Photo placement

U.S. Department of the Interior

U.S. Geological Survey

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 North San Francisco 

Bay Groundwater Basins, 2004: California GAMA Priority Basin Project

Scientific Investigations Report 2010–5089
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

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

Cover photographs:

Front cover: Well in Sonoma County, California. (Photograph taken by Tyler Johnson, U.S. Geolgoical Survey.)

Back cover: Vineyard in Sonoma, California. (Photograph taken by George Bennett, U.S. Geolgical Survey.) 

Sampling priority

Study area

Low-use basins

Priority basins

Areas that are outside DWR-defined

 groundwater basins
Status and Understanding of Groundwater 

Quality in the North San Francisco Bay 

Groundwater Basins, 2004: California 

GAMA Priority Basin Project

By Justin T. Kulongoski, Kenneth Belitz, Matthew K. Landon, and 

Christopher Farrar

Prepared in cooperation with the California State Water Resources Control Board

Scientific Investgations Report 2010–5089

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Kulongoski, J.T., Belitz, Kenneth, Landon, M.K., and Farrar, Christopher, 2010, Status and understanding of 

groundwater quality in the North San Francisco Bay groundwater basins, 2004: California GAMA Priority Basin 

Project: U.S. Geological Survey Scientific Investigations Report 2010–5089, 88 p. 
iii

Contents

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

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

Purpose and Scope .......................................................................................................................................3

Description of Study Unit: North San Francisco Bay...............................................................................5

Valleys and Plains Study Area............................................................................................................5

Volcanic Highlands Study Area..........................................................................................................5

Wilson Grove Formation Highlands Study Area ..............................................................................5

Hydrothermal Study..............................................................................................................................8

Hydrogeologic Setting of the North San Francisco Bay Study Unit.............................................8

Regional Geologic Setting.................................................................................................................11

Quaternary Sedimentary Deposits..........................................................................................13

Faults .........................................................................................................................................14

Methods ........................................................................................................................................................14

Relative-Concentrations and Water-Quality Benchmarks ..........................................................14

Datasets for Status Assessment......................................................................................................15

U.S. Geological Survey Grid Wells..........................................................................................15

California Department of Public Health Grid Wells..............................................................16

Additional Data Used for Spatially-Weighted Calculation..................................................16

Identification of Constituents for Status Assessment ..................................................................19

Calculation of Aquifer-Scale Proportions..............................................................................19

Understanding-Assessment Methods ............................................................................................24

Statistical Analysis ....................................................................................................................24

Potential Explanatory Factors....................................................................................................................25

Land Use ..............................................................................................................................................25

Well Depth............................................................................................................................................25

Normalized Position of Wells along Flowpaths .............................................................................25

Age Classification...............................................................................................................................25

Geochemical Condition and Water Temperature..........................................................................25

Correlations between Explanatory Factors....................................................................................25

Status and Understanding of Water Quality ...........................................................................................30

Inorganic Constituents.......................................................................................................................34

Trace Elements...........................................................................................................................34

Understanding Assessment for Arsenic.......................................................................34

Understanding Assessment for Boron..........................................................................46

Understanding Assessment for Manganese and Iron ...............................................48
iv

Radioactive Constituents..........................................................................................................48

Nutrients .....................................................................................................................................48

Understanding Assessment for Nitrate plus Nitrite....................................................48

Major and Minor Ions ...............................................................................................................50

Understanding Assessment for TDS .............................................................................50

Organic Constituents..........................................................................................................................51

Trihalomethanes.........................................................................................................................52

Understanding Assessment for Chloroform.................................................................53

Solvents.......................................................................................................................................56

Understanding Assessment for Solvents .....................................................................57

Other VOCs..................................................................................................................................57

Herbicides...................................................................................................................................57

Understanding Assessment for Simazine ....................................................................57

Insecticides..........................................................................................................................................57

Special-Interest Constituents...........................................................................................................57

Summary .......................................................................................................................................................60

Acknowledgments.......................................................................................................................................61

References....................................................................................................................................................61

Appendix A. Stratigraphy .......................................................................................................................66

Basement Rocks .................................................................................................................................66

Tolay and Donnel Ranch Volcanics .................................................................................................66

Sonoma Volcanics ..............................................................................................................................67

Petaluma Formation............................................................................................................................67

Wilson Grove Formation Highlands .................................................................................................67

Huichica Formation.............................................................................................................................68

Glen Ellen Formation...........................................................................................................................68

Appendix B. Use of Data from the California Department of Public Health (CDPH) 

Database..........................................................................................................................................69

Appendix C. Calculating Total Dissolved Solids ..................................................................................76

Appendix D. Estimation of Aquifer-Scale Proportions .......................................................................77

Appendix E. Ancillary Data .....................................................................................................................78

Land-Use Classification.....................................................................................................................78

Well-Construction Information .........................................................................................................78

Normalized Position of Wells along Flow Paths ............................................................................78

Classification of Geochemical Condition........................................................................................78

Groundwater-Age Classification......................................................................................................83

Appendix F. Comparison of California Department of Public Health (CDPH) and 

USGS–GAMA Data.........................................................................................................................85
v

Figures

Figure 1. Map of the location of the North San Francisco Bay study unit, California, and the

California hydrogeologic provinces ......................................................... 4

Figure 2. Map showing geographic features and study areas of the North San Francisco

Bay study unit, California ..................................................................... 6

Figure 3. Geologic map of the North San Francisco Bay study unit, California .................. 7

Figure 4. Map showing locations of grid cells, California Department of Public Health

(CDPH) wells, and the U.S. Geological Survey (USGS) grid and understanding

wells sampled during August to December 2004 for the North San Francisco Bay

study unit, California ........................................................................... 9

Figure 5. Map showing a conceptual model of the aquifer system in the Valleys and Plains

valleys of the North San Francisco Bay study unit, California ........................... 10

Figure 6. Ternary diagram showing percent urban, agricultural, and natural land use in the

study unit and study areas, and the 500-meter-radius area surrounding wells in

the North San Francisco Bay study unit, California ....................................... 11

Figure 7. Map showing land use in the North San Francisco Bay study unit, California, and

the locations of U.S. Geological Survey grid wells ....................................... 12

Figure 8. Boxplots showing construction characteristics for grid wells and understanding

wells, North San Francisco Bay study unit, California .................................... 26

Figure 9. Map showing normalized position of wells along a flowpath in the North San

Francisco Bay study unit, California ......................................................... 27

Figure 10. Boxplots showing the relation of classified groundwater age to depth to top of

perforations below land surface, well depth below land surface, and age

classification in relation to the depth of well perforations, North San Francisco

Bay study unit, California ..................................................................... 28

Figure 11. Graph showing the relation of oxidation-reduction condition to normalized

position of wells along a flowpath, and depth of perforated interval of wells, North

San Francisco Bay study unit, California ................................................... 30

Figure 12A.Map showing pH levels in U.S. Geological Survey (USGS) grid wells,

USGS-understanding wells, and California Department of Public Health (CDPH)

wells, North San Francisco Bay study unit, California .................................... 31

Figure 12B.Graph showing pH as a function of well depth and groundwater age

classification, North San Francisco Bay study unit, California ........................... 32

Figure 12B.Graph showing pH as a function of well depth and groundwater age

classification, North San Francisco Bay study unit, California ........................... 32

Figure 13. Graph showing maximum relative-concentration of constituents, by constituent

type, detected in grid wells in the North San Francisco Bay study unit, 

California ....................................................................................... 35

Figure 14. Plots showing maximum relative-concentrations of selected trace elements,

nutrients, and major and minor ions in grid wells, North San Francisco Bay study

unit, California ................................................................................. 37

Figure 15. Maps showing relative-concentrations of selected inorganic constituents in U.S.

Geological Survey (USGS) grid and USGS-understanding wells and California

Department of Public Health (CDPH) wells (data from the period August 30, 2001,

to September 1, 2004), North San Francisco Bay study unit, California: arsenic,

boron, lead, nitrate plus nitrite, as nitrogen, manganese, iron, and total dissolved

solids............................................................................................. 38

Figure 16. Plots showing arsenic concentration related to classifications of groundwater

age and well depth, manganese and iron concentrations, and pH in grid and

understanding wells sampled for the North San Francisco Bay study unit, 

California ....................................................................................... 45
vi

	Figure 17.	 Plots showing nitrate plus nitrite, as nitrogen, concentrations related to

classifications of groundwater age, land use, age classification of groundwater,

depth to the top of perforations, and dissolved oxygen concentrations in grid and

understanding wells sampled for the North San Francisco Bay study unit,

California ....................................................................................... 49

	Figure 18.	 Graph of well altitude versus total dissolved solid concentrations in grid and

understanding wells in the Wilson Grove Formation Highlands, Volcanic

Highlands, and Valleys and Plains study areas of the North San Francisco Bay

study unit, California. Positions along the flowpath (proximal, medial, or terminal)

are indicated for some wells in the Valleys and Plains study area ..................... 52

	Figure 19.	 Graph of detection frequency and maximum relative-concentration of organic

and special interest constituents detected in grid wells in the North San

Francisco Bay study unit, California ......................................................... 53

Figure 20.	 Graphs showing detection frequency and maximum relative-concentrations of

selected organic and special-interest constituents in grid wells in the North San

Francisco Bay study unit, California, August to December 2004 ........................ 54

	Figure 21.	 Map showing land-use classifications and the relative-concentrations of the

trihalomethane chloroform in U.S. Geological Survey (USGS) grid wells and

California Department of Public Health (CDPH) wells (data from the period August

30, 2001 to September 1, 2004), North San Francisco Bay study unit, California ...... 55

	Figure 22.	 Graph showing concentration of chloroform as a function of depth to the top

perforation and land-use classifications, North San Francisco Bay study unit,

California ....................................................................................... 56

	Figure 23.	 Map showing land-use classification and the relative-concentrations of the sum

of the solvents tetrachloroethene (PCE), trichloroethene (TCE), and

1,2-dichloroethane in U.S. Geological Survey (USGS) grid wells and California

Department of Public Health (CDPH) wells (data from the period August 30, 2001

to September 1, 2004), North San Francisco Bay study unit, California.................. 58

	Figure 24.	 Map showing land-use classifications and the relative-concentrations of the

herbicide simazine in U.S. Geological Survey (USGS) grid wells and California

Department of Public Health (CDPH) wells (data from the period August 30, 2001

to September 1, 2004), North San Francisco Bay study unit, California.................. 59

	Figure B1.	 Map showing identifiers and locations of U.S. Geological Survey (USGS) grid and

understanding wells sampled during August to December, 2004, and grid wells for

which data for inorganic constituents from the California Department of Public

Health (CDPH) were used, North San Francisco Bay study unit, California ............ 74

	Figure F1.	 Graph showing paired inorganic constituent concentrations comparing

USGS–GAMA data and California Department of Health (CDPH) data from the

period August 30, 2001, to September 1, 2004, North San Francisco Bay study unit,

California ....................................................................................... 86

	Figure F2.	 Piper diagram for wells sampled for the North San Francisco Bay study unit,

California, and wells that have constituents with a charge imbalance less than 10

percent recorded by the California Department of Public Health (CDPH) database... 87

	Figure F3.	 Piper diagram of constituents with a charge imbalance of less than 10 percent,

organized by study area in the North San Francisco Bay study unit, California ...... 88
vii

Tables

Table 1. Analyte groups and number of constituents and wells sampled for each analytical

schedule in the North San Francisco Bay study unit, California, August to

December, 2004................................................................................. 17

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

grid wells with USGS–GAMA data and California Department of Public Health

(CDPH) data, for each constituent, North San Francisco Bay study unit,

California ....................................................................................... 18

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

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

California Department of Public Health database and data collected by the U.S.

Geological Survey for the North San Francisco Bay study unit, California, August

to December, 2004.............................................................................. 19

Table 4. Aquifer proportions from grid-based and spatially-weighted methods for

constituents (1) for which high relative-concentrations were reported in the

California Department of Public Health (CDPH) database during August 30, 2001,

to September 1, 2004, or (2) that had moderate or high relative-concentrations in

samples collected from grid wells during August to December 2004, North San

Francisco Bay study unit, California ......................................................... 20

Table 5. Number of constituents analyzed and detected with associated benchmarks in

each constituent class, North San Francisco Bay study unit, California, August to

December 2004 ................................................................................. 22

Table 6. Constituents with historically high concentrations recorded in the California

Department of Public Health (CDPH) database............................................. 23

Table 7. Results of nonparametric analysis (Spearman’s method) of correlations between

selected potential explanatory factors, North San Francisco Bay study unit,

Callifornia ....................................................................................... 29

Table 8. Results of Wilcoxon rank sum tests on grid-well data used to determine

significant differences between constituent values grouped by explanatory factor

classes, North San Francisco Bay study unit, California ................................. 33

Table 9. Aquifer-scale proportions for constituent classes, North San Francisco Bay study

unit, California ................................................................................. 36

Table 10. Results of non-parametric (Spearman's method) analysis for correlations

between selected water-quality constituents and potential explanatory factors,

North San Franciso Bay study unit, California ............................................. 47

Table B1. Grid cell number, U.S. Geological Survey (USGS) well identification number, land

use categories, land use classification, well construction information, and the

normalized position along a flowpath for wells sampled August to December 2004

for the North San Francisco Bay study unit, California.................................... 70

Table E1. Oxidation-reduction constituents, redox classification, and iron, arsenic, and

chromium species ratios for samples from the North San Francisco Bay study

unit, California ................................................................................. 79

Table E2. Noble gas based recharge temperature, tritium, terrigenic helium, percent

modern carbon, and age classification of samples, North San Francisco Bay

study unit, California ........................................................................... 81
viii

Abbreviations and Acronyms

AB Assembly Bill (through the California State Assembly)

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 

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 limit

MRL minimum reporting level

na no data available

nc no significant correlation

nd constituent not detected

ns not sampled

NL-CA California Department of Public Health notification level 

QC quality control

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

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

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

TDS total dissolved solids

US United States

VOL Volcanic Highlands study area of the North San Francisco Bay study unit

VP Valleys and Plains study area of the North San Francisco Bay study unit

VPFP Valleys and Plains study area flow path well

WG Wilson Grove Formation Highlands study area of the North San Francisco Bay study unit

WGFP Wilson Grove Formation Highlands study area flow path well

Organizations

CDPH California Department of Public Health (Department of Health Services before July 1, 

2007)

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water Quality Assessment (USGS)

SWRCB State Water Resources Control Board (California)

USEPA U.S. Environmental Protection Agency

USGS U. S. Geological Survey
ix

Selected chemical names

Ammonia-N ammonia as nitrogen

DBCP 1,2-dibromo-3-chloropropane

DEHP bis(2-ethylhexyl) phthalate

DO dissolved oxygen

DOC dissolved organic carbon

EDB 1,2-dibromomethane (ethylene dibromide) 

NDMA N-nitrosodimethylamine

PCE tetrachloroethene

TCE trichloroethene

1,2,3-TCP 1,2,3-trichloropropane

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound

Units of Measure

a annum (year)

cm3

 STP g–1 cubic centimeters at standard temperature and pressure per gram 

δ delta notation; the ratio of a heavier isotope to the more common lighter isotope of an 

element, relative to a standard reference material, expressed as per mil

ft foot (feet)

gpm gallons per minute

in inch

kg kilogram

km kilometer

km2 square kilometer

L liter

L/m liters per minute

m meter

Ma Megaannum (million years)

mg/L milligrams per liter (parts per million)

mi mile

mi2 square mile

mL milliliter

mm millimeter

μg/L micrograms per liter (parts per billion)

μS/cm microsiemens per centimeter

pCi/L picocuries per liter

per mil parts per thousand

pmc percent modern carbon

ppm parts per million
x

TU tritium unit

yr year

> greater than

< less than

≤ less than or equal to 

— not detected at a concentration above the LRL

% percent

Notes

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

°F=(1.8×°C)+32

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88).

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 either in milligrams per liter (mg/L) or 

micrograms per liter (μg/L). 

Milligrams per liter is equivalent to parts per million (ppm) and micrograms per liter is equivalent to parts 

per billion (ppb).
Abstract

Groundwater quality in the approximately 1,000-squaremile (2,590-square-kilometer) North 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 northern 

California in Marin, Napa, and Sonoma 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 North San Francisco Bay study was designed 

to provide a spatially unbiased assessment of untreated 

groundwater quality in the primary aquifer systems. The 

assessment is based on water-quality and ancillary data 

collected by the USGS from 89 wells in 2004 and waterquality data from the California Department of Public Health 

(CDPH) database. The primary aquifer systems (hereinafter 

referred to as primary aquifers) were defined by the depth 

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

San Francisco Bay study unit. The quality of groundwater in 

shallower or deeper water-bearing zones may differ from that 

in the primary aquifers; 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 (VOC), pesticides, and naturally occurring 

inorganic constituents, such as major ions and trace elements. 

This status assessment is intended to characterize the quality 

of groundwater resources within the primary aquifers of the 

North San Francisco Bay study unit, not the treated drinking 

water delivered to consumers by water purveyors.

Relative-concentrations (sample concentration divided 

by the health- or aesthetic-based 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 above a benchmark, and less than or equal to (≤) 

1.0 indicates a concentration equal to or below a benchmark. 

Relative-concentrations of organic and special interest 

constituents were classified as “high” (relative-concentration 

> 1.0), “moderate” (0.1 < relative-concentration ≤ 1.0), or 

“low” (relative-concentration ≤ 0.1). Inorganic constituent 

relative-concentrations were classified as “high” (relativeconcentration > 1.0), “moderate” (0.5 < relative-concentration 

≤ 1.0), or “low” (relative-concentration ≤ 0.5).

Aquifer-scale proportion was used as a metric for 

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

aquifers that have a relative-concentration greater than 1.0; 

proportion is calculated on an areal rather than a volumetric 

basis. Moderate and low aquifer-scale proportions were 

defined as the percentage of the primary aquifers 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 North 

San Francisco Bay study unit (90-percent confidence 

intervals).

For inorganic constituents with human-health 

benchmarks, relative-concentrations were high in 14.0 percent 

of the primary aquifers, moderate in 35.8 percent, and 

low in 50.2 percent. The high aquifer-scale proportion of 

inorganic constituents primarily reflected high aquifer-scale 

proportions of arsenic (10.0 percent), boron (4.1 percent), 

and lead (1.6 percent). In contrast, relative-concentrations of 

organic constituents (one or more) were high in 1.4 percent, 

moderate in 4.9 percent, and low in 93.7 percent (not 

detected in 64.8 percent) of the primary aquifers. The high 

aquifer-scale proportion of organic constituents primarily 

reflected high aquifer-scale proportions of PCE (1.3 percent), 

TCE (0.1 percent), and 1,1-dichloroethene (0.1 percent). 

The inorganic constituents with secondary maximum 

contaminant levels (SMCL), manganese and iron, had relativeconcentrations that were high in 40.8 percent and 24.4 percent 

of the primary aquifers, respectively. Of the 255 organic and 

special-interest constituents analyzed for, 26 constituents were 

detected. Two organic constituents were frequently detected 

(in 10 percent or more of samples), the trihalomethane 

chloroform and the herbicide simazine, but both were detected 

at low relative-concentrations. 

Status and Understanding of Groundwater Quality in the 

North San Francisco Bay Groundwater Basins, 2004: 

California GAMA Priority Basin Project

By Justin T. Kulongoski, Kenneth Belitz, Matthew K. Landon, and Christopher Farrar
2 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

The second component of this study, the understanding 

assessment, identified the natural and human factors that 

affect groundwater quality by evaluating land use, physical 

characteristics of the wells, geochemical conditions of the 

aquifer, and water temperature. Results from these evaluations 

were used to explain the occurrence and distribution of 

constituents in the study unit. The understanding assessment 

indicated that a majority of the wells that contained nitrate 

also had an urban or agricultural land-use classification, 

had a modern or mixed age classification, and had depths 

to their top perforations <100 ft (30 m). Geochemical data 

are consistent with partial denitrification of nitrate in some 

reducing groundwaters in the terminal and deeper parts of the 

flow system.

High and moderate relative-concentrations of arsenic 

may be attributed to reductive dissolution of manganese or 

iron oxides, or to desorption or inhibition of arsenic sorption 

under alkaline conditions. Arsenic concentrations increased 

with increasing depth and groundwater age in the North 

San Francisco Bay study unit. High to moderate relativeconcentrations of boron were primarily associated with 

hydrothermal activity or high-salinity waters in the Napa 

Sonoma lowlands.

Simazine was detected in groundwater classified as 

modern and mixed age more often than in groundwater 

classified as pre-modern age, while chloroform was detected 

most often in groundwater classified as mixed age. Simazine 

and chloroform also were observed in wells that had 

surrounding land use classified as agricultural or land use 

classified as urban, and top of perforation depths less than 100 

ft (30 m). Together, the occurrence of chloroform and simazine 

in shallow wells with modern or mixed groundwater located 

in urban or agricultural areas suggests that these constituents 

result from anthropogenic activities during the last 50 years.

Tritium, helium-isotope, and carbon-14 data were used 

to classify the predominant age of groundwater samples into 

three categories: modern (water that has entered the aquifer in 

the last 50 years), pre-modern (water that entered the aquifer 

more than 50 years to tens of thousands of years ago), and 

mixed (mixtures of modern- and pre-modern-age waters). 

Arsenic, iron, and total dissolved solids (TDS) concentrations 

were significantly greater in groundwater having premodern-age classification than modern, suggesting that these 

constituents accumulate with groundwater residence time.

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 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 (http://www.waterboards.ca.gov/gama). 

The statewide GAMA Program currently consists of three 

projects: the GAMA Priority Basin Project, conducted by 

the USGS (http://ca.water.usgs.gov/gama/); the GAMA 

Domestic Well Project, conducted by the SWRCB; and 

GAMA Special Studies, conducted by LLNL. Statewide, 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. 

Shallow groundwater wells, such as private domestic and 

environmental monitoring wells, may be particularly at risk 

because of surficial contamination. As a result, concentrations 

of contaminants, such as VOCs and nitrate, in shallow wells 

can be higher than in wells screened in the deeper primary 

aquifers (Landon and others, 2010).

The SWRCB initiated the GAMA Program in 2000 in 

response to a legislative mandate (Supplemental Report of the 

1999 Budget Act 1999-00 Fiscal Year). The GAMA Priority 

Basin Project was initiated in response to the Groundwater 

Quality Monitoring Act of 2001 {Sections 10780-10782.3 

of the California Water Code, Assembly Bill 599} to assess 

and monitor the quality of groundwater in California. The 

GAMA Priority Basin Project is a comprehensive assessment 

of statewide groundwater quality designed to help better 

understand and identify 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 the 

monitoring plan to assess groundwater basins through direct 

and other statistically reliable sampling approaches (Belitz and 

others, 2003; 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). 
Purpose and Scope 3

The range of hydrologic, geologic, and climatic 

conditions that exist in California must be considered in an 

assessment of groundwater quality. Belitz and others (2003) 

partitioned the State into ten 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 California’s approximately 16,000 public-supply 

wells 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 public-supply 

wells in California.

Purpose and Scope

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

unit description: description of the hydrogeologic setting of 

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

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

assessment of the status of the current quality of groundwater 

in the primary aquifers in the NSF 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.

The status assessment includes analyses of water-quality 

data for 83 wells selected by the USGS for spatial coverage of 

one well per grid cell across the NSF study unit (hereinafter 

referred to as USGS grid wells). Most of these wells were 

public-supply wells, but 2 domestic and 1 irrigation well 

with similar perforation depth intervals to the USGS grid 

wells also were sampled. Samples were collected for analysis 

of anthropogenic constituents, such as volatile organic 

compounds (VOC) and pesticides, and naturally occurring 

inorganic constituents such as major ions and trace elements. 

Water-quality data from the California Department of Public 

Health (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 

primary aquifer systems in the NSF study unit; the primary 

aquifer systems (hereinafter referred to as primary aquifers) 

are defined by the depth interval of the wells listed in the 

CDPH database for the North San Francisco Bay study 

unit. GAMA status assessments are designed to provide a 

statistically robust characterization of groundwater quality 

in the primary aquifers 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 and 

statewide scales.

To provide context, the water-quality data discussed in 

this report were compared to State and Federal drinking-water 

benchmarks, both regulatory and non regulatory, for treated 

drinking water. The assessments in this report characterize 

the quality of untreated groundwater resources in the primary 

aquifers within the study unit, not the treated 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 treated water that is served to the 

consumer, not to groundwater.

The understanding assessment uses data from six wells 

sampled by the USGS for the purpose of understanding 

(hereinafter referred to as USGS understanding wells) 

in addition to the 83 grid wells sampled for the status 

assessment, to identify the natural and human factors affecting 

groundwater quality, and to explain the relations between 

water quality and selected potential explanatory factors. 

Potential explanatory factors examined included land use, 

depth, position of wells along the along the groundwater flow 

paths, indicators of groundwater age, geochemical conditions, 

and water temperature. 

Water-quality data for samples collected by the USGS 

for the GAMA Program in the NSF study unit and details of 

sample collection, analysis, and quality-assurance procedures 

for the NSF study unit are reported by Kulongoski and 

others (2006). Utilizing 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 data sets, classifying groundwater age, and assessing 

the status and understanding of groundwater quality by 

statistical and graphical approaches.
4 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

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

NORTH

SAN FRANCISCO

BAY GAMA

STUDY UNIT

(figure 2)

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

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

Provinces from Belitz and others, 2003.

Figure 1. Location of the North San Francisco Bay study unit, California, and the California hydrogeologic provinces.
Description of Study Unit: North San Francisco Bay 5

Description of Study Unit: 

North San Francisco Bay

The NSF study unit covers approximately 1,000 square 

miles (mi2) (2,590 square kilometers [km2]) in Napa, 

Sonoma and Marin Counties, which have a population of 

nearly 500,000. The NSF study unit lies in the Northern 

Coast Ranges hydrogeologic province (fig. 1) and contains 

eight groundwater basins (fig. 2): Alexander Valley, NapaSonoma Valley, Santa Rosa Valley, Wilson Grove Formation 

Highlands, Volcanic Highlands, Lower Russian River Valley, 

Petaluma Valley, and the Kenwood Valley. For the purpose 

of this study, these groundwater basins were grouped into 

three study areas based primarily on geology (fig. 3): the 

relatively flat-lying alluvium-filled basins were combined 

into the Valleys and Plains study area, the Wilson Grove 

Formation Highlands was selected as the second study area, 

and the Volcanic Highlands was identified as the third study 

area (fig. 2). As part of the Priority Basin Project, untreated 

groundwater samples were collected from 96 wells and 1 

spring in the NSF study unit from August 31 to November 18, 

2004 (Kulongoski and others, 2006).

Valleys and Plains Study Area

The Valleys and Plains (VP) study area extends from 

Alexander Valley in the north to the San Pablo Bay (San 

Francisco Bay) in the south, and includes most of the 

alluvium-filled basins in the NSF study unit (fig. 2). These 

basins result from a series of northwest-trending structural 

depressions in the southern part of the Coast Ranges of 

northern California (Bailey and others, 1964; Fox, 1983). 

Mountain ranges, 1,000 to more than 4,000 ft (300 to 1,200 m) 

in altitude, bound the VP study area to the north, northwest, 

and east, and separate the Napa Valley, Sonoma Valley, and 

the Santa Rosa Plain. The San Pablo Bay bounds the VP study 

area to the south, and the North Coast Ranges and Wilson 

Grove Formation Highlands bound the VP study area to the 

west.

The VP study area covers nearly 500 mi2 (1,295 km2) and 

includes the Napa, Sonoma, Santa Rosa, Kenwood, Petaluma, 

and Alexander Valleys, as well as the Napa-Sonoma lowlands 

to the south near San Pablo Bay (fig. 2). These valleys consist 

of a relatively thin cover of Quaternary alluvium overlying 

a thick section of Neogene volcanics and sedimentary 

rocks, Cretaceous sedimentary rocks, Franciscan Complex 

sedimentary and metamorphic rocks, and Jurassic serpentinite 

(Bailey and others, 1964; Fox, 1983). 

In these valleys, the main water bearing unit is the 

alluvium and Neogene sedimentary rocks that underlie 

and form the valley floors (fig. 3), the thickness of which 

increases progressively from north to south and from the 

periphery of the valleys towards the rivers. In most valleys, 

the thickness of the alluvium ranges from less than 10 ft (3 m) 

to more than 300 ft (90 m). Except for small localized areas 

of semiconfinement, water in the alluvium is unconfined and 

moves under a natural hydraulic gradient that conforms in a 

general way to the surface topography (Faye, 1973; Farrar 

and others, 2006; Metzger and others, 2006). Groundwater 

recharge to the alluvial aquifers occurs by stream-channel 

infiltration beneath the major rivers and their tributaries and by 

direct infiltration of precipitation on alluvial fans.

Volcanic Highlands Study Area

The Volcanic Highlands (VOL) study area (fig. 2) 

consists of hilly to mountainous areas of Neogene volcanic 

deposits, which include lava flows, agglomerates, tuffs, and 

intercalated sediments of volcanic debris (fig. 3). These 

deposits, identified as the Sonoma Volcanics, were subdivided 

in ascending order of members into the Mark West Andesite, 

the Sonoma tuff, and the St. Helena Rhyolite (Osmont, 1905). 

These rocks have been folded, faulted, and eroded so that 

they now form a series of elongate ridges separating narrow 

alluvial valleys.

The VOL study area is approximately 390 mi2

(1,010 km2) of discontinuous highlands east of the Napa 

Valley to the Santa Rosa Valley (fig. 2). For the purpose of this 

study, the VOL study area was defined as the composite of the 

1.16-mi2 (3-km2) areas surrounding identified public-supply 

wells, which were completed in the volcanic formations 

(fig. 3, 4).

Pumice tuff, tuff breccias, and redeposited stratified tuff 

form the most important water bearing units in the Sonoma 

Volcanics, and are interspersed with the andesitic and basaltic 

lavas that are mostly impervious and act as confining beds, 

which restrict the movement of groundwater (Cardwell, 1958). 

The thickness of the Sonoma Volcanics is not uniform. On 

the northeast side of Kenwood Valley, tuff and tuff-breccia 

deposits are up to 1,200 ft (360 m) thick and basalt flows are 

over 800 ft (240 m) thick.

Wilson Grove Formation Highlands Study Area

The Wilson Grove Formation Highlands (WG) study 

area is characterized by gently rolling hills, broad valleys, 

and rounded hilltops between the Santa Rosa Valley and the 

Pacific Ocean (fig. 2). The WG study area corresponds to 

the Wilson Grove Formation Highlands groundwater basin 

(California department of Water Resources, 2003) in Marin 

and Sonoma Counties of California. The WG study area 

covers approximately 140 mi2 (360 km2), and is bound to 

the north and south by rugged coastal mountains of up to 

1,000 ft (300 m) in altitude, to the west by the Pacific Ocean, 

and to the east by the Santa Rosa Valley. The Wilson Grove 

Formation Highlands is named for the main geological unit 

underlying the area, the Wilson Grove Formation (Fox, 1983), 
6 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Pacific Ocean

San Pablo Bay

Cache Creek

Sonoma Creek

Russian

River

Soda Creek

Napa River

Petaluma River

Stemple Cr.

Salmon Cr.

Americano Cr.Americano Cr.

Laguna de Santa Rosa River

CONTRA COSTA

COUNTY

LAKE

COUNTY

YOLO

COUNTY

NAPA

COUNTY

SONOMA

COUNTY

SOLANO

COUNTY

MARIN

COUNTY

MENDOCINO COUNTY

PETALUMA VALLEY 

SANTA ROSA VALLEY,

Santa Rosa Plain

NAPA-SONOMA

VALLEY,

Napa Valley

NAPA-SONOMA VALLEY,

Sonoma Valley

NAPA-SONOMA

VALLEY,

Napa-Sonoma

Lowlands

ALEXANDER VALLEY,

Alexander Area

SANTA ROSA VALLEY,

Healdsburg Area

LOWER RUSSIAN

RIVER VALLEY 

ALEXANDER VALLEY,

Cloverdale Area

KENWOOD 

VALLEY 

WILSON GROVE

FORMATION HIGHLANDS 

Santa Rosa

Calistoga

Rohnert

Park

Petaluma

Napa

Sonoma

Sebastopol

Graton

Healdsburg

Windsor

SANTA ROSA

VALLEY,

Rincon Valley

NAPA-SONOMA VALLEY,

Sonoma Valley

Groundwater basin

Groundwater basin

 boundary

Groundwater subbasin

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation Highlands

STUDY AREA

EXPLANATION

Lake Berryessa

0 5

0 5 10 Kilometers

10 Miles Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

N o r t h C o a s t R a n g e s

Sonoma Mts

M a y a c m a s M t s

H o w e l l M t s

Lake

Hennessey

Lake

Sonoma

Lake

Milliken 

Figure 2. Geographic features and study areas of the North San Francisco Bay study unit, California.
Description of Study Unit: North San Francisco Bay 7

San Pablo Bay

Pacific Ocean

?

?

?

?

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Geology modified from,

Division of Mines and Geology,

CD-ROM 2000-007 (2000),GIS Data

for the Geologic Map of California

Lake Berryessa

E

E

Ep

J

J

J

J

J

KJf

KJf

KJf

KJf

KJf

Kl

Kl

Kl

Kl

Kl

Kl

Ku

Ku

Ku Ku

Ku

M

Mzv

Mzv

Mzv

Mzv

P

P

P

Q

Q

Q

Q

QPc

QPc

Qv

Qv

TK

TK

TK

Tv

Tv

Tv

Tv

Tv

Tv

grMz

um

um

um

um

Soda Creek Fault

Rodgers Creek Fault

Green Valley Fault

Bennet Valley Fault

Healdsburg Fault

Maacama Fault

Eastside Fault

San Andreas Fault System

West Napa Fault

Figure 3. Geologic map of the North San Francisco Bay study unit, California.
8 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

and was previously described as the Merced Formation 

(Johnson, 1934). The marine deposits of the Wilson Grove 

Formation contain weakly cemented, fine grained-fossiliferous 

sandstones with lenses consisting of conglomerate and sandy 

shale, and are underlain by Franciscan Complex basement 

rock (fig. 3).

The Wilson Grove Formation is the main water-bearing 

unit because of its extent, high porosity, and moderate 

transmissivity (Cardwell, 1958). Most of the Wilson Grove 

Formation marine deposits are derived from eroded Franciscan 

Complex rocks and range from 300 to 2,000 ft (100 to 610 m) 

in total thickness. 

Hydrothermal Study

The hydrothermal study characterized 7 thermal wells 

and 1 thermal spring. Five thermal wells were sampled in 

the northern Napa Valley, two thermal wells were sampled 

in Sonoma Valley, and one thermal spring (HOT-08) was 

sampled in hills composed of Cretaceous and Jurassic 

Franciscan Complex rocks west of Napa Valley (fig. B1A).

The water from the wells and the spring is mainly used for 

recreation (mineral baths), not for drinking-water supply; 

therefore, these wells are not included in the status assessment 

of the data as they do not represent drinking-water supply. 

Thermal waters were considered as potential explanatory 

factors in the understanding assessment of water quality in the 

NSF study unit.

Hydrogeologic Setting of the North San 

Francisco Bay Study Unit

The climate in the North San Francisco Bay study unit 

is characterized by warm, dry summers and cool, moist 

winters. The National Climate Data Center station in Sonoma 

reports an average annual temperature of 59 oF (15 oC), and 

an average annual precipitation of 30 inches (0.76 m), while 

average annual precipitation to the west at Graton, California 

is 42 inches (1.1 m), where the average annual temperature is 

57 oF (13.9 oC). Rain occurs primarily during the winter and 

early spring. However, the distribution of precipitation across 

the study area depends on the topography and the prevailing 

winds; precipitation increases concomitant with an increase in 

altitude.

The study areas are drained by several rivers and their 

principal tributaries. The Napa-Sonoma and Kenwood 

Valleys are drained by the Napa River and Sonoma Creek, 

respectively; the Petaluma Valley by the Petaluma River; the 

Santa Rosa, Alexander, and Lower Russian River Valleys by 

the Laguna de Santa Rosa and Russian Rivers; and the Wilson 

Grove Formation Highlands by the Americano, Stemple, and 

Salmon Creeks (fig. 2). The Volcanic Highlands are drained by 

the tributaries of the Napa, Sonoma, and Petaluma Rivers. 

In the study areas, groundwater flow generally follows 

the topography of the basins, from high elevations towards 

the drainages, and down valleys towards the San Pablo Bay 

or the Pacific Ocean (fig. 5). Sources of groundwater recharge 

include percolation of precipitation, and river and stream 

runoff. 

Eocene marine

Paleocene marine

Jurassic marine

Franciscan Complex

Lower Cretaceous marine

Upper Cretaceous marine

Miocene marine

Volcanic and metavolcanic

Pliocene marine

Quaternary

Quaternary

Tertiary

Pliocene

Miocene

Eocene

Paleocene

Plio-Pleistocene and

 Pliocene nonmarine 

Recent alluvium, landslide,

 and sand dune deposits

Volcanic flow rocks

Coastal belt rocks

Tertiary-Cretaceous

Cretaceous

Jurassic

Volcanic flow and

 pyroclastic rocks

Granitic rocks

Ultramafic rocks

Water

Q

QPc

P

M

E

Ep

Qv

Tv

TK

Ku

Kl

J

grMz

um

Mzv

KJf

EXPLANATION

USGS grid well

USGS understanding well

Study unit and study area boundary

Water boundary

GEOLOGIC UNIT

?

Fault—Dashed where approximately

 located, dotted where concealed,

 queried where uncertain

 

Thrust fault—Dashed where

 approximately located

Sedimentary rocks Sedimentary and metasedimentary rocks

Plutonic, metavolcanic, and mixed rocks

Cenozoic Mesozoic

Volcanic rocks

Figure 3. Continued.
Description of Study Unit: North San Francisco Bay 9

Lake Berryessa

Pacific Ocean

San Pablo

Bay CONTRA COSTA

COUNTY

LAKE

COUNTY YOLO

COUNTY

NAPA

COUNTY

SONOMA

COUNTY

SOLANO

COUNTY

MARIN

COUNTY

MENDOCINO COUNTY

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

USGS grid well

USGS understanding well

CDPH wells

STUDY AREA

EXPLANATION

Valleys and Plains

Grid cell

Volcanic Highlands

Wilson Grove Formation

 Highlands

Figure 4. Locations of grid cells, California Department of Public Health (CDPH) wells, and the U.S. Geological Survey (USGS) grid and 

understanding wells sampled during August to December 2004 for the North San Francisco Bay study unit, California.
10 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Water resources for public drinking-water supply include 

surface water from Lake Hennessey, Lake Milliken, Lake 

Mendocino, Lake Sonoma, the Sacramento River Delta, 

and local public-supply wells (fig. 2). The primary aquifers 

targeted by this study included groundwater bearing zones in 

which public-supply wells are completed. These wells vary in 

depth from 60 to 1,040 ft (18 to 316 m), depending on their 

location and depth of the alluvium. In the Napa and Sonoma 

Valleys, groundwater movement is generally from north to 

south towards the San Pablo Bay (fig. 2). In the Alexander 

Valley and the Healdsburg area of the Santa Rosa Valley, 

groundwater movement is generally from north to south 

towards the Lower Russian River Valley, at which point the 

flow becomes from east to west towards the Pacific Ocean.

Land use in the study unit is 55 percent natural, 

31 percent agricultural, and 14 percent urban according 

to classifications from USGS National Land Cover Data 

(Vogelmann and others, 2001; Price and others, 2003) 

(figs. 6A, 7; Appendix E: Land-Use Classification). Most of 

the agricultural and urban land is in the Valleys and Plains 

study area, whereas most of the natural landscapes are in the 

Volcanic Highlands and Wilson Grove Formation Highlands 

study areas (figs. 6, 7). However, vineyards extend up into the 

Volcanic Highlands, and irrigation and pesticide application 

do occur in these areas. Natural lands are mostly grassland 

and forests, while the primary use of agricultural land is for 

pasture (cattle, sheep, and poultry), hay, vineyards, flowers, 

nurseries, and orchards (California Department of Water 

Evapotranspiration

Surface

water

outflow

Groundwater

outflow

Groundwater

inflow

Water table

Napa Valley

Soda Creek Fault

EXPLANATION

(Arrows show components of hydrologic budget)

Infiltration

Evapotranspiration

Precipitation

Direction of groundwater

 movement

Napa R

Howell

Mountains

Infiltration

Infiltration

Groundwater

inflow

N

2,000

Elevation,

in feet

above

or below

sea level

-2,000

0'

Precipitation

Figure 5. A conceptual model of the aquifer system in the Valleys and Plains valleys of the North San Francisco Bay study unit, 

California.
Description of Study Unit: North San Francisco Bay 11

Resources, 2001a,b). The largest urban areas are the cities of 

Santa Rosa, Petaluma, Rohnert Park, Healdsburg, Windsor, 

Napa, and Sonoma. Figure 6A shows the percentage of land 

use for selected categories that was calculated for the NSF 

study unit, each study area, and for the cumulative area 

within a 1,640-ft (500-m) radius around each well. Figure 6B

shows the land-use-category percentages for the area around 

individual wells in the NSF study unit. Figure 7 shows the 

land-use classification map that is based on satellite imagery 

(see Appendix E: Land-Use Classification for details), and the 

NSF USGS grid wells.

Regional Geologic Setting

The Northern Coast Ranges of California are 

characterized by a strong northwest trending physiography 

(figs. 1, 3). The core of the Coast Ranges consists of three 

major rock groups: the Franciscan Complex, the Great Valley 

Sequence, and the Coast Range Ophiolite which together 

make up the basement rocks. These rocks span an age range 

from Jurassic to Eocene and were originally deposited in 

marine environments west of the continent. Subsequently 

these sediments were transported toward and accreted against 

the North American continent. Cenozoic folding and faulting 

produced the topography of mountain ranges and intervening 

valleys present today. Basement rocks are blanketed by 

Cenozoic sedimentary and volcanic rocks along the Pacific 

coast, around San Francisco Bay, and in some of the inland 

valleys. The area now occupied by the Santa Rosa Plain was 

the continental margin during the later part of the Cenozoic 

and a site of sedimentary depositional transition between 

marine, estuarine, and terrestrial environments (Fox, 1983; 

Powell and others, 2004; McLaughlin and others, 2005). 

Similarly, the mouths of Petaluma, Sonoma, and Napa Valley 

were connected with the brackish waters of the San Francisco 

Bay and accumulated large thicknesses of bay mud associated 

with Pleistocene sea-level changes. All the larger inland 

valleys are underlain now by thick layers of sedimentary and 

volcanic rocks (Farrar and others, 2006). The volcanic rocks 

in the Clear Lake volcanic field (approximately 30 mi (48 

km) north of Calistoga) and the Sonoma Volcanics also cover 

large parts of the Sonoma, Howell, and Mayacmas Mountains 

(figs. 2, 3). Most of the valleys and ridges have formed in 

response to northwest trending, right-lateral, strike-slip 

20

40

60

80

100

0

100 80 60 40 20 0

100

80

60

40

20

0

PERCENT URBAN

PERCENT AGRICULTURE

PERCENT NATURAL

20

40

60

80

100

0

100 80 60 40 20 0

100

80

60

40

20

0

PERCENT URBAN

PERCENT AGRICULTURE

PERCENT NATURAL

Understanding

Valleys and Plains

Volcanic Highlands

Wilson Grove

Formation Highlands

STUDY UNIT

STUDY AREA

STUDY AREA

Data point:

Land use

based on

satellite

imagery

Data point:

Land use

based on a

500-meter-radius

area around

grid wells

Valleys and Plains

Volcanic Highlands

Wilson Grove

Formation Highlands

A B

Figure 6. Percent urban, agricultural, and natural land use in A. the study unit and study areas, and B. the 500-meter-radius area 

surrounding wells in the North San Francisco Bay study unit, California.
12 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

0 4 8 16 Miles

0 8 16 4 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

USGS National Land Cover Data

(Price and others, 2003)

EXPLANATION

Grid well

Study unit and

study area boundary Urban

LAND USE CLASSIFICATION

Agricultural

Natural

Santa Rosa

Napa

Sonoma

San Pablo Bay

Pacific Ocean

Lake Berryessa

Figure 7. Land use in the North San Francisco Bay study unit, California, and the locations of U.S. Geological Survey grid wells.
Description of Study Unit: North San Francisco Bay 13

faulting and reverse faulting on west-dipping, high-angle 

faults that characterize a 50-mi (80-km) wide strip of coastal 

California north of the San Francisco Bay.

The principal mountain ranges within the study area, 

from the Pacific Ocean coastline to east of Napa Valley, 

approximately 40 mi (66 km), are the North Coast Ranges, 

the Sonoma Mountains, the Mayacmas Mountains, and the 

Howell Mountains (fig. 2). The altitude of the highlands is 

generally less than 2,000 ft (600 m); the altitude of most ridge 

lines is between 500 and 1,500 ft (150 to 460 m). The Sonoma 

Mountains are higher relief than the North Coast Range and 

rise steeply to altitudes of 1,000 to 2,000 ft (300 to 600 m) east 

of Santa Rosa. The Mayacmas Mountains are less steep and 

generally range between 500 and 1,000 ft (150 to 300 m) in 

altitude. The North Coast Range is made up of low rounded 

hills that reach altitudes of 600 to 1,200 ft (180 to 370 m). 

Between the mountain ranges lie three prominent sub-parallel, 

northwest-trending valleys: Santa Rosa Valley, Sonoma Valley, 

and Napa Valley. 

Each of the valleys has substantial urban population 

centers, surrounded by large tracts of agricultural land and 

sparse rural populations (fig. 7). The valleys form separate 

watersheds, which have separate underlying groundwater flow 

systems. Thermal springs and wells are in parts of each of the 

three valleys. The thermal waters that issue from springs or are 

pumped from wells in each valley are derived from separate 

hydrothermal systems that have similar hydrogeologic 

settings. 

Quaternary Sedimentary Deposits

Quaternary sedimentary deposits recognized and mapped 

within the study area include alluvial deposits, near shore 

marine and estuarine sediments, colluvium, and landslide 

deposits. The depositional environment, source materials, and 

age of deposits determine the composition, sorting, bedding, 

degree of cementation, and other characteristics of the deposits 

relevant to their hydraulic properties.

The alluvial sediments of Quaternary age have been 

mapped as distinct deposits on the basis of degree of 

consolidation, cementation, clast size and sorting, and 

geomorphic expression. Alluvial fan deposits cover more 

area than the other alluvial sediments in the study area. The 

fan, river terrace, and stream-channel deposits generally 

consist of heterogeneous mixtures of poorly to well sorted 

sand, silt, clay, gravel, cobbles, and boulders in thin to 

massive interfingering beds of limited lateral extent (tens to 

hundreds of feet), often having lenticular form. Most of the 

deposits near valley axes lie flat, but near valley margins, 

beds in wedge-shaped fan deposits dip toward the valley. 

Large volumes of alluvial sediment are present in all the 

main valleys in the study area. The alluvial sediment may 

be divided into older (Pleistocene) and younger (Holocene) 

deposits, which, to some degree, is consistent with the amount 

of cementation and consolidation of the sediments (Cardwell, 

1958; Kunkel and Upson, 1960). The maximum thickness of 

the older alluvium is about 500 ft (152 m), and the younger 

alluvium is generally less than 150 ft (50 m) thick. 

The generally porous nature of the alluvial deposits and 

close proximity to modern streams allows for rapid recharge 

of precipitation to the groundwater system and for exchanges 

between groundwater and surface water in and near stream 

channels. Groundwater is unconfined in most places, but 

semiconfined conditions exist beneath thick layers of clay 

or silt in some areas. Early development of groundwater 

resources in the study area focused on the drilling of shallow 

wells in alluvial deposits, taking advantage of shallow 

depths to water, high porosities, and moderate hydraulic 

conductivities. However, because of the broad range of 

lithologic characteristics and correlative hydraulic properties 

of the alluvial deposits and their saturated thickness, well 

yields historically have ranged from dry to over 1,000 gpm 

(3,785 L/m). In some areas, as groundwater extraction 

increased, water levels declined and the alluvial deposits no 

longer could supply sufficient water to wells, especially for 

agriculture or municipal supply. The alluvial deposits are 

still the primary aquifers along major streams and in much of 

Napa Valley. Large collector wells completed in gravels along 

the Russian River provide thousands of gallons of water per 

minute for municipal supply, and smaller agricultural wells 

obtain yields of 100 to >1,000 gpm (378 to 3,785 L/m). In 

the Napa Valley, well yields from alluvial deposits generally 

are below 50 gpm (189 L/m), but can be as high as 400 gpm 

(1,514 L/m); specific yields are between 8 and 17 percent 

(California Department of Water Resources, 2003).

Large areas near the mouths of Petaluma, Sonoma, 

and Napa Valley are covered with fine-grained, poorly 

consolidated materials deposited in estuarine and near shore 

marine environments, mapped as Bay Mud. These materials 

were deposited near sea level in the Holocene (<10,000 years), 

occupy the topographically lowest parts of the valleys, and 

extend off-shore into the San Pablo Bay. The deposits are 

composed mostly of silt, clay, peat, and fine sand, with 

isolated accumulations of beach sand. The fine-grained texture 

and general lack of cementation provides high porosity 

(>40 percent) but low hydraulic conductivity and a specificyield range of 3–7 percent (California Department of Water 

Resources, 1982). Few wells, as of 2006, are completed in the 

Bay Mud because of the low yields obtained from the finegrained sediments and the poor quality, saline water contained 

in them.

The landslide and colluvial deposits occur as isolated 

patches or thin cover near the flanks of the valleys. The 

limited thickness or areal extent of these deposits, and their 

topographic position, generally above the autumnal watertable elevation, do not facilitate the development of water 

wells.
14 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Faults

The most important faults in the region are the northwest 

trending, right-stepping, en-echelon faults that are part of the 

San Andreas dextral transform system active from Miocene to 

Holocene (McLaughlin and others, 2005). These include the 

Rodgers Creek, Healdsburg, Bennett Valley, Mayacma, and 

Green Valley Faults (fig. 3). Total slip on the Rodgers Creek 

Fault is up to 40 mi (65 km) (McLaughlin and others, 1996). 

The faults have many of the classic geomorphic features of 

strike-slip fault movement: scarps, benches, stratigraphic 

discontinuities, deflected drainages, sag ponds, and springs. 

The youthful geomorphic expression and seismicity along 

the faults of the San Andreas system attest to their active 

classification for earthquake hazards. The distinctive 

geomorphic expression of the faults is usually obscured where 

they strike beneath alluvium covered valleys. 

The most important hydrologic impact of faults in the 

region is their role in developing the inland valleys. On a local 

scale, the faults are hydrologically important because they are 

planar features or zones across or within which the movement 

of groundwater may be inhibited or preferentially increased. 

Faulting breaks indurated rocks, producing zones of fractures 

that increase permeability and may provide preferential paths 

for groundwater flow. However, over time, the movement of 

groundwater through fractures may cause chemical weathering 

and cementation that reduces the permeability and converts 

the fault plane or zone into a groundwater barrier. Faulting 

in unconsolidated sediments or indurated rocks can produce 

zones of fine-grained fault gouge having low permeability, 

resulting in a groundwater barrier or zone of restricted 

groundwater flow. Faults may also displace rocks or sediments 

in such ways that formations with very different hydraulic 

properties are moved adjacent to one another.

Faults are thought to be important surfaces along which, 

in some circumstances, fluids may move vertically and allow 

deep waters to move to the surface or into shallow formations. 

This process is of special significance for the hydrothermal 

systems that are active in Napa, Sonoma, and Santa Rosa 

Valleys, where thermal springs and wells tend to be aligned 

with and near valley-bounding faults (Youngs and others, 

1983). It has been speculated that faults in Santa Rosa Valley, 

form barriers to groundwater movement. However, conclusive 

proof of this phenomenon has not been documented by 

hydraulic testing (California Department of Water Resources, 

1987). A more thorough discussion of the stratigraphy is 

included in Appendix A.

Methods 

The status assessment provides a spatially-unbiased 

assessment of groundwater quality within in the primary 

aquifers, while the understanding assessment was designed to 

evaluate the natural and human factors that affect groundwater 

quality of the NSF study unit. This section describes the 

methods used for (1) defining groundwater quality, (2) 

assembling the datasets used for the status assessment, (3) 

determining which constituents warrant assessment, (4) 

calculating aquifer-scale proportions, and (5) statistical 

analyses for the understanding assessment. Methods used 

for compilation of data on potential explanatory factors are 

described in Appendix E.

The primary metric for defining groundwater quality 

was relative-concentration, which references concentrations 

of constituents measured in groundwater to regulatory and 

nonregulatory benchmarks used to evaluate drinking-water 

quality. Constituents are included or not included in the 

assessment on the basis of objective criteria by using these 

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 relativeconcentrations: the “grid-based” method uses one value per 

cell to represent groundwater quality and the “spatiallyweighted” method uses many values per cell.

The CDPH database contains historical records from 

more than 25,000 wells, necessitating targeted retrievals to 

effectively access relevant water-quality data. For example, 

for the area representing the NSF study unit, the historical 

CDPH database contains more than 309,000 records from 

790 wells. The CDPH data were used in three ways in the 

status assessment: to fill in gaps in the USGS data for the gridbased calculations of aquifer-scale proportions, to identify 

constituents for inclusion in the assessment, and 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

Relative-concentrations were used because they provide 

context for the measured concentrations in the sample: 

relative-concentrations less than one indicate sample 

concentrations less than the benchmark, and values greater 

than one indicate sample concentrations greater than the 

benchmark. The use of relative-concentrations also permits 

comparison on a single scale of constituents present at a wide 

range of concentrations.
Methods 15

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 MCLs or Health-Based Screening Levels 

[HBSL]) and defined this ratio as the benchmark quotient. 

Relative-concentrations used in this report are equivalent 

to the benchmark quotient reported by Toccalino and others 

(2004) for constituents that have MCLs. However, HBSLs 

were not used in this report, as they are not currently used as 

benchmarks by California drinking-water regulatory agencies. 

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. 

 Regulatory and nonregulatory 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, 

200; California Department of Health Services, 2008a,b). 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 

technique levels (TT-US). 

2. Nonregulatory 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. Nonregulatory, health based CDPH notification 

levels (NL-CA), USEPA lifetime health advisory 

levels (HAL-US) and USEPA risk-specific doses for 

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 

constituents analyzed is provided by Kulongoski and others 

(2006).

For ease of discussion, relative-concentrations of 

constituents were classified into low, moderate, and high 

categories:

Category

Relativeconcentrations 

of organic 

constituents

Relativeconcentrations 

of 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 (Toccalino and others, 2004; U.S. Environmental 

Protection Agency, 1998). For inorganic constituents, the 

boundary between moderate and low relative-concentrations 

was set at 0.5. A larger boundary value was used because in 

the NSF study unit and elsewhere in California (Landon and 

others, 2010), the naturally occurring inorganic constituents 

tend to be more prevalent in groundwater. While 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.

Datasets for Status Assessment

U.S. Geological Survey Grid Wells

The primary data used for the grid-based calculations of 

aquifer-scale proportions of relative-concentrations were data 

from wells sampled by USGS–GAMA. Detailed descriptions 

of the methods used to identify wells for sampling are given 

in Kulongoski and others (2006). Briefly, each study area 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 State-wide databases maintained by the CDPH and 

the USGS. The NSF study unit contained a total of 100 grid 

cells, and the USGS sampled wells in 83 of those cells (USGS 

grid wells). Of the 83 USGS grid wells, 80 were listed in the 

CDPH database; the other 3 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 a prefix identifying the study area and 

a number indicating the order of sample collection (fig. B1; 

table B1). The following prefixes were used to indicate 

study area: VOL, Volcanic Highlands study area, VP, Valleys 

and Plains study area, and WG, Wilson Grove Formation 

Highlands study area. In this report, the well VOL-04 was 

reclassified as an understanding well because it occupied the 

same cell as well VOL-20. 
16 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Samples collected from USGS grid wells were analyzed 

for 270 to 342 constituents (table 1). Water quality indicators 

(field parameters), volatile organic compounds, pesticides, 

NDMA, perchlorate, 1,2,3-TCP, noble gases, and selected 

isotopes used as hydrologic tracers were analyzed in samples 

from all wells. Major and minor ions, trace elements, nutrients, 

and redox species were analyzed in samples from many wells, 

and radiochemical constituents, and carbon isotopes were 

analyzed in samples from some wells. The collection, analysis, 

and quality-control data for the analytes listed in table 1

are described by Kulongoski and others (2006). However, 

further quality assurance and quality controls (QA/QC) were 

applied to the data. Data for constituents detected in the blank 

samples were screened below the concentration equal to the 

constituent’s highest blank sample detection plus one half of 

the constituent’s laboratory reporting level. 

California Department of Public Health 

Grid Wells

The three study areas were divided into 100 grid cells, 

out of which 17 cells did not have a USGS grid well; 54 cells 

had a USGS grid well but no USGS data for major ions, 

trace elements, nutrients, and radiochemical constituents; 

and 11 cells had a USGS grid well but incomplete USGS 

data for nutrient and radiochemical constituents. The CDPH 

database was queried to provide these missing inorganic and 

radiochemical data. CDPH wells with data for the most recent 

three years available at the time of sampling (8/30/01 through 

9/1/04) were considered. If a well had more than one analysis 

for a constituent in the three year interval, the most recent data 

were selected. 

The decision tree used to identify suitable data from 

CDPH wells is described in Appendix B. Briefly, the first 

choice was to use CDPH data from the same well sampled by 

the USGS (USGS grid well). In this case, “DG” was added 

to the well’s GAMA ID to signify that it was a well sampled 

by the USGS but also used CDPH data (fig. B1; table B1). If 

the DG well did not have all of the needed data, a second well 

in the cell was randomly selected from the subset of CDPH 

wells with data and a new identification with “DPH” and a 

new number was assigned to that well (fig. B1; table B1). The 

combination of the USGS grid wells and the DG- and DPHCDPH-grid wells, produced a grid-well network covering 91 

of the 100 grid cells in the NSF study unit (table B1). The 

remaining 9 cells had no accessible wells. 

Note that 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 water-quality data, the CDPH data 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 perchlorate for the 

status assessment. A larger number of VOCs and pesticide 

compounds are analyzed for the USGS–GAMA Program 

than are available from CDPH. USGS–GAMA collected data 

for 88 VOCs plus 114 pesticides and pesticide degradates 

at every well in the NSF study unit (table 1). In addition, 

method detection limits for USGS–GAMA analyses of organic 

constituents were typically one to two orders of magnitude 

lower than the reporting limits for analyses compiled by 

CDPH (table 3). Data for these constituents were collected by 

USGS–GAMA at all 83 USGS grid wells sampled. 

Additional Data Used for Spatially-Weighted 

Calculation

The spatially-weighted calculations of aquifer-scale 

proportions of relative-concentrations used data from the 

USGS grid wells, additional wells sampled by USGS–GAMA, 

and all wells in the CDPH database having water-quality data 

during the three-year interval August 30, 2001 to September 

1, 2004. For wells with both USGS and CDPH data, only the 

USGS data were used. 

Six additional wells were sampled by the USGS to 

increase the sampling density in several areas to better 

understand specific groundwater-quality issues in the study 

unit (fig. B1A). These “USGS-understanding” wells were 

numbered with prefixes modified from those used for the 

USGS grid wells (VPFP-01, -02, -03, -04, and VOL-04, and 

WGFP-01) (fig. B1A, table B1). Seven hydrothermal wells 

and one hydrothermal spring also were sampled but were not 

included in this water-quality assessment because they were 

not used for drinking-water supply. The hydrothermal data is 

used for comparison purposes in Appendix F.
Methods 17

Analytical schedule

Basic Intermediate Hydrothermal Expanded

Total number of wells 56 11 8 22

Number of grid wells sampled 54 10 0 19

Number of understanding wells sampled 2 1 8 3

Analyte class Number of constituents

Water-quality indicators (field parameters)

 Specific conductance and temperature 2 2 2 2

 Dissolved oxygen and pH 1 2

 Field alkalinity, bicarbonate, and carbonate 3

 Turbidity 1

Organic constituents

Volatile organic compounds (VOC) and gasoline additives 1 88 88 0 88

Pesticides and pesticide degradates 61 61 0 61

Polar pesticides and degradates 53 53 0 53

Potential waste-water indicators 2 50 50 0 50

Dissolved organic carbon 1

Constituents of special interest

Perchlorate, NDMA, and low-level 1,2,3-TCP 3 3 3 0 3

Inorganic constituents

Major and minor ions, silica, total dissolved solids (TDS), and trace elements 36 36 36

Nutrients 5

Arsenic and iron species 6 6

Chromium species 3 3 3 3

Isotopes

Stable isotopes of hydrogen and oxygen 2 2 2 2

Carbon-13 and carbon-14 2

Radioactivity and dissolved gases

Tritium 4 1 1 1 1

Noble gases and tritium 5 7 7 7 7

Dissolved gases (carbon dioxide, argon, methane, nitrogen, oxygen) 5 5

Radon and radium isotopes 3

Gross alpha and beta radioactivity 4

Microbial constituents

Total coliforms, colifage (somatic and F-specific), E. coli 4

Total 270 306 63 342

 1 Includes nine constituents classified as fumigants or fumigant synthesis byproducts.

2

 Does not include 13 constituents in common with VOCs and pesticides.

3

 1,2,3-TCP was analyzed as a constituent of special interest with a method reporting level of 0.005 μg/L and on the USGS VOC schedule 2020, which has a 

laboratory reporting level of 0.12 μg/L.

4

 Analyzed at U.S. Geological Survey Tritium Laboratory, Menlo Park, California.

5

 Analyzed at Lawrence Livermore National Laboratory, Livermore, California.

Table 1. Analyte groups and number of constituents and wells sampled for each analytical schedule in the North San Francisco Bay 

study unit, California, August to December, 2004.

[1,2,3-TCP, 1,2,3-trichloropropane; NDMA, N-nitrosodimethylamine; USGS, U.S. Geological Survey. μg/L, microgram per liter]
18 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Constituent type Constituent Benchmark 

type

Benchmark 

value Units

Number of grid 

wells with 

USGS-GAMA 

data

Number of grid 

wells with 

CDPH data

Nutrient Ammonia, as nitrogen HAL-US 30 mg/L 22 0

Nutrient Nitrate plus nitrite, as nitrogen MCL-US 10 mg/L 22 63

Nutrient Nitrite, as nitrogen MCL-US 1 mg/L 22 54

Trace element Aluminum MCL-CA 1,000 μg/L 31 49

Trace element Antimony MCL-US 6 μg/L 31 49

Trace element Arsenic MCL-US 10 μg/L 31 49

Trace element Barium MCL-CA 1,000 μg/L 31 49

Trace element Beryllium MCL-US 4 μg/L 31 49

Trace element Boron NL-CA 1,000 μg/L 31 43

Trace element Cadmium MCL-US 5 μg/L 31 49

Trace element Chromium MCL-CA 50 μg/L 32 22

Trace element Copper AL-US 1,300 μg/L 31 39

Trace element Iron SMCL-CA 300 μg/L 32 47

Trace element Lead AL-US 15 μg/L 31 32

Trace element Manganese SMCL-CA 50 μg/L 31 45

Trace element Mercury MCL-US 2 μg/L 23 49

Trace element Molybdenum HAL-US 40 μg/L 31 1

Trace element Nickel MCL-CA 100 μg/L 31 49

Trace element Selenium MCL-US 50 μg/L 31 49

Trace element Silver SMCL-CA 100 μg/L 31 46

Trace element Strontium HAL-US 4,000 μg/L 31 0

Trace element Thallium MCL-US 2 μg/L 31 49

Trace element Uranium MCL-US 30 μg/L 31 0

Trace element Vanadium NL-CA 50 μg/L 31 44

Trace element Zinc SMCL-US 5,000 μg/L 31 38

Minor ion Fluoride MCL-CA 2 mg/L 28 49

Major ion Chloride SMCL-CA 500 mg/L 32 39

Major ion Sulfate SMCL-CA 500 mg/L 32 38

Major ion Total dissolved solids (TDS) SMCL-CA 1,000 mg/L 32 38

Radioactive Gross-alpha radioactivity, 30 day count MCL-US 15 pCi/L 21 31

Radioactive Gross-beta radioactivity, 30 day count MCL-CA 50 pCi/L 21 1

Radioactive Radium-226 MCL-US 5 pCi/L 21 0

Radioactive Radium-228 MCL-US 5 pCi/L 2 7

Radioactive Radon-222 MCL-US 4,000 pCi/L 21 2

Table 2. Inorganic constituents and associated benchmark information, and the number of grid wells with USGS–GAMA data and 

California Department of Public Health (CDPH) data, for each constituent, North San Francisco Bay study unit, California.

[MCL-US, U.S. Environmental Protection Agency (USEPA) maximum contaminant level; MCL-CA, CDPH maximum contaminant level; SMCL-CA, CDPH 

secondary maximum contaminant level; NL-CA, CDPH notification level; AL-US, USEPA action level; HAL-US, USEPA lifetime health advisory; USGS, U.S. 

Geological Survey. μg/L, microgram per liter; mg/L, milligram per liter; pCi/L, picocurie per liter]
Methods 19

Identification of Constituents for 

Status Assessment

Up to 342 constituents were analyzed in samples 

from NSF study unit wells; however, only a subset of these 

constituents is discussed in this report. Three criteria were 

used to identify constituents for inclusion:

1. Constituents present at high or moderate 

relative-concentrations in the CDPH database 

within the 3-year interval,

2. Constituents present at high or moderate 

relative-concentrations in the USGS grid wells 

or USGS-understanding wells or,

3. Organic constituents having study unit detection 

frequencies greater than 10 percent in the USGS 

grid well dataset.

These criteria identified 11 organic constituents and 

15 inorganic constituents for inclusion in the status 

assessment (table 4). An additional 15 organic constituents 

and 35 inorganic constituents were detected by USGS–

GAMA, and are not included in the status assessment because 

they either have no established benchmarks, or were only 

detected at low relative-concentrations (table 5). Constituents 

discussed in the understanding assessment had high relativeconcentration in >2 percent of the primary aquifers or were 

detected in >10 percent of the USGS grid well dataset. The 

remaining constituents that were not detected by USGS–

GAMA in the NSF study unit are listed in Kulongoski and 

others (2006).

The CDPH database also was used to identify 

constituents that have been reported at high relativeconcentrations historically, but not currently. The historical 

period was defined as from the earliest record maintained in 

the CDPH electronic database to August 29, 2001 (May 16, 

1974, through August 29, 2001). 

Constituents may be historically high, but not currently 

high, due to improvement of groundwater quality with time or 

abandonment of wells with high concentrations. Historically 

high 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 2001 to 2004. For the NSF study unit, there 

were nine historically high constituents (table 6).

Calculation of Aquifer-Scale Proportions

The status assessment is intended to characterize the 

quality of groundwater resources within the primary aquifers 

of the NSF study unit. The primary aquifers are defined by the 

depth intervals over which wells listed in the CDPH database 

are perforated. The use of the term “primary aquifers” does 

not imply that there exists a discrete aquifer unit. In most 

groundwater basins, municipal and community supply wells 

generally are perforated at greater depths than are domestic 

wells (for example, Farrar and others, 2006). Thus, because 

domestic wells are not listed in the CDPH database, the 

primary aquifer generally corresponds to the portion of the 

aquifer system tapped by municipal and community supply 

wells. A majority of the wells used in the status assessment

are listed in the CDPH databases, and are therefore classified 

as municipal and community drinking-water supply 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 may also be applicable to the portions 

of the aquifer systems used for domestic drinking-water 

supplies.

Constituent type

CDPH USGS GAMA

Number of Median unit

compounds Median MDL Number of compounds Median LRL

Volatile organic compounds and gasoline 

additives (including fumigants)

61 0.5 88 0.06 μg/L

Pesticides plus degradates 27 2 114 0.019 μg/L

Nutrients, major and minor ions 4 0.4 17 0.06 mg/L

Trace elements 20 8 25 0.12 μg/L

Radioactive constituents (SSMDC)1 5 1 8 0.05 pCi/L

Perchlorate 1 4 1 0.5 μg/L

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

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

constituent for data reported in the California Department of Public Health database and data collected by the U.S. Geological Survey 

for the North San Francisco Bay study unit, California, August to December, 2004.

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

liter; pCi/L, picocurie per liter; SSMDC, sample-specific minimum detectable concentration; ns, not sampled]

 1 Value reported in GAMA column is the median SSMDC for eight radioactive constituents collected and analyzed by GAMA.
20 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project Table 4. Aquifer proportions from grid-based and spatially-weighted methods for constituents (1) for which high relative-concentrations were reported in the California Department of Public Health (CDPH) database during August 30, 2001, to September 1, 2004, or (2) that had moderate or high relative-concentrations in samples collected from grid wells during August to December 2004, North San Francisco Bay study unit, California.—Continued [Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 83 grid wells during August to December, 2004. Spatially-weighted aquifer-scale proportions are based on CDPH data for August 30, 2001, to September 1, 2004, combined with grid-well and understanding-well data. High, concentrations greater than benchmark; moderate, concentrations less than benchmark and greater than or equal to 0.1 of benchmark for organic constituents or 0.5 of benchmark for inorganic constituents; low, concentrations less than 0.1 of benchmark for organic constituents or 0.5 of benchmark for inorganic constituents. AL-US, U.S. Environmental Protection Agency (USEPA) action level; HAL-US, USEPA lifetime health advisory; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligram per liter; μg/L, microgram per liter; ns, not sampled; pCi/L, picocurie per liter; %, percent] Constituent Benchmark Raw detection frequency 1 Spatially-weighted aquifer-scale proportion 1 Grid-based aquifer-scale proportion 90% confidence interval for grid-based high proportion 2 Number of wells Percent Number of cells Proportion, in percent Number of cells Proportion, in percent Limit, in percent Type Value Unit Moderate High Moderate High Moderate High Lower Upper Trace element Aluminum MCL-CA 1,000 μg/L 406 0.5 0.7 81 0.4 0.6 80 0.0 0.0 0.0 2.0 Antimony MCL-US 6 μg/L 404 0.0 0.7 81 0.0 0.7 80 0.0 0.0 0.0 2.0 Arsenic MCL-US 10 μg/L 407 21.6 10.3 81 24.1 12.2 80 25.0 10.0 5.7 16.9 Boron NL-CA 1,000 μg/L 288 3.1 3.5 77 4.7 3.6 74 5.4 4.1 1.6 9.7 Lead AL-US 15 μg/L 274 1.8 2.2 71 1.5 1.8 63 1.6 1.6 0.4 6.8 Mercury MCL-US 2 μg/L 401 1.0 0.0 80 1.1 0.0 72 1.4 0.0 0.0 2.2 Molybdenum HAL-US 40 μg/L 35 2.9 0.0 33 3.0 0.0 32 3.1 0.0 0.0 4.9 Nickel MCL-CA 100 μg/L 406 0.0 0.2 80 0.0 0.2 80 0.0 0.0 0.0 2.0 Trace element with a SMCL Iron SMCL-CA 300 μg/L 342 8.8 18.7 80 7.9 26.3 78 9.0 24.4 17.3 33.1 Manganese SMCL-CA 50 μg/L 336 8.6 34.5 79 8.6 40.9 76 7.9 40.8 32.0 50.2 Radioactive constituent Gross-alpha radioactivity, 72 hour count 3 MCL-US 15 pCi/L 194 0.0 3 0 55 0.0 0.0 51 0.0 0.0 0.0 3.1 Nutrient Nitrate plus nitrite, as nitrogen MCL-US 10 mg/L 549 2.7 0.5 85 3.2 0.4 76 2.6 0.0 0.0 2.1 Major and minor element Chloride SMCL-CA 500 mg/L 275 0.0 0.4 73 0.0 0.7 71 0.0 0.0 0.0 2.3 Fluoride MCL-CA 2 mg/L 397 0.3 1.5 82 0.1 1.2 77 0.0 0.0 0.0 2.1 Total dissolved solids (TDS) SMCL-CA 1,000 mg/L 273 4.4 0.4 71 5.0 0.7 70 7.1 1.2 0.0 2.3 Table 4. Aquifer proportions from grid-based and spatially-weighted methods for constituents (1) for which high relative-concentrations were reported in the California Department of Public Health (CDPH) database during August 30, 2001, to September 1, 2004, or (2) that had moderate or high relative-concentrations in samples collected from grid wells during August to December 2004, North San Francisco Bay study unit, California. [Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 83 grid wells during August to December, 2004. Spatially-weighted aquifer-scale proportions are based on CDPH data for August 30, 2001, to September 1, 2004, combined with grid-well and understanding-well data. High, concentrations greater than benchmark; moderate, concentrations less than benchmark and greater than or equal to 0.1 of benchmark for organic constituents or 0.5 of benchmark for inorganic constituents; low, concentrations less than 0.1 of benchmark for organic constituents or 0.5 of benchmark for inorganic constituents. AL-US, U.S. Environmental Protection Agency (USEPA) action level; HAL-US, USEPA lifetime health advisory; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligram per liter; μg/L, microgram per liter; ns, not sampled; pCi/L, picocurie per liter; %, percent]
Methods 

21

Table 4. Aquifer proportions from grid-based and spatially-weighted methods for constituents (1) for which high relative-concentrations were reported in the California 

Department of Public Health (CDPH) database during August 30, 2001, to September 1, 2004, or (2) that had moderate or high relative-concentrations in samples collected from grid wells during August to December 2004, North San Francisco Bay study unit, California.—Continued

[Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 83 grid wells during August to December, 2004. Spatially-weighted aquifer-scale proportions are based on CDPH data for August 30, 2001, to September 1, 2004, combined with grid-well and understanding-well data. High, concentrations greater than benchmark; moderate, concentrations less than benchmark and greater than or equal to 0.1 of benchmark for organic constituents or 0.5 of benchmark for inorganic constituents; low, concentrations less than 0.1 of benchmark for organic constituents or 0.5 of benchmark for inorganic constituents. AL-US, U.S. Environmental Protection Agency (USEPA) action level; HAL-US, USEPA lifetime health advisory; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligram per liter; μg/L, microgram per liter; ns, not sampled; pCi/L, picocurie per liter; %, percent]

Constituent Benchmark Raw detection frequency 1

Spatially-weighted 

aquifer-scale 

proportion 1

Grid-based 

aquifer-scale 

proportion

90% confidence 

interval for 

grid-based high 

proportion 2

Number 

of wells

Percent Number 

of cells

Proportion, in percent Number 

of cells

Proportion, in percent Limit, in percent

Type Value Unit Moderate High Moderate High Moderate High Lower Upper

Solvent

1,2-Dichloroethane 3 MCL-CA 0.5 μg/L 304 0.0 3 0.0 87 0.0 0.0 83 0.0 0.0 0.0 1.9

Tetrachloroethene (PCE) MCL-US 5 μg/L 307 0.7 0.7 87 0.4 1.3 83 0.0 0.0 0.0 1.9

Trichloroethene 

(TCE)

MCL-US 5 μg/L 312 1.6 0.6 87 2.1 0.1 83 0.0 0.0 0.0 1.9

Other VOC

1,1-Dichloroethene MCL-CA 6 μg/L 302 0.0 0.3 87 0 0.1 83 0.0 0.0 0.0 1.9

Bis(2-ethylhexyl) 

phthalate (DEHP) 3

MCL-CA 4 μg/L 62 0.0 3 0 62 0 0.0 ns ns ns ns ns

Benzene MCL-US 1 μg/L 305 0.7 0.0 87 1.7 0.0 83 1.2 0.0 0.0 1.9

Toluene 3 MCL-US 150 μg/L 309 0.0 3 0 87 0 0.0 83 0.0 0.0 0.0 1.9

1,2-Dibromo3-chloropropane 

(DBCP) 3

MCL-US 0.2 μg/L 160 0.0 3 0 84 0 0.0 83 0.0 0.0 0.0 1.9

1,2-Dibromo

ethane (EDB) 3

MCL-US 0.05 μg/L 235 0.0 3 0 84 0 0.0 83 0.0 0.0 0.0 1.9

Herbicides

Simazine MCL-CA 4 μg/L 418 0.0 0.0 86 0.0 0.0 81 0.0 0.0 0.0 2.0

Insecticide

Diazinon HAL-US 1 μg/L 175 0.6 0.0 84 1.2 0.0 83 1.2 0.0 0.0 1.9

1 Based on the most recent data for each CDPH well during the period August 30, 2001 to September 1, 2004, combined with GAMA grid- and understanding-well data.2 Based on the Jeffrey's interval for the binomial distribution (Brown and others, 2001).3 The high value was reported in the CDPH database between August 30, 2001 to September 1, 2004, but the high value was not the most recently reported value used for calculating aquifer-scale 

proportion. 
22 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project Organic constituent class Benchmark type Sum of organic and special interest compounds VOC and gasoline additives (excluding fumigants) Fumigants Pesticides and degradates Polar pesticides and degradates Potential wastewater indicator compounds Special interest compounds Number of constituents Number of constituents Number of constituents Number of constituents Number of constituents Number of constituents Number of constituents Analyzed Detected Analyzed Detected Analyzed Detected Analyzed Detected Analyzed Detected Analyzed Detected Analyzed Detected MCL 50 15 29 13 4 0 3 2 9 0 4 0 1 0 HAL 33 2 6 0 1 0 14 2 9 0 2 0 1 0 NL 17 1 15 0 0 0 0 0 0 0 1 1 1 0 RSD5 7 0 2 0 2 0 3 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SMCL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 None 148 8 27 0 2 0 41 1 35 2 43 5 0 0 Total: 255 26 79 13 9 0 61 5 53 2 50 6 3 0 Inorganic constituent class Benchmark type Sum of inorganic constituents Major and minor ions Nutrients Trace elements Radioactive constituents Number of constituents Number of constituents Number of constituents Number of constituents Number of constituents Analyzed Detected Analyzed Detected Analyzed Detected Analyzed Detected Analyzed Detected MCL 23 23 1 1 2 2 12 12 8 8 HAL 4 4 0 0 1 1 3 3 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 6 6 3 3 0 0 3 3 0 0 None 13 13 7 7 3 3 3 3 0 0 Total 50 50 11 11 6 6 25 25 8 8 Organic and inorganic constituents combined 305 76 Table 5. Number of constituents analyzed and detected with associated benchmarks in each constituent class, North San Francisco Bay study unit, California, August to December 2004. [AL, U.S. Environmental Protection Agency (USEPA) action level; CDPH, California Department of Public Health; HAL, USEPA lifetime health advisory; MCL, USEPA or CDPH maximum contaminant level; NL, CDPH notification level; RSD5, USEPA risk specific dose at 10–5; SMCL, USEPA or CDPH secondary maximum contaminant level; VOC, volatile organic compound]
Methods 23

Two statistical approaches, grid-based and spatiallyweighted, were applied to evaluate the proportions of the 

primary aquifers in the NSF study unit with high, moderate, 

and low relative-concentrations of constituents. For ease of 

discussion, these proportions are referred to as high, moderate, 

and low aquifer-scale proportions. Calculations of aquiferscale proportions were made for individual constituents 

meeting the criteria for inclusion in the status assessment, 

and for classes of constituents. Classes of constituents with 

health-based benchmarks included trace elements, radioactive 

constituents, nutrients, major and minor elements, solvents, 

other VOCs, herbicides, and insecticides. Aquifer-scale 

proportions were also calculated for the following constituents 

having aesthetic (SMCL) thresholds: total dissolved solids, 

chloride, manganese, and iron. 

The grid-based calculation uses the grid-well dataset 

assembled from the USGS grid and CDPH grid wells. For 

each constituent, the high aquifer-scale proportion was 

calculated by dividing the number of cells represented by a 

high value for that constituent by the total number of grid cells 

with data for that constituent. The moderate and low aquiferscale proportions were calculated similarly. Confidence 

intervals for the high aquifer-scale proportions were computed 

using the Jeffrey’s interval for the binomial distribution 

(Brown and others, 2001). The grid-based estimate is spatially 

unbiased. However, the grid-based approach may not detect 

constituents that are present at high concentrations in small 

proportions of the primary aquifers. For calculation of high 

aquifer-scale proportion for a class of constituents, cells were 

considered high if any of the constituents had a high value. 

Cells were considered moderate if any of the constituents had 

a moderate value, but none had a high value.

The spatially-weighted calculation uses the dataset 

assembled from all of the CDPH and USGS wells. For each 

constituent, the high aquifer-scale proportion was calculated 

by computing the proportion of “high” wells in each cell and 

then averaging the proportions for all the cells (Isaaks and 

Srivastava, 1989). The moderate aquifer-scale proportion 

was calculated similarly. Confidence intervals for spatiallyweighted detection frequencies of high concentrations are not 

described in this report. For calculation of high 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, but not a high value.

In addition, for each constituent, the detection frequencies 

of high and moderate values for individual constituents were 

calculated using the same dataset as used for the spatiallyweighted calculations. However, raw detection frequencies 

are not spatially unbiased because the wells in the CDPH 

database are not uniformly distributed (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 aquiferscale proportions (see Appendix D for details of statistical 

methods). 

Constituent

Benchmark

Unit

Date of most 

recent high 

concentration 

Number of 

historically 

high wells 1

Number of 

wells 1 Type Value

Trace element

Cadmium MCL-US 5 μg/L 6/28/1988 1 532

Chromium MCL-CA 50 μg/L 8/13/1987 1 513

Copper AL-US 1,300 μg/L 8/25/1999 1 449

Thallium MCL-US 2 μg/L 8/11/1999 1 496

Radioactive constituent

Radium-226 MCL-US 5 pCi/L 10/10/1996 2 18

Trihalomethane

Chloroform MCL-US 2 80 μg/L 8/30/1993 2 518

Solvent

1,1-Dichloroethane MCL-CA 5 μg/L 11/24/1987 1 518

Dichloromethane (methylene chloride) MCL-US 5 μg/L 11/2/1989 2 517

Constituent of special interest

Perchlorate MCL-CA 6 μg/L 6/14/2000 1 53

1

 Based on historical CDPH well data for May, 16, 1974–August 29, 2001.

2

 The MCL-US benchmark for trihalomethanes is the sum of the concentrations of chloroform, bromoform, bromodichloromethane, and 

dibromochloromethane.

Table 6. Constituents with historically high concentrations recorded in the California Department of Public Health (CDPH) database.

[High concentration, above human-health benchmark; AL-US, U.S. Environmental Protection Agency (USEPA) action level; MCL-CA, CDPH maximum 

contaminant level; MCL-US; USEPA maximum contaminant level; NL-CA, CDPH notification level; μg/L, microgram per liter; pCi/L, picocurie per liter]
24 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

The grid-based high aquifer-scale proportions were used 

to represent proportions in the primary aquifers unless the 

spatially-weighted proportions were significantly different 

than the grid-based values. Significantly different results were 

defined as follows:

• If the grid-base 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 arise when the concentration of a 

constituent is high in a small fraction of the primary 

aquifers.

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

nonzero and the spatially-weighted proportion was 

outside the 90-percent confidence interval (based on 

the Jeffrey’s 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 limits for many organic 

constituents and some inorganic constituents in the CDPH 

database were higher than the boundary 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-weighed 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 tops of the well perforations, normalized position 

along flow path, classified groundwater age, and geochemicalcondition (see Appendix E for more details)—were analyzed 

in relation to constituents of interest for the understanding 

assessment in order to establish context for physical and 

chemical processes 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 understanding factors influencing 

water quality are shown in figures. 

The wells selected for the understanding assessment 

were USGS grid and CDPH grid wells and USGSunderstanding wells. CDPH DPH wells were not used in the 

understanding assessment because carbon isotope, tritium, 

dissolved oxygen, and some well construction data were 

not available. Correlations between explanatory factors and 

groundwater constituents were tested using either the set of 

grid plus understanding wells or grid wells 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. However, 

six USGS-understanding wells were selected for analyses of 

relations between constituents and the vertically-distributed 

explanatory factors (depth, groundwater-age classification, 

and geochemical conditions). TDS was measured directly or 

calculated from specific conductance (see Appendix C).

Statistical Analysis

Nonparametric statistical methods were used to test the 

significance of correlations between water-quality variables 

and potential explanatory factors. Nonparametric statistics 

are robust techniques that are generally 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). Correlations 

were investigated using Spearman’s method to calculate the 

rank-order correlation coefficient (ρ) between continuous 

variables. The values of ρ can range from +1.0 (perfect 

positive correlation) to 0.0 (no correlation) to –1.0 (perfect 

negative correlation). 

The Wilcoxon rank-sum test was used to evaluate the 

correlation between water quality and categorical explanatory 

factors: for example, groundwater age (modern, mixed, or premodern), redox conditions (oxic, mixed, or anoxic/suboxic), 

and land-use classification (natural, agricultural, urban, or 

mixed). The Wilcoxon rank-sum test can be used to compare 

two independent populations (data groups or categories) to 

determine whether one population contains larger values 

than the other (Helsel and Hirsch, 2002). The null hypothesis 

for the Wilcoxon rank sum test is that there is no significant 

difference between the values of the two independent data 

groups being tested. The Wilcoxon rank sum test was used 

for multiple comparisons of two independent groups rather 

than the multiple-stage Kruskal-Wallis test for identifying 

differences between three or more groups, although a set of 

Wilcoxon rank sum tests is more likely to falsely indicate a 

significant difference between groups than the Kruskal-Wallis 

test (Helsel and Hirsch, 2002). However, given the potentially 

large and variable number of differences to be evaluated, 

the Wilcoxon rank sum test was selected as a consistent 

and practical direct test of differences. Because of the small 

sample size, the exact distribution with continuity correction 

also was applied.
Potential Explanatory Factors 25

Potential Explanatory Factors

Land Use 

Land use around USGS grid wells (radius of 1,640 ft 

[500 m]) in the NSF study unit was dominated by natural 

landscape (40 percent of the area) and agricultural land 

(38 percent of the area). Only 22 percent of the area was urban 

land (fig. 6A). Land classified as natural also made up the 

greatest percentage of the total land area (taking into account 

the entire area of the study unit rather than just the area around 

grid wells)(figs. 6A, 7). In the WG study area, 27 percent of 

the land within 1,640 ft (500 m) of grid wells was urban, but 

in the whole study areas, only 10 percent was urban (fig. 6A). 

The higher percentage of urbanized land surrounding the 

grid wells reflects the co-location of public-supply wells 

and population density. The area surrounding grid wells, 

particularly for the WG study area, may reflect greater urban 

influence than might be expected on the basis of the average 

land use of the study area (fig. 7).

Well Depth

Well construction information was available for 73 of 

the 83 grid wells sampled in the NSF study unit. Grid wells 

had depths ranging from 60 to 864 ft (18 to 263 m) below 

land surface; the median was 317 ft (97 m) (fig. 8; table B1). 

Depths to the tops of the perforations ranged from 20 to 690 ft 

(6 to 210 m) with a median of 95 ft (29 m). The perforation 

length was up to 560 ft (171 m) with a median of 155 ft 

(47 m). The wide range in construction depths reflects the 

geological differences between the VP, VOL, and WG study 

areas. The understanding wells, 5 out of 6 of which were 

public supply wells, have ranges in well depth and depth to top 

of perforations (fig. 8) very similar to those of the grid wells. 

Normalized Position of Wells along Flowpaths

Wells were sampled along three river valleys—the Napa, 

the Sonoma, and the Russian River systems—in order to 

assess the effects that position of wells along flow paths had 

on groundwater quality (“Normalized position of wells along 

flow paths” in Appendix E; table B1). This study examined the 

change in concentrations of major and minor ions, and trace 

elements as a function of the normalized position of wells 

along groundwater flowpaths. Types of wells considered for 

these flow paths were grid, understanding, and CDPH-other 

wells. There were 13 wells with designated position along 

the Napa flow-path, 7 wells along the Sonoma flow-path, and 

12 wells along the Russian River (Alexander Valley to the 

Lower Russian River Valley) flow-path (fig. 9). 

Age Classification

Groundwater samples were assigned age classifications 

on the basis of the tritium, carbon-14, and helium-4 content of 

the samples (Appendix E: “Groundwater Age Classification”). 

Age classifications were assigned to 87 USGS grid- and 

understanding-well groundwater samples; 14 were classified 

as modern, 23 were mixed, and 50 were pre-modern age 

(table E2). Samples from 10 wells could not be classified 

because the age-tracer data was incomplete or did not meet 

quality-assurance checks. 

Median groundwater ages generally increased with 

depth to the top of the well perforations (fig. 10A). The depth 

to the top perforations was significantly shallower in wells 

having modern age distributions compared to pre-modern age 

distributions (fig. 10A). The well depths of wells classified 

as modern were significantly shallower than those of wells 

classified as mixed or pre-modern (table B1, E2, fig. 10B). 

Water in five of the eleven wells perforated entirely within 

the upper 100 ft (30 m) of the aquifer was modern age, while 

water in most wells with perforations ≥100 ft (30 m) below 

land surface was pre-modern (fig. 10C). Water in wells 

with the top perforations <100 ft (30 m) and the bottom 

perforations ≥100 ft (30 m) was mostly of mixed age. 

Geochemical Condition and Water Temperature

An abridged classification of oxidation-reduction 

(redox) conditions adapted from the framework presented by 

McMahon and Chapelle (2008) for 56 wells sampled by the 

USGS–GAMA Priority Basin Project is given in Appendix E; 

table E1. The classification “indeterminate” was added to 

the framework for groundwater samples that did not have 

sufficient data available to be classified as oxic, anoxic/

suboxic, or mixed anoxic/oxic. Groundwater was anoxic/

suboxic (suboxic to anoxic) in 44 percent of the wells, mixed 

anoxic/oxic in 24 percent of the wells, and oxic in 32 percent 

of the wells. 

Correlations between Explanatory Factors

Apparent correlations between explanatory factors and 

a water-quality constituent could actually reflect correlations 

between two or more explanatory factors. Therefore, it is 

important to identify statistically significant correlations 

between explanatory factors (table 7).

Percent agricultural land use is significantly correlated 

(negatively) with normalized position along the flowpath 

(table 7). This correlation reflects agricultural land-use 

dominating in the proximal sections of the valleys (fig. 7).
26 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Figure 8. Construction characteristics for grid wells and understanding wells, North San Francisco Bay study unit, California.

Well

depth

Top of

perforations

Perforation

length

Well

depth

Top of

perforations

Perforation

length

Grid wells

72

Understanding wells

62 61 6 5 5

61 Number of wells with data

75th percentile

Median

25th percentile

25th percentile minus

1.5 * interquartile range

75th percentile plus

1.5 * interquartile range

Upper detached

Lower detached

1,000

800

600

400

200

0

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

EXPLANATION

Data value less than

 or equal to 1.5 times

 the interquartile range

Data value more than

 or equal to 1.5 times

 the interquartile range
Potential Explanatory Factors 27

Lake Berryessa

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

STUDY AREA

EXPLANATION

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

Generalized direction

 of groundwater flow

 (flowpath)

 

Russian River

flowpath

Sonoma Valley

flowpath

Napa Valley

flowpath

USGS Grid well

USGS Understanding well

Well with desiginated

 position along flowpath

San Pablo Bay

Pacific Ocean

Figure 9. Normalized position of wells along a flowpath in the North San Francisco Bay study unit, California.
28 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Modern

GROUNDWATER AGE

 CLASSIFICATION

Mixed

Pre-modern

NUMBER OF WELLS

DEPTH OF

PERFORATIONS

BELOW LAND

SURFACE

0 5 10 15 20 25

Entire perforated

interval less than

 100 feet 

Entire perforated

interval greater

than or equal to

100 feet 

Top of perforated

interval less than

100 feet; bottom of

perforated interval

greater than or

equal to 100 feet 

11 23 35 0

200

400

DEPTH TO TOP OF PERFORATIONS,

IN FEET BELOW LAND SURFACE

600

800

GROUNDWATER AGE CLASSIFICATION

0 12

200

400

WELL DEPTH, 

IN FEET BELOW LAND SURFACE 600

800

1,000

1,000

23 42

GROUNDWATER AGE CLASSIFICATION

11 Number of wells with data

EXPLANATION

EXPLANATION

75th percentile

Median

25th percentile

25th percentile minus

1.5 * interquartile range

75th percentile plus

1.5 * interquartile range

Upper detached

Lower detached

A

B

C

Modern Mixed Pre-modern

Modern Mixed Pre-modern

Figure 10. The relation of classified groundwater age to A. depth to top of perforations below land surface, B. well depth below land 

surface, and C. age classification in relation to the depth of well perforations, North San Francisco Bay study unit, California.
Potential Explanatory Factors 29

Depth to the top of the perforations is significantly 

correlated (positively) with normalized position along the 

flowpath, which may reflect deeper perforations in wells 

towards the terminal end of the valleys (table 7). Depth to the 

top perforations is significantly correlated (positively) with 

well depth also. Depth to the top perforations is significantly 

correlated (positively) with pH also, suggesting that higher pH 

in water is found deeper in the primary aquifers. 

Figure 11 presents a cross section of well perforations 

and well redox classification plotted as normalized position 

of wells along the flowpath on the x-axis, and depth of the 

perforation interval on the y-axis. This figure was derived 

from data for all of the flow-path wells plotted on a single 

composite flow path (see Appendix E for details). Shallow 

groundwater in the upgradient part of the flowpath is oxic 

trending towards less oxic water deeper in the aquifer and 

farther along the flowpath. The distal (downgradient) part of 

the normalized flowpath has mainly anoxic/suboxic and mixed 

conditions. The range of geochemical conditions may reflect 

natural spatial variability and mixing of water with variable 

reduction-oxidation characteristics of water in wells that have 

long perforated intervals.

The pH ranged from 4.1 to 9.4 in the USGS grid wells, 

USGS-understanding wells, and CDPH-other wells (fig. 12A). 

The relationship between pH and well depth is shown in 

figure 12B. Two trends are apparent. The pH of groundwater 

classified as pre-modern is generally higher (median = 7.4; 

n = 34) than modern groundwater (median = 6.9; n = 10). 

Second, groundwater classified as pre-modern tends to be 

from deeper wells, and groundwater classified as modern tends 

to be from shallower wells. The higher pH water (pH > 7.4) 

was classified as predominantly pre-modern age and most 

was from wells deeper than 200 ft (64 m) (fig. 12B), while the 

lower pH groundwater was characterized as shallow and of 

modern or mixed age.

Dissolved oxygen is significantly correlated (negatively) 

with pH, reflecting the decrease in oxygen in waters with 

reducing conditions (table 7). Concentrations of DO were 

significantly correlated (negatively) with normalized position 

of wells along flowpaths, consistent with the visual patterns 

evident in figure 11. Wilcoxon tests indicated significantly 

higher DO in wells classified as having water of modern ages, 

as compared with wells having water of pre-modern ages 

(table 8). 

The pH is significantly correlated (positively) with 

normalized position along flowpath and depth to top of 

perforations (table 7), suggesting that the ion balance 

changes so that groundwater is less acidic in the distal part 

of the valleys and in deep aquifers. Wilcoxon tests indicated 

significantly lower pH in wells classified as having modern 

and mixed ages compared to pre-modern ages (table 8). 

Groundwater found deeper in the aquifer, or in the distal 

parts of a flow path, generally has longer residence times than 

groundwater from shallow or proximal sites.

Implications of correlations between explanatory 

variables are discussed later in the report as part of the 

analysis of factors affecting individual constituents.

Type of well analyzed Explanatory factor

Normalized 

position 

along 

flowpath

Depth to 

top of 

perforations 

Depth 

of well

Dissolved 

oxygen 

concentration pH

ρ: Spearman’s correlation statistic

Grid wells Percent urban land use nc nc nc nc nc

Grid wells Percent agricultural land use –0.37 nc nc nc nc

Grid wells Percent natural land use nc nc nc nc nc

Grid wells Normalized position along 

flowpath

0.56 nc –0.50 0.43

Grid and understanding wells 1 Depth to top of perforations 0.62 0.68 nc 0.34

Grid and understanding wells 1 Depth of well nc 0.68 nc nc

Grid and understanding wells 1 Dissolved oxygen concentration –0.50 nc nc –0.64

Grid and understanding wells 1 pH 0.42 0.34 nc –0.64

Table 7. Results of nonparametric analysis (Spearman’s method) of correlations between selected potential explanatory factors, 

North San Francisco Bay study unit, Callifornia.

[ρ, Spearman's correlation statistic; significant positive correlation and significant negative correlations shown. nc, no significant correlation]

1

Understanding wells do not include hydrothermal wells or spring data.
30 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Status and Understanding of 

Water Quality 

The status assessment was designed to identify the 

constituents or classes of constituents most likely to be water 

quality concerns by virtue of their high concentrations or 

their prevalence. Approximately 28,000 individual analytical 

results were included in the assessment of groundwater quality 

for the NSF study unit. The status assessment applies only to 

constituents having regulatory or nonregulatory 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 

public-supply aquifers are weighted equally; regions with 

a high density of groundwater use or with high density of 

potential contaminants were not preferentially sampled. The 

understanding assessment identifies the natural and human 

factors affecting water quality in the NSF study unit, and 

focuses on the constituents identified as significant in the 

status assessment.

0

200

400

600

800

0 0.2 0.4 0.6 0.8 1.0

NORMALIZED POSITION ALONG FLOW PATH

DEPTH, IN FEET BELOW LAND SURFACE

Figure 11.

Top

Bottom

Perforated interval Oxic

Oxidation-reduction classification

EXPLANATION

Mixed Anoxic/suboxic

North (proximal) South (distal)

Upgradient Downgradient

Figure 11. The relation of oxidation-reduction condition to normalized position of wells along a flowpath, and depth of perforated 

interval of wells, North San Francisco Bay study unit, California.
Status and Understanding of Water Quality 31

San Pablo Bay

Pacific Ocean

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection EXPLANATION

Study unit and

study area boundary

USGS grid well

pH, IN STANDARD UNITS

6.5 to 7.4

7.5 to 7.9

8.0 to 9.4

CDPH well

4.1 to 7.4

7.5 to 7.9

8.0 to 8.8

A

Lake Berryessa

USGS National Land Cover Data

(Price and others, 2003)

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

 

Figure 12A. pH levels in U.S. Geological Survey (USGS) grid wells, USGS-understanding wells, and California Department of Public 

Health (CDPH) wells, North San Francisco Bay study unit, California.
32 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

The following discussion of the status and understanding 

assessment results is divided into inorganic and organic 

constituents. The 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 then are presented for the subset of constituents 

that met criteria for inclusion based on concentration, or for 

organic constituents, prevalence. 

Spatially-weighted high aquifer-scale proportions fell 

within the 90-percent confidence intervals for their respective 

grid-based aquifer-scale high proportions for all of the 

15 constituents listed on table 4, providing evidence that the 

grid-based approach yields statistically equivalent results to 

the spatially-weighted approach. 

6.0 7.0 8.0 9.0

0

200

400

600

800

1,000

pH, IN STANDARD UNITS

DEPTH, IN FEET BELOW LAND SURFACE

Modern

Mixed

Pre-modern

B

EXPLANATION

GROUNDWATER AGE CLASSIFICATION

Figure 12B. pH as a function of well depth and groundwater age classification, North San Francisco Bay study unit, California.
Status and Understanding of Water Quality 

33

Selected 

water-quality 

constituents

Potential explanatory factors

Groundwater age classification Geochemical conditions classifications Land-use classifications

Modern age 

compared 

with mixed 

age

Mixed age 

compared 

with premodern age

Modern age 

compared 

with premodern age

Oxic 

conditions 

compared 

with mixed 

conditions

Mixed 

conditions 

compared 

with anoxic/

suboxic 

conditions

Oxic conditions 

compared with 

anoxic/suboxic 

conditions

Natural 

compared 

with 

agricultural 

land use

Natural 

compared 

with mixed 

land use

Natural 

compared 

with urban 

land use

Agricultural 

compared 

with mixed 

land use

Agricultural 

compared 

with urban 

land use

Urban 

compared 

with mixed 

land use

Z: Test statistic for Wilcoxon test

Arsenic nc nc –2.38 nc nc nc nc nc nc nc nc ncBoron nc nc nc nc nc nc nc nc nc nc nc ncIron nc nc –2.20 –3.62 nc nc nc nc nc nc nc ncLead nc nc nc 2.49 nc nc nc nc nc nc nc ncManganese –2.04 nc –2.34 –2.23 nc nc nc nc nc nc nc ncTotal dissolved 

solids

nc nc –2.59 nc nc nc nc nc nc nc nc nc

Dissolved oxygen nc nc 2.12 nc 2.69 4.30 nc nc nc nc nc nc

pH nc –2.51 –3.03 –2.05 nc –2.82 nc nc nc nc nc nc

Nitrate plus nitrite, 

as nitrogen

nc 3.67 3.31 nc 2.86 2.98 nc nc nc nc nc 2.25

Chloroform nc 2.33 nc nc 2.06 2.06 nc –2.56 –2.04 nc nc nc

Solvents, sum of 

concentrations (PCE + TCE + 1,1-dichloroethane)

nc nc 2.68 nc nc nc nc nc nc nc nc nc

Simazine 2.68 2.34 5.04 nc nc nc nc nc nc nc nc nc

Table 8. Results of Wilcoxon rank sum tests on grid-well data used to determine significant differences between constituent values grouped by explanatory factor classes, North San Francisco Bay study unit, California.[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); nc, no significant correlation; PCE, tetrachloroethene; TCE, trichloroethene]
34 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Inorganic Constituents

Inorganic constituents generally occur naturally in 

groundwater, although their concentrations may be influenced 

by human as well as natural factors. All 50 inorganic 

constituents analyzed by the USGS–GAMA were detected 

in the NSF study unit, of which 31 had regulatory or 

nonregulatory health-based benchmarks, 6 had nonregulatory 

aesthetic/technical-based benchmarks, and 13 had no 

established benchmarks (table 5). The inorganic constituents 

detected at high relative-concentrations in one or more 

grid wells were arsenic, boron, lead, iron, manganese, and 

total dissolved solids (table 4). The maximum relativeconcentration (sample concentration divided by the benchmark 

concentration) for each constituent is indicated in figure 13.

 Nine inorganic constituents, the trace elements arsenic, 

boron, lead, mercury, molybdenum, iron and manganese; 

the nutrient nitrate (as nitrogen); and TDS, met the selection 

criterion of having maximum relative-concentrations greater 

than 0.5 in the grid-based aquifer proportions (fig. 13; 

table 4). Inorganic constituents, as a group (nutrients, trace 

elements, and radioactive constituents), had high proportions 

in 14.0 percent of the primary aquifers, moderate proportions 

in 35.8 percent, and low proportions in 50.2 percent (table 9). 

High aquifer proportion equals the percentage (%) of grid 

wells with high relative-concentrations, and therefore the 

percentage (%) of the primary aquifers with high values.

Trace Elements

Trace elements, as a class, had high relativeconcentrations (for one or more constituents) in 13.6 percent 

of the primary aquifers, moderate values in 33.3 percent, 

and low values in 53.1 percent (table 9). High relativeconcentrations of trace elements reflected high relativeconcentrations of arsenic (10.0 percent), boron (4.1 percent), 

and lead (1.6 percent) (table 4).

Figure 14 shows maximum relative-concentrations of 

inorganic constituents in grid wells. To illustrate the spatial 

distributions of inorganic constituents, figures 15A–F are 

maps showing arsenic, boron, lead, nitrate, manganese, iron, 

and TDS data for USGS grid wells and CDPH wells from the 

period August 30, 2001 to September 1, 2004.

Table 4 lists the percentage of the primary aquifers with 

high and moderate relative-concentrations for the individual 

constituents. Ten percent of the primary aquifers had high 

relative-concentrations of arsenic and 25 percent had moderate 

relative-concentrations (table 4). High and moderate relativeconcentrations of arsenic occurred in all three study areas 

(figs. 14, 15A). 4.1 percent of the primary aquifers had 

high relative-concentrations of boron and 5.4 percent had 

moderate relative-concentrations. Lead was detected at a high 

relative-concentration in one grid well (1.6 percent of the 

primary aquifers) and at a moderate relative-concentration 

in one grid well (1.6 percent of the primary aquifers), both 

located in the Valley and Plains study area (figs. 14, 15C). 

Mercury had high relative-concentration in 0 percent of the 

primary aquifers and moderate relative-concentrations in 1.4 

percent. Molybdenum had high relative-concentration in 0 

percent of the primary aquifers and moderate values in 3.1 

percent.

Three trace elements—aluminum, antimony, and 

nickel—had spatially-weighted high relative-concentrations 

in 0.6 percent, 0.7 percent, and 0.2 percent of the primary 

aquifers, respectively, as compared to 0 percent for these 

elements for the grid-based approach (table 4). The spatiallyweighted approach includes data from a larger number of 

wells than the grid-based approach, and therefore is more 

likely to include constituents present at high concentrations in 

small proportions of the primary aquifers.

The trace elements cadmium, chromium, copper, and 

thallium had high relative-concentrations in at least one well 

reported in the CDPH database before 2001 but not during 

the current period of study (table 6), and these high values 

represented historic values rather than current values.

Among constituents with SMCLs, iron had a high 

relative-concentration in 24.4 percent and a moderate relativeconcentration in 9.0 percent of the primary aquifers (table 4, 

fig. 14). High relative-concentrations of iron were in a greater 

percentage of the VP study area (20 percent) than in the VOL 

study area (5 percent) or the WG study area (0 percent). 

Manganese had a high relative-concentration in 40.8 percent 

and a moderate relative-concentration in 7.9 percent of the 

primary aquifers. High relative-concentrations of manganese 

were in a greater percentage of the VP study area (43 percent) 

than in the VOL study area (11 percent) or the WG study area 

(0 percent).

Understanding Assessment for Arsenic

 Arsenic is a naturally occurring semi-metallic trace 

element. Potential sources of arsenic to groundwater are 

both natural and anthropogenic. Natural sources include the 

dissolution of arsenic-rich minerals, such as arsenian pyrite, 

a common constituent of shales, and apatite, a common 

constituent of phosphorites. Arsenic can also be used as a 

wood preservative, in glass production, in paints, dyes, metals, 

drugs, soaps, and semiconductors, and in mining copper and 

gold (Welch and others, 2000). Arsenic solubility increases 

with increasing water temperature, such that hydrothermal 

fluids often have higher arsenic concentrations (Ballantyne 

and Moore, 1988; Webster and Nordstrom, 2003), as do older 

groundwaters that have had extended exposure to arsenic 

bearing minerals. 
Status and Understanding of Water Quality 35

0.001

0.01

0.1

1

10

100

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

 (more than 25 grid wells sampled) 

EXPLANATION

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

 (fewer than 25 grid wells sampled) 

Radon-222

Moderate

Low

Moderate

Low

Moderate

High High

Organic and special

interest consitituents

Inorganic

consitituents

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Volatile

organic

compounds

Fumigants Pesticides Wastewater

indicator

compounds

Nutrients Trace

elements

Major and

minor ions

and trace

elementsSMCL

Radioactive

constituents

Number of 

compounds

detected

< 0.001 2 0 1 0 0 0 0 0

Simazine

Naphthalene

Benzene

Carbon tetrachloride

Bromodichloromethane

Dibromochloromethane Bromoform

Chloroform

Methyl tert-butyl ether

Diazinon

Atrazine

Nitrite-N

Ammonia-N

Nitrate-N

Iron

Manganese

Silver

TDS

Zinc

Sulfate

Chloride

Fluoride

Molybdenum

Boron

Lead

Copper

Radium-226

Radon-222

Radium-228

Gross alpha

Gross beta

Vanadium

Cadmium

Antimony

Chromium

Selenium

Uranium

Beryllium

Arsenic

Mercury

Aluminum

Barium

Nickel

Thallium

Strontium

Figure 13. Maximum relative-concentration of constituents, by constituent type, detected in grid wells in the North San Francisco Bay 

study unit, California. N, nitrogen; TDS, total dissolved solids; SMCL, secondary maximum contaminant level.
36 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Arsenic mobilization and distribution in groundwater 

is influenced by the oxidation-reduction (redox) and pH 

conditions of the groundwater system. Arsenic is stable in 

two oxidation states in the environment: arsenite (As+3) 

and arsenate (As+5). Over a wide pH range and under oxic 

conditions, arsenate (As+5) is predicted to be the predominant 

species, whereas under more reducing (anoxic) conditions, 

arsenite (As+3) would likely be predominant (Welch and 

others, 1988). Laboratory reaction experiments by Islam and 

others (2004) indicate that arsenite was the dominant arsenic 

species resulting from reductive dissolution of iron oxides by 

microbial activity and the addition of organic carbon, even 

though the solid-phase arsenic was in the form of arsenate.

Previous investigations (Belitz and others, 2003) and 

reviews of arsenic (for example, Welch and others, 2000, 

2006; Frankenberger, 2002; Ravenscroft and others, 2009) 

have attributed elevated arsenic concentrations in groundwater 

to two mechanisms: the release of arsenic from dissolution 

of iron or manganese oxides under iron- or manganesereducing conditions, and the desorption of arsenic from 

aquifer sediments, or inhibition of arsenic sorption to aquifer 

sediments, as a result of alkaline groundwater conditions (pH 

values greater than 8.0).

Evidence for the first mechanism, release of arsenic 

under reducing conditions, in NSF groundwaters includes the 

association of high and moderate concentrations of arsenic 

with groundwater having manganese- or iron-reducing 

conditions, and the occurrence of arsenite as the predominant 

arsenic species in these groundwaters. Concentrations of 

arsenic were >10 μg/L (high relative-concentration) in 8 wells; 

trace element and pH data were available for 5 of these wells 

(fig. 16B). Two of the 5 wells having arsenic concentrations 

>10 μg/L had pH ≤7.5, manganese concentrations >100 μg/L 

(manganese-reducing conditions), and iron concentrations 

>100 μg/L (iron-reducing conditions). Eight of the 20 wells 

that had moderate relative-concentrations of arsenic (5 to 

10 μg/L) had manganese concentrations >50 μg/L, and 7 of 

these wells had iron concentrations >100 μg/L, suggesting 

that reductive dissolution of iron and manganese oxides may 

account for the high and moderate concentrations of arsenic in 

these wells. 

Constituent class Not detected Aquifer proportion, in percent

Low Moderate High

Inorganic constituents with human-health benchmark

Trace elements 0.0 53.1 33.3 13.6

Radioactive 0.0 100.0 0.0 0.0

Nutrients 0.0 97.0 2.6 1 0.4

Major and minor elements 0.0 98.7 1 0.1 1 1.2

Total for inorganic constituents with human-health benchmarks 0.0 50.2 35.8 1 14.0

Inorganic constituents with aesthetic benchmark

Total dissolved solids (SMCL) 0.0 91.7 7.1 1.2

Iron and manganese (SMCL) 0.0 39.8 17.9 42.3

Chloride (SMCL) 0.0 99.3 0.0 1 0.7

Organic constituents with human-health benchmark

Solvents 84.1 12.0 1 2.5 1 1.4

Other VOC 93.9 4.8 1.2 1 0.1

Herbicides 88.0 12.0 0.0 0.0

Insecticide 98.8 0.0 1.2 0.0

Total for organic constituents with human-health benchmarks 64.8 28.9 1 4.9 1 1.4

Constituents of special interest

Perchlorate and N-nitrosodimethylamine (NDMA) 100.0 0.0 0.0 0.0

1

 Spatially-weighted value.

Table 9. Aquifer-scale proportions for constituent classes, North San Francisco Bay study unit, California.

[High, concentrations greater than benchmark; moderate, concentrations less than benchmark and greater than or equal to 0.1 of benchmark for organic 

constituents or 0.5 of benchmark for inorganic constituents; low, concentrations less than 0.1 of benchmark for organic constituents or 0.5 of benchmark for 

inorganic constituents. SMCL, secondary maximum contaminant level; VOC, volatile organic compound. Values are grid based except where footnoted]
Status and Understanding of Water Quality 37

Arsenic speciation data was collected at 15 wells 

(Kulongoski and others, 2006, table 17). Arsenite (As+3) 

was the dominant arsenic species in four wells that had 

iron concentrations >100 μg/L; arsenate (As+5) was the 

dominant arsenic species in the other 11 wells, which had iron 

concentrations <100 μg/L. The presence of arsenite as the 

dominant dissolved arsenic species in iron-reduced water in 

the NSF is consistent with arsenic mobilization under reducing 

conditions.

Evidence for the second mechanism, desorption of 

arsenic from aquifer sediments or inhibition of arsenic 

sorption to aquifer sediments with increasing pH, in 

15 out of 25 wells that had high and moderate relativeconcentrations of arsenic, includes pH conditions ≥7.4, 

manganese concentrations <100 μg/L, and iron concentrations 

<100 μg/L. The pH in 2 of the wells was >8.0, indicating that 

alkaline conditions may contribute to high concentrations of 

arsenic in these wells. Under oxic conditions, it is expected 

that dissolved arsenate would be the dominant species in 

groundwater (Islam and others, 2004) and arsenate would be 

the dominant species in most of the NSF groundwater with 

iron concentrations <100 μg/L. 

STUDY AREA

0.001

0.01

0.1

1

10

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Total

dissolved

solids1

Iron Manganese Mercury MolybdenumNitrate

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation Highlands

Chloride Arsenic Boron Lead 1

Constituents that have secondary

maximum contaminant levels

Constituents that have health-based thresholds

(Maximum Contaminant Level, Health Advisory Level,

Notification Level, 1 secondary threshold) 

High

Moderate

Low

Figure 14. Maximum relative-concentrations of selected trace elements, nutrients, and major and minor ions in grid wells, North San 

Francisco Bay study unit, California.
38 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

CDPH well USGS grid well

0-5

5.1-10

10.1-98

ARSENIC, IN MICROGRAMS PER LITER

0-5

5.1-10

10.1-33

Low

Moderate

High

Relative-concentration

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

A

San Pablo Bay

Pacific Ocean

Figure 15. Relative-concentrations of selected inorganic constituents in U.S. Geological Survey (USGS) grid and USGS-understanding 

wells and California Department of Public Health (CDPH) wells (data from the period August 30, 2001, to September 1, 2004), North San 

Francisco Bay study unit, California: A. arsenic, B. boron, C. lead, D. nitrate plus nitrite, as nitrogen, E. manganese, 

F. iron, and G. total dissolved solids. 
Status and Understanding of Water Quality 39

San Pablo Bay

Pacific Ocean

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

B

0-500

500.1-1,000

1,000.1-4,600

BORON, IN MICROGRAMS PER LITER

0-500

500.1-1,000

1,000.1-3,830

Low

Moderate

High

CDPH well Relative-concentration USGS grid well

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

Figure 15. Continued.
40 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

C

0 .0-7.5

7.6-15.0

15.1-55.0

0.0-7.5

7.6-15.0

15.1-15.3

LEAD, IN MICROGRAMS PER LITER

Low

Moderate

High

CDPH well Relative-concentration USGS grid well

San Pablo Bay

Pacific Ocean

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

Figure 15. Continued.
Status and Understanding of Water Quality 41

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

D

0-5

5.1-10

10.1-52

NITRATE PLUS NITRITE AS NITROGEN, IN MILLIGRAMS PER LITER

0-5

5.1-10

10.1-15

Low

Moderate

High

CDPH well Relative-concentration USGS grid well

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

San Pablo Bay

Pacific Ocean

Figure 15. Continued.
42 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Figure 15. Continued.

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

E

0-25

25.1-50

50.1-1,220

Low

Moderate

High

0-25

25.1-50

50.1-4,600

MANGANESE, IN MICROGRAMS PER LITER

CDPH well Relative-concentration USGS grid well

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

San Pablo Bay

Pacific Ocean
Status and Understanding of Water Quality 43

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

F

0-150

151-300

301-13,000

0-150

151-300

301-1,090

IRON, IN MICROGRAMS PER LITER

Low

Moderate

High

CDPH well Relative-concentration USGS grid well

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

San Pablo Bay

Pacific Ocean

Figure 15. Continued.
44 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Figure 15. Continued.

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

EXPLANATION

G

100-500

501-1,000

1,001-1,400

162-500

501-1,000

1,001-1,500

CDPH well USGS grid well

TOTAL DISSOLVED SOLIDS, IN MILLIGRAMS PER LITER

Low

Moderate

High

Relative-concentration

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

San Pablo Bay

Pacific Ocean
Status and Understanding of Water Quality 45

Maximum contaminant level

0

1,200

TOP OF PERFORATION, IN FEET BELOW LAND SURFACE

10 20 30

ARSENIC CONCENTRATION, IN MICROGRAMS PER LITER 

0.5 * Maximum contaminant level

EXPLANATION

Manganese greater than 100 micrograms per liter 

Iron greater than 100 micrograms per liter

Manganese greater than 100 micrograms per liter

pH less than 7.5

Iron greater than or equal to 100 micrograms per liter

pH greater than 7.2

Manganese less than 100 micrograms per liter

Iron less than 100 micrograms per liter

pH greater than or equal to 7.4

Manganese less than 100 micrograms per liter

Iron less than 100 micrograms per liter

pH less than 7.2

0

200

400

600

800

1,000

GROUNDWATER AGE CLASSIFICATION

EXPLANATION

ARSENIC CONCENTRATION,

IN MICROGRAMS PER LITER

Modern Mixed Pre-modern

11 18 41

0

10

20

30

41 Number of wells with data

75th percentile

Median

25th percentile

25th percentile minus

1.5 * interquartile range

75th percentile plus

1.5 * interquartile range

Upper detached

Lower detached

A

B

Low Moderate High

Figure 16. Arsenic concentration related to A. classifications of groundwater age and B. well depth, manganese and iron 

concentrations, and pH in grid and understanding wells sampled for the North San Francisco Bay study unit, California.
46 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Ten percent of the primary aquifers had high relativeconcentrations of arsenic and 25 percent had moderate 

relative-concentrations (table 4). Arsenic distribution was not 

significantly correlated to either redox classification or pH 

conditions of groundwater in the NSF study unit (tables 8, 

10). This result suggests that several factors, or a combination 

thereof, are influencing arsenic concentrations in groundwater. 

Arsenic concentrations were significantly higher in samples 

having a groundwater age classified as pre-modern, than in 

samples classified as having modern ages (table 8, fig. 16A). 

This suggests that groundwater accumulates arsenic over time 

from longer exposure to arsenic-bearing minerals. Arsenic 

was positively correlated with groundwater temperature. 

The higher arsenic concentrations in warmer groundwater 

may result from the increase in solubility of arsenic with 

increasing water temperature or from mixing with the deeper 

hydrothermal system. The negative correlation of arsenic with 

percent natural land use likely reflects other factors (table 10). 

In the North San Francisco Bay study unit, nearly all of 

the samples with high arsenic relative-concentrations were 

from wells in the Valleys and Plains study area (fig. 15A). 

Areas containing groundwaters that have high arsenic 

concentrations are grouped into four areas: the Santa Rosa 

Valley, the southern Napa Valley, the Calistoga area, and 

the Agua Caliente (Sonoma) area (fig. 2). The locations of 

elevated concentrations of arsenic in the Santa Rosa Valley 

and southern Napa Valley (fig.15A) correspond to areas that 

have manganese- (or iron-) reducing (anoxic) conditions 

(figs. 15E and 15F) and likely result from the release of 

arsenic during reductive dissolution of iron or manganese 

oxides.

The elevated concentrations of arsenic in the Calistoga 

and Agua Caliente hydrothermal areas most likely result 

from shallow groundwater mixing with deep thermal waters. 

Deep thermal waters have high temperatures and high pH 

values. High temperatures will cause arsenic (and boron) to 

leach from nearly any rock (Webster and Nordstrom, 2003), 

whereas alkaline conditions facilitate the desorption of arsenic 

and inhibit the sorption of arsenic to aquifer sediments. The 

increase in arsenic solubility caused by an increase in water 

temperature, and the desorption or inhibition of sorption 

of arsenic under alkaline conditions results in the higher 

arsenic concentrations in groundwater from the Calistoga and 

Agua Caliente hydrothermal areas. A more comprehensive 

investigation of the hydrothermal system in the NSF study unit 

is being prepared and will lead to a separate publication.

Understanding Assessment for Boron

Boron is a naturally occurring metalloid that occurs in 

many minerals. Natural sources of boron include igneous 

rocks, such as granite and pegmatite (as the mineral tourmaline), and evaporite minerals, such as borax, kernite, and 

colemanite (Hem, 1970; Reimann and Caritat, 1998). Borax, 

a boron-containing evaporate mineral that is mined in California, is used as a cleaning agent and therefore may be present 

in sewage and industrial wastes. Seawater contains 4.5 mg/L 

of boron (Summerhayes and Thorpe, 1996), and boron is 

associated with thermal springs (Hem, 1970; Kulongoski and 

others, 2006). Boron also is used to produce semiconductors, 

insecticides, preservatives, and chemical reagents.

Boron speciation in groundwater is dependent on pH, 

salinity, and specific cation content. The neutral form of boron, 

B(OH)3, is predominant at pH <9.2, while the anionic form, 

B(OH)–

4 , is predominant at pH >9.2 (Dotsika and others, 

2006). Boron is highly mobile because no mineral has a low 

enough solubility to provide an upper limit to its concentration 

range. However, boron concentration in groundwater is limited 

by mixing of thermal water with nonthermal waters.

 Boron had high relative-concentrations in 4.1 percent of 

the primary aquifers and was detected at 3 VP wells at high 

relative-concentration, and at 4 wells (3 VP and 1 WG) at 

moderate relative concentrations (fig. 15B). Boron distribution 

was not significantly correlated to redox classification or pH 

(tables 8, 10), nor did it correlate to land use or groundwater 

age classifications.

 In the northern Alexander Valley, higher concentrations 

of boron may result from the infiltration of waters from 

Hot Creek, which drains the Geysers geothermal area near 

Middletown north of Calistoga. Similarly, northern Napa 

Valley has higher boron concentrations, which may result from 

shallow groundwater mixing with the deeper hydrothermal 

system that provides thermal waters to numerous spas and the 

“Old Faithful Geyser” in Calistoga. In the Sonoma and NapaSonoma lowlands, higher boron concentrations may result 

from the mixing of shallow groundwater with the discharge 

from hot springs in Sonoma and Agua Caliente, or with high 

salinity connate water from the dissolution of evaporites. 
Status and Understanding of Water Quality 

47

Selected 

water-quality 

constituent

Benchmark 

type

High 

proportion, 

in percent

Potential explanatory factors

Data analyzed: grid and understanding wells 1 Data analyzed: grid wells

Depth to top of 

perforations Well depth Dissolved oxygen pH Groundwater temperature Normalized position along flowpath Altitude of land surface at well Percent urban land use 2 Percent agricultural land use 2 Percent natural land use 2

ρ: Spearman's correlation statistic ρ: Spearman's correlation statistic

Inorganic constituent

Arsenic 3 MCL-US 10.0 nc nc nc nc 0.24 nc nc nc nc –0.23

Boron 3 NL-CA 4.1 nc nc nc nc nc nc nc nc nc nc

Iron 3 SMCL-CA 24.4 nc nc –0.40 nc nc nc nc nc nc nc

Lead AL-US 1.6 nc nc 0.38 –0.25 nc nc nc nc nc nc

Manganese 3 SMCL-CA 40.8 nc nc nc nc nc nc nc nc nc nc

TDS SMCL-CA 0.7 nc nc nc 0.43 nc nc –0.39 nc nc nc

Nitrate plus 

nitrite (as N)

MCL-US 0.4 nc nc 0.63 nc –0.40 nc nc nc nc –0.24

Organic constituent

Chloroform 4 MCL-US 0.0 nc nc nc –0.28 –0.23 nc nc nc nc nc

Simazine 4 MCL-US 0.0 –0.38 –0.49 nc –0.33 –0.29 nc nc nc nc nc

Table 10. Results of non-parametric (Spearman's method) analysis for correlations between selected water-quality constituents and potential explanatory factors, North San Franciso Bay study unit, California.[AL-US, U.S. Environmental Protection Agency (USEPA) action level, CDPH, California Department of Public Health; MCL-US, USEPA maximum contaminant level; N, nitrogen; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; TDS, total dissolved solids. nc, no significant correlation; ρ, Spearman’s correlation statistic]

 1 Excluding hydrothermal wells and spring. 2 Within a circle with a radius of 500 meters centered around hydrothermic well and spring data. 3 Constituents with greater than 2 percent high aquifer proportion. 4 Single constituents with detection frequency greater than 10 percent of grid wells.
48 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Understanding Assessment for Manganese and Iron

Potential natural sources of manganese and iron 

to groundwater include the dissolution of igneous and 

metamorphic rocks as well as the dissolution of various 

secondary minerals (Hem, 1970). Rocks that contain 

significant amounts of manganese and iron have a relatively 

high composition of the minerals olivine, pyroxene, and 

amphibole. Potential anthropogenic sources of iron and 

manganese to groundwater include effluents associated 

with the steel and mining industries (Reimann and Caritat, 

1998), and soil amendments, in the form of manganese and 

iron sulfates, that are added to deficient soils in order to 

stimulant crop growth. Distributions of iron and manganese 

concentrations are strongly influenced by redox conditions in 

the aquifer. In sediments, the oxyhydroxides of manganese 

and iron are common as coatings on mineral surfaces and as 

suspended particles (Sparks, 1995). These oxyhydroxides are 

stable in oxygenated systems at neutral pH. However, under 

anoxic conditions, the process of reductive dissolution of these 

minerals affects the mobility of manganese and iron in aquifer 

systems (Sparks, 1995). 

In the NSF study unit, concentrations of manganese were 

significantly correlated (negatively) with oxic compared with 

mixed conditions (table 8). Iron was significantly correlated 

(negatively) with DO (table 10) and with oxic compared with 

mixed redox conditions (table 8). Most high and moderate 

relative-concentrations of iron and manganese were in the VP 

study area, particularly the Santa Rosa Valley and the southern 

Napa Valley (figs. 2, 14, 15E, 15F), reflecting the natural 

distribution of iron- and manganese reducing conditions that 

result from reductive dissolution of iron and manganese oxides 

in the aquifer sediments.

Radioactive Constituents

The activities of radioactive constituents generally were 

low in the NSF study unit (table 4). Gross alpha radio-activity 

above the MCL-US of 15 pCi/L was detected in 1 well out of 

194 during the current period of study, however, it was not the 

most recent value used to calculate aquifer-scale proportion. 

Activities of radium-226 were detected above the MCL-US (5 

pCi/L) in two wells in 1996, before the current period of study, 

and were considered to be historical values (table 6). 

Nutrients 

Nutrients had high relative-concentrations in 0.4 percent 

of the primary aquifers and moderate in 2.6 percent (table 9) 

resulting from the detection of nitrate plus nitrite (table 4). 

Moderate relative-concentrations of nitrate plus nitrite 

primarily occur in the VP and WG study areas (fig.15D).

Understanding Assessment for Nitrate plus Nitrite

Although nitrate plus nitrite was not measured at high 

relative-concentrations in more than 2 percent of the primary 

aquifers, it is a constituent that is important to water quality 

and is discussed in this section. Nitrogen in groundwater 

occurs in the forms of dissolved nitrate and nitrite. Certain 

bacteria and algae naturally convert nitrogen from the atmosphere to nitrate, which is an important nutrient for plants. 

Nitrate is present in precipitation also, and is produced by 

desert plants (Hem, 1970). Anthropogenic sources of nitrate 

include its application as a fertilizer for agriculture, and livestock, when in concentrated numbers, produce nitrogenous 

waste that can leach into groundwater. Septic systems may 

also introduce nitrogenous waste into groundwater. In 

addition, nitrate may be associated with uranium mining and 

processing (Hem, 1970).

Nitrate plus nitrite concentrations were significantly 

greater in groundwater of modern age compared with premodern age, and mixed age compared with pre-modern age. 

Nitrate plus nitrite concentrations were significantly greater 

in wells with oxic conditions compared with anoxic/suboxic 

conditions, and mixed conditions compared with anoxic/

suboxic conditions and in wells classified as urban compared 

with mixed land use (table 8, fig. 17). Nitrate plus nitrite was 

significantly correlated positively with dissolved oxygen 

and correlated negatively with groundwater temperature and 

percent natural land use (table 10). Some of the explanatory 

variables related to nitrate plus nitrite, such as dissolved 

oxygen and groundwater age, are generally related to each 

other (table 8), such that older water tends to have lower 

dissolved oxygen and higher pH and water temperature. The 

negative correlation between nitrate plus nitrite and natural 

land use (table 10) suggests that higher concentrations of 

nitrate plus nitrite are likely from anthropogenic sources. 

Only one well had a moderate nitrate plus nitrite (as 

nitrogen) relative-concentration (>5 mg/L as nitrogen). 

This well had an urban/agricultural land-use classification, 

a 170-ft depth (52 m) to the top of the perforations, and a 

pre-modern age classification (fig. 17). Similarly, ten of the 

eleven samples that had the highest nitrate plus nitrite (as 

nitrogen) concentrations were from areas classified as urban/

agricultural land. Nine of those samples were classified as 

modern or mixed age and were from wells that had depths to 

the top perforations <100 ft (30 m). In summary, most of the 

wells in which nitrate plus nitrite was detected were in areas 

where land was classified as urban or agricultural, had a depth 

to the top perforations <100 ft (30 m), and had groundwater 

primarily classified as modern or mixed age.
Status and Understanding of Water Quality 49

Maximum

contaminant

level

0 2 4 6 8

DEPTH TO TOP OF PERFORATIONS, IN FEET BELOW LAND SURFACE

10

NITRATE PLUS NITRITE AS NITROGEN CONCENTRATION, IN MILLIGRAMS PER LITER 

0.5 * Maximum

contaminant

level

EXPLANATION

0

200

400

600

GROUNDWATER AGE CLASSIFICATION

EXPLANATION

NITRATE PLUS NITRITE AS NITROGEN CONCENTRATION,

IN MILLIGRAMS PER LITER

Modern Mixed Pre-modern

11 17 42

0

1

2

3

4

5

6

42 Number of wells with data

75th percentile

Median

25th percentile

25th percentile minus

1.5 * interquartile range

75th percentile plus

1.5 * interquartile range

Upper detached

Lower detached

A

B

Modern age

Mixed age

Pre-modern age

Low Moderate

Urban/Agriculture

Mixed

Natural

Urban/Agriculture

Mixed

Natural

Urban/Agriculture

Mixed

Natural

Figure 17. Nitrate plus nitrite, as nitrogen, concentrations related A. to classifications of groundwater age B. to land use, age 

classification of groundwater, and depth to the top of perforations, and C. to dissolved oxygen concentrations in grid and understanding

wells sampled for the North San Francisco Bay study unit, California.
50 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Under the anoxic conditions observed in some NSF 

wells, denitrification likely reduces nitrate in groundwaters, 

particularly in the deeper parts of the flow system. 

Denitrification of nitrate to dissolved nitrogen gas (N2) in 

reducing groundwater has been reported in numerous studies 

(for example, Vogel and others, 1981; Kendall, 1998). Excess 

N2 (Kulongoski and others, 2006, table 21) was measured 

in four groundwater samples in the VP study area, three of 

which were from groundwaters classified as anoxic/suboxic. 

These three samples had nitrate concentrations <0.06 mg/L. 

The fourth groundwater sample was oxic and had a nitrate 

plus nitrite (as nitrogen) concentration equal to 3.22 mg/L 

(Kulongoski and others, 2006, table 14). Data from the three 

samples are consistent with denitrification of nitrate in these 

wells.

Major and Minor Ions

The major ions chloride and sulfate, and TDS have upper 

SMCL-CA benchmarks based on aesthetic properties. The 

minor ion fluoride has an MCL-US, and the remaining seven 

major and minor ions do not have benchmarks.

TDS had high proportion of 1.2 percent of the primary 

aquifers, and moderate proportion of 7.1 percent (table 4). 

Chloride had aquifer high proportion of 0.7 percent, and 

fluoride had high proportion of 1.2 percent and moderate 

proportion of 0.1 percent. 

Understanding Assessment for TDS

Although TDS was not measured at high relativeconcentrations in more than 2 percent of the primary aquifers, 

it is a constituent that is important to water quality and is 

discussed in this section. The anion chloride is a major 

component of TDS, and its distribution, for the most part, 

reflects that of TDS. Natural sources of TDS include mixing 

of groundwater with deep saline groundwater (connate water), 

seawater intrusion from San Pablo Bay, concentration of 

salts by evaporation in discharge areas, and (or) rock/water 

interactions. Potential anthropogenic sources of TDS to 

groundwater in the NSF study unit include agricultural and 

urban irrigation evaporation, disposal of waste water and 

industrial effluent, and leaking water and sewer pipes. 

0

1

2

3

4

5

6

10

NITRATE PLUS NITRITE AS NITROGEN CONCENTRATION, IN MILLIGRAMS PER LITER 

DISSOLVED OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER 

Modern

GROUNDWATER AGE

CLASSIFICATION

Mixed

Pre-modern

C

0.5 * Maximum

 contaminant

 level 

EXPLANATION

Maximum

 contaminant

 level

Low Moderate

0 2 4 6 8

Figure 17. Continued.
Status and Understanding of Water Quality 51

In the NSF study unit, TDS had a significant positive 

correlation with pH and a negative correlation with the altitude 

of the well or altitude of the land surface (table 10). TDS 

had a significant negative correlation with modern compared 

with pre-modern groundwater age (table 8), suggesting 

that dissolved solid concentrations are lower in younger 

groundwater. The median concentration of TDS in modern, 

mixed, and pre-modern groundwater were 220, 246, and 

281 mg/L, respectively. Figure 18 compares the TDS and the 

altitude of land surface for NSF wells as a function of the 

normalized position of the wells along the flowpath (proximal, 

medial, or terminal). Concentrations of TDS in wells generally 

increased as the positions changed from proximal to medial to 

terminal (fig. 18). Evaporative enrichment of TDS as a result 

of irrigation practices was excluded as a cause for the increase 

in TDS along the flow paths because the groundwater samples 

from the NSF study unit follow the meteoric water-line 

relationship δD = 8*δ18O + 10, and no deviation indicative of 

evaporation is observed (Kulongoski and others, 2006). 

The high concentrations of TDS in wells VP-44, VP-36, 

and VP-28 most likely resulted from shallower groundwater 

mixing with deep saline (connate) water (Farrar and others, 

2006) in the Napa Sonoma lowlands (fig. 2). These wells have 

distinctive chemistry, including the highest concentrations 

of several inorganic constituents measured in the NSF 

study; chloride (191 mg/L), sodium (270 mg/L), bromide 

(1.44 mg/L), iodide (1.1 mg/L), and sulfate (239 mg/L) 

(Kulongoski and others, 2006). Concentrations of iodide 

greater than the seawater value (0.06 mg/L; Hem, 1970) 

suggest that connate water is the source of the salinity to these 

wells.

Organic Constituents

The organic compounds are organized by constituent 

class, including three classes of volatile organic compounds 

(VOC) and two classes of pesticides. VOCs may be in paints, 

solvents, fuels, and refrigerants; they can be byproducts of 

water disinfection and are characterized by their volatile 

nature, or tendency to evaporate. In this report, VOCs are 

classified into three categories: trihalomethanes, solvents, and 

other VOCs (including gasoline additives and fumigants). 

Pesticides are used to control weeds, insects, or fungi in 

agricultural, urban, and suburban settings. In this report, 

pesticides are classified as either herbicides or insecticides.

Organic constituents were not detected at high relativeconcentrations in grid wells; however, the gasoline additive 

benzene, and the insecticide diazinon were both detected at 

moderate relative-concentrations. Only 26 of the 255 organic 

compounds analyzed were detected, and most of these organic 

constituents (18 of the 26) have human-health benchmarks 

(table 7).

According to the spatially-weighted method, the 

proportion of the aquifer that had high relative-concentrations 

of organic constituents with human-health benchmarks 

was 1.4 percent (table 9), resulting from the high relativeconcentrations of the solvents tetrachloroethene (PCE) 

(1.3 percent) and trichloroethene (TCE) (0.1 percent), and the 

organic synthesis reagent 1,1-dichloroethene (0.1 percent) 

(table 4). The proportion of the aquifer having moderate 

relative-concentrations of organic constituents was 4.9 percent 

(table 9).

Organic constituents were found in 41 percent of the 

83 grid wells in the NSF study unit. The maximum relativeconcentrations of two organic constituents, benzene and 

diazinon, were greater than 0.1 (figs. 19, 20). Two additional 

compounds, chloroform and simazine were detected in 

10 percent or more of the primary aquifers. All concentrations 

of organic constituents in samples from grid and understanding 

wells were below human-health benchmarks.

VOCs were detected in 19 percent of the 83 grid 

wells representing the primary aquifers. Of the 10 VOCs 

detected, 9 were detected only at low relative-concentrations 

(fig. 19). One VOC, benzene, was detected at moderate 

relative-concentration in one sample. The trihalomethane 

(THM) chloroform was the only VOC detected in more than 

10 percent of the grid wells. 

Pesticides or pesticide degradates were detected in 

16 percent of the 83 grid wells representing the primary 

aquifers. Of the 114 pesticides and pesticide degradates 

analyzed, 7 were detected; 4 were parent compounds 

with benchmarks, one was a parent compound without a 

benchmark, and two were degradates without benchmarks. 

All concentrations of pesticides were below human-health 

benchmarks. One pesticide, diazinon, was detected at 

moderate relative-concentration in one sample (fig. 20). 

The insecticide simazine was the only pesticide detected 

in 10 percent of the grid wells, (fig. 20). The individual 

constituents that were not detected and the wells sampled in 

the NSF study unit are listed in the report by Kulongoski and 

others (2006).
52 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Trihalomethanes

Water used for drinking water and other household 

uses in both domestic and municipal systems commonly is 

disinfected with hypochlorite solutions (bleach). As a side 

effect to disinfecting the water, the hypochlorite reacts with 

organic matter to produce THMs and other chlorinated and/

or brominated disinfection byproducts. The THM chloroform 

had a low proportion of 100 percent of the primary aquifers. 

Chloroform was detected in 15.7 percent of the 83 grid wells 

0 200 400 600 800 1,000

TOTAL DISSOLVED SOLIDS (TDS), IN MILLIGRAMS PER LITER

0

500

1,000

1,500

0

500

1,000

1,500

2,000

2,000

2,000

0

500

1,000

1,500

ALTITUDE OF LAND SURFACE, IN FEET ABOVE NAVD 88

Volcanic Highlands Study Area

Wilson Grove Formation Highlands Study Area

Medial well

Well not on a

 selected

 flow path

Proximal well

Terminal well

EXPLANATION

Well position along flow path—

Valleys and Plains Study Area

Figure 18. Well altitude versus total dissolved solid concentrations in grid and understanding wells in the Wilson Grove Formation 

Highlands, Volcanic Highlands, and Valleys and Plains study areas of the North San Francisco Bay study unit, California. Positions 

along the flowpath (proximal, medial, or terminal) are indicated for some wells in the Valleys and Plains study area.

(figs. 20, 21). Chloroform also was the most frequently 

detected VOC in groundwater according to the USGS National 

Water Quality Assessment (NAWQA) program (Zogorski 

and others, 2006). Potential urban sources of THMs include 

recharge from landscape irrigation with disinfected water, 

leakage from distribution or sewer systems, and industrial and 

commercial sources (Ivahnenko and Barbash, 2004). 
Status and Understanding of Water Quality 53

Understanding Assessment for Chloroform

Chloroform was more prevalent in the VP study area 

(detection frequency 18 percent) than the VOL and WG study 

areas (fig. 20) (detection frequency 11percent, and 14 percent, 

respectively) (Kulongoski and others, 2006). Chloroform was 

significantly correlated (negatively) with land classified as 

natural compared with mixed land use and land classified as 

natural compared with urban land use (table 8). Chloroform 

was detected in two wells in land classified as natural, four 

wells each in land classified as urban and land classified as 

agricultural, and three wells in land classified as mixed (fig. 21

—not all wells had well construction information). Nationally, 

THM concentrations have been strongly correlated with 

percent urban land use (Zogorski and others, 2006). 

Although chloroform concentrations were not directly 

correlated with depth (table 10), eight of the thirteen wells in 

which chloroform was detected and for which construction 

information was available had depths to the top of the 

perforations of <102 ft (31 m) (fig. 22).

Moderate

Low

Moderate

High

Diazinon

Naphthalene

Simazine

Atrazine

Trifluralin

Chloroform

1,1,1-Trichloroethane

CFC-113

Benzene

Bromodichloromethane

Dibromochloromethane

Carbon tetrachloride

Bromoform

Methyl tert-butyl ether

EXPLANATION

Simazine

1 10 100

0.0001

0.00001

0.001

0.01

0.1

1

10

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

DETECTION FREQUENCY, PERCENT

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

 (more than 25 grid wells sampled) 

Figure 19. Detection frequency and maximum relative-concentration of organic and special interest constituents detected in grid 

wells in the North San Francisco Bay study unit, California. CFC-113, trichlorotrifluorethane.
54 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Chloroform concentrations were significantly greater 

in groundwater classified as mixed age than in groundwater 

classified as pre-modern age (table 8); however, there was 

no significant difference between groundwater classified as 

modern age compared with mixed age and modern age as 

compared with pre-modern age. Seven of the 13 chloroform 

detections occurred in groundwater with mixed ages, 3 in 

groundwater classified as modern age, and 3 in groundwater 

classified as pre-modern age. Because water supplies have 

been disinfected with chlorine over the past 100 years, 

chloroform may be in relatively deep wells that do not appear 

to have modern (from the last 50 years) recharge water.

Chloroform concentrations were significantly 

correlated (positively) with wells having oxic compared 

with anoxic/suboxic groundwater conditions, and between 

wells having mixed compared with anoxic/suboxic conditions 

(table 8), suggesting that chloroform concentrations are greater 

in oxic than anoxic waters, which may result from anoxic 

groundwaters commonly being older than oxic groundwaters. 

Chloroform was significantly correlated (negatively) with 

the explanatory variables pH and groundwater temperature 

(table 10). Cooler groundwater temperatures may result from 

more recent groundwater recharge, while warmer temperatures 

and, often, higher pH result from longer residence times or 

interactions with the deeper hydrothermal system. These 

correlations suggest that cooler, younger, oxic groundwater 

is more likely to contain chloroform, because it was more 

recently recharged at land surface where exposure to 

anthropogenic compounds could occur.

STUDY AREA

Valleys and Plains

Volcanic Highlands

Wilson Grove Formation

 Highlands

Benzene

Chloroform

Diazinon

Simazine

0.0001 0.001 0.01 0.1 1 10

MAXIMUM RELATIVE-CONCENTRATION,

DIMENSIONLESS

STUDY UNIT

DETECTION FREQUENCY,

IN PERCENT

NUMBER OF

WELLS CONTAINING

THE CONSTITUENT

9

2

2

1

8

1

0 20 40 60 80 100

10% detection

Low Moderate High

A B

Figure 20. A. Detection frequency and B. maximum relative-concentrations of selected organic and special-interest constituents in 

grid wells in the North San Francisco Bay study unit, California, August to December 2004. 
Status and Understanding of Water Quality 55

0 4 8 16 Miles

0 8 16 4 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Urban

Agricultural

Natural

CDPH well Relative-concentration 

Not detected

Low

< 0.01

0.01-40.00

USGS grid well

< 0.01

0.01-40.00

CHLOROFORM, IN MICROGRAMS PER LITER

Lake Berryessa

San Pablo Bay

Pacific Ocean

Study unit and

study area boundary

LAND USE CLASSIFICATION

USGS National Land Cover Data

(Price and others, 2003)

Figure 21. Land-use classifications and the relative-concentrations of the trihalomethane chloroform in U.S. Geological Survey (USGS) 

grid wells and California Department of Public Health (CDPH) wells (data from the period August 30, 2001 to September 1, 2004), North 

San Francisco Bay study unit, California. CDPH, California Department of Public Health; <, less than.
56 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

In summary, most wells containing chloroform had urban 

or agricultural land-use classifications, had top perforations 

<102 ft. (31 m), and had oxic groundwater conditions.

Solvents

Solvents are used for various industrial, commercial, 

and domestic purposes. Two solvents, PCE and TCE, had 

spatially-weighted high aquifer proportions of 1.3 percent 

and 0.1 percent, respectively (table 4). PCE is primarily used 

for dry-cleaning of fabrics and degreasing metal parts, and 

is an ingredient in a wide range of products including paint 

removers, polishes, printing inks, lubricants, and adhesives. 

TCE has similar uses as PCE, and also may be formed by 

Agricultural

No dominant land use classification

Natural

Urban

0.0 0.1 0.2 0.3 0.4 0.5 0.6

CHLOROFORM, IN MICROGRAMS PER LITER

600

400

200

0

DEPTH TO THE TOP PERFORATION, IN FEET BELOW LAND SURFACE

EXPLANATION

LAND USE CLASSIFICATION

Figure 22. Concentration of chloroform as a function of depth to the top perforation and land-use classifications, North San Francisco 

Bay study unit, California.

degradation of PCE in groundwater (Vogel and McCarty, 

1985). The solvent 1,2-dichloroethane was detected at high 

relative-concentration in one well during August 30, 2001 to 

September 1, 2004; however, the high relative-concentration 

was not the most recent value from the CDPH data used to 

represent that well (table 4). Solvents as a class had a high 

aquifer-scale proportion of 1.4 percent, and a moderate 

aquifer-scale proportion of 2.5 percent (table 9). None of the 

individual solvent compounds were detected in more than 

10 percent of the wells tested.

Historically high values for the solvents dichloromethane 

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

for the period before August 30, 2001, but not during the 

current period of study (table 6). 
Status and Understanding of Water Quality 57

Understanding Assessment for Solvents

Although solvents were not measured at high relativeconcentrations in more than 2 percent of the primary aquifers, 

they are constituents that are important to water quality. 

The three solvents selected for the summation were PCE, 

TCE, and 1,2-dichloroethane, on the basis of high relativeconcentrations (table 4). Solvent concentrations were 

significantly greater in wells classified as modern age than 

in wells classified as pre-modern age (table 8). However, 

solvents were detected in groundwaters classified as mixed 

age and pre-modern age, possibly because some solvents were 

used before 1950 and could be present in pre-modern water. 

Figure 23 shows the distribution of solvents in the NSF study 

unit, which mostly are located around the urban areas. 

Other VOCs

In the category “other VOCs,” there were no grid-based 

high relative-concentrations and only one grid-based moderate 

relative-concentration (benzene, 1.2 percent; table 4). 

1,1-Dichloroethene had a spatially-weighted high relativeconcentration in 0.1 percent of the primary aquifers.

The constituents bis(2-ethylhexyl)phthalate (DEHP), 

1,2-dibromo-3-chloropropane (DBCP), 1,2-dibromoethane 

(EDB), and toluene had high relative-concentrations recorded 

in the CDPH database for the period from August 30, 2001, to 

September 1, 2004, but the high relative-concentrations were 

not the most recent values selected for calculating aquifer 

proportion (table 4).

Herbicides

Low relative-concentrations of the herbicide simazine 

were detected in samples from the NSF study unit (figs. 19, 

20). Simazine was detected in 10 percent of the grid wells, 

all of which were in the VP study area; the maximum 

concentration was 0.013 μg/L. Historically, simazine is 

most commonly used on vineyards and orchards in the study 

unit, but also is used on rights-of-way for weed control 

(Domagalski and Dubrovsky, 1991). Simazine was the most 

frequently detected triazine herbicide in groundwater in 

California (Troiano and others, 2001). Simazine was among 

the most commonly detected herbicides in groundwater in 

major aquifers across the United States (Gilliom and others, 

2006).

Understanding Assessment for Simazine 

Simazine was detected only in grid wells with ages 

classified as modern (6 wells) and mixed (2 wells). Simazine 

was significantly correlated (positive) with modern compared 

with mixed, mixed compared with pre-modern, and modern 

compared with pre-modern groundwater ages (table 8).

Simazine was detected only in wells with depths to 

the top perforations <100 ft (30 m) and was significantly 

correlated (negatively) with the depth to the top perforation, 

well depth, pH, and groundwater temperature (table 10). 

Simazine concentration was not significantly correlated with 

land-use classification or percent land use (fig. 24; tables 8,

10); however, 4 of the 8 grid wells containing simazine were 

in agricultural land-use areas, 2 were from mixed land-use 

areas, and 1 each was in urban and natural land-use areas. 

Simazine has been used in agricultural applications on citrus 

and vineyards, and urban settings for weed control (Gilliom 

and others, 2006).

The wells in which simazine was detected may be 

characterized as shallow (<100 ft [30 m]), containing recently 

recharged groundwater (modern), and predominantly in 

agricultural land-use areas. 

Insecticides

The insecticide diazinon was detected at one grid well 

in the WG study area at a moderate relative-concentration 

(figs. 19, 20).

Special-Interest Constituents

Constituents of special interest analyzed for in the NSF 

study unit were NDMA, 1,2,3-TCP, and perchlorate. These 

constituents were selected because they recently have been 

found in, or are considered to have the potential to reach, 

drinking-water supplies (California Department of Public 

Health, 2008a,b,c). NDMA, 1,2,3-TCP, and perchlorate were 

not detected in the 83 grid wells sampled (Kulongoski and 

others, 2006). A high concentration of perchlorate in one 

well was recorded in the CDPH database before August 30, 

2001 (table 6), but not during the period August 30, 2001–

September 1, 2004.
58    Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Figure 23. Land-use classification and the relative-concentrations of the sum of the solvents tetrachloroethene (PCE), trichloroethene

(TCE), and 1,2-dichloroethane in U.S. Geological Survey (USGS) grid wells and California Department of Public Health (CDPH) wells (data

from the period August 30, 2001 to September 1, 2004), North San Francisco Bay study unit, California. <, less than.

San Pablo Bay

Pacific Ocean

0 4 8 16 Miles

0 8 16 4 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Study unit and

study area

boundary

Urban

Agricultural

Natural

CDPH well Relative-concentration

Not detected

Low

Moderate

High

< 0.02

0.02-0.5

0.51-5.0

5.1-31.0

USGS grid well

< 0.02

0.02-0.5

SUM OF SOLVENTS (PCE + TCE + 1,2,-DICHLOROETHANE),

IN MICROGRAMS PER LITER

Lake Berryessa

LAND USE CLASSIFICATION

USGS National Land Cover Data

(Price and others, 2003)
Status and Understanding of Water Quality 59

0 4 8 16 Miles

0 8 16 4 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection EXPLANATION

Urban

Agricultural

Natural

CDPH well

< 0.005

Relative-concentration USGS grid well

Not detected

Low

< 0.005

0.006-0.052

SIMAZINE, IN MICROGRAMS PER LITER

Study unit and

study area boundary

LAND USE CLASSIFICATION

USGS National Land Cover Data

(Price and others, 2003)

Lake Berryessa

San Pablo Bay

Pacific Ocean

Figure 24. Land-use classifications and the relative-concentrations of the herbicide simazine in U.S. Geological Survey (USGS) grid 

wells and California Department of Public Health (CDPH) wells (data from the period August 30, 2001 to September 1, 2004), North San 

Francisco Bay study unit, California.
60 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Summary 

Groundwater quality in the approximately 1,000 mi2

(2,590 km2) North 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 North San Francisco Bay provides a 

spatially unbiased characterization of untreated groundwater 

quality in the primary aquifers at the basin-scale. The 

assessment is based on water-quality and ancillary data 

collected by the U.S. Geological Survey (USGS) from 

89 wells in 2004, and water-quality data from the California 

Department of Public Health (CDPH) database. 

The first component of this study, the status of the 

current-quality of the groundwater resource, was assessed on 

the basis of data from samples analyzed for volatile organic 

compounds (VOC), pesticides, and naturally occurring 

inorganic constituents, such as major ions and trace elements. 

The status assessment characterizes the quality of groundwater 

resources within the primary aquifers of the North 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. 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 aquifers with relative-concentration 

greater than 1.0; 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 NSF study unit (90-percent 

confidence intervals). However, the spatially-weighted 

approach was superior to the grid-based proportion when a 

constituent was high in a small fraction of the aquifer.

For inorganic constituents with human-health 

benchmarks, relative-concentrations were high in 14.0 percent 

of the primary aquifers, moderate in 35.8 percent, and 

low in 50.2 percent. The high aquifer-scale proportion of 

inorganic constituents primarily reflected high aquifer-scale 

proportions of arsenic (10.0 percent), boron (4.1 percent), 

and lead (1.6 percent). In contrast, relative-concentrations of 

organic constituents (one or more) were high in 1.4 percent, 

moderate in 4.9 percent, and low in 93.7 percent (not detected 

in 64.8 percent) of the primary aquifers. The high aquiferscale proportion of organic constituents primarily reflected 

high aquifer-scale proportions of PCE (1.3 percent), TCE 

(0.1 percent), and 1,1-dichloroethene (0.1 percent). The 

inorganic constituents with secondary maximum contaminant 

levels, manganese and iron, had relative-concentrations that 

were high in 40.8 percent and 24.4 percent of the primary 

aquifers, respectively. Of the 255 organic and special-interest 

constituents analyzed for, 26 constituents were detected. Two 

organic constituents were frequently detected (in 10 percent 

or more of samples): the trihalomethane chloroform and 

the herbicide simazine, but both were detected at low 

relative-concentrations. 

The second component of this work, the understanding 

assessment, identified the natural and human factors that 

affect groundwater quality by evaluating land use, physical 

characteristics of the wells, geochemical conditions of the 

aquifer, and water temperature. Results from these analyses 

attempt to explain the occurrence and distribution of 

constituents in the study unit. The understanding assessment 

indicated that a majority of the wells that contained nitrate 

had an urban or agricultural land use classification, had water 

of modern or mixed age classification, and had depths to their 

top perforations of less than 100 ft (30 m). Geochemical data 

are consistent with partial denitrification of nitrate in some 

reducing groundwaters in the terminal and deeper parts of the 

flow system.

High and moderate relative-concentrations of arsenic 

may be attributed to reductive dissolution of manganese 

or iron oxides or to desorption by high pH waters. Arsenic 

concentrations increased with increasing depth and 

groundwater age in all three study areas, or from mixing with 

hydrothermal waters. High to moderate relative-concentrations 

of boron were primarily associated with hydrothermal activity 

or high salinity waters in the Napa-Sonoma lowlands. 

Simazine was detected more often in groundwater 

classified as having modern- and mixed-age than in premodern-age groundwater, while chloroform was detected most 

often in groundwater classified as mixed age. Simazine and 

chloroform also were observed in wells that had surrounding 

land-use classified as agricultural or land classified as urban, 

and which had top-perforation depths less than 102 ft (30 m). 

Together, the occurrence of chloroform and simazine in 

shallow wells with groundwater of modern or mixed age and 

that are located in urban or agricultural areas suggests that 

these constituents result from anthropogenic activities in the 

last 50 years.

Tritium, helium isotopes, and carbon-14 data were used 

to classify the predominant age of groundwater samples into 

three categories: modern (water that has entered the aquifer in 

the last 50 years), pre-modern (water that entered the aquifer 

more than 50 years, up to tens of thousands of years ago), and 

mixed (mixtures of waters with modern and pre-modern ages). 

Arsenic, iron, and TDS concentrations were significantly 

greater in groundwater having pre-modern age classification 

than in modern-age groundwaters, suggesting that these 

constituents accumulate with groundwater residence time.
References 61

Acknowledgments

The authors thank the following cooperators for their 

support: the State Water Board, Lawrence Livermore National 

Laboratory, California Department of Public Healtlh, 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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Open-File Report 83-27, 106 p., 6 plates.

Zogorski, J.S., Carter, J.M., Ivahnenko, Tamara, Lapham, 

W.W., Moran, M.J., Rowe, B.L., Squillace, P.J., and 

Toccalino, P.L., 2006, The quality of our Nation’s waters—

Volatile organic compounds in the Nation’s ground water 

and drinking-water supply wells: U.S. Geological Survey 

Circular 1292, 101 p. 
66 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Appendix A. Stratigraphy 

Mesozoic to Early Tertiary rocks of the Franciscan 

Complex, Great Valley Sequence, and Coast Range Ophiolite 

make up the basement rocks beneath the entire study unit. 

Tertiary sedimentary and volcanic rocks overlie the basement 

and are exposed in upland areas. The volcanic rocks are 

part of the belt of volcanic fields that extend from 60 mi 

(100 km) south of San Francisco Bay to Clear Lake and are 

progressively younger toward the northwest (Fox and others, 

1973). Quaternary sedimentary deposits underlie the valley 

floors and form fans along the valley margins.

Basement Rocks

The Franciscan Complex, as the name implies, includes 

rocks of several different lithologies, commonly: sandstone, 

graywacke, shale, conglomerate, chert, greenstone, and 

serpentinite. In places, the rocks are pervasively sheared and 

form mélange zones. The total thickness is unknown but is a 

few tens of thousands of feet (Bailey and others, 1964). These 

rocks have become highly indurated, having formed tens 

of millions of years ago and having been deeply buried and 

subjected to elevated temperatures. Porosity and permeability 

are low in Franciscan rocks because most of the original pore 

spaces are filled by minerals that cement the individual grains 

together. 

Exposures of the ophiolite in the Coast Ranges have 

been mapped as small outcrops within larger masses of the 

Franciscan Complex in the Mayacmas Mountains and the 

southern part of the Sonoma Mountains (Fox and others, 

1973; Wagner and others, 2003, 2004). The ophiolite consists 

of serpentinized peridotite, gabbro, and basalt that has been 

faulted and tectonically interleaved with the Franciscan 

Complex (McLaughlin and others, 2005). 

 Great Valley Sequence rocks are exposed on the east 

side of Mayacmas Mountains and have been identified in 

deep petroleum exploration wells at the southern end of the 

Sonoma Valley and beneath San Pablo Bay (Wright and 

Smith, 1992). The presence of Great Valley Sequence rocks 

beneath Sonoma Valley is supported, at least in places, by 

the consistently low ratios of dissolved boron to chloride in 

water samples from thermal wells along the east side of the 

valley (Donnelly-Nolan and others, 1993). Most Great Valley 

Sequence rocks are sandstone, shale, and minor conglomerate 

units. These rocks are typically well cemented and indurated. 

Wells drilled in these rocks generally yield little or no water 

(Kunkel and Upson, 1960; Page, 1986). 

Most of the modern permeability in the basement rocks 

is due to fractures that developed in response to deformation 

related to the style of emplacement of the various rock 

packages as they were transported from the sea floor to the 

continent and by later folding and faulting. Because of their 

low permeability and specific storage capacity, the basement 

rocks are commonly considered to be nonwater-bearing and 

to form the boundaries of groundwater basins throughout the 

Coast Ranges (Cardwell, 1958 and California Department of 

Water Resources, 1958). However, the basement rocks are 

not truly nonwater-bearing, and in places, especially where 

indurated rock units are highly fractured, these units may 

provide limited amounts of water to wells and springs. 

Younger volcanic and sedimentary rocks as well as 

unconsolidated sediments were deposited unconformably 

upon the basement rocks. The volcanic and sedimentary 

rocks are interbedded and, in places, are interfingered. The 

volcanic rocks include the Late Miocene to Early Pliocene 

Donnell Ranch Volcanics and Tolay Volcanics, and the 

younger Sonoma Volcanics. The sedimentary rocks include 

the Petaluma Formation, Wilson Grove Formation, Huichica 

Formation, Glen Ellen Formation, and several informally 

named mappable geologic units. Sedimentary rocks older 

than the Sonoma Volcanics outcrop in small patches in the 

southern parts of the Napa and Sonoma Valleys and are likely 

in the subsurface, but because their subsurface extent is poorly 

known and few water wells are drilled into them in this region, 

the sedimentary rocks are not discussed further.

Tolay and Donnel Ranch Volcanics 

The Tolay Volcanics stratigraphic unit was named by 

Morse and Bailey (1935) and includes a thick series of mafic 

lavas, breccia, tuff, and agglomerate, identified in the core 

from a petroleum test well drilled in the Petaluma oil district. 

At that location, 4,165 ft (1,270 m) were penetrated without 

reaching the bottom. These rocks are not exposed anywhere 

on the Santa Rosa Plain and are not penetrated by any water 

wells in the area. The uppermost part of the Tolay Volcanics 

is interbedded with the lowermost part of the Petaluma 

Formation. This stratigraphic relationship suggests the Tolay 

Volcanics is of Late Miocene or Early Pliocene age. 
Appendix A. Stratigraphy 67

The Donnell Ranch Volcanics were informally named 

by Youngerman (1989) for lavas and tuffs exposed between 

the Rodgers Creek and Tolay faults in the southern Sonoma 

Mountains. The Donnell Ranch Volcanics predominantly 

consist of mafic lavas and breccias and rhyolitic to dacitic 

lavas and tuffs. The ages of the Donnell Ranch and Tolay 

Volcanics overlap, and some uncertainty still exists in field 

identification and mapping of these two volcanic formations.

Sonoma Volcanics

The Sonoma Volcanics constitute an important aquifer 

in parts of the Santa Rosa Plain, Sonoma Valley, and Napa 

Valley. This heterogeneous assemblage of lithologic types 

has a broad range of hydraulic properties. Specific yields 

range from 0 to 15 percent (California Department of Water 

Resources, 1982, 2003). Water production from wells drilled 

into thick air-fall pumice units may exceed a few hundred gpm 

but wells drilled into unfractured lavas or welded tuffs may 

produce less than 10 gpm (38 L/m).

The Sonoma Volcanics are a thick, highly variable 

sequence of continental volcanic and volcaniclastic rocks 

including basalt, andesite, and rhyolite lavas interbedded with 

air-fall and ash-flow tuffs, debris-flow deposits, and lacustrine 

deposits. The total thickness of Sonoma Volcanics is at least a 

few thousand feet. The Sonoma Volcanics were first described 

by Osmont (1905) and named for Sonoma Mountain, east of 

Santa Rosa. The Sonoma Volcanics underlie parts of the Santa 

Rosa Plain where the formation is frequently penetrated by 

water wells. The Sonoma Volcanics are widely exposed in 

upland areas east of the Rodgers Creek Fault in the Sonoma 

Mountains, Mayacmas Mountains, and the Howell Mountains. 

Recent mapping (David L. Wagner and Robert J. 

McLaughlin, U.S. Geological Survey, oral commun., 2003) 

shows the Sonoma Volcanics can be separated into older 

and younger members on the basis of the structural attitude 

of individual units, with the older member dipping more 

steeply than the overlying rocks. On the basis of stratigraphic 

relations and several argon-40/argon-39 dates, the Sonoma 

Volcanics were extruded and deposited over an interval from 

approximately 8 to 2.5 million years (Ma) (McLaughlin and 

others, 2005). This formation is overlain by and interfingers 

with continental sedimentary rocks, including the Petaluma 

Formation along the eastern part of Santa Rosa Valley and the 

Glen Ellen Formation in Sonoma Valley.

Petaluma Formation

The Petaluma Formation is dominated by fine-grained 

materials, either in thick beds or as interstitial material in 

poorly sorted silty and clayey sands or gravels. The Petaluma 

Formation is an important aquifer only in the northern part of 

Petaluma Valley. Across the study area, specific yields range 

from 3 to 7 percent and well yields are generally only a few 

gallons per minute (Cardwell, 1958; California Department of 

Water Resources, 2003).

The Petaluma Formation is exposed in outcrops around 

the margins of Santa Rosa Valley and, on the basis of geologic 

logs from wells, is present under a large part of the valley. 

The Petaluma Formation is composed of sandstone, shale, 

siltstone, and clay, and minor beds of nodular limestone and 

conglomerate. Much of the formation was deposited under 

brackish-water conditions but includes a continental and a 

marine facies (Powell and others, 2004). The continental 

facies extends westward from the Sonoma and Mayacmas 

Mountains; the transition to marine facies occurs beneath the 

Santa Rosa Plain. Outcrops and cuttings from deep petroleum 

exploration wells indicate that the total thickness of the 

formation probably is at least 3,000 ft (914 m) (Powell and 

others, 2004). The formation is well exposed along a 1- to 

2-mi (1- to 3-km) wide area along the western base of the 

Sonoma Mountains that extends north from near Penngrove to 

the mouth of Tolay Creek. Age-dates on interbedded tuff units 

indicate the Petaluma Formation was deposited during the 

early Pliocene (McLaughlin and others, 2005).

Wilson Grove Formation Highlands

The Wilson Grove Formation is composed of fine- to 

medium-grained, moderate- to well-sorted, tan to gray, 

uncemented to weakly cemented marine sandstone. Dickerson 

(1922) correlated these strata with the Merced Formation at 

its type locality on the San Francisco Peninsula. Fox (1983) 

renamed the strata Wilson Grove Formation. The formation 

contains marine fossils, clasts of volcanic rocks, thin beds of 

tuff, pebble and gravel stringers, and clay lenses. The Wilson 

Grove Formation interfingers with the Petaluma Formation 

beneath the Santa Rosa Plain. The two formations show 

a transition from estuarine to bathyal marine depositional 

environments. In most places west of the Santa Rosa Plain 

the Wilson Grove Formation unconformably overlies the 

Franciscan Complex on a highly irregular contact. The 

maximum thickness of the Wilson Grove is about 2,700 ft 

(823 m) (Powell and others, 2004). The strata of the Wilson 

Grove Formation have generally been assigned an age of 

Pliocene to Pleistocene on the basis of molluscan fossils. A 

Pliocene age for at least part of the formation was validated 

using the 40Ar/39Ar isotopic technique run on samples of 

intercalated Roblar tuff, which gave an age of 6.26 Ma 

(McLaughlin and others, 2005).
68 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Huichica Formation

The Huichica Formation was first named by Weaver 

(1949) for massive silt beds that crop out in the southeastern 

part of Sonoma Valley and later were recognized in a small 

part of eastern Napa Valley (Kunkel and Upson, 1960). 

The Huichica Formation is overlain by older and younger 

alluvium and unconformably overlies the Sonoma Volcanics. 

The base of the Huichica contains pebble and gravel sized 

volcanic clasts derived from the Sonoma Volcanics, indicating 

coeval deposition for parts of the two formations. Most of the 

Huichica Formation consists of deformed continental yellow 

silt and clay deposits with lenses of silty gravel and sand. 

In Sonoma Valley, the maximum thickness of the Huichica 

Formation attains is 900 ft (274 m) (Kunkel and Upson, 1960). 

The high proportion of fine-grained materials making up 

the Huichica results in very low permeability and very low 

well yields. Few wells produce enough water even for single 

domestic users (California Department of Water Resources, 

2004).

Glen Ellen Formation

The Glen Ellen Formation was first described by Weaver 

(1949) for continental deposits that crop out near Glen Ellen in 

Sonoma Valley. The name has since been applied to rocks of a 

similar appearance in Petaluma Valley, the Santa Rosa Plain, 

and Alexander Valley. The formation consists of clay-rich 

stratified deposits of poorly sorted sand, silt, and gravel. Beds 

grade laterally and vertically between coarse- and fine-grained, 

commonly over distances of a few tens to a few hundreds 

of feet. Bedding is thick to massive and often has lenticular 

form. Most of the clasts and probably much of the matrix were 

derived from the Sonoma Volcanics. The sedimentary rocks 

making up this formation were probably originally deposited 

as alluvial fans and piedmont. Some of the material beneath 

and adjacent to Santa Rosa Valley was probably deposited in 

lagoons or shallow bays and may grade into a marine facies. 

The Glen Ellen Formation is of late Pliocene age as 

indicated by stratigraphic relations and 40Ar/39Ar radiometric 

age dates of intercalated Putah Tuff (McLaughlin and others, 

2005). Beneath Santa Rosa Valley, the Glen Ellen Formation 

rests directly upon the basement rocks of the Franciscan 

Complex in places but more generally overlies the Sonoma 

Volcanics, or the Wilson Grove or Petaluma Formations. The 

Glen Ellen Formation interfingers with the Wilson Grove and 

Sonoma Volcanics beneath parts of the Santa Rosa Plain. In 

Sonoma Valley, the Glen Ellen overlies Franciscan basement 

or the Sonoma Volcanics. Along the valley margins in Sonoma 

Valley and the Santa Rosa Plain, the Glen Ellen is overlain 

by alluvial units of Quaternary age. The thickness of the Glen 

Ellen Formation varies greatly but generally is a few hundred 

feet thick or less. However, the maximum thickness of the 

formation may be about 3,000 ft (914 m) in Santa Rosa Valley 

(Cardwell, 1958).

In Sonoma Valley, Santa Rosa Plain, and Alexander 

Valley, the Glen Ellen Formation is an important aquifer and 

provides water for domestic and agricultural uses. The large 

fraction of fine-grained material in the formation causes 

the permeability and well yields to be low. Average specific 

yields for the Glen Ellen range from 3 to 7 percent (California 

Department of Water Resources, 2003). Well yields depend 

on the total thickness of coarse-grained materials and vary 

from a few to a few hundred gpm The amount of groundwater 

existing under unconfined and semiconfined conditions 

depends on the thickness and continuity of overlying finegrained materials.
Appendix B. Use of Data from the California Department of Public Health (CDPH) Database 69

Appendix B. Use of Data from the 

California Department of Public Health 

(CDPH) Database

For the NSF study unit, the historical CDPH database 

contains over 309,000 records distributed across more than 

790 wells, requiring targeted retrievals to manageably use the 

data to assess water-quality. The paragraphs below summarize 

the selection process for wells and data from the CDPH 

database for use in the grid-based assessment of status. 

The strategy used to select CDPH inorganic data for 

a single well in each cell where the USGS did not obtain 

a sample for analysis for inorganic constituents involved 

prioritizing data from different sources. The first choice 

was to select CDPH data for the grid well sampled by the 

USGS for other constituents, provided the CDPH data met 

quality-control criteria. Cation/anion balance was used as 

the quality-control assessment metric. Because water is 

electrically neutral and must have a balance between positive 

(cations) and negative (anions) electrically charged dissolved 

species, the cation/anion balance is commonly used as a 

quality-assurance criterium for water sample analysis (Hem, 

1970). An imbalance greater than or equal to (≥) 10 percent 

may indicate uncertainty in the quality of the data. The most 

recent CDPH data from the well were evaluated to determine 

whether the cation/anion imbalance was <10 percent; if so, 

the CDPH inorganic data for the well were selected for use 

as grid-well data (USGS grid well with CDPH inorganic 

data). It was assumed that if analyses met the high-qualitycontrol criterion—cation/anion balance—for major and minor 

elements, then analyses at these wells for trace elements, 

nutrients, and radiochemical constituents would also be 

of high quality. This approach resulted in the selection of 

inorganic data from the CDPH database for 24 USGS grid 

wells. To identify the USGS wells that incorporated CHDPH 

inorganic data, a well ID was created that added –DG– to 

the GAMA ID for these wells (for example, VP-01 with 

CDPH data was assigned the well identification: VP-DG-01; 

table B1).

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 <10 percent in each grid cell. This step 

resulted in selecting CDPH inorganic data for non-USGSsampled wells for six grid cells. These six CDPH grid wells 

were not co-located with their cell’s respective USGS grid 

well. To identify these new CDPH wells, a well ID was 

created that added “–DPH–“ after the study unit prefix and 

then a sequential number starting after the last GAMA ID for 

the study area (for example, CDPH grid well VP-DPH-55, 

table B1). If no wells in a grid cell met the cation/anion 

balance criteria or there was insufficient data to evaluate 

charge balance, the third choice for the CDPH grid well was 

to select the highest randomly ranked CDPH well that had 

any of the needed inorganic data. This resulted in selecting 

CDPH inorganic data for 17 USGS grid wells and 9 additional 

wells for a total of 28 wells. If the well was a USGS grid well, 

a well ID was created that added “–DG–“ to the GAMA ID 

(for example, VP-DG-01), or if the well was a new CHDPgrid well, “–DPH–“ was added after the study unit prefix and 

then a sequential number starting after the last GAMA ID for 

the study area (for example, CDPH grid well VP-DPH-51).In 

some cases, to achieve one value for each constituent per cell, 

it was necessary to select an additional well in a cell for data, 

hence some cells have multiple CDPH wells. 

The result of these steps was one grid well per cell 

having data from the USGS database, the CDPH database, 

or both database. Inorganic data from the CDPH database 

were used for 63 grid wells. Data was available for 63 grid 

wells for nitrate plus nitrite and for 0 to 54 wells for most 

other inorganic constituents (table 2). In combination with 

USGS grid well inorganic data (19 to 29 wells), inorganic 

data was available for 89 of the 100 grid cells. 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 Jeffrey’s 

interval for the binomial distribution).

Differences in constituent LRLs or 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 F.
70 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Table B1. Grid cell number, U.S. Geological Survey (USGS) well identification number, land use categories, land use classification, 

well construction information, and the normalized position along a flowpath for wells sampled August to December 2004 for the GAMA 

Priority Basins Project North San Francisco Bay study unit, California.—Continued

[CDPH, California Department of Public Health; DOM, domestic well; FP, flow-path (understanding) well; ft, foot; IND, industrial well; IRR, irrigation well; 

m, meter; LSD, land surface datum; na, no data available; nd, no data collected; no CDPH data, no CDPH water-quality data available; no USGS well, USGS 

GAMA-grid well was not sampled in that cell; PSW, public supply well; USGS data, USGS-GAMA data available for the grid well; VP, Valleys and Plains 

study area well; VOL, Volcanic Highlands study area well; WG, Wilson Grove Formation Highlands study area well. A USGS GAMA well identification 

number indicates the use of data from a USGS-grid well; a CDPH GAMA well identification number with “DG” indicates the use of CDPH inorganic 

constituent data at a USGS-grid well, and a CDPH GAMA well identification number with “DPH” indicates a CDPH-grid well with no USGS data]

Grid 

cell 

number

USGS-GAMA well identification 

number indicating data source

Well 

type

Land use catagories

Land use 

classification

Construction information

Normalized 

position 

along 

flowpath

Agricultural 

land use

Natural 

land 

use

Urban 

land 

use

Well 

depth

Top of 

perforations

Bottom of 

perforations

Length 

from top of 

uppermost 

perforated 

interval to 

bottom of 

well

within 500 meters 

of the well, 

in percent

in feet below LSD dimensionless

Volcanic Highlands study area grid wells

1 VOL-06 VOL-DG-06 PSW 0 97 3 Natural 280 200 280 80 na

2 VOL-08 VOL-DG-08 PSW 0 100 0 Natural na na na na na

3 VOL-10 No CDPH data PSW 0 100 0 Natural 136 76 136 60 na

3 VOL-DPH-22 PSW 0 100 0 Natural na na na na na

4 VOL-05 VOL-DG-05 PSW 0 100 0 Natural 510 330 510 180 na

5 VOL-01 USGS data PSW 0 45 55 Urban 323 na na na na

6 VOL-02 No CDPH data PSW 0 96 4 Natural 380 na na na na

6 VOL-DPH-23 PSW 0 100 0 Natural na na na na na

7 VOL-09 VOL-DG-09 PSW 0 100 0 Natural na na na na na

8 VOL-07 VOL-DG-07 PSW 6 94 0 Natural 395 215 395 180 na

9 VOL-20 USGS data PSW 0 88 12 Natural 250 170 250 80 na

10 VOL-03 VOL-DG-03 PSW 44 27 28 Mixed 500 102 na na na

10 VOL-DPH-27 PSW na na na na na na na na na

11 VOL-14 USGS data PSW 45 44 12 Mixed 417 57 417 360 na

12 VOL-18 VOL-DG-18 PSW 50 42 8 Agricultural 670 135 660 525 na

13 VOL-19 USGS data PSW 4 90 6 Natural 705 285 705 420 na

14 VOL-17 No CDPH data PSW 0 100 0 Natural 265 na na na na

14 VOL-DPH-24 PSW 50 49 1 Agricultural 646 na 390 na na

15 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

16 VOL-11 VOL-DG-11 PSW 0 83 17 Natural 555 398 555 157 na

17 VOL-16 No CDPH data PSW 33 66 0 Natural 423 75 419 344 na

17 VOL-DPH-25 PSW 58 42 0 Agricultural na na na na na

18 VOL-15 VOL-DG-15 PSW 0 93 7 Natural 115 65 113 48 na

19 VOL-12 No CDPH data DOM 37 63 0 Natural 368 148 368 220 na

19 VOL-DPH-26 PSW 81 17 2 Agricultural na na na na na

20 VOL-13 VOL-DG-13 PSW 0 97 3 Natural 510 30 510 480 na

Valleys and Plains study area grid wells

1 VP-21 VP-DG-21 PSW 8 56 36 Natural 314 80 310 230 na

2 VP-04 VP-DG-04 PSW 95 4 1 Agricultural 397 na na na na

3 VP-07 VP-DG-07 PSW 40 5 55 Urban 685 295 670 375 na

4 VP-03 VP-DG-03 PSW 36 2 62 Urban 120 55 85 30 na

4 VP-DPH-58 PSW na na na na na na na na 0.92

5 VP-12 No CDPH data PSW 83 16 1 Agricultural 808 650 800 150 na

6 VP-14 VP-DG-14 PSW 74 0 25 Agricultural 550 507 547 40 na

6 VP-DPH-59 PSW na na na na na na na na na

Table B1. Grid cell number, U.S. Geological Survey (USGS) well identification number, land use categories, land use classification, well 

construction information, and the normalized position along a flowpath for wells sampled August to December 2004 for the North San 

Francisco Bay study unit, California.

[CDPH, California Department of Public Health; DOM, domestic well; FP, flow-path (understanding) well; ft, foot; IND, industrial well; IRR, irrigation well; 

m, meter; LSD, land surface datum; na, no data available; nd, no data collected; no CDPH data, no CDPH water-quality data available; no USGS well, USGS 

GAMA-grid well was not sampled in that cell; PSW, public supply well; USGS data, USGS-GAMA data available for the grid well; VP, Valleys and Plains study 

area well; VOL, Volcanic Highlands study area well; WG, Wilson Grove Formation Highlands study area well. A USGS GAMA well identification number 

indicates the use of data from a USGS-grid well; a CDPH GAMA well identification number with “DG” indicates the use of CDPH inorganic constituent data at 

a USGS-grid well, and a CDPH GAMA well identification number with “DPH” indicates a CDPH-grid well with no USGS data]
Appendix B. Use of Data from the California Department of Public Health (CDPH) Database 71

Table B1. Grid cell number, U.S. Geological Survey (USGS) well identification number, land use categories, land use classification, 

well construction information, and the normalized position along a flowpath for wells sampled August to December 2004 for the GAMA 

Priority Basins Project North San Francisco Bay study unit, California.—Continued

[CDPH, California Department of Public Health; DOM, domestic well; FP, flow-path (understanding) well; ft, foot; IND, industrial well; IRR, irrigation well; 

m, meter; LSD, land surface datum; na, no data available; nd, no data collected; no CDPH data, no CDPH water-quality data available; no USGS well, USGS 

GAMA-grid well was not sampled in that cell; PSW, public supply well; USGS data, USGS-GAMA data available for the grid well; VP, Valleys and Plains 

study area well; VOL, Volcanic Highlands study area well; WG, Wilson Grove Formation Highlands study area well. A USGS GAMA well identification 

number indicates the use of data from a USGS-grid well; a CDPH GAMA well identification number with “DG” indicates the use of CDPH inorganic 

constituent data at a USGS-grid well, and a CDPH GAMA well identification number with “DPH” indicates a CDPH-grid well with no USGS data]

Grid 

cell 

number

USGS-GAMA well identification 

number indicating data source

Well 

type

Land use catagories

Land use 

classification

Construction information

Normalized 

position 

along 

flowpath

Agricultural 

land use

Natural 

land 

use

Urban 

land 

use

Well 

depth

Top of 

perforations

Bottom of 

perforations

Length 

from top of 

uppermost 

perforated 

interval to 

bottom of 

well

within 500 meters 

of the well, 

in percent

in feet below LSD dimensionless

7 VP-11 VP-DG-11 PSW 13 43 45 Mixed 80 60 70 10 0.66

8 VP-10 USGS data PSW 0 68 32 Natural 99 75 95 20 0.76

9 VP-08 VP-DG-08 PSW 55 45 0 Agricultural 102 70 88 18 0.55

10 VP-15 VP-DG-15 PSW 46 2 52 Urban 60 40 60 20 na

11 VP-02 No CDPH data PSW 58 9 33 Agricultural 350 65 341 276 na

12 VP-05 VP-DG-05 PSW 47 4 49 Mixed 502 130 450 320 na

13 VP-06 VP-DG-06 PSW 75 2 23 Agricultural 700 170 680 510 na

14 VP-22 VP-DG-22 PSW 76 3 21 Agricultural 502 60 502 442 na

15 No USGS well VP-DPH-51 PSW 47 53 0 Natural na na na na na

17 VP-35 USGS data PSW 95 5 0 Agricultural 300 85 300 215 na

18 VP-01 VP-DG-01 PSW 77 16 7 Agricultural 864 441 862 421 na

19 VP-23 VP-DG-23 PSW 61 2 37 Agricultural 530 369 530 161 na

20 VP-20 No CDPH data IRR 89 0 11 Agricultural 110 70 110 40 na

20 VP-DPH-56 PSW 43 4 53 Urban na na na na na

21 VP-18 VP-DG-18 PSW 67 14 19 Agricultural 63 48 70 22 0.49

21 VP-DPH-60 PSW na na na na na na na na na

22 VP-42 VP-DG-42 PSW 83 11 6 Agricultural 209 55 209 154 0.10

23 VP-30 USGS data PSW 52 43 4 Agricultural na na na na 0.08

24 VP-29 USGS data PSW 67 12 21 Agricultural 120 62 120 58 0.18

25 VP-19 USGS data PSW 0 36 64 Urban 100 20 60 40 0.49

26 VP-09 VP-DG-09 PSW 17 14 68 Urban 400 260 400 140 na

27 VP-24 VP-DG-24 PSW 0 22 78 Urban 85 65 85 20 na

28 VP-16 VP-DG-16 PSW 0 12 88 Urban 206 50 100 50 na

29 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

30 VP-28 No CDPH data PSW 0 100 0 Natural 216 116 216 100 na

31 No USGS well VP-DPH-53 PSW 0 96 4 Natural na na na na 0.96

32 No USGS well VP-DPH-54 PSW 0 98 2 Natural na na na na 0.8

33 VP-43 No CDPH data DOM 0 94 6 Natural 790 690 790 100 0.41

33 VP-DPH-57 PSW na na na na na na na na na

34 VP-25 VP-DG-25 PSW 13 0 87 Urban 265 160 265 105 na

35 VP-13 VP-DG-13 PSW 0 100 0 Natural na na na na na

36 VP-17 No CDPH data PSW 84 16 0 Agricultural na na na na 0.32

37 VP-26 USGS data PSW 97 3 0 Agricultural 600 125 600 475 0.29

38 VP-31 VP-DG-31 PSW 74 26 0 Agricultural 315 20 315 295 0.19

39 VP-27 VP-DG-27 PSW 28 19 53 Urban 372 50 372 322 0.06

40 VP-34 USGS data PSW 80 10 10 Agricultural 258 41 258 217 0.17
72 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Table B1. Grid cell number, U.S. Geological Survey (USGS) well identification number, land use categories, land use classification, 

well construction information, and the normalized position along a flowpath for wells sampled August to December 2004 for the GAMA 

Priority Basins Project North San Francisco Bay study unit, California.—Continued

[CDPH, California Department of Public Health; DOM, domestic well; FP, flow-path (understanding) well; ft, foot; IND, industrial well; IRR, irrigation well; 

m, meter; LSD, land surface datum; na, no data available; nd, no data collected; no CDPH data, no CDPH water-quality data available; no USGS well, USGS 

GAMA-grid well was not sampled in that cell; PSW, public supply well; USGS data, USGS-GAMA data available for the grid well; VP, Valleys and Plains 

study area well; VOL, Volcanic Highlands study area well; WG, Wilson Grove Formation Highlands study area well. A USGS GAMA well identification 

number indicates the use of data from a USGS-grid well; a CDPH GAMA well identification number with “DG” indicates the use of CDPH inorganic 

constituent data at a USGS-grid well, and a CDPH GAMA well identification number with “DPH” indicates a CDPH-grid well with no USGS data]

Grid 

cell 

number

USGS-GAMA well identification 

number indicating data source

Well 

type

Land use catagories

Land use 

classification

Construction information

Normalized 

position 

along 

flowpath

Agricultural 

land use

Natural 

land 

use

Urban 

land 

use

Well 

depth

Top of 

perforations

Bottom of 

perforations

Length 

from top of 

uppermost 

perforated 

interval to 

bottom of 

well

within 500 meters 

of the well, 

in percent

in feet below LSD dimensionless

41 VP-37 USGS data PSW 5 1 94 Urban 360 60 350 290 0.46

42 VP-38 USGS data PSW 76 11 13 Agricultural 770 210 770 560 0.60

43 VP-44 USGS data PSW 80 15 5 Agricultural 318 140 302 na 0.77

44 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

45 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

46 No USGS well VP-DPH-55 PSW 2 98 0 Natural na na na na na

47 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

48 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

49 VP-36 USGS data PSW 1 87 12 Natural 306 145 300 155 1.00

50 VP-40 USGS data PSW 19 45 37 Mixed na na na na 0.87

51 VP-48 USGS data PSW 77 3 20 Agricultural 200 60 200 140 0.89

52 VP-47 USGS data PSW 49 42 8 Mixed 79 na na 79 0.09

53 VP-39 USGS data PSW 35 65 0 Natural 460 56 460 404 0.26

54 VP-50 USGS data PSW 35 17 48 Mixed 199 na na na 0.32

55 VP-46 USGS data PSW 93 4 3 Agricultural 180 40 180 140 0.44

56 VP-32 USGS data PSW 64 36 0 Agricultural 400 na na na 0.49

57 VP-33 USGS data PSW 68 18 14 Agricultural na na na na 0.59

58 VP-45 USGS data PSW 79 5 15 Agricultural na na na na 0.69

59 VP-41 USGS data PSW 20 29 51 Urban 235 60 235 175 0.75

60 VP-49 USGS data PSW 0 79 21 Natural 220 140 220 80 0.83

Wilson Grove Formation Highlands study area grid wells

1 WG-06 WG-DG-06 PSW 2 97 1 Natural 161 na na na na

2 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

3 No USGS well WG-DPH-15 PSW 43 55 2 Natural na na na na na

3 WG-DPH-22 PSW na na na na na na na na na

4 WG-13 WG-DG-13 PSW 0 100 0 Natural 275 na na na na

5 WG-05 No CDPH data PSW 0 98 2 Natural 215 135 215 na na

5 WG-DPH-21 PSW 0 100 0 Natural na na na na na

6 WG-09 No CDPH data PSW 31 7 61 Urban na na na na na

7 No USGS well WG-DPH-16 PSW 58 9 33 Agricultural na na na na na

8 No USGS well WG-DPH-17 PSW 0 95 5 Natural na na na na na

9 No USGS well WG-DPH-18 PSW 45 54 1 Natural na na na na na

10 WG-10 USGS data PSW 0 98 2 Natural 190 70 190 120 na

11 No USGS well No CDPH data nd nd nd nd nd nd nd nd nd

12 WG-14 WG-DG-14 PSW 24 59 17 Natural 295 155 295 140 na
Appendix B. Use of Data from the California Department of Public Health (CDPH) Database 73

Table B1. Grid cell number, U.S. Geological Survey (USGS) well identification number, land use categories, land use classification, 

well construction information, and the normalized position along a flowpath for wells sampled August to December 2004 for the GAMA 

Priority Basins Project North San Francisco Bay study unit, California.—Continued

[CDPH, California Department of Public Health; DOM, domestic well; FP, flow-path (understanding) well; ft, foot; IND, industrial well; IRR, irrigation well; 

m, meter; LSD, land surface datum; na, no data available; nd, no data collected; no CDPH data, no CDPH water-quality data available; no USGS well, USGS 

GAMA-grid well was not sampled in that cell; PSW, public supply well; USGS data, USGS-GAMA data available for the grid well; VP, Valleys and Plains 

study area well; VOL, Volcanic Highlands study area well; WG, Wilson Grove Formation Highlands study area well. A USGS GAMA well identification 

number indicates the use of data from a USGS-grid well; a CDPH GAMA well identification number with “DG” indicates the use of CDPH inorganic 

constituent data at a USGS-grid well, and a CDPH GAMA well identification number with “DPH” indicates a CDPH-grid well with no USGS data]

Grid 

cell 

number

USGS-GAMA well identification 

number indicating data source

Well 

type

Land use catagories

Land use 

classification

Construction information

Normalized 

position 

along 

flowpath

Agricultural 

land use

Natural 

land 

use

Urban 

land 

use

Well 

depth

Top of 

perforations

Bottom of 

perforations

Length 

from top of 

uppermost 

perforated 

interval to 

bottom of 

well

within 500 meters 

of the well, 

in percent

in feet below LSD dimensionless

13 WG-03 No CDPH data PSW 56 14 30 Agricultural 552 270 552 282 na

14 WG-08 USGS data PSW 0 1 99 Urban 600 332 600 268 na

15 WG-01 WG-DG-01 PSW 49 1 50 Urban 350 90 350 260 na

15 WG-DPH-23 PSW na na na na na na na na na

15 WG-DPH-25 PSW 87 10 3 Agricultural na na na na na

16 WG-04 No CDPH data PSW 28 5 67 Urban 452 432 452 20 na

16 WG-DPH-24 PSW na na na na na na na na na

17 WG-12 WG-DG-12 PSW 83 5 12 Agricultural 261 100 128 28 na

18 WG-02 WG-DG-02 PSW 88 8 4 Agricultural 240 na na na na

19 WG-11 WG-DG-11 PSW 73 3 24 Agricultural na na na na na

20 WG-07 WG-DG-07 PSW 95 1 4 Agricultural 550 140 540 400 na

USGS-Understanding wells

9 VOL-04 USGS data PSW 0 100 0 Natural 542 na na na na

5 VPFP-01 USGS data PSW 88 2 10 Agricultural 1,040 410 1,020 610 na

11 VPFP-02 USGS data PSW 3 0 96 Urban 231 61 231 170 na

53 VPFP-03 USGS data IRR 12 55 33 Natural 25 13 25 12 0.05

7 VPFP-04 USGS data PSW 0 71 29 Natural 99 75 95 20 0.86

14 WGFP-01 USGS data PSW 17 7 76 Urban 528 138 528 390 na
74 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

USGS grid well

USGS understanding well

San Pablo Bay

Pacific Ocean

EXPLANATION

HOT-02,03 HOT-01

HOT-05 HOT-04

HOT-07

HOT-06

HOT-08

VOL-04

VPFP-01

WGFP-01

VPFP-02

VPFP-03

VPFP-04

VOL-10

VOL-11

VOL-17

VOL-12

VOL-13

VOL-14

VOL-15

VOL-16

VOL-18

VOL-19

VOL-20

VP-01

VP-02

VP-03

VP-04

VP-05

VP-06

VP-07

VP-08

VP-09

VP-10

VP-11

VP-12

VP-13

VP-14

VP-15

VP-16

VP-17

VP-18

VP-19

VP-20

VP-21 VP-22

VP-23

VP-24 VP-25

VP-26

VP-27

VP-28

VP-29

VP-30

VP-31

VP-32

VP-33

VP-34

VP-35

VP-36

VP-37

VP-38

VP-39

VP-40

VP-41

VP-42

VP-43

VP-44

VP-45

VOL-01

VP-47

VP-48

VP-49

VP-50

WG-01

WG-02

WG-03

WG-04

WG-05

WG-06

WG-07

WG-08

VP-46

WG-10

WG-11

WG-12

WG-13

WG-14

VOL-02

VOL-03

VOL-05

VOL-06

VOL-07

VOL-08

VOL-09

WG-09

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

A

Study unit and

study area boundary

STUDY AREA

Valleys and Plains

Grid cell

Volcanic Highlands

Wilson Grove Formation

 Highlands

Figure B1. Identifiers and locations of A. U.S. Geological Survey (USGS) grid and understanding wells sampled during August to 

December, 2004, and B. grid wells for which data for inorganic constituents from the California Department of Public Health (CDPH) 

were used, North San Francisco Bay study unit, California. 
Appendix B. Use of Data from the California Department of Public Health (CDPH) Database 75

VP-DG-21

VP-DG-22

WG-DG-11

WG-DG-07

WG-DG-06 WG-DG-02 VP-DPH-57

VP-DG-04 VOL-DG-08 VOL-DG-03

WG-DG-14

WG-DPH-15 WG-DPH-18

WG-DPH-22

WG-DG-12

VOL-DPH-22

VP-DG-07 VP-DG-01 VOL-DPH-27

WG-DG-13

WG-DPH-17 VP-DG-05

WG-DPH-21

WG-DPH-16 WG-DPH-24

WG-DPH-23

VP-DG-03 VOL-DG-05

VP-DG-23

VOL-DPH-25

VOL-DPH-26

WG-DG-08 VOL-DG-15

VOL-DG-13

VP-DG-27 VOL-DG-07

WG-DG-01 VP-DG-14 VP-DG-16

VP-DG-15

VP-DG-25

VP-DG-24

VOL-DG-09

VP-DPH-56

VP-DPH-60

VP-DPH-59

VP-DPH-58

VP-DG-11

VOL-DG-18

VOL-DPH-23

VP-DPH-55

VP-DG-09

VP-DG-13 VP-DG-08 VOL-DG-11 VOL-DPH-24

VP-DG-18

VP-DG-42

VP-DG-31

VP-DPH-53

VP-DPH-51 VOL-DG-06

VP-DPH-54

VP-DG-06

0 4 8 16 Miles

0 4 8 16 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Lake Berryessa

Study unit and

study area boundary

EXPLANATION

CDPH well

B

San Pablo Bay

Pacific Ocean

STUDY AREA

Valleys and Plains

Grid cell

Volcanic Highlands

Wilson Grove Formation

 Highlands

Figure B1. Continued. 
76 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Appendix C. Calculating Total 

Dissolved Solids

Specific conductance, an electrical measure of TDS, 

was measured for the 83 grid and 6 understanding wells 

sampled by the USGS, whereas TDS was only measured 

for 41 of these wells. For wells that had no measured TDS, 

TDS values were calculated from specific conductance (SC) 

values using a linear regression equation (TDS = 0.56*SC + 

58). The TDS values estimated using the regression equation 

closely matched measured TDS values (r2 = 0.96). TDS values 

from the CDPH were combined with USGS measured and 

calculated TDS values. 
Appendix D. Estimation of Aquifer-Scale Proportions 77

Appendix D. Estimation of AquiferScale Proportions

Two statistical approaches, grid-based and spatiallyweighted, were selected to evaluate the proportions of 

the primary aquifers in the NSF study unit that had high, 

moderate or low relative-concentrations (compared to 

benchmarks) of constituents. The grid-based and spatiallyweighted 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 

aquifers, 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 

cells (spatially-weighted). Raw detection frequencies, derived 

from the percentage of the total number of wells with high 

or moderate relative-concentrations, were also 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.

Grid-based: One well in each grid cell, a “grid well,” 

was randomly selected to represent the primary aquifers. 

Most grid wells were USGS grid wells that were sampled for 

the NSF study. Additional data for CDPH grid wells were 

selected to provide data for grid cells that did not have USGS 

grid well data. The relative-concentration for each constituent 

(concentration relative to its benchmark) was then evaluated 

for each grid well. The proportion of the primary aquifers that 

had high relative-concentrations was calculated by dividing 

the number of cells with concentrations > the benchmark 

(relative-concentration >1) by the total number of grid-wells in 

the NSF study unit. Proportions containing moderate and low 

relative-concentrations were calculated similarly. Confidence 

intervals for grid-based aquifer proportions were computed 

using the Jeffrey’s interval for the binomial distribution 

(Brown and others, 2001). The grid-based estimate is spatially 

unbiased. However, the grid-based approach may not 

detect constituents that exist at high concentrations in small 

proportions of the primary aquifers.

Spatially-weighted: The spatially-weighted approach 

relied upon USGS grid well data, CDPH data from August 

30, 2001 to September 1, 2004 (most recent analyses per 

well for all wells within each grid cell), and selected USGSunderstanding 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 relativeconcentrations for all the wells in each cell. The high, 

moderate and low aquifer-scale proportions are calculated 

from the percentage of cells with high, moderate, or low 

relative-concentrations (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 August 

30, 2001 to September 1, 2004 for the USGS grid well data, 

the CDPH well data (the most recent analysis per well for 

all wells), and USGS-understanding wells. However, this 

approach is not spatially unbiased 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 

aquifers, and could be given a disproportionately high (or 

low) weight compared to that given by spatially unbiased 

approaches. Raw detection frequencies of high relativeconcentrations are provided to identify constituents for 

discussion in this report, but were not used to assess aquiferscale proportions. 
78 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Appendix E. Ancillary Data

Land-use classifications and percentages of each, well 

construction information, normalized position along flowpath, 

geochemical conditions, and groundwater age data and 

classifications are listed in tables B1, E1, and E2. 

Land-Use Classification

Land use was classified using an enhanced version of 

the satellite derived (98 ft (30 m) pixel resolution) USGS 

National Land Cover Dataset (Voglemann and others, 2001; 

Price and others, 2003). 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. One 

pixel in the dataset imagery represents a land area of 9,688 ft2

(900 m2), calculated from the pixel radius of 98 ft (30 m). 

The imagery was classified into 25 land-cover categories 

(Nakagaki and Wolock, 2005). These 25 land-cover categories 

were aggregated into 4 principal land-use classes: urban, 

agricultural, natural, and mixed. Each pixel was assigned a 

land use class if >50 percent of the land cover in that area 

could be associated with a single land use. If no land cover 

was >50 percent of the pixel area, the classification of mixed 

was assigned. 

Land-use classes for the study unit, for study areas, and 

for circles with a radius of 1,640 ft (500 m), around each well 

were assigned using the USGS National Land Cover Dataset 

(Johnson and Belitz, 2009). Land-use classifications for the 

study unit and the study areas (fig. 4) were calculated from 

the land cover of each pixel in the study unit and the study 

areas. Land use assigned to the area surrounding an individual 

well (table B1) was calculated from land use within the area 

of a circle around each well (radius of 1,640 ft (500 m) and 

land area of 8,449,627 ft2 (785,398 m2). For some analyses of 

constituent distributions (fig. 16B), the land-use classes urban 

and agricultural were combined into a single class urban/

agriculture to represent land used for anthropogenic purposes.

Well-Construction Information

Most well-construction data were from driller’s logs and 

are given in table B1. Other sources were ancillary records 

of well owners and the USGS National Water Information 

System database. Well identification verification procedures 

are described by Kulongoski and others (2006).

Normalized Position of Wells along Flow Paths

The normalized position of wells within the alluvial 

valleys in relation to the groundwater flow system was an 

additional factor examined for the understanding of water 

quality in the NSF study unit (table B1). Three flowpaths 

were considered in this study: the Napa Valley flowpath, the 

Sonoma Valley flowpath, and the Russian River flowpath. 

Groundwater in the alluvium moves under a natural hydraulic 

gradient that conforms in a general way to the surface 

topography (Faye, 1973). In the Napa and Sonoma Valleys, 

groundwater flows generally from the northern part of the 

valley southward towards the San Pablo Bay (fig. 8). In the 

Alexander Valley and the Healdsburg area of the Santa Rosa 

Valley, groundwater flows generally from north to south 

towards the Lower Russian River Valley and then flows 

west (figs. 2, 8). Normalized position along the flowpath 

was determined by calculating the valley length (from the 

northernmost point in the valley to the discharge location). 

Then a perpendicular line was drawn from each well to the 

valley upgradient-downgradient axis (typically the location 

of the river), demarking the normalized position, or distance 

along the flowpath. Positions were normalized by dividing 

the projected location’s distance along the flow path by the 

total length of the system, resulting in a value from 0 to 1; 

normalized positions are given in table B1. Lower values 

of normalized position indicate locations in the upgradient 

or proximal portion of the flow system and higher values 

of position indicate locations in the downgradient or distal 

portion of the flow system. Plotting data with respect to 

normalized position along the flowpath also allows aerially 

distributed data to be aggregated into a single diagrammatic 

cross-section.

Classification of Geochemical Condition

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidationreduction characteristics, dissolved oxygen, and ratios of iron, 

arsenic and chromium species (table E1). 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 well-documented sequence of Terminal Electron 

Acceptor Processes (TEAP); one TEAP typically dominates 

at a particular time and aquifer location (Chapelle and 

others, 1995; Chapelle, 2001). The predominant TEAPs are 

oxygen-reduction (causing oxic conditions), 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. Different 

redox elements (for example; iron, manganese, and sulfur) 

tend not to reach overall equilibrium in most natural water 

systems (Lindberg and Runnels, 1984); therefore, a single 

redox measurement usually cannot represent the system, 

further complicating the assessment of redox conditions. 
Appendix E. Ancillary Data 79

Table E1. Oxidation-reduction constituents, redox classification, and iron, arsenic, and chromium species ratios for samples from the 

North San Francisco Bay study unit, California.—Continued

[anoxic/suboxic, cm3STP/g, cubic centimeter at standard temperature and pressure per gram of water; dissolved oxygen <0.5 mg/L (milligram per liter); 

indeterminant, insufficient data to determine redox classification; μg/L, microgram per liter; N, nitrogen; na, no data available; oxic, dissolved oxygen 

≥0.5 mg/L; redox, oxidation-reduction; O2, oxygen; NO3, nitrate; Mn, manganese; Fe, iron; As, arsenic; Cr, chromium. >, greater than; ≥, greater than or equal 

to; <, less than; —, not detected]

Oxidation-reduction constituent

Dissolved 

oxygen

Nitrate plus 

nitrite, as 

nitrogen

Manganese Iron Sulfate Methane

x 10–4

Oxidation-reduction threshold value

>0.5 >0.5 >50 >100 >4 >1

Possible redox type if concentration >redox threshold value

USGSGAMA well 

identification 

number

O2 NO3 Mn Fe

Analysis reporting level and associated units

0.1 0.06 0.18 5.0 0.18 0.1

Redox 

classification

Fe(III)/ 

Fe(II)

As(V)/ 

As(III)

Cr(VI)/ 

mg/L mg/L μg/L μg/L mg/L Cr(III) x 10–4

cm3STP/g

VOL-01 3.2 0.05 4.8 80 1.42 na Oxic 0.2 >10 2.0

VOL-05 na 1.45 na na 2.40 na Indeterminate na na >10

VOL-07 na 1.38 na na 6.10 na Indeterminate na na 9.2

VOL-09 na na 27 na 9.10 na Indeterminate na na >10

VOL-11 na 1.06 na na 8.70 na Indeterminate na na 5.8

VOL-14 4.3 0.79 10.7 na 7.40 na Oxic <0.01 >10 >10

VOL-15 na na 110 1,900 13 na Indeterminate na na 10.4

VOL-18 0.2 na na 240 3.10 na Anoxic/suboxic na na <0.01

VOL-19 1.9 0.03 127 235 5.94 na Mixed 0.1 1.1 >10

VOL-20 2.9 0.10 0.3 4 4.56 na Oxic >10 >10 >10

VP-01 na na 37 570 5.00 na Indeterminate na na >10

VP-04 na na 150 1,600 11 na Indeterminate na na <0.01

VP-06 na 5.20 na na 8.60 na Indeterminate na na 3.5

VP-07 na 1.04 150 190 7.90 na Mixed na na —

VP-08 na 1.27 na na 18 na Indeterminate na na 2.5

VP-09 na 0.99 na na 2.90 na Indeterminate na na >10

VP-10 0.1 0.31 203 117 11.3 na Anoxic/suboxic na na na

VP-11 na 1.20 na 190 14 na Indeterminate na na —

VP-16 na na 75 180 0.00 na Indeterminate na na na

VP-18 na 0.72 na na 12 na Indeterminate na na >10

VP-19 0.4 0.09 42.2 10 9.63 na Anoxic/suboxic 0.3 — >10

VP-21 na 3.16 na na 23 na Indeterminate na na >10

VP-22 na 2.48 na na 19 na Indeterminate na na >10

VP-23 na 4.07 na na 43 na Indeterminate na na >10

VP-25 na 1.02 42 na 1.40 na Indeterminate na na 3.6

VP-26 0.1 0.04 241 21 21.7 na Anoxic/suboxic 1.4 >10 >10

VP-27 na 3.16 150 210 13 na Mixed na na 4.2

VP-29 5.7 1.89 0.1 na 26.2 na Oxic — — >10

VP-30 0.6 0.62 36.8 13 19.1 na Oxic 0.4 — >10

VP-32 0.0 na 129 5 32.1 na Anoxic/suboxic na na 8.3

VP-33 na na 139 1,090 19.3 na Indeterminate na na >10

VP-34 0.2 0.06 434 999 2.58 0.41 Anoxic/suboxic 0.05 0.15 7.8

Table E1. Oxidation-reduction constituents, redox classification, and iron, arsenic, and chromium species ratios for samples from the 

North San Francisco Bay study unit, California.

[anoxic/suboxic, cm3STP/g, cubic centimeter at standard temperature and pressure per gram of water; dissolved oxygen <0.5 mg/L (milligram per liter); 

indeterminant, insufficient data to determine redox classification; μg/L, microgram per liter; N, nitrogen; na, no data available; oxic, dissolved oxygen 

≥0.5 mg/L; redox, oxidation-reduction; O2, oxygen; NO3, nitrate; Mn, manganese; Fe, iron; As, arsenic; Cr, chromium. >, greater than; ≥, greater than or equal 

to; <, less than; —, not detected]
80 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Table E1. Oxidation-reduction constituents, redox classification, and iron, arsenic, and chromium species ratios for samples from the 

North San Francisco Bay study unit, California.—Continued

[anoxic/suboxic, cm3STP/g, cubic centimeter at standard temperature and pressure per gram of water; dissolved oxygen <0.5 mg/L (milligram per liter); 

indeterminant, insufficient data to determine redox classification; μg/L, microgram per liter; N, nitrogen; na, no data available; oxic, dissolved oxygen 

≥0.5 mg/L; redox, oxidation-reduction; O2, oxygen; NO3, nitrate; Mn, manganese; Fe, iron; As, arsenic; Cr, chromium. >, greater than; ≥, greater than or equal 

to; <, less than; —, not detected]

Oxidation-reduction constituent

Dissolved 

oxygen

Nitrate plus 

nitrite, as 

nitrogen

Manganese Iron Sulfate Methane

x 10–4

Oxidation-reduction threshold value

>0.5 >0.5 >50 >100 >4 >1

Possible redox type if concentration >redox threshold value

USGSGAMA well 

identification 

number

O2 NO3 Mn Fe

Analysis reporting level and associated units

0.1 0.06 0.18 5.0 0.18 0.1

Redox 

classification

Fe(III)/ 

Fe(II)

As(V)/ 

As(III)

Cr(VI)/ 

mg/L mg/L μg/L μg/L mg/L Cr(III) x 10–4

cm3STP/g

VP-35 na 4.52 3.6 17 21.2 na Mixed na na —

VP-36 na 0.00 1,220 671 12.4 na Mixed na na 1.0

VP-37 3.5 3.22 6.4 na 14.8 0.32 Oxic >10 >10 3.9

VP-38 0.1 0.06 14.5 12 1.72 4.87 Anoxic/suboxic 0.3 4.8 3.3

VP-39 0.8 na 78.1 43 5.46 na Oxic na na <0.01

VP-40 0.1 0.06 40.1 331 3.17 8.03 Anoxic/suboxic 0.09 — 4.0

VP-41 1.2 3.61 928 na 23.6 na Oxic na na 0.0

VP-42 na 1.38 130 670 2.10 na Mixed na na na

VP-43 na na na 65 12 na Indeterminate na na >10

VP-44 na 2.94 135 310 239 na Mixed na na <0.01

VP-45 0.3 0.06 50.1 135 19.9 na Anoxic/suboxic — — >10

VP-46 1.1 2.48 1.6 22 43.7 na Oxic 0.3 — 16.0

VP-47 na 0.68 0.4 na 37.9 na Indeterminate na na >10

VP-48 0.0 0.00 181 714 14.9 na Anoxic/suboxic na na 2.5

VP-49 0.1 0.06 435 362 7.12 na Anoxic/suboxic <.01 <.01 5.5

VP-50 0.7 0.05 687 57 3.84 na Mixed 0.5 >10 >10

VPFP-01 0.1 0.06 66.4 36 4.70 na Anoxic/suboxic 0.2 >10 0.2

VPFP-02 0.3 1.55 355 na 15.4 na Anoxic/suboxic >10 >10 >10

VPFP-03 7.4 na 938 4 32.7 na Oxic na na >10

WG-06 na 1.08 na na 28 na Indeterminate na na 1.5

WG-08 0.8 1.21 12.9 40 20.3 na Oxic — — 2.3

WG-10 0.1 0.06 0.5 na 5.87 na Anoxic/suboxic >10 — 12.5

WG-11 na 1.29 na 99 20 na Indeterminate na na na

WGFP-01 0.2 0.06 16 29 15.9 na Anoxic/suboxic na na >10
Appendix E. Ancillary Data 81

Table E2. Noble gas based recharge temperature, tritium, terrigenic helium, percent modern carbon, and age classification of 

samples, North San Francisco Bay study unit, California.—Continued

[°C, degrees Celsius; ns, not sampled]

USGS GAMA well 

identification number

Noble gas 

based recharge 

temperature, 

in °C

Tritium, 

in tritium units

Terrigenic helium, 

percentage of 

total helium

Percent modern 

carbon Age classification

VOL-01 15.8 <1 1.4 68.7 Pre-Modern

VOL-02 ns <1 ns ns ns

VOL-03 20.0 <1 68.6 ns Pre-Modern

VOL-04 13.0 <1 11.7 ns Pre-Modern

VOL-05 14.6 <1 0.0 ns Mixed

VOL-06 16.1 <1 72.9 ns Pre-Modern

VOL-07 17.5 <1 8.4 ns Pre-Modern

VOL-08 16.9 <1 2.1 ns Mixed

VOL-09 13.7 <1 15.4 ns Pre-Modern

VOL-10 11.8 2.4 0.0 ns Modern

VOL-11 13.8 <1 0.7 ns Mixed

VOL-12 15.2 <1 31.0 ns Pre-Modern

VOL-13 13.6 3.1 0.0 93.1 Modern

VOL-14 15.2 1.3 0.0 ns Modern

VOL-15 13.2 <1 9.3 ns Pre-Modern

VOL-16 20.2 <1 86.0 ns Pre-Modern

VOL-17 14.1 <1 12.0 ns Pre-Modern

VOL-18 14.8 <1 70.9 ns Pre-Modern

VOL-19 16.1 <1 4.1 52.0 Pre-Modern

VOL-20 13.5 <1 4.7 47.7 Pre-Modern

VP-01 11.4 <1 80.3 ns Pre-Modern

VP-02 16.8 <1 1.6 ns Mixed

VP-03 14.6 <1 28.4 ns Pre-Modern

VP-04 14.8 <1 12.9 ns Pre-Modern

VP-05 12.1 <1 80.1 ns Pre-Modern

VP-06 14.4 <1 17.9 ns Pre-Modern

VP-07 13.8 <1 34.8 ns Pre-Modern

VP-08 15.4 2.3 4.3 ns Modern

VP-09 13.1 <1 20.1 ns Pre-Modern

VP-10 14.6 2.3 21.2 104.4 Mixed

VP-11 20.6 2.0 0.0 ns Modern

VP-12 20.5 <1 0.0 ns Mixed

VP-13 15.0 <1 3.4 ns Pre-Modern

VP-14 10.9 <1 76.5 ns Pre-Modern

VP-15 13.4 1.6 0.0 ns Modern

VP-16 13.8 <1 71.6 ns Pre-Modern

VP-17 16.1 2.1 4.6 ns Modern

VP-18 14.6 2.0 0.0 ns Modern

VP-19 21.4 2.1 4.3 97.4 Modern

VP-20 13.4 2.1 13.5 ns Mixed

Table E2. Noble gas based recharge temperature, tritium, terrigenic helium, percent modern carbon, and age classification of 

samples, North San Francisco Bay study unit, California.

[°C, degrees Celsius; ns, not sampled]
82 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

Table E2. Noble gas based recharge temperature, tritium, terrigenic helium, percent modern carbon, and age classification of 

samples, North San Francisco Bay study unit, California.—Continued

[°C, degrees Celsius; ns, not sampled]

USGS GAMA well 

identification number

Noble gas 

based recharge 

temperature, 

in °C

Tritium, 

in tritium units

Terrigenic helium, 

percentage of 

total helium

Percent modern 

carbon Age classification

VP-21 14.1 2.5 0.0 ns Modern

VP-22 15.9 2.0 19.8 ns Mixed

VP-23 12.1 <1 76.8 ns Pre-Modern

VP-24 15.0 <1 50.4 ns Pre-Modern

VP-25 15.3 <1 22.8 ns Pre-Modern

VP-26 14.5 <1 65.6 13.1 Pre-Modern

VP-27 14.4 1.5 27.2 ns Mixed

VP-28 14.9 1.7 40.2 ns Mixed

VP-29 15.2 2.3 0.0 104.5 Modern

VP-30 14.3 2.6 41.0 70.0 Mixed

VP-31 16.8 1.4 19.5 ns Mixed

VP-32 15.0 <1 60.0 ns Pre-Modern

VP-33 16.3 <1 11.5 ns Pre-Modern

VP-34 20.1 <1 93.9 19.7 Pre-Modern

VP-35 14.4 <1 1.2 ns Modern

VP-36 11.4 <1 74.8 ns Pre-Modern

VP-37 18.4 1.8 11.7 96.5 Mixed

VP-38 13.7 <1 95.8 0.6 Pre-Modern

VP-39 15.0 <1 48.7 ns Pre-Modern

VP-40 14.7 <1 92.7 13.3 Pre-Modern

VP-41 16.2 2.2 65.4 ns Mixed

VP-42 14.9 <1 0.0 ns Mixed

VP-43 14.9 <1 19.3 ns Pre-Modern

VP-44 ns <1 ns ns ns

VP-45 12.9 <1 79.3 6.7 Pre-Modern

VP-46 17.2 2.3 65.4 ns Mixed

VP-47 18.5 2.4 8.1 ns Mixed

VP-48 14.9 <1 83.1 ns Pre-Modern

VP-49 ns <1 96.9 2.2 Pre-Modern

VP-50 13.0 <1 44.6 19.1 Pre-Modern

VPFP-01 12.7 <1 54.6 24.0 Pre-Modern

VPFP-02 14.0 <1 7.5 84.7 Pre-Modern

VPFP-03 16.3 2.4 95.9 ns Mixed

VPFP-04 12.5 2.5 13.1 ns Mixed

WG-01 14.4 <1 0.0 ns Mixed

WG-02 16.2 <1 1.1 ns Mixed

WG-03 15.0 <1 0.0 ns Mixed

WG-04 15.5 <1 0.0 ns Mixed

WG-05 ns <1 ns ns ns

WG-06 ns 1.9 ns ns ns
Appendix E. Ancillary Data 83

Groundwater-Age Classification

Groundwater recharge temperature from noble gases, age 

data and classifications are listed in table E2. Groundwater 

dating techniques indicate the time since the groundwater 

was last in contact with the atmosphere. Techniques used to 

estimate groundwater residence times or ‘age’ include those 

based on tritium (for example: Tolstikhin and Kamenskiy, 

1969; Torgersen and others, 1979),tritium combined with its 

decay product helium-3 (for example, Takaoka and Mizutani, 

1987; Poreda and others, 1988), carbon-14 activities (for 

example, Vogel and Ehhalt, 1963; Plummer and others, 1993), 

and dissolved noble gases, particularly helium-4 accumulation 

(for example: Davis and DeWiest, 1966; Andrews and Lee, 

1979; Kulongoski and others, 2008). 

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 (HTO). Consequently, the presence of 3H in 

groundwater may be used to identify water that has exchanged 

with the atmosphere in the past 50 years. By determining 

the ratio of 3H to 3He, resulting from the radioactive decay 

of 3H, the time that the water has resided in the aquifer can 

be calculated more precisely than by using tritium alone (for 

example: Takaoka and Mizutani, 1987; Poreda and others, 

1988). 

Carbon-14 is a widely used chronometer based on the 

radiocarbon content of dissolved inorganic carbonate species 

in 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. 14C is incorporated 

into carbon dioxide and mixed throughout the atmosphere, 

dissolved in precipitation, and incorporated into the hydrologic 

cycle. 14C activity in groundwater, expressed as percent 

modern carbon (pmc), reflects exposure to the atmospheric 14C 

source and is governed by the decay constant of 14C (with a 

half-life of 5,730 yr). 14C can be used to estimate groundwater 

ages ranging from 1,000 to less than 30,000 years before 

present because of its half-life. 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). The 14C age (residence 

time) is calculated on the basis of the decrease in 14C activity 

due to radioactive decay since groundwater recharge, relative 

to an assumed initial 14C concentration (Clarke and Fritz, 

1997). Average initial 14C activity of 99 percent modern 

carbon (pmc) was assumed for this study, with estimated 

errors on calculated groundwater ages of up to ±20 percent.

Helium (He) is a naturally occurring inert gas initially 

included during the accretion of the planet, and later produced 

by the radioactive decay of lithium, thorium, and uranium in 

the earth. Measured He concentrations in groundwater is the 

sum of several He components including air-equilibrated He 

(Heeq), He from dissolved-air bubbles (Hea), terrigenic He 

(Heter), and tritiogenic He-3 (3Het). Helium (3He and 4He) 

concentrations in groundwater often exceed the expected 

solubility equilibrium values, a function of the temperature 

of the water, as a result of subsurface production of both 

isotopes and their subsequent release into the groundwater 

(for example, Morrison and Pine, 1955; Andrews and Lee, 

1979; Torgersen, 1980; Andrews, 1985; Torgersen and Clarke, 

1985). The presence of terrigenic He in groundwater, from 

its production in aquifer material or deeper in the crust, is 

indicative of long groundwater residence times. The amount 

of terrigenic helium is defined as the concentration of the total 

measured helium minus the fraction due to air equilibration 

Table E2. Noble gas based recharge temperature, tritium, terrigenic helium, percent modern carbon, and age classification of 

samples, North San Francisco Bay study unit, California.—Continued

[°C, degrees Celsius; ns, not sampled]

USGS GAMA well 

identification number

Noble gas 

based recharge 

temperature, 

in °C

Tritium, 

in tritium units

Terrigenic helium, 

percentage of 

total helium

Percent modern 

carbon Age classification

WG-07 12.0 <1 64.7 ns Pre-Modern

WG-08 15.7 <1 1.5 57.5 Pre-Modern

WG-09 15.2 <1 0.0 ns Mixed

WG-10 ns <1 ns 23.4 Pre-Modern

WG-11 15.6 <1 20.8 ns Pre-Modern

WG-12 14.6 <1 14.4 ns Pre-Modern

WG-13 ns <1 ns ns ns

WG-14 15.3 <1 48.8 ns Pre-Modern

WGFP-01 14.4 <1 3.9 45.2 Pre-Modern
84 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

[Heeq] and dissolved air bubbles [Hea]. For the purposes of this 

study, percent terrigenic He is used to identify groundwater 

with residence times greater than 100 yr. Percent terrigenic 

He is defined as the concentration of terrigenic He (as defined 

previously) divided by the total measured He in the sample 

(corrected for air-bubble entrainment). Samples with greater 

than 5 percent terrigenic He indicate groundwater has a 

residence time of more than 100 yr.

Recharge temperatures for 82 samples were calculated 

from dissolved neon, argon, krypton, and xenon data using 

methods described by Aeschbach-Hertig and others (1999). 

The only modeled recharge temperatures accepted were those 

for which the probability was greater than 1 percent that the 

sum of the squared deviations between the modeled and the 

measured concentrations (weighted with the experimental 

1-sigma errors) was equal to or greater than the observed 

value (Aeschbach-Hertig and others, 2000). The recharge 

temperature with the highest probability for each sample was 

used in this report. 

3H/3He ages were computed as described by Poreda and 

others (1988). The 3He/4He of samples was determined by the 

linear regression of the percentage of terrigenic He and δ3He 

[δ3He = (Rmeas/(Ratm–1) x 100] of samples containing less than 

1 tritium unit. Calculations of the noble gas temperature and 

3He to 4He ratios are useful because they constrain heliumbased groundwater ages further.

In this study, the age distributions of samples are 

classified as pre-modern, modern, and mixed. Groundwater 

with tritium activity less than 1 tritium unit (TU), percent 

terrigenic He greater than 5 percent, and 14C less than 90 pmc 

was designated as pre-modern, defined as having been 

recharged before 1950. Groundwater with tritium activities 

greater than 1 TU, percent terrigenic He less than 5 percent 

and 14C greater than 90 pmc is designated as modern, defined 

as having been recharged after 1950. Samples with premodern and modern components are designated as mixed 

groundwater, which includes substantial fractions of old 

and young waters. In reality, pre-modern groundwater could 

contain very small fractions of modern water and modern 

groundwater could contain small fractions of pre-modern 

water. Previous investigations have used a range of tritium 

values from 0.3 to 1.0 TU as thresholds for distinguishing 

pre-1950 from post-1950 water (Michel, 1989; Plummer and 

others, 1993; Michel and Schroeder, 1994; Clark and Fritz, 

1997; Manning and Solomon, 2005). By using a tritium value 

of 1.0 TU for the threshold in this study, the age classification 

scheme allows a slightly larger fraction of modern water to be 

classified as pre-modern than if a lower threshold were used. 

A lower threshold for tritium would result in fewer samples 

classified as pre-modern than mixed when other tracers, 

such as carbon-14 and terrigenic helium, would suggest that 

they were primarily pre-modern. This higher threshold was 

considered more appropriate for this study since many of the 

wells were production wells with long screens and mixing of 

waters of different ages is likely to occur. 

Tritium, percent modern carbon, and percent terrigenic 

helium, and sample age classifications are reported in 

Appendix E: table E2. Because of uncertainties in age 

distributions, in particular those caused by mixing waters of 

different ages in wells with long perforation intervals and high 

withdrawal rates, these age estimates were not specifically 

used for statistically quantifying the relation between age 

and water quality in this report. While more sophisticated 

lumped parameter models used for analyzing age distributions 

that incorporate mixing are available (for example, Cook 

and Böhlke, 2000), use of these alternative models to 

characterize age mixtures was beyond the scope of this report. 

Rather, classification into modern, mixed, and pre-modern 

categories was sufficient to provide an appropriate and useful 

characterization for the purposes of examining groundwater 

quality.
Appendix F. Comparison of California Department of Public Health (CDPH) and USGS–GAMA Data 85

Appendix F. Comparison of California 

Department of Public Health (CDPH) 

and USGS–GAMA Data

CDPH and GAMA data were compared to assess the 

validity of combining data from these different sources. 

Because the LRLs or MDLs for most organic constituents and 

trace elements were substantially lower for data collected by 

the USGS GAMA Priority Basin Project than 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. However, 

concentrations of inorganic constituents (calcium, magnesium, 

sodium, alkalinity, chloride, sulfate, TDS, nitrate plus nitrite 

as nitrogen), which generally are prevalent at concentrations 

substantially above LRLs, were compared for each well using 

data from both sources. Forty-one wells had major ion or 

nitrate data from the USGS-database and the CDPH database. 

Wilcoxon signed rank tests of paired analyses for these eight 

constituents indicated no significant differences between 

USGS–GAMA and CDPH data for these constituents. While 

differences between the paired data sets existed for a few 

wells, most sample pairs plotted close to a 1 to 1 line (fig. F1). 

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. F2). Piper diagrams are often 

used to define groundwater type (Hem, 1970). All cation/

anion data in the CDPH database that had a cation/anion 

balance <10 percent were retrieved and plotted on these Piper 

diagrams for comparison with grid well data.

The range of water types in grid wells and noted in 

historical CDPH data were similar (fig. F2). In most wells, no 

single cation accounted for more than 10 percent of the total 

cations, and bicarbonate accounted for more than 10 percent 

of the total anions. Waters in these wells are described as 

mixed cation-bicarbonate type waters. There were also many 

wells that contained mixed cation-mixed anion type waters, 

indicating that no single cation and no single anion accounted 

for more than 10 percent of the total. Waters in a minority of 

wells are classified as sodium-chloride type waters, indicating 

that sodium and chloride accounted for more than 10 percent 

of the total 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 the NSF study unit. 

A minor difference between grid data and CDPH data 

was evident: a minority of GAMA wells (8 wells) had higher 

sodium plus potassium as the dominant cations and sulfate 

plus chloride as the dominant anions (right side of center 

diagram, fig. F2). These were the hydrothermal wells and 

spring, which are not used for public drinking water supply 

(fig. F3), so it was not surprising that this type of water was 

not recorded in the CDPH database. Results from the analyses 

of these wells are provided here for reference and will be 

discussed in a subsequent report.
86 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

0.1

1

10

100

1,000

0.1 1 10 100 1,000

CONCENTRATION, CDPH DATA, IN MILLIGRAMS PER LITER

CONCENTRATION, USGS-GAMA DATA, IN MILLIGRAMS PER LITER

Calcium

EXPLANATION

Magnesium

Sodium

Chloride

Arsenic

Sulfate

Total Dissolved

 Solids

1:1 line

Figure F1. Paired inorganic constituent concentrations comparing USGS–GAMA data and California Department of Health (CDPH) 

data from the period August 30, 2001, to September 1, 2004, North San Francisco Bay study unit, California. USGS, U.S. Geological 

Survey; TDS, total dissolved solids. 
Appendix F. Comparison of California Department of Public Health (CDPH) and USGS–GAMA Data 87

PERCENT

Calcium

 100  80 

 60  40 

 20  0 

 0 

 20 

 40 

 60 

 80 

 100 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Carbonate plus Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

 0 

 20 

 40 

 60 

 80 

 100 

 0 

 20 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 40 

 20 

 0 

 0 

 0 

 0 

 100 

 80 

 60 

 40 

 20 

 0 

 60 

Magnesium

 0 

 100 

 80 

 60 

 40 

 20 

 0 

Sulfate

Sulfate plus Chloride

PERCENT

PERCENT

CDPH well (most recent analysis with charge imbalance <10%)

USGS GAMA well

EXPLANATION

Figure F2. Wells sampled for the North San Francisco Bay study unit, California, and wells that have constituents with a charge 

imbalance less than 10 percent recorded by the California Department of Public Health (CDPH) database. <, less than; %, percent.
88 Status and Understanding of Groundwater Quality, North San Francisco Bay Groundwater Basins, 2004: California GAMA Project

PERCENT

Calcium

 0 100 

 80 

 60 

 40 

 20  0 

 20 

 40 

 80 

 100 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Carbonate plus Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

 0 

 20 

 40 

 60 

 80 

 100 

 0 

 20 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 40 

 20 

 0 

 0 

 60 

 0 

Magnesium

Sulfate

Sulfate plus Chloride

PERCENT

PERCENT

Valleys and Plains well

Volcanic Highlands well

Wilson Grove Formation Highlands well

Hydrothermal well or spring

EXPLANATION

Figure F3. Constituents with a charge imbalance of less than 10 percent, organized by study area in the North San Francisco Bay study 

unit, California. 
Printed on recycled paper

Photo placement

Kulongoski and others—Status and Understanding of Groundwater Quality in the North San Francisco Bay

Groundwater Basins, 2004: California GAMA Priority Basin Project—SIR 2010–5089