U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the California State Water Resources Control Board

A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program

Status and Understanding of Groundwater Quality in the San Francisco 

Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Photo placement

Scientific Investigations Report 2012–5248
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

boundaries from California Department of Water Resources (CDWR, 2003).

Cover photographs:

Front cover: Monitoring well in San Francisco County, California. (Photograph taken by Michael Wright, 

U.S. Geological Survey.)

Back cover: Santa Clara Valley, California, April 2007. (Photograph taken by Michael Solt, U.S. 

Geological Survey.)

Sampling priority

Study

area

Low-use basins

Priority basins

Areas that are outside CDWR-defined

 groundwater basins
Status and Understanding of Groundwater Quality in 

the San Francisco Bay Groundwater Basins, 2007: 

California GAMA Priority Basin Project

By Mary C. Parsons, Justin T. Kulongoski, and Kenneth Belitz

A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program

Prepared in cooperation with the California State Water Resources Control Board

Scientific Investigations Report 2012–5248

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Parsons, M.C., Kulongoski, J.T., and Belitz, Kenneth, 2013, Status and understanding of groundwater quality in 

the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project: U.S. Geological Survey 

Scientific Investigations Report 2012–5248, 76 p.
iii

Contents

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

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

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

Hydrogeologic Setting of the San Francisco Bay Study Unit.................................................................4

Methods ..........................................................................................................................................................9

Relative-Concentrations and Water-Quality Benchmarks ............................................................9

Data Used for Status Assessment ..........................................................................................11

U.S. Geological Survey Grid Data ..................................................................................11

California Department of Public Health Grid Data ......................................................12

Data Used for Spatially Weighted Calculation.............................................................13

Selection of Constituents for Status and Understanding Assessments....................................13

Calculation of Aquifer-Scale Proportions.......................................................................................14

Understanding-Assessment Methods ............................................................................................15

Statistical Analysis ....................................................................................................................15

Potential Explanatory Factors....................................................................................................................16

Land Use...............................................................................................................................................16

Well Depth and Depth to Top-of-Perforation .................................................................................16

Groundwater Age................................................................................................................................20

Geochemical Conditions....................................................................................................................20

Correlations Between Explanatory Factors ...................................................................................23

Status and Understanding of Water Quality ...........................................................................................24

Inorganic Constituents.......................................................................................................................24

Trace Elements...........................................................................................................................33

Uranium and Radioactive Constituents..................................................................................33

Nutrients......................................................................................................................................33

Understanding Assessment for Nitrate ........................................................................33

Constituents with Aesthetic Benchmarks .............................................................................36

Understanding Assessment for TDS and Chloride......................................................36

Understanding Assessment for Iron and Manganese ...............................................38

Organic Constituents..........................................................................................................................38

Trihalomethanes.........................................................................................................................45

Understanding Assessment for Chloroform.................................................................45

Solvents.......................................................................................................................................45

Understanding Assessment for Solvents .....................................................................45

Other VOCs..................................................................................................................................45

Understanding Assessment for CFC-113 ......................................................................46

Herbicides...................................................................................................................................46

Special-Interest Constituents...........................................................................................................47

Understanding Assessment for Perchlorate.........................................................................47

Summary .......................................................................................................................................................50

Acknowledgments.......................................................................................................................................50

References Cited..........................................................................................................................................51
iv

Contents—Continued

Appendix A. Use of Data from The California Department of Public Health (CDPH) Database ....55

Appendix B. Calculating Total Dissolved Solids .....................................................................................59

Appendix C. Estimation of Aquifer-Scale Proportions...........................................................................59

Appendix D. Ancillary Datasets.................................................................................................................60

Appendix E. Comparison of Data from the CDPH and USGS-GAMA Program..................................73

Figures

Figure 1. Map of the San Francisco Bay study unit, California GAMA Priority Basin

Project, and the California hydrogeologic provinces.................................... 3

Figure 2. Map showing the geographic features of the San Francisco Bay study unit,

California GAMA Priority Basin Project................................................... 5

Figure 3. Map showing the geologic formations and areal distribution of USGS-grid

and understanding wells sampled in the San Francisco Bay study unit,

California GAMA Priority Basin Project................................................... 6

Figure 4. Map showing the locations of study unit grid cells, U.S. Geological Survey

(USGS) grid and understanding wells, and California Department of Public

Health (CDPH) wells, San Francisco Bay study unit, California GAMA Priority

Basin Project, April–June 2007 ............................................................ 10

Figure 5. Ternary diagrams showing percentages of urban, agricultural, and natural

land use in the study unit and in the area surrounding each USGS-grid and

USGS-understanding well in the San Francisco Bay study unit, California

GAMA Priority Basin Project............................................................... 17

Figure 6. Map showing classification of land use in the San Francisco Bay study unit,

California GAMA Priority Basin Project................................................... 18

Figure 7. Boxplots showing well depths, depths to top-of-perforations, and perforation

lengths for grid, understanding production, and understanding monitor wells,

San Francisco Bay Study Unit, California GAMA Priority Basin Project,

April–June 2007.............................................................................. 19

Figure 8. Boxplots showing relation of groundwater age classification to depth to

top-of-perforations and to well depth, and bar chart showing age

classification in relation to well depth classification for USGS-grid wells and

USGS-understanding production wells, San Francisco Bay study unit,

California GAMA Priority Basin Project, April–June 2007 .............................. 21

Figure 9. Graph showing pH plotted as a function of well depth, well type, and age

classification, San Francisco Bay Study Unit, California GAMA Priority Basin

Project, April–June 2007 .................................................................. 22

Figure 10. Map showing dissolved oxygen concentrations in USGS-grid and

USGS-understanding wells, San Francisco Bay Study Unit, California GAMA

Priority Basin Project, April–June 2007 ................................................... 22

Figure 11. Graph showing maximum relative-concentration of constituents detected in

grid wells, by constituent class, San Francisco Bay study unit, California

GAMA Priority Basin Project, April–June 2007 .......................................... 28

Figure 12. Graph showing maximum relative-concentrations of selected inorganic

constituents in grid wells, San Francisco Bay Study Unit, California GAMA

Priority Basin Project, April–June 2007 ................................................... 29
v

Figures—Continued

Figure 13. Maps showing concentrations of selected inorganic constituents for

USGS-grid and USGS-understanding wells, April–June 2007, and from the

California Department of Public Health database for the period April 1,

2004–March 31, 2007, San Francisco Bay study unit, California GAMA Priority

Basin Project: nitrate, total dissolved solids, chloride, iron, and manganese ...... 30

Figure 14. Plots showing nitrate, as nitrogen, concentrations related to: classifications

of groundwater age, dissolved oxygen, well type, and classification of

groundwater age, and depth to top-of-perforations, well type, and land use

in USGS-grid and USGS-understanding wells sampled for the San Francisco

Bay study unit, California GAMA Priority Basin Project, April–June 2007 ......... 34

Figure 15. Graph of total total dissolved solids concentrations relative to depth to

top-of-perforation, well type, and geochemical classifications in USGS-grid

and USGS-understanding wells in the San Francisco Bay study unit,

California GAMA Priority Basin Project, April–June 2007 .............................. 37

Figure 16. Graph showing detection frequency and maximum relative-concentrations

of organic and special-interest constituents detected in USGS-grid wells in

the San Francisco Bay study unit, California GAMA Priority Basin Project,

April–June 2007.............................................................................. 39

Figure 17. Graph showing the detection frequency and maximum

relative-concentrations of selected organic and special-interest

constituents in USGS-grid wells in the San Francisco Bay Study Unit,

California GAMA Priority Basin Project, April–June 2007 .............................. 40

Figure 18. Map showing concentrations of selected organic and special-interest

constituents detected in USGS-grid and USGS-understanding wells for

April–June 2007, San Francisco Bay study unit, California GAMA Priority

Basin Project: chloroform, solvents, atrazine, and perchlorate ..................... 41

Figure 19. Graph showing concentrations of CFC-113 relative to well depth and

groundwater age classification and geochemical classification for the San

Francisco Bay study unit, California GAMA Priority Basin Project, April–June

2007 .......................................................................................... 46

Figure 20. Graph showing concentrations of perchlorate relative to groundwater age

classification for the San Francisco Bay study unit, California GAMA Priority

Basin Project, April–June 2007 ............................................................ 48

Figure 21. Graph showing concentrations of perchlorate relative to dissolved oxygen

concentrations, land-use classification, and groundwater age classification

for the San Francisco Bay study unit, California GAMA Priority Basin Project,

April–June 2007.............................................................................. 48

Figure 22. Predicted probability of detecting perchlorate in groundwater as a function

of aridity index and Anthropogenic Score and observed detection frequency

and average aridity index in groups of samples for perchlorate

concentrations greater than or equal to 0.5 microgram per liter (μg/L) and 

1.0 μg/L, San Francisco Bay study unit, California GAMA Priority Basin Project ... 49
vi

Tables

Table 1. Number of wells sampled by the U.S. Geological Survey for the fast and slow

sampling schedules, and number of constituents sampled in each

constituent class for the San Francisco Bay study unit, California GAMA

Priority Basin Project, April–June 2007 ................................................... 11

Table 2. Inorganic constituents and associated benchmark information, and number

of grid wells with USGS-GAMA data and CDPH data, for each constituent,

San Francisco Bay study unit, California GAMA Priority Basin Project............... 12

Table 3. Comparison of the number of compounds and median laboratory reporting

levels or method detection levels, by type of constituent, for data reported in

the California Department of Public Health (CDPH) database and for data

collected by the U.S. Geological Survey (USGS) for the San Francisco Bay

study unit, California GAMA Priority Basin Project, April–June 2007 ............... 13

Table 4. Historically high constituents from the California Department of Public

Health (CDPH) database, July 5, 1977, to March 31, 2004, San Francisco Bay

study unit, California GAMA Priority Basin Project .................................... 14

Table 5. Results of non-parametric (Spearman’s rho method) analysis of correlations

in grid and understanding wells between selected potential explanatory

factors, San Francisco Bay study unit, California GAMA Priority Basin

Project ....................................................................................... 23

Table 6. Results of Wilcoxon rank-sum tests for differences in values of selected

potential explanatory factors between samples classified into groundwater

age, San Francisco Bay study unit, California GAMA Priority Basin Project......... 24

Table 7. Aquifer-scale proportions from grid-based and spatially weighted

approaches for constituents that: (1) had moderate or high

relative-concentrations during April 1, 2004, to March 31, 2007, from the

California Department of Public Health (CDPH) database, (2) had moderate or

high relative-concentrations in samples collected from USGS-grid wells

(April–June 2007), or (3) are organic constituents having detection

frequencies greater than 10 percent in USGS-grid wells, San Francisco Bay

study unit, California GAMA Priority Basin Project .................................... 25

Table 8. Numbers of constituents analyzed and detected by the U.S. Geological

Survey, with associated benchmarks in each constituent class, San

Francisco Bay study unit, California GAMA Priority Basin Project, April–June

2007 .......................................................................................... 27

Table 9A. Aquifer-scale proportions for constituent classes of inorganics with

health-based and aesthetic benchmarks, San Francisco Bay study unit,

California GAMA Priority Basin Project................................................... 29

Table 9B. Aquifer-scale proportions for constituent classes of organics with

health-based benchmarks, San Francisco Bay study unit, California GAMA

Priority Basin Project........................................................................ 29

Table 10. Results of Wilcoxon rank-sum tests on grid-well data used to determine

significant differences between selected water-quality constituents grouped by 

potential explanatory factor classifications, San Francisco Bay study unit, California 

GAMA Priority Basin Project............................................................... 35

Table 11. Results of non-parametric (Spearman’s method) analysis of correlations between 

selected water-quality constituents and potential explanatory factors, San Francisco 

Bay study unit, California GAMA Priority Basin Project .............................. 36
vii

Conversion Factors, Datums, and 

Abbreviations and Acronyms

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 2.590 square kilometer (km2) 

Flow rate

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

Radioactivity

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

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

 °F= (1.8×°C)+32.

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

 °C= (°F–32)/1.8.

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 milligrams per liter (mg/L) or 

micrograms per liter (μg/L). One milligram per liter is equivalent to 1 part per million (ppm); 

1 microgram per liter is equivalent to 1 part per billion (ppb). Concentrations or activities for 

radioactive constituents in water are given in picocuries per liter (pCi/L), percent modern 

carbon (pmc), or tritium units (TU).

Datums

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

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

Abbreviations and Acronyms

AL-US U.S. Environmental Protection Agency action level 

E estimated or having a higher degree of uncertainty

GAMA Groundwater Ambient Monitoring and Assessment Program

HAL-US U.S. Environmental Protection Agency lifetime health advisory level 

HBSL health-based screening level

LRL laboratory reporting level

LSD land-surface datum

LT-MDL long-term method detection level

MCL-CA California Department of Public Health maximum contaminant level 

MCL-US U.S. Environmental Protection Agency maximum contaminant level 

MDL method detection level

MRL minimum reporting level

nc no significant correlation

NL-CA California Department of Public Health notification level 

NWIS National Water Information System (USGS)

PSW public-supply well

QC quality control

RSD5-US U.S. Environmental Protection Agency risk-specific dose at a risk factor of 10–5

SFBAY San Francisco Bay study unit

SMCL-CA California Department of Public Health secondary maximum contaminant level 

SMCL-US U.S. Environmental Protection Agency secondary maximum contaminant level 

TEAP terminal electron acceptor process

TT-US U.S. Environmental Protection Agency treatment technique

Organizations

CDPH California Department of Public Health (Department of Health Services prior to 

July 1, 2007)

CDPR California Department of Pesticide Regulation

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water-Quality Assessment Program (USGS)

NWQL National Water Quality Laboratory (USGS)

SWRCB State Water Resources Control Board (California)

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
ix

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued

Selected chemical names

Ammonia-N ammonia as nitrogen

14C carbon-14

CaCO3 calcium carbonate

CFC-113 1,1,2-trichlorotrifluoroethane

DO dissolved oxygen

3H tritium

MTBE Methyl tert-butyl ether

NDMA N-nitrosodimethylamine

Nitrate-N nitrate as nitrogen

Nitrite-N nitrite as nitrogen

PCE tetrachloroethene

TCA 1,1,1-trichloroethane

TCE trichloroethene

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound
x

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Abstract

Groundwater quality in the approximately 620-squaremile (1,600-square-kilometer) San Francisco Bay study unit 

was investigated as part of the Priority Basin Project of the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program. The study unit is located in the Southern Coast 

Ranges of California, in San Francisco, San Mateo, Santa 

Clara, Alameda, and Contra Costa Counties. The GAMA 

Priority Basin Project is being conducted by the California 

State Water Resources Control Board in collaboration with the 

U.S. Geological Survey (USGS) and the Lawrence Livermore 

National Laboratory. 

The GAMA San Francisco Bay study was designed 

to provide a spatially unbiased assessment of the quality of 

untreated groundwater within the primary aquifer system, as 

well as a statistically consistent basis for comparing water 

quality throughout the State. The assessment is based on 

water-quality and ancillary data collected by the USGS from 

79 wells in 2007 and is supplemented with water-quality data 

from the California Department of Public Health (CDPH) 

database. The primary aquifer system is defined by the depth 

interval of the wells listed in the CDPH database for the San 

Francisco Bay study unit. The quality of groundwater in 

shallower or deeper water-bearing zones may differ from that 

in the primary aquifer system; shallower groundwater may be 

more vulnerable to surficial contamination.

The first component of this study, the status of the 

current quality of the groundwater resource, was assessed 

by using data from samples analyzed for volatile organic 

compounds (VOCs), pesticides, and naturally occurring 

inorganic constituents, such as major ions and trace elements. 

Water- quality data from the CDPH database also were 

incorporated for this assessment. This status assessment is 

intended to characterize the quality of groundwater resources 

within the primary aquifer system of the San Francisco 

Bay study unit, not the treated drinking water delivered to 

consumers by water purveyors.

Relative-concentrations (sample concentration 

divided by the benchmark concentration) were used for 

evaluating groundwater quality for those constituents 

that have Federal and (or) California benchmarks. A 

relative-concentration greater than (>) 1.0 indicates a 

concentration greater than a benchmark, and a relativeconcentration less than or equal to (≤) 1.0 indicates a 

concentration equal to or less than a benchmark. Relativeconcentrations of organic and special-interest constituents 

were classified as low (relative- concentration ≤ 0.1), 

moderate (0.1 < relative- concentration ≤ 1.0), or high

(relative-concentration > 1.0). Inorganic constituent relativeconcentrations were classified as low (relativeconcentration ≤ 0.5), moderate (0.5 < relative-concentration 

≤ 1.0), or high (relative- concentration > 1.0). A lower 

threshold value of relative-concentration was used to 

distinguish between low and moderate values of organic 

constituents because organic constituents are generally less 

prevalent and have smaller relative-concentrations than 

naturally occurring inorganic constituents.

Aquifer-scale proportion was used as the metric for 

evaluating regional-scale groundwater quality. High aquiferscale proportion is defined as the percentage of the primary 

aquifer system that has relative-concentration greater than 1.0 

for a particular constituent or class of constituents; proportion 

is based on an areal rather than a volumetric basis. Moderate 

and low aquifer-scale proportions were defined as the 

percentages of the primary aquifer system that have moderate 

and low relative-concentrations, respectively. Two statistical 

approaches—grid-based and spatially weighted—were used 

to evaluate aquifer-scale proportion for individual constituents 

and classes of constituents. Grid-based and spatially weighted 

estimates were comparable in the San Francisco Bay study 

unit (90-percent confidence intervals).

Inorganic constituents with health-based benchmarks 

were present at high relative-concentrations in 5.1 percent 

of the primary aquifer system, and at moderate relativeconcentrations in 25 percent. The high aquifer-scale proportion 

of inorganic constituents primarily reflected high aquifer-scale 

proportions of barium (3.0 percent) and nitrate (2.1 percent). 

Inorganic constituents with secondary maximum contaminant 

levels were present at high relative-concentrations in 

Status and Understanding of Groundwater Quality in the 

San Francisco Bay Groundwater Basins, 2007: 

California GAMA Priority Basin Project

By Mary C. Parsons, Justin T. Kulongoski, and Kenneth Belitz
2 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

14 percent of the primary aquifer system and at moderate 

relative-concentrations in 33 percent. The constituents present 

at high relative-concentrations included total dissolved solids 

(7.0 percent), chloride (6.1 percent), manganese (12 percent), 

and iron (3.0 percent).

Organic constituents with health-based benchmarks were 

present at high relative-concentrations in 0.6 percent and at 

moderate relative-concentrations in 12 percent of the primary 

aquifer system. Of the 202 organic constituents analyzed 

for, 32 were detected. Three organic constituents were 

frequently detected (in 10 percent or more of samples): the 

trihalomethane chloroform, the solvent 1,1,1-trichloroethane 

and the refrigerant 1,1,2-trichlorotrifluoroethane. One specialinterest constituent, perchlorate, was detected at moderate 

relative-concentrations in 42 percent of the primary aquifer 

system.

The second component of this work, the understanding 

assessment, identified some of the primary natural and human 

factors that may affect groundwater quality by evaluating land 

use, physical characteristics of the wells, and geochemical 

conditions of the aquifer. Results from these evaluations were 

used to explain the occurrence and distribution of constituents 

in the study unit. 

Introduction 

To assess the quality of ambient groundwater in aquifers 

used for drinking-water supply and to establish a baseline 

groundwater-quality monitoring program, the California State 

Water Resources Control Board (SWRCB), in collaboration 

with the U.S. Geological Survey (USGS) and Lawrence 

Livermore National Laboratory (LLNL), implemented the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program (website at: http://www.waterboards.ca.gov/gama). 

The statewide GAMA Program currently consists of four 

projects: (1) the GAMA Priority Basin Project, conducted 

by the USGS (website at: http://ca.water.usgs.gov/gama/); 

(2) the GAMA Domestic Well Project, conducted by the 

SWRCB; (3) GAMA Special Studies, conducted by LLNL, 

and (4) the online database GeoTracker GAMA, conducted by 

the SWRCB. On a statewide basis, the Priority Basin Project 

focused primarily on the deeper part of the groundwater 

resource, and the SWRCB Domestic Well Project generally 

focused on the shallower aquifer systems. The primary aquifer 

system may be at less risk of contamination than the shallow 

wells, such as private domestic and environmental monitoring 

wells, which are closer to surficial sources of contamination. 

As a result, concentrations of contaminants, such as volatile 

organic compounds (VOCs) and nitrate, in shallow wells can 

be greater than in wells screened in the deeper primary aquifer 

(Kulongoski and others, 2010; Landon and others, 2010).

The SWRCB initiated the GAMA Program in 2000 in 

response to a legislative mandate (State of California, 1999; 

2001a). The GAMA Priority Basin Project was initiated 

in response to the Groundwater Quality Monitoring Act of 

2001 to assess and monitor the quality of groundwater in 

California (State of California, 2001b). The GAMA Priority 

Basin Project is a comprehensive assessment of statewide 

groundwater quality designed to improve understanding 

and identification of risks to groundwater resources and to 

increase the availability of information about groundwater 

quality to the public. For the Priority Basin Project, the USGS, 

in collaboration with the SWRCB, developed monitoring 

plans to assess groundwater basins through direct sampling 

of groundwater and other statistically reliable sampling 

approaches (Belitz and others, 2003; California State Water 

Resources Control Board, 2003). Additional partners in 

the GAMA Priority Basin Project include the California 

Department of Public Health (CDPH), the California 

Department of Pesticide Regulation (CDPR), the California 

Department of Water Resources (CDWR), and local water 

agencies and well owners (Kulongoski and Belitz, 2004). 

The ranges of hydrologic, geologic, and climatic 

conditions in California were considered in this program’s 

assessment of groundwater quality. Belitz and others (2003) 

partitioned the State into 10 hydrogeologic provinces, 

each with distinctive hydrologic, geologic, and climatic 

characteristics (fig. 1). All of these hydrogeologic provinces 

contain groundwater basins and subbasins designated by the 

CDWR (California Department of Water Resources, 2003). 

Groundwater basins generally consist of relatively permeable, 

unconsolidated deposits of alluvial or volcanic origin. Eighty 

percent of the approximately 16,000 public-supply wells in 

California are in designated groundwater basins. Groundwater 

basins and subbasins were prioritized for sampling on the 

basis of the number of public-supply wells, with secondary 

consideration given to municipal groundwater use, agricultural 

pumping, the number of historically leaking underground fuel 

tanks, and registered pesticide applications (Belitz and others, 

2003). The 116 priority basins, and additional areas outside 

defined groundwater basins, were grouped into 35 study units, 

which include approximately 95 percent of the public-supply 

wells in California. The San Francisco Bay study unit is 

composed of eight groundwater basins in the Southern Coast 

Ranges hydrogeologic province.
Introduction 3

Basin and Range

Central

Valley

Klamath

Mountains

Desert

Cascades and

Modoc Plateau

Transverse Ranges and

selected Peninsular Ranges

Southern Coast

Ranges

Sierra

Nevada

Northern

Coast

Ranges

San Diego

Drainages

Bakersfield

San

Francisco

Sacramento

OREGON

NEVADA

MEXICO

ARIZONA

Redding

Los Angeles

San Diego

PA C I F I C O C E A N

0 200 MILES

0 200 KILOMETERS

100

100

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

Provinces from Belitz and others, 2003.

SAN FRANCISCO BAY 

GAMA STUDY UNIT

(figure 2)

IP_017719_Figure 01 LocationMap

Figure 1. San Francisco Bay study unit, California GAMA Priority Basin Project, and the California hydrogeologic 

provinces.
4 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Purpose and Scope

The purposes of this report are to provide a (1) study 

unit description: description of the hydrogeologic setting 

of the San Francisco Bay study unit (fig. 1), hereinafter 

referred to as the SFBAY study unit, (2) status assessment: 

assessment of the status of the current quality of groundwater 

in the primary aquifer system in the SFBAY study unit, and 

(3) understanding assessment: identification of the natural and 

human factors affecting groundwater quality and explanation 

of the relations between water quality and selected potential 

explanatory factors. 

Water-quality data for samples collected by the USGSGAMA Program in the SFBAY study unit and details of 

sample collection, analysis, and quality-assurance procedures 

for the SFBAY study unit are reported in Ray and others 

(2009). Using those same data, this report describes methods 

used in designing the sampling network, identifying CDPH 

data for use in the status assessment, estimating aquifer-scale 

proportions of relative-concentrations, analyzing ancillary 

datasets, classifying groundwater age, and assessing the status 

and understanding of groundwater quality by statistical and 

graphical approaches. 

The status assessment includes analyses of water-quality 

data for 43 production wells selected by the USGS for spatial 

coverage of 1 well per grid cell across the SFBAY study unit 

(hereinafter referred to as USGS-grid wells). Most of these 

wells were public-supply wells, but a few other types of wells 

with similar perforation depth intervals to the USGS-grid 

wells also were sampled. Samples were collected for analysis 

of organic constituents, such as VOCs and pesticides, and 

inorganic constituents, such as major ions and trace elements. 

Water-quality data from the CDPH database also were used to 

supplement data collected by USGS for the GAMA Program. 

The resulting set of water-quality data from USGS-grid wells 

and selected CDPH wells was considered to be representative 

of the water quality in the primary aquifer system in the 

SFBAY study unit; the primary aquifer system is defined by 

the depth intervals of the wells listed in the CDPH database 

for the SFBAY study unit. USGS-GAMA status assessments 

are designed to provide a statistically robust characterization 

of groundwater quality in the primary aquifer system at the 

basin-scale (Belitz and others, 2003). The statistically robust 

design also allows basins to be compared and results to be 

synthesized at regional, statewide, and national scales.

To provide context, the water-quality data discussed 

in this report were compared to State and Federal drinkingwater benchmarks, both regulatory and non-regulatory. The 

assessments in this report characterize the quality of untreated 

groundwater resources in the primary aquifer system within 

the study unit, not the drinking water delivered to consumers 

by water purveyors. After withdrawal from the ground, water 

typically is treated, disinfected, and (or) blended with other 

waters to maintain acceptable water quality. Benchmarks 

apply to water that is served to the consumer, not to untreated 

groundwater.

The understanding assessment uses data from 36 wells 

sampled by the USGS for the purpose of understanding 

(hereinafter referred to as USGS-understanding wells), in 

addition to data from the 43 USGS-grid wells sampled for 

the status assessment, to determine the relations between 

water quality and selected potential explanatory factors and to 

identify the natural and human factors affecting groundwater 

quality. Potential explanatory factors examined included 

land use, depth of well, indicators of groundwater age, and 

geochemical conditions. 

Hydrogeologic Setting of the 

San Francisco Bay Study Unit

The SFBAY study unit covers approximately 

620 square miles (mi2) in San Francisco, San Mateo, Santa 

Clara, Alameda, and Contra Costa Counties. The SFBAY 

study unit lies in the Southern Coast Ranges hydrogeologic 

province (fig. 1), and contains eight groundwater basins 

defined by CDWR: Marina, Lobos, Downtown, Islais Valley, 

South San Francisco, Visitacion Valley, Westside, and Santa 

Clara Valley (fig. 2). For the purpose of this study, these 

eight groundwater basins were grouped into one study unit. 

As part of the Priority Basin Project, untreated groundwater 

samples were collected from 79 wells in the SFBAY study unit 

from April 23 to June 21, 2007 (Ray and others, 2009). This 

study unit is bounded on the east by the Diablo Range and 

Franciscan complex, on the west by the Santa Cruz Mountains 

and the San Andreas Fault, on the north by the Golden Gate 

Strait, and on the south by the Santa Clara Valley groundwater 

divide (figs. 2, 3). This groundwater divide at Cochrane Road 

in Morgan Hill separates the northerly flow of water toward 

San Francisco Bay from the southerly flow of water toward 

Monterey Bay (Moran and others, 2002).
Introduction 5

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Aqueduct

Canal

Stream

Counties

SAN FRANCISCO

CO

ALAMEDA CO

SANTA CRUZ CO

CONTRA COSTA CO

MARIN CO

SAN MATEO CO

SANTA CLARA CO

SAN JOAQUIN CO

SACRAMENTO CO

Hayward

Alameda

San Francisco

San Jose

San Mateo

Palo Alto

Santa Clara

Morgan Hill

Arroyo Calero

Arroyo Valle

Guadalupe River

Arroyo Hondo

Llagas Creek

Arroyo de la Laguna

Arroyo Valle

California Aqueduct

Delta-Mendota

Canal

Coyote Creek

Diablo Range

Old River

Contra Costa Canal

Dutch Slough

P a c i f i c O c e a n

San Francisco

Bay

Golden Gate Strait

EXPLANATION

0 10 20 MILES

Groundwater basins in the San Francisco Bay (SFBAY) Study Unit 0 10 20 KILOMETERS

Downtown

Islais Valley

Lobos

Marina

South San Francisco

Visitacion Valley

Westside

Santa Clara Valley

IP_017719_Figure 02 Area Map

Percolation

ponds

Figure 2. Geographic features of the San Francisco Bay study unit, California GAMA Priority Basin Project.
6 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Figure 3. Geologic formations and areal distribution of USGS-grid and understanding wells sampled in the San Francisco Bay study 

unit, California GAMA Priority Basin Project.

San Andreas Fault

Hayward Fault

Q

K

P

M

KJf

E

KJfm

Ku

QPc

O

grMz

Ti

Kl

Mzv

um

Tv

Ep

J

Oc

Tv

gb

KJfs

sch

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Geology from Jennings, 1977, 

and Saucedo and others, 2000

SAN FRANCISCO

CO

ALAMEDA CO

SANTA CRUZ CO

CONTRA COSTA CO

MARIN CO

SAN MATEO CO

SANTA CLARA CO

SAN JOAQUIN CO

SACRAMENTO CO

Hayward

Alameda San Francisco

San Jose

San Mateo

Palo Alto

Santa

Clara

Morgan Hill

Arroyo Calero

Arroyo Valle

Guadalupe River

Arroyo Hondo

Llagas Creek

Arroyo de la Laguna

Arroyo Valle

Coyote Creek

Diablo Range

Santa Cruz Mts

Old River

Contra Costa Canal

Dutch Slough

P a c i f i c O c e a n

San Francisco

Bay

Golden Gate Strait

0 10 20 MILES

0 10 20 KILOMETERS

IP017719_Figure 03 Geology

California Aqueduct

Delta-Mendota

Canal
Introduction 7

EXPLANATION

USGS-grid well

USGS-understanding well

Study unit and study area boundary

Water boundary

?

Fault—Dashed where approximately

 located, dotted where concealed,

 queried where uncertain

 

Quaternary

Tertiary

Tertiary

Cretaceous

Water

GEOLOGIC UNIT

Sedimentary rocks Sedimentary and metasedimentary rocks

Plutonic, metavolcanic, and mixed rocks

Cenozoic Mesozoic

Volcanic rocks

Aqueduct

Canal

Stream

Counties

Plio-Pleistocene sediment

Quaternary alluvium

Quaternary other sediment

Marine sediment

Nonmarine sediment

Mafic volcanics

Franciscan complex

Granitic rocks

Metamorphic other

Metasediment

Pyroclastic volcanics

Ultramafic/mafic

Q

Qs

QPc

E, P

Oc

Tv, Ti

K, Ku

grMz

Mzv

um

KJf(m)

Tvp

Figure 3.—Continued
8 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

largely composed of the Mesozoic-age Franciscan Group, 

which is a large complex of highly altered submarinedeposited volcanic rocks and chert deposits. The basement 

also is composed of Cretaceous-age sedimentary formations, 

known as the Great Valley Sequence, consisting of bedded 

series of sandstone, siltstone, shale, and thick conglomerate 

layers, and the Cenozoic-age Santa Clara Formation, which 

is a broadly folded, lightly consolidated, fluvial unit of 

conglomerate, sandstone, and mudstone. The Santa Clara 

Formation is slightly water bearing in the areas of the study 

unit where the formation dips below younger alluvial deposits. 

 The Hayward Fault in the Diablo Range and the San 

Andreas Fault in the Santa Cruz Mountains form the present 

eastern and western boundaries of the synform, respectively. 

Both faults are part of a complex structural system of 

northwesterly trending valleys and mountain ranges, created 

by tectonic uplift caused by the collision of the North 

American Plate with the Pacific Ocean Plate. Most faults in the 

area are no longer active; however, the Hayward Fault and the 

San Andreas Fault remain prominent, active faults (Figuers, 

1998). The Hayward Fault separates Mesozoic-age units (on 

the west) from Cenozoic-age units (on the east) and impedes 

the westward flow of groundwater (California Environmental 

Protection Agency, San Francisco Bay Regional Water Quality 

Control Board, 2003). The Diablo Range separates the Santa 

Clara Valley on the west from the San Joaquin Valley on the 

east. The Santa Cruz Mountains form a ridge along the San 

Francisco Peninsula, which separates the Pacific Ocean from 

the San Francisco Bay and the Santa Clara Valley. The San 

Andreas Fault runs near the ridgeline (average altitude of 

3,000 feet) throughout the range. The Santa Cruz Mountains 

are the result of tectonic uplift caused by a leftward bend in 

the San Andreas Fault (Wallace, 1990).

The climate in the SFBAY area is Mediterranean and is 

characterized by warm, dry summers and cool, moist winters 

(Hanson and others, 2004). The average annual temperature 

is 61 degrees Fahrenheit (°F) in Santa Clara and 58 °F in San 

Francisco (U.S. Department of Commerce, 2010). Average 

rainfall across the study unit ranges from 14 inches per 

year (in/yr) in the southern and southwestern parts of the 

study unit to 28 in/yr in the northern parts of the study unit 

(PRISM Climate Group, Oregon State University, 2010); most 

precipitation falls between November and April. Precipitation 

can be as much as 90 in/yr in the surrounding mountains, 

falling mostly as rain with some snow in the higher elevations 

(California Department of Water Resources, 2003). Most of 

the precipitation falls in the western side of the study unit; the 

Santa Cruz Mountains block the delivery of moisture to the 

Diablo Range, which is situated approximately 40 miles inland 

from the ocean, and as a result receives less precipitation than 

the Santa Cruz Mountains.

The Coyote Creek (fig. 2) is the main drainage feature 

of the Santa Clara Valley groundwater basin. It originates 

in the Diablo Range and flows northwesterly through the 

valley before entering the San Francisco Bay (California 

Environmental Protection Agency, San Francisco Bay 

The primary aquifer system that is the focus of the 

GAMA Priority Basin Project represents the water-bearing 

units that supply water for wells listed in the CDPH database. 

The main water-bearing units within the Visitacion Valley, 

Islais Valley, Westside, and South San Francisco basins consist 

of Pleistocene-age unconsolidated sediments of dune sand, 

the Pleistocene-age Colma Formation (deposits consisting 

of fine-grained sand, silty sand, and discontinuous beds of 

clay), marine estuarine deposits (locally referred to as the 

“Bay Mud”), and artificial fill (California Department of 

Water Resources, 2003). Impermeable bedrock underlying the 

water basins is composed of consolidated sediments of the 

Franciscan Group (Schlocker, 1974) and interbedded strata of 

marine mudstone, sandstone, and conglomerate of late Jurassic 

and Cretaceous age (Bailey and others, 1964; California 

Department of Water Resources, 2003). The water-bearing 

units within the Lobos, Marina, and Downtown basins include 

Pleistocene-age deposits consisting of fine-grained sand, silty 

sand, and discontinuous beds of clay, and alluvial fan deposits 

(California Department of Water Resources, 2003). In the 

Santa Clara Valley, the aquifers are composed of Holoceneage and Pleistocene-age fluvial deposits, the Bay Mud, and 

the Pleistocene-age Santa Clara Formation (pebbly sandstone, 

siltstone, and clay) where it is exposed on the west and east 

sides of the Santa Clara Valley (California Department of 

Water Resources, 2003; Hanson and others, 2004). 

Sources of natural recharge to the groundwater flow 

system in the study unit include infiltration of mountain-front 

runoff, streamflow, and local precipitation. However, the 

predominant recharge sources for the groundwater flow system 

are (1) artificial recharge from the infiltration of imported 

water from the Hetch Hetchy Reservoir at percolation ponds, 

(2) leakage from transmission pipelines that transport the 

imported water, and (3) deep percolation of return flow from 

landscape irrigation (Hanson and others, 2004). Groundwater 

discharge occurs as pumpage, baseflow to streams, 

evapotranspiration, and direct discharge to the San Francisco 

Bay (Hanson and others, 2004). Groundwater generally flows 

toward the center of the basin and the San Francisco Bay. 

Saltwater intrusion to the groundwater resources is a 

known water-quality concern in the SFBAY study unit. Due 

to overdraft of the groundwater resources in the Santa Clara 

Valley basin, land subsidence has allowed saline waters 

from the Bay and adjacent salt ponds to flow upstream in 

rivers and streams and enter the shallow aquifers (California 

Environmental Protection Agency, San Francisco Bay 

Regional Water Quality Control Board, 2003). Pumping 

and abandoned wells have allowed these brackish waters to 

infiltrate deeper into the primary aquifer system (Figuers, 

1998; California Environmental Protection Agency, San 

Francisco Bay Regional Water Quality Control Board, 2003).

The SFBAY study unit overlies Mesozoic- and Cenozoicage formations, resting in a synform (downward-arched fold) 

(California Environmental Protection Agency, San Francisco 

Bay Regional Water Quality Control Board, 2003). The 

impermeable basement beneath the water-bearing units is 
Methods 9

Regional Water Quality Control Board, 2003). The Guadalupe 

River also runs through the Santa Clara Valley, originating 

in the Santa Cruz Mountains and entering the south end of 

the Bay (California Department of Water Resources, 2003). 

Other creeks and streams of the natural streamflow network 

throughout the study unit discharge directly to the Bay. The 

surface-water system also includes a system of reservoirs, 

aqueducts, and pipelines. The reservoirs discharge directly 

to creeks, whereas aqueducts and pipelines transfer imported 

water to artificial-recharge facilities (Hanson and others, 

2004).

Methods 

The status assessment provides a spatially unbiased 

assessment of groundwater quality in the primary aquifer 

system, while the understanding assessment was designed 

to evaluate the natural and human factors that may affect 

groundwater quality of the SFBAY study unit. This section 

describes the methods used to: (1) define groundwater quality, 

(2) assemble the datasets used for the status assessment, 

(3) determine which constituents warrant additional 

evaluation, (4) calculate aquifer-scale proportions, and 

(5) analyze statistics for the understanding assessment. 

Methods used for compilation of data on potential explanatory 

factors are described in appendix D.

In this study, groundwater-quality data are presented as 

relative-concentrations, the concentrations of constituents 

measured in groundwater relative to regulatory and nonregulatory benchmarks used to evaluate drinking-water 

quality. Constituents were selected for additional evaluation 

in the assessment on the basis of objective criteria defined in 

terms of relative-concentrations. Groundwater-quality data 

collected by USGS-GAMA and data compiled in the CDPH 

database are used in the status assessment. Two statistical 

methods based on spatially unbiased equal-area grids are used 

to calculate aquifer-scale proportions of low, moderate, or high 

relative-concentrations: (1) the “grid-based” method uses one 

value per grid cell to represent groundwater quality, and 

(2) the “spatially weighted” method uses many values per grid 

cell (Belitz and others, 2010).

The CDPH database contains historical records from 

more than 27,000 wells, necessitating targeted data retrievals 

to effectively access relevant water-quality data. For example, 

for the area representing the SFBAY study unit, the historical 

CDPH database contains more than 26,000 records for 

400 wells (fig. 4). The CDPH data were used in three ways 

in the status assessment: (1) to fill in gaps in the USGS data 

for the grid-based calculations of aquifer-scale proportions, 

(2) to aid in selecting constituents for additional evaluation 

in the assessment, and (3) to provide the majority of the data 

used in the spatially weighted calculations of aquifer-scale 

proportions.

Relative-Concentrations and Water-Quality 

Benchmarks

Concentrations of constituents are presented as relativeconcentrations in the status assessment:

Relative-concentration = Sample concentration

Benchmark concentration

Regulatory and non-regulatory benchmarks apply to treated 

water that is served to the consumer, not to untreated 

groundwater. However, to provide some context for the 

results, concentrations of constituents measured in the 

untreated groundwater were compared with benchmarks 

established by the U.S. Environmental Protection Agency 

(USEPA) and CDPH (U.S. Environmental Protection 

Agency, 2006; California Department of Public Health, 

2008a,b). Relative-concentrations less than 1 (< 1.0) 

indicate sample concentrations less than the benchmark, 

and relative-concentrations greater than 1 (> 1.0) indicate 

sample concentrations greater than the benchmark. The 

use of relative-concentrations also permits comparison 

of constituents on a single scale for a wide range of 

concentrations. Relative-concentrations can only be computed 

for constituents with water-quality benchmarks; therefore, 

constituents lacking water-quality benchmarks are not 

included in the status assessment. 

The benchmarks used for each constituent were selected 

in the following order of priority:

1. Regulatory, health-based CDPH and USEPA maximum 

contaminant levels (MCL-CA and MCL-US), action 

levels (AL-US), and treatment techniques (TT-US). 

2. Non-regulatory, aesthetic-based CDPH and USEPA 

secondary maximum contaminant levels (SMCL-CA and 

SMCL-US). For constituents with both recommended and 

upper SMCL-CA levels, the values for the upper levels 

were used. 

3. Non-regulatory, health-based CDPH notification levels 

(NL-CA), USEPA lifetime health advisory levels 

(HAL-US), and USEPA risk-specific doses for a risk of 

1:100,000 (RSD5-US).

Note that for constituents with multiple types of benchmarks, 

this hierarchy may not result in selection of the benchmark 

with the lowest concentration. Additional information on the 

types of benchmarks and listings of the benchmarks for all 

analyzed constituents is provided by Ray and others (2009).

Toccalino and others (2004), Toccalino and Norman 

(2006), and Rowe and others (2007) previously used the 

ratio of the measured sample concentration to the benchmark 

concentration [either USEPA MCLs or health-based screening 

levels (HBSLs)], and defined this ratio as the benchmark 

quotient. HBSLs were not used in this report because they 

are not currently used as benchmarks by California drinkingwater regulatory agencies. Because different water-quality 
10 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

SAN FRANCISCO

CO

ALAMEDA CO

SANTA CRUZ CO

CONTRA COSTA CO

MARIN CO

SAN MATEO CO

SANTA CLARA CO

SAN JOAQUIN CO

SACRAMENTO CO

Hayward

Alameda

San Francisco

San Jose

San Mateo

Palo Alto

Santa Clara

Morgan Hill

Arroyo Calero

Arroyo Valle

Guadalupe River

Arroyo Hondo

Llagas Creek

Arroyo de la Laguna

Arroyo Valle

Coyote Creek

Diablo Range

Old River

Contra Costa Canal

Dutch Slough

P a c i f i c O c e a n

San Francisco

Bay

Golden Gate Strait

EXPLANATION

0 10 20 MILES

0 10 20 KILOMETERS

IP_017719_Figure 04 Wells Map

Study unit Grid cell USGS-grid well USGS-understanding well CDPH-grid well CDPH other well

California Aqueduct

Delta-Mendota

Canal

Figure 4. Locations of study unit grid cells, U.S. Geological Survey (USGS) grid and understanding wells, and California Department 

of Public Health (CDPH) wells, San Francisco Bay study unit, California GAMA Priority Basin Project, April–June 2007.
Methods 11

benchmarks may be used to calculate relative-concentrations 

and benchmark quotients, the values of these ratios may not 

be the same for all constituents (for example, Fram and Belitz, 

2012). 

For ease of discussion, relative-concentrations of 

constituents were classified into low, moderate, and high

categories:

Category

Relative-concentrations for 

organic and special-interest 

constituents

Relative-concentrations 

for inorganic 

constituents

High > 1 > 1

Moderate > 0.1 and ≤ 1 > 0.5 and ≤ 1

Low ≤ 0.1 ≤ 0.5

The boundary between “moderate” and “low” relativeconcentrations was set at 0.1 for organic and special-interest 

constituents for consistency with other studies and reporting 

requirements (U.S. Environmental Protection Agency, 1998; 

Toccalino and others, 2004). For inorganic constituents, 

the boundary between “moderate” and “low” relativeconcentrations was set at 0.5. The primary reason for using 

a higher threshold was to focus attention on the inorganic 

constituents of most immediate concern (Fram and Belitz, 

2012). Most inorganic constituents are naturally occurring 

and tend to be more prevalent than organic constituents in 

groundwater. Although more complex classifications could be 

devised based upon the properties and sources of individual 

constituents, use of a single moderate/low boundary value 

for each of the two major groups of constituents provided a 

consistent objective criterion for distinguishing constituents 

occurring at moderate rather than low concentrations.

Data Used for Status Assessment

U.S. Geological Survey Grid Data

The primary data used for the grid-based calculations 

of aquifer-scale proportions were from wells sampled by 

USGS-GAMA. Detailed descriptions of the methods used 

to identify wells for sampling in this study unit are given in 

Ray and others (2009). Briefly, the study unit was divided 

into 10-mi2 (~25-km2) equal-area grid cells, and in each cell, 

one well was randomly selected to represent the cell (Scott, 

1990). Wells were selected from the population of wells in 

the statewide database maintained by the CDPH. The SFBAY 

study unit contained 68 grid cells, and the USGS sampled 

wells in 43 of those cells (USGS-grid wells). Of the 43 USGSgrid wells, 30 were listed in the CDPH database; the other 13 

were irrigation or domestic wells perforated at depths similar 

to the depths of CDPH wells in the cell. USGS-grid wells 

were named with an alphanumeric GAMA ID consisting of the 

prefix “SF” and a number indicating the relative location of 

the well around the San Francisco Bay (fig. A1; table A1). 

Samples collected from USGS-grid wells were analyzed 

for 229 to 266 constituents (table 1). Water-quality indicators, 

VOCs, pesticides, perchlorate, nutrients, oxidation/reduction 

Table 1. Number of wells sampled by the U.S. Geological 

Survey for the fast and slow sampling schedules, and number 

of constituents sampled in each constituent class for the San 

Francisco Bay study unit, California GAMA Priority Basin Project, 

April–June 2007.

[NDMA, N-Nitrosodimethylamine; USGS, U.S. Geological Survey; ns, not 

sampled]

Schedule

Fast Slow 

Total number of wells 48 31

Number of grid wells sampled 40 3

Number of understanding wells sampled 8 28

Analyte groups Number of constituents

Inorganic constituents

Specific conductance 1 1

Major and minor ions, alkalinity, and 

total dissolved solids

ns 11

Trace elements ns 24

Nutrients 5 5

Organic constituents

Volatile organic compounds (VOCs) 1 85 85

Pesticides and degradates 63 63

Polar pesticides and metabolites 2 54 54

Special-interest constituents

NDMA ns 1

Perchlorate 1 1

Geochemical and age-dating tracers

Dissolved oxygen, temperature, pH 3 3

Arsenic, chromium, and iron redox 

species ratios

3 3

δ2H and δ18O of water 2 2

δ14N and δ18O isotopes of nitrate 2 2

Carbon14 and δ13C of dissolved 

carbonate

2 2

Noble gases (He, Ne, Ar, Kr, Xe), 3He/4He, and tritium3

7 7

Tritium4 1 1

Radon-222 ns 1

Sum5: 229 266

1

 Includes nine constituents classified as fumigants.

2

 Does not include four constituents in common with pesticides and 

degradates.

3

 Analyzed at Lawrence Livermore National Laboratory, Livermore, 

California.

4

 Analyzed at USGS Stable Isotope and Tritium Laboratory, Menlo Park, 

California.

5

 Fourteen pharmaceutical compounds were analyzed at slow wells, and 

results are discussed in Fram and Belitz, 2011.
12 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

(redox) species ratios, noble gases, and selected isotopes were 

analyzed in samples from all wells. Major and minor ions, 

trace elements, and radioactive constituents were analyzed in 

samples from 31 wells on the slow sampling schedule. The 

sampling schedules, collection, analysis, and quality-control 

data for the analyte groups listed in table 1 are described by 

Ray and others (2009). Total dissolved solids (TDS) was 

measured directly or calculated from specific conductance (see 

appendix B).

California Department of Public Health Grid Data

Data collected by USGS-GAMA at the USGS-grid wells 

provided part of the data used for the status assessment for 

inorganic constituents; the rest of the data were obtained 

from the CDPH database. Of the 68 grid cells in the study 

unit, 25 cells did not have USGS-grid-well data, and 40 cells 

had USGS-grid-well data but did not have USGS data for 

major ions, trace elements, or radioactive constituents. The 

CDPH database was queried to provide these missing data for 

inorganic constituents. CDPH wells with data for the most 

recent 3 years available at the time of sampling (April 1, 

2004–March 31, 2007) were considered. If a well had more 

than one analysis for a constituent in the 3-year interval, the 

most recent data were selected. Selected wells from the CDPH 

database that were used to supplement the USGS data are 

referred to as CDPH-grid wells in this report.

The procedures used to identify suitable data from CDPH 

wells are described in appendix A. Briefly, the first choice was 

to use CDPH data from the same well sampled by the USGS. 

In this case, “DG” was added to the data’s GAMA ID to 

signify that it was from a well sampled by the USGS and that 

the USGS data were supplemented from the CDPH database 

(fig. A1B; table A1). If the DG well did not have all of the 

needed data, then a second well in the cell was randomly 

selected from the subset of CDPH wells with data, and a new 

identification with “DPH” was assigned to that well (fig. A1B; 

table A1). The combination of the USGS-grid-well data and 

the CDPH-grid-well data produced a network covering 47 of 

the 68 grid cells in the SFBAY study unit (table A1). The 

remaining 21 cells had no available data (fig. 4). 

The CDPH database generally did not contain data for 

all of the missing inorganic constituents at every CDPH-grid 

well; therefore, the number of wells used for the grid-based 

assessment was different for different inorganic constituents 

(table 2). Although other organizations also collect waterquality data, the CDPH database is the only statewide database 

of groundwater-chemistry data available for comprehensive 

analysis. 

CDPH data were not used to supplement USGS-grid-well 

data for VOCs, pesticides, or special-interest constituents. A 

larger number of VOCs and pesticide compounds are analyzed 

for by the USGS-GAMA Program than are available from 

the CDPH database. USGS-GAMA collected samples for 

85 VOCs plus 117 pesticides and pesticide degradates from all 

43 grid wells in the SFBAY study unit (table 1). In addition, 

Table 2. Inorganic constituents and associated benchmark 

information, and number of grid wells with USGS-GAMA data and 

CDPH data, for each constituent, San Francisco Bay study unit, 

California GAMA Priority Basin Project.

[CDPH, California Department of Public Health; N, nitrogen; SMCL, 

secondary maximum contaminant level; HBB, health-based benchmark 

(including all benchmark types except SMCL); USGS, U.S. Geological 

Survey]

Constituent

Number of grid

 wells sampled 

by USGS GAMA

Number of grid 

wells selected 

from CDPH

Nutrient-HBB

Ammonia 43 0

Nitrite 43 3

Nitrate 43 4

Trace element-HBB

Aluminum 3 29

Antimony 3 29

Arsenic 3 29

Barium 3 29

Beryllium 3 29

Boron 3 9

Cadmium 3 29

Chromium 3 28

Copper 3 29

Lead 3 25

Molybdenum 3 4

Nickel 3 29

Selenium 3 29

Strontium 3 0

Thallium 3 30

Vanadium 3 18

Minor element-HBB

Fluoride 3 29

Trace element-SMCL

Iron 3 29

Manganese 3 29

Silver 3 29

Zinc 3 29

Major element-SMCL

Chloride 3 29

Sulfate 3 29

Total dissolved solids (TDS) 3 29

Radioactive-HBB

Uranium 3 5

Radon-222 3 5

laboratory reporting levels used for USGS-GAMA analyses 

of organic and special-interest constituents were typically one 

to two orders of magnitude lower than the reporting limits for 

analyses compiled by the CDPH (table 3). 
Methods 13

Data Used for Spatially Weighted Calculation

The spatially weighted aquifer-scale proportions of 

relative-concentrations were calculated with data from the 

USGS-grid wells, from selected additional wells sampled 

by USGS-GAMA, and from all wells in the CDPH database 

having water-quality data collected during the 3-year interval 

April 1, 2004–March 31, 2007. For wells with both USGS and 

CDPH data, only the USGS data were used. 

Thirty-six additional wells were sampled by the USGS 

to provide more data in several areas to better understand 

specific groundwater-quality issues in the study unit (Ray and 

others, 2009). These “USGS-understanding” wells included 

12 production wells and 6 monitoring well clusters containing 

a total of 24 monitoring wells. The production wells were 

given GAMA IDs consisting of the prefix “SFU” and a 

number indicating the relative location of the well around the 

Bay. The monitoring wells were given GAMA IDs consisting 

of the prefix “SFM,” a letter A–F indicating the relative 

location of the well around the Bay (where A is the northwest 

side of the Bay, and letters are assigned counterclockwise), 

and a number indicating relative well depth within the cluster 

(where 1 is the deepest well) (fig. A1A; table A1). Only the 

USGS-understanding production wells were used in the 

spatially weighted calculations. The USGS-understanding 

monitoring wells were not representative of the primary 

aquifer system, due to well completion depths and well cluster 

locations (i.e., in areas not near CDPH wells). 

Selection of Constituents for Status and 

Understanding Assessments

As many as 266 constituents were analyzed in samples 

from SFBAY study unit wells; however, only a subset of 

these constituents is identified for additional evaluation in 

this report. A complete list of the constituents investigated 

by USGS-GAMA in the SFBAY study unit can be found in 

Ray and others (2009). Of the 266 constituents analyzed, 

137 constituents did not have benchmarks. Because relativeconcentrations cannot be calculated for constituents without 

benchmarks, these 137 constituents were not evaluated in 

this report. The 129 constituents having benchmarks were 

assessed, and a subset of these constituents was selected for 

additional evaluation in the status assessment on the basis of 

the following criteria:

1. Constituents present at high or moderate relativeconcentrations in the CDPH database within the 3-year 

period (April 1, 2004–March 31, 2007);

2. Constituents present at high or moderate relativeconcentrations in the USGS-grid wells or USGSunderstanding wells; or

3. Organic constituents having study unit detection 

frequencies greater than 10 percent in the USGS-grid well 

dataset for the study unit.

 Constituents discussed in the understanding assessment 

had high relative-concentrations in greater than 2 percent of 

the primary aquifer system, or were detected in greater than 

10 percent of the USGS-grid well dataset. 

The CDPH database also was used to identify 

constituents that have been reported at high relativeconcentrations historically, but not currently (table 4). 

The historical period was defined as from the earliest 

record maintained in the CDPH electronic database to 

March 31, 2004 (July 5, 1977, through March 31, 2004). 

Constituent concentrations may be historically 

high, but not currently high, because of improvement of 

groundwater quality with time or abandonment of wells 

with high concentrations. Historically high concentrations 

Table 3. Comparison of the number of compounds and median laboratory reporting levels or method detection levels, by type 

of constituent, for data reported in the California Department of Public Health (CDPH) database and for data collected by the 

U.S. Geological Survey (USGS) for the San Francisco Bay study unit, California GAMA Priority Basin Project, April–June 2007.

[CDPH, California Department of Public Health; MDL, method detection level; LRL, laboratory reporting level; mg/L, milligrams per liter; μg/L, 

micrograms per liter; pCi/L, picocuries per liter; SSMDC, sample-specific minimum detectable concentration; nc, not collected]

Constituent type

CDPH GAMA

Median

Number of unit

compounds

Median

MDL

Number of 

compounds

Median

LRL

Volatile organic compounds (including fumigants) 61 0.5 85 0.06 μg/L

Pesticides plus degradates 28 2.5 117 0.019 μg/L

Nutrients, major and minor ions 12 0.4 16 0.02 mg/L

Trace elements 24 4 24 0.11 μg/L

Radioactive constituents (SSMDC) 1 6 2 2 0.6 pCi/L

Perchlorate 1 4 1 0.5 μg/L

N-Nitrosodimethylamine (NDMA) nc nc 1 0.002 μg/L

1

 Value reported in GAMA column is a median SSMDC for two radioactive constituents collected and analyzed by GAMA.
14 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

of constituents that do not otherwise meet the criteria 

for inclusion in the status assessment are not considered 

representative of potential groundwater-quality concerns in 

the study unit from 2004 to 2007. For the SFBAY study unit, 

10 constituents had historically high concentrations (table 4). 

Half of the constituents reported at high concentrations 

only during the historical period were reported at high 

concentrations in only 1 well.

Calculation of Aquifer-Scale Proportions

The status assessment is intended to characterize the 

quality of groundwater resources within the primary aquifer 

system of the SFBAY study unit. The primary aquifer system 

is defined by the depth intervals over which wells listed in 

the CDPH database are perforated; these wells primarily are 

classified as municipal and community public-supply wells. 

The use of the term “primary aquifer system” does not imply 

a discrete aquifer unit. In most groundwater basins, municipal 

and community drinking-water supply wells generally are 

perforated at greater depths than are domestic wells. However, 

to the extent that domestic wells are perforated over the same 

depth intervals as the CDPH wells, the assessments presented 

in this report also may apply to the portions of the aquifer 

system used for domestic drinking-water supplies.

Two statistical approaches, grid-based and spatially 

weighted, were applied to evaluate the proportions of the 

primary aquifer system in the SFBAY study unit with high, 

moderate, and low relative-concentrations of constituents 

(Belitz and others, 2010). For ease of discussion, these 

proportions are referred to as “high,” “moderate,” and “low” 

aquifer-scale proportions. Calculations of aquifer-scale 

proportions were made for individual constituents meeting the 

criteria for additional evaluation in the status assessment and 

for classes of constituents. The classes consisted of groups of 

related individual constituents. For constituents with humanhealth benchmarks, the classes included trace elements, 

radioactive constituents, nutrients, trihalomethanes, solvents, 

other VOCs, and herbicides. For constituents with aestheticbased benchmarks, the classes included salinity indicators and 

trace elements. 

The grid-based calculation uses the dataset assembled 

from the USGS-grid-well and CDPH-grid-well data. For each 

constituent, the high aquifer-scale proportion was calculated 

by dividing the number of cells that had a high relativeconcentration value for that constituent by the total number 

of grid cells with data for that constituent (Belitz and others, 

2010). The moderate and low aquifer-scale proportions 

were calculated similarly. Confidence intervals for the high 

aquifer-scale proportions were computed using the Jeffreys 

Table 4. Historically high constituents from the California Department of Public Health (CDPH) database, July 5, 

1977, to March 31, 2004, San Francisco Bay study unit, California GAMA Priority Basin Project.

[CDPH, California Department of Public Health; high, concentration greater than human-health threshold; MCL-US, U.S. Environmental 

Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; SMCL-CA, CDPH secondary maximum 

contaminant level; mg/L, milligrams per liter; μg/L, micrograms per liter]

Constituent Threshold 

type

Threshold 

value Units

Date of most 

recent high 

value 

Number of 

historically 

high wells1

Number of 

wells with 

analysis1

Trace elements

Antimony MCL-US 6 μg/L 12-29-94 6 330

Cadmium MCL-US 5 μg/L 03-01-01 7 365

Chromium MCL-CA 50 μg/L 12-03-03 5 365

Thallium MCL-US 2 μg/L 03-29-94 1 330

Major and minor elements

Sulfate SMCL-CA 500 mg/L 12-18-03 1 368

Fluoride MCL-CA 2 mg/L 06-07-01 5 368

Solvents

1,2-Dichloroethane MCL-CA 0.5 μg/L 02-02-90 1 384

Fumigants

1,2-Dichloropropane MCL-CA 5 μg/L 12-08-97 1 380

Insecticide/degradate

Aldicarb Sulfone MCL-US 3 μg/L 06-16-93 1 227

Constituent of special interest

Perchlorate MCL-CA 6 μg/L 12-19-00 2 193

1

 Based on historical CDPH well data from July 5, 1977, to March 31, 2004.
Methods 15

interval for the binomial distribution (Brown and others, 

2001; Belitz and others, 2010). Although the grid-based 

approach is spatially unbiased, it may not detect constituents 

that are present at high concentrations in small proportions of 

the primary aquifer system. For calculation of aquifer-scale 

proportion for a class of constituents, cells were considered 

high if any of the constituents in that class had a high value. 

Cells were considered moderate if any of the constituents had 

a moderate value and if none of the values were high.

The spatially weighted calculation uses the dataset 

assembled from the grid wells, selected USGS-understanding 

wells, and all CDPH wells. For each constituent, the high 

aquifer-scale proportion was calculated by computing the 

proportion of wells with “high” values in each cell and then 

averaging the proportions for all of the cells (Isaaks and 

Srivastava, 1989; Belitz and others, 2010). The moderate 

aquifer-scale proportion was calculated similarly. Confidence 

intervals for spatially weighted detection frequencies of high 

concentrations are not described in this report. For calculation 

of the aquifer-scale proportion for a class of constituents, 

wells were considered high if any of the constituents had 

a high value. Wells were considered moderate if any of 

the constituents had a moderate value and if none of the 

constituents had a high value.

In addition, for each constituent, the raw detection 

frequencies of high and moderate values were calculated 

using the same dataset as was used for the spatially weighted 

calculations. However, these raw detection frequencies are 

spatially biased because the wells in the CDPH database 

are not uniformly distributed throughout the study unit 

(fig. 4). For example, if a constituent were present at high 

concentrations in a small region of the aquifer that had a high 

density of wells, the raw detection frequency of high values 

would be greater than the high aquifer-scale proportion. Raw 

detection frequencies are provided for reference, but were not 

used to assess aquifer-scale proportions (see appendix C for 

details of statistical methods). 

The grid-based high aquifer-scale proportions were used 

to represent proportions in the primary aquifer system unless 

the spatially weighted proportions were significantly different 

from the grid-based values. Significantly different results were 

defined as follows:

• If the grid-based high aquifer-scale proportion was 

zero and the spatially weighted proportion was greater 

than zero, then the spatially weighted result was used. 

This situation can happen when the concentration of 

a constituent is high in a small fraction of the primary 

aquifer system.

• If the grid-based high aquifer-scale proportion was 

greater than zero and the spatially weighted proportion 

was outside the 90-percent confidence interval (based 

on the Jeffreys interval for the binomial distribution), 

then the spatially weighted proportion was used.

The grid-based moderate and low proportions were 

used in most cases because the reporting levels for many 

organic constituents and some inorganic constituents in the 

CDPH database were greater than the boundaries between 

the moderate and low categories. However, if the grid-based 

moderate proportion was zero and the spatially weighted 

proportion was greater than zero, then the spatially weighted 

value was used as a minimum estimate for the moderate 

proportion.

Understanding-Assessment Methods

The potential explanatory factors—land use, well 

depth, depth to the top of the well perforations, classified 

groundwater age, and geochemical condition (see appendix D

for more details)—were analyzed in relation to constituents 

of interest in order to understand the physical and chemical 

processes occurring within the groundwater system. Statistical 

tests were used to identify significant correlations between the 

constituents of interest and potential explanatory factors. The 

strongest correlations for explanatory factors influencing water 

quality are shown graphically. 

The data selected for the understanding assessment were 

from grid wells and selected USGS-understanding wells. Data 

from USGS-understanding wells that are monitoring wells 

were not included in the statistical tests; only data for USGSunderstanding wells that are production wells were included. 

Statistical and graphical relations between explanatory factors 

and groundwater constituent concentrations were tested 

using either the set of grid plus understanding data or grid 

data only. Because the USGS-understanding wells were not 

randomly selected on a spatially distributed grid, these wells 

were excluded from analyses of relations of water quality to 

areally-distributed factors (land use) to avoid areal-clustering 

bias. Data from the USGS-understanding wells were used for 

analyses of relations between constituents and the vertically 

distributed explanatory factors (depth of well, groundwater 

age classification, and geochemical conditions). 

Statistical Analysis

Nonparametric statistical methods were used to test 

hypotheses about the relation between water-quality variables 

and potential explanatory factors in this report. Nonparametric 

statistics are robust techniques that generally are not 

affected by outliers and do not require that the data follow 

any particular distribution (Helsel and Hirsch, 2002). The 

significance level (p) used to test hypotheses for this report 

was compared to a threshold value (α) of 5 percent (α = 0.05) 

to evaluate whether the relation was statistically significant 

(p < α). Throughout this report, the term “correlated” is used 

to indicate that the relation was statistically significant.
16 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Two different statistical tests were used because the set 

of potential explanatory factors included both categorical and 

continuous variables. Groundwater age class and oxidationreduction class were treated as categorical variables because 

there were a finite number of values a well could be assigned: 

for example, groundwater ages were classified as modern, 

pre-modern, or mixed. Land use, well depth, depth to top 

of perforations, dissolved oxygen, and pH were treated as 

continuous variables because there were an infinite number 

of values a well could be assigned: for example, land use was 

represented by percentages of land-use types. Concentrations 

of water-quality constituents were treated as continuous 

variables.

Relations between potential explanatory factors, and 

between potential explanatory factors and water-quality 

constituents were tested for significance. Correlations between 

continuous variables were evaluated using the Spearman’s 

rho test to calculate the rank-order coefficient (ρ, rho) and the 

significance level of the correlation (p). Relations between 

categorical variables and continuous variables were evaluated 

using the Wilcoxon rank-sum test. The test was applied pairwise to the groups to determine which pairs had significantly 

different median values of the continuous variable. For 

example, the relation between well depth and groundwater 

age was evaluated by testing for differences in median value 

of well depth between each of the three pairs of groundwater 

age classes: modern and mixed, modern and pre-modern, and 

mixed and pre-modern.

Potential Explanatory Factors

Brief descriptions of potential explanatory factors, 

including land use, physical characteristics of the wells, 

indicators of groundwater age, and geochemical conditions 

of the aquifer, are given in this section. Data sources 

and methodology used for assigning values for potential 

explanatory factors are described in appendix D. 

Land Use

Land use was described by three land-use types: urban, 

agricultural, and natural (appendix D). Percentages of the 

three types were calculated for the study unit, and for areas 

within a radius of 500 meters (m) (500-m buffers) around 

wells (Johnson and Belitz, 2009). Some constituent sources 

are associated with land use; for example, anthropogenic 

constituents and some naturally occurring constituents may 

be introduced to groundwater at urban and (or) agricultural 

lands. Land use within the SFBAY study unit was 73 percent 

urban, 23 percent natural, and 4 percent agricultural (fig. 5A; 

appendix D). The urban landscape consists of residential, 

commercial, and industrial areas, and the large urban centers 

include the cities of San Francisco, San Mateo, Santa Clara, 

San Jose, Hayward, and Alameda (fig. 6). Natural lands occur 

along the south and southwest edges of the Bay, in Santa 

Clara and Alameda Counties, and are mostly grasslands. 

Agricultural lands are primarily in the southern tip of the study 

unit extending north of Morgan Hill in Santa Clara County; 

the primary use of agricultural land is for pasture for livestock 

(cattle, sheep, and poultry) and hay. Average land use within 

the 500-m buffers around USGS wells was similar to land 

use in the study unit as a whole (fig. 5A). Land use around 

individual USGS-grid and understanding wells primarily 

consisted of mixtures of urban and natural land use (fig. 5B). 

Five USGS-grid wells were surrounded by greater than 

5 percent agricultural land use (fig. 5B).

Well Depth and Depth to Top-of-Perforation

Some constituent sources are associated with depth: 

anthropogenic constituents are usually introduced at land 

surface, and some naturally occurring constituents are 

introduced to groundwater from deeper zones. Therefore, well 

depth can help determine the source of a particular constituent. 

Well construction information was available for 38 of the 43 

grid wells sampled by the USGS in the SFBAY study unit. 

USGS-grid wells had depths ranging from 80 to 1,120 ft 

below land-surface datum (LSD); the median was 517 ft 

below LSD (fig. 7A; table D2). Wells deeper than 700 ft below 

LSD (the 25th percentile) are located in the Santa Clara Valley 

groundwater basin. Depths to the top-of-perforations ranged 

from 35 to 540 ft below LSD, with a median of 262 ft below 

LSD. The perforation length was as much as 831 ft, with a 

median of 270 ft. The USGS-understanding production wells 

have median well depths, depths to top-of-perforations, and 

perforation lengths (fig. 7B) similar to those of the grid wells. 

The USGS-understanding monitor wells have a median 

perforation length of 20 ft, and 23 of the 24 wells have 

lengths less than or equal to 50 ft (fig. 7C). The median depth 

and depth to the top-of-perforation (329 ft below LSD and 

314 ft below LSD, respectively) of the monitor wells were 

not significantly different than the median depth and depth 

to the top-of-perforation of the USGS-grid wells (Wilcoxon 

rank-sum test, p=0.23 and p=0.09, respectively); however, the 

monitor wells generally were not perforated over the same 

depth interval as the USGS-grid wells. Eight monitor wells 

were perforated at depths shallower than the 75th percentile of 

the depth to the top-of-perforations of the grid wells, and three 

monitor wells were perforated at depths deeper than the 25th

percentile of the depth of the grid wells (table D2). In addition, 

several of the monitor well clusters were located in areas that 

were not near any CDPH wells (fig. A1A). These well clusters 

are used for various tasks, including monitoring of seawater 

intrusion and pumping for desalination plants. Because the 

monitor wells are located in depth zones and areas which are 

not used for public supply, they are not representative of the 

primary aquifer system.
Potential Explanatory Factors 17

USGS-grid wells

Understanding wells

SAN FRANCISCO BAY

STUDY UNIT

Data point:

Land use

based on

satellite

imagery

Data point:

Land use

based on a

500-meter radius

area around

grid wells

B

A

20

40

60

80

100

0

PERCENT URBAN

20

40

60

80

100

0

PERCENT URBAN

100 80 60 40 20 0

PERCENT AGRICULTURE

100 80 60 40 20 0

PERCENT AGRICULTURE

100

80

60

40

20

0

PERCENT NATURAL

100

80

60

40

20

0

PERCENT NATURAL

IP_017719_Figure 05ab Land use percentages Figure 5. Percentages of urban, agricultural, and natural land use (A) in the study unit and (B) in the area 

surrounding each USGS-grid and USGS-understanding well in the San Francisco Bay study unit, California 

GAMA Priority Basin Project.
18 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Figure 6. Classification of land use in the San Francisco Bay study unit, California GAMA Priority Basin Project.

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

Aqueduct

Canal

Stream

Counties

SAN FRANCISCO

CO

ALAMEDA CO

SANTA CRUZ CO

CONTRA COSTA CO

MARIN CO

SAN MATEO CO

SANTA CLARA CO

SAN JOAQUIN CO

SACRAMENTO CO

Hayward

Alameda

San Francisco

San Jose

San Mateo

Palo Alto

Santa Clara

Morgan Hill

Arroyo Calero

Arroyo Valle

Guadalupe River

Arroyo Hondo

Llagas Creek

Arroyo de la Laguna

Arroyo Valle

Coyote Creek

Diablo Range

Old River

Contra Costa Canal

Dutch Slough

P a c i f i c O c e a n

San Francisco

Bay

Golden Gate Strait

EXPLANATION

0 10 20 MILES

0 10 20 KILOMETERS

LAND-USE CLASSIFICATION

IP_017719_Figure 06 Land use

Study unit USGS-grid well

 boundary Urban Agricultural Natural

California Aqueduct

Delta-Mendota

Canal
Potential Explanatory Factors 19

IP_017719_Figure 7ab. Well perfs 

1,000

1,200

800

600

400

200

0

1,000

1,200

800

600

400

200

0

33 Number of wells with data

75th percentile

Median

25th percentile

Data point that is outside of

1.5 x IQR of the 75th percentile

Data point that is outside of

1.5 x IQR of the 25th percentile

Nearest data point that is within

1.5 x IQR of the 75th percentile

Nearest data point that is within

1.5 x IQR of the 25th percentile

Interquartile

range (IQR)

EXPLANATION

A

Well depth Top-of-perforations Perforation length

Grid wells

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

B

Well depth Top-of-perforations Perforation length

Understanding production wells

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

41 33 34

10 9 9

Figure 7. Well depths, depths to top-of-perforations, and perforation lengths for (A) grid, (B) understanding 

production, and (C) understanding monitor wells, San Francisco Bay Study Unit, California GAMA Priority Basin Project, 

April–June 2007.
20 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

IP_017719_Figure 7c. Well perfs 

1,000

1,200

800

600

400

200

0

24 Number of wells with data

75th percentile

Median

25th percentile

Data point that is outside of

1.5 x IQR of the 75th percentile

Data point that is outside of

1.5 x IQR of the 25th percentile

Nearest data point that is within

1.5 x IQR of the 75th percentile

Nearest data point that is within

1.5 x IQR of the 25th percentile

Interquartile

range (IQR)

EXPLANATION

C

Well depth Top-of-perforations Perforation length

USGS-understanding monitor wells

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

24 24 24

Figure 7.—Continued.

Groundwater Age

Groundwater samples were assigned age classifications 

based on the tritium and carbon-14 content of the samples 

(appendix D). Age classifications were assigned to 79 USGSgrid and understanding well groundwater samples. Among 

the USGS-grid and understanding production wells, 17 were 

classified as modern, 23 were mixed, 12 were pre-modern, 

and 3 were mixed or modern (table D5). In USGS-GAMA 

production wells, the median depths to the top-of-perforations 

did not increase with groundwater age (fig. 8A). Well depths or 

depths to top-of-perforations in USGS grid and understanding 

production wells were not significantly different between 

groundwater age classifications (fig. 8B). This suggests that 

wells classified as modern and pre-modern in age in the 

SFBAY study unit cannot be easily separated into depth 

classes. However, pre-modern-age groundwater was not found 

in USGS grid and understanding production wells perforated 

shallower than 300 ft below LSD (fig. 8C). 

In USGS-understanding monitor wells, 2 wells were 

classified as modern, 9 were mixed, and 13 were pre-modern. 

The median well depths in monitor wells are shallower 

than in production wells among all age classifications. Well 

depths and depths to top-of-perforations in monitor wells 

were not significantly different between groundwater age 

classifications. Unlike production wells, pre-modern-age 

groundwater exists at all depths in monitor wells. 

Geochemical Conditions

An abridged classification of oxidation-reduction 

(redox) conditions adapted from the framework presented by 

McMahon and Chapelle (2008) is given in appendix D. The 

classifications used in this report for 79 wells sampled by 

the USGS-GAMA Priority Basin Project are oxic, anoxic, or 

mixed (Jurgens and others, 2009) (table D3). In USGS-grid 

wells and understanding production wells, groundwater was 

73 percent oxic, 5 percent mixed, and 22 percent anoxic. In 

the USGS-understanding monitor wells, groundwater was 

12.5 percent oxic, 12.5 percent mixed, and 75 percent anoxic. 

Dissolved oxygen concentrations ranged from less than 0.2 to 

11.9 milligrams per liter (mg/L). Many of the anoxic samples, 

including many of the monitor wells, were from the East Bay, 

towards the distal end of the groundwater flow path (fig. 10). 

Under natural conditions, the East Bay aquifers dominantly are 

recharged with water along the mountains, and groundwater 

flows westward towards the Bay. The pH ranged from 6.5 to 

9.4 in the USGS-grid wells and USGS-understanding wells 

(fig. 9; table D3). 
Potential Explanatory Factors 21

Modern

GROUNDWATER AGE

 CLASSIFICATION

Mixed

Pre-modern

NUMBER OF WELLS

DEPTH OF

PERFORATIONS

BELOW LAND

SURFACE

0 5 10

Entire perforated

interval less than

 300 feet 

Entire perforated

interval greater

than or equal to

300 feet 

Top of perforated

interval less than

300 feet; Bottom of

perforated interval

greater than or

equal to 300 feet 

12 22 8 0

200

400

DEPTH TO TOP-OF-PERFORATIONS,

IN FEET BELOW LAND SURFACE

600

800

GROUNDWATER AGE CLASSIFICATION

WELL DEPTH, 

IN FEET BELOW LAND SURFACE

1,000

1,200

EXPLANATION

A

B

C

Modern Mixed Pre-modern

15 22 11 0

200

400

600

800

GROUNDWATER AGE CLASSIFICATION

1,000

1,200

Modern Mixed Pre-modern

IP_017719_Figure 8abc GW age

21 Number of wells with data

75th percentile

Median

25th percentile

Nearest data point that is within

1.5 x IQR of the 75th percentile

Nearest data point that is within

1.5 x IQR of the 25th percentile

Interquartile

range (IQR)

EXPLANATION

Figure 8. Relation of groundwater age classification to (A) depth to top-of-perforations and to (B) well depth, and (C) bar 

chart showing age classification in relation to well depth classification for USGS-grid wells and USGS-understanding 

production wells, San Francisco Bay study unit, California GAMA Priority Basin Project, April–June 2007.
22 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

6.5 7.0 7.5 8.0 8.5 9.0 9.5

EXPLANATION

Modern

Mixed

Pre-modern

GROUNDWATER AGE

 CLASSIFICATION

IP_017719_Figure 09 pH_GWage

Monitoring well

Modern

Mixed

Pre-modern

Production well

pH, IN STANDARD UNITS

WELL DEPTH BELOW LAND SURFACE, IN FEET

1,000

1,200

800

600

400

200

0

Figure 9. pH plotted as a function of well depth, well type, and age classification, San Francisco 

Bay Study Unit, California GAMA Priority Basin Project, April–June 2007.

Figure 10. Dissolved oxygen concentrations in USGS-grid and USGS-understanding wells, San Francisco 

Bay Study Unit, California GAMA Priority Basin Project, April–June 2007.

P a c i f i c O c e a n

San Francisco

Bay

0 10 20 Miles

0 10 20 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

DISSOLVED OXYGEN CONCENTRATION,

IN MICROGRAMS PER LITER

USGS-understanding

 monitor wells

< 0.5

< is less than

0.5 – 5.5

USGS-grid and understanding

 production wells

< 0.5

0.5 – 11.9
Potential Explanatory Factors 23

Correlations Between Explanatory Factors

Statistical correlations between explanatory factors and 

water-quality constituents could reflect correlations between 

two or more explanatory factors presented here, among other 

factors. Therefore, it is important to identify statistically 

significant correlations between explanatory factors (table 5).

In USGS-grid wells and USGS-understanding production 

wells, depth to top-of-perforations is positively correlated 

with well depth (table 5). Depths of wells and depths to topof-perforations were significantly deeper in wells located in 

urban land-use areas than in wells located in agricultural or 

natural land-use areas (table 5). Well depth also is positively 

correlated with pH, indicating greater pH in water drawn 

from deeper aquifers (table 5; fig. 9). Dissolved oxygen 

(DO) concentrations are significantly greater in mixed and 

modern-age groundwater than in pre-modern-age groundwater 

(table 6). 

In USGS-understanding monitor wells, depth to topof-perforations was greater in wells classified as mixed age 

than in wells classified as modern age (table 6). pH was also 

greater in pre-modern-age groundwater than in mixed-age 

groundwater. Groundwater deeper in the aquifer generally has 

longer residence times than groundwater from shallow sites. 

Implications of correlations between explanatory variables are 

discussed later in the report as part of the analysis of factors 

affecting individual constituents.

Table 5. Results of non-parametric (Spearman’s rho method) analysis of correlations in grid and understanding wells 

between selected potential explanatory factors, San Francisco Bay study unit, California GAMA Priority Basin Project.

[ρ, Spearman’s correlation statistic; significant (p<0.05) positive correlation and significant negative correlations shown; nc, no significant 

correlation (p>0.05)]

Type of wells analyzed Explanatory factors

Depth to top of 

perforations

Depth of 

well

Dissolved 

oxygen 

concentration

pH

ρ: Spearman’s correlation statistic

Grid wells Percent urban land use 0.48 0.63 nc nc

Grid wells Percent agricultural land use –0.38 nc nc nc

Grid wells Percent natural land use –0.47 –0.63 nc nc

Grid and understanding 

production wells Depth to top of perforations

Grid and understanding 

production wells Depth of well 0.77

Grid and understanding 

production wells Dissolved oxygen concentration nc nc

Grid and understanding 

production wells pH nc 0.36 nc
24 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Status and Understanding of 

Water Quality 

The status assessment was designed to identify the 

constituents or classes of constituents most likely to be waterquality concerns by virtue of their high relative-concentrations 

or their prevalence. Approximately 24,000 individual 

analytical results were included in the assessment of 

groundwater quality for the SFBAY study unit. The status 

assessment applies only to constituents having regulatory 

or non-regulatory health-based or aesthetic/technical-based 

benchmarks established by the USEPA or the CDPH (as of 

2009). The spatially distributed, randomized approach to 

well selection and data analysis yields a view of groundwater 

quality in which all areas of the primary aquifer system are 

weighted equally with respect to well density; regions with 

a high density of groundwater use or with a high density of 

potential contaminants were not preferentially sampled (Belitz 

and others, 2010). The understanding assessment identifies 

correlations between selected natural and human factors and 

water quality in the SFBAY study unit. The understanding 

assessment focuses on the constituents identified as important 

in the status assessment. 

The following discussion of the status and understanding 

assessment results is divided into inorganic, organic, and 

special-interest constituents. The status assessment begins 

with a survey of how many constituents were detected 

at any concentration compared to the number analyzed, 

and a graphical summary of the relative-concentrations of 

constituents detected in the grid wells. Results are presented 

for the subset of constituents that met criteria for evaluation 

based on relative-concentration, or for organic constituents, 

prevalence.

The aquifer-scale proportions calculated using the 

spatially weighted approach were within the 90-percent 

confidence intervals for their respective grid-based aquifer 

high proportions for the constituents listed in table 7, 

indicating that the grid-based approach yields statistically 

equivalent results to the spatially weighted approach. 

Inorganic Constituents

Inorganic constituents generally occur naturally in 

groundwater, although their concentrations may be influenced 

by human as well as natural factors. Of the 41 inorganic 

constituents analyzed by the USGS-GAMA Program, 38 were 

detected in the SFBAY study unit. Of those 38 constituents, 

20 had health-based benchmarks, 6 had aesthetic-based 

benchmarks, and 12 had no established benchmarks (table 8). 

The inorganic constituents detected at high relativeconcentrations in one or more grid wells were barium, 

iron, manganese, chloride, TDS, and nitrate. The maximum 

relative-concentration for each constituent is indicated in 

figure 11. 

Table 6. Results of Wilcoxon rank-sum tests for differences in 

values of selected potential explanatory factors between samples 

classified into groundwater age, San Francisco Bay study unit, 

California GAMA Priority Basin Project.

[Modern, sample with water recharged after 1952; Pre-modern, sample with 

water recharged before 1952; Mixed age, sample with modern and pre-modern 

components; only results with p-values ≤ 0.05 are considered significant in 

this study; Wilcoxon rank-sum tests with exact distribution and continuity 

correction; Z, test statistic for Wilcoxon test; significantly positive Z value 

(first classification is larger than second); significantly negative Z value (first 

classification is smaller than the second); nd, no significant difference 

(p-value > 0.05)]

Selected waterquality constituents

Groundwater age classification

Modern age 

compared with 

mixed age

Mixed age 

compared with 

pre-modern age

Modern age 

compared with 

pre-modern age

Z: Test statistic for Wilcoxon test

USGS-grid and understanding production wells

Percent urban 

land use

nd nd nd

Percent agricultural 

land use

nd nd nd

Percent natural 

land use

nd nd nd

Depth to top-ofperforationsnd nd nd

Depth of well nd nd nd

Dissolved oxygen 

concentration

nd 3.45 3.53

pH nd nd nd

USGS-understanding monitor wells

Percent urban 

land use

nd nd nd

Percent agricultural 

land use

nd nd nd

Percent natural 

land use

nd nd nd

Depth to top-ofperforations–2.13 nd nd

Depth of well nd nd nd

Dissolved oxygen 

concentration

nd nd nd

pH nd –2.18 nd
Status and Understanding of Water Quality 25

Table 7. Aquifer-scale proportions from grid-based and spatially weighted approaches for constituents that: (1) had moderate or high relative-concentrations during April 1, 2004, to March 31, 2007, from the California Department of Public Health (CDPH) database, (2) had moderate or high relative-concentrations in samples collected from USGS-grid wells (April–June 2007), or (3) are organic constituents having detection frequencies greater than 10 percent in USGS-grid wells, San Francisco Bay study unit, California GAMA Priority Basin Project.[High, concentrations greater than benchmark; moderate, concentrations less than benchmark and greater than or equal to 0.1 (for organic constituents) or 0.5 (for inorganic constituents) of benchmark; low, concentrations less than 0.1 (for organic constituents) or 0.5 (for inorganic constituents) of benchmark; MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; AL-US, U.S. Environmental Protection Agency (USEPA) action level; HAL-US, USEPA Lifetime Health Advisory; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligrams per liter; μg/L, micrograms per liter; na, not available]

Constituent Threshold type Threshold value Units

Raw detection frequency 1 Spatially weighted aquifer proportions 1 Grid-based aquifer proportions

90-percent 

confidence interval 

for grid-based 

high proportion 

(percent) 2

Number 

of 

wells

Percent 

moderate

Percent 

high

Number 

of 

cells

Proportion 

moderate 

(percent)

Proportion 

high 

(percent)

Number 

of 

cells

Proportion 

moderate 

(percent)

Proportion 

high 

(percent)

Lower 

limit

Upper 

limit

Trace elementsAluminum MCL-CA 1,000 μg/L 255 0.4 0.4 34 0.2 2.9 33 0 0 0 4.0

Arsenic MCL-US 10 μg/L 255 0.8 0 34 2.1 0 33 3.0 0 0 4.0

Barium MCL-CA 1,000 μg/L 255 0 0.4 34 0 1.5 33 0 3.0 0.5 11

Boron NL-CA 1,000 μg/L 50 4.0 0 14 3.6 0 12 0 0 0 11

Copper AL-US 1,300 μg/L 253 0 0.4 35 0 0.4 33 0 0 0 4.0

Lead AL-US 15 μg/L 215 0 0.9 32 0 1.6 29 0 0 0 4.5

Thallium MCL-US 2 μg/L 255 0.4 0 34 0.6 0 33 0 0 0 4.0

Radioactive constituentsRadium activity MCL-US 5 pCi/L 109 1.8 0 22 0.9 0 na3 na na na na

NutrientsNitrate MCL-US 10 mg/L 281 18 2.8 47 20 3.7 47 19 2.1 0.4 8.0

Nitrite MCL-US 1 mg/L 274 0 0.4 46 0 0.3 46 0 0 0 2.9Inorganic constituents with aesthetic benchmarksChloride SMCL-CA 500 mg/L 252 2.4 1.2 34 3.5 3.7 33 0 6.1 1.8 16

Total dissolved solids (TDS) SMCL-CA 1,000 mg/L 297 21 1.7 47 28 5.3 43 37 7.0 1.8 16Iron SMCL-CA 300 μg/L 254 6.7 5.5 34 6.1 9.7 33 6.1 3.0 0.5 11

Manganese SMCL-CA 50 μg/L 254 3.5 10 34 5.5 20 33 6.1 12 5.2 24

Trihalomethanes (THM)Chloroform 

(trichloromethane)

MCL-US 80 μg/L 263 0.4 0 46 2.2 0 43 2.3 0 0 3.1

Solvents1,1,1-Trichloroethane (TCA) MCL-CA 200 μg/L 265 0 0 46 0 0 43 0 0 0 3.1
26 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project Constituent Threshold type Threshold value Units Raw detection frequency 1 Spatially weighted aquifer proportions 1 Grid-based aquifer proportions 90-percent confidence interval for grid-based high proportion (percent) 2 Number of wells Percent moderate Percent high Number of cells Proportion moderate (percent) Proportion high (percent) Number of cells Proportion moderate (percent) Proportion high (percent) Lower limit Upper limit Solvents—Continued Tetrachloroethene (PCE) MCL-US 5 μg/L 265 0.4 0.4 46 2.2 0.3 43 2.3 0 0 3.1 Trichloroethene (TCE) MCL-US 5 μg/L 265 0.8 0 46 2.4 0 43 2.3 0 0 3.1 Other VOCs 1,1,2-Trichlorotrifluoroethane (CFC-113) MCL-CA 1,200 μg/L 263 0 0 46 0 0 43 0 0 0 3.1 1,1-Dichloroethene MCL-CA 6 μg/L 265 0.4 0 46 0.4 0 43 2.3 0 0 3.1 Methyl tert-butyl ether (MTBE) MCL-CA 13 μg/L 267 0 0.4 46 0 0.3 43 0 0 0 3.1 Herbicides Atrazine MCL-CA 1 μg/L 190 0.4 0 46 0 0 43 2.3 0 0 3.1 Constituents of special interest Perchlorate MCL-CA 6 μg/L 79 32 0 45 28 0 43 42 0 0 3.1

1 Based on the most recent data for each CDPH well during the period April 1, 2004–March 31, 2007, combined with GAMA grid and understanding production well data. 2 Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001). 3 USGS GAMA did not collect samples for analysis of radium activity in the SFBAY study unit.

Table 7. Aquifer-scale proportions from grid-based and spatially weighted approaches for constituents that: (1) had moderate or high relative-concentrations during April 1, 2004, to March 31, 2007, from the California Department of Public Health (CDPH) database, (2) had moderate or high relative-concentrations in samples collected from USGS-grid wells (April–June 2007), or (3) are organic constituents having detection frequencies greater than 10 percent in USGS-grid wells, San Francisco Bay study unit, California GAMA Priority Basin Project.—Continued[High, concentrations greater than benchmark; moderate, concentrations less than benchmark and greater than or equal to 0.1 (for organic constituents) or 0.5 (for inorganic constituents) of benchmark; low, concentrations less than 0.1 (for organic constituents) or 0.5 (for inorganic constituents) of benchmark; MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; AL-US, U.S. Environmental Protection Agency (USEPA) action level; HAL-US, USEPA Lifetime Health Advisory; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligrams per liter; μg/L, micrograms per liter; na, not available]
Status and Understanding of Water Quality 27

Table 8. Numbers of constituents analyzed and detected by the U.S. Geological Survey, with associated benchmarks in each constituent class, San Francisco Bay study unit, California GAMA Priority Basin Project, April–June 2007. [VOCs, volatile organic compounds; NWQL, National Water Quality Laboratory; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health; MCL, USEPA or CDPH maximum contaminant level; HAL, USEPA health advisory level; NL, CDPH notification level; RSD5, USEPA risk specific dose at 10–5; AL, USEPA action level; SMCL, USEPA or CDPH secondary 

maximum contaminant level]

Benchmark

type

Sum organic and 

special interest VOCs Pesticides and degradates, and polar pesticides and metabolites Special interest

Number of constituents Number of constituents Number of constituents Number of constituents

Analyzed Detected at any concentration Analyzed Detected at any concentration Analyzed Detected at any concentration Analyzed Detected at any concentration

Groups of organic constituentsMCL 46 18 33 14 12 3 1 1

HAL 31 4 7 2 24 2 0 0

NL 15 3 14 2 0 0 1 1

RSD5 8 0 4 0 4 0 0 0

AL 0 0 0 0 0 0 0 0

SMCL 0 0 0 0 0 0 0 0

None 104 7 27 4 77 3 0 0

Total: 204 32 85 22 117 8 2 2

Benchmark

type

Sum of inorganic Major and minor ions Nutrients Trace elements Radioactive 

Number of constituents Number of constituents Number of constituents Number of constituents Number of constituents

Analyzed Detected at any concentration Analyzed Detected at any concentration Analyzed Detected at any concentration Analyzed Detected at any concentration Analyzed Detected at any concentration

Groups of inorganic constistuentsMCL 15 13 1 1 2 2 11 9 1 1

HAL 3 3 0 0 1 1 2 2 0 0

NL 2 2 0 0 0 0 2 2 0 0

RSD5 0 0 0 0 0 0 0 0 0 0

AL 2 2 0 0 0 0 2 2 0 0

SMCL 7 6 3 3 0 0 4 3 0 0

None 12 12 7 7 2 2 3 3 0 0

Total: 41 38 11 11 5 5 24 21 1 1

Organic, 

special interest, inorganic total1: 245 70

1 Does not include geochemical and age-dating tracers listed in table 1.
28 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Figure 11. Maximum relative-concentration of constituents detected in grid wells, by 

constituent class, San Francisco Bay study unit, California GAMA Priority Basin Project, April–

June 2007.

0.001

0.01

0.1

1

10

Silver Name and center of symbol is the maximum relative-concentration for that constituent-

 Unless indicated by following location line: (more than 25 grid wells sampled) 

EXPLANATION

Low

Moderate

Low

Moderate

High High

Organic and specialinterest constituentsInorganic

constituents

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Volatile

organic

compounds

Solvents Pesticides Special

interest

Nutrients Trace

elements

Major and

minor ions

and trace

elementsSMCL

Radioactive

constituents

Number of 

compounds

detected

< 0.001 2 0 2 0 0 0 0 0

IP_017719_Figure 11 Maximum Relative Concentration

Simazine

Perchlorate

Bromodichloromethane

Chloromethane

1,1,2-Trichlorotrifluoroethane

1,1-Dichloroethene

1,1-Dichloroethane

cis-1,2-Dichloroethene

Carbon tetrachloride

Tetrachloroethene

(PCE)

Trichloroethene

(TCE)

1,1,1-Trichloroethane

Chloroform

Methyl tert-butyl ether

Atrazine

Nitrite-N

Ammonia-N

Nitrate-N

Iron

Manganese

Silver

TDS

Sulfate

Zinc

Chloride

Fluoride

Molybdenum

Boron

Lead

Copper

Radon-222

Uranium

Vanadium

Cadmium

Thallium

Antimony

Chromium

Selenium

Beryllium

Arsenic

Aluminum

Barium

Nickel

Strontium
Status and Understanding of Water Quality 29

Figure 12. Maximum relative-concentrations of 

selected inorganic constituents in grid wells, San 

Francisco Bay Study Unit, California GAMA Priority Basin 

Project, April–June 2007.

Fourteen inorganic constituents—aluminum, arsenic, 

barium, boron, copper, lead, thallium, nitrate, nitrite, chloride, 

TDS, iron, manganese, and radium—met the selection 

criterion of having maximum relative-concentrations > 0.5 

(moderate or high) in the grid-based aquifer proportions 

or in the CDPH database for the period April 1, 2004, to 

March 31, 2007 (table 7). The percentages of the primary 

aquifer system with high and moderate relative-concentrations 

for the individual inorganic constituents are listed in 

table 7. Inorganic constituents, as a group (trace elements, 

nutrients, and radioactive constituents), had high proportions 

in 5.1 percent of the primary aquifer system, moderate 

proportions in 25 percent of the primary aquifer system, and 

low proportions in 70 percent of the primary aquifer system 

(table 9). 

Inorganic constituents with relative-concentrations 

greater than 0.5 in one or more of the grid wells are shown 

in figure 12. The spatial distributions of selected inorganic 

constituents are illustrated with maps for USGS-grid wells, 

USGS-understanding wells, and CDPH-grid wells for the 

period April 1, 2004, to March 31, 2007 (fig. 13).

0.001

0.01

0.1

1

10

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Total

dissolved

solids

Chloride Iron Manganese Arsenic Barium Nitrate

Constituents that have secondary

maximum contaminant levels

SFBAY grid-well sample

Constituents that have

health-based thresholds

(maximum contaminant level,

health advisory level,

notification level) 

High

Moderate

Low

Table 9A. Aquifer-scale proportions for constituent classes of 

inorganics with health-based and aesthetic benchmarks, San 

Francisco Bay study unit, California GAMA Priority Basin Project.

[SMCL, secondary maximum contaminant level; values are grid based except 

where footnoted]

Constituent class

Aquifer-scale proportion

Low 

relativeconcentration 

(percent)

Moderate 

relativeconcentration 

(percent)

High 

relativeconcentration

(percent)

Inorganics with health-based benchmarks

Trace elements 94 3.0 3.0

Radioactive 99 1

0.9 0

Nutrients 79 19 2.1

Any inorganic with healthbased benchmarks70 25 5.1

Inorganics with aesthetic benchmarks

Salinity indicators 56 37 7.0

Manganese and (or) iron 

(SMCL)

76 12 12

Any inorganic with aesthetic 

benchmark

53 33 14

1

 Spatially weighted value

Table 9B. Aquifer-scale proportions for constituent classes of 

organics with health-based benchmarks, San Francisco Bay study 

unit, California GAMA Priority Basin Project.

[VOCs, volatile organic compounds; values are grid based except where 

footnoted]

Constituent class

Low relativeconcentration 

(percent)

Moderate 

relativeconcentration 

(percent)

High 

relativeconcentration 

(percent) Not 

detected

Detected 

low

Organics with health-based benchmarks

Trihalomethanes 70 28 2.3 0

Solvents 72 23 4.7 1

0.3

Other VOCs 81 17 2.3 1

0.3

Herbicides 86 12 2.3 0

Any organic with 

health-based 

benchmarks

49 39 12 1

0.6

1

 Spatially weighted value
30 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Figure 13. Concentrations of selected inorganic constituents for USGS-grid and USGS-understanding wells, 

April–June 2007, and from the California Department of Public Health (CDPH) database for the period April 1, 

2004–March 31, 2007, San Francisco Bay study unit, California GAMA Priority Basin Project: (A) nitrate, 

(B) total dissolved solids, (C) chloride, (D) iron, and (E) manganese.

0 10 20 Miles

0 10 20 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

A EXPLANATION

IP_017719_Figure 13a NO3 Concentration

NITRATE AS NITROGEN CONCENTRATION,

IN MILLIGRAMS PER LITER

P a c i f i c O c e a n

San Francisco

Bay

USGS- and CDPH-well 

CDPH other well

< 5 (low)

>5 and < 10 (moderate)

>10 (high)

USGS understanding production well 

< 5 (low)

>5 and < 10 (moderate)

>10 (high)

< 5 (low)

< is less than or equal to;

> is greater than

>5 and < 10 (moderate)

>10 (high)
Status and Understanding of Water Quality 31

P a c i f i c O c e a n

San Francisco

Bay

P a c i f i c O c e a n

San Francisco

Bay

0 10 20 Miles

0 10 20 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

CHLORIDE CONCENTRATION,

IN MILLIGRAMS PER LITER

USGS- and CDPH-well 

CDPH other well

< 250 (low)

>500 (high)

USGS understanding production well 

< 250 (low)

< 250 (low)

>250 and < 500 (moderate)

>500 (high)

TOTAL DISSOLVED SOLIDS CONCENTRATION,

IN MILLIGRAMS PER LITER

USGS- and CDPH-well 

CDPH other well

< 500 (low)

>500 and < 1,000 (moderate)

>1,000 (high)

USGS understanding production well 

< 500 (low)

>500 and < 1,000 (moderate)

< 500 (low)

< is less than or equal to;

> is greather than

>500 and < 1,000 (moderate)

>1,000 (high)

B

IP_017719_Figure 13bc TDS-Cl Concentration

C

Figure 13.—Continued.
32 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

P a c i f i c O c e a n

San Francisco

Bay

P a c i f i c O c e a n

San Francisco

Bay

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

MANGANESE CONCENTRATION,

IN MICROGRAMS PER LITER

D

IP_017719_Figure 13de Mn_Fe Concentration

IRON CONCENTRATION,

IN MICROGRAMS PER LITER

E

USGS- and CDPH-well 

CDPH other well

< 150 (low)

>150 and < 300 (moderate)

>300 (high)

USGS understanding production well 

< 150 (low)

< 150 (low)

>150 and < 300 (moderate)

>300 (high)

USGS- and CDPH-well 

CDPH other well

< 25 (low)

>25 and < 50 (moderate)

>50 (high)

< 25 (low)

>25 and < 50 (moderate)

>50 (high)

< is less than or equal to;

> is greater than

Figure 13.—Continued.
Status and Understanding of Water Quality 33

Trace Elements

Trace elements, as a class, had high relativeconcentrations (for one or more constituents) in 3.0 percent 

of the primary aquifer system, moderate values in 3.0 percent, 

and low values in 94 percent (table 9). High relativeconcentrations of trace elements reflected a high relativeconcentration of barium (tables 7, 9) in one grid well. 

Moderate relative-concentrations of trace elements reflected 

moderate relative-concentrations of arsenic. Because there was 

only one well with high relative-concentration of barium in the 

entire dataset, no understanding assessment will be made.

Three trace elements—aluminum, copper, and lead—had 

spatially weighted high relative-concentrations in 2.9 percent, 

0.4 percent, and 1.6 percent, respectively, compared to 

0 percent for these elements for the grid-based approach 

(table 7). The spatially weighted approach includes data from 

a larger number of wells than were used in the grid-based 

approach, and therefore is more likely to include constituents 

that exist in very small proportions of an aquifer. Strontium 

was sampled for in only 8 of the 47 cells in the grid network, 

and no data were available from the CDPH database; thus, 

strontium was not evaluated in the status assessment.

The trace elements antimony, cadmium, chromium, and 

thallium had high relative-concentrations in at least one well 

reported in the CDPH database before April 2004 but not 

during the current period of study (table 4). Because these 

high relative-concentrations represented historical values 

rather than current values, these trace elements are not of 

concern for present-day conditions and were not selected for 

additional evaluation in the status assessment.

Uranium and Radioactive Constituents

In addition to uranium, the USGS sampled one 

radioactive constituent—radon-222—for the status and 

understanding assessments. The relative-concentrations 

of these radioactive constituents were low in the SFBAY 

study unit (table 9). No detections of uranium or radon-222 

were above their respective MCL-US values of 30 μg/L 

and 4,000 picocuries per liter (pCi/L) during the current 

period of study or in the historical data (table 4). Radium 

had a moderate spatially weighted relative-concentration of 

0.9 percent, but was not sampled for by the USGS in SFBAY 

wells.

Nutrients

Nutrients as a class had high relative-concentrations 

in 2.1 percent of the primary aquifer system and moderate 

relative-concentrations in 19 percent (table 9). The nutrient 

detected at high relative-concentrations was nitrate (table 7; 

fig. 11). High relative-concentrations of nitrate were detected 

on the west side of the San Francisco Peninsula and at the 

southern tip of the Santa Clara valley (fig. 13A).

Understanding Assessment for Nitrate

Nitrate concentrations were significantly greater in 

wells having water of modern or mixed ages compared with 

pre-modern age (table 10; fig. 14A). Nitrate was positively 

correlated with DO, and nitrate concentrations were 

significantly greater in wells with oxic conditions compared 

with anoxic conditions and mixed redox conditions compared 

with anoxic conditions (table 10; fig. 14B). Nitrate was 

positively correlated with percentage of urban land use and 

negatively correlated with percentage of natural land use 

(table 11; fig.14C). The negative correlation between nitrate 

and natural land use (table 11) suggests that the nitrate is likely 

from anthropogenic sources. 

Nitrate was not correlated with well depth or depth 

to top-of-perforations (table 11, fig. 14C), and there were 

no significant differences in well depth or depth to top-ofperforations between wells with high or moderate relativeconcentrations of nitrate and wells with low relativeconcentrations of nitrate. Nitrate concentrations were 

significantly higher in modern and mixed-age groundwater 

compared to pre-modern-age groundwater, but because age 

classifications in the SFBAY study unit do not yield clear 

depth classes, moderate and high relative-concentrations of 

nitrate occur at varying depths of the aquifer.

Nitrate has both natural and anthropogenic sources to 

groundwater; however, concentrations greater than 2 mg/L 

(relative-concentration of 0.2) generally are considered to 

indicate presence of nitrate from anthropogenic sources 

(Mueller and Helsel, 1996). Potential anthropogenic sources 

of nitrate include use of fertilizers in agricultural and urban 

areas, nitrate in water used for engineered recharge, seepage 

from septic and sewage systems, and animal and human 

wastes. Most of the wells with high or moderate relativeconcentrations of nitrate were located in the southern half of 

the study unit, in the Santa Clara Valley (fig. 13A). Although 

most of the Santa Clara Valley is currently dominated by urban 

land use, the valley was dominated by agricultural land use 

prior to World War II, and there were still extensive tracts 

of orchards remaining in the 1980s. Distinguishing between 

urban and agricultural sources of nitrate is beyond the scope of 

this report. 

Four USGS-understanding monitor wells (three well 

clusters) had moderate or high relative-concentrations of 

nitrate. All four wells had a mixed-age classification and were 

in areas with production wells with moderate or high relativeconcentrations of nitrate.
34 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

EXPLANATION

B

Modern

Mixed

Pre-modern

GROUNDWATER AGE CLASSIFICATION

EXPLANATION

NITRATE, AS NITROGEN, CONCENTRATION,

IN MILLIGRAMS PER LITER

Modern Mixed Pre-modern

19 35 25

0

4

8

12

25 Number of values

75th percentile

Median

25th percentile

25th percentile minus

1.5 x interquartile range

75th percentile plus

1.5 x interquartile range

Upper detached

A

IP_017719_Figure 14ab_nitrate

0.5 x Maximum

 contaminant

 level 

Maximum

 contaminant

 level

Monitoring well

Production

Monitoring well

Production

Monitoring well

Production

0

2

4

6

8

10

12

10 12 14

NITRATE, AS NITROGEN, CONCENTRATION, IN MILLIGRAMS PER LITER 

DISSOLVED OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER 

Low Moderate High

GROUNDWATER AGE

CLASSIFICATION

Interquartile range (IQR)

0 2 4 6 8

Figure 14. Nitrate, as nitrogen, concentrations related to: (A) classifications of groundwater age, (B) dissolved oxygen, well 

type, and classification of groundwater age, and (C) depth to top-of-perforations, well type, and land use in USGS-grid and 

USGS-understanding wells sampled for the San Francisco Bay study unit, California GAMA Priority Basin Project, 

April–June 2007.
Status and Understanding of Water Quality 35

0

DEPTH TO TOP-OF-PERFORATIONS, IN FEET BELOW LAND SURFACE

2 4 6 8 10 12 14

NITRATE, AS NITROGEN, CONCENTRATION, IN MILLIGRAMS PER LITER 

0

400

200

600

800

1,000

1,200

C

0.5 x Maximum

 contaminant

 level 

EXPLANATION

Maximum

 contaminant

 level

Monitoring

Production

IP_017719_Figure 14c NO3vs depth by land use

WELL TYPE

Figure 14.—Continued.

Table 10. Results of Wilcoxon rank-sum tests on grid-well data used to determine significant differences between selected waterquality constituents grouped by potential explanatory factor classifications, San Francisco Bay study unit, California GAMA Priority 

Basin Project.

[Modern, sample with water recharged after 1952; Pre-modern, sample with water recharged before 1952; Mixed age, sample with modern and pre-modern 

components; mg/L, milligrams per liter; ≥, greater than or equal to; <, less than; ≤, less than or equal to; oxic, dissolved oxygen ≥ 0.5 mg/L; anoxic/

suboxic, dissolved oxygen < 0.5 mg/L; mixed redox, more than one redox state; only results with p-values ≤ 0.05 are considered significant in this study; 

Wilcoxon rank-sum tests with exact distribution and continuity correction; Z, test statistic for Wilcoxon test; significantly positive Z-value (first classification 

is larger than second); significantly negative Z-value (first classification is smaller than the second); nd, no significant difference (p-value > 0.05); CFC-113, 

1,1,2-Trichlorotrifluoroethane]

Selected water-quality 

constituents

Potential explanatory factors

Groundwater age classification Redox classifications

Modern age 

compared with 

mixed age

Mixed age 

compared with 

pre-modern age

Modern age 

compared with 

pre-modern age

Oxic conditions 

compared with 

mixed conditions

Mixed conditions 

compared with 

anoxic/suboxic 

conditions

Oxic conditions 

compared with 

anoxic/suboxic 

conditions

Z: Test statistic for Wilcoxon test Z: Test statistic for Wilcoxon test

Iron nd nd nd –3.54 2.55 nd

Manganese nd nd nd –2.99 nd –2.27

Total dissolved solids nd nd nd nd nd nd

Chloride nd nd nd nd nd nd

Nitrate nd 2.71 2.62 nd 2.60 3.17

Chloroform nd nd nd nd nd nd

Solvents, sum of concentrations 2.16 nd nd nd 2.93 2.01

CFC-113 nd nd 2.18 nd 2.00 2.01

Perchlorate nd 2.76 nd nd nd 2.24
36 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Constituents with Aesthetic Benchmarks

Eight inorganic constituents have SMCL-CA benchmarks 

that are based on aesthetic properties. Four of these—chloride, 

TDS, iron, and manganese—met the criteria for additional 

evaluation (table 7). Relative-concentrations of TDS were 

high in 7.0 percent of the primary aquifer system, and were 

moderate in 37 percent (table 7). Chloride was detected at 

a high relative-concentration in 6.1 percent of the primary 

aquifer system. Iron had a high relative-concentration 

in 3.0 percent and a moderate relative-concentration 

in 6.1 percent of the primary aquifer system (table 7; 

figs. 11, 12). Manganese had a high relative-concentration 

in 12 percent and a moderate relative-concentration in 

6.1 percent of the primary aquifer system.

Understanding Assessment for TDS and Chloride

Natural sources of TDS include mixing of groundwater 

with deep saline groundwater that is influenced by interactions 

with deep marine or lacustrine sediments, saltwater intrusion, 

and water-rock interactions in older waters. Potential 

anthropogenic sources of TDS to groundwater in the SFBAY 

study unit include urban irrigation, evaporation, wastewater 

disposal and industrial effluent, artificial recharge, and leaking 

sewer pipes. 

The anion chloride is a major component of TDS in the 

SFBAY study unit, and its distribution, for the most part, 

reflects that of TDS. TDS is negatively correlated with DO 

(table 11; fig. 15). Chloride concentrations are negatively 

correlated with pH and percentage of agricultural land use 

(table 11), suggesting that the source of high chloride and 

high TDS waters is not from agricultural irrigation return. 

The moderate and high TDS grid wells are located in three 

areas of the study unit (fig. 13B). On the east side of the 

Bay, high and moderate relative-concentrations of TDS were 

detected in areas with history of intrusion of water from the 

Bay in response to pumping of freshwater from aquifers. A 

combination of drought periods and pumping in the early 

1900s allowed the groundwater level to fall below sea level 

in well fields near the Bay, and saltwater intruded into the 

shallow aquifers (Figuers, 1998). Continuous pumping in the 

Niles Cone subbasin allowed saline waters to migrate from the 

shallow aquifers through the Bay Mud to the deeper aquifers 

used for public supply (Figuers, 1998). On the southwest 

Table 11. Results of non-parametric (Spearman’s method) analysis of correlations between selected water-quality constituents and 

potential explanatory factors, San Francisco Bay study unit, California GAMA Priority Basin Project.

[Only results with p-values ≤ 0.05 are considered significant in this study. A positive value indicates postive correlations; negative values indicate negative 

correlations. ρ, Spearman’s correlation statistic; >, greater than; ≥, greater than or equal to; nc, no significant correlation (p-value > 0.05); TDS, total dissolved 

solids; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health; CFC-113, 1,1,2-Trichlorotrifluoroethane; MCL-US, 

USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; SMCL-CA, CDPH secondary maximum contaminant level]

Constituent Benchmark 

type

High aquifer 

proportion, 

percent

Data analyzed: USGS-grid and -understanding 

production wells Data analyzed: USGS-grid wells

Well depth Depth to top-ofperforations pH

Dissolved 

oxygen 

(DO)

Percent 

urban land 

use1

Percent 

agricultural 

land use1

Percent natural 

land use1

ρ: Spearman’s correlation statistic ρ: Spearman’s correlation statistic

Inorganic constituents

Iron2 SMCL-CA 3.0 nc nc nc nc nc nc nc

Manganese2 SMCL-CA 12 nc nc nc nc nc nc nc

Nitrate2 MCL-US 2.1 nc nc nc 0.49 0.29 nc –0.29

TDS2 SMCL-CA 7.0 nc nc nc –0.31 nc nc nc

Chloride2 SMCL-CA 6.1 nc nc –0.49 nc nc –0.31 nc

Organic constituents and constituent classes

Chloroform3 MCL-US 0.0 nc nc nc nc nc nc nc

Solvents, sum of 

concentrations3

variable 0.3 nc nc nc nc nc –0.25 nc

CFC-1133 MCL-CA 0.0 0.31 nc nc 0.33 0.33 nc –0.33

Constituents of special interest

Perchlorate3 MCL-US 0.0 nc nc nc 0.27 nc nc –0.24

1

 Land-use percentages are within a radius of 500 meters centered around each well included in analysis.

2

 Constituents with ≥ 2% high aquifer proportion.

3

 Classes of compounds that include constituents with high and (or) moderate values or detection frequencies at any concentration ≥ 10%.
Status and Understanding of Water Quality 37

side of the Bay, moderate relative-concentrations of TDS 

occur on an alluvial fan near the town of Atherton. Upwelling 

of connate saline water from marine sediments may be a 

source of TDS to the aquifers (Metzger and Fio, 1997). On 

the east side of the Santa Clara Valley, moderate relativeconcentrations of TDS occur in wells along Coyote Creek, 

which drains marine sediments in the Diablo Range to the east 

of the study unit. Coyote Creek flows northwest, through the 

fan deposits of the Santa Clara Valley, to the Bay. Numerous 

TOTAL DISSOLVED SOLIDS, IN MILLIGRAMS PER LITER

50 100 1,000 10,000 100,000

1,000

800

600

400

200

0

DEPTH TO TOP-OF-PERFORATIONS, IN FEET BELOW LAND SURFACE

Monitoring well Production well

Oxic

Classification

EXPLANATION

Anoxic

Mixed

Oxic

Anoxic

Mixed

IP_017719_Figure 15 TDS vs depth by OX state

0.5 x Maximum

 contaminant

 level 

Maximum

 contaminant

 level

impoundments in the southern part of the valley store 

imported water and then release the water into Coyote Creek 

for groundwater recharge. Accumulation of salts in Coyote 

Creek along the flowpath from both of these waters may be a 

source of TDS to the aquifers. 

Many monitoring wells in the SFBAY study unit were 

selected for sampling in order to collect data in areas with 

known salinity issues. The SFM-B cluster is dedicated to 

monitoring seawater intrusion to aquifers on the west side of 

Figure 15. Total dissolved solids concentrations relative to depth to top-of-perforation, well type, and 

geochemical classifications in USGS-grid and USGS-understanding wells in the San Francisco Bay study unit, 

California GAMA Priority Basin Project, April–June 2007.
38 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

the Bay. The SFM-D and SFM-E clusters are in areas of the 

East Bay where brackish waters are pumped for desalination 

plants. High relative-concentrations of TDS and (or) chloride 

were detected in these monitoring well clusters, in addition 

to the shallowest well of the SFM-F cluster in the East Bay. 

The highest concentrations of TDS were detected in wells 

SFM-B2 (18,200 mg/L) and SFM-F6 (77,800 mg/L) and 

are consistent with observations of brackish to hypersaline 

waters in the shallow aquifers near the San Francisco Bay 

(California Environmental Protection Agency, San Francisco 

Bay Regional Water Quality Control Board, 2003). As noted 

previously, continuous pumping in the Niles Cone subbasin of 

the Santa Clara Valley (East Bay) also has allowed bay water 

and saline water from adjacent salt ponds to enter the shallow 

and deeper aquifers (Figuers, 1998; California Environmental 

Protection Agency, San Francisco Bay Regional Water Quality 

Control Board, 2003).

Understanding Assessment for Iron and Manganese

Potential natural sources of iron and manganese to 

groundwater include the dissolution of iron and manganese 

oxyhydroxide minerals present in many sediments and 

rocks, and of primary iron- and manganese-bearing silicate 

minerals found in igneous and metamorphic rocks (Hem, 

1970). Potential anthropogenic sources of these constituents 

to groundwater include effluents associated with the steel and 

mining industries (Reimann and de Caritat, 1998) and soil 

amendments, in the form of manganese and iron sulfates, that 

are added to deficient soils in order to stimulate crop growth. 

Concentrations of iron and manganese are strongly influenced 

by redox conditions in the aquifer. In sediments, iron and 

manganese oxyhydroxide minerals are common as suspended 

particles and as coatings on mineral surfaces (Sparks, 1995). 

These minerals are stable in oxygenated systems at neutral pH. 

Under anoxic conditions, however, the process of reductive 

dissolution can mobilize iron and manganese cations from 

oxyhydroxides, increasing their concentrations in groundwater 

(Sparks, 1995). Oxidation-reduction conditions for the SFBAY 

study unit are reported in appendix D (table D3).

In the SFBAY study unit, concentrations of iron in 

production wells were significantly lower in oxic groundwater 

than in mixed groundwater (table 10). Concentrations of 

manganese were also significantly lower in oxic compared 

with mixed or anoxic groundwater (table 10). These results are 

consistent with reductive dissolution of iron and manganese 

oxyhydroxides in the aquifer sediments, which takes place in 

the downgradient parts of the aquifer system.

Fourteen monitor wells (from five well clusters) had 

high relative-concentrations of iron and (or) manganese. 

As discussed previously, most monitor wells have waters 

classified as anoxic and classified as pre-modern age and 

are at the distal end of the groundwater flow path. Elevated 

concentrations of iron and (or) manganese in these wells 

would also be consistent with reductive dissolution of iron and 

manganese oxyhydroxides in the aquifer sediments.

Organic Constituents

The organic compounds are organized by constituent 

class, including volatile organic compounds (VOCs) and 

pesticides. VOCs are found in paints, solvents, fuels, and 

refrigerants; they can be byproducts of water disinfection 

and are characterized by their volatile nature, or tendency to 

evaporate. Pesticides include herbicides, insecticides, and 

fungicides and are used to control weeds, insects, or fungi in 

agricultural, urban, and suburban settings.

The proportion of the aquifer with high relativeconcentrations of organic constituents with health-based 

benchmarks was 0.6 percent (spatially weighted value) 

(table 9), resulting from high spatially weighted relativeconcentrations of the solvent tetrachloroethene (PCE) 

(0.3 percent) and the gasoline additive methyl tert-butyl 

ether (MTBE) (0.3 percent) (table 7). The proportion of the 

aquifer having moderate relative-concentrations of organic 

constituents was 12 percent (table 9). Only 30 of the 202 

organic compounds analyzed for were detected, and most of 

these detected constituents (23 of the 30) have health-based 

benchmarks (table 8). 

VOCs were detected in 49 percent of the primary 

aquifer system. Of the 14 VOCs detected, 10 were detected 

only at low relative-concentrations. The maximum relativeconcentrations of four VOCs—PCE, trichloroethene 

(TCE), chloroform, and 1,1-dichloroethene—were 

greater than 0.1 but less than 1.0 (figs. 16, 17). Three 

VOCs—chloroform,1,1,1- trichloroethane (TCA), and 

1,1,2-trichlorotrifluoroethane (CFC-113)—were detected 

in more than 10 percent of the grid wells (figs. 17, 18). All 

concentrations of VOCs in samples from USGS-grid and 

understanding wells were less than health-based benchmarks.

Pesticides or pesticide degradates were detected in 

19 percent of the primary aquifer system. Of the 117 pesticides 

and pesticide degradates analyzed for, 6 were detected in 

the USGS-grid wells; 3 were pesticide parent compounds 

with benchmarks, 2 were parent compounds without a 

benchmark, and 1 was a degradate without a benchmark. 

All concentrations of pesticides were below health-based 

benchmarks. One herbicide, atrazine, was detected at 

moderate relative-concentration in one sample (table 7; 

fig. 17). The herbicide degradate deethylatrazine was the only 

pesticide detected in 10 percent or more of the grid wells. 

Deethylatrazine does not have a benchmark, and therefore is 

not included in the status or understanding assessments. The 

individual constituents that were not detected and the wells 

sampled in the SFBAY study unit are listed in Ray and others 

(2009).
Status and Understanding of Water Quality 39

Figure 16. Detection frequency and maximum relative-concentrations of organic and specialinterest constituents detected in USGS-grid wells in the San Francisco Bay study unit, California 

GAMA Priority Basin Project, April–June 2007.

RELATIVE-CONCENTRATION, DIMENSIONLESS

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

100

Moderate

High

Low

DETECTION FREQUENCY AT ANY CONCENTRATION, IN PERCENT

0 10 20 30 40 50

Prometon Name and center of symbol is the maximum relative-concentration for that constituent-

 Unless indicated by following location line: (more than 25 grid wells sampled) 

EXPLANATION

IP_017719_Figure 16 Maximum RelConcen vs Detections

Carbon

tetrachloride

Methyl tert-butyl ether

cis-1,2-Dichloroethene

1,1,1-Trichloroethane

1,1,2-Trichlorotrifluoroethane

Carbon disulfide

Perchlorate Chloroform

Bromodichloromethane

Prometon

1,1-Dichloroethane

Chloromethane

Trichloroethene

(TCE)

CFC-11

Diphenamid

Atrazine

Simazine

1,1-Dichloroethene

Tetrachloroethene

(PCE)
40 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Atrazine

Chloroform

Tetrachloroethene

(PCE)

1,1-Dichloroethene

CFC-113

Perchlorate

Trichloroethene

(TCE)

1,1,1-Trichloroethane

(TCA)

Low Moderate High

RELATIVE-CONCENTRATION, DIMENSIONLESS

0.0001 0.001 0.01 0.1 1 10

STUDY UNIT

DETECTION FREQUENCY,

IN PERCENT

0 10 20 30 40 50

10% detection

IP_017719_Figure 17ab VOC

A B

Figure 17. (A) Detection frequency and (B) maximum relative-concentrations of selected organic and special-interest 

constituents in USGS-grid wells in the San Francisco Bay Study Unit, California GAMA Priority Basin Project, April–June 2007.
Status and Understanding of Water Quality 41

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

CHLOROFORM, IN MICROGRAMS

PER LITER

IP_017719_Figure 18a_Chloroform MAP

< 0.04 (not detected)

>0.04 and < 8.0 (low)

< 0.04 (not detected)

>0.04 (low) 

>8.0 (moderate)

P a c i f i c O c e a n

San Francisco

Bay

USGS- and CDPH-well 

CDPH other well

USGS-understanding production well 

< is less than or equal to;

> is greater than

Not detected

< 8 (low)

A

Figure 18. Concentrations of selected organic and special-interest constituents detected in USGS-grid and USGSunderstanding wells for April–June 2007, San Francisco Bay study unit, California GAMA Priority Basin Project: 

(A) chloroform, (B) solvents, (C) atrazine, and (D) perchlorate.
42 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

SUM OF SOLVENTS,

IN MICROGRAMS PER LITER

< 0.02 (not detected)

0.02 to 2.5 (low)

< 0.03 (not detected)

0.03 to 0.51 (low)

2.6 to 5.0 (moderate)

IP_017719_Figure 18b_Sum of solvents

P a c i f i c O c e a n

San Francisco

Bay

USGS- and CDPH-well 

CDPH other well

Concentration is the sum of the concentrations of tetrachloroethene (PCE),

trichloroethene (TCE), 1,1,1-trichloroethane (TCA), 1,1-dichloroethane,

cis-1,2-dichloroetheylene, and

tetrachloromethane.

USGS-understanding production well 

0.03 to 2.5 (low)

< 0.03 (not detected)

< is less than

2.6 to 5.0 (moderate)

>5.1 to 6.57 (high)

B

Figure 18.—Continued.
Status and Understanding of Water Quality 43

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

ATRAZINE CONCENTRATION,

IN MICROGRAMS PER LITER

IP_017719_Figure 18c_Atrazine

P a c i f i c O c e a n

San Francisco

Bay

< 0.007 (not detected)

0.007 to 0.1 (low)

< 0.007 (not detected)

0.101 to 0.107 (moderate)

USGS- and CDPH-well 

USGS-understanding production well 

< is less than

C

Figure 18.—Continued.
44 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

0 10

0 10 20 KILOMETERS20 MILES

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Surface waters

San Francisco Bay

STUDY UNIT

EXPLANATION

PERCHLORATE CONCENTRATION,

IN MICROGRAMS PER LITER

IP_017719_Figure 18d_Perchlorate map

P a c i f i c O c e a n

San Francisco

Bay

< 0.5 (not detected)

0.02 to 0.6 (low)

< 0.5 (not detected)

0.5 to 0.6 (low)

0.7 to 3.8 (moderate)

USGS- and CDPH-well 

USGS-understanding production well 

0.7 to 1.40 (moderate)

< is less than

D

Figure 18.—Continued.
Status and Understanding of Water Quality 45

Trihalomethanes

Chloroform was detected in 30 percent of the 43 grid 

wells in the study unit (figs. 16, 17) and was detected at low 

relative-concentrations in 28 percent of the primary aquifer 

system and at moderate relative-concentrations in 2.3 percent 

of the primary aquifer system (table 7). Chloroform also was 

the most frequently detected VOC in groundwater according 

to the USGS National Water-Quality Assessment (NAWQA) 

Program (Zogorski and others, 2006). Figure 18A shows the 

distribution of chloroform in the SFBAY study unit.

Understanding Assessment for Chloroform

Chloroform concentrations in production wells were 

not significantly correlated with any explanatory factor 

(tables 10, 11). Nationally, THM concentrations have been 

positively correlated with percentage of urban land use 

(Zogorski and others, 2006). Potential urban sources of 

THMs include recharge from landscape irrigation that uses 

disinfected water, leakage from distribution or sewer systems, 

and industrial and commercial sources (Ivahnenko and 

Barbash, 2004).

Five of the 14 chloroform detections in production wells 

occurred in groundwater with mixed ages, 6 detections were in 

groundwater classified as modern age, and 3 detections were 

in groundwater classified as pre-modern age. Because water 

supplies have been disinfected with chlorine during the past 

100 years, chloroform may be in wells that do not appear to 

have modern recharge (since 1952) water.

Solvents

Solvents are used for various industrial, commercial, and 

domestic purposes. One solvent compound met the selection 

criteria of greater than 10 percent detection frequency; 

1,1,1-trichloroethane (TCA) was detected in 16 percent of the 

grid wells. Two solvents, PCE and TCE, each had moderate 

aquifer proportions of 2.3 percent (table 7). Solvents had 

a high aquifer-scale proportion of 0.3 percent (spatially 

weighted), reflecting detections of PCE (tables 7, 9). The 

proportion of the aquifer having moderate values of solvents 

was 4.7 percent.

Historically high concentrations of the solvent 

1,1-dichloroethane were recorded in the CDPH database for 

the period before April 1, 2004, but high concentrations were 

not detected during the current period of study (table 4).

Understanding Assessment for Solvents

The sum of solvent concentrations was calculated by 

summing the concentrations of all six solvents detected: PCE, 

TCE, TCA, 1,1-dichloroethane, cis-1,2-dichloroethylene, and 

carbon tetrachloride. The sum of solvents in production wells 

was negatively correlated with percentage of agricultural 

land use (table 11). Nationally, solvent concentrations also 

have been correlated strongly with percentage of urban land 

use (Zogorski and others, 2006; Moran and others, 2007). 

Figure 18B shows the distribution of solvents in the SFBAY 

study unit.

Solvent concentrations in production wells were 

significantly greater in modern compared with mixed-age 

waters. Some solvents that were used before 1952 could 

be present in pre-modern or mixed-age water. Solvent 

concentrations in production wells were significantly greater 

in oxic or mixed redox waters compared with anoxic waters. 

This reflects the relation between DO concentrations and 

groundwater age (table 6).

Historical releases of chlorinated solvents to the 

groundwater aquifers in the SFBAY study unit are related to 

electronics manufacturing in the Silicon Valley and dry cleaner 

locations in Santa Clara Valley (Santa Clara Valley Water 

District, 2011). However, these releases are typically above 

the confined zone of the aquifer and do not reach the deeper 

parts of the aquifer used for public supply. Thus, similar to 

previous work in the Bay area (Moran and others, 2002), 

solvent occurrences in groundwater samples cannot be traced 

to known solvent plumes.

One monitoring well had a detection of solvents, 

reflecting a detection of carbon tetrachloride. This was the 

shallowest well (depth to top of perforations of 140 ft) of a 

cluster in the northwestern portion of the study unit, near San 

Francisco (SFM-A cluster). Similar to production wells with 

solvent detections, this monitor well had oxic, mixed-age 

groundwater.

Other VOCs

Other VOCs were present at high relative-concentrations 

in 0.3 percent of the primary aquifer system and at 

moderate relative-concentrations in 2.3 percent (table 9). 

The gasoline oxygenate methyl tert-butyl ether was present 

at high relative-concentration in 0.3 percent (spatially 

weighted) of the primary aquifer system (table 7). The VOC 

1,1,2-trichlorotrifluoroethane (CFC-113)—often used as a 

refrigerant—was detected in 16 percent of the grid wells 

(fig. 16). Refrigerants are among the most frequently detected 

VOCs in the nation (Zogorski and others, 2006).

The fumigant 1,2-dichloropropane had historically high 

relative-concentrations in the CDPH database from the period 

before April 1, 2004, but not during the current period of study 

(table 4).
46 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Understanding Assessment for CFC-113

Concentrations of CFC-113 in production wells were 

significantly greater in modern-age waters compared with 

pre-modern waters and in oxic and mixed redox waters 

compared with anoxic/suboxic waters (table 10; fig. 19). 

CFC-113 was positively correlated with DO concentrations 

and negatively correlated with well depth (table 11). CFC-113 

was positively correlated with percentage of urban land use 

and equally negatively correlated with percentage of natural 

land use, as is expected because of the anthropogenic source 

of the compound. Some of the explanatory variables related 

to CFC- 113, such as DO and groundwater age, are generally 

related to each other (table 6). There were no detections of 

CFC-113 in any monitoring well.

Herbicides

Herbicides were not detected at high relativeconcentrations in the SFBAY study unit and were detected at 

moderate relative-concentrations in 2.3 percent of the primary 

aquifer system (table 9). Atrazine was detected in 9.3 percent 

of the grid wells and was the only herbicide detected at 

moderate relative-concentrations (figs. 16, 18C; table 7). 

Atrazine was among the most commonly detected herbicides 

in groundwater in major aquifers across the United States 

(Gilliom and others, 2006). 

CFC-113, IN MICROGRAMS PER LITER

0 5 10 15

1,000

800

600

400

200

0

WELL DEPTH, IN FEET BELOW LAND SURFACE

EXPLANATION

Modern

Mixed

Pre-modern

GROUNDWATER AGE

CLASSIFICATION

Oxic

Anoxic

Mixed

Oxic

Anoxic

Mixed

Oxic

Anoxic

Figure 19. Concentrations of CFC-113 relative to well depth and groundwater age classification and 

geochemical classification for the San Francisco Bay study unit, California GAMA Priority Basin Project, 

April–June 2007.
Status and Understanding of Water Quality 47

Special-Interest Constituents

Constituents of special interest analyzed for in the 

SFBAY study unit were N-nitrosodimethylamine (NDMA) 

and perchlorate. These constituents were selected because 

they recently have been detected in drinking-water supplies or 

are considered to have the potential to reach drinking-water 

supplies (California Department of Public Health, 2008a,b,c). 

NDMA was not detected in the three grid wells sampled (Ray 

and others, 2009). Perchlorate was analyzed for in samples 

from all grid wells (table 1). Perchlorate was not detected at 

high relative-concentrations and was detected at moderate 

relative-concentrations in 42 percent of the primary aquifer 

system (table 7; fig. 18D). 

Understanding Assessment for Perchlorate

Perchlorate concentrations in production wells 

were negatively correlated with percentage of natural 

land use (table 11). Concentrations of perchlorate were 

significantly higher in mixed-age waters than in pre-modern 

water (table 10; fig. 20). Perchlorate concentrations were 

positively correlated with DO concentrations (table 11), and 

concentrations of perchlorate were significantly higher in 

oxic waters compared with anoxic/suboxic waters (table 10; 

fig. 21). While perchlorate biodegrades under anoxic 

conditions in some aquifers (Sturchio and others, 2007), the 

apparent relation between perchlorate and DO in the San 

Francisco Bay study unit may result from relations of DO and 

perchlorate with modern and mixed-age groundwater. 

Perchlorate has natural and anthropogenic sources to 

groundwater. Perchlorate forms naturally in the atmosphere 

and is present dissolved in precipitation and deposited in 

unsaturated zones (Rajagopalan and others, 2006, 2009). 

The distribution of perchlorate under natural conditions in 

groundwater is likely correlated with climate because the 

extent of evaporative concentration of precipitation in the 

hydrologic cycle is likely to increase with increasing aridity 

(Fram and Belitz, 2011). Potential anthropogenic sources of 

perchlorate include industrial, manufacturing, or commercial 

uses, such as rocket fuel, explosives, road flares, automobile 

air-bag systems, and other products, and nitrate fertilizers 

mined from the Atacama Desert of Chile that have been used 

historically on some orchard crops (U.S. Environmental 

Protection Agency, 2005; Dasgupta and others, 2006; Bohlke 

and others, 2009). In addition, irrigation may remobilize 

naturally deposited perchlorate salts in the unsaturated zone. 

Concentrations greater than 1 μg/L (relative-concentration of 

0.17) have a high probability of being anthropogenic in origin 

(Fram and Belitz, 2011).

Fram and Belitz (2011) used logistic regression to 

quantify the relation between the probability of perchlorate 

detection in groundwater resources sampled by the GAMA 

Priority Basin Project and water-quality indicators and other 

proxies of natural and anthropogenic perchlorate sources and 

processes. Their best-fit model was constructed with aridity 

index as the proxy for perchlorate under natural conditions 

and with an Anthropogenic Score (AS) as the proxy for 

anthropogenic sources and processes affecting perchlorate. 

The AS summed four binary categorical variables: presence 

or absence of herbicides and fumigants, presence or absence 

of solvents and fuel components, nitrate concentration above 

or below 3 mg/L, and presence or absence of known sites of 

perchlorate contamination within a specific distance of the 

well. This model was applied to the data from the SFBAY 

study unit to assess whether the observed concentrations and 

detection frequencies of perchlorate can be accounted for by 

natural conditions or require additional inputs of perchlorate 

from anthropogenic sources or processes.

USGS production wells from the SFBAY study unit 

were divided into four groups having AS values of 0, 1, 2, and 

3 or  4. The predicted probabilities of detecting perchlorate 

under natural conditions (AS=0) at concentrations greater 

than 0.5 μg/L and greater than 1.0 μg/L are approximately 

8.1 percent and 2.7 percent, respectively, at the average aridity 

index for USGS production well samples (0.37) (fig. 22A, B; 

Fram and Belitz, 2011). The predicted probabilities increase as 

AS increases, reaching 80 percent and 66 percent, respectively, 

when AS=4. The detection frequencies observed in samples 

from the SFBAY study unit also increase as AS increases and 

are generally higher than the predicted probabilities at each 

value of AS (fig. 22A, B). 

The fact that the observed detection frequencies are 

greater than the predicted probabilities of detection indicates 

that the observed distribution of perchlorate in the SFBAY 

study unit is not representative of the distribution expected 

under natural conditions, and indicates that anthropogenic 

sources of perchlorate are dominant. This was expected, given 

that there are several known sites of perchlorate contamination 

to groundwater from industrial sources in the study unit (U.S. 

Environmental Protection Agency, 2005).

Five monitoring wells (from three well clusters) 

had detections of perchlorate, all at moderate relativeconcentrations. All monitoring wells with perchlorate 

detections were classified as mixed-age groundwaters. Three 

wells (two well clusters, SFM-C and SFM-E) were classified 

as anoxic, and one well each (from the same cluster, SFMA) was classified as mixed and oxic. The two wells from the 

SFM-A cluster that had detections of perchlorate also had 

detections of other organic compounds—chloroform and 

solvents. The other wells with perchlorate detections did not 

have detections of other organic compounds.
48 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

GROUNDWATER AGE CLASSIFICATION

EXPLANATION

PERCHLORATE CONCENTRATION, IN MICROGRAMS PER LITER

Modern Mixed Pre-modern

17 26 12

0

1

2

4

3

10 Number of values

75th percentile

Median

25th percentile

25th percentile minus

1.5 x interquartile range

75th percentile plus

1.5 x interquartile range

Upper detached

IP_017719_Figure 23 Perchlorate vs GW age

Interquartile

range (IQR)

Figure 20. Concentrations of perchlorate relative to groundwater age classification for the San Francisco Bay study 

unit, California GAMA Priority Basin Project, April–June 2007.

0

0

12

10

8

6

4

2

PERCHLORATE CONCENTRATION, IN MICROGRAMS PER LITER

1 2 3 4

DISSOLVED OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER

Pre-modern

Modern

Mixed

GROUNDWATER AGE

CLASSIFICATION

EXPLANATION

Figure 21. Concentrations of perchlorate relative to dissolved oxygen concentrations, land-use classification, 

and groundwater age classification for the San Francisco Bay study unit, California GAMA Priority Basin Project, 

April–June 2007.
Status and Understanding of Water Quality 49

IP017719_fig 22ab perchlorate_anthro

ARIDITY INDEX, DIMENSIONLESS 

A

B

0

20

40

60

80

100

0

0 0.2 0.4 0.6 0.8 1

20

40

60

80

100

PREDICTED PROBABILITY OF DETECTION OR

OBSERVED DETECTION FREQUENCY FOR

CONCENTRATION GREATER THAN OR EQUAL TO

0.5 MICROGRAM PER LITER, IN PERCENT

PREDICTED PROBABILITY OF DETECTION OR

OBSERVED DETECTION FREQUENCY FOR

CONCENTRATION GREATER THAN OR EQUAL TO

1 MICROGRAM PER LITER, IN PERCENT

EXPLANATION

AS=0

AS = 1

AS = 2

AS = 3 or 4

AS=0

AS=0

AS=1

AS=1

AS=2

AS=3

AS=2

AS=3

Average anthropogenic score (AS)

Anthropogenic

Score (AS) = 4

Anthropogenic

Score (AS) = 4

Figure 22. Predicted probability of detecting perchlorate in groundwater as a function of aridity index and 

Anthropogenic Score and observed detection frequency and average aridity index in groups of samples for perchlorate 

concentrations greater than or equal to (A) 0.5 microgram per liter (μg/L) and (B) 1.0 μg/L, San Francisco Bay study unit, 

California GAMA Priority Basin Project.
50 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Summary 

Groundwater quality in the approximately 620-squaremile San Francisco Bay study unit was investigated as part 

of the Priority Basin Project of the Groundwater Ambient 

Monitoring and Assessment (GAMA) Program. The GAMA 

San Francisco Bay (SFBAY) study was designed to provide a 

spatially unbiased characterization of the quality of untreated 

groundwater in the primary aquifer system at the basin-scale. 

The assessment is based on water-quality and ancillary data 

collected by the U.S. Geological Survey (USGS) from 79 

wells in 2007 and water-quality data from the California 

Department of Public Health (CDPH) database.

The first component of this study, the status of the quality 

of the current groundwater resource, was assessed by using 

data from samples analyzed for inorganic constituents, such 

as major ions and trace elements, volatile organic compounds 

(VOCs), and pesticides. The status assessment characterizes 

the quality of groundwater resources within the primary 

aquifer system of the SFBAY study unit, not the drinking 

water delivered to consumers by water purveyors.

Relative-concentrations (sample concentration divided 

by the benchmark concentration) were used to evaluate 

groundwater quality for those constituents that have Federal 

and (or) California benchmarks. Aquifer-scale proportion was 

used as a metric for evaluating regional-scale groundwater 

quality. High aquifer-scale proportion is defined as the 

percentage of the primary aquifer system with relativeconcentration greater than 1.0 for a particular constituent or 

class of constituents; proportion is based on an areal rather 

than a volumetric basis. Moderate and low aquifer-scale 

proportions were defined as the percentage of the aquifer 

with moderate and low relative-concentrations, respectively. 

Two statistical approaches, grid-based and spatially weighted, 

were used to evaluate aquifer-scale proportion for individual 

constituents and classes of constituents. Grid-based and 

spatially weighted estimates were comparable in the SFBAY 

study unit (90-percent confidence intervals). However, the 

spatially weighted approach was superior to the grid-based 

proportion when concentrations of a constituent were high in a 

small portion of the aquifer.

For inorganic constituents with health-based benchmarks, 

relative-concentrations were high in 5.1 percent of the primary 

aquifer system, moderate in 25 percent, and low in 70 percent. 

The high aquifer-scale proportion of inorganic constituents 

reflected high values of trace elements (3.0 percent) and 

nutrients (2.1 percent). Inorganic constituents with high values 

were barium (3.0 percent) and nitrate (2.1 percent). The 

inorganic constituents with secondary maximum contaminant 

levels (total dissolved solids, chloride, manganese, and 

iron) were high in 14 percent of the primary aquifer system, 

moderate in 33 percent, and low in 53 percent. 

In contrast, relative-concentrations of organic 

constituents (one or more) were high in 0.6 percent, 

moderate in 12 percent, and low in 88 percent (not detected 

in 49 percent) of the primary aquifer system. The high 

aquifer-scale proportion of organic constituents reflected 

high aquifer-scale proportions of solvents (0.3 percent 

spatially weighted) and methyl tert- butyl ether (0.3 percent 

spatially weighted). Of the 204 organic and special-interest 

constituents analyzed for, 34 were detected. Three organic 

constituents were frequently detected (in 10 percent or more 

of samples)—the trihalomethane chloroform and the VOCs 

1,1,1-trichloroethane and 1,1,2-trichlorotrifluoroethane 

(CFC-113)—but none of these three were detected at high 

relative-concentrations.

In the second component of this report, the understanding 

assessment, some of the primary natural and human factors 

that affect groundwater quality were identified through 

evaluation of correlations between land use, physical 

characteristics of the wells, geochemical conditions of the 

aquifer, and relative-concentrations of constituents. Results 

from these analyses attempt to explain the occurrence and 

distribution of constituents in the SFBAY study unit. 

The understanding assessment indicated that nitrate 

concentrations were negatively correlated with percentage 

of natural land use and were greater in groundwater 

having modern or mixed-age classification compared 

to groundwater having pre-modern-age classification, 

suggesting anthropogenic sources for the nitrate. High and 

moderate relative-concentrations of iron and manganese 

may be attributed to reductive dissolution of manganese and 

iron oxides. Concentrations of total dissolved solids were 

highest in areas of known groundwater salinity concerns. 

Concentrations and detection frequency of perchlorate were 

greater than predicted for natural conditions, indicating 

dominance of anthropogenic sources of perchlorate. 

Acknowledgments

The authors thank the following cooperators for their 

support: the California State Water Resources Control 

Board, Lawrence Livermore National Laboratory, California 

Department of Public Health, and California Department of 

Water Resources. We especially thank the cooperating well 

owners and water purveyors for their generosity in allowing 

the USGS to collect samples from their wells. Funding for this 

work was provided by State of California bonds authorized by 

Proposition 50 and administered by the State Water Board.
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Appendix A 55

For the SFBAY study unit, the historical CDPH database 

contains more than 26,000 records of chemistry data for 

samples from more than 400 wells, requiring targeted 

retrievals to manageably use the data to assess water quality. 

The following paragraphs summarize the selection process for 

wells and data from the CDPH database for use in the gridbased status assessment.

The grid-based calculation of aquifer-scale proportion 

uses one value per grid cell. Where USGS data for inorganic 

constituents were not available, additional data to represent 

a cell were selected from the CDPH database. Of the 68 

grid cells in the SFBAY study unit, 3 cells had USGS-grid 

wells with the full complement of inorganic constituent data 

collected by USGS-GAMA, 40 cells had USGS-grid wells 

with data for just nutrients, and 25 cells did not have USGSgrid wells. The CDPH database was queried to provide these 

missing data for inorganic constituents. CDPH wells with data 

for the most recent 3 years available at the time of sampling 

(April 1, 2004–March 31, 2007) were considered. If a well had 

more than one analysis for a constituent in the 3-year interval, 

then the most recent data were selected.

The data in the CDPH database are of unknown quality, 

and the database does not contain data for quality-control 

samples with which to make a comprehensive quality-control 

assessment of the data. Cation-anion imbalance was used as 

a rough quality-control metric. Because water is electrically 

neutral and must have a balance between positive (cations) 

and negative (anions) electrically charged dissolved species, 

the cation/anion imbalance commonly is used as a qualityassurance check for water-sample analysis (Hem, 1985). An 

imbalance of less than 10 percent was defined as indicating 

acceptable quality of the major ion data for the sample. It 

was assumed that if the quality of the major ion data were 

acceptable, then the quality of the data for the other inorganic 

constituents also would be acceptable. In practice, however, 

some wells did not have data for major ion constituents, so the 

cation-anion imbalance could not be evaluated.

The first choice was to select CDPH inorganic data for 

the grid well sampled by the USGS for other constituents, 

provided the CDPH data met the cation/anion balance criteria. 

This approach resulted in the selection of supplemental 

inorganic data from the CDPH database for 23 USGS-grid 

wells. To identify the USGS-grid wells that incorporated 

CDPH inorganic data, a well ID was created that added “DG” 

to the GAMA ID for these data (for example, SF-04 with 

CDPH data was assigned the well identification SF-DG-04; 

table A1).

If the first step did not yield CDPH inorganic data for 

the USGS-grid well, the second step was to search the CDPH 

database to identify the highest-ranked well with a cation/

anion imbalance less than 10 percent in each grid cell. This 

step resulted in selecting CDPH inorganic data from additional 

CDPH wells that were not USGS-grid wells for 16 grid cells. 

To identify the data from these new CDPH-grid wells, an 

ID was created that added “DPH” after the study unit prefix. 

For cells that contained a USGS-grid well, the identification 

number of the CDPH-grid well remains the same as that 

of the USGS-grid well identifier (for example, CDPH-grid 

well SF-DPH-07; table A1). For cells that did not contain a 

USGS-grid well, the CDPH-grid well was given a sequential 

number starting after the last GAMA ID for the study unit (for 

example, CDPH-grid well SF-DPH-44; table A1). 

If no wells in a grid cell met the cation/anion balance 

criteria or if there were insufficient data to evaluate charge 

balance, the third choice was to select the highest CDPH well 

that was randomly ranked with any of the needed inorganic 

data. This resulted in selecting CDPH inorganic data for seven 

additional wells. These new CDPH-grid wells were labeled 

using the same prefix as the other new CDPH-grid wells. 

The result of these steps was that 47 of the 68 grid cells 

were represented by inorganic constituent data from the USGS 

database, the CDPH database, or both databases. In some 

cases, to achieve one value for each constituent per cell, it 

was necessary to select an additional well in a cell for certain 

constituents; hence, some cells have data from multiple CDPH 

wells. Inorganic data from the CDPH database were used for 

34 grid cells (figs. A1A, A1B). CDPH data were available for 

29 or 30 grid cells for most inorganic constituents, with the 

exception of data for nutrients, boron, lead, molybdenum, 

strontium, vanadium, and radioactive constituents, which were 

available for 0 to 25 wells (table 2). Estimates of aquifer-scale 

proportion for constituents based on a smaller number of wells 

are subject to a larger error associated with the 90-percent 

confidence intervals (on the basis of Jeffreys interval for the 

binomial distribution).

Differences in constituent laboratory reporting levels 

(LRLs) or method detection levels (MDLs) associated with 

USGS and CDPH data did not affect analysis of high or 

moderate relative-concentrations because concentrations 

greater than one-half of water-quality benchmarks were 

substantially higher than the reporting levels. Several types 

of comparisons between USGS-collected and CDPH data are 

described in Appendix E.

Appendix A. Use of Data from The California Department of 

Public Health (CDPH) Database
56 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

USGS 

GAMA well 

identification 

number

Grid cell 

number

CDPH 

GAMA well 

identification 

number

Grid 

supplemented by 

CDPH data from 

different well

Grid wells

SF-01 1 – –

SF-02 2 – –

SF-03 15 – –

SF-04 16 SF-DG-04 –

SF-05 17 SF-DG-05 –

SF-06 18 – –

SF-07 34 – SF-DPH-07

SF-08 33 – SF-DPH-08

SF-09 36 SF-DG-09 SF-DPH-09

SF-10 37 SF-DG-10 SF-DPH-10

SF-11 50 SF-DG-11 –

SF-12 51 SF-DG-12 –

SF-13 49 SF-DG-13 –

SF-14 48 SF-DG-14 SF-DPH-14

SF-15 47 SF-DG-15 –

SF-16 46 SF-DG-16 SF-DPH-16

SF-17 45 SF-DG-17 SF-DPH-17

SF-18 44 SF-DG-18 SF-DPH-18

SF-19 53 – –

SF-20 54 SF-DG-20 SF-DPH-20

SF-21 55 SF-DG-21 –

SF-22 56 SF-DG-22 SF-DPH-22

SF-23 57 SF-DG-23 SF-DPH-23

SF-24 58 – –

SF-25 59 SF-DG-25 SF-DPH-25

SF-26 67 SF-DG-26 –

SF-27 66 SF-DG-27 SF-DPH-27

SF-28 64 – –

SF-29 65 SF-DG-29 –

SF-30 60 SF-DG-30 –

SF-31 63 SF-DG-31 –

SF-32 62 SF-DG-32 SF-DPH-32

SF-33 61 SF-DG-33 –

SF-34 43 – –

SF-35 28 – –

SF-36 27 SF-DG-36 –

SF-37 26 SF-DG-37 –

SF-38 23 – –

SF-39 24 SF-DG-39 –

SF-40 25 – SF-DPH-40

SF-41 11 – –

SF-42 10 – –

SF-43 13 – –

Table A1. Cell number and USGS GAMA well identification numbers for well data used in the San Francisco Bay study unit, California 

GAMA Priority Basin Project.

[A USGS GAMA well identification number indicates the use of USGS data from the grid well; a CDPH GAMA well identification number with ‘DG’ 

indicates the use of CDPH inorganic data from the grid well; a CDPH GAMA well identification number with ‘DPH’ indicates the use of CDPH data from a 

different well. SF, San Francisco Bay study unit well; SFU or SFM, understanding well; –, no wells sampled or selected; na, not applicable]

USGS 

GAMA well 

identification 

number

Grid cell 

number

CDPH 

GAMA well 

identification 

number

Grid 

supplemented by 

CDPH data from 

different well

Grid wells

None 29 – SF-DPH-44

None 32 – SF-DPH-45

None 35 – SF-DPH-46

None 68 – SF-DPH-47

Understanding production wells

SFU-01 17 – –

SFU-02 48 – –

SFU-03 48 – –

SFU-04 49 – –

SFU-05 49 – –

SFU-06 58 – –

SFU-07 58 – –

SFU-08 63 – –

SFU-09 27 – –

SFU-10 27 – –

SFU-11 na1 – –

SFU-12 na1 – –

Monitor wells

SFM-A1 1 – –

SFM-A2 1 – –

SFM-A3 1 – –

SFM-A4 1 – –

SFM-B1 16 – –

SFM-B2 16 – –

SFM-C1 58 – –

SFM-C2 58 – –

SFM-C3 58 – –

SFM-C4 58 – –

SFM-C5 58 – –

SFM-D1 28 – –

SFM-D2 28 – –

SFM-D3 28 – –

SFM-D4 28 – –

SFM-E1 28 – –

SFM-E2 28 – –

SFM-E3 28 – –

SFM-F1 23 – –

SFM-F2 23 – –

SFM-F3 23 – –

SFM-F4 23 – –

SFM-F5 23 – –

SFM-F6 23 – –

1

These wells were outside the study unit boundaries.
Appendix A 57

Figure A1. Identifiers and locations of (A) USGS-grid and USGS-understanding wells sampled during April to June 2007 and 

(B) CDPH-grid wells, San Francisco Bay study unit, California GAMA Priority Basin Project.

2

8

1

3 4

68

6

42

38

52

34

40

20

55

58 57

59

36

27

41

37

28

56

50

45

54

46

47

44

53

49

29

48

33 32

67

30

51

14

22

9

15

66

5

18

61

39

35

43

31

7

11

16

26

10

17

24

23

60

19

25

65

6362

21

13 12

64

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Aqueduct

Canal

Stream

Counties

SAN FRANCISCO

CO

ALAMEDA CO

SANTA CRUZ CO

CONTRA COSTA CO

MARIN CO

SAN MATEO CO SANTA CLARA

VALLEY

SANTA CLARA CO

SAN JOAQUIN CO

SACRAMENTO CO

Hayward

Alameda

San Francisco

San Jose

San Mateo

Palo Alto

Santa Clara

Morgan Hill

Arroyo Calero

Arroyo Valle

Guadalupe River

Arroyo Hondo

Llagas Creek

Arroyo de la Laguna

Arroyo Valle

Coyote Creek

Diablo Range

Old River

Contra Costa Canal

Dutch Slough

P a c i f i c O c e a n

San Francisco

Bay

Golden Gate Strait

EXPLANATION

0 10 20 MILES

0 10 20 KILOMETERS

IP_017719_Figure A1a_Well Map

USGS-grid well and identifier

USGS-understanding well and identifier

SF-11

SFU-03

Study unit

Grid cell and

 number 36

SFU-12

SFU-11

SFU-10 SFU-09

SFU-08

SFU-07

SFU-06

SFU-05

SFU-04

SFU-03

SFU-01

SFM-A1–A4

SFM-B1–B2

SFM-F3–F6

SFM-E1–E3

SFM-D1–D4

SFM-C2–C5

SFU-02

SF-43

SF-42

SF-41

SF-40

SF-39

SF-38

SF-37

SF-36

SF-35

SF-34

SF-33

SF-32

SF-31

SF-30

SF-29

SF-28

SF-27

SF-26

SF-25

SF-24

SF-23

SF-22

SF-21

SF-20 SF-19

SF-18 SF-17

SF-16

SF-06

SF-05

SF-04

SF-03

SF-02

SF-01

SF-14

SF-13

SF-12

SF-11

SF-10

SF-09

SF-08

SF-07

SF-15

A
58 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Figure A1.—Continued

B

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Aqueduct

Canal

Stream

Counties

SAN FRANCISCO

CO

ALAMEDA CO

SANTA CRUZ CO

CONTRA COSTA CO

MARIN CO

SAN MATEO CO

SANTA CLARA

VALLEY

SANTA CLARA CO

SAN JOAQUIN CO

SACRAMENTO CO

Hayward

Alameda

San Francisco

San Jose

San Mateo

Palo Alto

Santa Clara

Morgan Hill

Arroyo Calero

Arroyo Valle

Guadalupe River

Arroyo Hondo

Llagas Creek

Arroyo de la Laguna

Arroyo Valle

Coyote Creek

Diablo Range

Old River

Contra Costa Canal

Dutch Slough

P a c i f i c O c e a n

San Francisco

Bay

Golden Gate Strait

EXPLANATION

0 10 20 MILES

0 10 20 KILOMETERS

IP017719_Figure A1b_CDPH Well Map

Study unit

CDPH-grid well and identifier

SF-DG-27

SF-DG-31

SF-DG-29

SF-DG-17

SF-DG-12

SF-DG-13

SF-DG-11

SF-DG-23

SF-DG-15

SF-DG-16

SF-DG-21

SF-DG-20 SF-DG-22

SF-DG-25

SF-DG-27

SF-DG-10 SF-DG-18

SF-DG-09

SF-DG-33

SF-DG-32

SF-DG-30

SF-DG-05

SF-DG-04

SF-DG-36

SF-DG-37

SF-DG-39

SF-DPH-17

SF-DPH-08

SF-DPH-07

SF-DPH-20

SF-DPH-22

SF-DPH-25

SF-DPH-23

SF-DPH-16

SF-DPH-27

SF-DPH-10

SF-DPH-32

SF-DPH-09

SF-DPH-46

SF-DPH-47

SF-DPH-45

SF-DPH-44

SF-DPH-40

SF-DG-14

SF-DPH-14

SF-DPH-18
Appendix B 59

Appendix B. Calculating Total Dissolved Solids

Specific conductance, an electrical measure of total 

dissolved solids (TDS), was available for all 79 grid and 

understanding wells sampled by the USGS, whereas TDS was 

only measured for 30 of these wells. As a result, TDS values 

were calculated for wells that had no measured TDS from 

specific conductance (SC) values using a linear regression 

equation (TDS = 0.56*SC +58) developed from the SC and 

TDS relations from the 30 wells with TDS data so that all 

grid wells would have TDS values. The predicted TDS values 

using the regression equation closely matched measured TDS 

values (R2 = 0.99). TDS values from CDPH were combined 

with USGS measured and calculated TDS values. 

Appendix C. Estimation of Aquifer-Scale Proportions

Two statistical approaches, grid-based and spatially 

weighted, were selected to evaluate the proportions of the 

primary aquifer system in the SFBAY study unit that had 

high, moderate, or low relative-concentrations (compared to 

benchmarks) of constituents. The grid-based and spatially 

weighted estimations of aquifer-scale proportions, based on 

a spatially distributed grid cell network across the study unit, 

are intended to characterize the water quality of the primary 

aquifer system, or at depths from which drinking water is 

usually drawn. These approaches assign weights to wells 

based upon a single well per cell (grid-based) or the number of 

wells per cell (spatially weighted). Raw detection frequencies, 

derived from the percentage of the total number of wells with 

high or moderate relative-concentrations, also were calculated 

for individual constituents, but were not used for estimating 

aquifer-scale proportion because this method creates spatial 

bias towards regions with large numbers of wells.

1. Grid-based: One well in each grid cell, a “grid well,” was 

randomly selected to represent the primary aquifer system 

(Belitz and others, 2010). For organic constituents, the 

one value in each grid cell was obtained from samples 

analyzed by USGS-GAMA from 43 grid wells. For 

inorganic constituents, the one value in each grid cell 

was obtained from samples analyzed by USGS-GAMA 

and data selected from the CDPH database. The relativeconcentration for each constituent (concentration relative 

to its benchmark) was then evaluated for each grid well. 

The proportion of the primary aquifer system that had 

high relative-concentrations was calculated by dividing 

the number of cells with concentrations greater than the 

benchmark (relative-concentration greater than 1) by 

the number of grid wells with data in the SFBAY study 

unit. Proportions containing moderate and low relativeconcentrations were calculated similarly. Confidence 

intervals for grid-based aquifer proportions were 

computed using the Jeffreys interval for the binomial 

distribution (Brown and others, 2001). The grid-based 

estimate is spatially unbiased. However, the grid-based 

approach may not identify constituents that exist at high 

concentrations in small proportions of the primary aquifer 

system.

The grid-based aquifer-scale proportions for constituent 

classes also are calculated on a one-value-per-cell basis. 

A cell with a high value for any constituent in the class 

is defined as a high cell, and the high proportion is the 

number of high cells divided by the number of cells with 

data for any of the constituents in the class. The moderate 

proportion for the constituent class is calculated similarly, 

except that a cell already defined as high cannot also 

be defined as moderate. A cell with a moderate value 

for any constituent in the class that does not also have a 

high value for any constituent in the class is defined as 

moderate.

2. Spatially weighted: The spatially weighted approach 

relied upon USGS-grid well data, CDPH data from 

April 1, 2004, to March 31, 2007 (most recent analyses 

per well for all wells within each grid cell), and selected 

USGS-understanding public-supply well data. However, 

instead of data from only one well per grid cell, the 

spatially weighted approach calculates the high, moderate, 

and low relative-concentrations for all of the wells in 

each cell. The high, moderate, and low aquifer-scale 

proportions are calculated for each cell, and then the 

proportions are averaged for all of the cells with data 

for the constituent (Isaaks and Srivastava, 1989). The 

resulting proportions are spatially unbiased. Confidence 

intervals for spatially weighted estimates of aquifer-scale 

proportion are not described in this report.

The raw detection frequency approach is merely the 

percentage (frequency) of wells within the study unit that had 

high relative-concentrations. It was calculated by considering 

all of the available data in the period from April 1, 2004, to 

March 31, 2007, for the USGS-grid well data, the CDPHwell data (the most recent analysis per well for all wells), and 

USGS-understanding wells. However, this approach could be 

spatially biased because the CDPH and USGS-understanding 

wells are not uniformly distributed. Consequently, high 

values (or low values) for wells clustered in a particular area 

represent a small part of the primary aquifer system, and could 

be given a disproportionately high (or low) weight compared 

to that given by spatially unbiased approaches. Raw detection 

frequencies of high relative-concentrations are provided to 

identify constituents for discussion in this report, but were not 

used to assess aquifer-scale proportions. 
60 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Land-use classifications and percentages of each 

classification, well construction information, geochemical 

conditions, and groundwater age data and classifications are 

listed in tables D1–D5. 

Land-Use Classification

Land use was classified by using an enhanced version 

of the satellite-derived [98-ft (30-m) pixel resolution] USGS 

Enhanced National Land Cover Dataset (Nakagaki and others, 

2007). This dataset has been used in previous national and 

regional studies relating land use to water quality (Gilliom 

and others, 2006; Zogorski and others, 2006). The dataset 

characterizes land cover during the early 1990s. The imagery 

was classified into 25 land-cover categories (Nakagaki 

and Wolock, 2005). These 25 land-cover categories were 

aggregated into three principal land-use classes for the purpose 

of general characterization of land use: urban, agricultural, and 

natural. Average land use (proportions of urban, agricultural, 

and natural) for the study unit, for each study area, and for 

areas within a radius of 1,640 ft (500 m) surrounding each 

well (table D1) were calculated using ArcGIS (version 9.2) 

(Johnson and Belitz, 2009). 

Well Construction Information

Well construction data primarily were obtained from 

driller’s logs filed with CDWR. Other sources of well 

construction data were ancillary records from well owners and 

the USGS National Water Information System database. Well 

construction data are not available in the CDPH database. Well 

identification verification procedures are described by Ray and 

others (2009). Well depths, depths to the tops and bottoms of 

the perforated intervals, and lengths of the perforated intervals 

for wells are listed in table D2.

Classification of Geochemical Condition

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidationreduction (redox) characteristics and pH (table D3). 

Oxidation-reduction (redox) conditions influence 

the mobility of many organic and inorganic constituents 

(McMahon and Chapelle, 2008). Along groundwater flow 

paths, redox conditions commonly proceed along a welldocumented sequence of terminal electron acceptor processes 

(TEAPs); one TEAP typically dominates at a particular time 

and aquifer location (Chapelle and others, 1995; Chapelle, 

2001). The predominant TEAPs are oxygen-reduction, 

nitrate-reduction, manganese-reduction, iron-reduction, 

sulfate-reduction, and methanogenesis. The presence of 

redox-sensitive chemical species suggesting more than one 

TEAP may indicate mixed waters from different redox zones 

upgradient of the well, a well screened across more than one 

redox zone, or spatial heterogeneity in microbial activity in the 

aquifer. 

Redox conditions were assigned to each sample using a 

modified version of the classification scheme of McMahon 

and Chapelle (2008) and Jurgens and others (2009) (tables D3, 

D4). Samples with DO > 0.5 mg/L were classified as oxic, and 

samples with DO ≤ 0.5 mg/L were classified as anoxic. The 

anoxic samples were further classified according to the TEAPs 

inferred from data for nitrate, manganese, and iron. Data for 

these constituents were obtained from USGS-GAMA where 

available and from the CDPH database (“DG” CDPH-grid 

wells). Inorganic constituent data were not available for all 

anoxic samples. 

Groundwater Age Classification

Groundwater dating techniques provide a measure of 

the time since the groundwater was last in contact with the 

atmosphere (residence time in the aquifer). The techniques 

used in this report to estimate groundwater residence times or 

‘age’ were those based on tritium (for example: Tolstikhin and 

Kamensky, 1969; Torgersen and others, 1979) and carbon-14 

activities (for example: Vogel and Ehhalt, 1963; Plummer and 

others, 1993). 

Tritium (3H) is a short-lived radioactive isotope 

of hydrogen with a half-life of 12.32 years (Lucas and 

Unterweger, 2000). Tritium is produced naturally in the 

atmosphere from the interaction of cosmogenic radiation with 

nitrogen (Craig and Lal, 1961), by above-ground nuclear 

explosions, and by the operation of nuclear reactors. Tritium 

enters the hydrological cycle following oxidation to tritiated 

water. Natural background levels of tritium in precipitation are 

approximately 3 to 15 tritium units (TU) (Craig and Lal, 1961; 

Clark and Fritz, 1997). Above-ground nuclear explosions 

resulted in a large increase in tritium values in precipitation, 

beginning in about 1952 and peaking in 1963 at values 

over 1,000 TU in the northern hemisphere (Michel, 1989). 

Radioactive decay over a period of 60 years would decrease 

tritium values of 10 TU to 0.6 TU. 

Previous investigations have used a range of tritium 

values from 0.3 to 1.0 TU as thresholds for indicating presence 

of water that has exchanged with the atmosphere since 

1952 (Michel, 1989; Plummer and others, 1993; Michel and 

Schroeder, 1994; Clark and Fritz, 1997; Manning and others, 

2005; Kulongoski and others, 2010; Landon and others, 

2010). For samples collected for the SFBAY study unit in 

2007, tritium values greater than a threshold of 1.0 TU were 

defined as indicating the presence of groundwater recharged 

since 1952 (Kulongoski and others, 2010; Landon and others, 

2010). Water recharged since 1952 is defined as “modern” 

groundwater.

Appendix D. Ancillary Datasets
Appendix D 61

Carbon-14 (14C) is a widely used chronometer based 

on the radiocarbon content of organic and inorganic 

carbon. Dissolved inorganic carbon species, carbonic acid, 

bicarbonate, and carbonate typically are used for 14C dating 

of groundwater. 14C is formed in the atmosphere by the 

interaction of cosmic-ray neutrons with nitrogen and, to 

a lesser degree, with oxygen and carbon, and by aboveground nuclear explosions. 14C is incorporated into carbon 

dioxide and mixed throughout the atmosphere. The carbon 

dioxide enters the hydrologic cycle because it dissolves in 

precipitation and surface water in contact with the atmosphere. 

14C activity in groundwater, expressed as percent modern 

carbon (pmc), reflects the time since groundwater was last 

exposed to the atmospheric 14C source. 14C has a half-life of 

5,730 years and can be used to estimate groundwater ages 

ranging from 1,000 to approximately 30,000 years before 

present. 

The 14C age (residence time, presented in years) is 

calculated on the basis of the decrease in 14C activity as 

a result of radioactive decay since groundwater recharge, 

relative to an assumed initial 14C concentration (Clark and 

Fritz, 1997). An average initial 14C activity of 99 pmc is 

assumed for this study, with estimated errors on calculated 

groundwater ages up to ± 20 percent. Calculated 14C ages in 

this study are referred to as “uncorrected” because they have 

not been adjusted to consider exchanges with sedimentary 

sources of carbon (Fontes and Garnier, 1979). Groundwater 

with a 14C activity of >88 pmc is reported as having an age 

of <1,000 years; no attempt is made to refine 14C ages <1,000 

years. Measured values of pmc can be >100 in groundwater 

samples containing a significant component of water recharged 

after 1952 because the definition of pmc is based on 14C 

activity in the absence of 14C contributed by above-ground 

nuclear explosions (Clark and Fritz, 1997). For the SFBAY 

study unit, 14C activity <90 pmc was defined as indicating 

the presence of groundwater recharged before the modern 

era (Kulongoski and others, 2010; Landon and others, 2010). 

Water recharged before the modern era is defined as “premodern” groundwater. 

Tritium and 14C data and age classifications are reported 

in table D5. Because of uncertainties in age distributions, 

particularly the uncertainties caused by mixing of waters of 

different ages in wells with long screened or open intervals 

and high withdrawal rates, the uncorrected 14C ages were not 

specifically used for quantifying the relation between age 

and water quality in this report. While more sophisticated 

lumped parameter models for analyzing age distributions that 

incorporate mixing are available (Cook and Böhlke, 2000), 

use of these alternative models to understand age mixtures was 

not needed for the assessments in this report. Classification 

into modern, mixed, and pre-modern categories was sufficient 

to provide an appropriate and useful characterization for the 

purposes of examining groundwater quality.

For the SFBAY study unit, groundwater samples were 

assigned the following age classifications:

Classification Tritium (TU) 14C (pmc)

Modern >1.0 >90

Modern or Mixed >1.0 No data

Mixed >1.0 <90

Mixed <1.0 >90

Pre-modern <1.0 <90
62 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Table D1. Well type, percent land-use by category, land-use classification, septic and leaking underground fuel tank (LUFT) density, 

and USGS-GAMA well identification number for GAMA well data and CDPH grid-well data used in the San Francisco Bay study unit, 

California GAMA Priority Basin Project.

[m, meter; tanks/km2, tanks per square kilometer; na, not available; USGS, U.S. Geological Survey; DPH, California Department of Public Health; Well types: 

PSW, public supply well; IRR, irrigation well; MON, monitor well; INS, institutional well; IND, industrial well; DES, desalination well]

USGS-GAMA well 

identification 

number

Well 

type

Agricultural land 

use within 500 m 

of the well

(percent)

Natural land use 

within 500 m of 

the well, percent

Urban land use 

within 500 m of the 

well, percent

Land-use 

classification

Septic 

density1

(tanks/km2

)

LUFT 

density2

(tanks/

km2

)

USGS-grid wells

SF-01 IRR 0 61 39 Natural 0 1

SF-02 IRR 0 73 27 Natural 0 2

SF-03 PSW 0 2 98 Urban 0 1

SF-04 PSW 0 5 95 Urban 1 5

SF-05 PSW 0 3 97 Urban 0 2

SF-06 IRR 0 3 97 Urban 1 3

SF-07 INS 0 8 92 Urban 0 2

SF-08 IRR 0 5 95 Urban 0 2

SF-09 PSW 0 0 100 Urban 16 1

SF-10 PSW 0 3 97 Urban 22 5

SF-11 PSW 0 1 99 Urban 6 2

SF-12 PSW 0 2 98 Urban 8 1

SF-13 PSW 0 5 95 Urban 6 1

SF-14 PSW 0 2 98 Urban 5 1

SF-15 PSW 0 4 96 Urban 0 5

SF-16 PSW 0 2 98 Urban 0 12

SF-17 PSW 1 37 62 Urban 1 2

SF-18 PSW 0 0 100 Urban 0 0

SF-19 IRR 0 14 86 Urban 16 1

SF-20 PSW 0 1 99 Urban 34 2

SF-21 PSW 0 1 99 Urban 3 1

SF-22 PSW 0 2 98 Urban 10 3

SF-23 PSW 0 5 95 Urban 0 12

SF-24 PSW 0 2 98 Urban 3 6

SF-25 PSW 0 10 90 Urban 5 3

SF-26 PSW 7 19 74 Urban 0 1

SF-27 PSW 2 14 84 Urban 9 2

SF-28 PSW 0 34 66 Urban 1 1

SF-29 PSW 0 2 98 Urban 1 1

SF-30 PSW 17 67 16 Natural 5 0

SF-31 PSW 20 21 59 Urban 0 0

SF-32 PSW 1 81 18 Natural 3 0

SF-33 PSW 46 49 5 Natural 11 0

SF-34 IRR 0 57 43 Natural 0 1

SF-35 DES 0 15 85 Urban 0 2

SF-36 PSW 0 28 72 Urban 10 2

SF-37 PSW 0 29 71 Urban 14 3

SF-38 PSW 3 21 77 Urban 0 1

SF-39 PSW 0 15 85 Urban 69 2

SF-40 PSW 0 6 94 Urban 2 3

SF-41 IRR 0 14 86 Urban 5 6

SF-42 IND 0 5 95 Urban 0 13

SF-43 IRR 0 36 64 Urban 0 4
Appendix D 63

USGS-GAMA well 

identification 

number

Well 

type

Agricultural land 

use within 500 m 

of the well

(percent)

Natural land use 

within 500 m of 

the well, percent

Urban land use 

within 500 m of the 

well, percent

Land-use 

classification

Septic 

density1

(tanks/km2

)

LUFT 

density2

(tanks/

km2

)

CDPH-grid wells

SF-DPH-44 PSW 0 5 95 Urban na na

SF-DPH-45 PSW 0 1 99 Urban na na

SF-DPH-46 PSW 0 3 97 Urban na na

SF-DPH-47 PSW 0 28 72 Urban na na

SF-DPH-48 PSW 0 0 100 Urban na na

SF-DPH-49 PSW 0 1 99 Urban na na

SF-DPH-50 PSW 0 1 99 Urban na na

SF-DPH-51 PSW 0 0 100 Urban na na

SF-DPH-52 PSW 69 7 24 Agricultural na na

SF-DPH-53 PSW 0 13 87 Urban na na

SF-DPH-54 PSW 0 2 98 Urban na na

SF-DPH-55 PSW 0 1 99 Urban na na

SF-DPH-56 PSW 0 1 99 Urban na na

SF-DPH-57 PSW 0 3 97 Urban na na

SF-DPH-58 PSW 92 6 2 Agricultural na na

SF-DPH-59 PSW 1 11 88 Urban na na

SF-DPH-60 PSW 0 25 75 Urban na na

SF-DPH-61 PSW 0 3 97 Urban na na

SF-DPH-62 PSW 0 22 78 Urban na na

USGS-understanding wells

SFM-A1 MON 0 45 55 Urban 0 1

SFM-A2 MON 0 45 55 Urban 0 1

SFM-A3 MON 0 45 55 Urban 0 1

SFM-A4 MON 0 45 55 Urban 0 1

SFM-B1 MON 0 8 92 Urban 0 2

SFM-B2 MON 0 8 92 Urban 0 2

SFM-C1 MON 0 14 86 Urban 3 2

SFM-C2 MON 0 14 86 Urban 3 2

SFM-C3 MON 0 14 86 Urban 3 2

SFM-C4 MON 0 14 86 Urban 3 2

SFM-C5 MON 0 14 86 Urban 3 2

SFM-D1 MON 0 12 88 Urban 1 2

SFM-D2 MON 0 12 88 Urban 1 2

SFM-D3 MON 0 12 88 Urban 1 2

SFM-D4 MON 0 12 88 Urban 1 2

SFM-E1 MON 0 0 100 Urban 0 1

SFM-E2 MON 0 0 100 Urban 0 1

SFM-E3 MON 0 0 100 Urban 0 1

SFM-F1 MON 0 60 40 Natural 0 3

SFM-F2 MON 0 60 40 Natural 0 3

SFM-F3 MON 0 60 40 Natural 0 3

SFM-F4 MON 0 60 40 Natural 0 3

SFM-F5 MON 0 60 40 Natural 0 3

SFM-F6 MON 0 60 40 Natural 0 3

SFU-01 PSW 0 7 93 Urban 1 7

SFU-02 PSW 0 0 100 Urban 7 3

SFU-03 PSW 0 1 99 Urban 4 3

Table D1. Well type, percent land-use by category, land-use classification, septic and leaking underground fuel tank (LUFT) density, 

and USGS-GAMA well identification number for GAMA well data and CDPH grid-well data used in the San Francisco Bay study unit, 

California GAMA Priority Basin Project.—Continued

[m, meter; tanks/km2; tanks per square kilometer; na, not available; USGS, U.S. Geological Survey; DPH, California Department of Public Health; Well types: 

PSW, public supply well; IRR, irrigation well; MON, monitor well; INS, institutional well; IND, industrial well; DES, desalination well]
64 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

USGS-GAMA well 

identification 

number

Well 

type

Agricultural land 

use within 500 m 

of the well

(percent)

Natural land use 

within 500 m of 

the well, percent

Urban land use 

within 500 m of the 

well, percent

Land-use 

classification

Septic 

density1

(tanks/km2

)

LUFT 

density2

(tanks/

km2

)

USGS-understanding wells

SFU-04 PSW 0 2 98 Urban 0 1

SFU-05 PSW 0 12 88 Urban 0 1

SFU-06 PSW 0 12 88 Urban 2 4

SFU-07 PSW 0 10 90 Urban 0 2

SFU-08 PSW 0 23 77 Urban 1 7

SFU-09 PSW 0 29 71 Urban 9 2

SFU-10 PSW 0 24 76 Urban 8 2

SFU-11 PSW 0 98 2 Natural 3 0

SFU-12 PSW 0 99 1 Natural 3 0

1

 Septic tank density within 500-meter radius of well site, based on 1990 U.S. Census data.

2

 LUFT density within 500-meter radius of well site, based on GEIMS LUFT database (California Environmental Protection Agency, 2001).

Table D1. Well type, percent land-use by category, land-use classification, septic and leaking underground fuel tank (LUFT) density, 

and USGS-GAMA well identification number for GAMA well data and CDPH grid-well data used in the San Francisco Bay study unit, 

California GAMA Priority Basin Project.—Continued

[m, meter; tanks/km2; tanks per square kilometer; na, not available; USGS, U.S. Geological Survey; DPH, California Department of Public Health; Well types: 

PSW, public supply well; IRR, irrigation well; MON, monitor well; INS, institutional well; IND, industrial well; DES, desalination well]
Appendix D 65

USGS-GAMA well 

identification number 

indicating data source

Well 

type

Construction information

Aridity index1

Well depth (dimensionless)

(ft below LSD)

Top of perforations 

(ft below LSD)

Bottom of 

perforations 

(ft below LSD)

Length from top 

of uppermost 

perforated interval 

to bottom of well 

(ft)

USGS-grid wells

SF-01 IRR na na na na 0.48

SF-02 IRR 360 170 350 190 0.54

SF-03 PSW 410 170 375 240 0.51

SF-04 PSW na na na na 0.54

SF-05 PSW 480 na na na 0.50

SF-06 IRR 180 na na na 0.45

SF-07 INS 220 na na na 0.37

SF-08 IRR 275 240 275 35 0.37

SF-09 PSW 1,120 289 1120 831 0.37

SF-10 PSW 680 290 660 390 0.31

SF-11 PSW 596 348 526 248 0.36

SF-12 PSW na na na na 0.37

SF-13 PSW 760 340 750 420 0.35

SF-14 PSW 528 165 363 363 0.32

SF-15 PSW 810 540 790 270 0.31

SF-16 PSW 665 295 665 370 0.31

SF-17 PSW na na na na 0.32

SF-18 PSW 816 300 816 516 0.34

SF-19 IRR 540 200 520 340 0.46

SF-20 PSW 840 358 798 482 0.38

SF-21 PSW 815 350 795 465 0.35

SF-22 PSW 827 378 818 449 0.35

SF-23 PSW 890 300 870 590 0.31

SF-24 PSW 780 265 774 515 0.32

SF-25 PSW 612 267 603 345 0.32

SF-26 PSW 427 107 376 320 0.37

SF-27 PSW 437 186 400 251 0.35

SF-28 PSW 517 385 454 132 0.33

SF-29 PSW 275 102 266 173 0.37

SF-30 PSW 80 41 80 39 0.47

SF-31 PSW 286 na na na 0.41

SF-32 PSW na na na na 0.42

SF-33 PSW 366 161 346 205 0.43

SF-34 IRR 153 60 153 93 0.35

SF-35 DES 248 216 240 32 0.34

SF-36 PSW 190 100 180 90 0.37

SF-37 PSW 200 80 177 120 0.37

SF-38 PSW 535 na na na 0.37

SF-39 PSW 600 480 580 120 0.40

SF-40 PSW 550 245 530 305 0.43

SF-41 IRR 155 35 155 120 0.42

SF-42 IND 495 324 479 171 0.45

SF-43 IRR 353 262 300 91 0.44

Table D2. Well construction information for wells used in the San Francisco Bay study unit, California GAMA Priority Basin Project.

[ft, feet; LSD, land-surface datum; USGS, U.S. Geological Survey; SF, San Francisco Bay study unit; DPH, California Department of Public Health; na, not 

available; Well types: PSW, public supply well; IRR, irrigation well; MON, monitor well; INS, institutional well; IND, industrial well; DES, desalination well]
66 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

USGS-GAMA well 

identification number 

indicating data source

Well 

type

Construction information

Aridity index1

Well depth (dimensionless)

(ft below LSD)

Top of perforations 

(ft below LSD)

Bottom of 

perforations 

(ft below LSD)

Length from top 

of uppermost 

perforated interval 

to bottom of well 

(ft)

CDPH-grid wells

SF-DPH-44 PSW na na na na na

SF-DPH-45 PSW 608 181 532 427 na

SF-DPH-46 PSW 828 144 624 684 na

SF-DPH-47 PSW 320 142 301 178 na

SF-DPH-48 PSW 515 260 500 255 na

SF-DPH-49 PSW na na na na na

SF-DPH-50 PSW na na na na na

SF-DPH-51 PSW na 299 823 na na

SF-DPH-52 PSW na na na na na

SF-DPH-53 PSW na 260 716 na na

SF-DPH-54 PSW na na na na na

SF-DPH-55 PSW na 358 790 na na

SF-DPH-56 PSW na na na na na

SF-DPH-57 PSW na 440 531 na na

SF-DPH-58 PSW na na na na na

SF-DPH-59 PSW na na na na na

SF-DPH-60 PSW na na na na na

SF-DPH-61 PSW na na na na na

SF-DPH-62 PSW na na na na na

USGS-understanding wells

SFM-A1 MON 575 555 565 20 0.49

SFM-A2 MON 440 410 430 30 0.49

SFM-A3 MON 270 240 260 30 0.49

SFM-A4 MON 155 140 150 15 0.49

SFM-B1 MON 146 126 136 20 0.45

SFM-B2 MON 74 54 64 20 0.45

SFM-C1 MON 1,000 820 840 180 0.32

SFM-C2 MON 640 620 640 20 0.32

SFM-C3 MON 540 520 540 20 0.32

SFM-C4 MON 425 405 425 20 0.32

SFM-C5 MON 72 62 72 10 0.32

SFM-D1 MON 480 450 480 30 0.34

SFM-D2 MON 340 330 340 10 0.34

SFM-D3 MON 260 230 260 30 0.34

SFM-D4 MON 80 50 80 30 0.34

SFM-E1 MON 470 430 470 40 0.35

SFM-E2 MON 200 180 200 20 0.35

SFM-E3 MON 100 50 100 50 0.35

SFM-F1 MON 1,010 990 1,010 20 0.42

SFM-F2 MON 860 830 860 30 0.42

SFM-F3 MON 640 530 640 110 0.42

SFM-F4 MON 318 298 318 20 0.42

SFM-F5 MON 138 128 138 10 0.42

SFM-F6 MON 45 35 45 10 0.42

SFU-01 PSW 630 na na na 0.46

SFU-02 PSW 570 309 557 261 0.32

Table D2. Well construction information for wells used in the San Francisco Bay study unit, California GAMA Priority Basin Project.—

Continued

[ft, feet; LSD, land-surface datum; USGS, U.S. Geological Survey; SF, San Francisco Bay study unit; DPH, California Department of Public Health; na, not 

available; Well types: PSW, public supply well; IRR, irrigation well; MON, monitor well; INS, institutional well; IND, industrial well; DES, desalination well]
Appendix D 67

USGS-GAMA well 

identification number 

indicating data source

Well 

type

Construction information

Aridity index1

Well depth (dimensionless)

(ft below LSD)

Top of perforations 

(ft below LSD)

Bottom of 

perforations 

(ft below LSD)

Length from top 

of uppermost 

perforated interval 

to bottom of well 

(ft)

USGS-understanding wells

SFU-03 PSW 594 310 563 284 0.32

SFU-04 PSW 800 445 780 355 0.33

SFU-05 PSW 604 302 507 302 0.34

SFU-06 PSW 800 315 745 485 0.32

SFU-07 PSW 560 295 467 265 0.32

SFU-08 PSW 749 314 737 435 0.34

SFU-09 PSW 320 220 300 100 0.37

SFU-10 PSW 465 189 455 276 0.35

SFU-11 PSW na na na na 0.58

SFU-12 PSW na na na na 0.88

1

Aridity index is average annual precipitation divided by average annual evapotranspiration.

Table D2. Well construction information for wells used in the San Francisco Bay study unit, California GAMA Priority Basin Project.—

Continued

[ft, feet; LSD, land-surface datum; USGS, U.S. Geological Survey; SF, San Francisco Bay study unit; DPH, California Department of Public Health; na, not 

available; Well types: PSW, public supply well; IRR, irrigation well; MON, monitor well; INS, institutional well; IND, industrial well; DES, desalination well]
68 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Table D3. pH and oxidation-reduction constituent concentrations and classifications for samples from the San Francisco Bay study 

unit, California GAMA Priority Basin Project. 

[redox, oxidation-reduction; mg/L, milligram per liter; μg/L, microgram per liter; <, less than; >, greater than; ≥, greater than or equal to; >, greater than; <, less 

than; na, not available; O2, oxygen; NO3, nitrate; Mn, manganese; Fe, iron. Redox classifications: oxic, dissolved oxygen ≥ 0.5 mg/L; anoxic/suboxic, dissolved 

oxygen < 0.5 mg/L; NO3-red, nitrate reducing conditions; Mn-red, manganese reducing conditions; MnFe-red, manganese and iron reducing conditions]

Oxidation-reduction constituents

Dissolved

oxygen

Nitrate plus

nitrite, as 

nitrogen

Magnanese Iron Sulfate

Redox threshold value ≥0.5 >0.5 >50 >100 >4

Possible redox type if concentration > redox 

threshold value O2 NO3 Mn Fe

Analysis reporting level 

(unless otherwise noted) 0.2 0.06 0.18 5.0 0.18

Well

No. 

Source of 

inorganic 

data1

pH 

(standard 

units)

mg/L mg/L μg/L μg/L mg/L Redox classification

Grid wells

SF-01 CDPH 7.5 9.4 7.47 na na na Oxic

SF-02 CDPH 7.3 6.3 12.7 na na na Oxic

SF-03 CDPH 7.5 2.6 5.66 na na na Oxic

SF-04 CDPH 8.1 1.7 2.02 41.0 <100 10.0 Oxic

SF-05 CDPH 7.1 4.8 1.29 na na 29.0 Oxic

SF-06 CDPH 6.5 4.7 5.28 na na na Oxic

SF-07 CDPH 6.9 1.9 6.58 na na na Oxic

SF-08 CDPH 7.6 6.5 0.3 na na na Oxic

SF-09 CDPH 7.3 4.8 7.66 na na 41.1 Oxic

SF-10 CDPH 7.6 1.1 3.8 na na 37.1 Oxic

SF-11 CDPH 7.3 11.9 2.96 <20 <100 32 Oxic

SF-12 CDPH 7.3 7.9 2.15 <20 <100 39 Oxic

SF-13 CDPH 7.1 5.8 6.2 <20 <100 21 Oxic

SF-14 CDPH 7.4 4.1 4.44 <20 <100 39 Oxic

SF-15 CDPH 7.7 <0.2 1.36 <20 <100 45 Anoxic (NO3-red)

SF-16 CDPH 7.9 <0.2 0.52 27 <100 50 Anoxic (NO3-red)

SF-17 CDPH 7.4 0.6 3.51 na na na Oxic

SF-18 CDPH 7.4 5.1 5.33 88.9 101 72.7 Mixed

SF-19 CDPH 7.7 0.2 0.2 na na na Anoxic

SF-20 CDPH 7.3 5.9 3.06 15.2 287 46.2 Mixed

SF-21 CDPH 7.2 4.9 4.85 5.08 173 35.9 Mixed

SF-22 CDPH 7.5 3.1 0.69 1.25 0 44.9 Oxic

SF-23 CDPH 7.6 3.8 1.93 <20 <100 60 Oxic

SF-24 USGS 7.4 1.1 3.81 0.70 11 64.9 Oxic

SF-25 CDPH 7.4 7.3 6.14 19.8 69.2 62.4 Oxic

SF-26 CDPH 7.3 0.8 0.56 3.76 73.5 32.0 Oxic

SF-27 CDPH 7.4 1.1 0.98 na na 37.0 Oxic

SF-28 USGS 7.5 1.1 3.99 <0.2 <6 81.3 Oxic

SF-29 CDPH 7.4 1.8 2.91 na na 68.0 Oxic

SF-30 CDPH 7.1 5.7 5.6 na na 210 Oxic

SF-31 CDPH 7.5 2.1 1.2 na na 37.0 Oxic

SF-32 CDPH 7.7 1.1 0.89 na na 23.0 Oxic

SF-33 CDPH 7.3 3.3 3.43 na na 32.0 Oxic

SF-34 CDPH 7.3 0.2 2.67 na na na Anoxic

SF-35 USGS 7.2 0.5 0.49 408 24 142 Anoxic (Mn-red)
Appendix D 69

Oxidation-reduction constituents

Dissolved

oxygen

Nitrate plus

nitrite, as 

nitrogen

Magnanese Iron Sulfate

Redox threshold value ≥0.5 >0.5 >50 >100 >4

Possible redox type if concentration > redox 

threshold value O2 NO3 Mn Fe

Analysis reporting level 

(unless otherwise noted) 0.2 0.06 0.18 5.0 0.18

Well

No. 

Source of 

inorganic 

data1

pH 

(standard 

units)

mg/L mg/L mg/L mg/L mg/L Redox classification

Grid wells—Continued

SF-36 CDPH 7.1 2.7 1.27 na na 72.0 Oxic

SF-37 CDPH 7.2 0.3 0.69 na na 90.0 Anoxic

SF-38 CDPH 7.5 <0.2 <0.06 na na na Anoxic

SF-39 CDPH 7.3 <0.2 0.15 92.0 <100 51.0 Anoxic (Mn-red)

SF-40 CDPH 7.4 0.4 2.64 na na na Anoxic

SF-41 CDPH 9.4 10.5 E0.05 na na na Oxic

SF-42 CDPH 7.2 0.7 2.04 na na na Oxic

SF-43 CDPH 7.3 1.0 0.19 na na na Oxic

Understanding wells

SFM-A1 USGS 7.6 0.2 <0.06 239 202 81.5 Anoxic (MnFe-red)

SFM-A2 USGS 8.0 0.2 4.82 63.6 6 7.97 Anoxic (Mn-red)

SFM-A3 USGS 7.4 0.6 8.54 590 56 34.1 Mixed oxic/Mn-red

SFM-A4 USGS 7.5 3.0 11.2 0.60 E4 43.4 Oxic

SFM-B1 USGS 7.7 1.2 <0.06 264 106 33.6 Mixed oxic/MnFe-red

SFM-B2 USGS 6.9 0.2 <0.06 8,560 1,370 717 Anoxic (MnFe-red)

SFM-C1 USGS 8.0 0.2 <0.06 22.5 E4 18.0 Anoxic (suboxic)

SFM-C2 USGS 7.9 0.3 6.57 21.3 E4 58.0 Anoxic (NO3-red)

SFM-C3 USGS 7.5 0.2 3.73 8.70 7 55.0 Anoxic (NO3-red)

SFM-C4 USGS 7.6 0.3 3.85 10.4 10 62.5 Anoxic (NO3-red)

SFM-C5 USGS 7.3 0.6 2.56 0.60 E6 87.4 Oxic

SFM-D1 USGS 8.0 0.2 <0.06 165 114 35.0 Anoxic (MnFe-red)

SFM-D2 USGS 7.7 0.2 <0.06 128 9 45.3 Anoxic (Mn-red)

SFM-D3 USGS 7.2 <0.2 <0.06 2,030 E12 114 Anoxic (Mn-red)

SFM-D4 USGS 7.2 <0.2 3.63 3.40 14 101 Anoxic (NO3-red)

SFM-E1 USGS 7.8 0.2 0.28 99.9 E4 60.6 Anoxic (suboxic)

SFM-E2 USGS 7.4 0.3 11 29.6 E4 73.7 Anoxic (NO3-red)

SFM-E3 USGS 7.2 0.7 3.56 8.9 19 129 Oxic

SFM-F1 USGS 7.8 5.5 <0.06 92.4 10 61.5 Mixed oxic/Mn-red

SFM-F2 USGS 8.2 0.1 <0.06 25.4 E5 37.2 Anoxic (suboxic)

SFM-F3 USGS 7.6 <0.2 0.36 55.1 <6 33.1 Anoxic (Mn-red)

SFM-F4 USGS 7.7 0.2 <0.06 152 E5 50.7 Anoxic (Mn-red)

SFM-F5 USGS 7.8 0.2 <0.06 90.3 <6 37.5 Anoxic (Mn-red)

SFM-F6 USGS 6.5 0.5 <0.06 37,000 <300 5,540 Mixed oxic/Mn-red

SFU-01 USGS 7.5 0.2 0.33 84.8 16 55.3 Anoxic (Mn-red)

SFU-02 CDPH 7.5 3.0 3.78 na na na Oxic

Table D3. pH and oxidation-reduction constituent concentrations and classifications for samples from the San Francisco Bay study 

unit, California GAMA Priority Basin Project.—Continued

[redox, oxidation-reduction; mg/L, milligram per liter; μg/L, microgram per liter; <, less than; >, greater than; ≥, greater than or equal to; >, greater than; <, less 

than; na, not available; O2, oxygen; NO3, nitrate; Mn, manganese; Fe, iron. Redox classifications: oxic, dissolved oxygen ≥ 0.5 mg/L; anoxic/suboxic, dissolved 

oxygen < 0.5 mg/L; NO3-red, nitrate reducing conditions; Mn-red, manganese reducing conditions; MnFe-red, manganese and iron reducing conditions]
70 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

Oxidation-reduction constituents

Dissolved

oxygen

Nitrate plus

nitrite, as 

nitrogen

Magnanese Iron Sulfate

Redox threshold value ≥0.5 >0.5 >50 >100 >4

Possible redox type if concentration > redox 

threshold value O2 NO3 Mn Fe

Analysis reporting level 

(unless otherwise noted) 0.2 0.06 0.18 5.0 0.18

Well

No. 

Source of 

inorganic 

data1

pH 

(standard 

units)

mg/L mg/L mg/L mg/L mg/L Redox classification

Understanding wells—Continued

SFU-03 CDPH 7.5 6.3 4.07 na na na Oxic

SFU-04 CDPH 7.8 7.9 5.34 na na na Oxic

SFU-05 CDPH 7.3 8.8 5.89 na na na Oxic

SFU-06 USGS 7.5 0.4 1.14 <0.2 <6 72.9 Anoxic (NO3-red)

SFU-07 USGS 7.4 2.4 3.3 <0.2 <6 77.1 Oxic

SFU-08 USGS 7.5 1.0 3.93 <0.2 7 78.0 Oxic

SFU-09 CDPH 7.2 1.9 2.73 na na na Oxic

SFU-10 CDPH 7.4 0.2 2.02 na na na Anoxic (NO3-red)

SFU-11 CDPH 7.0 2.5 E0.06 na na na Oxic

SFU-12 CDPH 6.8 5.0 0.11 na na na Oxic

1

 Only data for manganese, iron, and/or sulfate were supplemented. Data for pH, dissolved oxygen, and nitrate were collected by the USGS at all wells.

Table D3. pH and oxidation-reduction constituent concentrations and classifications for samples from the San Francisco Bay study 

unit, California GAMA Priority Basin Project.—Continued

[redox, oxidation-reduction; mg/L, milligram per liter; μg/L, microgram per liter; <, less than; >, greater than; ≥, greater than or equal to; >, greater than; <, less 

than; na, not available; O2, oxygen; NO3, nitrate; Mn, manganese; Fe, iron. Redox classifications: oxic, dissolved oxygen ≥ 0.5 mg/L; anoxic/suboxic, dissolved 

oxygen < 0.5 mg/L; NO3-red, nitrate reducing conditions; Mn-red, manganese reducing conditions; MnFe-red, manganese and iron reducing conditions]
Appendix D 71

Table D4. Oxidation-reduction classification system applied to 

samples from the San Francisco Bay study unit, California GAMA 

Priority Basin Project.

[Oxidation-reduction classes: NO3-red, nitrate-reducing; Mn-red, manganesereducing; MnFe-red, manganese and iron reducing; Fe-red, iron-reducing. Other 

abbreviations: mg/L, milligram per liter; μg/L, microgram per liter; >, greater 

than; <, less than; na, data not available; any, any concentration]

Oxidation-reduction 

class

Dissolved 

oxygen 

(mg/L)

Nitrate, as 

nitrogen 

(mg/L)

Manganese 

(μg/L)

Iron 

(μg/L)

Oxic classifications

Oxic > 0.5 any < 50 < 100

Oxic > 0.5 any na na

Oxic > 0.5 na na na

Anoxic classifications

Anoxic (suboxic) < 0.5 < 0.5 < 50 < 100

Anoxic (NO3-red) < 0.5 > 0.5 < 50 < 100

Anoxic (Mn-red) < 0.5 < 0.5 > 50 < 100

Anoxic (MnFe-red) < 0.5 < 0.5 > 50 > 100

Anoxic (Fe-red) < 0.5 < 0.5 < 50 > 100

Anoxic < 0.5 < 0.5 na na

Anoxic (NO3-red) < 0.5 > 0.5 na na

Anoxic < 0.5 na na na

Mixed classifications

Mixed 

(oxic/Mn-red)

> 0.5 any > 50 < 100

Mixed 

(oxic/MnFe-red)

> 0.5 any > 50 > 100
72 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

USGS 

GAMA well 

identification 

number

Tritium, 

tritium 

units

Percent 

modern 

carbon 

(percent)

Age 

classification

SF-01 5.3 88 Mixed

SF-02 3.2 85 Mixed

SF-03 2.1 na Modern or mixed

SF-04 < 1.0 66 Pre-modern

SF-05 < 1.0 51 Pre-modern

SF-06 2.2 107 Modern

SF-07 5.4 95 Modern

SF-08 1.4 45 Mixed

SF-09 1.2 82 Mixed

SF-10 < 1.0 56 Pre-modern

SF-11 2.4 91 Modern

SF-12 2.9 100 Modern

SF-13 1.4 81 Mixed

SF-14 1.2 77 Mixed

SF-15 < 1.0 51 Pre-modern

SF-16 < 1.0 43 Pre-modern

SF-17 2.1 82 Mixed

SF-18 1.8 53 Mixed

SF-19 < 1.0 37 Pre-modern

SF-20 3.2 102 Modern

SF-21 3.1 98 Modern

SF-22 2.7 108 Modern

SF-23 3.0 79 Mixed

SF-24 3.1 85 Mixed

SF-25 3.5 75 Mixed

SF-26 1.5 102 Modern

SF-27 2.0 92 Modern

SF-28 3.3 97 Modern

SF-29 2.6 100 Modern

SF-30 2.2 87 Mixed

SF-31 3.2 99 Modern

SF-32 2.7 94 Modern

SF-33 1.9 79 Mixed

SF-34 4.0 89 Mixed

SF-35 6.3 36 Mixed

SF-36 3.9 102 Modern

SF-37 3.8 94 Modern

SF-38 < 1.0 24 Pre-modern

SF-39 < 1.0 24 Pre-modern

SF-40 < 1.0 57 Pre-modern

Table D5. Tritium, percent modern carbon, and age classification of samples, San Francisco Bay study unit, California GAMA Priority 

Basin Project.

[<, less than; Modern, sample with water recharged since 1952; Pre-modern, sample with water recharged before 1952; Mixed, sample with modern and premodern components; na, not available]

USGS 

GAMA well 

identification 

number

Tritium, 

tritium 

units

Percent 

modern 

carbon 

(percent)

Age 

classification

SF-41 2.9 na Modern or mixed

SF-42 < 1.0 2 Pre-modern

SF-43 < 1.0 12 Pre-modern

SFM-A1 < 1.0 16 Pre-modern

SFM-A2 < 1.0 72 Pre-modern

SFM-A3 1.5 66 Mixed

SFM-A4 4.3 87 Mixed

SFM-B1 < 1.0 41 Pre-modern

SFM-B2 < 1.0 51 Pre-modern

SFM-C1 < 1.0 8 Pre-modern

SFM-C2 2.8 82 Mixed

SFM-C3 2.5 81 Mixed

SFM-C4 2.5 85 Mixed

SFM-C5 3.0 86 Mixed

SFM-D1 < 1.0 9 Pre-modern

SFM-D2 < 1.0 51 Pre-modern

SFM-D3 3.4 78 Mixed

SFM-D4 4.2 92 Modern

SFM-E1 < 1.0 38 Pre-modern

SFM-E2 3.4 87 Mixed

SFM-E3 31 97 Modern

SFM-F1 < 1.0 5 Pre-modern

SFM-F2 < 1.0 2 Pre-modern

SFM-F3 1.3 15 Mixed

SFM-F4 < 1.0 17 Pre-modern

SFM-F5 < 1.0 32 Pre-modern

SFM-F6 < 1.0 75 Pre-modern

SFU-01 < 1.0 45 Pre-modern

SFU-02 1.6 78 Mixed

SFU-03 1.8 81 Mixed

SFU-04 1.9 85 Mixed

SFU-05 2.3 87 Mixed

SFU-06 2.0 69 Mixed

SFU-07 3.4 90 Mixed

SFU-08 3.2 95 Modern

SFU-09 6.2 98 Modern

SFU-10 14 88 Mixed

SFU-11 < 1.0 93 Mixed

SFU-12 na 92 Modern or mixed
Appendix E 73

CDPH and USGS-GAMA data were compared to assess 

the validity of combining data from these different sources. 

Because the reporting levels for most organic constituents and 

trace elements were substantially lower for data collected by 

the USGS GAMA Priority Basin Project than for data from the 

CDPH database (table 3), only relatively high concentrations 

of constituents could be compared, and as a result, there were 

insufficient data to rigorously evaluate agreement between 

CDPH and USGS-GAMA data. However, concentrations 

of inorganic constituents (calcium, magnesium, sodium, 

alkalinity, chloride, sulfate, TDS, and nitrate), which generally 

are prevalent at concentrations substantially greater than 

reporting levels, were compared for each well using data from 

both sources. Six wells had major ion data, and 39 wells had 

nitrate data from the USGS database and from the CDPH 

database. Although differences between the paired datasets 

existed for a few wells, most sample pairs plotted close to a 

1:1 line (r2 = 0.98) (fig. E1). These plots indicated that the 

GAMA and CDPH inorganic data were comparable. 

Major ion data for grid wells with sufficient data (USGS 

and CDPH data) were plotted on Piper diagrams (Piper, 1944) 

with all CDPH major ion data to determine whether the grid 

wells represented the range of groundwater types that have 

historically been observed in the study unit. Piper diagrams 

show the relative abundance of major cations and anions (on 

a charge equivalent basis) as a percentage of the total ion 

content of the water (fig. E2). Piper diagrams often are used to 

define groundwater type (Hem, 1985). All cation/anion data in 

the CDPH database that had a cation/anion balance less than 

10 percent were retrieved and plotted on these Piper diagrams 

for comparison with USGS-grid well data.

The ranges of water types in grid wells and noted 

historical CDPH data were similar (fig. E2). In most wells, no 

single cation accounted for more than 60 percent of the total 

cations, and bicarbonate accounted for more than 60 percent of 

the total anions. Waters in these wells are described as mixed 

cation–bicarbonate type waters. There were also wells that 

contained mixed cation–mixed anion type waters, indicating 

that no single cation and no single anion accounted for more 

than 60 percent of the total, and some wells contained calcium/

magnesium–bicarbonate type waters, for which calcium plus 

magnesium and bicarbonate account for more than 60 percent 

of the cations and anions, respectively. 

The determination that the range of relative abundance of 

major cations and anions in grid wells is similar to the range of 

those in all CDPH wells indicates that the grid wells represent 

the types of water present within primary aquifer system in the 

SFBAY study unit. 

Appendix E. Comparison of Data from the CDPH and USGS-GAMA Program
74 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

0.1 1 10 100 1,000

CONCENTRATION, USGS-GAMA DATA, IN MILLIGRAMS PER LITER

0.1

1

10

100

1,000

CONCENTRATION, CDPH DATA, IN MILLIGRAMS PER LITER

1:1 correlation line

IP_017719_Figure E01 Concentration correlation

EXPLANATION

Calcium

Magnesium

Sodium

Alkalinity

Chloride

Sulfate

Total dissolved solids

Nitrate as nitrogen

Figure E1. Paired inorganic constituent concentrations from wells sampled by USGS GAMA Priority Basin Project 

from April to June 2007, and California Department of Public Health (CDPH) from April 1, 2004, to March 31, 2007, 

San Francisco Bay study unit, California GAMA Priority Basin Project.
Appendix E 75

Percent

Calcium

 100 

 80 

 60 

 40 

 20  0 

 0 

 20 

 40 

 60 

 80 

 100 

Magnesium

 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrite plus Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

Sulfate

 0 

 20 

 40 

 60 

 80 

 100 

Sulfate plus Chloride

 0 

 20 

 40 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 20 

 0 

Percent

Percent

EXPLANATION

USGS-grid well

CDPH well

IP_017719_Figure E02 Grid wells_SFB

Figure E2. Selected inorganic data from USGS-grid wells and from all wells in the California Department 

of Public Health (CDPH) database that have a charge imbalance of less than 10 percent, San Francisco 

Bay study unit, California GAMA Priority Basin Project.
76 Status of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007: California GAMA Priority Basin Project

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Parsons and others—Status and Understanding of Groundwater Quality in the San Francisco Bay Groundwater Basins, 2007—

California GAMA Priority Basin Project—SIR 2012–5248