Survey digital data, 1:2,000,000, 2005, and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

Map area Area of

lerilusrc1

EXPLANATION

Extent of the urban land used for 

 well selection in the Lake Erie–

 Lake St. Clair Drainages urban 

 land-use study network 

 (lerilusrc1)

Boundary of primary aquifer

Sampled well and identification 

 number—See table 1 list for 

 network name lerilusrc1

01

Figure 6. Study area and wells sampled as part of the Lake Erie-Lake St. Clair drainages urban land-use study network (lerilusrc1) near 

Detroit, Michigan for the U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 11

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107°30' 107°20' 107°10' 107°

32°50'

32°40'

32°30'

Caballo

Reservoir

Grande

Percha Creek

Trujillo Lateral

Tierra Blanca Creek

Arroyo Cuervo

Berrenda Creek

Rincon Arroyo

Las Animas Creek

Rio

Hatch

Radium

Springs

Rincon

Salem

NEW MEXICO

Base from U.S. Geological Survey digital data, 1:1,000,000, 2005, and Esri City Boundaries, 2010

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Map area

Area of

rioglusag1

EXPLANATION

Extent of the agricultural land used 

 for well selection in the Rio

 Grande aquifer system 

 agricultural land-use study 

 network (rioglusag1)

Boundary of primary aquifer

Sampled well and identification 

 number—See table 1 list for 

 network name rioglusag1

01

Figure 7. Study area and wells sampled as part of the Rio Grande aquifer system agricultural land-use study network (rioglusag1) for 

the U.S. Geological Survey National Water-Quality Assessment Project.
12 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

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107°10' 107° 106°50' 106°40' 106°30' 106°20'

35°20'

35°10'

35°

34°50'

34°40'

34°30'

Jemez

Canyon

Reservoir

Rio Puerco

Arroyo Alamito

Rio Grande

Comanche

Hells Canyon Wash

Coyote

Rio

Arroyo 

Arroyo

Albuquerque Main Canal

Draw

Arroyo

Monte Largo

Jose San

Montoyas

los 

de

Arroyo 

Calabacillas

las 

de

Albuquerque

Los Lunas

NEW MEXICO

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

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Map area

Area of

rioglusrc1

EXPLANATION

Extent of the urban land used for 

 well selection in the Rio Grande

 aquifer system urban land-use 

 study network (rioglusrc1)

Boundary of primary aquifer

Sampled well and identification 

 number—See table 1 list for 

 network name rioglusrc1

01

Figure 8. Study area and wells sampled as part of the Rio Grande aquifer system urban land-use study network (rioglusrc1) near 

Albuquerque, New Mexico for the U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 13

River

06 07

08 09 17

23

26

Wateree River

Saluda River

Savannah 

South Fork Edisto River

Congaree

North Fork 

Little River

Lake Murray

Lake Marion

Lake Wateree

River

River

Edisto

North Augusta

Columbia

Sumter

Augusta

Aken

SOUTH CAROLINA

GEORGIA

81°40' 81°20' 81° 80°40' 80°20'

34°20'

34°

33°40'

33°20'

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

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

Lake Marion

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01

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Creek

Creek

Mill

Polk Swamp

Map area

Area of santlusrc1

EXPLANATION

Extent of the urban land used for 

 well selection in the Santee River

 Basin and Coastal Drainages

 urban land-use study network 

 (santlusrc1)

Boundary of primary aquifer

Sampled well and identification 

 number—See table 1 list for 

 network name santlusrc1

01

Figure 9. Study area and wells sampled as part of the Santee River Basin and Coastal Drainages urban land-use study network 

(santusrc1) near Columbia, South Carolina, for the U.S. Geological Survey National Water-Quality Assessment Project.
14 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

94° 93°30' 93°

45°30'

45°

44°30'

Mississippi

Rum

Elk

River

River

Minnesota

River

River

Minneapolis–St. Paul

St. Cloud

MINNESOTA

WI

Base from U.S. Geological Survey digital data, 1:2,000,000, 2005, and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

Rum River

Mississippi

River

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Creek

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Map area Area of umislusrc1

EXPLANATION

Extent of the urban land used for 

 well selection in the Upper

 Mississippi River Basin urban 

 land-use study network 

 (umislusrc1)

Boundary of primary aquifer

Sampled well and identification 

 number—See table 1 list for 

 network name umislusrc1

01

Figure 10. Study area and wells sampled as part of the Upper Mississippi River Basin urban land-use study network (umislusrc1) near 

Minneapolis/St. Paul, Minnesota, for the U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 15

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42°50'

42°40'

42°30'

Marsh Creek

A Canal

Main North Side Canal

Goose

Main Drain

Snake River

H Canal

Main

Lake Walcott

Creek

South

Side

Canal

Acequia

Burley

Heyburn

Minidoka

Paul

Rupert

Declo

IDAHO

Base from U.S. Geological Survey digital data, 1:2,000,000, 2005, and Esri City Boundaries, 2010

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Map area

Area of usnkluscr2

EXPLANATION

Extent of the agricultural land used for well 

 selection in the Upper Snake River Basin 

 agricultural land-use study network (usnkluscr2)

Boundary of primary aquifer

Sampled well and identification number—See table 1

 list for network name usnkluscr2

01

Figure 11. Study area and wells sampled as part of the Upper Snake River Basin agricultural land-use study network (usnkluscr2) for 

the U.S. Geological Survey National Water-Quality Assessment Project.
16 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

The LUS sampling network consists of 28 monitoring 

wells. The wells were typically about 16 to 46 ft deep (appendix 2, table 2.1) with about 4 to 8 ft screen intervals (appendix 

2, table 2.2). The same 28 wells were also sampled as part of 

the glacvfps1 network (table 1) described later in this report. 

A total of 30 monitoring wells in the network were previously sampled in 1996 or 1997 (Thomas, 2000b) and in 2006. 

Subsets of wells also were sampled periodically from 2002 

through 2010. Samples for the current phase of monitoring 

were collected from August through September 2016 and are 

reported here.

Rio Grande Aquifer System Agricultural 

Land-Use Study Network (rioglusag1)

The Rio Grande Valley agricultural LUS network (rioglusag1; fig. 7) was designed to characterize shallow groundwater quality in an agricultural area overlying the Rio Grande 

aquifer system in southern New Mexico. The study area of 

about 57 mi2 lies within the flood plain of the Rio Grande 

in the narrow Rincon Valley. The aquifer system in the area 

consists of nonglacial sediments that can be divided into finegrained basin-fill deposits of Tertiary age overlain by coarser 

valley-fill deposits of Quaternary age (Anderholm, 2002).

The rioglusag1 network consists of 24 monitoring wells 

completed near the water table in the Quaternary valleyfill deposits. The wells are of similar depths, ranging from 

about 15 to 37 ft, with a median of about 23 ft (appendix 

2, table 2.1). The wells have open intervals of around 10 ft 

(appendix 2, table 2.2). The rioglusag1 network was previously sampled in 1994 and 2006; however, because of generally declining water levels, several wells in the original 1994 

network have since gone dry. Samples for the current phase of 

monitoring were collected in April and May of 2016. One well 

in this network was also sampled as part of the Rio Grande 

aquifer system ETN (rgaqetn1) network (table 1) described 

later in this report.

Rio Grande aquifer system urban land-use study 

network (rioglusrc1)

The Rio Grande Basin urban LUS network (rioglusrc1, 

fig. 8) was designed to characterize shallow groundwater quality in an urban area overlying the Rio Grande aquifer system 

in central New Mexico. The study area of about 190 mi2 in 

and near Albuquerque lies within the flood plain of the Rio 

Grande in the alluvial Middle Rio Grande Basin. The aquifer 

system in the basin consists of nonglacial sand and gravel that 

can be divided into basin-fill deposits of Tertiary age, generally thousands of feet thick, overlain by valley-fill deposits of 

Quaternary age, typically less than about 130 ft thick (Hawley 

and Haase, 1992). The aquifer system is used mainly for 

municipal and domestic water supply. Although the network 

is intended to focus on water quality underlying areas of residential and commercial land use, the study area also includes 

agricultural land (Anderholm, 1997).

The rioglusrc1 network consists of 27 monitoring wells 

completed near the water table in the Quaternary valley-fill 

deposits. The wells range in depth from about 13 to 57 ft, with 

a median of about 27 ft (appendix 2, table 2.1). Open intervals 

range from 5 to 15 ft and are typically about 10 ft (appendix 2, 

table 2.2). Several wells in the rioglusrc1 network were previously sampled in 1993, 2006, or both. There were 15 wells in 

this network sampled also as part of the Rio Grande aquifer 

system VFPS (rgaqvfps1) network (table 1) described later in 

this report. Samples for the current phase of monitoring were 

collected in July and August 2016.

Santee River Basin and Coastal Drainages Urban 

Land-Use Study Network (santusrc1)

The Santee River Basin and Coastal drainages urban LUS 

network (santlusrc1; fig. 9) consists of 27 monitoring wells 

installed between 8 and 54 ft deep (appendix 2, table 2.1) in 

semiconsolidated sand. The study area of about 3,400 mi2 is 

near Columbia, South Carolina, and was designed to characterize shallow groundwater quality in a commercial and 

residential area. The wells are screened in the Middendorf 

Formation, which is part of the sandhills aquifers (Hughes and 

others, 2000) and the McQueen Branch Aquifer (Campbell 

and Landmeyer, 2014). This network previously was sampled 

in 1996 and 2006. Samples for the current phase of monitoring 

were collected in August 2016.

Upper Mississippi River Basin Urban Land-Use 

Study Network (umislusrc1)

The Upper Mississippi River Basin urban LUS network 

(umislusrc1; fig. 10) was designed to characterize the effects 

of urban residential and commercial development activities on shallow groundwater in a surficial sand and gravel 

aquifer system in the northwestern part of Minneapolis-St. 

Paul, Minnesota. The study area covers about 1,600 mi2. and 

overlies an area of 50 to greater than 400 ft of sand, gravel, 

and clay, which were deposited as river terraces or overbank 

deposits by melt water from the Des Moines Lobe during the 

late Wisconsinin glacial period (Andrews and others 1997). 

Estimated transport times from the land surface to the water 

table indicate that this sand and gravel aquifer is highly susceptible to contamination from activities at the land surface 

(Peigat, 1989; Meyer, 1993).

The umislusrc1 consists of 30 shallow monitoring wells 

completed immediately below the water table at depths 

from about 9 to 33 ft with a median of 18 ft, (appendix 2, 

table 2.1). Open intervals generally were 5 ft (appendix 2, 

table 2.2). Wells from the umislusrc1 network were previously 
Groundwater Study Design 17

sampled in 1997–98 and 2006. Samples for the current 

phase of monitoring were collected during July, August, and 

September 2016, and these data are presented in this report.

Upper Snake River Basin Agricultural Land-Use 

Study Network (usnkluscr2)

The Upper Snake River Basin agricultural LUS network 

(usnkluscr2; fig. 11), with a study area of about 400 mi2, was 

designed to characterize groundwater quality underlying 

agricultural land use in the East Snake River Plain aquifer in 

the Upper Snake River Basin in southern Idaho. The aquifer 

is composed of a series of Quaternary basalt flows that are 

vesicular and broken and able to transmit large volumes of 

water (Whitehead, 1992). The study area is characterized by a 

history of elevated nitrate concentrations in groundwater and 

is predominantly an area of groundwater-sourced irrigated 

agriculture (Rupert, 1997; Skinner, 2003).

The usnkluscr2 network includes 31 wells, mostly used 

for domestic supply. The wells range in depth from 125 to 

469 ft (median of 256 ft) with a large range of open intervals from 2 to about 337 ft (median of 171 ft; appendix 2; 

table 2.1). Most wells are cased at the surface only with 

open-hole well construction. Samples for the current phase of 

monitoring were collected during June 2016.

Decadal Trends Networks—Major Aquifer 

Study Networks

The MAS networks were designed to reflect the resource 

used for domestic supply. The MAS networks generally 

consist of domestic-supply wells but also may include publicsupply or other types of wells. Domestic-supply wells typically draw smaller volumes of groundwater and from shallower depths of the aquifer than do public-supply wells. The 

MAS areas are determined by the areal extent of the primary 

aquifer and physiography and are designed to assess the condition of groundwater quality in the most heavily used aquifer 

in the area (Lapham and others, 1995; Koterba and others, 

1995). Wells in MAS networks are sampled once per decade to 

assess temporal trends in water quality. Data from the following MAS networks are included in this report: Lake Erie-Lake 

St. Clair drainages MAS network (lerisus1; fig. 12), Nevada 

Basin and Range MAS network (nvbrsus2; fig. 13), and Puget 

Sound drainages MAS network (pugtsus1; fig. 14).

Lake Erie-Lake St. Clair Drainages Major Aquifer 

Study Network (lerisus1)

The Lake Erie-Lake St. Clair drainages MAS network 

(lerisus1; fig. 12) was established to assess the water quality 

of an aquifer that is an important source of drinking water 

in the developed area north and west of Detroit, Mich. The 

lerisus1 study area covers about 2,500 mi2 and coincides with 

the boundary of the lerilusrc1 network (described previously). Groundwater is drawn from discontinuous layers of 

sand and gravel that are present at various depths within the 

glacial till. Collectively, these layers of sand and gravel are an 

aquifer system that is variably confined (Fleming, 1994). The 

hydrogeology is complex because of heterogeneity of glacial 

materials, fracturing in the surficial till, and the network of 

agricultural tile drains. (Thomas, 2000a).

The lerisus1 consists of 24 domestic wells. The wells 

ranged from 35 to 205 ft deep (appendix 2, table 2.1) with 

3- to 10-ft screen intervals (appendix 2, table 2.2). A total 

of 27 wells in the network were previously sampled in 1997 

(Thomas, 2000b). Samples for the current phase of monitoring were collected from August through September 2016, and 

these data are reported here.

Nevada Basin and Range Major Aquifer Study 

Network (nvbrsus2)

The Nevada Basin and Range MAS Network (nvbrsus2; 

fig. 13) was designed to broadly assess the quality of groundwater in the Basin and Range basin-fill aquifers, specifically in 

the deep Truckee Meadows and Eagle Valley alluvial aquifers within the Truckee and Carson River Basins in western 

Nevada. The nvbrsus2 study area is about 380 mi2 overlying 

an aquifer of nonglacial sand and gravel. Historically, agriculture was the chief land use in both valleys; however, most land 

has been urbanized with only about 1,200 acres for agriculture 

remaining in the Truckee Meadows and less than 500 acres in 

Eagle Valley (Homer and others, 2015).

The nvbrsus2 includes 30 wells, mostly public-supply 

wells (table 1). The wells range from 105 to 815 ft deep and 

have a median depth of 347 ft (appendix 2, table 2.1). The 

wells have a wide range of open intervals from 10 to 670 ft, 

with a median of around 193 ft (appendix 2, table 2.2). This 

network was previously sampled in1995 and 2003. Samples 

for the current phase of monitoring were collected in July 

through September 2016.

Puget Sound Drainages Major Aquifer Study 

Network (pugtsus1)

The Puget Sound Drainages MAS network (pugtsus1; 

fig. 14) was established to characterize the quality of shallow 

groundwater in the unconfined parts of the Fraser aquifer. 

The pugtsus1 study area covers about 2,100 mi2 between the 

Canadian border, the Olympic Mountains, and the Cascade 

Range. Aquifers in the Puget Sound area are contained mostly 

within the Puget Sound Lowland, which is a structural basin 

filled with alluvial, glacial, and interglacial unconsolidated 

sediments, which can be locally more than 3,000 ft thick. 

(Jones, 1999). These aquifers are vulnerable to contamination where they are shallow, coarse-grained, unconfined, and 

overlain by urban or agricultural land use (Tesoriero and 

Voss, 1997). The Fraser aquifer is composed primarily of 
18 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19 20

21

22

23

24

84°30' 84° 83°30' 83° 82°30' 82°

43°30'

43°

42°30'

42°

41°30'

Grand

River

Detroit

River

St. Clair River

Battle Creek

LAKE HURON

St. Joseph

Lake St. Clair

Saginaw Bay

LAKE ERIE

River

Ann Arbor

Bay City

Detroit

Flint

Jackson

Lansing

Monroe

Saginaw

South Lyon–Howell

Toledo

Port

Huron

CANADA

MICHIGAN

OHIO

INDIANA

Base from U.S. Geological Survey digital data, 1:10,000,000, 2005, 

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N., Central meridian 96°00' W.

North American Datum of 1983

0 10 20 30 40 50 MILES

0 10 20 30 40 50 KILOMETERS

Map

area

Area of lerisus1

EXPLANATION

Boundary of primary aquifer and

 Lake Erie-Lake St. Clair 

 drainages major aquifer 

 study network (lerisus1)

Sampled well and identification 

 number—See table 1 list for 

 network name lerisus1

01

Figure 12. Study area and wells sampled as part of the Lake Erie-Lake St. Clair drainages major aquifer study network (lerisus1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 19

02 01

03

04

05

06

07

08 09

10

11 12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

Creek

Steamboat

Truckee

Carson River

Lake Tahoe

Washoe Lake

River

Ditch

Steamboat

Boca

Reservoir

Stampede

Reservoir

Carson City

Dayton

Incline Village

Reno

Truckee

Washoe

Valley

NEVADA CALIFORNIA

Johnson Lane

120°10' 120° 119°50' 119°40' 119°30'

39°30'

39°20'

39°10'

39°

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

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N., Central meridian 96°00' W.

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Map

area

Area of

nvbrsus2

EXPLANATION

Boundary of primary aquifer and

 Nevada Basin and Range

 major aquifer study network 

 (nvbrsus2)

Sampled well and identification 

 number—See table 1 list for 

 network name nvbrsus2

01

Figure 13. Study area and wells sampled as part of the Nevada Basin and Range major aquifer study network (nvbrsus2) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
20 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

01

02

03

04

05 06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

24

25

26

27

28

29

124° 123°30' 123° 122°30' 122° 121°30' 121°

48°30'

48°

47°30'

47°

46°30'

Chehalis

White

Skagit

Strait of Juan de Fuca

River

Lake

Shannon

River

River

Bellingham

Marysville

Mount

Vernon

Olympia–Lacey

Seattle

Tacoma

CANADA

WASHINGTON

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

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N., Central meridian 96°00' W.

North American Datum of 1983

0 10 20 30 40 50 MILES

0 10 20 30 40 50 KILOMETERS

Map area

Area of pugtsus1

EXPLANATION

Boundary of primary aquifer and

 Puget Sound Drainages

 major aquifer study network 

 (pugtsus1)

Sampled well and identification 

 number—See table 1 list for 

 network name pugtsus1

01

Figure 14. Study area and wells sampled as part of the Puget Sound drainages major aquifer study network (pugtsus1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 21

coarse-grained outwash deposited during glacial advances 

and retreats, with some finer materials interbedded (Jones, 

1999). The quality of groundwater of the Puget Sound Basin 

area is of concern because of its importance for municipal and 

domestic supplies of potable water (Staubitz and others, 1997).

The pugtsus1 consists of 29 primarily drinking water 

wells (table 1). The wells were between about 22 and 309 ft 

deep (appendix 2, table 2.1) with screens generally between 

5 and 10 ft long but as much as 60 ft long (appendix 2, 

table 2.2). A total of 30 wells in the network were previously 

sampled in 1996 (Inkpen and others, 2000). Samples for the 

current phase of monitoring were collected in August through 

December 2016, and these data are reported here.

Enhanced Trends Networks

An ETN consists of a small number of wells (typically 

two to four) that are sampled frequently to evaluate the time 

scales during which groundwater quality changes. Such 

changes might result from seasonal or annual variability in 

recharge, discharge, or contaminant loading (Rowe and others, 2013). Data from eight ETNs are included in this report 

(figs. 15–16): Central Valley ETN (cvaletn1), Columbia 

Plateau ETN (clptetn1), Edwards-Trinity aquifer system ETN 

(edtretn1), glacial aquifer system ETN (glacetn1), Mississippi 

Embayment aquifer system ETN (metxent1), Northern 

Atlantic Coastal Plain ETN (nacpetn1), New England 

crystalline-rock and glacial aquifer system ETN (negxetn1), 

and the Rio Grande aquifer system ETN (rgaqetn1).

Wells in an ETN are instrumented for high-frequency 

measurement of selected parameters and they periodically 

have discrete measurements of additional parameters. The 

parameters measured at a high frequency differ among wells 

and networks but generally include parameters like temperature, dissolved oxygen, pH, and specific conductance. 

Data collected at a high frequency for wells in the ETNs are 

available online; links to the data are provided in appendix 3, 

table 3.1.

For periodic discrete sampling, the ETNs are divided 

into two groups of four networks that are sampled on a 4-year 

alternating cycle. Four networks are sampled about once 

every 2 months for 4 years, whereas the other four networks 

are sampled annually. After the first 4-year period, the sampling frequency switches; the networks that were sampled 

every 2 months during the first period are sampled annually, 

and the other four networks are sampled every 2 months. 

Water-quality data from the discrete sampling during 2016 are 

included in this report and in Arnold and others (2020).

Central Valley Enhanced Trends Network 

(cvaletn1)

The Central Valley ETN (cvaletn1; fig. 15A, C) in 

the Central Valley aquifer system is intended to aid in the 

understanding of the subsurface movement of groundwater 

constituents (in some cases contaminants from land-use 

practices) between the shallow and deep parts of the aquifer 

system. The environmental setting of cvaletn1 previously was 

described in Arnold and others (2017a,b) and is not repeated in 

this report.

The cvaletn1 is made up of three wells that represent 

different depths in the regional aquifer (table 1). Two wells 

are relatively shallow (CVALETN1–02, 320 ft deep; and 

CVALETN1–03, 234 ft deep), and one well is relatively 

deep (CVALETN1–01, 620 ft deep). All three wells in the 

cvaletn1 were sampled previously in 2013 (Arnold and others, 2016a,b), 2014 (Arnold and others, 2017a,b), and 2015 

(Arnold and others, 2018a,b). CVALETN1–02 also was 

sampled in 2013 as part of the cvalfps2 network (table 1) 

described later in this report. Data from the 2016 sampling are 

included in this report and in Arnold and others (2020).

Columbia Plateau Enhanced Trends Network 

(clptetn1)

The Columbia Plateau ETN (clptetn1; fig. 15A, B) in the 

Columbia Plateau aquifer system was designed to investigate 

questions about how groundwater quality differs between 

the shallow basin-fill (unconsolidated deposits) aquifers and 

the deeper, underlying basaltic-rock aquifers and how water 

quality varies along the flow paths. The environmental setting 

of clptetn1 previously was described in Arnold and others 

(2017a,b) and is not repeated in this report.

The clptetn1 is made up of wells that represent different positions within the regional groundwater flow system 

at different depths (table 1). Well CLPTETN1–01 is a shallow (80 ft), domestic well in the sand and gravel aquifer. 

Well CLPTETN1–04 is a deep (1,116 ft), long-screened 

(926–1,100 ft) supply well that is open to the basaltic-rock 

aquifers; this well is about 20 mi south of the Columbia 

River and is the most proximal of the three wells. Well 

CLPTETN1–05 is a shallow to moderate depth (170 ft) industrial well, cased to 144 ft also in the sand and gravel aquifer.

Wells CLPTETN1–02 and CLPTETN1–03 were sampled 

as part of the clptetn1 in 2014 (Arnold and others, 2017a,b); 

however, because of issues with sample quality and sampling 

access, these wells are no longer included in the clptetn1. 

Well CLPTETN1–01 also was sampled in 2014 (Arnold and 

others, 2017a,b). Wells CLPTETN1–01, CLPTETN1–04, 

and CLPTETN1–06 were sampled in 2015, and the data were 

reported in Arnold and others (2018a,b). Well CLPTETN1–06 

was not resampled in 2016 because access to the well 

was denied; well CLPTETN1–05 was sampled instead. 

CLPTETN1–04 was also sampled as part of the clptpas1 

network (table 1) described earlier in this report. Wells 

CLPTETN1–01, CLPTETN1–04, and CLPTETN1–05 were 

sampled in 2016 and the data are included in this report and in 

Arnold and others (2020).
PACIFIC OCEAN

Columbia River

Rio Grande

San Antonio River

Quadalupe

R iver

Medina River

M E X I C O

C A N A D A

WASHINGTON

IDAHO

MONTANA

OREGON

NORTH

DAKOTA

CALIFORNIA

WYOMING

SOUTH

DAKOTA

NEVADA

UTAH

NEBRASKA

COLORADO

KANSAS

ARIZONA

NEW

MEXICO OKLAHOMA

TEXAS

Boardman

He

Irrigon

Sta

Umatill

Calwa

Easton

Fresno

Hatch

R

Salem

Lakehills 

New

Berl

Ne

Brau

San Antonio

EDTRETN1–02EDTRETN1–01EDTRETN1–03

RGAQETN1–03RGAQETN1–02RGAQETN1–01

CVALETN1–01CVALETN1–02CVALETN1–03

CLPTETN1–01CLPTETN1–06CLPTETN1–04

CLPTETN1–04

CLPTETN1–01

CLPTETN1–06

CVALETN1–01CVALETN1–02CVALETN1–03

RGAQETN1–

RGAQETN1–03

EDTRETN1–03

EDTRETN1–01

EDTRETN1–02

0 100 200 300 400 MILES 5000 100 200 300 400 KILOMETERS 500

0 1 2 3 4 MILES 50 1 2 3 4 KILOMETERS 5

0 2 4 6 8 MILES 100 102 4 6 8 KILOMETERS

0 0 

Base from U.S. Geological Survey digital data, 1:2,000,000 Albers Equal-Area Conic projectionStandard parallels 29°30' N. and 45°30' N.Central meridian 96°00' W.North American Datum of 1983

0 2 4 6 8 MILES 10

0 10 2 4 6 8 KILOMETERS

125° 120° 115° 110° 105° 100°

45°40°35°30°25° EXPLANATION

Sampled well

CVALETN1-01 Well identification number (see table 1)

A

BCDE

Figure 15. The, 

A, study areas and wells sampled in the western United States as part of the enhanced trends networks for the U.S. Geological S

Water-Quality Assessment Project; and details of well locations for, 

B, clptetn1; 

C, cvaletn1; 

D, rgaqetn1; and, 

E, edtretn1.
GULF OF MEXICO

Wolf River

ATLANTIC OCEAN

Mississippi River

CT

C A N A D A

MINNESOTA

MAINE

WISCONSIN

NH

VT

MICHIGAN

NEW YORK

IOWA

MA

PENNSYLVANIA

ILLINOIS

RI

OHIO

NJ

INDIANA

WEST VIRGINIA

MISSOURI

VIRGINIA

KENTUCKY

MD

TEXAS

NORTH

CAROLINA

TENNESSEE

ARKANSAS

SOUTH CAROLINA

GEORGIA

ALABAMA

MISSISSIPPI

LOUISIANA FLORIDA

Amesbury 

Exeter

Ham

NEW

HAMPSHIRE

MASSACHUS

Bridgeville

Ellendale

Georgetown

Greenwood

Lewe

Milton

TENNESSEE

ARKANSASWest

Memphis

Memphis

MICHIGAN

WISCONSIN

DELAWARE

Fitchburg

La Crosse

Sh

StevensPoint

MINNESOTA

NACPETN1–01NACPETN1–02NACPETN1–03

NEGXETN1–03NEGXETN1–02NEGXETN1–01

METXETN1–02METXETN1–01

GLACETN1–01GLACETN1–02

GLACETN1–03GLACETN1–04GLACETN1–05

GLACETN1–01GLACETN1–02

GLACETN1–03GLACETN1–04GLACETN1–05

NEGXET

NEGXETN1–02

NEGXETN1–03

NACPETN1–03

N

NACPETN1–01

0 1 2 3 4 5 MILES0 1 2 3 4 5 KILOMETERS

0 2 4 6 8 10 MILES0 2 4 6 8 10 KILOMETERS

0 1 2 3 4 5 MILES0 1 2 3 4 5 KILOMETERS 0 100 200 300 400 500 MILES

0 100 200 300 400 500 KILOMETERS

Base from U.S. Geological Survey digital data, 1:2,000,000Albers Equal-Area Conic projectionStandard parallels 29°30' N. and 45°30' N.Central meridian 96°00' W.North American Datum of 1983

0 20 MILES

0 20 KILOMETERS

IOWA

95° 90° 85° 80° 75° 70° 65°

50°45°40°35°30°25°

EXPLANATION

A

BCDE

Sampled well

GLACETN1-01 Well identification number (see table 1)

Figure 16. The, 

A, study areas and wells sampled in the eastern United States as part of the enhanced trends networks for the U.S. Geological S

Water-Quality Assessment Project; and details of well locations for, 

B, glacetn1; 

C, negxetn1; 

D, nacpetn1; and, 

E, metxetn1.
24 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Edwards-Trinity Aquifer System Enhanced 

Trends Network (edtretn1)

The Edwards-Trinity aquifer system ETN (edtretn1; 

fig. 15A, E) was designed to evaluate temporal variability in 

groundwater quality in a dynamic karst aquifer. The environmental setting of edtretn1 previously was described in Arnold 

and others (2017a,b) and is not repeated in this report.

There are three wells in the edtretn1 (table 1) that are 

along an approximately north-to-south aquifer transect within 

the San Antonio metropolitan area (fig. 15E). One well is 

in the upgradient, unconfined recharge zone, and two wells 

are downgradient in the confined zone. The upgradient well 

(EDTRETN1–02) is 300 ft deep and open to the aquifer along 

the bottom 80 ft of its depth (table 1). The downgradient wells 

are 550 ft (EDTRETN1–01) and 1,550 ft (EDTRETN1–03) 

deep and are open to the aquifer throughout their length below 

the confined zone (table 1). The farthest downgradient well 

(EDTRETN1–03) is close to the southern boundary of the 

aquifer. Wells in the edtretn1 were first sampled as part of 

the edtretn1 in 2013 (Arnold and others, 2016a,b), and were 

sampled again in 2014 (Arnold and others, 2017a,b) and 2015 

(Arnold and others, 2018a,b). During 2016, the wells were 

sampled about bimonthly from January through October 2016, 

and these data are included in this report and in Arnold and 

others (2020).

Glacial Aquifer System Enhanced Trends 

Network (glacetn1)

The glacial aquifer system ETN (glacetn1; fig. 16A, B) 

was designed to identify the temporal variability and magnitude of observed changes in groundwater quality in agricultural areas of the glacial aquifer system. The environmental 

setting of glacetn1 previously was described in Arnold and 

others (2017a,b) and is not repeated in this report.

The glacetn1 consists of five wells distributed in two 

locations. A location in central Wisconsin has two monitoring wells: GLACETN1–01 (83 ft deep) and GLACETN1–02 

(34.5 ft deep). A location in southwestern Wisconsin has 

three wells: monitoring wells GLACETN1–03 (50 ft deep) 

and GLACETN1–04 (89 ft deep), and public-supply well 

GLACETN1–05 (125 ft deep). All wells in the glacetn1 were 

sampled in 2016, and these data are included in this report 

and in Arnold and others (2020). Wells GLACETN1–01 and 

GLACETN1–02 also were sampled in 2014 as part of the 

Glacial aquifer FPS (glacfps1) (table 1) described later in 

this report. Wells GLACETN1–01 and GLACETN1–02 have 

been sampled every year since 2014; and GLACETN1–03, 

GLACETN1–04, and GLACETN1–05 have been sampled every year since 2015 (Arnold and others, 2017a,b, 

2018a,b, 2020) .

Mississippi Embayment Aquifer System 

Enhanced Trends Network (metxetn1)

The Mississippi Embayment aquifer system ETN (metxetn1; fig. 16A, E) was designed to study how water quality 

in shallow and deep parts of the regional aquifer changes in 

response to changing hydrologic conditions and pumping. The 

environmental setting of metxetn1 previously was described in 

Arnold and others (2017a,b) and is not repeated in this report.

The metxetn1 consists of one well in the shallow aquifer 

(METXETN1–02, 90 ft deep) and one well in the Memphis 

aquifer (METXETN1–01, 624 ft deep) (table 1). The wells 

were first sampled as part of the metxetn1 in 2013 (Arnold 

and others, 2016a,b) and again in 2014 (Arnold and others, 

2017a,b) and 2015(Arnold and others, 2018a,b). In 2013, the 

wells also were used as part of the Mississippi Embayment 

aquifer system FPS (metxfps1) network (table 1) described 

later in this report. Sampling in 2016 was approximately 

bimonthly, and these data are included in this report and in 

Arnold and others (2020).

Northern Atlantic Coastal Plain Enhanced Trends 

Network (nacpetn1)

The Northern Atlantic Coastal Plain ETN (nacpetn1; 

fig. 16A, D) in the Northern Atlantic Coastal Plain aquifer 

system provides an opportunity to study the movement of 

contaminants from the land surface downward into aquifers 

and the effects of recharge and pumping on the temporal variability of water quality. The environmental setting of nacpetn1 

previously was described in Arnold and others (2017a,b) and 

is not repeated in this report.

The nacpetn1 has three wells that are located across 

southern Delaware in different parts of the flow system at different depths (table 1). Well NACPETN1–03 (119 ft deep) is 

a public-supply well in southwestern Delaware near the center 

of the Delmarva Peninsula. Well NACPETN1–02 (135ft deep) 

also is a public-supply well, one of several supply wells for a 

coastal town. Well NACPETN1–01 is a shallow monitoring 

well (22 ft) that is surrounded locally by agricultural land use. 

All three wells in the nacpetn1 were sampled once in 2014 

(Arnold and others, 2017a,b), once in August 2015 (Arnold 

and others, 2017a,b), and approximately bi-monthly during 

2016. Data from the 2016 sampling are included in this report 

and in Arnold and others (2020).

New England Crystalline-Rock and Glacial 

Aquifer System Enhanced Trends Network 

(negxetn1)

The New England crystalline-rock and glacial aquifer 

system ETN (negxetn1, fig. 16A, C) provides the opportunity 

to study the temporal variability of contaminants in groundwater from geologic sources as well as contaminants from 
Groundwater Study Design 25

man-made sources with changing inputs. The environmental 

setting of negxetn1 previously was described in Arnold and 

others (2017a,b) and is not repeated in this report.

The negxetn1 consists of three wells at different depths 

(table 1). Two of the wells are public-supply wells, one 

completed in glacial sediments (NEGXETN1–01, 83 ft deep) 

and the other one completed in the crystalline-rock aquifer 

(NEGXETN1–02, 492 ft deep), and are in the southern part 

of the network area. The third well is a domestic-supply well 

in the northern part (NEGXETN1–03, about 176 ft deep). 

Wells in the negxetn1 were first sampled in 2014 (Arnold 

and others, 2017a,b) and again in 2015 (Arnold and others, 

2018a,b). The wells were sampled bimonthly January through 

November 2016, and these data are included in this report and 

in Arnold and others (2020).

Rio Grande Aquifer System Enhanced Trends 

Network (rgaqetn1)

The Rio Grande aquifer system ETN (rgaqetn1; fig. 15A, 

D) provides the opportunity to study temporal variability in 

the water quality of shallow groundwater affected by irrigation, river water infiltration, and variable hydrologic conditions in an arid climate. The environmental setting of rgaqetn1 

previously was described in Arnold and others (2017a,b) and 

is not repeated in this report.

The rgaqetn1 consists of three wells completed in the 

valley fill at different depths (table 1): two shallow wells that 

are screened across the water table (RGAQETN1–01, about 

23 ft deep; and RGAQETN1–03, 22 ft deep) and one deeper 

well (RGAQETN1–02, 60 ft deep). The wells were sampled as 

part of the rgaqetn1 in 2014 (Arnold and others, 2017a,b) and 

in 2015 (Arnold and others, 2018a,b). The wells were sampled 

again in 2016, and these data are included in this report and 

in Arnold and others (2020). One well, RGAQETN1–01 also 

was sampled as part of rioglusag1 in 2016 (table 1).

Vertical Flow-Path Study Networks

Vertical flow-path study (VFPS) networks are designed to 

evaluate changes in groundwater quality over longer periods 

of time than the enhanced trends networks (ETNs) and decadal 

trends networks (LUS and MAS networks). The wells in a 

VFPS network are selected from public, domestic, or monitoring wells and located such that there is a representation 

of wells at different depths and collocated within the area to 

be studied. Evaluating vertical gradients of groundwater age 

and contaminant concentrations facilitates the understanding 

of changes in groundwater quality over periods greater than 

10 years. VFPS networks generally are sampled once. Data 

from the Columbia Plateau VFPS (clptvfps1, fig. 17), Glacial 

aquifer system VFPS (glacvfps1, fig. 18), and Rio Grande 

aquifer system VFPS (rgaqvfps1, fig. 19) are included in this 

report.

Columbia Plateau Vertical Flow-Path Study 

Network (clptvfps1)

The Columbia Plateau VFPS network (clptvfps1; fig. 17) 

was designed to examine changes in water quality over time 

and to evaluate whether agriculturally derived contaminants 

are moving deeper into the aquifer system. The clptvfps1 

is in south-central Washington and is contained within the 

Columbia Basin Irrigation Project area, a large-scale irrigation project through which a series of canals divert Columbia 

River water to a highly productive agricultural basin. The 

clptvps1 network consists of 19 wells from 2 decadal networks 

(Frans and others, 2012): 10 shallow monitoring wells from 

the Columbia Plateau agricultural land use study (ccptlusag2b; 

Arnold and others, 2017a,b) and 9 deeper domestic or publicsupply wells from the Columbia Plateau major aquifer study 

(ccptsus1b; Arnold and others, 2017a,b). The 10 agricultural 

land-use study wells are typically screened in unconsolidated 

basin-fill deposits, whereas the 9 major aquifer study wells are 

typically screened in underlying basaltic rocks. Well depths 

range from 19 to 1,000 ft (appendix 2, table 2.1). The ccptsus1b and ccptlusag2b networks were previously sampled in 

1994–95 and 2002. Samples for the clptvfps1 were collected 

from July to September 2014.

Glacial Aquifer System Vertical Flow-Path Study 

Network (glacvfps1)

The Glacial aquifer system VFPS network (glacvfps1; 

fig. 18) was designed to add an age framework to results 

from decadal networks in the glacial aquifer system to help 

address questions of changes in water quality over time. The 

glacvfps1 network is a group of 43 wells and is a combination of 28 wells from the lerilusrc1 network described earlier 

in this report and 15 additional wells completed at various 

depths ranging from about 10 to 172 ft (appendix 2, table 2.1). 

The wells were open to the aquifer across intervals of 4 to 

10 ft, but most were open less than 5-ft intervals (appendix 

2, table 2.2). Samples for the vertical flow-path study were 

collected August through September 2016, and these data are 

reported here.

Rio Grande Aquifer System Vertical Flow-Path 

Study Network (rgaqvfps1)

The Rio Grande aquifer system VFPS (rgaqvfps1; fig. 19) 

is in the alluvial Middle Rio Grande Basin, lying within the 

Rio Grande flood plain in and near Albuquerque. The study is 

focused on areas of the aquifer that are recharged by seepage 

from the Rio Grande. High concentrations of dissolved solids, 

arsenic, or both have previously been observed within the 

study area (Plummer and others, 2004).

The rgaqvfps1 network consists of 19 monitoring wells, 

15 of which are part of the rioglusrc1 urban land-use network 

previously described in this report (ranging in depth from 
26 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

River

Potholes

Reservoir

Columbia

WASHINGTON

Connell

Ellensburg

Ephrata

George

Kennewick

Moses

Lake

Othello

Mattawa

Pasco

Quincy

Royal

City Warden

Grandview Richland

city

Granger

city

Mabton

city

Prosser

city

Sunnyside

city

Toppenish

city

Wapato

city

Wenatchee

02

03

06

08

09

10 11

12 14 13

16

17

18

19

20

21,24

22

23

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

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

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

120°40' 120°20' 120° 119°40' 119°20' 119°

47°

46°40'

46°20'

46°

Map area

EXPLANATION

Boundary of primary aquifer

Sampled well and identification 

 number—See table 1 list for 

 network name clptvfps1

02

Figure 17. Study and wells sampled as part of the Columbia Plateau vertical flow-path study network (clptvfps1) for the U.S. Geological 

Survey National Water-Quality Assessment Project.
Groundwater Study Design 27

Huron

Cass Lake

Kent Lake

Strawberry

Lake

Huron

Saline River

Mann Creek

South

Clinton River

Paint

Stony Creek

River

Creek

Ore

River

Creek

Flint

South Lyon-Howell

Ann Arbor

Detroit

01,02

03 04,05

06

07

08 09,10

11

12 13,14 15

16,17

18

19

20,21 22

23,24 25,26

27,28

29,30 31 32

33 34

35,36

37

38

39,40 41

42

43

83°50' 83°40' 83°30' 83°20' 83°10'

42°50'

42°40'

42°30'

42°20'

42°10'

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

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

WGS 1984 World Mercator projection.

North American Datum of 1983

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name glacvfps1

01

Figure 18. Study and wells sampled as part of the Glacial aquifer system vertical flow-path study network (glacvfps1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
28 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Tijeras Arroyo

Arroyo de Domingo Baca

Arroyo del Pino

Bear Arroyo

Alameda Drain

Barr Canal

Gallegos

Lateral

Barelas Ditch

Atrisco

Riverside

Drain

Albuquerque

Riverside

Drain

Isleta Drain

Arenal Main Canal

Arenal Ditch

Chamizal Lateral

Atrisco Drain

Arroyo del Embudo

Harwood Lateral

Rio Grande

Griegos Lateral

San Jose Drain

Griegos Drain

Plover River

Flume Creek

Albuquerque

10

11

12 13

14

15

16

17

18

19

01

02

03 04

05

06

07

08,09

106°44' 106°42' 106°40' 106°38' 106°36' 106°34' 106°32'

35°10'

35°8'

35°6'

35°4'

35°2'

Base from U.S. Geological Survey digital data, 1:24,000

WGS 1984 World Mercator projection.

North American Datum of 1983

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name rgaqvfps1

01

Figure 19. Study and wells sampled as part of the Rio Grande aquifer system vertical flow-path study network (rgaqvfps1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 29

about 18 to 57 ft). The additional 4 wells are deeper, ranging from 95 to 254 ft in depth (appendix 2, table 2.1). The 

wells are typically open to the aquifer across 10-ft intervals, 

although intervals range from about 5 to 82 ft (appendix 2, 

table 2.2). Samples were collected in July and August 2016.

Flow-Path Study Networks

Flow-path study (FPS) networks are designed to evaluate changes in groundwater quality along flow paths from 

recharge areas to discharges areas (streams or withdrawal from 

wells). For each network, monitoring wells were installed or 

selected at four or five locations along hypothesized groundwater flow paths, with three or four wells at each of these locations screened at various depths. These networks examine flow 

paths that are usually shorter (often less than about 3 miles) 

and shallower than VFPS networks. FPS networks are typically collocated with land-use study networks. FPS networks 

are designed to examine the transport and transformation of 

contaminants along primarily horizontal flow paths. Like the 

VFPS, FPS networks may be used to examine trends over 

decades but for a smaller spatial extent. Data from two Central 

Valley aquifer system FPS networks (cvalfps1 and cvalfps2; 

figs. 20–21); Glacial aquifer system FPS network (glacfps1; 

fig. 22), and Mississippi Embayment aquifer system FPS network (metxfps1; fig. 23) are included in this report.

Central Valley Aquifer System Flow-Path Study 

Networks (cvalfps1, cvalfps2)

The two Central Valley aquifer system FPS networks near 

Fresno, Calif. (cvalfps1, cvalfps2; figs. 20–21) were designed 

to examine the fate and transport of agricultural contaminants such as 1,2-dibromo-3-chloropropane and nitrate along 

groundwater flow paths. The cvalfps1 study area is west of 

the foothills of the Sierra Nevada and east of the San Joaquin 

Valley trough on the high alluvial fan of the Kings River 

(Burow and others, 1999). Twenty monitoring wells were 

installed at six well nest sites along a 2.9-mile transect to characterize changes in water quality along approximate groundwater flow paths. The monitoring well transect generally was 

aligned in the direction of regional groundwater movement in 

the study area. Well depths in the network range from 70 to 

268 ft (appendix 2; table 2.1). The cvalfps1 network was 

previously sampled in 1994–95 (Burow and others, 1999) and 

2003 (Burow and others, 2008). Samples for the current phase 

of monitoring for the cvalfps1 were collected from July to 

September 2013.

The cvalfps2 network is an extension in space from 

the cvalfps1 network using a mix of observation, irrigation, 

domestic-supply, and public-supply wells. There are 21 wells 

is the network, with well depths ranging from 100 to 532 ft 

below land surface (appendix 2; table 2.1). Samples for the 

current phase of monitoring for the cvalfps2 were collected 

from July to September 2013.

Glacial Aquifer System Flow-Path Study Network 

(glacfps1)

The Glacial aquifer system FPS network (glacfps1, fig. 22) 

was designed to examine the migration and transformation of 

contaminants along groundwater flow paths and groundwatersurface water interactions (Saad, 2008). The study site is in the 

Tomorrow River Watershed in Portage County, Wisconsin. The 

site includes four nests of wells installed along groundwater 

flow paths (as inferred from groundwater flow simulations). 

There are 18 total wells in these 4 nests, located upgradient 

from the Tomorrow River at distances of about 200 ft (well 

nest 4), 700 ft (well nest 3), 2,000 ft (well nest 2), and 5,000 ft 

(well nest 1). These well nests represent the downgradient part 

of the entire flow path length that discharges to the stream at 

this location. Eight additional samples were taken from streambed piezometers (tables 2.1 and 2.2 include the piezometers in 

the count of wells) that were about 1 ft below the streambed. 

The Tomorrow River Watershed is nested within a previously 

conducted agricultural land-use study area (wmiclusag2, Saad, 

2008; Arnold and others, 2017a,b) and is in the same location as a previous groundwater-surface water interaction study 

(Tesoriero and others, 2013). Samples for the current phase of 

monitoring for the glacfps1 were collected in June 2014.

Mississippi Embayment Aquifer System 

Flow-Path Study Network (metxfps1)

The Mississippi Embayment aquifer system FPS network 

(metxfps1; fig. 23) was designed to examine the intrinsic 

vulnerability of the Memphis aquifer along two inferred flow 

paths (Kingsbury and others, 2017). The study site is in the 

northeastern part of the Mississippi Embayment in southwestern Tennessee and northwestern Mississippi and is nested 

with a previous urban land-use study (miselusrc1). Wells are 

along flow paths that begin in the outcrop area of the Memphis 

aquifer and end at public supply wells that are in a confined 

part of the aquifer. This study is designed to evaluate whether 

younger water is migrating into confined parts of the aquifer 

that are used for drinking-water supplies. The network of 22 

wells consists of a mix of monitoring, domestic, commercial, and public-supply wells that range in depth from 23 to 

624 ft (appendix 2; table 2.1). Samples for the current phase 

of monitoring for the metxfps1 were collected in August and 

September 2013.

Modeling Support Study Networks

Modeling support study (MSS) networks are used to 

support modeling efforts where more data are needed to 

calibrate a model. They are designed based on a specific need 

so that each is somewhat unique in its design. Data from two 

Northern Atlantic Coastal Plain MSS networks (nacpmss1 and 

nacpmss2; figs. 24–25); and one Glacial aquifer system MSSS 

network (glacmss1; fig. 26) are included in this report.
30 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Fowler Switch Canal

Briggs Canal

Hansen Canal

Fancher Creek

South Branch

Collins Canal

Hansen Canal

Fancher Creek Canal

Mill Ditch

East

Branch

Ditch

Sanger

Fresno

B1-1

B1-2

B1-3

B2.5-1

B2.5-2 B2-1

B2-2

B2-3

B3-1

B3-2

B3-3

B3-4

B4-1

B4-2

B4-3

B4-4

B5-1

B5-2

B5-3

119°40' 119°38' 119°36' 119°34'

36°46'

36°44'

36°42'

36°40'

Base from U.S. Geological Survey digital data, 1:24,000

WGS 1984 World Mercator projection

North American Datum of 1983

0 1 2 MILES

0 1 2 KILOMETERS

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name cvalfps1

B1-1

Figure 20. Study and wells sampled as part of the Central Valley aquifer system flow-path study network (cvalfps1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 31 Fowler Switch Canal

Fish Slough

Hansen

Washington

Sandridge Canal

Central Canal

Dry Creek Canal

Slough

Fresno

Stinson

Canal

Creek

Dog Creek

Dry

Canal

Mill Ditch

Ventura Canyon Canal

Braley Canal

Fresno

Canal

Colony

N. Central Canal

Canal

S. Br. Washington Canal

Selma

Fresno

Kerman

Riverdale

Sanger

! !

!

!

!

!

!

! !

!

!

!!

!

!

!

!

!

!

FPC1-shallow

FPC2-deep

FPC2-shallow FPC3-deep

FPC3-shallow

FPF1-deep

FPF1-shallow

FPF2-deep

FPF2-shallow

FPR1-deep

FPR1-shallow

FPR2-shallow

FPR3-deep

FPR3-shallow

FPR4-deep

FPR4-shallow

FPR5-deep

FPR5-shallow

FPR6-deep

FPR6-shallow

FPR2-deep

36°55'

36°50'

36°45'

36°40'

36°35'

36°30'

36°25'

120°5' 120° 119°55' 119°50' 119°45' 119°40' 119°35'

Base from U.S. Geological Survey digital data, 1:24,000

WGS 1984 World Mercator projection

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name cvalfps2

FPF1-deep

Figure 21. Study and wells sampled as part of the Central Valley aquifer system flow-path study network (cvalfps2) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
32 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Tomorrow River

Lake Du Bay

Plover River

Nace Creek

Flume Creek

Stevens Point

Wausau

Waupaca

89°40' 89°30' 89°20' 89°10' 89°

45°

44°50'

44°40'

44°30'

44°20'

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

WGS 1984 World Mercator projection.

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Fresno

24,25,26

21,22,23

16,17,18

09,10,11,12,13,14

03,04,05

01,02,27

06,07,08

Tomorrow River

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name glacfps1 

01

Figure 22. Study and wells sampled as part of the Glacial aquifer system flow-path study network (glacfps1) for the U.S. Geological 

Survey National Water-Quality Assessment Project.
Groundwater Study Design 33

Nonconnah Creek

Shaws

Marys Creek

Cypress Creek

Wolf River

Loosahatchie River

Wolf River

Indian

Grays Creek

Wolf River

Loosahatchie

Fletcher

Creek

Creek

River

Creek

Arlington

Memphis

Oakland

TENNESSEE

MISSISSIPPI

01

02

03

04

05

06

07

08

09

10

11

12 13

14 15

16

17 18

19

20 21

22

89°50' 89°40' 89°30' 89°20' 89°10'

35°20'

35°10'

35°

34°50'

34°40'

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

WGS 1984 World Mercator projection.

North American Datum of 1983

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name metxfps1

01

Figure 23. Study and wells sampled as part of the Mississippi Embayment aquifer system flow-path study network (metxfps1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
34 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

01 02

03

04

05

06

07

08

09

11 10

12,14

13

15

16

17,18

19

20

Chesapeake Bay

Potomac River

Patuxent River

Baltimore

Waldorf

Washington, D.C.

Arlington

Alexandria

Reston

Woodbridge

Manassas

Johnstown

Lexington

Park

Bethesda

Annapolis

Columbia

Ellicott

City

VIRGINIA

MARYLAND

MARYLAND

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

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

77°20' 77° 76°40' 76°20' 76°

39°20'

39°

38°40'

38°20'

Fredericksburg

Map

area

Area of

nacpmss1

EXPLANATION

Equal-area grid cells used for

 randomized site selection

Northern Atlantic Coastal Plain

 modeling support study in

 Maryland (nacpmss1) boundary

Sampled well and identification 

 number—See table 1 list for 

 network name nacpmss1

01

Figure 24. Study and wells sampled as part of the Northern Atlantic Coastal Plain modeling support study network (nacpmss1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Groundwater Study Design 35

01

02

03

04

05

06

07

08

09

10 11

12

13

15 14

16

17

18

19

20

21

22

23

24

25

Long Island Sound

ATLANTIC OCEAN

Stamford

Danbury

New Haven

New

York

Norwich

Hartford

Waterbury

Hempstead

Bridgeport

Medford

Huntington

Norwalk

CONNECTICUT

NEW YORK

NEW

JERSEY

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

and U.S. Census Bureau Urban Areas, 2017

Albers Equal-Area Conic projection

Standard parallels 29°30' N. and 45°30' N.

Central meridian 96°00' W.

North American Datum of 1983

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

73°20' 73° 72°40' 72°20' 72° 71°40'

41°40'

41°20'

41°

40°40'

40°20'

Map

area

Area of

nacpmss2

EXPLANATION

Equal-area grid cells used for

 randomized site selection

Northern Atlantic Coastal Plain

 modeling support study in

 New York (nacpmss2) boundary

Sampled well and identification 

 number—See table 1 list for 

 network name nacpmss2

01

Figure 25. Study and wells sampled as part of the Northern Atlantic Coastal Plain modeling support study network (nacpmss2) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
36 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Peshtigo

Wisconsin

Fox River

Wolf River

Oconto

River

Fox River

Pigeon

River

Wolf

Wisconsin

River

Montello

River

South Branch

Beaver Creek

Black

Evergreen River

Embarrass River

Red

South

Pine River

Neenah

Creek

Willow

Creek

S. Br.

Little Wolf

River

Waupaca

River

Middle

Plover River

Little

GREEN BAY

LAKE

MICHIGAN

River

Creek

Wolf

River

River

Branch

Branch

River

River

Lake Nokomis

Lake Mohawksin

Alexander

Lake

Lake Wausau

Shawano Lake

Lake

Dubay

Petenwell

Lake

Lake Winnebago

Lake

Poygan

Lake

Winneconne

Lake Butte

des Morts

Castle

Rock Lake

Buffalo

Lake Puckaway Lake

Appleton

Green Bay

Waupaca New London

Stevens

Point

Shawano

Clintonville

Wausau

Seymour

Oconto

Falls

Antigo

Wautoma

Portage

Oshkosh

Winneconne

01_SD

01_TD

01_TS

02_SD

02_TS

02a_PD

02a_PS

02b_PD

03_SD

03_TD

04_SD

05_SD

05_TD

05_TS

06_PD

06_PS

06_TS

07_SD

07_TS

08_SD

08_TS

09_TD

09_TS

10_SD

10_TS

02b_PS

06_SD

09_SD 03_TS

04_TS

90° 89° 88°

45°

44°

Base from U.S. Geological Survey digital data, 1:24,000

WGS 1984 World Mercator projection.

North American Datum of 1983

0 5 10 15 20 25 MILES

0 5 10 15 20 25 KILOMETERS

Map

area

EXPLANATION

Sampled well and identification 

 number—See table 1 list for 

 network name glacmss1

01_TS

Figure 26. Study and wells sampled as part of the Glacial aquifer system modeling support study network (glacmss1) for the 

U.S. Geological Survey National Water-Quality Assessment Project.
Sample Collection and Analysis 37

Glacial Aquifer System Modeling Support Study 

Network (glacmss1)

The Glacial aquifer system MSS network (glacmss1, 

fig. 24), in eastern Wisconsin, was designed to provide additional groundwater quality and age information for the parts 

of the glacial aquifer in the Fox-Wolf-Peshtigo groundwater 

model area that were not already covered by other NAWQA 

groundwater networks. More specifically, this network 

provides information about the deeper parts of the sand and 

gravel aquifer, and about both the shallow and deep parts of 

the till in the Fox-Wolf-Peshtigo model area. The network also 

provides some vertical flow path information for the sand and 

gravel aquifer.

The eastern part of the network area is underlain by 

east-sloping sedimentary rocks of Cambrian, Ordovician, 

and Silurian age (Batten and Bradbury, 1996). Rocks of 

Precambrian age, primarily granite, underlie the western part 

of the network area. Quaternary age unconsolidated deposits 

overlie the entire area, and these deposits are generally thickest along major river valleys (Batten and Bradbury, 1996; 

Soller and others, 2012). Groundwater is an important source 

of drinking water in the study area, especially for domestic 

wells. Both naturally occurring and anthropogenic contaminants adversely affect drinking-water quality in the area. In the 

Central Sands, nitrate is a common contaminant, while arsenic 

is common in parts of Paleozoic aquifers in eastern Wisconsin 

(Luczaj and Masarik, 2015).

The network includes a total of 30 wells: 10 deep sand 

and gravel wells, 6 shallow sand and gravel wells that are 

paired with deep sand and gravel wells, 10 shallow till wells, 

and 4 deep till wells. Well depths range from 45 to 245 ft 

below ground surface. Samples for the model support study 

were collected August through September 2014, and these data 

are reported here.

Northern Atlantic Coastal Plain Modeling 

Support Study Networks (nacpmss1, nacpmss2)

There are two MSS networks in the Northern Atlantic 

Coastal Plain aquifer (nacpmss1, fig. 25; nacpmss2, fig. 26). 

One is in Maryland (nacpmss1), and the other one is in New 

York (nacpmss2). The nacpmss1 underlies and is to the east 

of the cities of Baltimore, Maryland, and Washington, D.C. 

This study is within the broader Northern Atlantic Coastal 

Plain principal aquifer setting described in Arnold and others, 

(Arnold and others, 2017b). It consists of unconsolidated to 

partly consolidated Early Cretaceous to Holocene age sediments that thicken and deepen toward the Atlantic Coast. 

Twenty wells ranging in depth from 60 to 645 ft were sampled 

from regional layered aquifers in the Piney Point, Nanjemoy, 

Aquia, Magothy, Potomac, Patapsco (upper and lower), and 

Patuxent Formations (Shedlock and others, 2007).

The Northern Atlantic Coastal Plain aquifer modeling 

support study area in New York, nacpmss2, underlies the half 

of Long Island (split the long way) along the Atlantic Coast 

to the east and north of New York City. This study is within 

the broader Northern Atlantic Coastal Plain principal aquifer 

setting described in Arnold and others (2017b). Twenty-five 

wells ranging in depth from 200 to 900 ft were sampled in the 

Magothy Formation regional layered aquifer.

Sample Collection and Analysis

Water-quality data from samples collected at 648 wells 

(fig. 1; table 1) are available in Arnold and others (2020). 

Groundwater samples were collected and processed using 

methods designed to yield samples that were representative 

of environmental conditions, minimally affected by contamination, and consistent nationwide (Koterba and others, 

1995; Lapham and others, 1995; USGS, variously dated). All 

samples were collected at the wellhead (the point at which 

the groundwater exits the well near land surface) or as close 

to the wellhead as possible. This location was selected so 

that samples were collected before any treatment or blending 

potentially could alter constituent concentrations. Samples 

were collected and processed using prescribed protocols 

described in Koterba and others (1995), Lapham and others 

(1995), and the USGS National Field Manual (USGS, variously dated). Samples were analyzed at the USGS National 

Water-Quality Laboratory (NWQL) in Denver, Colorado, 

for water-quality indicators, nutrients and dissolved organic 

carbon, major and minor ions, trace elements, VOCs, pesticides, radon radiochemistry, and one item of special interest, 

arsenic speciation. Four radionuclide constituent concentrations (lead–210, polonium–210, radium–224, radium–226, 

and radium–228) were analyzed by ALS Environmental in 

Fort Collins, Colo. Perchlorate concentrations were analyzed 

by Weck Laboratories, Inc. in Industry, Calif. Hexavalent 

chromium (chromium [VI]) concentrations were analyzed by 

the USGS Trace Metal Laboratory in Boulder, Colo. Strontium 

concentrations were analyzed by the Metal and Metalloid 

Isotope Research Laboratory in Menlo Park, Calif.

The constituents for which samples were collected are 

listed in table 2 of Arnold and others (2020) and are organized 

by constituent class; constituent primary uses and sources; 

analytical schedules and sampling period; analytical method 

references; USGS parameter codes; comparison thresholds; 

reporting levels for the 2016 sampling period; number of 

analyses, detections, and detections above the reporting level; 

and the table in which the data for the constituent class are 

shown. The reporting levels shown are for the samples collected in 2016. Reporting levels for earlier data are shown in 

Arnold and others (2016b, 2017a, 2018a). Analytical schedules are groups of constituents for which laboratory analysis is 

requested. The USGS parameter code identifies the constituents, and the method reference indicates the laboratory method 
38 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

used to analyze the samples. The reported concentration of a 

constituent can be evaluated using the comparison threshold 

value. Of the comparison thresholds listed in table 2 of Arnold 

and others (2020), only the secondary maximum contaminant 

level is not health based.

In addition to discrete water-quality samples that are 

collected periodically, the ETN wells also are instrumented to 

measure basic water-quality parameters at a high frequency 

during specific periods throughout each day. Each well is 

instrumented with a water-quality sonde that contains temperature, specific conductance, pH, and dissolved oxygen 

probes. Some wells also are instrumented to measure nitrate. 

The sonde sits in a flow-through chamber that receives 

groundwater flow from near the wellhead. Measurements 

of the basic water-quality parameters are made when the 

well is pumping and groundwater is flowing through the 

system, which may range from 1 to 24 hours per day. Waterquality data are recorded by the sonde at different intervals, 

from 2 minutes to 12 hours, depending on the network. The 

water-quality data are transmitted to a data collection platform where the data are stored and transmitted to the USGS 

National Water Information System (USGS, 2018) database 

by the Geostationary Operational Environmental Satellite 

network. The high-frequency data are reviewed, corrected, 

and approved according to recommendations for publishing 

continuous water-quality records (Wagner and others, 2006).

Data Reporting

Laboratories use specified values, referred to as reporting 

levels, in reporting results determined during analysis of water 

samples. Different reporting levels are used depending on the 

constituent and the laboratory method used to analyze the 

sample. Concentrations not measured above a certain threshold concentration for that constituent are reported as less than 

the reporting level; these are censored data.

Reporting levels are defined differently by the NWQL 

for inorganic and organic constituents. Inorganic constituents 

(major ions, nutrients, and trace elements) for samples analyzed in 2016 are each reported using a reporting level (RL) 

that is equivalent to the detection limit (DL). The DL is the 

smallest concentration that can be measured and reported with 

99-percent confidence that the concentration is greater than 

zero, which means less than or equal to a 1-percent chance of 

a false positive (Williams and others, 2015). The DLs used 

in 2016 for both inorganic and organic constituents generally were determined using the DQCALC method described 

in Williams and others (2015); this is indicated as DLDQC 

in table 2 of Arnold and others (2020). However, for a few 

constituents that have commonly been detected in laboratory 

blank samples, the DLs were determined using an approach 

that calculates a DL directly from blank data (Williams and 

others, 2015); this is indicated as DLBLNK in table 2 of 

Arnold and others (2020). Organic constituents (VOCs and 

pesticides) are each reported using an RL that typically is 

about twice, but may be more than twice, the DL (Williams 

and others, 2015); this is indicated by RLDQC in table 2 of 

Arnold and others (2020) when calculated from a DL determined using the DQCALC method. This approach to setting 

the RL is estimated to limit the chance of incorrectly reporting the constituent as absent to less than or equal to 1 percent 

(Williams and others, 2015). In other words, there is at least a 

99-percent confidence that the constituent really is absent from 

the sample when it is reported relative to the RL. The RLs are 

used for reporting analytical results for VOCs and pesticides 

to allow for the robust analysis and interpretation of detection frequencies. The NWQL uses information-rich analytical 

methods such as gas chromatography or high-performance 

liquid chromatography for these constituents and often provides results that indicate the presence of these constituents at 

concentrations less than their RLs, and even at concentrations 

less than their DLs.

A few constituents are reported using minimum reporting levels (MRLs). The MRLs are calculated according to the 

EPA definition of an MRL, described previously as the minimum concentration of a substance that can be measured and 

reported with 99-percent confidence that the value is above 

zero (Childress and others, 1999). An MRL is a reporting level 

that is chosen by the laboratory.

Radionuclides are reported using units of radioactive activity (picocuries per liter) rather than concentration. Reporting levels for these constituents are based on 

the sample-specific critical level (ssLc) or sample-specific 

minimum detectable concentrations (ssMDCs) (McCurdy and 

others, 2008). The ssLc and ssMDC are calculated for each 

sample from parameter values used during the actual analysis of the sample. The ssLc and ssMDC are analogous to the 

DL and RL, respectively. The ssLc is defined as the smallest 

measured activity that indicates detection of the radionuclide, 

with no more than a 5-percent chance of a false positive 

detection (EPA, 2004). The specified probability associated 

with a critical level can vary, but it is typically 5 percent for 

radionuclides. Like the DL, the ssLc is a reporting level that 

is based on a specified probability of false positive errors; that 

is, incorrectly reporting that the radionuclide is present. The 

ssMDC, like the RL, is a reporting level that is based on a 

specified probability of false negative errors; that is, failing to 

report that the radionuclide is present. The ssMDC is defined 

as the activity at which there is 5-percent chance of a false 

negative error and typically is about two times greater than the 

ssLc (McCurdy and others, 2008).

The laboratory method used to analyze samples for VOCs 

in schedules 4436 and 4437 was approved and published in 

2016 (Rose and others, 2016). Results for laboratory methods 

that are unapproved generally are not made available to the 

public by the USGS because the quality of the results could 

be affected by problems subsequently discovered during the 

process of method approval; however, the method approval 

process revealed no substantial problems and resulted in no 

changes in the analytical process (Duane Wydoski, USGS, 
Quality-Assurance and Quality-Control Methods 39

written commun., 2015). This indicates that the data reported 

before approval for these methods were of sufficient quality 

for public release.

Concentrations below DLs, and concentrations between 

DLs and RLs, are reported without any qualifiers in this 

report. Concentrations below DLs or between DLs and RLs 

can be identified by comparing the reported concentrations 

with the DLs and RLs listed by compound in table 2 of Arnold 

and others (2020). It is important to note that there is greater 

uncertainty associated with values less than DLs (regarding 

risk of false positive errors or inaccurate detections) and with 

values less than RLs (regarding risk of false negative values 

or inaccurate nondetections) than with values that are greater 

than DLs, RLs, or both.

The data presented in this report and associated data 

release (Arnold and others, 2020) are current as of the date 

of retrieval (December 18, 2018) from the National Water 

Information System (USGS, 2018). For example, RLs may be 

adjusted when additional QC data for the method are examined. The well information and water-quality data presented 

in this report and the associated data release were reviewed by 

USGS personnel and subsequently verified by coauthors who 

are responsible for tracking the data.

Quality-Assurance and Quality-Control 

Methods

The quality-assurance plan for NAWQA Project groundwater samples was derived from previous NAWQA Project 

cycles of study (Koterba and others, 1995) and the USGS 

National Field Manual (USGS, variously dated). About 14 percent of samples collected during any period are for data quality assurance and quality control (QC). Types of QC samples 

include equipment blanks, source solution blanks, field blanks, 

replicates, field spikes, and laboratory spikes. Data and results 

from statistical analysis of blank QC samples are presented in 

appendix 4 (tables 4.1 and 4.2).

Blanks are used to test for bias from an unintentional 

introduction of contamination to environmental samples. 

Equipment blanks are used to test whether equipment is clean 

and free of contamination. Source solution blanks are used 

to test whether the water used for the blank sample is free of 

contamination. Field blanks are used to test for contamination 

that may be introduced during sample collection, processing, 

handling, and analysis. Field blanks also are used to test for 

contamination from the environment around where the sample 

was collected. Replicates are samples that are collected at the 

same time and using the same method as the environmental 

sample. Replicates measure the variability of determining a 

concentration in samples that should be essentially identical. 

Spiked samples are used to measure the performance of analytical methods on an environmental water sample. A sample 

can be spiked in the field or the laboratory.

The number and type of QC samples planned for each 

network study depend on the number of wells sampled, the 

number of sampling teams that are involved in the sampling, 

and the constituents for which samples will be analyzed, as 

described in the following criteria:

• Equipment blanks are collected for nutrients, trace elements, and VOCs at the quantity of one blank for each 

team sampling the network.

• Source solution blanks are collected for nutrients, trace 

elements, and VOCs at the quantity of one blank for 

each team sampling the network. The VOCs have additional source solution blanks that are collected with 

each field blank.

• Field blanks are collected for major ions, nutrients, dissolved organic carbon, trace elements, and pesticides 

at the quantity of 1 blank for every 15 wells sampled 

or 1 blank for each team sampling the network 

(whichever results in a greater number of blanks). 

Field blanks are collected for VOCs at the quantity of 

1 blank for every 10 wells sampled or 1 blank for each 

team sampling the network (whichever results in a 

greater number of blanks).

• Replicate samples are collected for major ions, nutrients, dissolved organic carbon, trace elements, VOCs, 

and radionuclides at the quantity of 1 replicate for 

every 30 wells sampled. Replicate samples are collected for pesticides at the quantity of 1 replicate for 

every 15 wells.

• Field spikes are collected for pesticides at the quantity 

of 1 spike sample for every 30 wells sampled.

• Laboratory spikes are collected for VOCs at the quantity of 1 spike sample for every 30 wells sampled.

Statistical analysis of QC sample data can be used to 

evaluate the variability or bias of the data, sampling and sample handling procedures, and laboratory and (or) field methods 

and to ensure the environmental assessment samples represent 

true groundwater chemistry. The QC sample data provided in 

Arnold and others (2020) include water quality for all blank 

QC samples collected between January and December 2016 

in association with the environmental sample data and a few 

results from earlier sampling periods that were not previously published. Data from the 2012–13 sampling period are 

presented in Arnold and others (2016a,b), data from the 2014 

sampling period are presented in Arnold and others (2017a,b), 

and data from the 2015 sampling period are presented in 

Arnold and others (2018a,b).
40 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Groundwater-Quality Data

Groundwater-quality data from 648 wells are included 

in this report (table 1). Samples were analyzed for 376 constituents (table 2 of Arnold and others, 2020); however, not all 

wells were sampled for all constituents. Results of analyses are 

presented in tables 3–13 of Arnold and others (2020), which 

are organized by constituent class: water-quality indicators 

(table 3); nutrients and dissolved organic carbon (table 4); 

major and minor ions (table 5); trace elements (table 6); VOCs 

(tables 7); pesticides (table 8); radiochemistry (table 9); and 

special-interest constituents, including arsenic speciation 

(table 10), chromium (VI) (table 11), perchlorate (table 12), 

and strontium (table 13). The constituents for which samples 

were analyzed and the table in which the data are presented 

are listed in table 2 of Arnold and others (2020). Comparative 

benchmarks (thresholds) listed in that table provide context 

for evaluating the constituent concentration data in terms of 

human health and other characteristics relevant for drinkingwater use. Several types of thresholds are listed. The EPA 

maximum contaminant levels are legally enforceable drinkingwater standards that specify the maximum permissible level of 

a constituent that can be delivered to a user of a public water 

system. The EPA human-health benchmarks for pesticides 

(HHBPs) are nonenforceable screening levels for evaluating if 

a pesticide concentration in drinking-water sources may indicate a potential human-health risk (EPA, 2012). The HHBPs 

include benchmarks for cancer and noncancer health effects 

(EPA, 2013). The USGS Health-Based Screening Levels are 

nonenforceable benchmarks for constituents that do not have 

HHBPs or maximum contaminant levels (MCLs) that can be 

used to evaluate if constituent concentrations may indicate a 

potential human-health concern (Toccalino, 2007; Toccalino 

and others, 2014). Like EPA HHBPs, USGS health-based 

screening levels are categorized in terms of cancer and noncancer health effects.

The groundwater-quality data from January to 

December 2016 are presented in the format of tab-delimited 

ASCII text files and are available for download from Arnold 

and others (2020) along with complete metadata files that 

describe the contents of each text file. The data may be 

imported into a spreadsheet, database, or statistical software 

for manipulation and analysis. The data available from Arnold 

and others (2020) are referenced as tables 1–13 and appendix 

tables 4.10–4.19 in this report.

Water-Quality Indicators

Water-quality indicators include water temperature, dissolved oxygen, specific conductance, pH, alkalinity, carbonate 

and bicarbonate (calculated from alkalinity), and turbidity 

(table 3 of Arnold and others, 2020). Water-quality indicators 

are measured in the field when the other water samples are 

collected (USGS, variously dated), and pH and specific conductance sometimes are also measured in the laboratory.

Water-quality indicators provide basic information about 

the general quality and geochemical conditions of the water. 

Dissolved oxygen is the concentration of oxygen dissolved in 

the water and is an indicator of reduction-oxidation (redox) 

conditions in the aquifer. Measurements of pH indicate the 

acidity or basicity of water. Dissolved oxygen and pH are 

important controls on the chemical reactions that can happen 

in water. Specific conductance is a measure of how well the 

water conducts electricity and indicates the relative amount of 

dissolved solids in the water. Alkalinity, carbonate, and bicarbonate indicate the hardness of water and are related to pH. 

Turbidity is a measure of the suspended solids in the water.

Inorganic Constituents

Inorganic constituents are most often naturally present 

in groundwater. Groundwater samples were analyzed for the 

following inorganic constituent classes: major and minor ions, 

nutrients and dissolved organic carbon, and trace elements 

(including metals; tables 4–6 of Arnold and others, 2020).

Nutrients include nitrogen and phosphorus compounds 

and dissolved organic carbon. Data for ammonia, nitrate plus 

nitrite, nitrite, total nitrogen, and phosphorus measured as 

orthophosphate are presented in table 4 of Arnold and others 

(2020). Nutrients are present naturally, but nutrient concentrations also are affected by human activities such as farming and 

wastewater disposal (Hem, 1992). Nitrogen was measured as 

total nitrogen and as the individual nitrogen species of nitrite, 

nitrate, and ammonia. Nutrient concentrations can affect the 

quality of groundwater for use as drinking water.

Major and minor ions are cations and anions that can be 

dissolved in water from geologic materials. Concentrations of 

major and minor ions can be used to classify water into different types (Hem, 1992; Hiscock, 2005). Waters with similar 

ion concentrations often have similar history, recharge areas, 

climate, mineralogy, and residence time (Güler and others, 

2002). Some major ions can affect the quality of water for 

drinking and other uses. Groundwater samples were analyzed 

for 10 major and minor ions and total dissolved solids (table 5 

of Arnold and others, 2020).

Trace elements consist of metals that are usually present in the environment in very small quantities (Hem, 1992). 

Trace elements often are dissolved in water from geologic 

materials, but concentrations of these elements also can be 

affected by human activities such as mining. Many trace elements can affect the quality of groundwater for use as drinking water. Groundwater samples were analyzed for 22 trace 

elements (table 6 of Arnold and others, 2020).

Organic Compounds

Organic compounds are man-made chemicals and include 

VOCs and pesticides. VOCs are chemicals that tend to evaporate into the air and are in a variety of substances including 

disinfectants, solvents, paint, fumigants, asphalt, and fuel 
Summary 41

additives (Zagorsky and others, 2006). Pesticides are chemical 

compounds used to control plant or insect pests and include 

fungicides, herbicides, and insecticides (Gilliom and others, 

2006). Many VOCs and pesticides, if present, can affect the 

quality of groundwater used for drinking water. Groundwater 

samples from 2016 were analyzed for 85 VOCs and 225 pesticides (tables 7 and 8 of Arnold and others, 2020).

Radiochemistry

Radiochemical constituents include radionuclides and 

measurements of radioactivity. Radionuclides are chemical 

constituents that are produced naturally by the decay of radioactive parent elements such as uranium and thorium. Sources 

of radionuclides in groundwater are geologic material such 

as rocks and soils (Hem, 1992). Radionuclides and measurements of radioactivity included in this report are α radioactivity, β radioactivity, radon (a dissolved gas), several isotopes 

of radium (radium-224, radium-226, and radium-228), 

polonium-210, and lead-210 (table 9 of Arnold and others, 

2020). Uranium, which also is a radionuclide, is included with 

trace elements (table 6 of Arnold and others, 2020) because 

uranium is measured in units of mass concentration rather than 

as units of radioactivity. In total, groundwater samples were 

analyzed for eight radionuclides and measures of radioactivity.

Constituents of Special Interest

Several constituents of special interest were included for 

selected networks. Constituents of special interest were arsenic 

species (arsenate, arsenite, monomethylarsonate, and dimethylarsinate), which are derived from arsenic, hexavalent chromium (chromium [VI]), perchlorate, and strontium isotopes 

(87Sr/86Sr) (tables 10–13 of Arnold and others, 2020). Arsenic 

and chromium are predominantly natural in origin but may 

have localized anthropogenic sources. Anthropogenic arsenic 

uses include metal and ore processing, glass production, fossil 

fuel combustion, wood preservatives, pesticides, semiconductor production, and pharmaceuticals (Garelick and others, 

2008). Hexavalent chromium is chromium in the +6 oxidation 

state (six electrons lost from the atom) and is used in textile 

dyes, wood preservation, anticorrosive agents, and other 

surface coatings (Nriagu and Niebor, 1988). Geochemical 

conditions such as redox and pH affect the speciation of chromium and arsenic in groundwater (Hem, 1992). Most arsenic 

and chromium in groundwater is from geologic sources in 

rocks and soils. Perchlorate is an inorganic constituent used 

in rocket fuels, fireworks, safety flares, and other products; 

it is present in some fertilizers and may be present naturally 

at low concentrations in groundwater (Srinivasan and Sorial, 

2009; Jackson and others, 2015). Strontium isotope variations 

in groundwater provide insight into the sources of dissolved 

constituents to groundwater and have been used to trace flow 

paths and mineral-solution reactions in soils and aquifer rocks 

(Banner, 2004).

Summary

As part of the third decadal cycle of the U.S. Geological 

Survey National Water-Quality Assessment Project, 

groundwater-quality data are being collected from well networks to assess water-quality conditions in the Nation’s principal aquifers and investigate changes in groundwater-quality 

conditions in selected land use and hydrogeologic settings. 

Groundwater-quality data are published in annual data series 

reports, of which this report is the third in the series.

Groundwater-quality data from 648 wells were collected 

from 7 types of well networks: principal aquifer study networks, land-use study networks, major aquifer study networks, 

enhanced trends networks, vertical flow-path study networks, 

flow-path study (FPS), and modeling support study (MSS). 

Within principal aquifer, land-use, and major aquifer study 

networks, study areas were divided into equal-area grid cells 

and wells were selected for sampling using a stratified random 

sampling design. The number of wells in principal aquifer 

networks ranged from about 40 to 60 wells per network for the 

studies included in this report. About 30 wells typically made 

up each land-use or major aquifer study network. Enhanced 

trends networks that were sampled in 2016 consisted of two to 

five wells that were selected at locations within aquifers where 

temporal changes in groundwater quality might be expected. 

Three vertical flow-path study network, four flow-path studies, 

and three modeling support studies are described in this report.

Groundwater samples were analyzed for water-quality 

indicators and constituents, including nutrients, major and 

minor ions, trace elements, volatile organic compounds, pesticides, radiochemistry, and select special-interest constituents 

such as arsenic speciation, hexavalent chromium, and perchlorate. These groundwater-quality data are tabulated in this 

report and associated data release. Quality-control samples 

were collected along with environmental samples, and data 

from blank quality-control samples also are included in this 

report. The data release includes data collected during 2015 

and previously unpublished data from selected environmental 

samples collected in 2013 and selected quality-control samples 

collected in 2014. These previously unpublished data are associated with networks described in this report.
42 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Acknowledgments

Many people in addition to the authors participated in 

providing information for this report. Bryant Jurgens and Tyler 

Johnson (both with the U.S. Geological Survey) provided tools 

for data compilation. Personnel in U.S. Geological Survey 

water science centers, too numerous to name individually, 

collected, packaged, and shipped water samples to the laboratories for analysis and ensured results were entered into the 

U.S. Geological Survey National Water Information System.

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Table 1. Information about wells that have environmental data included in this report.[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–01 NV Washoe 7/5/2016 4,690 NGVD 29 Public supply 5

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–02 NV Washoe 7/5/2016 5,193 NGVD 29 Public supply 7

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–03 NV Washoe 7/6/2016 4,700 NGVD 29 Domestic 2

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–04 NV Carson City 7/7/2016 4,620.00 NGVD 29 Observation 1

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–05 NV Washoe 7/18/2016 4,490 NGVD 29 Public supply 4

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–06 NV Washoe 7/19/2016 4,480 NGVD 29 Public supply 2

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–07 NV Washoe 7/20/2016 4,458 NGVD 29 Public supply 3

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–08 NV Carson City 7/21/2016 4,889.10 NGVD 29 Observation 1

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–09 NV Carson City 7/25/2016 4,666.70 NGVD 29 Public supply 4

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–10 NV Carson City 7/26/2016 4,652 NGVD 29 Public supply 7

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–11 NV Washoe 7/27/2016 4,485 NGVD 29 Public supply 8

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–12 NV Washoe 7/28/2016 4,504 NGVD 29 Public supply 7

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–13 NV Washoe 8/1/2016 4,391.60 NAVD 88 Observation 1

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–14 NV Carson City 8/2/2016 4,718 NGVD 29 Domestic 1

Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–15 NV Washoe 8/16/2016 4,530 NGVD 29 Public supply 3
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–16 NV Washoe 8/16/2016 4,400 NGVD 29 Public supply 1Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–17 NV Washoe 8/17/2016 5,800 NGVD 29 Public supply 2Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–18 NV Carson City 8/18/2016 4,803.90 NGVD 29 Observation 2Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–19 NV Washoe 8/22/2016 4,500 NAVD 88 Observation 7Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–20 NV Washoe 8/23/2016 4,410 NGVD 29 Public supply 2Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–21 NV Washoe 8/23/2016 4,438 NGVD 29 Public supply 3Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–22 NV Washoe 9/7/2016 4,488 NAVD 88 Other 1Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–23 NV Carson City 9/8/2016 5,181.50 NGVD 29 Observation 1Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–24 NV Washoe 9/12/2016 4,550 NGVD 29 Public supply 4Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–25 NV Washoe 9/13/2016 4,510 NGVD 29 Public supply 4Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–26 NV Washoe 9/13/2016 4,563 NGVD 29 Public supply 3Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–27 NV Washoe 9/14/2016 4,475 NGVD 29 Public supply 5Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–28 NV Washoe 9/14/2016 4,420 NGVD 29 Public supply 6Basin and Range aquifers MAS nvbrsus2 NVBRSUS2–29 NV Washoe 9/15/2016 4,410 NGVD 29 Public supply 6Basin and Range 

aquifers

MAS nvbrsus2 NVBRSUS2–30 NV Washoe 9/21/2016 4,495 NGVD 29 Public supply 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Biscayne aquifer PAS biscpas1 BISCPAS1–01 FL Palm Beach 8/22/2016 15 NAVD 88 Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–02 FL Palm Beach 8/22/2016 na na Public supply 7Biscayne aquifer PAS biscpas1 BISCPAS1–03 FL Broward 8/23/2016 na na Public supply 9Biscayne aquifer PAS biscpas1 BISCPAS1–04 FL Broward 8/30/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–05 FL Broward 6/20/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–06 FL Broward 6/20/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–07 FL Broward 8/30/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–08 FL Broward 6/21/2016 14 NGVD 29 Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–09 FL Broward 6/21/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–10 FL Broward 9/1/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–11 FL Broward 6/22/2016 14.7 NGVD 29 Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–12 FL Broward 6/23/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–13 FL Broward 6/22/2016 8 NGVD 29 Public supply 9Biscayne aquifer PAS biscpas1 BISCPAS1–14 FL Broward 8/23/2016 8 NGVD 29 Observation 2Biscayne aquifer PAS biscpas1 BISCPAS1–15 FL Broward 6/22/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–16 FL Broward 8/29/2016 na na Public supply 7Biscayne aquifer PAS biscpas1 BISCPAS1–17 FL Broward 6/23/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–18 FL Broward 8/29/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–19 FL Broward 6/23/2016 na na Public supply 6Biscayne aquifer PAS biscpas1 BISCPAS1–20 FL Broward 8/24/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–21 FL Broward 8/24/2016 na na Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–22 FL Miami-Dade 6/16/2016 na na Public supply 6Biscayne aquifer PAS biscpas1 BISCPAS1–23 FL Miami-Dade 6/16/2016 na na Public supply 6Biscayne aquifer PAS biscpas1 BISCPAS1–24 FL Miami-Dade 6/15/2016 7 NGVD 29 Public supply 9Biscayne aquifer PAS biscpas1 BISCPAS1–25 FL Miami-Dade 6/15/2016 12 NGVD 29 Public supply 9Biscayne aquifer PAS biscpas1 BISCPAS1–26 FL Miami-Dade 6/13/2016 na na Observation 4Biscayne aquifer PAS biscpas1 BISCPAS1–27 FL Miami-Dade 6/14/2016 9.5 NGVD 29 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Biscayne aquifer PAS biscpas1 BISCPAS1–28 FL Miami-Dade 8/31/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–29 FL Miami-Dade 6/14/2016 8.2 NGVD 29 Public supply 1Biscayne aquifer PAS biscpas1 BISCPAS1–30 FL Miami-Dade 6/13/2016 na na Public supply 9Biscayne aquifer PAS biscpas1 BISCPAS1–31 FL Miami-Dade 8/31/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–32 FL Miami-Dade 6/8/2016 na na Public supply 3Biscayne aquifer PAS biscpas1 BISCPAS1–33 FL Miami-Dade 6/9/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–34 FL Miami-Dade 6/8/2016 na na Public supply nBiscayne aquifer PAS biscpas1 BISCPAS1–35 FL Miami-Dade 6/8/2016 na na Public supply 4Biscayne aquifer PAS biscpas1 BISCPAS1–36 FL Miami-Dade 6/7/2016 na na Public supply 6Biscayne aquifer PAS biscpas1 BISCPAS1–37 FL Miami-Dade 8/25/2016 na na Observation 6Biscayne aquifer PAS biscpas1 BISCPAS1–38 FL Miami-Dade 6/7/2016 5.3 NGVD 29 Observation 2Biscayne aquifer PAS biscpas1 BISCPAS1–39 FL Miami-Dade 6/6/2016 7 NGVD 29 Public supply 6Biscayne aquifer PAS biscpas1 BISCPAS1–40 FL Miami-Dade 6/6/2016 na na Public supply nCentral Valley aquifer system FPS cvalfps1 CVALFPS1–B1–1 CA Fresno 7/25/2013 353 NGVD 29 Observation 8Central Valley aquifer system FPS cvalfps1 CVALFPS1–B1–2 CA Fresno 7/25/2013 353 NGVD 29 Observation 1Central Valley aquifer system FPS cvalfps1 CVALFPS1–B1–3 CA Fresno 8/12/2013 353 NGVD 29 Observation 2Central Valley aquifer system FPS cvalfps1 CVALFPS1–B2.5–1 CA Fresno 9/25/2013 343 NGVD 29 Observation 1Central Valley aquifer system FPS cvalfps1 CVALFPS1–B2.5–2 CA Fresno 9/25/2013 343 NGVD 29 Observation 1Central Valley aquifer system FPS cvalfps1 CVALFPS1–B2–1 CA Fresno 9/10/2013 350 NGVD 29 Observation 8Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B2–2 CA Fresno 9/9/2013 351 NGVD 29 Observation 8

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B2–3 CA Fresno 9/9/2013 351 NGVD 29 Observation 1
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B3–1 CA Fresno 8/13/2013 342 NGVD 29 Observation 7

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B3–2 CA Fresno 8/14/2013 342 NGVD 29 Observation 1

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B3–3 CA Fresno 8/13/2013 342 NGVD 29 Observation 1

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B3–4 CA Fresno 8/14/2013 342 NGVD 29 Observation 1

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B3–5 CA Fresno 8/13/2013 342 NGVD 29 Observation 2

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B4–1 CA Fresno 8/19/2013 337 NGVD 29 Observation 7

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B4–2 CA Fresno 8/20/2013 337 NGVD 29 Observation 1

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B4–3 CA Fresno 8/20/2013 337 NGVD 29 Observation 1

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B4–4 CA Fresno 8/19/2013 337 NGVD 29 Observation 2

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B5–1 CA Fresno 7/23/2013 329 NGVD 29 Observation 8

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B5–2 CA Fresno 7/23/2013 329 NGVD 29 Observation 1

Central Valley 

aquifer system

FPS cvalfps1 CVALFPS1–B5–3 CA Fresno 7/24/2013 329 NGVD 29 Observation 2

Central Valley 

aquifer system

FPS cvalfps2 CVALFPS2–FPC1–

shallow

CA Fresno 7/24/2013 356 NGVD 29 Domestic 1

Central Valley 

aquifer system

FPS cvalfps2 CVALFPS2–FPC2–

deep

CA Fresno 8/28/2013 328 NAVD 88 Public supply 4

Central Valley 

aquifer system

FPS cvalfps2 CVALFPS2–FPC2–

shallow

CA Fresno 7/22/2013 331 NGVD 29 Domestic 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPC3– shallow CA Fresno 9/17/2013 312 NAVD 88 Domestic 1Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPF1– deep CA Fresno 8/27/2013 300 NAVD 88 Public supply 5Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPF1– shallow CA Fresno 9/11/2013 300 NAVD 88 Observation 1Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPF2– deep CA Fresno 8/27/2013 313 NAVD 88 Public supply 4Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPF2– shallow CA Fresno 9/11/2013 313 NAVD 88 Observation 1Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR1– deep CA Fresno 8/28/2013 298 NAVD 88 Public supply 4Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR1– shallow CA Fresno 9/24/2013 295 NAVD 88 Domestic 1Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR2– deep CA Fresno 9/19/2013 275 NAVD 88 Public supply 3Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR2– shallow CA Fresno 8/22/2013 261 NAVD 88 Domestic 1Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR3– deep CA Fresno 9/18/2013 232 NAVD 88 Other 5Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR3– shallow CA Fresno 8/29/2013 231 NAVD 88 Public supply 2Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR4– deep CA Fresno 8/26/2013 214 NAVD 88 Other 5Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR4– shallow CA Fresno 9/18/2013 213 NAVD 88 Domestic 2Central Valley aquifer system FPS cvalfps2 CVALFPS2–FPR5– deep CA Fresno 8/26/2013 193 NAVD 88 Other 5Central Valley 

aquifer system

FPS cvalfps2 CVALFPS2–FPR5–

shallow

CA Fresno 9/12/2013 205 NAVD 88 Domestic 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Central Valley 

aquifer system

FPS cvalfps2 CVALFPS2–FPR6–

deep

CA Fresno 8/21/2013 170 NGVD 29 Other 5

Central Valley 

aquifer system

FPS cvalfps2 CVALFPS2–FPR6–

shallow

CA Fresno 9/16/2013 188 NAVD 88 Domestic 3

Central Valley 

aquifer system

ETN cvaletn1 CVALETN1–01 CA Fresno 3/3, 3/17, 

5/17, 11/17, 12/7/2016

308 NAVD 88 Public supply 6

Central Valley 

aquifer system

ETN/ cvaletn1/ CVALETN1–02/ CA Fresno 9/17/2013 

(fps2); 3/3, 7/17/2016

308 NAVD 88 Public supply 3

FPS cvalfps2 CVALFPS2––FPC3–

deep

Central Valley 

aquifer system

ETN cvaletn1 CVALETN1–03 CA Fresno 3/17, 5/17, 8/24, 

12/7/2016

308 NAVD 88 Observation 2

Columbia 

Plateau basin-fill and basaltic-rock aquifers

ETN clptetn1 CLPTETN1–01 OR Morrow 1/20, 3/18, 

4/21, 6/22, 7/28, 9/8, 11/17/2016

292 NGVD 29 Domestic 8

Columbia 

Plateau basin-fill and basaltic-rock aquifers

ETN clptetn1 CLPTETN1–05 OR Umatilla 3/17, 4/21, 6/23, 

9/9, 10/25, 11/18/2016

616 NAVD 88 Other 1

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–01 WA Whitman 8/4/2016 1,557.90 NAVD 88 Public supply 2

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–02 WA Yakima 6/27/2016 1,110 NGVD 29 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–03 WA Grant 6/28/2016 533 NAVD 88 Public supply 9Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–04 WA Kittitas 6/29/2016 1,570 NAVD 88 Public supply nColumbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–05 WA Kittitas 6/30/2016 1,625 NAVD 88 Public supply 7Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–06 WA Kittitas 6/30/2016 2,240 NGVD 29 Public supply 5Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–07 WA Adams 7/11/2016 931 NAVD 88 Public supply 2Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–08 WA Grant 7/11/2016 1,129 NAVD 88 Public supply 3Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–09 WA Franklin 7/12/2016 860 NGVD 29 Public supply 1Columbia Plateau 

basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–10 WA Benton 7/13/2016 na na Public supply n
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–11 WA Yakima 7/14/2016 745 NGVD 29 Public supply 3

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–12 WA Grant 7/18/2016 1,317 NAVD 88 Public supply 4

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–13 WA Douglas 7/19/2016 2,760 NAVD 88 Public supply 6

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–14 WA Lincoln 7/20/2016 1,692 NGVD 29 Public supply 5

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–15 WA Grant 7/21/2016 1,122 NAVD 88 Public supply 4

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–16 WA Grant 7/21/2016 1,175 NAVD 88 Public supply 7

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–17 WA Lincoln 7/25/2016 1,922 NGVD 29 Public supply 1

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–18 WA Lincoln 7/25/2016 2,480 NGVD 29 Public supply 4
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–19 WA Lincoln 7/26/2016 2,141 NGVD 29 Public supply 3Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–20 WA Spokane 7/26/2016 2,444 NAVD 88 Public supply 2Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–21 WA Spokane 7/27/2016 2,364 NAVD 88 Public supply 7Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–22 WA Grant 7/28/2016 1,210 NAVD 88 Public supply 1Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–23 WA Adams 8/1/2016 1,979 NGVD 29 Public supply 7Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–24 WA Whitman 8/1/2016 1,984 NAVD 88 Public supply 2Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–25 WA Whitman 8/2/2016 2,578 NAVD 88 Public supply 3Columbia Plateau 

basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–26 WA Garfield 8/3/2016 1,891 NAVD 88 Public supply 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–27 WA Garfield 8/3/2016 1,892 NAVD 88 Public supply 9

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–28 WA Walla Walla 8/8/2016 1,624 NAVD 88 Public supply 5

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–29 WA Walla Walla 8/8/2016 1,062 NAVD 88 Public supply 4

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–30 WA Walla Walla 8/9/2016 802 NAVD 88 Public supply 7

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–31 WA Walla Walla 8/9/2016 802 NAVD 88 Public supply 7

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–32 WA Franklin 8/10/2016 723 NAVD 88 Public supply 3

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–33 WA Klickitat 8/16/2016 2,168 NAVD 88 Public supply 2

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–34 WA Klickitat 8/17/2016 1,381 NAVD 88 Public supply 6
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia Plateau basin-fill and basaltic-rock aquifers PAS/ clptpas1/ CLPTPAS1–35/ OR Umatilla 1/21, 3/18, 4/20, 6/23, 7/27, 9/9, 11/18/2016 693 NAVD 88 Public supply 1

ETN clptetn1 CLPTETN1–04 Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–36 OR Wasco 7/11/2016 168 NAVD 88 Public supply 2

Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–37 OR Union 7/12/2016 2,798 NAVD 88 Public supply 2Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–38 OR Wallowa 7/13/2016 3,832 NAVD 88 Public supply 1Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–39 OR Wallowa 7/14/2016 2,947 NAVD 88 Public supply 8Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–40 OR Gilliam 7/25/2016 463 NAVD 88 Public supply 4Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–41 OR Union 7/26/2016 2,730 NGVD 29 Public supply 6Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–42 OR Umatilla 8/1/2016 485 NGVD 29 Public supply 7
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–43 OR Union 8/2/2016 2,792 NGVD 29 Public supply 1

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–44 OR Morrow 8/3/2016 2,700 NGVD 29 Public supply 4

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–45 OR Wasco 8/4/2016 3,070 NAVD 88 Public supply 1

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–46 OR Umatilla 8/8/2016 1,649 NAVD 88 Public supply 3

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–47 OR Umatilla 8/9/2016 3,403 NGVD 29 Public supply 5

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–48 OR Umatilla 8/10/2016 1,005 NAVD 88 Public supply 5

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–49 OR Gilliam 8/11/2016 2,300 NGVD 29 Public supply 1

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–50 OR Wasco 8/22/2016 13,40 NGVD 29 Public supply 6
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–51 OR Morrow 8/23/2016 1,100 NGVD 29 Public supply 6Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–52 OR Grant 8/24/2016 3,710 NGVD 29 Public supply 2Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–53 OR Wasco 8/25/2016 1,765 NAVD 88 Public supply 3Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–54 OR Sherman 9/1/2016 2,292 NAVD 88 Public supply 4Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–55 WA Asotin 8/4/2016 1,180 NGVD 29 Public supply 1Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–56 ID Idaho 8/1/2016 1,440 NGVD 29 Public supply 1Columbia Plateau basaltic-rock aquifers PAS clptpas1 CLPTPAS1–57 ID Idaho 8/2/2016 3,255 NAVD 88 Public supply 1Columbia Plateau 

basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–58 ID Idaho 8/3/2016 3,752 NAVD 88 Public supply n
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–59 ID Lewis 8/3/2016 3,917 NAVD 88 Public supply 3

Columbia 

Plateau basaltic-rock aquifers

PAS clptpas1 CLPTPAS1–60 ID Latah 8/4/2016 2,617 NGVD 29 Public supply 1

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–02 WA Grant 7/30/2014 1,246 NAVD 88 Observation 2

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–03 WA Franklin 8/18/2014 457 NAVD 88 Public supply 1

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–06 WA Franklin 8/26/2014 423 NAVD 88 Observation 8

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–08 WA Grant 9/19/2014 1,236 NAVD 88 Observation 1

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–09 WA Grant 7/24/2014 1,202 NAVD 88 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–10 WA Grant 7/24/2014 1,117 NAVD 88 Observation 3Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–11 WA Adams 7/23/2014 1,256.40 NAVD 88 Public supply 4Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–12 WA Grant 7/23/2014 1,144 NAVD 88 Observation 3Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–13 WA Grant 7/28/2014 1,063 NAVD 88 Public supply 1Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–14 WA Grant 7/30/2014 1,132 NAVD 88 Observation 2Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–16 WA Adams 8/12/2014 1,205.10 NAVD 88 Observation 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–17 WA Adams 9/11/2014 1,044 NAVD 88 Public supply 4

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–18 WA Adams 8/12/2014 1,078.80 NAVD 88 Observation 3

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–19 WA Franklin 8/11/2014 976.1 NAVD 88 Observation 1

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–20 WA Franklin 8/26/2014 728.6 NAVD 88 Public supply 7

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–21 WA Franklin 8/27/2014 927 NAVD 88 Public supply 1

Columbia 

Plateau basin-fill and basaltic-rock aquifers

VFPS clptvfps1 CLPTVFPS1–22 WA Franklin 8/19/2014 713.3 NAVD 88 Observation 4
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–23 WA Douglas 7/7/2014 640.5 NAVD 88 Public supply 3Columbia Plateau basin-fill and basaltic-rock aquifers VFPS clptvfps1 CLPTVFPS1–24 WA Franklin 8/25/2014 926.9 NAVD 88 Public supply 1Edwards-Trinity aquifer system ETN edtretn1 EDTRETN1–01 TX Bexar 11/12, 12/17/2013; 2/10, 4/1, 7/17, 8/28, 9/2, 11/24/2014; 1/27, 3/24, 5/20, 6/4, 8/18, 10/22, 12/9/2015; 3/10, 5/9, 6/2, 8/23, 10/19/2016 946 NAVD 88 Public supply 5
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Edwards-Trinity 

aquifer system

ETN edtretn1 EDTRETN1–02 TX Bexar 11/13, 

12/16/2013; 2/11, 3/31, 6/2, 7/16, 9/3, 11/25/2014; 1/26, 3/25, 5/15, 6/3, 8/19, 10/21, 12/7/2015; 3/9, 4/21, 6/1, 8/22, 10/18/2016

975 NGVD 29 Observation 3

Edwards-Trinity 

aquifer system

ETN edtretn1 EDTRETN1–03 TX Bexar 11/14, 

12/17/2013; 2/10, 4/1, 6/3, 7/16, 9/2, 11/24/2014; 1/27, 3/26, 5/27, 6/4, 8/18, 10/22, 12/8/2015; 3/10, 5/13, 6/2, 8/23, 10/19/2016

585 NAVD 88 Public supply 1

Glacial aquifer 

and New England crystallinerock aquifers

ETN negxetn1 NEGXETN1–01 NH Rockingham 1/13, 3/11, 5/24, 

7/20, 9/30, 11/16/2016

60 NGVD 29 Public supply 8

Glacial aquifer 

and New England crystallinerock aquifers

ETN negxetn1 NEGXETN1–02 NH Rockingham 1/13, 3/11, 5/23, 

7/13, 9/30, 11/16/2016

80 NGVD 29 Public supply 4
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer and New England crystalline- rock aquifers ETN negxetn1 NEGXETN1–03 NH Rockingham 1/13, 3/9, 5/25, 7/12, 9/29, 11/17/2016 110 NGVD 29 Domestic 1Glacial aquifer system MSS glacmss1 GLACMSS1–01_SD WI Columbia 8/21/2014 824 NGVD 29 Public supply 1Glacial aquifer system MSS glacmss1 GLACMSS1–01_TD WI Marquette 9/15/2014 865 NGVD 29 Other 1Glacial aquifer system MSS glacmss1 GLACMSS1–01_TS WI Marquette 8/20/2014 na na Observation nGlacial aquifer system MSS glacmss1 GLACMSS1–02_SD WI Waupaca 8/12/2014 na na Public supply 1Glacial aquifer system MSS glacmss1 GLACMSS1–02_TS WI Waushara 8/20/2014 na na Observation nGlacial aquifer system MSS glacmss1 GLACMSS1–02a_PD WI Portage 8/11/2014 na na Public supply 1Glacial aquifer system MSS glacmss1 GLACMSS1–02a_PS WI Portage 8/12/2014 1,090 NAVD 88 Domestic 6Glacial aquifer system MSS glacmss1 GLACMSS1–02b_PD WI Waupaca 8/11/2014 844.5 NGVD 29 Public supply 8Glacial aquifer system MSS glacmss1 GLACMSS1–02b_PS WI Waupaca 9/16/2014 833 NAVD 88 Other 4Glacial aquifer system MSS glacmss1 GLACMSS1–03_SD WI Shawano 8/13/2014 1,160 NGVD 29 Public supply 5Glacial aquifer system MSS glacmss1 GLACMSS1–03_TD WI Shawano 9/24/2014 1,026 NAVD 88 Public supply 8Glacial aquifer 

system

MSS glacmss1 GLACMSS1–03_TS WI Shawano 9/22/2014 1,287 NAVD 88 Other 6

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–04_SD WI Forest 8/13/2014 1,637 NGVD 29 Public supply 9
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–04_TS WI Oconto 9/22/2014 1,348 NAVD 88 Domestic 7

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–05_SD WI Langlade 8/18/2014 1,290 NGVD 29 Public supply 9

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–05_TD WI Oconto 9/22/2014 1,362 NAVD 88 Domestic 1

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–05_TS WI Langlade 9/22/2014 1,317 NAVD 88 Public supply 5

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–06_PD WI Waupaca 8/11/2014 780 NGVD 29 Public supply 9

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–06_PS WI Waupaca 9/17/2014 792 NAVD 88 Other 1

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–06_SD WI Waupaca 9/23/2014 852 NGVD 29 Public supply 1

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–06_TS WI Waupaca 9/24/2014 907 NAVD 88 Domestic 8

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–07_SD WI Waupaca 8/19/2014 760 NGVD 29 Public supply 1

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–07_TS WI Waushara 9/24/2014 788 NAVD 88 Other 6

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–08_SD WI Outagamie 9/16/2014 800 NGVD 29 Public supply 1

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–08_TS WI Shawano 9/16/2014 812 NAVD 88 Other 6

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–09_SD WI Oconto 8/19/2014 810 NGVD 29 Public supply 2

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–09_TD WI Shawano 9/23/2014 838 NGVD 29 Public supply 1

Glacial aquifer 

system

MSS glacmss1 GLACMSS1–09_TS WI Oconto 9/23/2014 741 NAVD 88 Domestic 5
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer system MSS glacmss1 GLACMSS1–10_SD WI Marinette 8/18/2014 697 NAVD 88 Public supply 5Glacial aquifer system MSS glacmss1 GLACMSS1–10_TS WI Oconto 8/18/2014 na na Observation nGlacial aquifer system MAS lerisus1 LERISUS1–01 MI Livingston 8/4/2016 951 NGVD 29 Domestic 1Glacial aquifer system MAS lerisus1 LERISUS1–02 MI Livingston 8/4/2016 987 NGVD 29 Domestic 7Glacial aquifer system MAS lerisus1 LERISUS1–03 MI Oakland 8/8/2016 943 NGVD 29 Domestic 7Glacial aquifer system MAS lerisus1 LERISUS1–04 MI Lenawee 8/22/2016 808 NGVD 29 Domestic 1Glacial aquifer system MAS lerisus1 LERISUS1–05 MI Hillsdale 8/22/2016 900 NAVD 88 Domestic 5Glacial aquifer system MAS lerisus1 LERISUS1–06 MI Hillsdale 8/23/2016 1,048 NGVD 29 Domestic 9Glacial aquifer system MAS lerisus1 LERISUS1–07 MI Hillsdale 8/23/2016 922 NGVD 29 Domestic 9Glacial aquifer system MAS lerisus1 LERISUS1–08 MI Lenawee 8/24/2016 865 NGVD 29 Domestic 8Glacial aquifer system MAS lerisus1 LERISUS1–09 MI Lenawee 8/24/2016 863 NGVD 29 Domestic 8Glacial aquifer system MAS lerisus1 LERISUS1–10 MI St. Clair 9/6/2016 788 NGVD 29 Domestic 1Glacial aquifer system MAS lerisus1 LERISUS1–11 MI Sanilac 9/6/2016 814 NGVD 29 Domestic 5Glacial aquifer system MAS lerisus1 LERISUS1–12 MI Lapeer 9/6/2016 833 NAVD 88 Domestic 1Glacial aquifer 

system

MAS lerisus1 LERISUS1–13 MI Oakland 9/7/2016 815 NGVD 29 Domestic 7
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer 

system

MAS lerisus1 LERISUS1–14 MI Lapeer 9/8/2016 862 NGVD 29 Domestic 1

Glacial aquifer 

system

MAS lerisus1 LERISUS1–15 MI Macomb 9/8/2016 630 NGVD 29 Domestic 3

Glacial aquifer 

system

MAS lerisus1 LERISUS1–16 MI Oakland 9/12/2016 1,117 NGVD 29 Domestic 1

Glacial aquifer 

system

MAS lerisus1 LERISUS1–17 MI Sanilac 9/12/2016 812 NGVD 29 Domestic 2

Glacial aquifer 

system

MAS lerisus1 LERISUS1–18 MI Oakland 9/12/2016 1,020 NGVD 29 Domestic 1

Glacial aquifer 

system

MAS lerisus1 LERISUS1–19 MI Washtenaw 9/13/2016 995 NGVD 29 Domestic 9

Glacial aquifer 

system

MAS lerisus1 LERISUS1–20 MI Washtenaw 9/13/2016 921 NGVD 29 Domestic 1

Glacial aquifer 

system

MAS lerisus1 LERISUS1–21 OH Williams 8/23/2016 927 NGVD 29 Domestic 7

Glacial aquifer 

system

MAS lerisus1 LERISUS1–22 OH Williams 8/23/2016 980 NGVD 29 Domestic 8

Glacial aquifer 

system

MAS lerisus1 LERISUS1–23 OH Williams 8/24/2016 912 NGVD 29 Domestic 7

Glacial aquifer 

system

MAS lerisus1 LERISUS1–24 OH Williams 8/24/2016 863 NGVD 29 Domestic 1

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–01 WA Pierce 8/22/2016 1,450 NGVD 29 Domestic 7

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–02 WA Pierce 8/22/2016 480 NGVD 29 Domestic 7

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–03 WA Pierce 8/23/2016 360 NGVD 29 Domestic 4

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–04 WA Pierce 8/23/2016 360 NGVD 29 Domestic 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer system MAS pugtsus1 PUGTSUS1–05 WA Snohomish 8/25/2016 1,040 NGVD 29 Domestic 1Glacial aquifer system MAS pugtsus1 PUGTSUS1–06 WA Snohomish 8/25/2016 220 NGVD 29 Domestic 7Glacial aquifer system MAS pugtsus1 PUGTSUS1–07 WA Pierce 9/6/2016 720 NGVD 29 Domestic 4Glacial aquifer system MAS pugtsus1 PUGTSUS1–08 WA Thurston 9/6/2016 430 NGVD 29 Domestic 5Glacial aquifer system MAS pugtsus1 PUGTSUS1–09 WA Thurston 9/13/2016 14 NGVD 29 Domestic 7Glacial aquifer system MAS pugtsus1 PUGTSUS1–10 WA Thurston 9/13/2016 210 NGVD 29 Domestic 6Glacial aquifer system MAS pugtsus1 PUGTSUS1–11 WA Snohomish 9/15/2016 230 NGVD 29 Domestic 7Glacial aquifer system MAS pugtsus1 PUGTSUS1–12 WA Whatcom 9/21/2016 144 NGVD 29 Domestic 2Glacial aquifer system MAS pugtsus1 PUGTSUS1–13 WA Whatcom 9/21/2016 65 NGVD 29 Domestic 2Glacial aquifer system MAS pugtsus1 PUGTSUS1–14 WA King 11/1/2016 320 NGVD 29 Domestic 7Glacial aquifer system MAS pugtsus1 PUGTSUS1–15 WA Snohomish 11/8/2016 240 NGVD 29 Domestic 4Glacial aquifer system MAS pugtsus1 PUGTSUS1–16 WA Kitsap 11/14/2016 67 NAVD 88 Domestic 8Glacial aquifer system MAS pugtsus1 PUGTSUS1–17 WA Whatcom 11/16/2016 60 NGVD 29 Domestic 4Glacial aquifer system MAS pugtsus1 PUGTSUS1–18 WA Whatcom 11/16/2016 130 NGVD 29 Domestic 9Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–19 WA King 11/17/2016 346 NAVD 88 Public supply 2
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–20 WA Kitsap 11/21/2016 304 NAVD 88 Domestic 3

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–21 WA Mason 11/21/2016 262 NAVD 88 Domestic 3

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–22 WA Mason 11/22/2016 480 NAVD 88 Domestic 6

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–23 WA Mason 11/22/2016 227 NAVD 88 Domestic 1

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–24 WA Kitsap 11/28/2016 219 NAVD 88 Other 2

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–25 WA King 11/29/2016 532 NGVD 29 Public supply 8

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–26 WA King 12/5/2016 47 NGVD 29 Public supply 9

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–27 WA King 12/5/2016 40 NGVD 29 Public supply 8

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–28 WA Pierce 12/7/2016 261 NAVD 88 Public supply 1

Glacial aquifer 

system

MAS pugtsus1 PUGTSUS1–29 WA Thurston 12/7/2016 29 NAVD 88 Public supply 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–01 MN Anoka 7/11/2016 857.9 NAVD 88 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–02 MN Anoka 7/12/2016 856.9 NGVD 29 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–03 MN Anoka 7/12/2016 894.7 NAVD 88 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–04 MN Anoka 7/13/2016 879.1 NAVD 88 Observation 2

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–05 MN Anoka 7/13/2016 877.4 NGVD 29 Observation 2
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer system LUS umislusrc1 UMISLUSRC1–06 MN Anoka 7/14/2016 854.1 NAVD 88 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–07 MN Hennepin 7/14/2016 849.1 NGVD 29 Observation 9 Glacial aquifer system LUS umislusrc1 UMISLUSRC1–08 MN Hennepin 8/1/2016 867.9 NGVD 29 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–09 MN Hennepin 8/2/2016 858.4 NGVD 29 Observation 2Glacial aquifer system LUS umislusrc1 UMISLUSRC1–10 MN Hennepin 8/2/2016 910.8 NGVD 29 Observation 2Glacial aquifer system LUS umislusrc1 UMISLUSRC1–11 MN Anoka 8/3/2016 862 NGVD 29 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–12 MN Hennepin 8/4/2016 872.8 NGVD 29 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–13 MN Hennepin 8/15/2016 841.8 NAVD 88 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–14 MN Hennepin 8/15/2016 866.6 NGVD 29 Observation 2Glacial aquifer system LUS umislusrc1 UMISLUSRC1–15 MN Hennepin 8/16/2016 845.8 NAVD 88 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–16 MN Hennepin 8/17/2016 880.6 NAVD 88 Observation 2Glacial aquifer system LUS umislusrc1 UMISLUSRC1–17 MN Hennepin 8/18/2016 872.3 NAVD 88 Observation 2Glacial aquifer system LUS umislusrc1 UMISLUSRC1–18 MN Hennepin 8/22/2016 864.9 NAVD 88 Observation 1Glacial aquifer system LUS umislusrc1 UMISLUSRC1–19 MN Hennepin 8/22/2016 857.7 NGVD 29 Observation 1Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–20 MN Anoka 8/23/2016 895.7 NGVD 29 Observation 2
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–21 MN Hennepin 8/23/2016 844.1 NGVD 29 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–22 MN Hennepin 8/23/2016 863.3 NGVD 29 Observation 2

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–23 MN Hennepin 8/24/2016 868.1 NGVD 29 Observation 2

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–24 MN Hennepin 8/25/2016 876.2 NGVD 29 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–25 MN Hennepin 8/25/2016 851 NGVD 29 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–26 MN Hennepin 8/25/2016 849.6 NGVD 29 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–27 MN Hennepin 8/29/2016 875.2 NGVD 29 Observation 2

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–28 MN Hennepin 8/29/2016 850.4 NGVD 29 Observation 3

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–29 MN Anoka 8/30/2016 867.9 NGVD 29 Observation 1

Glacial aquifer 

system

LUS umislusrc1 UMISLUSRC1–30 MN Hennepin 9/8/2016 857.9 NGVD 29 Observation 9 

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–01/ MI Livingston 8/1/2016 895 NGVD 29 Observation 2VFPS glacvfps1 GLACVFPS1–11

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–02/ MI Livingston 8/1/2016 880 NGVD 29 Observation 2VFPS glacvfps1 GLACVFPS1–09

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–03/ MI Livingston 8/2/2016 905 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–07

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–04/ MI Washtenaw 8/2/2016 870 NAVD 88 Observation 2VFPS glacvfps1 GLACVFPS1–06
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–05/ MI Livingston 8/3/2016 925 NGVD 29 Observation 2

VFPS glacvfps1 GLACVFPS1–18 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–06/ MI Livingston 8/3/2016 935 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–19 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–07/ MI Livingston 8/3/2016 995 NGVD 29 Observation 3VFPS glacvfps1 GLACVFPS1–20 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–08/ MI Oakland 8/9/2016 915 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–13 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–09/ MI Oakland 8/10/2016 935 NGVD 29 Observation 2VFPS glacvfps1 GLACVFPS1–25 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–10/ MI Oakland 8/11/2016 940 NAVD 88 Observation 4VFPS glacvfps1 GLACVFPS1–22 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–11/ MI Livingston 8/15/2016 935 NGVD 29 Observation 4VFPS glacvfps1 GLACVFPS1–16 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–12/ MI Livingston 8/15/2016 1,010 NGVD 29 Observation 6VFPS glacvfps1 GLACVFPS1–27 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–13/ MI Oakland 8/16/2016 990 NGVD 29 Observation 3VFPS glacvfps1 GLACVFPS1–33 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–14/ MI Oakland 8/16/2016 1,035 NGVD 29 Observation 3VFPS glacvfps1 GLACVFPS1–29 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–15/ MI Oakland 8/16/2016 1,025 NGVD 29 Observation 2VFPS glacvfps1 GLACVFPS1–31 Glacial aquifer system LUS/ lerilusrc1/ LERILUSRC1–16/ MI Oakland 8/17/2016 1,045 NGVD 29 Observation 4VFPS glacvfps1 GLACVFPS1–35 Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–17/ MI Oakland 8/17/2016 1,040 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–39
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–18/ MI Oakland 8/18/2016 1,045 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–42

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–19/ MI Oakland 9/7/2016 1,055 NAVD 88 Observation 3VFPS glacvfps1 GLACVFPS1–38

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–20/ MI Macomb 9/7/2016 840 NAVD 88 Observation 3VFPS glacvfps1 GLACVFPS1–34

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–21/ MI Oakland 9/7/2016 995 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–37

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–22/ MI Oakland 9/13/2016 920 NGVD 29 Observation 3VFPS glacvfps1 GLACVFPS1–23

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–23/ MI Oakland 9/14/2016 1,045 NGVD 29 Observation 3VFPS glacvfps1 GLACVFPS1–41

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–24/ MI Washtenaw 9/14/2016 845 NGVD 29 Observation 2VFPS glacvfps1 GLACVFPS1–01

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–25/ MI Washtenaw 9/15/2016 835 NGVD 29 Observation 2VFPS glacvfps1 GLACVFPS1–03

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–26/ MI Washtenaw 9/15/2016 855 NGVD 29 Observation 3VFPS glacvfps1 GLACVFPS1–04

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–27/ MI Oakland 9/22/2016 768 NAVD 88 Observation 2VFPS glacvfps1 GLACVFPS1–32

Glacial aquifer 

system

LUS/ lerilusrc1/ LERILUSRC1–28/ MI Oakland 9/27/2016 875 NGVD 29 Observation 1VFPS glacvfps1 GLACVFPS1–15

Glacial aquifer 

system

VFPS glacvfps1 GLACVFPS1–02 MI Washtenaw 9/14/2016 845 NGVD 29 Domestic 1

Glacial aquifer 

system

VFPS glacvfps1 GLACVFPS1–05 MI Washtenaw 9/15/2016 855 NGVD 29 Domestic 1

Glacial aquifer 

system

VFPS glacvfps1 GLACVFPS1–08 MI Livingston 8/2/2016 910 NGVD 29 Domestic 4
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer system VFPS glacvfps1 GLACVFPS1–10 MI Livingston 8/1/2016 880 NGVD 29 Domestic 6Glacial aquifer system VFPS glacvfps1 GLACVFPS1–12 MI Livingston 8/2/2016 895 NGVD 29 Domestic 1Glacial aquifer system VFPS glacvfps1 GLACVFPS1–14 MI Oakland 8/9/2016 915 NGVD 29 Domestic 3Glacial aquifer system VFPS glacvfps1 GLACVFPS1–17 MI Livingston 8/15/2016 935 NGVD 29 Domestic 1Glacial aquifer system VFPS glacvfps1 GLACVFPS1–21 MI Livingston 8/3/2016 995 NGVD 29 Domestic 9Glacial aquifer system VFPS glacvfps1 GLACVFPS1–24 MI Oakland 9/13/2016 920 NGVD 29 Domestic 1Glacial aquifer system VFPS glacvfps1 GLACVFPS1–26 MI Oakland 8/10/2016 933 NAVD 88 Domestic 6Glacial aquifer system VFPS glacvfps1 GLACVFPS1–28 MI Livingston 8/15/2016 1,000 NGVD 29 Domestic 1Glacial aquifer system VFPS glacvfps1 GLACVFPS1–30 MI Oakland 8/16/2016 1,035 NGVD 29 Domestic 9Glacial aquifer system VFPS glacvfps1 GLACVFPS1–36 MI Oakland 8/17/2016 1,035 NGVD 29 Domestic 1Glacial aquifer system VFPS glacvfps1 GLACVFPS1–40 MI Oakland 8/17/2016 1,040 NGVD 29 Domestic 1Glacial aquifer system VFPS glacvfps1 GLACVFPS1–43 MI Oakland 8/18/2016 1,045 NGVD 29 Domestic 8Glacial aquifer system ETN/ glacetn1/ GLACETN1–01/ WI Portage 6/9/2014; 2/17, 5/6, 6/29, 8/2, 9/5/2016 1,135.70 NAVD 88 Observation 8

FPS glacfps1 GLACFPS1–17 Glacial aquifer 

system

ETN/ glacetn1/ GLACETN1–02/ WI Portage 6/9/2014; 2/17, 

5/6, 6/29, 8/2, 9/5/2016

1,135.70 NAVD 88 Observation 3

FPS glacfps1 GLACFPS1–18
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer 

system

ETN glacetn1 GLACETN1–03 WI Sauk 8/3/2016 723.4 NAVD 88 Observation 5

Glacial aquifer 

system

ETN glacetn1 GLACETN1–04 WI Sauk 8/26/2016 723.4 NAVD 88 Observation 8

Glacial aquifer 

system

ETN glacetn1 GLACETN1–05 WI Sauk 8/3/2016 730 NAVD 88 Public supply 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–01 WI Portage 6/11/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–02 WI Portage 6/11/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–03 WI Portage 6/12/2014 1,111 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–04 WI Portage 6/12/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–05 WI Portage 6/12/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–06 WI Portage 6/10/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–07 WI Portage 6/10/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–08 WI Portage 6/11/2014 1,100 NAVD 88 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–09 WI Portage 6/10/2014 1,109 NGVD 29 Observation 1

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–10 WI Portage 6/10/2014 1,109 NGVD 29 Observation 2

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–11 WI Portage 6/11/2014 1,109 NGVD 29 Observation 3

Glacial aquifer 

system

FPS glacfps1 GLACFPS1–12 WI Portage 6/11/2014 1,109 NGVD 29 Observation 4
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Glacial aquifer system FPS glacfps1 GLACFPS1–13 WI Portage 6/11/2014 1,109 NGVD 29 Observation 6Glacial aquifer system FPS glacfps1 GLACFPS1–14 WI Portage 6/11/2014 1,109 NGVD 29 Observation 7Glacial aquifer system FPS glacfps1 GLACFPS1–15 WI Portage 6/10/2014 1,135 NGVD 29 Observation 2Glacial aquifer system FPS glacfps1 GLACFPS1–16 WI Portage 6/10/2014 1,135 NGVD 29 Observation 5Glacial aquifer system FPS glacfps1 GLACFPS1–19 WI Portage 6/12/2014 1,148 NGVD 29 Observation 3Glacial aquifer system FPS glacfps1 GLACFPS1–20 WI Portage 6/12/2014 1,148 NGVD 29 Observation 4Glacial aquifer system FPS glacfps1 GLACFPS1–21 WI Portage 6/17/2014 1,148 NGVD 29 Observation 8Glacial aquifer system FPS glacfps1 GLACFPS1–22 WI Portage 6/18/2014 1,148 NGVD 29 Observation 8Glacial aquifer system FPS glacfps1 GLACFPS1–23 WI Portage 6/17/2014 1,148 NGVD 29 Observation 9Glacial aquifer system FPS glacfps1 GLACFPS1–24 WI Portage 6/16/2014 1,150 NGVD 29 Observation 3Glacial aquifer system FPS glacfps1 GLACFPS1–25 WI Portage 6/17/2014 1,150 NGVD 29 Observation 6Glacial aquifer system FPS glacfps1 GLACFPS1–26 WI Portage 6/16/2014 1,150 NGVD 29 Observation 9Glacial aquifer system FPS glacfps1 GLACFPS1–27 WI Portage 6/11/2014 1,100 NAVD 88 Observation 1High Plains aquifer PAS hpaqpas1 HPAQPAS1–64 TX Hockley 5/23/2016 3,508 NAVD 88 Public supply 2High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–65 TX Hale 5/24/2016 3,402 NGVD 29 Public supply 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–66 TX Bailey 5/25/2016 3,808 NGVD 29 Public supply 2

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–67 TX Lubbock 5/26/2016 3,186 NGVD 29 Public supply 3

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–68 TX Gaines 5/31/2016 3,341 NAVD 88 Public supply 2

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–69 TX Martin 5/31/2016 2,909 NGVD 29 Public supply 2

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–70 TX Martin 6/1/2016 2,683 NAVD 88 Public supply 1

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–71 TX Dawson 6/2/2016 3,020 NAVD 88 Public supply 1

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–72 TX Lipscomb 6/13/2016 2,624 NGVD 29 Public supply 4

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–73 TX Roberts 6/14/2016 3,089 NGVD 29 Public supply 7

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–74 TX Wheeler 6/15/2016 2,748 NAVD 88 Public supply 2

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–75 TX Carson 6/16/2016 3,529 NGVD 29 Public supply 8

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–76 TX Dallam 6/20/2016 4,620 NAVD 88 Public supply 3

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–77 TX Parmer 6/21/2016 4,017 NAVD 88 Public supply 3

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–78 TX Deaf Smith 6/21/2016 3,823 NGVD 29 Public supply 3

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–79 TX Moore 6/23/2016 3,666 NAVD 88 Public supply 6

High Plains 

aquifer

PAS hpaqpas1 HPAQPAS1–80 TX Sherman 7/13/2016 3,695 NAVD 88 Public supply 3
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–01 TN Hardeman 9/9/2013 500 NGVD 29 Observation 6Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–02 TN Fayette 9/11/2013 351 NAVD 88 Public supply 1Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–03 TN Fayette 9/10/2013 385 NGVD 29 Public supply 1Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–04 TN Fayette 9/10/2013 350 NGVD 29 Domestic 2Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–05 TN Shelby 9/11/2013 332 NGVD 29 Other 1Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–06 TN Shelby 8/21/2013 298 NGVD 29 Observation 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–07 TN Shelby 8/29/2013 316 NGVD 29 Public supply 5

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–09 MS Benton 8/15/2013 411 NAVD 88 Observation 2

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–10 TN Fayette 8/14/2013 421 NGVD 29 Domestic 9

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–11 TN Fayette 8/22/2013 432 NGVD 29 Domestic 2

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–12 TN Fayette 8/20/2013 311 NGVD 29 Public supply 1

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–13 TN Shelby 8/14/2013 383 NGVD 29 Public supply 2
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–14 TN Shelby 8/20/2013 352 NGVD 29 Public supply 2Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–15 TN Shelby 8/21/2013 360 NGVD 29 Public supply 3Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–16 TN Shelby 8/27/2013 280 NGVD 29 Public supply 5Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–18 TN Shelby 8/19/2013 290 NGVD 29 Observation 8Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–19 TN Shelby 9/12/2013 360 NGVD 29 Other 1Miss. Embayment- TX Coastal Uplands aqui- fer system FPS metxfps1 METXFPS1–20 TN Shelby 8/13/2013 300 NGVD 29 Observation 1
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–21 TN Shelby 8/12/2013 268 NGVD 29 Observation 7

Miss. 

EmbaymentTX Coastal Uplands aquifer system

FPS metxfps1 METXFPS1–22 TN Shelby 8/27/2013 259 NGVD 29 Observation 9

Miss. 

EmbaymentTX Coastal Uplands aquifer system

ETN/ metxetn1/ METXETN1–01/ TN Shelby 8/28/2013; 

4/26, 6/22, 8/22, 10/31, 12/13/2016

320 NGVD 29 Public supply 6

FPS metxfps1 METXFPS1–08

Miss. 

EmbaymentTX Coastal Uplands aquifer system

ETN/ metxetn1/ METXETN1–02/ TN Shelby 8/28, 11/19, 

12/16/2013; 2/3/2014, 2/29, 4/26, 6/22, 8/23, 10/31, 12/13/2016

310 NGVD 29 Observation 9

FPS metxfps1 METXFPS1–17

Northern Atlantic 

Coastal Plain aquifer system

ETN nacpetn1 NACPETN1–01 DE Sussex 3/2, 3/23, 4/26, 

6/28, 8/10, 10/4/2016

16 NGVD 29 Observation 2

Northern Atlantic 

Coastal Plain aquifer system

ETN nacpetn1 NACPETN1–02 DE Sussex 3/2, 4/26, 

8/9, 8/29, 10/5/2016

13.8 NGVD 29 Public supply 1

Northern Atlantic 

Coastal Plain aquifer system

ETN nacpetn1 NACPETN1–03 DE Sussex 3/3, 4/27, 5/6, 

6/29, 8/11, 10/4/2016

41.6 NGVD 29 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–01 DC District of Columbia 12/19/2016 60.6 NAVD 88 Observation 2Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–02 DC District of Columbia 12/6/2016 58.8 NAVD 88 Observation 2Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–03 DC District of Columbia 12/20/2016 142.6 NAVD 88 Observation 6Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–04 MD Prince George’s 8/22/2016 180 NAVD 88 Other 3Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–05 MD Prince George's 8/23/2016 185 NGVD 29 Other 4Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–06 MD Anne Arundel 8/24/2016 126 NGVD 29 Public supply 2Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–07 MD Prince George's 10/26/2016 212 NAVD 88 unknown 6Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–08 MD Charles 10/27/2016 204.6 NGVD 29 Other 3Northern Atlantic Coastal Plain aquifer system MSS nacpmss1 NACPMSS1–09 MD Anne Arundel 11/15/2016 70 NGVD 29 Other 2Northern Atlantic Coastal Plain 

aquifer system

MSS nacpmss1 NACPMSS1–10 MD Prince 

George's

11/16/2016 240.9 NGVD 29 Other 3
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–11 MD Prince 

George's

11/17/2016 240 NGVD 29 Other 6

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–12 MD Anne 

Arundel

11/21/2016 162.8 NAVD 88 Observation 2

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–13 MD Prince 

George's

11/21/2016 28 NAVD 88 unknown 4

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–14 MD Anne 

Arundel

11/22/2016 163.4 NAVD 88 Observation 1

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–15 MD Anne 

Arundel

11/22/2016 51 NAVD 88 Other 2

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–16 MD Anne 

Arundel

11/30/2016 35 NAVD 88 Other 3

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–17 MD Calvert 12/12/2016 137.9 NGVD 29 Domestic 3

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–18 MD Calvert 12/13/2016 138.8 NGVD 29 Observation 1

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–19 MD Prince 

George's

12/20/2016 95.8 NGVD 29 Observation 1

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss1 NACPMSS1–20 MD Anne 

Arundel

12/28/2016 51.3 NGVD 29 Observation 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–01 NY Suffolk 8/16/2016 75 NGVD 29 Public supply 3Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–02 NY Suffolk 8/17/2016 71 NGVD 29 Public supply 5Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–03 NY Suffolk 8/17/2016 108 NAVD 88 Domestic 7Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–04 NY Nassau 8/22/2016 145 NGVD 29 Public supply 2Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–05 NY Suffolk 8/23/2016 185 NGVD 29 Public supply 4Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–06 NY Suffolk 8/23/2016 157 NAVD 88 Domestic 4Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–07 NY Suffolk 8/24/2016 110 NGVD 29 Public supply 9Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–08 NY Suffolk 8/24/2016 120 NGVD 29 Public supply 3Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–09 NY Suffolk 8/25/2016 39 NAVD 88 Domestic 6Northern Atlantic Coastal Plain 

aquifer system

MSS nacpmss2 NACPMSS2–10 NY Nassau 8/30/2016 26 NGVD 29 Public supply 6
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–11 NY Nassau 8/31/2016 61 NGVD 29 Public supply 4

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–12 NY Nassau 8/31/2016 101 NGVD 29 Public supply 2

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–13 NY Nassau 9/1, 9/20/2016 253 NGVD 29 Public supply 5

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–14 NY Suffolk 9/7/2016 24 NAVD 88 Domestic 3

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–15 NY Suffolk 9/7/2016 26 NGVD 29 Observation 7

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–16 NY Suffolk 9/8/2016 12 NAVD 88 Domestic 2

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–17 NY Suffolk 9/12/2016 79 NGVD 29 Public supply 2

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–18 NY Suffolk 9/13/2016 26 NGVD 29 Public supply 7

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–19 NY Suffolk 9/14/2016 21 NAVD 88 Public supply 7

Northern Atlantic 

Coastal Plain aquifer system

MSS nacpmss2 NACPMSS2–20 NY Suffolk 9/15/2016 105 NGVD 29 Public supply 2
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–21 NY Suffolk 9/21/2016 61 NAVD 88 Domestic 2Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–22 NY Nassau 9/28/2016 98 NGVD 29 Public supply 3Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–23 NY Nassau 10/5/2016 235 NAVD 88 Public supply 7Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–24 NY Suffolk 10/11/2016 113 NAVD 88 Public supply 4Northern Atlantic Coastal Plain aquifer system MSS nacpmss2 NACPMSS2–25 NY Suffolk 10/18/2016 54 NAVD 88 Public supply 3Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–01 OK Cherokee 5/23/2016 965 NAVD 88 Public supply 4Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–02 OK Delaware 5/24/2016 891 NAVD 88 Public supply 3Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–03 OK Cherokee 6/13/2016 707 NAVD 88 Public supply nOzark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–04 OK Ottawa 6/14/2016 810 NGVD 29 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–05 MO Franklin 6/2/2016 905 NGVD 29 Public supply 7Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–06 MO Stoddard 4/27/2016 379 NAVD 88 Public supply 5Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–07 MO Reynolds 4/28/2016 859 NAVD 88 Public supply 6Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–08 MO Carter 5/3/2016 720 NGVD 29 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–09 MO Shannon 5/4/2016 986 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–10 MO Howell 5/17/2016 1,055 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–11 MO Oregon 5/18/2016 755 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–12 MO Howell 5/19/2016 1,414 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–13 MO Taney 5/23/2016 1,060 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–14 MO Taney 5/24/2016 1,010 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–15 MO Wright 5/25/2016 15,30 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–16 MO Howell 5/26/2016 1,165 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–17 MO Phelps 5/31/2016 1,192 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–18 MO Crawford 6/1/2016 1,027 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–19 MO Texas 6/6/2016 1,472 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–20 MO Gasconade 6/7/2016 987 NGVD 29 Public supply 8

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–21 MO Taney 6/7/2016 981 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–22 MO Franklin 6/8/2016 598 NGVD 29 Public supply 9

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–23 MO Barry 6/8/2016 1,497 NGVD 29 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–24 MO Jasper 6/9/2016 1,054 NAVD 88 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–25 MO Pulaski 6/9/2016 1,110 NGVD 29 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–26 MO Jasper 6/14/2016 1,149 NAVD 88 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–27 MO Texas 6/14/2016 1,267 NGVD 29 Public supply 9Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–28 MO Barry 6/15/2016 1,480 NGVD 29 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–29 MO Dent 6/15, 7/7/2016 1,380 NGVD 29 Public supply 8Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–30 MO McDonald 6/16/2016 1,040 NGVD 29 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–31 MO Webster 6/27/2016 1,490 NGVD 29 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–32 MO Cole 6/29/2016 815 NAVD 88 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–33 MO Cole 6/30/2016 795 NGVD 29 Public supply 7Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–34 MO Jefferson 7/6, 8/17/2016 800 NGVD 29 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–35 MO Barton 7/11/2016 995 NAVD 88 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–36 MO Barton 7/12/2016 1,020 NGVD 29 Public supply 9Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–37 MO Vernon 7/13/2016 985 NAVD 88 Public supply 1Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–38 MO St. Clair 7/14/2016 822 NGVD 29 Public supply 8
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–39 MO Hickory 7/18/2016 277 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–40 MO Dallas 7/19/2016 1,195 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–41 MO Christian 7/20/2016 1,340 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–42 MO Camden 7/21/2016 1,040 NGVD 29 Public supply 5

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–43 MO Morgan 7/25/2016 1,070 NGVD 29 Public supply 5

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–44 MO Benton 7/26/2016 952 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–45 MO Pettis 7/27/2016 865 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–46 MO Cooper 7/28/2016 855 NGVD 29 Public supply 6

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–47 MO Laclede 8/16/2016 1,357 NAVD 88 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–48 MO Perry 8/22, 8/24/2016 652 NAVD 88 Public supply 5

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–49 MO Cape 

Girardeau

8/23/2016 450 NGVD 29 Public supply 2

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–50 MO Cape 

Girardeau

8/24/2016 na na Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–51 MO Ste. 

Genevieve

8/25/2016 770 NGVD 29 Public supply 1

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–52 AR Lawrence 4/25/2016 318 NGVD 29 Public supply 5

Ozark Plateaus 

aquifer system

PAS ozrkpas1 OZRKPAS1–53 AR Sharp 4/26/2016 653 NAVD 88 Public supply 1
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–54 AR Sharp 5/2/2016 1,667 NGVD 29 Public supply nOzark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–55 AR Stone 5/5/2016 980 NGVD 29 Public supply 3Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–56 AR Baxter 5/31/2016 720 NAVD 88 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–57 AR Boone 6/1/2016 1,146 NAVD 88 Public supply 1Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–58 AR Newton 6/1/2016 1,344.50 NGVD 29 Public supply 2Ozark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–59 AR Madison 6/6/2016 1,446 NGVD 29 Public supply nOzark Plateaus aquifer system PAS ozrkpas1 OZRKPAS1–60 KS Crawford 6/13/2016 887 NGVD 29 Public supply 1Rio Grande aqui- fer system ETN rgaqetn1 RGAQETN1–02 NM Dona Ana 5/10/2016 4,058 NAVD 88 Other 6Rio Grande aqui- fer system ETN rgaqetn1 RGAQETN1–03 NM Sierra 5/9/2016 4,134 NAVD 88 Observation 2Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–01 NM Dona Ana 4/4/2016 4,000.70 NAVD 88 Observation 1Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–02 NM Dona Ana 4/4/2016 4,005.10 NAVD 88 Observation 1Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–03 NM Dona Ana 4/5/2016 4,015.40 NAVD 88 Observation 2Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–04 NM Dona Ana 4/5/2016 4,023.40 NAVD 88 Observation 1Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–05 NM Dona Ana 4/5/2016 4,028.40 NAVD 88 Observation 1Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–06 NM Dona Ana 4/5/2016 4,028.40 NAVD 88 Observation 1
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–07 NM Dona Ana 4/6/2016 4,071 NAVD 88 Observation 2Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–08 NM Dona Ana 4/6/2016 4,057.40 NAVD 88 Observation 1Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–09 NM Dona Ana 4/6/2016 4,081.50 NAVD 88 Observation 2Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–10 NM Dona Ana 4/6/2016 4,069.10 NAVD 88 Observation 1Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–11 NM Sierra 4/7/2016 4,118.60 NAVD 88 Observation 2Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–12 NM Sierra 4/7/2016 4,132.60 NAVD 88 Observation 1Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–13 NM Sierra 4/7/2016 4,119.90 NAVD 88 Observation 3Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–14 NM Sierra 4/12/2016 4,135.30 NAVD 88 Observation 2Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–15 NM Sierra 4/12/2016 4,138.70 NAVD 88 Observation 2Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–16 NM Sierra 4/13/2016 4,138.20 NAVD 88 Observation 2Rio Grande aquifer system LUS/ rioglus- ag1/ RIOGLUSAG1–17/ NM Dona Ana 5/10/2016 4,015.30 NAVD 88 Observation 2

ETN rgaqetn1 RGAQETN1–01

Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–18 NM Sierra 5/16/2016 4,133 NAVD 88 Observation 3Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–19 NM Dona Ana 5/16/2016 4,055 NAVD 88 Observation 3Rio Grande aquifer system LUS rioglusag1 RIOGLUSAG1–20 NM Dona Ana 5/17/2016 4,100 NAVD 88 Observation 3
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–21 NM Dona Ana 5/17/2016 4,104 NAVD 88 Observation 3Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–22 NM Dona Ana 5/18/2016 4,110 NAVD 88 Observation 3Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–23 NM Dona Ana 5/18/2016 4,078 NAVD 88 Observation 3Rio Grande aqui- fer system LUS rioglusag1 RIOGLUSAG1–24 NM Dona Ana 4/12/2016 4,047.40 NAVD 88 Observation 2Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–01/ NM Bernalillo 7/11/2016 4,991 NGVD 29 Observation 4

VFPS rgaqvfps1 RGAQVFPS1–04 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–02/ NM Bernalillo 7/12/2016 4,965 NGVD 29 Observation 2VFPS rgaqvfps1 RGAQVFPS1–13 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–03/ NM Bernalillo 7/12/2016 4,927 NGVD 29 Observation 2VFPS rgaqvfps1 RGAQVFPS1–19 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–04/ NM Bernalillo 7/13/2016 4,945 NGVD 29 Observation 2VFPS rgaqvfps1 RGAQVFPS1–15 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–05/ NM Bernalillo 7/13/2016 4,935 NGVD 29 Observation 1VFPS rgaqvfps1 RGAQVFPS1–18 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–06/ NM Bernalillo 7/13/2016 4,990 NGVD 29 Observation 2VFPS rgaqvfps1 RGAQVFPS1–02 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–07/ NM Bernalillo 7/13/2016 4,940 NGVD 29 Observation 1VFPS rgaqvfps1 RGAQVFPS1–16 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–08/ NM Bernalillo 7/14/2016 4,967 NGVD 29 Observation 3VFPS rgaqvfps1 RGAQVFPS1–11 Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–09/ NM Bernalillo 7/14/2016 4,980 NGVD 29 Observation 3VFPS rgaqvfps1 RGAQVFPS1–06

Rio Grande aquifer system LUS/ rioglusrc1/ RIOGLUSRC1–10/ NM Bernalillo 7/14/2016 4,969.80 NGVD 29 Observation 3

VFPS rgaqvfps1 RGAQVFPS1–07
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Rio Grande aquifer system LUS/ rioglusrc1/ RIOGLUSRC1–11/ NM Bernalillo 7/14/2016 4,972.30 NGVD 29 Observation 4

VFPS rgaqvfps1 RGAQVFPS1–08

Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–12 NM Valencia 7/18/2016 4,800 NGVD 29 Observation 2Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–13 NM Bernalillo 7/18/2016 5,002 NGVD 29 Observation 2Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–14 NM Valencia 7/18/2016 4,852 NGVD 29 Observation 2Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–15 NM Valencia 7/19/2016 4,848 NGVD 29 Observation 2Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–16 NM Bernalillo 7/19/2016 4,925 NGVD 29 Observation 1Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–17 NM Bernalillo 7/19/2016 4,919 NGVD 29 Observation 2Rio Grande aquifer system LUS/ rioglusrc1/ RIOGLUSRC1–18/ NM Bernalillo 7/19/2016 4,935 NGVD 29 Observation 2

VFPS rgaqvfps1 RGAQVFPS1–17

Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–19 NM Valencia 7/20/2016 4,845 NGVD 29 Observation 2Rio Grande aquifer system LUS/ rioglusrc1/ RIOGLUSRC1–20/ NM Bernalillo 7/20/2016 4,989 NGVD 29 Observation 5

VFPS rgaqvfps1 RGAQVFPS1–05

Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–21 NM Bernalillo 7/20/2016 4,920 NGVD 29 Observation 1Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–22 NM Sandoval 7/20/2016 5,053 NGVD 29 Observation 2Rio Grande aquifer system LUS rioglusrc1 RIOGLUSRC1–23 NM Bernalillo 7/21/2016 4,925 NGVD 29 Observation 1Rio Grande aquifer system LUS/ rioglusrc1/ RIOGLUSRC1–24/ NM Bernalillo 7/21/2016 4,995 NGVD 29 Observation 3

VFPS rgaqvfps1 RGAQVFPS1–01
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Rio Grande aqui- fer system LUS rioglusrc1 RIOGLUSRC1–25 NM Sandoval 8/1/2016 5,015 NGVD 29 Observation 2Rio Grande aqui- fer system LUS/ rioglusrc1/ RIOGLUSRC1–26/ NM Bernalillo 8/4/2016 4,965 NGVD 29 Observation 2

VFPS rgaqvfps1 RGAQVFPS1–10 Rio Grande aqui- fer system LUS rioglusrc1 RIOGLUSRC1–27 NM Bernalillo 8/8/2016 4,865 NGVD 29 Observation 2

Rio Grande aqui- fer system VFPS rgaqvfps1 RGAQVFPS1–03 NM Bernalillo 7/22/2016 4,988.60 NGVD 29 Observation 9Rio Grande aqui- fer system VFPS rgaqvfps1 RGAQVFPS1–09 NM Bernalillo 7/26/2016 4,972.30 NGVD 29 Observation 1Rio Grande aqui- fer system VFPS rgaqvfps1 RGAQVFPS1–12 NM Bernalillo 7/25/2016 5,110 NAVD 88 Observation 2Rio Grande aqui- fer system VFPS rgaqvfps1 RGAQVFPS1–14 NM Bernalillo 7/21/2016 4,961.70 NGVD 29 Observation 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–01 ID Minidoka 6/13/2016 4,151.40 NAVD 88 Domestic 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–02 ID Minidoka 6/13/2016 4,191.50 NAVD 88 Domestic 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–03 ID Minidoka 6/14/2016 4,151.60 NAVD 88 Domestic 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–04 ID Minidoka 6/14/2016 4,216.10 NAVD 88 Domestic 2Snake River Plain aquifer 

system

LUS usnkluscr2 USNKLUSCR2–05 ID Minidoka 6/14/2016 4,157.20 NAVD 88 Domestic 2
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–06 ID Blaine 6/15/2016 4,338.30 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–07 ID Minidoka 6/15/2016 4,279.80 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–08 ID Minidoka 6/15/2016 4,305 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–09 ID Minidoka 6/15/2016 4,244.20 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–10 ID Minidoka 6/16/2016 4,344.60 NAVD 88 Domestic 4

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–11 ID Minidoka 6/16/2016 4,181.20 NAVD 88 Domestic 1

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–12 ID Minidoka 6/20/2016 4,299.60 NAVD 88 Domestic 3

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–13 ID Minidoka 6/20/2016 4,153 NAVD 88 Domestic 1

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–14 ID Minidoka 6/21/2016 4,302.40 NGVD 29 Domestic 3

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–15 ID Lincoln 6/21/2016 4,289.50 NAVD 88 Domestic 3
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–16 ID Lincoln 6/21/2016 4,292.10 NGVD 29 Other 3Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–17 ID Minidoka 6/21/2016 4,304.10 NAVD 88 Domestic nSnake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–18 ID Minidoka 6/22/2016 4,193 NAVD 88 Domestic 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–19 ID Minidoka 6/22/2016 4,170.10 NAVD 88 Domestic 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–20 ID Minidoka 6/22/2016 4,253 NAVD 88 Domestic 2Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–21 ID Minidoka 6/22/2016 4,209.10 NAVD 88 Domestic 1Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–22 ID Minidoka 6/23/2016 4,303 NAVD 88 Domestic 3Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–23 ID Minidoka 6/27/2016 4,260.80 NAVD 88 Domestic 2Snake River Plain aquifer system LUS usnkluscr2 USNKLUSCR2–24 ID Minidoka 6/27/2016 4,278.70 NAVD 88 Domestic 3Snake River Plain aquifer 

system

LUS usnkluscr2 USNKLUSCR2–25 ID Minidoka 6/27/2016 4,249.80 NAVD 88 Domestic 2
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–26 ID Minidoka 6/28/2016 4,210.10 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–27 ID Minidoka 6/28/2016 4,201.20 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–28 ID Minidoka 6/28/2016 4,210.20 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–29 ID Minidoka 6/29/2016 4,223.60 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–30 ID Minidoka 6/29/2016 4,239.30 NAVD 88 Domestic 2

Snake River 

Plain aquifer system

LUS usnkluscr2 USNKLUSCR2–31 ID Minidoka 6/29/2016 4,211 NAVD 88 Domestic 2

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–01 SC Richland 8/1/2016 244 NAVD 88 Observation 4

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–02 SC Richland 8/2/2016 219 NGVD 29 Observation 1

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–03 SC Richland 8/3/2016 261 NGVD 29 Observation 4

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–04 SC Richland 8/3/2016 260 NGVD 29 Observation 2
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–05 SC Richland 8/4/2016 250 NGVD 29 Observation 1Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–06 SC Richland 8/8/2016 290 NGVD 29 Observation 2Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–07 SC Richland 8/9/2016 400 NGVD 29 Observation 5Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–08 SC Richland 8/15/2016 320 NGVD 29 Observation 2Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–09 SC Richland 8/15/2016 380 NGVD 29 Observation 1Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–10 SC Richland 8/16/2016 215 NGVD 29 Observation 1Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–11 SC Richland 8/16/2016 240 NGVD 29 Observation 3Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–12 SC Richland 8/16/2016 293 NGVD 29 Observation 5Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–13 SC Richland 8/17/2016 230 NGVD 29 Observation 1Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–14 SC Richland 8/17/2016 210 NGVD 29 Observation 1
Table 1. Information about wells that have environmental data included in this report.—Continued[NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington]

Principal and 

regional and (or) 

other aquifer 

information

Network 

type

Network 

name

NAWQA well

ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–15 SC Richland 8/17/2016 301 NGVD 29 Observation 5

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–16 SC Richland 8/17/2016 240 NGVD 29 Observation 2

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–17 SC Richland 8/18/2016 383 NAVD 88 Observation 2

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–18 SC Richland 8/22/2016 142 NGVD 29 Observation 1

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–19 SC Richland 8/23/2016 280 NGVD 29 Observation 2

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–20 SC Richland 8/23/2016 170 NGVD 29 Observation 8

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–21 SC Richland 8/24/2016 289 NGVD 29 Observation 5

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–22 SC Richland 8/24/2016 212 NGVD 29 Observation 1

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–23 SC Lexington 8/25/2016 290 NGVD 29 Observation 9 

Southeastern 

Coastal Plain aquifer system

LUS santlusrc1 SANTLUSRC1–24 SC Richland 8/29/2016 200 NGVD 29 Observation 8
Table 1. Information about wells that have environmental data included in this report.—Continued [NAWQA, National Water-Quality Assessment; ID, identification; no., number; LS, land surface; ft bls, foot below land surface; lat., latitude, in degrees and minutes; long., longPAS, principal aquifer study; FL, Florida; na, not available; NGVD 29, National Geodetic Vertical Datum of 1929; NAVD 88, North American Vertical Datum of 1988; ETN, enTexas; NM, New Mexico; LUS, land-use study; SC, South Carolina; FPS, flow-path study; MS, Mississippi; VFPS; vertical flow-path study; TN, Tennessee; OK, Oklahoma; ARMissouri; KS, Kansas; MD, Maryland; MSS, modeling support study; DE, Delaware; DC, District of Columbia; MAS, major aquifer study; NV, Nevada; NY, New York; OH, ONH, New Hampshire; WI, Wisconsin; OR, Oregon; MN, Minnesota; WA, Washington] Principal and regional and (or) other aquifer information Network type Network name NAWQA well ID no. State County Sample date Altitude LS Altitude datum Water use 

(

Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–25 SC Richland 8/30/2016 200 NGVD 29 Observation 1Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–26 SC Richland 8/31/2016 277 NAVD 88 Observation 2Southeastern Coastal Plain aquifer system LUS santlusrc1 SANTLUSRC1–27 SC Richland 9/1/2016 215 NGVD 29 Observation 5

aThere was no information available about the bottom of perforation but was assumed to be the bottom of well (Well_depth). bThe depth to the bottom was less than the depth to the top of the open interval and was assumed to be the well depth.
Appendix 1. Information Contained in Previous Reports in This Series 103

Appendix 1. Information Contained in Previous Reports in This Series

A list of reports in this series and the networks discussed are listed in table 1.1.

Table 1.1. Index to which report contains each network description.

Reference Year network 

was sampled

Network name 

abbreviation Network name used in report

Arnold and others 

(2016a,b)

2013 acfbluscr3 Apalachicola-Chattahoochee-Flint River Basin agricultural land-use study 

network

2013 albesus7 Albemarle-Pamlico Drainage Basin major aquifer study network

2013 bnrfpas1 Basin and Range Basin-Fill Aquifers principal aquifer study network

2013 clowpas1 Coastal Lowlands Aquifer System principal aquifer study network

2013 cvaletn1 Central Valley Aquifer System enhanced trends network

2012 dlmvluscr1 Delmarva Peninsula agricultural land-use study network

2013 dlmvsus1 Delmarva Peninsula major aquifer study network

2013 edtretn1 Edwards-Trinity Aquifer System enhanced trends network

2012 gafllusrc1a Georgia-Florida urban land-use study network near Tampa, Florida

2012 gafllusrc1b Georgia-Florida urban land-use study network near Tampa, Florida

2013 gaflsus4 Georgia-Florida major aquifer study network

2013 glacpas1 Glacial Aquifer System principal aquifer study network

2013 metxetn1 Mississippi Embayment Aquifer System enhanced trends network

2012 nacppas1 Northern Atlantic Coastal Plain Aquifer System principal aquifer study networks

2013 nacppas2 Northern Atlantic Coastal Plain Aquifer System principal aquifer study networks

2013 nvbrlusrc1 Nevada Basin and Range urban land-use study network near Reno and Carson 

City, Nevada

2013 sanjlusor1a San Joaquin Valley agricultural land-use study network

2013 secppas1 Southeastern Coastal Plain Aquifer System principal aquifer study network

2013 spltluscr1 South Platte River agricultural land-use study network

2013 vpdcpas1 Valley and Ridge, and Piedmont and Blue Ridge Carbonate-Rock Aquifers principal aquifer study network

2013 wmicsus2 Western Lake Michigan Drainages major aquifer study network
104 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Table 1.1. Index to which report contains each network description.—Continued

Reference Year network 

was sampled

Network name 

abbreviation Network name used in report

Arnold and others 

(2017a,b)

2014 albelusag1 Albemarle-Pamlico Drainage Basin agricultural land-use study network

2014 ccptlusag2b Columbia Plateau agricultural land-use study network

2014 ccptsus1b Columbia Plateau major aquifer study network

2014 clptetn1 Columbia Plateau enhanced trends network

2014 cmorpas1 Cambrian-Ordovician Aquifer System principal aquifer study network

2014 cvaletn1 Central Valley Aquifer System enhanced trends network

2014 edtretn1 Edwards-Trinity Aquifer System enhanced trends network

2014 glacetn1 Glacial Aquifer System enhanced trends network

2014 glacpas1 Glacial Aquifer System principal aquifer study network

2014 metxetn1 Mississippi Embayment Aquifer System enhanced trends network

2014 metxpas1 Mississippi Embayment-Texas Coastal Uplands Aquifer Systems principal 

aquifer study network

2014 nacpetn1 Northern Atlantic Coastal Plain enhanced trends network

2014 negxetn1 New England Crystalline-Rock and Glacial Aquifer System enhanced trends 

network

2014 piedpas1 Piedmont and Blue Ridge Crystalline-Rock Aquifers principal aquifer study 

network

2014 potolusag1 Potomac River Basin agricultural land-use study network

2014 potosus1 Potomac River Basin crystalline-rock major aquifer study network

2014 rgaqetn1 Rio Grande Aquifer System enhanced trends network

2014 rgaqpas1 Rio Grande Aquifer System principal aquifer study network

2014 sanjlusor2a San Joaquin Valley agricultural land-use study network

2014 trinsus3 Trinity River Basin major aquifer study network

2014 whitluscr1 White River Basin agricultural land-use study network

2014 wmiclusag2 Western Lake Michigan Drainages agricultural land-use study network
Appendix 1. Information Contained in Previous Reports in This Series 105

Table 1.1. Index to which report contains each network description.—Continued

Reference Year network 

was sampled

Network name 

abbreviation Network name used in report

Arnold and others 

(2018a,b)

2015 acfbsus1 Apalachicola-Chattahoochee-Flint River Basins major aquifer study network

2015 bnrcpas1 Basin and Range Carbonate-Rock Aquifers principal aquifer study network

2015 ccptlusor1b Central Columbia Plateau Orchard agricultural land-use study network

2015 clptetn1 Columbia Plateau enhanced trends network

2015 cnbrluscr1 Central Nebraska Basins agricultural land-use study network

2015 cvaletn1 Central Valley Aquifer System enhanced trends network

2015 edtretn1 Edwards-Trinity Aquifer System enhanced trends network

2015 florpas1 Floridan Principal Aquifer System principal aquifer study network

2015 gaflluscr1 Georgia-Florida Coastal Plain Drainages agricultural land-use study network

2015 glacetn1 Glacial Aquifer System enhanced trends network

2015 hpaqpas1 High Plains aquifer principal aquifer study network

2015 hpaqvfps1 High Plains Aquifer vertical flow-path study network

2015 lirblusrc1 Lower Illinois River Basin urban land-use study network near St. Louis, 

Missouri

2015 metxetn1 Mississippi Embayment Aquifer System enhanced trends network

2015 metxpas1 Mississippi Embayment-Texas Coastal Uplands Aquifer Systems principal 

aquifer study network

2015 nacpetn1 Northern Atlantic Coastal Plain enhanced trends network

2015 negxetn1 New England Crystalline-Rock and Glacial Aquifer System enhanced trends 

network

2015 podllusrc1 Potomac River Basin and Delmarva Peninsula urban land-use study network 

near Washington, D.C.

2015 rgaqetn1 Rio Grande Aquifer System enhanced trends network

2015 rgaqpas1 Rio Grande Aquifer System principal aquifer study network

2015 sanjluscr1a San Joaquin-Tulare River Basins agricultural land-use study network

2015 sanjsus1 San Joaquin-Tulare River Basins major aquifer study network

2015 trinlusrc1 Trinity River Basin urban land-use study network near Houston, Texas

2015 wmicsus1 Western Lake Michigan Drainages major aquifer study network
106 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Table 1.1. Index to which report contains each network description.—Continued

Reference Year network 

was sampled

Network name 

abbreviation Network name used in report

Arnold and others 

(2020a,b)

2016 biscpas1 Biscayne aquifer principal aquifer study network

2016 clptpas1 Columbia Plateau Basaltic-Rock Aquifers principal aquifer study network

2016 hpaqpas1 High Plains aquifer system principal aquifer study network

2016 ozrkpas1 Ozark Plateaus aquifer system principal aquifer study network

2016 lerilusrc1 Lake Erie-Lake St. Clair drainages urban land-use study network near Detroit, 

Michigan

2016 rioglusag1 Rio Grande aquifer system agricultural land-use study network

2016 rioglusrc1 Rio Grande aquifer system urban land-use study network near Albuquerque, 

New Mexico

2016 santlusrc1 Santee River Basin and Coastal Drainages urban land-use study network near 

Columbia, South Carolina

2016 umislusrc1 Upper Mississippi River Basin urban land-use study network near Minneapolis/

St. Paul, Minnesota

2016 usnkluscr2 Upper Snake River Basin agricultural land-use study network

2016 lerisus1 Lake Erie-Lake St. Clair drainages major aquifer study network

2016 nvbrsus2 Nevada Basin and Range major aquifer study network

2016 pugtsus1 Puget Sound drainages major aquifer study network

2016 clptetn1 Columbia Plateau enhanced trends network

2016 cvaletn1 Central Valley enhanced trends network

2016 edtretn1 Edwards-Trinity aquifer system enhanced trends network

2016 glacetn1 Glacial aquifer system enhanced trends network

2016 metxetn1 Mississippi Embayment aquifer system enhanced trends network

2016 nacpetn1 Northern Atlantic Coastal Plain enhanced trends network

2016 negxetn1 New England crystalline-rock and glacial aquifer system enhanced trends network

2016 rgaqetn1 Rio Grande aquifer system enhanced trends network

2016 cvalfps1 Central Valley aquifer system flow-path study network 1

2016 cvalfps2 Central Valley aquifer system flow-path study network 2

2016 glacfps1 Glacial aquifer system flow-path study network

2016 metxfps1 Mississippi Embayment aquifer system flow-path study network

2016 clptvfps1 Columbia Plateau vertical flow-path study network

2016 glacvfps1 Glacial aquifer system vertical flow-path study network

2016 rgaqvfps1 Rio Grande aquifer system vertical flow-path study network

2016 glacmss1 Glacial Aquifer System Modeling Support Study

2016 nacpmss1 Northern Atlantic Coastal Plain modeling support study network 1

2016 nacpmss2 Northern Atlantic Coastal Plain modeling support study network 2
Appendix 1. Information Contained in Previous Reports in This Series 107

References Cited

Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., 

Kingsbury, J.A., Tesoriero, A.J., Kulongoski, J.T., and 

Belitz, K., 2020a, Groundwater-quality and select qualitycontrol data from the National Water-Quality Assessment 

Project, January through December 2016, and previously 

unpublished data from 2012 to 2015: U.S. Geological 

Survey Data Series 1124, 135 p., https://doi.org/10.3133/

ds1124.

Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, 

B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., 

Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, 

B.J., and Belitz, K., 2017a, Datasets from groundwater 

quality data from the National Water Quality Assessment 

Project, January through December 2014 and select qualitycontrol data from May 2012 through December 2014: 

U.S. Geological Survey data release, accessed March 28, 

2018, at https://doi.org/10.5066/F7W0942N.

Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, 

B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., 

Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, 

B.J., and Belitz, K., 2017b, Groundwater-quality data from 

the National Water-Quality Assessment Project, January 

through December 2014 and select quality-control data 

from May 2012 through December 2014: U.S. Geological 

Survey Data Series 1063, 83 p., accessed March 28, 2018, 

at https://doi.org/10.3133/ds1063.

Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, 

P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and 

Belitz, K., 2018a, Groundwater-quality and select qualitycontrol data from the National Water-Quality Assessment 

Project, January through December 2015, and previously 

unpublished data from 2013 to 2014: U.S. Geological 

Survey Data Series 1087, 68 p., accessed February 2020 at 

https://doi.org/10.3133/ds1087.

Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., 

Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., Belitz, 

K., and Sharpe, J.B., 2018b, Datasets from groundwaterquality and select quality-control data from the National 

Water-Quality Assessment Project, January through 

December 2015 and previously unpublished data from 

2013–2014: U.S. Geological Survey data release, accessed 

September 4, 2019, at https://doi.org/10.5066/F7XK8DHK.

Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, 

P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., 

Belitz, K., and Sharpe, J.B., 2020b, Datasets from 

groundwater-quality and select quality-control data from 

the National Water-Quality Assessment Project, January 

through December 2016, and previously unpublished data 

from 2013 to 2015: U.S. Geological Survey data release, 

https://doi.org/10.5066/P9W4RR74.

Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., 

Barlow, J.R., Kulongoski, J.T., Musgrove, M., Kingsbury, 

J.A., and Belitz, K., 2016a, Groundwater quality data 

from the National Water-Quality Assessment Project, May 

2012 through December 2013: U.S. Geological Survey 

Data Series 997, 56 p., accessed March 28, 2018, at 

https://doi.org/10.3133/ds997.

Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., 

Barlow, J.R., Kulongoski, J.T., Musgrove, M., Kingsbury, 

J.A., and Belitz, K., 2016b, Groundwater quality data from 

the National Water Quality Assessment Project, May 2012 

through December 2014 and select quality-control data from 

May 2012 through December 2013: U.S. Geological Survey 

data release, accessed March 28, 2018, at https://doi.org/

10.5066/F7HQ3X18.
Table 2.1. Well depth by study network. [ETN, enhanced trends network; nc, not calculated; LUS, land-use study; MAS, major aquifer study; PAS, principal aquifer study; VFPS, vertical flow-path study] Network type Network name Number of wells in network with data presented in this report Number of wells with well depth data Well depth, in feet below land surface Minimum 10th percentile 25th percentile Median 75th percentile 95th percentile Ma

ETN clptetn1 3 3 80 nc nc 170 nc nc 1ETN cvaletn1 3 3 234 nc nc 320 nc nc ETN edtretn1 3 3 300 nc nc 550 nc nc 1ETN glacetn1 5 5 34.5 nc nc 83 nc nc ETN metxetn1 2 2 90 nc nc 357 nc nc ETN nacpetn1 3 3 22 nc nc 119 nc nc ETN negxetn1 3 3 83 nc nc 176.3 nc nc ETN rgaqetn1 3 3 22 nc nc 22.6 nc nc FPS cvalfps1 20 20 70 79.7 87 149 187.25 268 FPS cvalfps2 21 21 100 132 180 300 455 530 FPS glacfps1 26a 27 1.25 1.3 1.3 33 62 90 FPS metxfps1 22 22 23 77.19 92 177 267.5 597.2 LUS lerilusrc1 28 28 10.5 16.03 19.6 26.9 32.55 46.445 LUS rioglusag1 24 24 14.9 16.34 18.275 22.75 32.3 36.625 LUS rioglusrc1 27 27 12.5 19.38 20.8 26.5 30.5 48.06 LUS santlusrc1 27 27 8.6 10.26 13.7 21.5 40.15 53.37 LUS umislusrc1 30 30 9 11.98 15 18 23.75 28.775 LUS usnkluscr2 31 30 125 163.5 197.75 256 283.75 360.5 MAS lerisus1 24 24 35 61.6 72.75 90.5 107.25 168.2 MAS nvbrsus2 30 30 105 162.8 236.5 347 569.75 789.35 MAS pugtsus1 29 29 22.5 40 60 78 112 290.8 MSS glacmss1 30 27 45 52.6 62.5 90 140.5 190.9 MSS nacpmss1 20 20 60 152 194.25 292.5 369.25 608.9 MSS nacpmss2 25 25 200 257.6 303 466 626 731.4 PAS biscpas1 40 31 22 40 60 91 113.5 140 PAS clptpas1 60 57 91 222 309 484 743 1,328 2PAS hpaqpas1 17 17 180 196.6 242 321 395 731.2 PAS ozrkpas1 60 57 370 568.6 900 1,115 1,425 1,943.4 3VFPS clptvfps1 19 19 19 27.5 35 88 175 772.3 1VFPS glacvfps1 43 43 10.5 16.62 22.55 33.9 76.25 136.9 VFPS rgaqvfps1 19 19 18.3 20.1 27.15 34.8 53.4 160.22 

a18 wells and 8 piezometers.
Table 2.2. Length of open interval by study network.[ETN, enhanced trends network; nc, not calculated; LUS, land-use study; MAS, major aquifer study; PAS, principal aquifer study; na, not available; VFPS, vertical flow-path stu

Network 

type

Network 

name

Number of wells in 

network with data 

presented in this report

Number of wells 

with open interval 

data

Length of open interval, in feet

Minimum 10th percentile 25th percentile Median 75th percentile 95th percentile Ma

ETN clptetn1 3 3 1 nc nc 26 nc nc 17ETN cvaletn1 3 3 10 nc nc 150 nc nc 20ETN edtretn1 3 3 80 nc nc 230 nc nc 23ETN glacetn1 5 4 3 nc nc 5 nc nc 1ETN metxetn1 2 2 10 nc nc 57 nc nc 10ETN nacpetn1 3 3 3 nc nc 19 nc nc 5ETN negxetn1 3 2 10 nc nc 207 nc nc 40ETN rgaqetn1 3 3 10 nc nc 10 nc nc 2FPS cvalfps1 20 20 5 5 5 5 5 5 FPS cvalfps2 21 20 0 0 17.5 60 240 296.3 34FPS glacfps1 26 26 0.05 0.1 0.1 3 3 8.75 1FPS metxfps1 22 21 10 10 10 20 44 100 10LUS lerilusrc1 28 28 4.5 4.5 4.5 4.5 4.5 6.625 LUS rioglusag1 24 24 9.3 9.3 9.3 9.4 9.6 10 1LUS rioglusrc1 27 27 5 7.76 10 10 10 15 1LUS santlusrc1 27 27 5 5 5 5 5 13.5 1LUS umislusrc1 30 30 4.5 5 5 5 5 5 1LUS usnkluscr2 31 25 2 35.8 58 171 256 322.8 33MAS lerisus1 24 24 3 3 4 4 4 8 1MAS nvbrsus2 30 30 10 10 68.75 193 271.25 509.2 67MAS pugtsus1 29 27 0 0 0.5 5 10 48.4 6MSS glacmss1 30 14 4 8.63 20 25 31.5 41.75 4MSS nacpmss1 20 20 5 6.9 10 20 32.5 112 15MSS nacpmss2 25 24 30 47.9 50.75 59.5 60 60.68 10PAS biscpas1 40 25 3 5.4 7 22 40 64.6 7PAS clptpas1 60 51 0 70 109 207 342 695.75 1,29PAS hpaqpas1 17 7 82 86.8 125 161 187.5 242.5 26PAS ozrkpas1 60 51 8 330 479 621 936 1,200 1,96VFPS clptvfps1 19 18 3 5 5 5 140 463.4 68VFPS glacvfps1 43 43 4 4 4.25 4.5 4.5 7.35 1VFPS rgaqvfps1 19 19 5 5 7.5 10 12.5 21.7 8
110 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Appendix 3. High-Frequency Data from Enhanced Trends Networks

High-frequency data collected at enhanced trends 

network sites are available from the National Water 

Information System (U.S. Geological Survey, 2018) online 

database (table 3.1). The links in table 3.1 below provide 

access to the high-frequency data on the web. To access the 

data for the period covered by this report, the user should 

open the National Water Information System web page at 

https://doi.org/10.5066/F7P55KJN. The user should then 

change the begin and end dates to retrieve the data for the 

period January 1, 2015, to December 31, 2015.

Some of the enhanced trends network sites may have 

different equipment installed and may collect different 

parameters than others. Additionally, some sites have missing 

records for various properties because of equipment failures at 

various times during the data-collection period. Locations of 

the enhanced trends networks are shown in figures 15–16 of 

this report.

Table 3.1. Web links to data collected at a high frequency from enhanced trends networks.

[See figures 15–16 of this report for locations of enhanced trends networks. NAWQA, National Water-Quality Assessment]

Network name NAWQA Project well 

identification number

Link to data collected at a high frequency 

(U.S. Geological Survey, 2018)

clptetn1 CLPTETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=455415119314601

clptetn1 CLPTETN1–04 https://waterdata.usgs.gov/nwis/uv?site_no=454554119121801

clptetn1 CLPTETN1–05 https://waterdata.usgs.gov/nwis/uv?site_no=454827119173401

cvaletn1 CVALETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=364200119420001

cvaletn1 CVALETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=364200119420002

cvaletn1 CVALETN1–03 https://waterdata.usgs.gov/nwis/uv?site_no=364200119420003

edtretn1 EDTRETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=293116098334101

edtretn1 EDTRETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=293516098325501

edtretn1 EDTRETN1–03 https://waterdata.usgs.gov/nwis/uv?site_no=292331098294501

glacetn1 GLACETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=443320089212303

glacetn1 GLACETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=443320089212304

glacetn1 GLACETN1–03 https://waterdata.usgs.gov/nwis/uv?site_no=431053090042702

glacetn1 GLACETN1–04 https://waterdata.usgs.gov/nwis/uv?site_no=431053090042701

glacetn1 GLACETN1–05 https://waterdata.usgs.gov/nwis/uv?site_no=431037090043401

metxetn1 METXETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=351113089513401

metxetn1 METXETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=351111089512501

nacpetn1 NACPETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=384637075153201

nacpetn1 NACPETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=384526075091601

nacpetn1 NACPETN1–03 https://waterdata.usgs.gov/nwis/uv?site_no=384428075355701

negxetn1 NEGXETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=425311070535801

negxetn1 NEGXETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=425400070545401

negxetn1 NEGXETN1–03 https://waterdata.usgs.gov/nwis/uv?site_no=425651070573701

rgaqetn1 RGAQETN1–01 https://waterdata.usgs.gov/nwis/uv?site_no=323733107011002

rgaqetn1 RGAQETN1–02 https://waterdata.usgs.gov/nwis/uv?site_no=324007107095501

rgaqetn1 RGAQETN1–03 https://waterdata.usgs.gov/nwis/uv?site_no=324955107180902
Appendix 3. High-Frequency Data from Enhanced Trends Networks 111

Reference Cited

U.S. Geological Survey, 2018, USGS water data for the 

Nation: U.S. Geological Survey National Water Information 

System database, accessed December 18, 2018, at 

https://doi.org/10.5066/F7P55KJN.
112 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Appendix 4. Quality-Control Samples and Data Analysis

Samples

Quality-control (QC) samples are routinely collected 

along with the environmental groundwater samples. The third 

cycle of the National Water-Quality Assessment (NAWQA) 

groundwater studies began in 2013, but a small pilot study 

was completed in 2012. The entire third cycle sampling 

period currently is May 2012–December 2016; this period is 

hereafter referred to as “the cycle 3 sampling period.” Data 

from the environmental and QC blank and replicate samples 

from the 2012–13 sampling period were presented in Arnold 

and others (2016a,b) and from the 2014 sampling period 

were presented in Arnold and others (2017a,b). Data from the 

environmental and QC blank samples from the 2015 sampling 

period were presented in Arnold and others (2018a,b). The 

Arnold and others (2017a,b) publications also presented data 

for selected spike samples collected in 2012–14. This current 

report presents a summary of QC samples from the entire 

cycle 3 sampling period (May 2012–December 2016) as well 

as the January–December 2016 sampling period covered by 

this report. A summary of results from blank samples collected during the sampling period January–December 2016 is 

provided in table 4.1, and a summary for the cycle 3 sampling period is provided in table 4.2. A summary of replicate 

samples, an analysis of the variability in detections and 

concentrations of selected analytes from replicate samples, 

and a summary of spike samples for the entire sampling period 

May 2012–December 2016 are provided in tables 4.3 through 

4.9. Data from the environmental and QC samples from the 

January–December 2016 sampling period are presented in 

tables 4.10–4.19 of Arnold and others (2020).

Table 4.1. Summary of results for field blanks collected by the National Water-Quality Assessment Project from January to 

December 2016.

[DOC, dissolved organic carbon; VOC, volatile organic compound; HHB, human-health benchmark; --, not applicable; SMCL, secondary maximum contaminant level]

Type of summary

Major and 

minor elements

Trace elements Nutrients and DOC VOCs Pesticide compounds

Total number of blank samples 48 80 43 to 82 87 29

Number of field blanks 43 46 41 to 47 38 26

Number of constituents analyzed 10 22 6 85 225

Number of constituents detected in field blanks 9 20 4 19 1

Number of constituents detected in field blanks 

that have an HHB

1 17 1 7 0

Number of constituents detected in field blanks 

that have an SMCL

4 5 0 0 0

Largest ratio of the maximum concentration in 

a field blank to the corresponding HHB, in 

percent

2 4.5 0.16 0.36 --

Largest ratio of the maximum concentration in 

a field blank to the corresponding SMCL, in 

percent

26 38 -- -- --
Appendix 4. Quality-Control Samples and Data Analysis 113

Table 4.2. Summary of results for field blanks collected by the National Water-Quality Assessment Project from May 2012 to 

December 2016.ww

[DOC, dissolved organic carbon; VOC, volatile organic compound; HHB, human-health benchmark; SMCL, secondary maximum contaminant level; --, not 

applicable]

Type of summary

Major and 

minor 

elements

Trace 

elements

Nutrients 

and DOC VOCs Pesticide compounds

Specialinterest 

analytes

Total number of blank samples 227 458 209 to 457 564 184 6 to 54

Number of field blanks 208 219 200 to 220 200 165 6 to 20

Number of constituents analyzed 10 22 6 129 272 7

Number of constituents detected in field blanks 10 21 6 27 27 3

Number of constituents detected in field blanks 

that have an HHB

1 18 2 12 18 2

Number of constituents detected in field blanks 

that have an SMCL

4 5 0 0 0 0

Largest ratio of the maximum concentration in 

a field blank to the corresponding HHB, in 

percent

2 4.8 0.67 2.4 0.25 750

Largest ratio of the maximum concentration in 

a field blank to the corresponding SMCL, in 

percent

26 199 -- -- -- --

Blank Sample Approach

Blank samples are QC samples that are used to determine if water samples become contaminated during sample 

collection, field processing, transport, or laboratory analysis. 

Blank samples are collected using blank water that has been 

prepared to be free of detectable concentrations of the constituents of interest. An equipment blank generally is collected 

in a controlled environment (such as a laboratory) before field 

sampling begins and is intended to evaluate the suitability of 

the equipment and equipment cleaning protocols for the established data-quality requirements. A field blank is subjected 

to all the same aspects of sample collection, field processing, 

preservation, transportation, and laboratory handling as an 

environmental sample and is intended to evaluate the potential for these procedures to be sources of contamination. A 

source solution blank is a sample of the water used to collect 

the equipment and field blanks and is intended to verify that 

the blank water itself has no detectable concentrations of the 

constituents of interest. Because field blanks are collected 

under conditions most comparable to conditions affecting 

environmental samples, these blanks are most directly representative of potential sources of contamination to environmental samples and were the focus of this initial evaluation of 

blank-sample results.

Results of the initial evaluation of data from field blanks 

for major and trace elements, nutrients, volatile organic compounds (VOCs), and pesticide compounds collected during 

the 2016 sampling period of January–December 2016 and the 

cycle 3 sampling period are presented in this report. Data from 

2016 are published in this report, data from 2012 to 2013 were 

published in Arnold and others (2016a,b), and data from 2014 

were published in Arnold and others (2017a,b). About 70 to 

75 percent of the field blanks collected for each of these constituents during the cycle 3 sampling period have been associated with groundwater sites that are sampled using a dedicated 

pump (primarily public-supply and domestic wells), and the 

rest have been associated with groundwater sites that are sampled using a portable sampling pump (monitoring wells). The 

objective of this initial evaluation of field blanks was to determine if environmental concentrations of these constituents as 

reported by the relevant laboratories are suitable for comparison to their corresponding human-health benchmarks (HHBs) 

or to U.S. Environmental Protection Agency (EPA) secondary 

maximum contaminant levels (SMCLs) if HHBs have not 

been established. The HHBs are a set of health-based comparison thresholds that include EPA maximum contaminant levels 

(MCLs), health-based screening levels (HBSLs), and humanhealth benchmarks for pesticides (HHBPs). Further evaluation 

of results for blank samples, such as through methods used by 

Olsen and others (2010), Bender and others (2011), Fram and 

others (2012), or Davis and others (2014), would be needed to 

determine if inadvertent contamination of samples with certain 

constituents would affect the interpretation of environmental 

concentrations of those constituents for objectives other than 

those presented in this report.
Table 4.3. Summary of results for replicate samples collected by the National Water-Quality Assessment Project from May 2012 to December 2016. [VOC, volatile organic compound; mg/L, milligram per liter; μg/L, microgram per liter; ng/L, nanogram per liter; pCi/L, picocurie per liter] Type of summary Major and minor elements and physical parameters Trace elements Nutrients and dissolved organic carbon VOCs Pesticide compounds 

Total number of replicate samples 58 to 119 128 103 to 120 110 142 Number of samples included in analysis of replicate results (2013–16 samples for VOCs and pesticide compounds) 58 to 119 128 103 to 120 105 138 Total number of analytes included in analysis of replicate results 14 22 6 85 225 Number of analytes that include censored values and have at least 10 replicate pairs without con- sistent non-detections 5 19 6 4 11 Range in mean detection rate for replicate pairs having at least 1 detection, in percent 90.6–100 80.0–100 84.6–99.5 93.8–100 78.1–97.8 Number of analytes with mean detection rate less than 75 percent 0 0 0 0 0 Range in percentage of replicate pairs with incon- sistent detections 0–18.8 0–40.0 0.9–30.8 0–12.5 4.3–43.8 Number of analytes with percentage of replicate pairs with inconsistent detections greater than 50 percent 0 0 0 0 0 Number of analytes that have at least 10 replicate pairs with consistent analyte detections 14 18 6 4 9 Range in standard deviation for lower concentration range of two-range model 0.0014–7.3 mg/L 0.0022–1.3 μg/L 0.0004–0.039 mg/L 0.0013–0.013 μg/L 1.1–19 ng/L Range in relative standard deviation for higher con- centration range of two-range model, in percent 0.24–5.7 1.2–9.3 0.98–10.8 5.8 3.6–13 
Appendix 4. Quality-Control Samples and Data Analysis 115

Blank Sample Counts

The total number of blank samples and the number of 

field blanks collected for groundwater sites differs by constituent group during the 2016 sampling period and the cycle 3 

sampling period (tables 4.1 and 4.2, respectively). Data for all 

blank samples from the 2016 sampling period are presented 

in tables 4.10–4.17 of Arnold and others (2020). All blank 

samples collected during the cycle 3 sampling period were 

analyzed using the corresponding laboratory methods listed 

in table 2 of Arnold and others (2020, 2016a, 2017a, 2018a). 

Of the 564 VOC blank samples collected during the cycle 3 

sampling period (table 4.2), 26 were collected in 2012 or early 

2013 and analyzed for an older analytical schedule using purge 

and trap gas chromatography/mass spectrometry (Gilliom and 

others, 2006; Zogorski and others, 2006); and 538 were collected in 2013–16 and analyzed using the most recent analytical schedule and laboratory methods (purge and trap gas 

chromatography/mass spectrometry and heated purge and trap 

gas chromatography/mass spectrometry). Of the 184 pesticide 

blank samples collected during the cycle 3 sampling period 

(table 4.2), 8 were collected in 2012 and analyzed for an older 

analytical schedule using gas chromatography/mass spectrometry; and 176 were collected in 2013–16 and analyzed using 

the most recent analytical schedule and laboratory method 

(direct aqueous injection liquid chromatography tandem 

mass spectrometry). One blank sample collected in 2014 was 

analyzed using both an older and the most recent analytical 

schedule. Not included in table 4.1 are sample counts for special analytes collected only in selected well networks during 

the cycle 3 sampling period: arsenic species, perchlorate, and 

hexavalent chromium. In January–December 2016, no field 

blanks were collected for arsenic species, and only two each 

were collected for perchlorate and hexavalent chromium.

Constituent Concentrations in Blank 

Samples

Of the 10 major or minor elements included in laboratory 

analysis (not including analysis for dissolved-solids concentration), 9 were detected in at least 1 field blank collected in 2016 

(table 4.1); all 10 elements were detected in at least 1 field 

blank collected during the cycle 3 sampling period (table 4.2). 

Only one of the major or minor elements detected during 

the cycle 3 sampling period (fluoride) had an HHB (table 2 

of Arnold and others, 2020); four (chloride, fluoride, sulfate, and iron) had SMCLs. The maximum concentration for 

fluoride in any field blank from the cycle 3 sampling period 

was 2.0 percent of its corresponding HHB. For chloride and 

sulfate, the maximum concentration in any field blank from 

the cycle 3 sampling period was less than 1 percent of the 

corresponding SMCL. For fluoride, the maximum concentration was 3.9 percent of the corresponding SMCL. For iron, the 

maximum concentration in any field blank from the cycle 3 

sampling period was 26 percent of its corresponding SMCL of 

300 micrograms per liter (μg/L), reported for an August 2016 

sample. Results for blind blanks submitted to the NWQL to 

evaluate laboratory data quality indicate a slight high bias for 

iron during 2016, but no evidence of a laboratory contamination issue (Tedmund Struzeski, U.S. Geological Survey 

[USGS] Inorganic Blind Sample Project, written commun., 

2017, 2018). Out of 207 NAWQA cycle 3 field blanks for iron, 

the two results that exceed 10 percent of the SMCL (42.0 and 

78.6 μg/L for samples collected in September 2014 and 

August 2016, respectively) likely reflect isolated events.

Of the 22 trace elements included in laboratory analysis, 

20 were detected in at least 1 field blank collected in 2016 

(table 4.1), and 21 were detected in at least 1 field blank 

from the cycle 3 sampling period (table 4.2). Of the 21 trace 

elements detected in field blanks from the cycle 3 sampling 

period, 18 (antimony, arsenic, barium, beryllium, boron, 

cadmium, chromium, copper, lead, manganese, molybdenum, 

nickel, selenium, silver, strontium, thallium, uranium, and 

zinc) had HHBs (table 2 of Arnold and others, 2020); and 5 

(aluminum, copper, manganese, silver, and zinc) had SMCLs. 

For 7 of the 18 detected trace elements with HHBs, the maximum concentration measured in a field blank from the cycle 3 

sampling period was less than 1 percent of the corresponding 

HHB; for the remaining 11 trace elements (antimony, arsenic, 

beryllium, cadmium, copper, lead, manganese, molybdenum, nickel, thallium, and zinc), the maximum concentration 

was less than 5 percent of the HHB. For silver and zinc, the 

maximum concentration measured in a field blank from the 

cycle 3 sampling period was less than 1 percent of the corresponding SMCL; for copper, the maximum concentration 

was less than 9 percent of the corresponding SMCL. However, 

for aluminum, the maximum concentration was nearly 

200 percent of the corresponding SMCL of 50 μg/L. Results 

for blind blanks submitted to NWQL to evaluate laboratory 

data quality indicate false positive detections of aluminum 

in blank samples submitted to the laboratory during July and 

August 2014, August–December 2015, and June 2016 (USGS 

Inorganic Blind Sample Project, https://bqs.usgs.gov/ibsp/). 

Investigation of this issue by the NWQL indicated the occurrence of sporadic contamination at concentrations of as much 

as 63 μg/L from at least July 2014 through December 2015, 

although the source of contamination and, therefore, the exact 

magnitude of contamination and the period(s) affected were 

not established (Tedmund Struzeski, USGS Inorganic Blind 

Sample Project, written commun., 2015). For NAWQA field 

blanks collected through the end of December 2016, reported 

detections of aluminum above the laboratory reporting limit 

occurred in blank samples collected primarily between May 29 

and July 16, 2014, between March 25 and September 8, 2015, 

and between April 7 and August 10, 2016.

Also, regarding trace elements, the USGS Office of Water 

Quality has documented random low-level contamination of 

water samples with cobalt and manganese from certain capsule 

filters used by the NAWQA Project and across the USGS from 
116 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

about October 1, 2008, to about September 30, 2014 (USGS 

Office of Water Quality, written commun., July 1, 2016). 

Blank samples for cobalt and manganese that were collected 

for NAWQA during this period were evaluated to determine 

the possible effects of this contamination on environmental 

samples, and evaluation results were described in appendix 3

of Arnold and others (2017b).

Of the five nutrients or groups of nutrients that the 

laboratory analyzes directly (as opposed to the nutrients with 

calculated results), three were detected in at least one field 

blank collected in 2016, as was dissolved organic carbon 

(DOC) (table 4.1). All five nutrients and DOC were detected 

in at least one field blank from the cycle 3 sampling period 

(table 4.2). Two of the nutrients detected in field blanks from 

the cycle 3 sampling period (nitrite and nitrate) have HHBs 

(table 2 of Arnold and others, 2020); none have SMCLs. For 

each of the two nutrients with HHBs, the maximum concentration measured in a field blank from the cycle 3 sampling 

period was less than 1 percent of the corresponding threshold. 

DOC does not have an HHB but was detected in 15 of 41 field 

blanks collected during 2016, and in 62 of 200 field blanks 

collected during cycle 3. Concentrations during cycle 3 ranged 

from 0.23 to 633 milligrams per liter (mg/L) and included 

multiple values above 1 mg/L. However, concentrations of 

this magnitude probably reflect inadequate rinsing of sampling 

equipment with blank water between use of methanol during 

the cleaning process and subsequent collection of the blank 

sample. Therefore, these results likely are not representative 

of the actual potential for contamination of environmental 

samples, which are collected only after flushing of sampling 

equipment with copious quantities of native groundwater.

Blank samples collected in 2012 were analyzed for 

85 VOCs, and blank samples collected in 2013 through 2016 

were analyzed for a different (but partially overlapping) list of 

85 VOCs; the change in laboratory methods and constituent 

lists resulted in a total of 129 different VOCs being included 

in the overall dataset of blank results. In total, 19 VOCs were 

detected in at least 1 field blank collected in 2016 (table 4.1), 

and 27 VOCs were detected in at least 1 field blank from 

the cycle 3 sampling period (table 4.2). Twelve compounds 

detected in field blanks from the cycle 3 sampling period 

(1,1-dichloroethene, 1,2-dibromo-3-chloropropane, 1,4-dichlorobenzene, benzene, carbon disulfide, dichloromethane, ethylbenzene, m-xylene plus p-xylene, o-xylene, styrene, toluene, 

and trichloromethane) have HHBs (table 2 of Arnold and 

others, 2020) and none have SMCLs. For 9 of the 12 VOCs 

with HHBs, the maximum concentration measured in a blank 

was less than 1 percent of the corresponding HHB threshold; 

for the remaining 3 VOCs (1,1-dichloroethene, 1,2-dibromo3-chloropropane, and dichloromethane), the maximum concentration was less than 3 percent of the corresponding HHB 

threshold.

Blank samples collected in 2012 were analyzed for 

137 pesticide compounds, and blank samples collected in 

2013 through 2016 were analyzed for 225 to 227 pesticide 

compounds; the change in laboratory methods and constituents 

resulted in 272 pesticide compounds being included in the 

overall dataset of blank results. One pesticide compound 

was detected in 1 field blank collected in 2016 (table 4.1), 

and 27 pesticide compounds were detected in at least 1 field 

blank from the cycle 3 sampling period (table 4.2). Eighteen 

compounds detected in field blanks from the cycle 3 sampling 

period (aldicarb sulfone, atrazine, bromacil, desulfinylfipronil, 

diflubenzuron, 2-hydroxy-4-isopropylamino-6-ethylaminos-triazine, imazethapyr, methyoxyfenozide, metolachlor, 

metribuzin, nicosulfuron, pendimethalin, cis-permethrin, 

trans-permethrin, piperonyl butoxide, prometryn, propiconazole, and tebuthiuron) have HHBs (table 2 of Arnold and 

others, 2020) and none have SMCLs. For all the pesticide 

compounds with HHBs, the maximum concentration measured 

in a field blank was about 0.25 percent or less of the corresponding HHB threshold.

A few of the special analytes collected in only selected 

well networks were detected in one or more field blanks from 

the cycle 3 sampling period. The one field blank collected 

in 2016 for arsenic speciation had no detections. Six field 

blanks were collected for arsenic speciation during the cycle 3 

sampling period. In these six field blanks, there were no 

detections of three arsenic species (arsenite, dimethylarsinate, 

and monomethylarsonate), but arsenate was detected in one 

field blank at a concentration of 1.51 μg/L, which is about 

15 percent of the HHB of 10 μg/L for total arsenic. Because 

it is possible that contamination could limit the suitability of 

arsenate results for comparison with the arsenic HHB, total 

arsenic results by NWQL laboratory schedule 2710, which 

are available for all samples that have arsenate results, should 

be used for comparison with the HHB. Perchlorate was not 

detected in any of the 4 field blanks collected during 2016, or 

in any of the 18 total field blanks collected during the cycle 3 

sampling period. Hexavalent chromium was detected in one 

of the seven field blanks collected in 2015 at a concentration 

of 0.2 μg/L, which is 5.0 percent of the upper cancer HBSL of 

4 μg/L. Hexavalent chromium was detected in 2 of the 20 field 

blanks from the cycle 3 sampling period at a maximum 

concentration of 0.3 μg/L, which is 7.5 percent of the upper 

cancer HBSL of 0.04 μg/L. Corresponding total chromium 

values typically are reported by the same USGS Trace Metal 

Laboratory in Boulder, Colo., that analyzes for hexavalent 

chromium. For the four hexavalent chromium field blanks collected in 2016, the corresponding total chromium values were 

below detection. Of the 19 samples from the cycle 3 sampling period that had total chromium results reported by this 

laboratory, 2 field blanks had a detection of total chromium at 

concentrations as much as 0.6 μg/L, which is 0.6 percent of 

the HHB of 100 μg/L. Therefore, it seems that there is minimal potential for contamination of total chromium from the 

USGS Trace Metal Laboratory to affect comparison of these 

values to HHBs.

The maximum concentrations of nutrients, VOCs, and 

pesticide compounds in field blanks from the cycle 3 sampling period are all substantially less than the thresholds 

used by the NAWQA Project to distinguish between low and 
Appendix 4. Quality-Control Samples and Data Analysis 117

moderate concentrations (50 percent of the HHB or SMCL for 

inorganic constituents and 10 percent of the HHB for organic 

constituents); therefore, results of the field blank samples for 

these constituent groups indicate minimal potential for effects 

of contamination on the number of groundwater samples that 

would be classified as having moderate or high concentrations 

relative to current HHBs or SMCLs. For major, minor, and trace 

elements, the maximum concentrations in field blanks from 

the cycle 3 sampling period also are substantially less than the 

relevant thresholds except for iron and aluminum. Data from 

third-party blind blanks and from NAWQA Project field blanks 

do not indicate systematic contamination issues for iron that 

would substantially affect classification of results relative to 

the SMCL of 300 μg/L. Because data from blind blanks and 

field blanks indicate that laboratory contamination might have 

affected aluminum results considerably for some environmental 

samples from late May 2014 through at least August 2016, aluminum results from this period cannot positively be classified as 

moderate or high relative to the SMCL of 50 μg/L.

Replicate Sample Approach

Replicate samples are QC samples that are used to estimate variability of analytical results caused by random measurement error (Mueller and others, 2015). Replicate samples 

are two or more water samples that are collected, processed, 

and analyzed in a manner that allows them to be considered 

essentially identical in composition and analysis (Mueller and 

others, 2015). Replicate groundwater samples for NAWQA 

consist of two samples collected one after the other in the field 

(sequential field replicates).

Replicate samples typically are used to evaluate variability in analyte concentration by estimating standard deviation 

(SD) as a function of concentration (Mueller and others, 

2015). The presence of censored values affects the calculation 

of these estimates and generally necessitates estimation of the 

variability in analyte detection, as well. One measure of the 

variability in analyte detection is the mean detection rate for 

all replicate pairs having at least one detection (Martin, 2002; 

Mueller and others, 2015). Another measure is the percentage 

of replicate sets with inconsistent detections, which is calculated as the number of replicate sets with inconsistent detections divided by the total number of replicate sets minus the 

number of sets with consistent non-detections (Martin, 2002; 

Mueller and others, 2015). A one-sided upper confidence 

limit for the percentage of inconsistent replicate sets can be 

calculated as described by Mueller and others (2015, p. 32). 

Multiple approaches are available to estimate the variability 

of analyte concentrations as a function of concentration. Three 

of these approaches and the requirements to apply them were 

described by Mueller and others (2015, p. 32).

Data and results of the initial evaluation of data from 

replicate samples for a variety of analytes collected during the entire cycle 3 sampling period of May 2012 through 

December 2016 are presented in this report. The objective 

of this initial evaluation of replicate samples was to broadly 

characterize variability in analyte detection and concentration to explore implications for comparisons of environmental 

concentrations of analytes as reported by the relevant laboratories with their corresponding HHBs or SMCLs. For example, 

large variability in analyte detection and (or) concentration 

near an HHB could reduce confidence in the reported concentration representing a true exceedance of an HHB in the 

environment. As part of this evaluation, for analytes that 

include censored values and have at least 10 replicate pairs 

not composed of consistent nondetections, the mean detection 

rate (for all replicate pairs having at least 1 detection) and the 

percentage of replicate sets with inconsistent detections were 

calculated. A piecewise-linear model used by Mueller and 

Titus (2005) and described by Mueller and others (2015) was 

used to estimate variability in concentrations for analytes having at least 10 replicate pairs with consistent detections. This 

two-range model divides concentrations into: (1) a low range 

for which the SD of replicates generally is uniform and the 

average SD is used to estimate variability; and (2) a high range 

for which the relative standard deviation (RSD; the ratio, in 

percent, of SD to mean concentration) generally is uniform 

and the average RSD is used to estimate variability. Graphs 

of SD and RSD against mean concentration are used to select 

an appropriate boundary concentration between the low and 

high ranges (Mueller and others, 2015). In some cases, either 

SD or RSD is fairly uniform throughout the range of available 

concentrations and, therefore, no boundary is needed.

Replicate Sample Counts

The total number of replicate samples collected for 

groundwater sites during cycle 3 varies by analyte group 

(table 4.3). Data for all replicate samples from the 2015 and 

2016 sampling periods are presented in tables 4.10–4.19 of 

Arnold and others (2020). All cycle 3 replicate samples were 

analyzed using the corresponding laboratory methods listed in 

table 2 of Arnold and others (2020). Of the 110 cycle 3 VOC 

replicate samples, 5 were collected in 2012 and analyzed for 

an older analytical schedule using purge and trap gas chromatography/mass spectrometry (Gilliom and others, 2006; 

Zogorski and others, 2006). The remaining 105 were collected 

in 2013–16 and analyzed using the most recent analytical 

schedule and laboratory methods (purge and trap gas chromatography/mass spectrometry and heated purge and trap 

gas chromatography/mass spectrometry). Of the 142 cycle 3 

pesticide replicate samples, 4 were collected in 2012 and 

analyzed for an older analytical schedule using gas chromatography/mass spectrometry. The remaining 138 were collected in 

2013–16 and analyzed using the most recent analytical schedule and laboratory method (direct aqueous injection liquid 

chromatography tandem mass spectrometry).
118 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Replicate Sample Results

Analysis of variability in analyte detection, analyte 

concentration, or both was attempted for only the subset of 

analytes that met certain requirements. Analysis of replicate 

results was not attempted for analyte/method combinations 

for which fewer than 10 replicate pairs had been analyzed. 

Therefore, VOCs and pesticide compounds determined with 

older laboratory methods used for analysis of replicates collected in 2012 were not evaluated for variability. In addition, 

analysis of variability in analyte detection was performed only 

for analytes that included censored values and had at least 

10 replicate pairs without consistent non-detections (table 4.4). 

Analysis of analyte concentrations was performed only for 

analytes that had at least 10 replicate pairs with consistent 

detections (table 4.5).

Among all constituents for which variability in detection 

was estimated, the mean detection rate ranged from 78.1 to 

100 percent and the percentage of pairs with inconsistent 

detections ranged from 0.0 to 43.8 percent (table 4.4). In his 

assessment of pesticides, Martin (2002) used a mean detection rate of 75 percent or less or a percentage of inconsistent 

replicate sets of 50 percent or more to indicate high variability 

of detection. Using those same criteria, none of the constituents analyzed for this study would be considered to have high 

variability of detection. Martin (2002) also used a mean detection rate of 90 percent or more or a percentage of inconsistent 

replicate sets of 25 percent or less to indicate low variability of 

detection. Under these criteria, the following constituents do 

not have low variability of detection but rather would be considered to have moderate variability: the nutrient nitrite; the 

pesticide compounds hexazinone, metolachlor, and OIAT; and 

the radiological analytes alpha radioactivity (30-day count), 

radium-226, and radium-228. The initial analysis of variability of detection presented here did not include calculation 

of an upper confidence bound on percentages of inconsistent 

replicate sets to quantify uncertainty and did not account for 

changes in variability across concentration ranges. In general, 

variability of detection is higher at low concentrations and 

decreases with increasing concentrations (Martin, 2002). More 

detailed analysis of this type might be needed for the interpretation of environmental concentrations for objectives other 

than those presented in this report.

For all constituents for which variability in concentration 

was estimated, the mean SD, RSD, or both, were determined 

for specified concentration ranges by means of the two-range 

model and are presented in table 4.5. For major and minor 

elements and physical parameters, the mean SD at lower or all 

concentrations was 0.22 mg/L or less, except for alkalinity and 

residue on evaporation (dissolved solids), and the mean RSD 

at higher concentrations was less than 3 percent, except for 

bromide and iron. For trace elements, the mean SD at lower or 

all concentrations was 0.3 microgram per liter or less, except 

for aluminum and zinc, and the mean RSD at higher concentrations was less than 5 percent, except for cobalt and lead. 

For nutrients and dissolved organic carbon, the mean SD at 

lower concentrations was less than 0.04 mg/L, and the mean 

RSD at higher concentrations was 3 percent or less, except for 

dissolved organic carbon. For VOCs, only 4 compounds had at 

least 10 replicate pairs with consistent detections. Mean SDs 

were calculated for lower concentration ranges for three compounds and were all 0.013 microgram per liter or less; only 

one compound had sufficient data to calculate a mean RSD 

(5.8 percent) for the upper concentration range. For pesticide 

compounds, 9 compounds had at least 10 replicate pairs with 

consistent detections. Six compounds had sufficient data to 

calculate a mean SD for lower concentrations; the mean SD 

for each of these compounds was less than 10 nanograms per 

liter, except for metolachlor sulfonic acid. Data for pesticide 

compounds generally were insufficient to reasonably estimate 

the mean RSD at higher concentrations. For radiological analytes, the mean SD at lower concentrations was 0.76 picocurie 

per liter or less, except for radon, and the mean RSD at higher 

concentrations was less than 10 percent, except for α radioactivity (72-hour count).

None of the special-interest analytes collected in only 

selected well networks had more than 10 replicate pairs 

without consistent nondetections for a rigorous evaluation of 

variability. However, the six available replicate pairs for the 

four arsenic species indicated no inconsistent detections. Only 

arsenate and arsenite were detected; within the four to five 

pairs with consistent detections of these analytes, concentrations were similar. Out of 10 sample pairs, perchlorate was not 

detected in either sample of 3 pairs, was detected in 1 sample 

of a single pair (at a concentration nearly equivalent to the 

laboratory reporting level) and was detected in both samples 

of 6 pairs (at similar concentrations). Out of 9 samples pairs, 

hexavalent chromium was not detected in either sample of 

3 pairs and had inconsistent detections in 3 of the 6 other 

pairs; concentrations within the 3 pairs with consistent detections were similar. Out of 8 sample pairs for total chromium 

from the Boulder, Colo., laboratory, chromium was not 

detected in either sample of 2 pairs, was detected in 1 sample 

of 2 pairs, and was detected in both samples of 4 pairs (2 of 

which had similar concentrations between samples). Four 

replicate samples were analyzed for strontium isotope ratios, 

for which a non-detection is not a possible result; the isotope 

ratios within all four sample pairs were identical.

Overall, the available results indicate generally low variability in analyte detection and concentration, meaning that 

random measurement error has minimal potential to affect 

the number of groundwater samples that would be classified 

as having moderate or high concentrations relative to current 

HHBs or SMCLs. However, further analysis beyond the scope 

of this initial evaluation of replicate results, such as the calculation of confidence intervals, would be needed to quantify the 

likely effects of variability for use of the environmental data 

for specific purposes.
Appendix 4. Quality-Control Samples and Data Analysis 119

Table 4.4. Estimated variability in detection of selected analytes based on field replicate samples collected by the U.S. Geological 

Survey National Water-Quality Assessment Project, May 2013 through December 2016.

[Variability was evaluated only for constituents having censored values and at least 10 replicate pairs without consistent nondetections. n, number of pairs with 

at least one detection; CAAT, chlorodiamino-s-triazine; CEAT, 2-chloro-6-ethylamino-4-amino-s-triazine; OIAT, 2-hydroxy-4-isopropylamino-6-amino-striazine; OIET, 2-hydroxy-4-isopropylamino-6-ethylamino-s-triazine; SA, sulfonic acid]

Constituent n

Mean detection rate 

(percent) Pairs with inconsistent detections (percent)

Major and minor elements

Bromide 95 97.4 5.3

Fluoride 113 100 0

Dissolved solids 118 99.6 0.8

Iron 80 90.6 18.8

Sulfate 115 100 0

Trace elements

Aluminum 52 90.4 19.2

Antimony 50 95 10

Arsenic 102 98.5 2.9

Beryllium 30 100 0

Boron 111 99.5 0.9

Cadmium 28 94.6 10.7

Chromium 54 97.2 5.6

Cobalt 82 94.5 11

Copper 54 89.8 20.4

Lead 81 92.6 14.8

Lithium 118 100 0

Manganese 92 97.8 4.3

Molybdenum 107 100 0

Nickel 94 98.9 2.1

Selenium 85 96.5 7.1

Thallium 16 96.9 6.3

Uranium 102 99 2

Vanadium 99 96 8.1

Zinc 66 94.7 10.6

Nutrients and dissolved organic carbon

Ammonia 50 93 14

Nitrite plus nitrate 77 98.7 2.6

Nitrite 26 84.6 30.8

Total nitrogen 108 99.5 0.9

Orthophosphate 106 99.5 0.9

Dissolved organic carbon 80 95.6 8.8
120 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Spike Sample Approach

Spike samples are QC samples that are used to estimate 

any positive or negative bias that might result from method 

performance, effects of the sample matrix, and (or) analyte 

degradation during sample shipment and storage (Mueller 

and others, 2015). Spike samples are collected by fortifying (spiking) a water sample with known concentrations of 

analytes. For VOCs, NAWQA collects laboratory matrix 

spikes, meaning that the spike solution is added to an environmental sample at the laboratory. For pesticide compounds 

and arsenic species, NAWQA collects field matrix spikes, 

meaning that the spike solution is added to an environmental 

sample in the field. Both types of spikes estimate recovery bias 

in an environmental water sample that could be caused by a 

problem with performance of the laboratory method, by the 

chemical, physical, or biological characteristics of the water, 

or by both (Mueller and others, 2015). Field spikes also reflect 

any degradation that might have occurred in an analyte during 

the time between sample collection and laboratory analysis. 

Evaluations of recoveries for laboratory spikes for VOCs and 

field spikes for arsenic species are included in this report, but 

an evaluation of recoveries for field spikes for pesticides is not 

included here because Shoda and others (2018) presented this 

type of evaluation for pesticide spike samples collected for the 

NAWQA Project in 2013–15.

Table 4.4. Estimated variability in detection of selected analytes based on field replicate samples collected by the U.S. Geological 

Survey National Water-Quality Assessment Project, May 2013 through December 2016.—Continued

[Variability was evaluated only for constituents having censored values and at least 10 replicate pairs without consistent nondetections. n, number of pairs with 

at least one detection; CAAT, chlorodiamino-s-triazine; CEAT, 2-chloro-6-ethylamino-4-amino-s-triazine; OIAT, 2-hydroxy-4-isopropylamino-6-amino-striazine; OIET, 2-hydroxy-4-isopropylamino-6-ethylamino-s-triazine; SA, sulfonic acid]

Constituent n

Mean detection rate 

(percent) Pairs with inconsistent detections (percent)

Volatile organic compounds

1,2-Dichloropropane 11 100 0

Carbon disulfide 16 93.8 12.5

Methyl tert-butyl ether 17 100 0

Trichloromethane 28 100 0

Pesticide compounds

Atrazine 39 91 17.9

CAAT 41 96.3 7.3

CEAT 23 97.8 4.3

Deethylatrazine (CIAT) 41 89 22

Hexazinone 16 81.3 37.5

Metolachlor 16 78.1 43.8

Metolachlor SA 43 95.3 9.3

OIAT 15 80 40

OIET 12 95.8 8.3

Prometon 13 92.3 15.4

Simazine 18 91.7 16.7

Radiological analytes

α radioactivity, 30-day count 57 85.1 29.8

α radioactivity, 72-hour count 68 89 22.1

β radioactivity, 30-day count 76 93.4 13.2

β radioactivity, 72-hour count 79 94.3 11.4

Radium-224 59 89.8 20.3

Radium-226 74 81.8 36.5

Radium-228 54 84.3 31.5
Appendix 4. Quality-Control Samples and Data Analysis 121

Table 4.5. Estimated variability in concentrations of selected analytes based on field replicate samples collected by the U.S. 

Geological Survey National Water-Quality Assessment Project, May 2013 through December 2016.

[Variability was evaluated only for constituents having at least 10 replicate pairs with consistent detections. n, number of values in that category; IPT, inflection point titration method; mg/L, milligram per liter; CaCO3, calcium carbonate; μS/cm, microsiemen per centimeter at 25 degrees Celsius; μg/L, microgram 

per liter; InsD, insufficient data; ng/L, nanogram per liter; SD, standard deviation; RSD, relative standard deviation; CAAT, chlorodiamino-s-triazine; CEAT, 

2-chloro-6-ethylamino-4-amino-s-triazine; OIET, 2-hydroxy-4-isopropylamino-6-ethylamino-s-triazine; SA, sulfonic acid; pCi/L, picocurie per liter]

Constituent Concentration range (unit) n

Variability

Statistic Value Unit

Major and minor elements and physical parameters

Alkalinity by IPT All available values 

(mg/L as CaCO3)

58 Mean SD 6.4 mg/L as CaCO3

pH All available values (pH units) 118 Mean SD 0.031 pH units

Specific conductance All available values (μS/cm) 118 Mean RSD 0.24 percent

Calcium <10 23 Mean SD 0.059 mg/L

>10 96 Mean RSD 1.3 percent

Magnesium All available concentrations 119 Mean RSD 1 percent

Potassium <1.5 45 Mean SD 0.016 mg/L

>1.5 74 Mean RSD 1.9 percent

Sodium All available concentrations 119 Mean RSD 1.5 percent

Bromide <0.1 53 Mean SD 0.0026 mg/L

>0.1 37 Mean RSD 5.7 percent

Chloride <40 91 Mean SD 0.05 mg/L

>40 28 Mean RSD 1.2 percent

Fluoride <0.2 62 Mean SD 0.0014 mg/L

>0.2 51 Mean RSD 2.5 percent

Silica All available concentrations 119 Mean SD 0.22 mg/L

Sulfate <40 72 Mean SD 0.029 mg/L

>40 43 Mean RSD 0.93 percent

Residue on evaporation <900 107 Mean SD 7.3 mg/L

>900 10 Mean RSD 0.82 percent

Iron <100 36 Mean SD 5.3 μg/L

>100 29 Mean RSD 5.4 percent

Trace elements

Aluminum All available concentrations 42 Mean SD 1 μg/L

Antimony <0.1 33 Mean SD 0.0026 μg/L

>0.1 12 Mean RSD 4.3 percent

Arsenic <1.0 48 Mean SD 0.0083 μg/L

>1.0 51 Mean RSD 1.6 percent

Barium <50 73 Mean SD 0.22 μg/L

>50 47 Mean RSD 1.6 percent

Beryllium All available concentrations 30 Mean SD 0.0028 μg/L

Boron <30 44 Mean SD 0.3 μg/L

>30 66 Mean RSD 1.9 percent

Cadmium All available concentrations 25 Mean SD 0.0036 μg/L

Chromium <2 39 Mean SD 0.028 μg/L

>2 12 Mean RSD 2.6 percent
122 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Table 4.5. Estimated variability in concentrations of selected analytes based on field replicate samples collected by the U.S. 

Geological Survey National Water-Quality Assessment Project, May 2013 through December 2016.—Continued

[Variability was evaluated only for constituents having at least 10 replicate pairs with consistent detections. n, number of values in that category; IPT, inflection point titration method; mg/L, milligram per liter; CaCO3, calcium carbonate; μS/cm, microsiemen per centimeter at 25 degrees Celsius; μg/L, microgram 

per liter; InsD, insufficient data; ng/L, nanogram per liter; SD, standard deviation; RSD, relative standard deviation; CAAT, chlorodiamino-s-triazine; CEAT, 

2-chloro-6-ethylamino-4-amino-s-triazine; OIET, 2-hydroxy-4-isopropylamino-6-ethylamino-s-triazine; SA, sulfonic acid; pCi/L, picocurie per liter]

Constituent Concentration range (unit) n

Variability

Statistic Value Unit

Trace elements—Continued

Cobalt <0.2 53 Mean SD 0.0061 μg/L

>0.2 20 Mean RSD 7.9 percent

Copper <8 37 Mean SD 0.17 μg/L

>8 6 Mean RSD InsD percent

Lead <0.4 52 Mean SD 0.021 μg/L

>0.4 17 Mean RSD 9.3 percent

Lithium All available concentrations 118 Mean RSD 1.8 percent

Manganese <10 44 Mean SD 0.11 μg/L

>10 44 Mean RSD 4.2 percent

Molybdenum <1 53 Mean SD 0.0076 μg/L

>1 54 Mean RSD 1.6 percent

Nickel <2 76 Mean SD 0.041 μg/L

>2 16 Mean RSD 2 percent

Selenium <0.5 57 Mean SD 0.0069 μg/L

>0.5 22 Mean RSD 2.9 percent

Strontium All available concentrations 123 Mean RSD 1.2 percent

Thallium All available concentrations 15 Mean SD 0.0022 μg/L

Uranium <1 56 Mean SD 0.0061 μg/L

>1 44 Mean RSD 2 percent

Vanadium <2 49 Mean SD 0.018 μg/L

>2 42 Mean RSD 1.3 percent

Zinc All available concentrations 59 Mean SD 1.3 μg/L

Nutrients

Ammonia <0.2 23 Mean SD 0.0019 mg/L

>0.2 20 Mean RSD 3 percent

Nitrite plus nitrate <3 40 Mean SD 0.0092 mg/L

>3 35 Mean RSD 0.98 percent

Nitrite All available concentrations 18 Mean SD 0.0004 mg/L

Total nitrogen <3 73 Mean SD 0.039 mg/L

>3 34 Mean RSD 1.5 percent

Orthophosphate <0.07 84 Mean SD 0.001 mg/L

>0.07 21 Mean RSD 2.3 percent

Dissolved organic carbon >1 41 Mean SD 0.03 mg/L

<1 32 Mean RSD 10.8 percent

Volatile organic compounds

1,2-Dichloropropane All available concentrations 11 Mean RSD 5.8 percent

Carbon disulfide <0.3 11 Mean SD 0.013 μg/L

>0.3 3 Mean RSD InsD percent
Appendix 4. Quality-Control Samples and Data Analysis 123

Table 4.5. Estimated variability in concentrations of selected analytes based on field replicate samples collected by the U.S. 

Geological Survey National Water-Quality Assessment Project, May 2013 through December 2016.—Continued

[Variability was evaluated only for constituents having at least 10 replicate pairs with consistent detections. n, number of values in that category; IPT, inflection point titration method; mg/L, milligram per liter; CaCO3, calcium carbonate; μS/cm, microsiemen per centimeter at 25 degrees Celsius; μg/L, microgram 

per liter; InsD, insufficient data; ng/L, nanogram per liter; SD, standard deviation; RSD, relative standard deviation; CAAT, chlorodiamino-s-triazine; CEAT, 

2-chloro-6-ethylamino-4-amino-s-triazine; OIET, 2-hydroxy-4-isopropylamino-6-ethylamino-s-triazine; SA, sulfonic acid; pCi/L, picocurie per liter]

Constituent Concentration range (unit) n

Variability

Statistic Value Unit

Volatile organic compounds—Continued

Methyl tert-butyl ether <0.3 14 Mean SD 0.0014 μg/L

>0.3 3 Mean RSD InsD percent

Trichloromethane <0.2 20 Mean SD 0.0013 μg/L

>0.2 9 Mean RSD InsD percent

Pesticide compounds

Atrazine <90 29 Mean SD 1.1 ng/L

>90 3 Mean RSD InsD percent

Deethylatrazine (CIAT) <200 27 Mean SD 4.8 ng/L

>200 5 Mean RSD InsD percent

CEAT <500 21 Mean SD 7.7 ng/L

>500 1 Mean RSD InsD percent

CAAT <65 12 Mean SD 4 ng/L

>65 26 Mean RSD 13 percent

Hexazinone <15 8 Mean SD InsD ng/L

>15 2 Mean RSD InsD percent

Metolachlor SA <500 14 Mean SD 19 ng/L

>500 5 Mean RSD 3.6 percent

OIET <50 9 Mean SD InsD ng/L

>50 2 Mean RSD InsD percent

Prometon <10 6 Mean SD InsD ng/L

>10 5 Mean RSD InsD percent

Simazine <50 14 Mean SD 2.1 ng/L

>50 1 Mean RSD InsD percent

Radiological analytes

α radioactivity, 30-day count <5 25 Mean SD 0.5 pCi/L

>5 15 Mean RSD 9.7 percent

α radioactivity, 72-hour count <10 41 Mean SD 0.55 pCi/L

>10 12 Mean RSD 14 percent

β radioactivity, 30-day count <8 44 Mean SD 0.59 pCi/L

>8 22 Mean RSD 8.3 percent

β radioactivity, 72-hour count <10 57 Mean SD 0.76 pCi/L

≥10 13 Mean RSD 8.6 percent

Radium-224 All available concentrations 48 Mean SD 0.15 pCi/L

Radium-226 All available concentrations 48 Mean SD 0.13 pCi/L

Radium-228 All available concentrations 38 Mean SD 0.16 pCi/L

Radon <500 32 Mean SD 12 pCi/L

>500 14 Mean RSD 2.9 percent
124 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

The percent recovery of an analyte in an individual 

spike sample is calculated by subtracting the concentration 

of the paired unspiked sample (collected closely in time) 

from the concentration of the spiked sample, then dividing 

by the expected concentration and multiplying by 100. The 

expected concentration is equal to the concentration of the 

spike solution times the amount of spike solution added to the 

sample, divided by the volume of the spiked sample. When 

the NWQL reported that an analyte was not detected in the 

paired unspiked sample, a concentration of zero was assumed 

for the purposes of the calculating the percent recovery. Spike 

solutions were obtained from the NWQL, which provides the 

concentration of each analyte included in an individual spike 

lot. Analytes included in spike solutions were assumed not to 

have degraded before use of the spike solution, although it is 

possible that future evaluation of spike sample results might 

indicate that the assumption is violated for certain spike lots, 

compounds, or both. For VOCs, the NWQL was assumed to 

have added 20 microliters of spike solution to a 43-milliliter 

sample. For arsenic species, field crews were assumed to have 

added 100 microliters of spike solution to an 11.5-milliliter 

sample. Samples were excluded from analysis of recoveries 

when there was evidence that they had been collected after 

chlorination, which can affect the analysis of many compounds. Individual results were excluded from analysis of 

recoveries when the concentration present for the compound 

in the unspiked sample exceeded the expected spike concentration because this can result in increased uncertainty in 

recovery (Shoda and others, 2018).

Results of an initial evaluation of recovery data from 

laboratory spikes for VOCs analyzed under laboratory schedules 4436 (S4436) and 4437 (S4437) and from field spikes 

for arsenic species analyzed under laboratory code 3142 

(LC3142) during the entire cycle 3 sampling period are presented in this report (laboratory schedules and codes are listed 

in table 2 of Arnold and others (2020, 2017b, 2018b). Data for 

laboratory spikes collected for VOCs and field spikes collected 

for arsenic species in January 2015–December 2016 also are 

published in this report. Data and results of earlier evaluations 

of recovery data from laboratory spikes collected for VOCs 

in 2012 under older laboratory schedules 2020 and 4024 and 

from laboratory spikes collected for VOCs collected in May 

2013 through December 2014 for S4436 and S4437 are presented in Arnold and others (2017a,b); data and results of an 

earlier evaluation of recovery data for field spikes collected for 

arsenic speciation in 2014 also are presented there. Although 

an evaluation of recovery results for field spikes collected 

for pesticides is not included in this report, data for pesticide 

spike samples collected in May 2013–December 2016 are 

published here.

The objective of performing an initial evaluation of spike 

samples was to determine if substantial positive or negative 

recovery bias exists for any analytes. Substantial positive 

or negative bias could have implications for comparisons of 

environmental concentrations of these analytes as reported 

by the NWQL with their corresponding HHBs. For example, 

a large negative recovery bias could result in the laboratory 

reporting a concentration that is substantially less than the 

concentration actually present in the environment, leading to 

an incorrect conclusion that the concentration in the environment does not exceed an HHB when it actually does. For the 

purposes of this initial evaluation of spike samples, a median 

recovery between 70 and 130 percent is considered acceptable. Further evaluation of results for spike samples would 

be needed to determine if recovery bias for certain analytes 

would affect the interpretation of environmental concentrations of those analytes for objectives other than those presented in this report.

Spike Sample Counts

Between May 2013 and December 2016, a total of 

96 laboratory spikes for VOCs by S4436, 97 laboratory spikes 

for VOCs by S4437, and 9 field spikes for arsenic species 

were collected for the NAWQA Project (tables 4.6–4.9). Data 

for VOC and arsenic species spike samples from January 2015 

through December 2016 are presented in tables 4.13–4.15 of 

Arnold and others (2020). Data for the 60 field spikes collected for pesticides by S2437 during cycle 3 are presented in 

table 4.14 of Arnold and others (2020).

Spike Sample Results

For VOC laboratory spikes collected during cycle 3 for 

analysis by S4436 or S4437, the median recovery for individual compounds ranged from 69.4 to 110.4 percent (tables 

4.6–4.8; figs. 4.1 and 4.2; table 4.13 of Arnold and others, 

2020). Only butane, which is included on S4436 and has no 

HHB, had a median recovery less than 70 percent. No VOCs 

had median recoveries greater than 130 percent. Therefore, 

laboratory spike recovery results do not indicate any issues 

with comparing reported VOC concentrations to their corresponding HHBs.

For the nine arsenic speciation field spikes collected 

during cycle 3 for analysis by LC3142, recoveries ranged 

from 77.3 to 124.7 percent for arsenate, 60.6 to 111.2 percent 

for arsenite, 63.6 to 100.0 percent for dimethylarsinate, and 

70.6 to 103.3 percent for monomethylarsonate (table 4.15 of 

Arnold and others, 2020; fig. 4.3). Median recoveries for all 

four species were between 85.3 and 90.6 percent (table 4.6). 

These results indicate bias from method performance, effects 

of the sample matrix, or analyte degradation, or all of the 

above generally is small but is more likely to be slightly negative than positive. No HHBs exist that are specific to individual arsenic species, although there is an HHB for total arsenic.
Appendix 4. Quality-Control Samples and Data Analysis 125

Table 4.6. Summary of results for spike samples collected by the National Water-Quality Assessment Project from May 2013 

to December 2016.

[VOC, volatile organic compound; HHB, human-health benchmark; --, not applicable]

Type of summary VOC by schedule 

4436

VOC by schedule 

4437 Arsenic species

Total number of field spikes 0 0 9

Total number of laboratory blanks 96 97 0

Number of constituents analyzed 49 38 4

Range of median spike recoveries, in percent 69.4–107.5 88.3–110.4 85.3–90.6

Number of compounds with median spike recovery less than 

70 percent

1 0 0

Number of compounds with median spike recovery less than 

70 percent with a corresponding HHB

0 -- --

Number of compounds with median spike recovery greater 

than 130 percent

0 0 0

Number of compounds with median spike recovery greater 

than 130 percent with a corresponding HHB

-- -- --
126 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Table 4.7. Statistical summary of laboratory spike recovery results for volatile organic compounds included in laboratory schedule 

4436.

[n, number of values for that constituent; min, minimum; 10th, 25th, 75th, and 90th, 10th, 25th, 75th, and 90th percentiles; med, median, max, maximum]

Parameter 

code Constituent

Statistic

n Min 10th 25th Med 75th 90th Max

P62174 2,2-Dichloro-1,1,1-trifluoroethane, water, unfiltered, recoverable, micrograms per liter96 84.3 94.1 98.7 101.7 107.4 110.9 118.6

P45028 Chlorodifluoromethane, water, unfiltered, recoverable, micrograms per liter96 67.4 79.0 87.1 94.0 105.6 117.2 133.0

P77119 Dichlorofluoromethane, water, unfiltered, recoverable, micrograms per liter96 90.2 95.1 100.6 103.1 108.5 114.9 124.2

P81590 Hexane, water, unfiltered, recoverable, micrograms per liter96 42.3 55.4 64.5 75.1 83.2 92.1 103.8

P81604 n-Pentane, water, unfiltered, recoverable, micrograms per liter96 55.2 65.2 76.3 88.8 98.6 106.9 124.7

P77562 1,1,1,2-Tetrachloroethane, water, unfiltered, 

recoverable, micrograms per liter

96 78.8 88.4 90.8 95.3 100.7 106.9 120.3

P34506 1,1,1-Trichloroethane, water, unfiltered, recoverable, micrograms per liter96 81.9 91.8 96.2 100.6 105.5 107.7 122.2

P34511 1,1,2-Trichloroethane, water, unfiltered, recoverable, micrograms per liter96 86.3 91.8 96.9 100.7 104.7 109.8 118.9

P34496 1,1-Dichloroethane, water, unfiltered, recoverable, micrograms per liter96 82.7 93.3 98.0 101.3 106.1 110.2 124.2

P34501 1,1-Dichloroethene, water, unfiltered, recoverable, micrograms per liter96 81.0 86.3 91.8 99.4 103.3 108.5 117.7

P34551 1,2,4-Trichlorobenzene, water, unfiltered, 

recoverable, micrograms per liter

96 75.4 84.8 88.1 92.2 98.2 104.7 116.5

P77222 1,2,4-Trimethylbenzene, water, unfiltered, 

recoverable, micrograms per liter

96 73.5 80.5 84.5 95.8 107.0 117.3 133.0

P34536 1,2-Dichlorobenzene, water, unfiltered, recoverable, micrograms per liter96 83.7 99.8 103.5 107.5 111.5 115.9 124.5

P32103 1,2-Dichloroethane, water, unfiltered, recoverable, micrograms per liter96 87.4 95.5 97.9 100.5 103.2 107.2 115.9

P34571 1,4-Dichlorobenzene, water, unfiltered, recoverable, micrograms per liter96 84.1 93.9 97.6 101.6 107.1 112.6 126.1

P34030 Benzene, water, unfiltered, recoverable, micrograms per liter96 83.5 90.8 97.9 101.3 106.1 110.2 123.9

P77297 Bromochloromethane, water, unfiltered, recoverable, micrograms per liter96 86.2 95.2 99.9 103.7 109.5 114.4 121.6

P32101 Bromodichloromethane, water, unfiltered, 

recoverable, micrograms per liter

96 79.7 85.6 89.1 94.3 104.3 110.9 127.0

P34413 Bromomethane, water, unfiltered, recoverable, 

micrograms per liter

96 42.6 82.8 95.3 107.0 125.8 149.6 195.6

P77041 Carbon disulfide, water, unfiltered, micrograms 

per liter

93 42.4 73.2 78.9 88.7 97.8 110.0 175.5

P34301 Chlorobenzene, water, unfiltered, recoverable, 

micrograms per liter

96 80.9 85.6 89.0 93.4 100.4 105.2 116.3

P34418 Chloromethane, water, unfiltered, recoverable, 

micrograms per liter

96 56.0 74.8 89.1 102.6 113.9 129.8 154.8

P77093 cis-1,2-Dichloroethene, water, unfiltered, recoverable, micrograms per liter96 84.4 92.0 94.8 99.0 102.6 110.1 121.1
Appendix 4. Quality-Control Samples and Data Analysis 127

Table 4.7. Statistical summary of laboratory spike recovery results for volatile organic compounds included in laboratory schedule 

4436.—Continued

[n, number of values for that constituent; min, minimum; 10th, 25th, 75th, and 90th, 10th, 25th, 75th, and 90th percentiles; med, median, max, maximum]

Parameter 

code Constituent

Statistic

n Min 10th 25th Med 75th 90th Max

P34704 cis-1,3-Dichloropropene, water, unfiltered, 

recoverable, micrograms per liter

96 78.3 84.9 88.3 92.1 98.4 104.4 115.3

P32105 Dibromochloromethane, water, unfiltered, 

recoverable, micrograms per liter

96 80.8 83.4 87.1 92.3 102.1 107.2 127.8

P34423 Dichloromethane, water, unfiltered, recoverable, 

micrograms per liter

96 74.3 93.9 98.6 101.6 105.1 110.9 117.0

P34371 Ethylbenzene, water, unfiltered, recoverable, 

micrograms per liter

96 73.6 78.7 83.3 88.6 98.0 104.9 125.0

P85795 m-Xylene plus p-xylene, water, unfiltered, 

recoverable, micrograms per liter

96 69.2 76.3 79.7 89.9 106.0 118.2 134.1

P34696 Naphthalene, water, unfiltered, recoverable, 

micrograms per liter

96 67.2 72.9 77.0 83.0 94.4 104.1 110.9

P77224 n-Propylbenzene, water, unfiltered, recoverable, 

micrograms per liter

96 70.9 75.1 78.3 84.0 102.7 111.9 130.0

P77135 o-Xylene, water, unfiltered, recoverable, micrograms per liter96 79.0 80.6 84.5 90.5 100.4 107.0 126.2

P77350 sec-Butylbenzene, water, unfiltered, recoverable, micrograms per liter96 72.6 77.7 80.5 88.9 100.0 108.2 126.9

P77128 Styrene, water, unfiltered, recoverable, micrograms per liter96 0.0 77.0 80.7 88.3 103.4 112.9 130.2

P78032 Methyl tert-butyl ether, water, unfiltered, recoverable, micrograms per liter96 73.4 92.0 94.9 98.8 103.4 109.4 125.5

P34475 Tetrachloroethene, water, unfiltered, recoverable, micrograms per liter95 86.7 91.4 96.2 103.9 111.6 120.9 138.7

P32102 Tetrachloromethane, water, unfiltered, recoverable, micrograms per liter96 83.9 88.9 91.7 96.5 101.9 108.9 122.3

P34010 Toluene, water, unfiltered, recoverable, micrograms per liter96 86.7 93.7 97.6 101.6 108.3 112.1 123.9

P34546 trans-1,2-Dichloroethene, water, unfiltered, 

recoverable, micrograms per liter

96 80.7 89.6 94.9 100.5 106.0 109.6 123.0

P34699 trans-1,3-Dichloropropene, water, unfiltered, 

recoverable, micrograms per liter

96 70.7 78.5 83.3 87.2 94.5 100.0 123.6

P32104 Tribromomethane, water, unfiltered, recoverable, micrograms per liter96 80.0 84.4 88.4 92.2 99.4 107.2 133.9

P39180 Trichloroethene, water, unfiltered, recoverable, 

micrograms per liter

95 77.9 84.2 87.3 94.0 99.6 102.6 111.3

P32106 Trichloromethane, water, unfiltered, recoverable, micrograms per liter93 89.9 96.0 100.5 104.6 107.9 112.8 121.5

P39175 Vinyl chloride, water, unfiltered, recoverable, 

micrograms per liter

96 73.0 81.6 89.1 96.4 103.8 110.0 140.5

P68726 1,3-Butadiene, water, unfiltered, recoverable, 

micrograms per liter

96 4.4 71.3 78.8 89.8 102.6 112.7 131.7

P49538 1,1-Difluoroethane, water, unfiltered, recoverable, micrograms per liter96 72.0 81.3 85.1 90.7 96.3 101.2 108.9

P77323 1,2,3,4-Tetrahydronaphthalene, water, unfiltered, recoverable, micrograms per liter96 58.0 65.3 75.3 84.3 107.0 115.5 127.7
128 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Table 4.7. Statistical summary of laboratory spike recovery results for volatile organic compounds included in laboratory schedule 

4436.—Continued

[n, number of values for that constituent; min, minimum; 10th, 25th, 75th, and 90th, 10th, 25th, 75th, and 90th percentiles; med, median, max, maximum]

Parameter 

code Constituent

Statistic

n Min 10th 25th Med 75th 90th Max

P50985 1,2-Dichloro-1,1,2,2-tetrafluoroethane, water, 

unfiltered, recoverable, micrograms per liter

96 47.1 62.0 68.0 76.0 84.0 91.3 101.7

P85668 1-Chloro-1,1-difluoroethane, water, unfiltered, 

recoverable, micrograms per liter

96 59.4 78.8 87.8 94.8 101.5 104.6 131.6

P81563 Butane, water, unfiltered, recoverable, micrograms per liter96 52.1 58.8 64.5 69.4 77.9 85.5 109.0
Appendix 4. Quality-Control Samples and Data Analysis 129

Table 4.8. Statistical summary of laboratory spike recovery results for volatile organic compounds included in laboratory schedule 

4437.

[n, number of values for that constituent; min, minimum; 10th, 25th, 75th, and 90th, 10th, 25th, 75th, and 90th percentiles; med, median, max, maximum]

Parameter 

code Constituent

Statistic

n Min 10th 25th Med 75th 90th Max

P80336 1,1-Dichloro-2-propanone, water, unfiltered, recoverable, micrograms per liter97 61.5 95.5 100.3 105.4 111.4 117.6 138.7

P81582 1,4-Dioxane, water, unfiltered, recoverable, micrograms per liter97 69.6 90.1 93.6 97.5 102.0 106.3 114.5

P77076 2-Nitropropane, water, unfiltered, recoverable, micrograms per liter97 64.1 92.8 95.3 98.6 103.7 107.5 117.4

P76997 Acetonitrile, water, unfiltered, recoverable, micrograms 

per liter

97 75.5 99.4 103.5 110.0 118.7 126.6 142.4

P34210 Acrolein, water, unfiltered, recoverable, micrograms 

per liter

77 0.0 81.6 87.4 94.6 103.8 108.4 118.2

P81578 Dimethoxymethane, water, unfiltered, recoverable, 

micrograms per liter

97 71.5 97.5 100.7 106.2 111.8 116.2 125.8

P81585 Ethyl acetate, water, unfiltered, recoverable, micrograms per liter97 60.7 88.4 94.3 101.5 107.0 110.9 125.8

P34386 Hexachlorocyclopentadiene, water, unfiltered, recoverable, micrograms per liter97 46.7 71.1 79.2 89.3 108.4 121.2 149.8

P34408 Isophorone, water, unfiltered, recoverable, micrograms 

per liter

97 57.2 83.7 91.7 105.7 119.5 129.3 137.8

P77032 Methyl acetate, water, unfiltered, recoverable, micrograms per liter97 71.6 93.9 98.3 103.4 110.5 118.1 132.0

P34447 Nitrobenzene, water, unfiltered, recoverable, micrograms per liter97 54.9 82.5 89.3 97.2 106.5 118.0 133.6

P78200 N-Nitrosodiethylamine, water, unfiltered, recoverable, 

micrograms per liter

97 65.2 83.8 92.3 107.4 120.0 127.3 143.8

P77035 tert-Butyl alcohol, water, unfiltered, recoverable, micrograms per liter97 64.9 94.6 97.8 100.8 106.3 111.8 119.6

P77443 1,2,3-Trichloropropane, water, unfiltered, recoverable, 

micrograms per liter

97 64.1 92.4 97.2 100.0 104.6 108.4 117.2

P82625 1,2-Dibromo-3-chloropropane, water, unfiltered, recoverable, micrograms per liter97 61.3 89.6 93.8 98.2 103.7 109.9 122.4

P77651 1,2-Dibromoethane, water, unfiltered, recoverable, 

micrograms per liter

97 64.0 93.6 97.1 99.7 104.3 108.0 120.4

P34541 1,2-Dichloropropane, water, unfiltered, recoverable, 

micrograms per liter

97 68.9 92.1 96.4 100.2 105.3 108.8 120.4

P78032 Methyl tert-butyl ether, water, unfiltered, recoverable, 

micrograms per liter

97 67.4 93.8 99.0 102.7 107.3 112.1 118.6

P68728 2-Ethoxyethyl acetate, water, unfiltered, recoverable, 

micrograms per liter

97 72.7 93.7 100.6 110.4 120.3 126.7 135.1

P68729 2-Propen-1-ol, water, unfiltered, recoverable, micrograms per liter97 68.4 93.8 99.7 107.0 114.3 118.4 125.1

P68730 α-Terpineo, water, unfiltered, recoverable, micrograms 

per liter

97 51.9 78.0 87.0 98.4 113.2 126.2 145.1

P68732 Butanal, water, unfiltered, recoverable, micrograms per 

liter

97 69.3 87.3 90.2 95.5 102.9 107.4 120.4

P77548 Chloropicrin, water, unfiltered, recoverable, micrograms per liter97 67.1 92.9 99.8 105.1 114.7 123.1 133.8
130 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

Table 4.8. Statistical summary of laboratory spike recovery results for volatile organic compounds included in laboratory schedule 

4437.—Continued

[n, number of values for that constituent; min, minimum; 10th, 25th, 75th, and 90th, 10th, 25th, 75th, and 90th percentiles; med, median, max, maximum]

Parameter 

code Constituent

Statistic

n Min 10th 25th Med 75th 90th Max

P68733 trans-Crotonaldehyde, water, unfiltered, recoverable, 

micrograms per liter

97 73.5 87.7 92.9 96.9 101.7 106.2 115.8

P81583 1,3-Dioxolane, water, unfiltered, recoverable, micrograms per liter97 75.1 93.3 98.2 103.3 108.9 113.5 122.2

P77034 1-Butanol, water, unfiltered, recoverable, micrograms 

per liter

97 62.0 91.8 95.3 101.0 105.3 110.7 120.5

P68066 1-Methoxy-4-(2-propenyl)benzene, water, unfiltered, 

recoverable, micrograms per liter

97 58.4 75.7 81.6 89.7 100.7 111.9 130.0

P77310 1-Octanol, water, unfiltered, recoverable, micrograms 

per liter

97 56.5 79.9 84.5 89.9 94.9 100.4 115.7

P77419 2,6-Dimethyl-4-heptanone, water, unfiltered, recoverable, micrograms per liter97 53.8 81.5 85.3 90.2 97.2 103.3 116.9

P77311 2-Ethyl-1-hexanol, water, unfiltered, recoverable, 

micrograms per liter

97 51.6 79.1 84.0 89.7 96.0 102.3 112.8

P77201 2-Methylpropyl acetate, water, unfiltered, recoverable, 

micrograms per liter

97 60.2 79.9 84.3 88.3 93.6 97.2 109.1

P77113 4-Methyl-2-pentanol, water, unfiltered, recoverable, 

micrograms per liter

97 60.1 89.1 93.3 97.3 102.0 104.7 109.5

P77179 5-Methyl-2-hexanone, water, unfiltered, recoverable, 

micrograms per liter

97 60.7 89.1 93.1 98.8 104.5 108.0 115.0

P77097 Cyclohexanone, water, unfiltered, recoverable, micrograms per liter97 64.8 88.8 92.9 98.5 104.8 111.7 168.3

P45013 Isopropyl acetate, water, unfiltered, recoverable, micrograms per liter97 67.6 94.2 99.9 103.3 108.1 113.5 125.4

P77015 Isopropyl alcohol, water, unfiltered, recoverable, micrograms per liter97 64.9 89.0 93.5 97.6 103.1 110.9 332.4

P77061 n-Pentanal, water, unfiltered, recoverable, micrograms 

per liter

97 63.9 85.3 88.9 93.1 97.3 102.2 115.5

P45022 Propyl acetate, water, unfiltered, recoverable, micrograms per liter97 69.3 85.6 89.0 93.9 100.1 105.8 118.2
Appendix 4. Quality-Control Samples and Data Analysis 131

Table 4.9. Statistical summary of field spike recovery results for arsenic species analyzed under laboratory code 3142.

[n, number of values for that constituent; min, minimum; 10th, 25th, 75th, and 90th, 10th, 25th, 75th, and 90th percentiles; med, median, max, maximum]

Parameter 

code Constituent

Statistic

n Min 10th 25th Med 75th 90th Max

P62453 Arsenate (H2AsO4

−), water, filtered, micrograms per liter 

as arsenic

7 77.3 82.8 86.6 88.0 97.8 113.2 124.7

P62452 Arsenite (H3AsO3), water, filtered, micrograms per liter 

as arsenic

9 60.6 79.1 84.9 90.6 97.4 111.0 111.2

P62455 Dimethylarsinate ([CH3]2HAsO2), water, filtered, recoverable, micrograms per liter as arsenic9 63.6 68.4 87.3 89.9 93.8 99.3 100.0

P62454 Monomethylarsonate ([CH3]HAsO3

−), water, filtered, 

recoverable, micrograms per liter as arsenic

9 70.6 79.3 83.1 85.3 96.9 100.2 103.3
020406080 100 120 140 160 180 200

Spike recovery, in percent

2,2-Dichloro-1,1,1-trifluoroethane Chlorodifluoromethane Dichlorofluoromethane Hexane n-Pentane 1,1,1,2-Tetrachloroethane 1,1,1-Trichloroethane 1,1,2-Trichloroethane 1,1-Dichloroethane 1,1-Dichloroethene 1,2,4-Trichlorobenzene 1,2,4-Trimethylbenzene 1,2-Dichlorobenzene 1,2-Dichloroethane 1,4-Dichlorobenzene Benzene Bromochloromethane Bromodichloromethane Bromomethane Carbon disulfide Chlorobenzene Chloromethane cis-1,2-Dichloroethene cis-1,3-Dichloropropene Dibromochloromethane Dichloromethane Ethylbenzene m-Xylene plus p-xylene Naphthalene n-Propylbenzene o-Xylene sec-Butylbenzene Styrene Methyl tert-butyl ether Tetrachloroethene Tetrachloromethane Toluene trans-1,2-Dichloroethene trans-1,3-Dichloropropene Tribromomethane Trichloroeth

Trichlor

Volatile organic compounds

EXPLANATI

Maximum75th percentileMedian25th percentileMinimum

Rang by  Lab me100-p

Figure 4.1. Laboratory spike recovery results for volatile organic compounds included in laboratory schedule 4436.
020

406080

100120140160180200

Spike recovery, in percent

1,1-Dichloro-2-propanone 1,4-Dioxane 2-Nitropropane Acetonitrile Acrolein Dimethoxymethane Ethyl acetate Hexachlorocyclopentadiene Isophorone Methyl acetate Nitrobenzene N-Nitrosodiethylamine tert-Butyl alcohol 1,2,3-Trichloropropane 1,2-Dibromo-3-chloropropane 1,2-Dibromoethane 1,2-Dichloropropane Methyl tert-butyl ether 2-Ethoxyethyl acetate 2-Propen-1-ol alpha-Terpineo Butanal Chloropicrin trans-Crotonaldehyde 1,3-Dioxolane 1-Butanol 1-Methoxy-4-(2-propenyl)benzene 1-Octanol 2,6-Dimethyl-4-heptanone 2-Ethyl-1-hexanol 2-Methylpropyl acetate 4-Methyl-2-pent

5-Methyl

Volatile organic compounds

EXPLANATIO

Maximum75th percentileMedian25th percentileMinimum

Range  by t Lab met100-p

Figure 4.2. Laboratory spike recovery results for volatile organic compounds included in laboratory schedule 4437.
134 Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016

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0

20

40

60

80

100

120

140

160

180

200

Spike recovery, in percent

Arsenate

Arsenite

Dimethylarsenate

Monomethylarsonate

EXPLANATION

Maximum

75th percentile

Median

25th percentile

Minimum

Range of spike recovery considered

 by the National Water Quality

 Laboratory to indicate acceptable 

 method performance

100-percent recovery reference line

Arsenic species

Figure 4.3. Field spike recovery results for arsenic speciation by 

laboratory code 3142.
Appendix 4. Quality-Control Samples and Data Analysis 135

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For more information about this publication, contact:

Director, USGS Central Midwest Water Science Center 

405 North Goodwin

Urbana, IL 61801

217–328–8747

For additional information, visit: https://www.usgs.gov/centers/cm-water

Publishing support provided by the 

Rolla Publishing Service Center

Arnold and others—Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, 2016—DS 1124

ISSN 2327-638X (online)

https://doi.org/10.3133/ds1124