U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the California State Water Resources Control Board

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

Status and Understanding of Groundwater Quality in the San Diego 

Drainages Hydrogeologic Province, 2004: California GAMA Priority 

Basin Project

Photo placement

Scientific Investigations Report 2011–5154
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

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

Sampling priority

Study area

Low-use basins

Priority basins

Areas that are outside CDWR-defined

 groundwater basins

Cover photographs:

Front cover: Lake Henshaw in San Diego County, California. (Photograph taken by Barbara Dawson,

U.S. Geological Survey).

Back cover: Citrus orchard in Pauma Valley, San Diego County, California. (Photograph taken by

Jill Densmore, U.S. Geological Survey.) 
Status and Understanding of Groundwater 

Quality in the San Diego Drainages 

Hydrogeologic Province, 2004: California 

GAMA Priority Basin Project

By Michael T. Wright and Kenneth Belitz

A product of the California Groundwater Ambient Monitoring and Assessment 

(GAMA) Program

Prepared in cooperation with the California State Water Resources Control Board

Scientific Investigations Report 2011–5154

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: 2011

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U.S. Government.

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Wright, M.T., and Belitz, K., 2011, Status and understanding of groundwater quality in the San Diego Drainages 

Hydrogeologic Province, 2004—California GAMA Priority Basin Project: U.S. Geological Survey Scientific 

Investigations Report 2011–5154, 100 p.
iii

Contents

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

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

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

Description of Study Unit .............................................................................................................................4

Description of Study Areas ..............................................................................................................10

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

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

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

USGS-Grid and -Understanding Wells ...................................................................................15

CDPH Grid Wells ........................................................................................................................19

Additional Data Used For Spatially Weighted Calculation.........................................21

Identification of Constituents for Status Assessment .................................................................21

Calculation of Aquifer-Scale Proportions.......................................................................................26

Understanding-Assessment Methods ............................................................................................27

Statistical Analysis .............................................................................................................................27

Potential Explanatory Factors...........................................................................................................28

Land Use......................................................................................................................................28

Well Depth...................................................................................................................................28

Groundwater Age Classification .............................................................................................29

Geochemical Condition.............................................................................................................30

Correlations between Explanatory Variables........................................................................30

Status and Understanding of Water Quality ...........................................................................................31

Inorganic Constituents.......................................................................................................................31

Trace Elements...........................................................................................................................31

Factors Affecting Vanadium Distribution .....................................................................38

Factors Affecting Arsenic Distribution..........................................................................38

Factors Affecting Boron Distribution.............................................................................42

Major and Minor Ions ...............................................................................................................43

Factors Affecting Manganese and Iron........................................................................43

Factors Affecting Total Dissolved Solids ......................................................................43

Radioactive Constituents..........................................................................................................49

Factors Affecting Radon-222 ..........................................................................................49

Organic and Special-Interest Constituents....................................................................................51

Trihalomethanes ........................................................................................................................51

Factors Affecting Trihalomethane Distribution............................................................51

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

Factors Affecting Solvent Distribution ..........................................................................56

Methyl Tert-Butyl Ether (MTBE) and Gasoline Components ..............................................56

Factors Affecting the Distribution of Methyl Tert-Butyl Ether (MTBE) and other 

Gasoline Components.........................................................................................60
iv

Contents—Continued

Status and Understanding of Water Quality—Continued

Pesticides....................................................................................................................................61

Factors Affecting Pesticide Distribution.......................................................................61

Perchlorate and Special-Interest Constituents....................................................................61

Factors Affecting Perchlorate Distribution ..................................................................65

Summary .......................................................................................................................................................65

Acknowledgments.......................................................................................................................................66

References....................................................................................................................................................66

Appendix A. Selection of CDHP-Well Data ............................................................................................72

Appendix B. Aquifer Proportion ...............................................................................................................78

Appendix C. Ancillary Datasets.................................................................................................................93

Appendix D. Comparison of CDPH and USGS-GAMA Data.................................................................97

Appendix E. Calculating Total Dissolved Solids...................................................................................100

Figures

Figure 1. Map showing location of the California hydrogeologic provinces and the 

San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California ........................................................................ 3

Figure 2. Map showing geographic features and study area boundaries of the 

San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California ........................................................................ 5

Figure 3. Graphs showing ternary diagram of proportions of urban, agricultural, 

and natural land-uses in the San Diego Groundwater Ambient Monitoring 

and Assessment (GAMA) study unit, California.......................................... 6

Figure 4. Maps showing land-use classification in the San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, and locations of 

grid and understanding wells ............................................................ 7

Figure 5. Maps showing the geology of the San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit and study areas..................... 11

Figure 6. Map showing locations of grid cells, California Department of Public Health 

(CDPH) wells, and the USGS-grid and -understanding wells sampled during 

May to July, 2004 for the San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit............................................................ 16

Figure 7. Graphs showing boxplots of construction attributes for grid and understanding 

wells, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California, May–July 2004 ................................................... 28

Figure 8. Graphs showing boxplots of relation of classified groundwater age to depth to 

top of the uppermost open interval below land surface and well depth below 

land surface, San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California, May–July 2004 .......................................... 29

Figure 9. Graph showing maximum relative-concentration in grid wells for constituents 

detected by type of constituent in the San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–July 2004......... 32
v

Figures—Continued

Figure 10. Graph showing dot plots of relative-concentrations of selected trace 

elements, radioactive constituents, nutrients, and major and minor elements 

in grid wells, San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California, May–July 2004 .......................................... 33

Figure 11. Maps showing values of selected inorganic constituents in USGS-grid and 

-understanding wells representative of the primary aquifers and the most 

recent analysis (July 30, 2001–July 29, 2004) for CDPH wells, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California, May–July 2004 .................................................................. 35

Figure 12. Graphs showing relation of vanadium to explanatory variables, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California .................................................................................... 40

Figure 13. Graphs showing relation of arsenic to explanatory variables in the San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California, May–July 2004 .................................................................. 41

Figure 14. Graph showing relation of boron concentrations to redox conditions and 

pH, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California, May–July 2004 ................................................... 42

Figure 15. Maps showing values of selected inorganic constituents in USGS-grid and 

-understanding wells representative of the primary aquifers and the most 

recent analysis July 30, 2001–July 29, 2004, for CDPH wells, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California, May–July 2004 .................................................................. 44

Figure 16. Graph showing relation of manganese and iron concentrations to redox 

conditions and pH, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004 ........................... 47

Figure 17. Graph showing boxplots of relation of total dissolved solids (TDS) 

concentrations to agricultural land use, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–July 2004......... 48

Figure 18. Graph showing relation of TDS to depth to the top of the uppermost open 

interval and to groundwater age classification, San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California, 

May–July 2004 .............................................................................. 49

Figure 19. Map showing values of radon-222 in USGS-grid and -understanding wells 

representative of the primary aquifers and the most recent analysis July 30, 

2001–July 29, 2004, for CDPH wells, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–July 2004......... 50

Figure 20. Graph showing detection frequency (non-area-weighted) and maximum 

relative-concentration for organic and special-interest constituents detected 

in grid wells, San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California, May–July 2004 .......................................... 52

Figure 21. Graph showing detection frequency (non-area-weighted) and relativeconcentrations in grid wells of selected organic and special-interest 

constituents, San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California, May–July 2004 .......................................... 53
vi

Figures—Continued

Figure 22. Map showing sum of trihalomethanes in USGS-grid and -understanding 

wells representative of the primary aquifers, and CDPH data from the prior 

3-year period of study (July 30, 2001 to July 29, 2004), San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California, 

May–July 2004 .............................................................................. 54

Figure 23. Graphs showing detection frequency and concentration of the trihalomethanes 

in relation to urban land use, and concentration of trihalomethanes in relation 

to depth to the top of the uppermost open interval and groundwater age 

classification, San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California, May–July 2004 .......................................... 55

Figure 24. Map showing sum of the solvents 1,2-dichloropropane, tetrachloroethylene (PCE), 

trichloroethylene (TCE), and carbon tetrachloride in USGS-grid wells 

representative of the primary aquifers, and CDPH data from the current period 

(July 30, 2001–July 29, 2004), San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004 ........................... 57

Figure 25. Graph showing detection frequency and sum of solvents concentration in 

relation to urban land use, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004 ........................... 58

Figure 26. Map showing sum of the gasoline components methyl tert-butyl ether (MTBE) 

and benzene in USGS-grid wells and from CDPH data for the prior 3-year period 

of study (July 30, 2001–July 29, 2004), San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California ........................... 59

Figure 27. Graph showing relation of methyl tert-butyl ether (MTBE) concentrations to 

distance from nearest leaking underground fuel tank (LUFT), San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California, May–July 2004 .................................................................. 60

Figure 28. Map showing sum of herbicides in USGS-grid wells and from CDPH data for 

the prior 3-year period of study (July 30, 2001–July 29, 2004), San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California, May–July 2004 .................................................................. 62

Figure 29. Graph showing sum of herbicide concentrations in relation to depth to the 

top of the uppermost open interval and groundwater age classification, and in 

relation to redox condition of groundwater, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–July 2004......... 63

Figure 30. Map showing perchlorate in USGS-grid wells and from CDPH data for the prior 

3-year period of study (July 30, 2001–July 29, 2004), San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California, 

May–July 2004 .............................................................................. 64
vii

Tables

Table 1. Constituent class and numbers of constituents and wells sampled for each 

analytical group in the San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May 17–July 29, 2004. .................. 19

Table 2. Inorganic constituents and number of grid wells per constituent, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

May–July 2004 .............................................................................. 20

Table 3. Comparison of number of compounds and median method detection limit 

or laboratory reporting levels by type of constituent for data stored in the 

California Department of Public Health database and data collected for the 

San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit, California, May 17–July 29, 2004 ................................................... 21

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

constituents detected in the Alluvial Fill study areas (Temecula Valley, 

Warner Valley, and Alluvial Basins) (1) with high relative-concentrations 

reported in the California Department of Public Health (CDPH) database 

during July 30, 2001–July 29 2004, or (2) with moderate or high relativeconcentrations in samples collected from grid wells during May–July 2004, 

or (3) with organic or special-interest constituents detected in more than 

10 percent of samples collected from grid wells during May–July 2004, 

San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California ........................................................................ 22

Table 5. Number of constituents analyzed and detected by benchmark and constituent 

type, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit grid wells, California, May 17 to July 29, 2004 .............................. 25

Table 6. Constituents with one or more concentrations above health-based benchmarks 

for the period of May 1983 to June 2001, based on the California Department 

of Public Health data for public-supply wells in the San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California ............... 26

Table 7. Results of non-parametric analysis of correlations between selected potential 

explanatory variables, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004 ........................... 30

Table 8. Grid-based aquifer-scale proportions for constituent classes in the Alluvial 

Fill study areas, (Temecula Valley, Warner Valley, and Alluvial Basins), San 

Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California .................................................................................... 34

Table 9. Results of non-parametric analysis of correlations between selected 

water-quality constituents and potential explanatory factors, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California .................................................................................... 39
viii

Conversion Factors, Datums, and Abbreviations and Acronyms

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2

) 2.590 square kilometer (km2)

Flow rate

gallon per minute (gal/min) 0.06309 liter per second (L/s)

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

kilometer (km) 0.6214 mile (mi)

meter (m) 3.281 foot (ft) 

Volume

liter (L) 0.2642 gallon (gal)

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)

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

°F=(1.8×°C)+32.

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).

cm3 STP g–1 cubic centimeters at standard temperature and pressure per gram 

δiE delta notation; the ratio of the heavier isotope (i) to the more common lighter isotope of an 

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

mL milliliter

pCi/L picocuries per liter

per mil parts per thousand

pmc percent modern carbon

TU tritium unit

% percent
ix

Datums

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

(NAVD 88).

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

Abbreviations and Acronyms

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

BLS below land surface

DG CDPH data from well sampled by GAMA

DPH CDPH data from well not sampled by GAMA

E estimated or having a higher degree of uncertainty

GAMA Groundwater Ambient Monitoring and Assessment Program

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

HBSL health-based screening level

LRL laboratory reporting level

LSD land-surface datum

LT-MDL long-term method detection level

MCL-CA California Department of Public Health maximum contaminant level 

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

MDL method detection limit

MRL minimum reporting level

NL-CA California Department of Public Health notification level

NWIS National Water Information System (USGS)

PSW public-supply wells

RPD relative percentage difference

RSD relative standard deviation

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

SDALLV Alluvial Basins study area

SDALLVU Alluvial Basins study area understanding well

SDHDRK Hard Rock study area

SDHDRKU Hard Rock study area understanding well

SDTEM Temecula Valley study area

SDTEMFP Temecula Valley study area flow path well

SDWARN Warner Valley study area

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

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

US United States

> greater than

≥ greater than or equal to

< less than

≤ less than or equal to

 

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
x

Organizations

CDPH California Department of Public Health (was California Department of Health Services 

prior to July 1, 2007)

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water Quality Assessment (USGS)

NWQL National Water Quality Laboratory (USGS)

SWRCB State Water Resources Control Board (California)

USEPA U.S. Environmental Protection Agency

USGS U. S. Geological Survey

Selected Chemical Names

DO dissolved oxygen

Fe iron

Nitrate-N nitrate as nitrogen

Nitrite-N nitrite as nitrogen

Mn manganese

MTBE methyl tert-butyl ether

PCE perchloroethene (tetrachloroethene)

TCE trichloroethene

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
Status and Understanding of Groundwater Quality in the 

San Diego Drainages Hydrogeologic Province, 2004: 

California GAMA Priority Basin Project

By Michael T. Wright and Kenneth Belitz

Abstract

Groundwater quality in the approximately 3,900-squaremile (mi2) San Diego Drainages Hydrogeologic Province 

(hereinafter San Diego) study unit was investigated from 

May through July 2004 as part of the Priority Basin Project 

of the Groundwater Ambient Monitoring and Assessment 

(GAMA) Program. The study unit is located in southwestern 

California in the counties of San Diego, Riverside, and 

Orange. The GAMA Priority Basin Project is being conducted 

by the California State Water Resources Control Board in 

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

Lawrence Livermore National Laboratory. 

The GAMA San Diego study was designed to provide 

a statistically robust assessment of untreated-groundwater 

quality within the primary aquifer systems. The assessment 

is based on water-quality and ancillary data collected by the 

USGS from 58 wells in 2004 and water-quality data from the 

California Department of Public Health (CDPH) database. The 

primary aquifer systems (hereinafter referred to as the primary 

aquifers) were defined by the depth interval of the wells 

listed in the California Department of Public Health (CDPH) 

database for the San Diego study unit. The San Diego study 

unit consisted of four study areas: Temecula Valley (140 mi2), 

Warner Valley (34 mi2), Alluvial Basins (166 mi2), and Hard 

Rock (850 mi2). The quality of groundwater in shallow or 

deep water-bearing zones may differ from that in the primary 

aquifers. For example, shallow groundwater may be more 

vulnerable to surficial contamination than groundwater in deep 

water-bearing zones. 

This study had two components: the status assessment

and the understanding assessment. The first component of 

this study—the status assessment 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. The status assessment is 

intended to characterize the quality of groundwater resources 

within the primary aquifers of the San Diego study unit, 

not the treated drinking water delivered to consumers by 

water purveyors. The second component of this study—the 

understanding assessment—identified the natural and human 

factors that affect groundwater quality by evaluating land use, 

well construction, and geochemical conditions of the aquifer. 

Results from these evaluations were used to help explain the 

occurrence and distribution of selected constituents in the 

study unit. 

Relative-concentrations (sample concentration divided 

by benchmark concentration) were used as the primary metric 

for relating concentrations of constituents in groundwater 

samples to water-quality benchmarks for those constituents 

that have Federal and (or) California benchmarks. For organic 

and special-interest constituents, relative-concentrations were 

classified as high (> 1.0), moderate (> 0.1 and ≤ 1.0), and low 

(≤ 0.1). For inorganic constituents, relative concentrations 

were classified as high (> 1.0), moderate (> 0.5 and ≤ 1.0), 

and low (≤ 0.5). Grid-based and spatially weighted approaches 

were then used to evaluate the proportion of the primary 

aquifers (aquifer-scale proportions) with high, moderate, and 

low relative-concentrations for individual compounds and 

classes of constituents. 

One or more of the inorganic constituents with healthbased benchmarks were high (relative to those benchmarks) 

in 17.6 percent of the primary aquifers in the Temecula 

Valley, Warner Valley, and Alluvial Basins study areas 

(hereinafter also collectively referred to as the Alluvial 

Fill study areas because they are composed of alluvial fill 

aquifers), and in 25.0 percent of the Hard Rock study area. 

Inorganic constituents with health-based benchmarks that were 

frequently detected at high relative-concentrations included 

vanadium (V), arsenic (As), and boron (B). Vanadium and 

As concentrations were not significantly correlated to either 

urban or agricultural land use indicating natural sources as the 

primary contributors of these constituents to groundwater. The 

positive correlation of B concentration to urban land-use was 

significant which indicates that anthropogenic activities are a 
2 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

contributing source of B to groundwater. The correlation of V, 

As and B concentrations to pH was positive, indicating that 

in alkaline groundwater these constituents are being desorbed 

from, or being inhibited from adsorbing to, particle surfaces. 

Inorganic constituents with aesthetic benchmarks 

that were detected at high relative-concentrations include 

manganese (Mn), iron (Fe), and total dissolved solids (TDS). 

In the Alluvial Fill study areas, Mn and TDS were detected 

at high relative-concentrations in 13.7 percent of the primary 

aquifers, and Fe in 6.9 percent of the primary aquifers. In 

the Hard Rock study area, Mn was detected at high relativeconcentrations in 33.3 percent of the primary aquifers, and 

TDS in 16.7 percent; Fe was not detected at high relativeconcentrations. Total dissolved solids concentrations were 

significantly correlated to agricultural land use suggesting 

that agricultural practices are a contributing source of TDS to 

groundwater. Manganese and Fe concentrations were highest 

in groundwater with low dissolved oxygen and pH indicating 

that the reductive dissolution of oxyhydroxides may be an 

important mechanism for the mobilization of Mn and Fe in 

groundwater. TDS concentrations were highest in shallow 

wells and in modern (< 50 yrs) groundwater which indicates 

anthropogenic activities as a source of TDS concentrations in 

groundwater.

The relative-concentrations of organic constituents 

with health-based benchmarks were high in 3.0 percent 

of the primary aquifers in the Alluvial Fill study areas. A 

single detection in the Alluvial Basins study area of the 

discontinued gasoline oxygenate methyl tert-butyl ether 

(MTBE) was the only organic constituent detected at a high 

relative-concentration; high relative-concentrations of these 

constituents were not detected in the Hard Rock study area. 

Twelve of 88 VOCs and 14 of 123 pesticides and pesticide 

degradates analyzed in grid wells were detected. Chloroform 

was the only VOC detected in more than 10 percent of the 

grid wells. The herbicides simazine, atrazine, and prometon 

were each detected in greater than 10 percent of the grid 

wells. Perchlorate was detected in 22 percent of the grid wells 

sampled.

The understanding assessment showed a significant 

correlation of trihalomethanes (THMs) and solvents to urban 

land-use, indicating that detections of these constituents are 

more likely to occur in groundwater underlying urbanized 

areas of the study unit. MTBE concentrations were 

negatively correlated to the distance from the nearest leaking 

underground fuel tank, indicating that point sources are the 

most significant contributing factor for MTBE concentrations 

to groundwater in the study unit. The positive correlation of 

THM and herbicide concentrations to modern groundwater 

was significant, as was the negative correlation of herbicide 

concentrations to pH and anoxic groundwater. The negative 

correlation of herbicides to pH and anoxic groundwater was 

likely due to the fact that these constituents were detected 

more frequently in shallow wells where groundwater 

conditions tend to be oxic with relatively low pH.

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 (State 

of California, 2011, at http://www.waterboards.ca.gov/gama). 

The statewide GAMA program currently consists of three 

projects: the GAMA Priority Basin Project, conducted by the 

USGS (U.S. Geological Survey, 2011, at 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 primarily focused 

on the deep part of the groundwater resource, and the SWRCB 

Domestic Well Project generally focused on the shallow 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 deep primary 

aquifers (Landon and others, 2010).

The SWRCB initiated the GAMA Program in 2000 in 

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

2001a, 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 (State of California, 2001b, 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 improve 

understanding and identification of risks to groundwater 

resources and to increase the availability of information 

about groundwater quality to the public. For the Priority 

Basin Project, the USGS, in collaboration with the SWRCB, 

developed 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). 

The range of hydrologic, geologic, and climatic 

conditions 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). 
Introduction 3

Figure 1. Location of the California hydrogeologic provinces and the San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California.

Basin and Range

Central

Valley

Klamath

Mountains

Desert

Cascades and

Modoc Plateau

Transverse Ranges and

Selected Peninsular Ranges

Southern Coast

Ranges

Sierra

Nevada

Northern

Coast

Ranges

San Diego

Drainages

Bakersfield

San

Francisco

Sacramento

OREGON

NEVADA

MEXICO

ARIZONA

Redding

Los Angeles

San Diego

PA C I F I C O C E A N

0 200 MILES

0 200 KILOMETERS

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

Provinces from Belitz and others, 2003

17-0006 Wright_Figure 1 Calilfornia provinces and study area.

Study unit
4 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

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 (PSW) 

are in designated groundwater basins. Groundwater basins 

and subbasins were prioritized for sampling on the basis of 

the number of PSWs, 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 PSWs 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 

San Diego Drainages Hydrogeologic Province Groundwater 

Ambient Monitoring and Assessment (GAMA) study 

unit (hereinafter San Diego study unit) (fig. 1), (2) status 

assessment: assessment of the status of the current quality of 

groundwater in the primary aquifer systems in the San Diego 

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 

those factors.

Water-quality data for samples collected by the USGS for 

the GAMA Program in the San Diego study unit and details of 

sample collection, analysis, and quality-assurance procedures 

for the San Diego study unit are reported by Wright and 

others (2005). 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 datasets, classifying groundwater age, and assessing 

the status and understanding of groundwater quality by using 

statistical and graphical approachess.

The status assessment includes analyses of water-quality 

data for 47 PSWs selected by the USGS for spatial coverage 

of one well per grid cell (hereinafter referred to as USGSgrid wells) across the San Diego study unit. 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 23 PSWs in 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 San 

Diego 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 San 

Diego 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 untreated groundwater.

In addition to the 47 grid-wells sampled for the status 

assessment, the understanding assessment also uses data 

from the 11 wells sampled by the USGS for the purposes of 

understanding (hereinafter referred to as USGS-understanding 

wells). Data from these wells are used 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 to the top of the uppermost 

open interval, indicators of groundwater age, and geochemical 

conditions. 

Description of Study Unit 

The San Diego study unit boundaries are the same 

as those of the San Diego Drainages Hydrogeologic 

Province described by Belitz and others (2003) and covers 

approximately 3,900 square miles (mi2). The San Diego study 

unit encompasses the majority of San Diego County, as well as 

parts of southwestern Orange and Riverside Counties (fig. 2). 

Geographic boundaries of the San Diego study unit are the 

Transverse Ranges and Selected Peninsular Ranges Province 

to the north, the Desert Province to the east, the country of 

Mexico to the south, and the Pacific Ocean to the west. 

The climate in the coastal areas of the San Diego study 

unit generally is mild, with temperatures averaging 64 degrees 

Fahrenheit (°F) and average annual precipitation ranging 

from 10 to 13 inches (in.) (California Regional Water Quality 

Control Board, San Diego Region, 1994). In the eastern 

part of the study unit, annual temperatures in the Peninsular 

Ranges average 55 °F, with average annual precipitation of 

approximately 45 in. 
Description of Study Unit 5

Figure 2. Geographic features and study area boundaries of the San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California.

Santa

Margarita

River

San Luis Rey River

San Diego River

Sweetwater

River

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

Temecula

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 2 Study areas.

PENINSULAR 

RANGES

Temecula Valley 

Warner Valley

Alluvial Basins

Hard Rock

Study unit boundary

EXPLANATION

Oceanside

PACIFIC OCEAN

Study areas
6 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

The San Diego study unit is drained by a number of 

creeks and rivers, including the Santa Margarita and San Luis 

Rey Rivers in the north, and the San Diego and Sweetwater 

Rivers in the south (fig. 2). Runoff in the study unit is 

attributed mainly to rainfall; however, smaller amounts of 

runoff come from urban water use, snowmelt, and artesian 

springs. Groundwater and surface-water flow direction is 

primarily from the mountainous east towards the west and the 

Pacific Ocean. Groundwater recharges in the study unit by 

precipitation, irrigation returns, infiltration of reservoir and 

river water, and engineered recharge. Groundwater primarily 

discharges through pumping from wells.

The San Diego study unit is composed of relatively small 

groundwater basins that underlie approximately 400 mi2 of 

land surface, corresponding to the Temecula Valley, Warner 

Valley, and Alluvial Basins study areas (fig. 2). In addition, 

a part of the groundwater resources in the San Diego study 

unit are in areas outside of defined groundwater basins. This 

area underlies approximately 850 mi2 of the study unit land 

surface and was defined as all areas located outside a CDWRdefined groundwater basin, but within 1.9 miles (mi) of a PSW 

documented in the CDPH database, and corresponds to the 

Hard Rock study area (fig. 2). 

The land use in the study unit is 7 percent agricultural, 

84 percent natural, and 9 percent urban based on classification 

by the USGS National Land Cover Data (Vogelman and 

others, 2001; Price and others, 2003) (fig. 3A). The natural 

land-use areas are mostly shrub land, with lesser amounts of 

evergreen forest and grass lands. Natural land-use is most 

predominant in the eastern parts of the study unit (fig. 4A–C). 

Figure 3A–B. Ternary diagram of proportions of urban, agricultural, and natural land-uses in the San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California. (A) Study unit and study areas, (B) wells. 

Hard Rock study-area grid wells

Temecula Valley study-area grid wells 

Warner Valley study-area grid wells

Understanding wells

Alluvial Basins study-area grid wells

20

40

60

80

100

0

100 80 60 40 20 0

100

80

60

40

20

0

PERCENT AGRICULTURE

20

40

60

80

100

0

100 80 60 40 20 0

100

80

60

40

20

0

PERCENT NATURAL

PERCENT AGRICULTURE

PERCENT URBAN

PERCENT NATURAL

PERCENT URBAN

EXPLANATION EXPLANATION

Study unit

Study areas

Alluvial Basins

Hard rock

Temecula Valley

Warner Valley

A B

Data point:

Land use 

averaged for

entire study

unit or

entire individual

study area

Data point:

Land use based on

an average for

the 500-meter radius 

area around

each grid well
Description of Study Unit 7

Figure 4A–C. Land-use classification in the San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, and 

locations of grid and understanding wells. (A) Temecula Valley, (B) Warner Valley, (C) Alluvial Basins, and Hard Rock study areas.

Railroad

Canyon

Reservoir

Santa

Margarita

River

Temecula Creek

Murrieta

Creek

Lake Skinner

Vail Lake

Grid well

Urban Understanding well

Land-use classification

EXPLANATION

Agricultural

Natural

Shaded relief derived from U.S. Geological Survey

National Elevation digital Dataset, 2006,

Albers Equal Area Conic Projection

117 15' 117 10' 117 05' 117 00' 116 55' 116 50'

33

30'

33

35'

33

40'

Temecula

RIVERSIDE COUNTY

SAN DIEGO COUNTY

0 5 MILES

0 5 KILOMETERS

Study area boundary

Study unit boundary

A
8 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 4A–C.—Continued

Lake 

Henshaw

San Luis Rey River

San Luis Rey

River

Agua Caliente Creek

Buena Vista Creek

Matagual Creek

Carrista

Creek

Urban

Land-use classification

EXPLANATION

Agricultural

Natural

Study area boundary

Study unit boundary

0 2 4 MILES

0 2 4 KILOMETERS

116 45' 116 40' 116 35'

33

20'

33

15'

PENINSULAR 

RANGES

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 4b Land use.

Grid well

B
Description of Study Unit 9

Figure 4A–C.—Continued

Shaded relief derived from U.S. Geological Survey

National Elevation digital Dataset, 2006,

Albers Equal Area Conic Projection

17-0006 Wright_Figure 4c Land use.

EXPLANATION

Urban

Landuse

classification

Agricultural

Natural

Hard rock

Study

Alluvial areas

basin Study unit boundary

0 10 20 MILES

0 10 20 KILOMETERS

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

PENINSULAR 

RANGES

Temecula

Grid well

Understanding well

C
10 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Agricultural land-use in the study unit is equal parts orchards 

and pasture land, with a small percentage of row crops. Urban 

land-use primarily is found in the coastal areas of the study 

unit and the largest urban center is the San Diego metropolitan 

area. The majority of land use in all study areas is natural; 

the Warner Valley and Hard Rock study areas are classified 

as 99 and 91 percent natural, respectively (fig. 3A). The 

Alluvial Basins and Temecula Valley study areas are the most 

urbanized in the San Diego study unit (28 and 13 percent, 

respectively); the largest amount of agricultural land-use also 

is in these study areas (17 and 20 percent respectively). 

Description of Study Areas 

The boundaries of the Temecula Valley study area (fig. 2) 

are the same as those of the Temecula Valley groundwater 

basin as described by the California Department of Water 

Resources, (2004a). The Temecula Valley study area primarily 

is in southwestern Riverside County with a very small part 

of the basin extending into northern San Diego County. The 

Temecula Valley study area covers approximately 140 mi2

and is bounded by the relatively impermeable rocks of the 

Peninsular Ranges on three sides. The main water-bearing 

units are Quaternary alluvium that is estimated to be as great 

as 2,500 feet (ft) thick; generally it is unconfined except in 

areas where faults cut across the basin (California Department 

of Water Resources, 1956; Kennedy, 1977). Rock types that 

bound the groundwater-bearing deposits in the study area 

include Mesozoic granites and gabbros and Jurassic marine 

sedimentary rocks (fig. 5A) (Saucedo, 2000). Sources of 

groundwater recharge in the basin include percolation of 

precipitation, infiltration of irrigation and domestic return 

water, and engineered recharge from spreading basins along 

Temecula Creek. Groundwater primarily discharges through 

groundwater pumping. Average annual precipitation ranges 

from 7 to 15 in. Surface water drains to several creeks, 

including Temecula and Murrieta Creeks that discharge into 

the Santa Margarita River, which then flows westward out of 

the valley. 

The boundaries of the Warner Valley study area (fig. 2) 

are the same as those of the Warner Valley groundwater basin, 

which is located in northeastern San Diego County (California 

Department of Water Resources, 2004b). The Warner Valley 

study area has a surface area of 37 mi2; it is bounded on the 

west by Lake Henshaw and on all other sides by the crystalline 

rocks of the Peninsular Ranges. The main water-bearing unit 

consists of alluvium and residuum (California Department of 

Water Resources, 1971). The alluvium is as great as 900 ft 

thick and generally is unconsolidated. The crystalline rocks 

that bound the groundwater-bearing deposits in this study 

area consist primarily of Mesozoic granite and metamorphic 

rocks of pre-Cenozoic age (fig. 5B) (Saucedo, 2000). Sources 

of groundwater recharge include percolation of precipitation, 

and river and stream runoff. Groundwater discharges primarily 

through groundwater pumping. Annual precipitation ranges 

from 15 to 21 in. The Warner Valley study area is primarily 

drained by the Agua Caliente and Buena Vista Creeks, and 

the San Luis Rey River, all of which flow westward into Lake 

Henshaw. 

The Alluvial Basins study area (fig. 2) is composed of all 

alluvial basins in the study unit that have one or more PSWs. 

The 12 groundwater basins in this study area are the San Juan, 

San Mateo, Santa Margarita, San Luis Rey, San Pasqual, Santa 

Maria, San Diego River, El Cajon, Sweetwater, Cottonwood, 

Campo, and Potrero Valleys (California Department of 

Water Resources, 2003). The collective surface area of 

the study area is approximately 166 mi2, with individual 

basins ranging in area from as small as 3 mi2 (California 

Department of Water Resources, 2004c), to as large as 46 mi2

(California Department of Water Resources, 2004d). The 

main water-bearing units are Quaternary age alluvium and 

residuum, with an average thickness of alluvium that ranges 

from approximately 15 ft in the San Mateo Valley groundwater 

basin (California Department of Water Resources, 1991) to 

60 ft in the San Luis Rey groundwater basin (Izbicki, 1985). 

Inland alluvial basins generally are bound by the Mesozoic 

granites of the Peninsular Ranges, whereas coastal alluvial 

basins generally are bounded by Cenozoic-aged sedimentary 

rocks (fig. 5C) (Saucedo, 2000). Sources of groundwater 

recharge include percolation of precipitation, river and 

stream runoff, agricultural and domestic returns, discharge 

of wastewater to rivers, and septic systems. Groundwater 

primarily discharges through groundwater pumping. The 

average annual precipitation in these basins range from as 

little as 8 in. to as great as 21 in. Runoff from precipitation 

primarily is drained to the southwest towards the Pacific 

Ocean, but some basins are internally drained. 

The Hard Rock study area (fig. 2) consists of all areas 

outside of CDWR-defined groundwater basins that are within 

3 km of a PSW. The study area covers approximately 850 mi2

and most of the study area is in the inland areas of the study 

unit. Surficial geology in the study area primarily is composed 

of granitic and metamorphic rocks with small amounts of 

Mesozoic volcanic and Cenozoic marine sedimentary rocks 

(fig. 5C). Well completion reports for the PSWs sampled by 

the GAMA program indicate that wells are withdrawing water 

primarily from fractured and decomposed granite. Sources of 

groundwater recharge include percolation of precipitation, and 

river and stream runoff. 
Description of Study Unit 11

Figure 5A–C. The geology of the San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit and study areas: 

(A) Temecula Valley, (B) Warner Valley, (C) Alluvial Basins, and Hard Rock study areas.

Santa

Margarita

River

Temecula Creek

Murrieta

Creek

Lake Skinner

Railroad

Canyon

Reservoir

Vail Lake

Plio-Pleistocene nonmarine;

Pliocene nonmarine

Alluvium (mostly Holocene,

some Pleistocene); Quaternary

nonmarine; Quaternary marine

Grid well (USGS

 GAMA and CDPH)

Understanding well

 (USGS GAMA)

Geologic units

EXPLANATION

Cenozoic sedimentary rocks

Shaded relief derived from U.S. Geological Survey

National Elevation digital 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

17-0006 Wright_Figure 5a Geology of Temecula area.

117 15' 117 10' 117 05' 117 00' 116 55' 116 50'

33

30'

33

35'

Temecula

Q

QPc

QPc

QPc

Q

Q

Q

Q

Q

Q

Q

gb

gb

gb

gb

gb

gb gb

gb

gb

gb

gb

J

J

J

J

J

J

J

J

J

J

J

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz grMz

grMz grMz

Qv

Qv

Qv

gr-m

gr-m

gr-m

gr-m

gr-m

m

m

Mzv

Mzv

m

m

Quaternary volcanic flow rocks

(or predominantly flow rocks)

Mesozoic granitic rocks

Jurassic marine rocks

Granitic and metamorphic rocks,

undivided, of pre-Cenozoic age

Mesozoic gabbroic rocks

Cenozoic volcanic rocks

Mesozoic and pre-Cenozoic plutonic, volcanic,

metamorphic, and sedimentary rocks

Mesozoic volcanic and metavolcanic

rocks; Franciscan volcanic rocks

Undivided pre-Cenozoic metasedimentary and metavolcanic

rocks of great variety

RIVERSIDE COUNTY

SAN DIEGO COUNTY

33

40'

0 5 MILES

0 5 KILOMETERS

A

Fault-Dashed where

approximately located;

dotted where concealed

Geologic contact

Study unit boundary
12 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 5A–C.—Continued

Lake 

Henshaw

San Luis Rey River

San Luis Rey

River

Agua Caliente Creek

Buena Vista Creek

Matagual Creek

Carrista Creek

Q

Q

Q

Q

Q

gb

grMz

grMz

grMz

grMz

grMz grMz grMz

gr-m

gr-m

gr-m

gr-m

gr-m

Mesozoic gabbroic rocks

Mesozoic granitic rocks

Granitic and metamorphic rocks, undivided,

 of pre-Cenozoic age

Cenozoic sedimentary rocks

Mesozoic and pre-Cenozoic mixed and plutonic rocks

Grid well (USGS GAMA

 and CDPH)

Geologic units

Q

B

gb

grMz

gr-m

0 2 4 MILES

0 2 4 KILOMETERS

EXPLANATION

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Fault-Dashed where

 approximately located;

 dotted where concealed

Geologic contact

Study unit boundary

116 45' 116 40' 116 35'

33

20'

33

15'

PENINSULAR 

RANGES

Alluvium (mostly Holocene, some Pleistocene);

 Quaternary nonmarine; Quaternary marine

17-0006 Wright_Figure 5b Geology of Warner area.

Geology modified from

Division of Mines and Geology,

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

for the Geologic Map of California
Description of Study Unit 13

Figure 5A–C.—Continued

Q

Q

Q

Q

Q

Q

Q

Q

Q

Q

Q

Q

Q

QPc

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

gb

J

J

J

J

J

J

J

J

J

J

J

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

grMz

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m

gr-m gr-m

M

M

M

m

m

m

m m

P

P

P

Ec

Ec

Ec

Ec

Ec

Ec

Ku

Ku

Ku

Ku

sch

Mzv

Mzv

Mzv

Mzv

Mzv

Mzv

Mzv

Geologic units

Cenozoic sedimentary rocks

Q

Ec

QPc

P

Qv Quaternary volcanic flow rocks

 (or predominantly flow rocks)

Cenozoic volcanic rocks

Plio-Pleistocene nonmarine;

 Pliocene nonmarine

Pliocene nonmarine

Eocene nonmarine

Alluvium (mostly Holocene,

 some Pleistocene); Quaternary

 nonmarine; Quaternary marine

EXPLANATION

gb

J

grMz

gr-m

m

Mzv

Mesozoic granitic rocks

Jurassic marine

Granitic and metamorphic rocks,

 undivided, of pre-Cenozoic age

Mesozoic gabbroic rocks

Mesozoic and pre-Cenozoic plutonic,

volcanic, metamorphic, and

 sedimentary rocks

Mesozoic volcanic and metavolcanic

 rocks; Franciscan volcanic rocks

Undivided pre-Cenozoic meta-

 sedimentary and metavolcanic

 rocks of great variety

Hard Rock study unit Alluvial BasinS study unit 

Fault-Dashed where

approximately located;

dotted where concealed

Geologic contact

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

MEXICO

UNITED STATES

ORANGE

COUNTY

Dana Point

San Diego

PENINSULAR 

RANGES

Temecula

Shaded relief derived from U.S. Geological Survey National

Elevation Dataset, 2006, Albers Equal Area Conic Projection

17-0006 Wright_Figure 5c Geology.

PACIFIC OCEAN

QPc

QPc

C

0 10 20 MILES

0 10 20 KILOMETERS

Grid well (USGS

GAMA and CDPH)

Understanding well

 (USGS GAMA)
14 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Methods 

The status assessment provides a spatially unbiased 

assessment of groundwater quality within in the primary 

aquifers, whereas the understanding assessment was designed 

to evaluate the natural and human factors that affect the 

groundwater quality of the San Diego study unit. The status 

assessment was conducted for each study area. 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) analyzing statistics for the understanding assessment. 

The primary metric for defining groundwater quality 

is relative-concentration, which references concentrations 

of constituents measured in groundwater to regulatory and 

non-regulatory 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 calculated 

aquifer-scale proportions of low, moderate, or high relativeconcentrations: the “grid-based” method uses one value 

per cell to represent groundwater quality and the “spatially 

weighted” 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. The CDPH 

data were used in three ways in the status assessment: (1) to 

fill in gaps in the USGS data for the grid-based calculations 

of aquifer-scale proportions, (2) to identify constituents for 

inclusion in the assessment, and (3) to provide the majority 

of the data used in the spatially-weighted calculations of 

aquifer-scale proportions. 

Relative-Concentrations and 

Water-Quality Benchmarks

Concentrations of constituents are presented as 

relative-concentrations in the status assessment:

- . Sampleconcentration Relative concentration

Benchmark concentration =

Relative-concentrations were used because they 

provide context for the measured concentrations in the 

sample: relative-concentrations less than 1 indicate sample 

concentrations less than the benchmark, and values greater 

than 1 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 concentration.

Toccalino and others (2004), Toccalino and Norman 

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

ratio of measured sample concentration to the benchmark 

concentration (either maximum contaminant levels (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 be computed only for constituents with water-quality 

benchmarks; therefore, constituents lacking water-quality 

benchmarks are not included in the status assessment.

Regulatory and non-regulatory benchmarks apply to 

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

groundwater. However, to provide some context for the 

results, concentrations of constituents measured in the 

untreated groundwater were compared with benchmarks 

established by the U.S. Environmental Protection Agency 

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

2006; California Department of Public Health, 2008a, b). 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, 

respectively), USEPA action levels and treatment 

technique levels (AL-US and TT-US, respectively). 

2. Non-regulatory CDPH and USEPA secondary maximum 

contaminant levels (SMCL-CA and SMCL-US, 

respectively). For constituents with both recommended 

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

levels were used. 

3. Non-regulatory, health based CDPH notification levels 

(NL-CA), USEPA lifetime health advisory levels 

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

For ease of discussion, relative-concentrations of 

constituents were classified into low, moderate, and high 

categories:

Category

Relativeconcentrations for 

organic constituents

Relativeconcentrations 

for inorganic 

constituents

High > 1 > 1

Moderate > 0.1 and < 1 > 0.5 and < 1

Low < 0.1 < 0.5
Methods 15

The boundary between “moderate” and “low” relativeconcentrations was set at 0.1 for organic and specialinterest 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 San Diego study unit, and elsewhere 

in California (Landon and others, 2010), the naturally 

occurring inorganic constituents tend to be more prevalent in 

groundwater. Although more complex classifications could be 

devised based upon the properties and sources of individual 

constituents, use of a single moderate/low boundary value 

for each of the two major groups of constituents provided a 

consistent objective criteria for distinguishing constituents 

occurring at moderate rather than low concentrations.

Datasets for Status Assessment

USGS-Grid and -Understanding 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 Wright and others (2005). Briefly, each study area was 

divided into equal-area grid cells that ranged in size and 

number from 10 4-mi2 cells in the Warner Valley study area 

to 20 approximately 15-mi2 cells in the Temecula Valley and 

Alluvial Basins study areas (fig. 6A–C). Because the Hard 

Rock study area was so large (850 mi2), grids were configured 

to provide a sampling density of approximately one well per 

85 mi2 which equaled ten grid cells. The objective of the 

sampling design in the Hard Rock study area was to provide 

an initial reconnaissance of groundwater quality outside 

of CDWR-defined groundwater basins. Consequently the 

analyses from groundwater wells sampled in the Hard Rock 

study area were not included when calculating aquifer-scale 

proportions for constituents at the study unit level.

Within each grid cell, one well was randomly selected to 

represent the cell (Scott, 1990). It should be noted that some 

cells were divided into several sections because of geographic 

features (fig. 6A–C). Wells were selected from the population 

of wells in state-wide databases maintained by the CDPH 

and the USGS. USGS-grid wells in the San Diego study unit 

were numbered in the order of sample collection with the 

prefix varying by study area: the Temecula Valley study area 

(SDTEM), the Warner Valley study area (SDWARN), the 

Alluvial Basins study area (SDALLV), and the Hard Rock 

study area (SDHDRK). Grid well numbers in the San Diego 

study unit are not always sequential because some grid wells 

have been re-designated as understanding wells subsequent to 

the publication of the San Diego study unit USGS Data Series 

report (table A1). Wells were redesignated in order to obtain 

a spatially distributed grid sampling-network that would meet 

the requirements of the status assessment. 

The San Diego study unit contained a total of 60 grid 

cells, and the USGS sampled wells in 47 of those cells 

(USGS-grid wells) (fig. 6A–C). All 47 USGS-grid wells 

sampled in the San Diego study unit were PSWs that are listed 

in the CDPH water-quality database. Some grid cells could not 

be sampled because wells were not available, the wells were 

inoperable or the owner declined to participate in the program. 

However, if there was a well adjacent (≤ 1 km) to an empty 

grid cell, then the adjacent well was sampled and the water 

quality was used to represent the previously empty grid cell. 

Of the 20 grid cells in the Temecula Valley and Alluvial Basins 

study areas, 12 and 16 grid cells, respectively, were sampled 

or water-quality data was available from CDPH. In the Warner 

Valley, 9 of 10 grid cells were sampled and in the Hard Rock 

study area all 10 grid cells were sampled.

Eleven understanding wells were sampled for the purpose 

of understanding water quality changes along flow paths 

or in areas where historically little water-quality data were 

available. USGS-understanding (nonrandomized) wells were 

designated with the suffix FP for flow path wells and U for 

other understanding wells in addition to the regular GAMA 

ID. The understanding wells were not included in the gridbased characterization of water quality, but were used in the 

spatially weighted approach and were used to examine the 

effects of explanatory factors, such as land use, on water 

quality. An in-depth analysis of how water quality changes 

along flow paths in the San Diego study is not presented in 

this report.

Wells were sampled using a tiered analytical approach. 

All wells were sampled for a standard set of constituents, 

including VOCs, pesticides and pesticide degradates, stable 

isotopes of water, dissolved noble gases, and tritium (table 1). 

The standard set of constituents was termed the “fast” 

schedule. Wells on the “intermediate” schedule were sampled 

for all the constituents on the fast schedule, plus NDMA, 

perchlorate, potential waste-water indicators, and chromium 

species. Wells sampled on the “slow” schedule were sampled 

for all the constituents on the intermediate schedule, plus 

nutrients and dissolved organic carbon, major and minor ions, 

trace elements, arsenic and iron species, carbon isotopes, 

radon-222, radium isotopes, gross alpha and beta radiation, 

1,4-dioxane and microbial constituents. Approximately 

60 percent of the wells were sampled on a fast or intermediate 

schedule, and 40 percent were sampled on a slow schedule. 

Wells in areas of interest, such as along flow paths, or in 

places where water quality data were scarce, were given 

priority for slow schedule sampling. 
16 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 6A–C. Locations of grid cells, California Department of Public Health (CDPH) wells, and the USGS-grid and -understanding 

wells sampled during May to July, 2004 for the San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit: 

(A) Temecula Valley, (B) Warner Valley, and (C) Alluvial Basins, and Hard Rock study areas.

Santa

Margarita

River

Temecula Creek

Murrieta

Creek

Lake Skinner

Vail Lake

Railroad

Canyon

Reservoir

EXPLANATION

Shaded relief derived from U.S. Geological Survey

National Elevation digital Dataset, 2006,

Albers Equal Area Conic Projection

117 15' 117 10' 117 05' 117 00' 116 55' 116 50'

33

30'

33

35'

Temecula

33

40'

RIVERSIDE COUNTY

SAN DIEGO COUNTY

Study cell (Temecula Valley)

Study area boundary

0 5 MILES

0 5 KILOMETERS

Grid well (USGS GAMA and CDPH)

Understanding well (USGS GAMA)

Study unit boundary

A
Methods 17

Figure 6A–C.—Continued

Lake 

Henshaw

San Luis Rey River

San Luis Rey

River

Agua Caliente Creek

Buena Vista Creek

Matagual Creek

Carrista

Creek

0 2 4 MILES

0 2 4 KILOMETERS

EXPLANATION

116 45' 116 40' 116 35'

33

20'

33

15'

PENINSULAR 

RANGES

Study cell (Warner Valley)

Study area boundary

Study unit boundary

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 6b Cells.

B

Grid well (USGS GAMA and CDPH)
18 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 6A–C.—Continued

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

Temecula

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 6c Grid Cells.

EXPLANATION

PENINSULAR 

RANGES

PACIFIC OCEAN

Grid well (USGS GAMA and CDPH)

Understanding well (USGS GAMA)

Study cell (Alluvial Basins) Study unit boundary

Study cell (Hard Rock)

C
Methods 19

CDPH Grid Wells

The four study areas were divided into 60 grid cells, out 

of which no USGS-grid wells were available for 13 cells; 

USGS-grid wells were available for 28 cells but no USGS data 

for major ions, trace elements, nutrients, and radiochemical 

constituents were available. Data from the CDPH database 

were used to provide missing inorganic and radiochemical 

data. CDPH wells with data for the most recent 3 years 

available at the time of sampling (July 30, 2001 through 

July 29, 2004) were considered. If more than one analysis for 

a constituent was available in the 3-year interval for a well, 

then the most recent data were selected. 

The decision tree used to identify suitable data from 

CDPH wells is described in appendix A. 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 whose data were supplemented from 

the CDPH database (fig.A1A–C; table A1). If all the needed 

data for the DG well were not available, then 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. The combination of 

the USGS-grid wells and the CDPH-grid wells produced 

a grid-well network covering 54 of the 60 grid cells in the 

San Diego study unit. 

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 grid-based 

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 64 pesticides and pesticide degradates at 

every well in the San Diego study unit (table 1). In addition, 

method detection limits for USGS-GAMA analyses of organic 

constituents typically were one to two orders of magnitude 

lower than the reporting limits for analyses compiled by 

CDPH (table 3). 

Table 1. Constituent class and numbers of constituents and 

wells sampled for each analytical group in the San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit, California, May 17–July 29, 2004.

[NDMA, N-Nitrosodimethylamine; USGS, U.S. Geological Survey]

Sampling schedule

Fast Intermediate Slow

Well summary Number of wells

Total number of wells 8 26 24

Number of grid wells sampled 6 22 19

Number of understanding wells sampled 2 4 5

Analyte Groups1 Number of constituents

Specific conductance and temperature 2 2 2

Volatile organic compounds (VOCs) 88 88 88

Pesticides and degradates 64 64 64

Noble gases and tritium2 7 7 7

Stable isotopes of hydrogen and oxygen 2 2 2

Potential waste-water indicators3 48 48

Pharmaceuticals4 16 16

Perchlorate and NDMA 2 2

Chromium species 2 2

Tritium5 1 1

 pH, dissolved oxygen, alkalinity, 

turbidity 4

Polar pesticides and degradates6 59

1,4-Dioxane 1

Nutrients and dissolved organic carbon 6

Major and minor ions, and trace elements 36

Arsenic and iron species 4

Carbon isotopes 2

Radon-222 1

Radium isotopes 2

Gross alpha and beta radioactivity 4

Microbial constituents 4

Sum of constituents for each schedule: 163 232 355

1

Not all analyte groups or analytes are discussed in the report.

2

Analyzed at Lawrence Livermore National Laboratory, Livermore, 

California.

3

Counts do not include analytes in common with VOCs, pesticides and 

degradates, pharmaceuticals or polar pesticides and degradates. Wastewater 

data is not used for assessment of status or understanding in this report.

4

Pharmaceutical data is not used for assessment of status or understanding 

in this report.

5

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

California.

6

Counts do not include analytes in common with pesticides and degradates.
20 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Table 2. Inorganic constituents and number of grid wells per 

constituent, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, May–July 2004.

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

Secondary Maximum Contaminant Level; HBB, Health Based Benchmark 

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

Survey]

Constituent type Constituent

Number of grid wells

Sampled 

by USGS 

GAMA

Selected 

from 

CDPH

Major element—SMCL

Chloride 19 14

Sulfate 19 14

Total dissolved solids 19 16

Minor element—HBB

Fluoride 19 14

Nutrient—HBB

Nitrite-N 19 15

Ammonia-N 19 0

Nitrate-N 19 18

Radioactive—HBB

Gross alpha radioactivity 19 16

Gross beta radioactivity 19 4

Ra226+228 19 0

Rn222 19 0

Uranium 19 8

Trace element—HBB

Aluminum 19 15

Antimony 19 14

Arsenic 19 15

Barium 19 15

Beryllium 19 14

Boron 19 15

Cadmium 19 15

Chromium 19 14

Copper 19 16

Lead 19 14

Mercury 19 15

Nickel 19 14

Selenium 19 15

Strontium 19 0

Thallium 19 14

Vanadium 19 14

Trace element—SMCL

Iron 19 15

Manganese 19 15

Silver 19 15

Zinc 19 15
Methods 21

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 

(understanding wells), and all wells in the CDPH database 

with water-quality data during the 3-year interval July 30, 

2001, through July 29, 2004. For wells with both USGS and 

CDPH data, only the USGS data were used. 

Identification of Constituents for 

Status Assessment 

Three criteria were used to identify constituents for 

additional evaluation in the status assessment of groundwater 

in the San Diego study:

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 well; 

3. Organic constituents with study unit detection 

frequencies greater than 10 percent in the 

USGS-grid well dataset.

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

of constituent for data stored in the California Department of Public Health database and data collected for the San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May 17–July 29, 2004.

[CDPH, California Department of Public Health; MDL, method detection limit; LRL, laboratory reporting level; MRL, method reporting level; 

mg/L, milligrams per liter; μg/L, micrograms per liter; nc, not collected; pCi/L, picocuries per liter]

Constituent type

CDPH GAMA

Number ofcompounds MDL Number ofcompounds MedianLRL/MRL

Volatile organic compounds (μg/L) 61 0.5 88 0.06

Pesticides and degradates (μg/L) 27 2 123 0.019

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

Trace elements (μg/L) 20 8 25 0.12

Radioactive constituents (SSMDC)1 (pCi/L) 5 1 8 0.54

Perchlorate (μg/L) 1 4 1 0.5

1,4-Dioxane (μg/L) 1 3 1 2

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

Pharmaceutical constituents (μg/L) nc nc 16 0.021

1

 Value reported for the median LRL/MRL is a median sample-specific critical level for eight radioactive constituents collected and analyzed by 

GAMA.

These criteria identified 11 organic and special-interest 

constituents and 26 inorganic constituents for additional 

evaluation in the status assessment (table 4). An additional 

23 organic constituents and 20 inorganic constituents were 

detected by USGS-GAMA, and are not included for further 

analysis in the status assessment because they either have no 

established benchmarks (table 5), or were only detected at low 

relative-concentrations.

The CDPH database also was used to identify 

constituents that have been reported at high relativeconcentrations historically, but not at the time of this study. 

The historical period was defined as from the earliest record 

maintained in the CDPH electronic database within the period 

May 1983 to June, 2001. Constituents may be historically 

high, but not currently high, because of improvement in 

groundwater quality with time or abandonment of wells with 

high relative-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 San Diego study unit, there were six historically 

high constituents (table 6).
22 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table 4. Aquifer proportions from grid-based and spatially weighted methods for constituents detected in the Alluvial Fill study areas (Temecula Valley, Warner Valley, and Alluvial Basins) (1) with high relative-concentrations reported in the California Department of Public Health (CDPH) database during July 30, 2001–July 29 2004, or (2) with moderate or high relative-concentrations in samples collected from grid wells during May–July 2004, or (3) with organic or special-interest constituents detected in more than 10 percent of samples collected from grid wells during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. [Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 47 grid wells during May–July 2004. Spatially weighted aquifer-scale proportions are based on CDPH data for July 30, 2001–July 29, 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. HAL-US, USEPA lifetime health advisory level; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health; μg/L, microgram per liter; ns, not sampled; mg/L, milligram per liter; pCi/L, picocurie per liter] Constituent Threshold type Threshold value Threshold units Grid-based aquifer-scale proportion1 Raw detection frequency2 Spatially weighted aquifer-scale proportion1,2 Number of wells High (percent) Moderate (percent) Low (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High (percent) Trace elements Vanadium NL-CA 50 μg/L 28 7.5 28.5 64.0 117 12 10.3 32 9.8 Arsenic MCL-US 10 μg/L 29 4.1 2.8 93.1 117 4 3.4 32 3.8 Boron NL-CA 1,000 μg/L 28 4.1 0.0 95.9 117 2 1.7 32 1.3 Antimony MCL-US 6 μg/L 28 3.4 0.0 96.6 106 1 0.9 31 3.2 Selenium MCL-US 50 μg/L 29 0.0 3.2 96.8 117 0 0.0 32 0.0 Fluoride MCL-CA 2 mg/L 28 30.7 2.8 97.2 115 1 0.9 31 0.7 Aluminum MCL-CA 1,000 μg/L 29 0.0 0.0 100.0 117 0 0.0 32 0.0 Thallium MCL-US 2 μg/L 28 0.0 0.0 100.0 106 0 0.0 32 0.0 Chromium MCL-CA 50 μg/L 28 0.0 0.0 100.0 109 0 0.0 32 0.0 Lead AL-US 15 μg/L 28 0.0 0.0 100.0 114 0 0.0 31 0.0 Nickel MCL-CA 100 μg/L 28 0.0 0.0 100.0 105 0 0.0 32 0.0 Cadmium MCL-US 5 μg/L 29 0.0 0.0 100.0 117 0 0.0 32 0.0 Mercury MCL-US 2 μg/L 29 0.0 0.0 100.0 117 0 0.0 32 0.0 Radioactive constituents Gross-alpha radioactivity MCL-US 15 pCi/L 29 3.2 10.5 86.3 115 4 3.5 30 3.4 Uranium MCL-US 30 μg/L 22 0.0 13.1 86.9 53 1 1.9 26 0.9 Radon-222 Proposed AMCL-US 4,000 pCi/L 15 0.0 0.0 100.0 21 0 0.0 18 0.0 Radium-228 MCL-US 5 pCi/L 14 0.0 0.0 100.0 34 0 0.0 20 0.0 Gross-beta radioactivity MCL-CA 50 pCi/L 19 0.0 0.0 100.0 47 0 0.0 22 0.0 Nutrients Nitrate as nitrogen MCL-US 10 mg/L 31 3.4 6.8 89.8 148 7 4.7 36 3.4 Nitrite, as nitrogen MCL-US 1 mg/L 28 0.0 0.0 100.0 127 0 0.0 33 0.0
Methods 23

Table 4. Aquifer proportions from grid-based and spatially weighted methods for constituents detected in the Alluvial Fill study areas (Temecula Valley, Warner Valley, and Alluvial Basins) (1) with high relative-concentrations reported in the California Department of Public Health (CDPH) database during July 30, 2001–July 29 2004, or (2) with moderate or high relative-concentrations in samples collected from grid wells during May–July 2004, or (3) with organic or special-interest constituents detected in more than 10 percent of samples collected from grid wells during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.—Continued.[Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 47 grid wells during May–July 2004. Spatially weighted aquifer-scale proportions are based on CDPH data for July 30, 2001–July 29, 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. HAL-US, USEPA lifetime health advisory level; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health; μg/L, microgram per liter; ns, not sampled; mg/L, milligram per liter; pCi/L, picocurie per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based aquifer-scale proportion1 Raw detection frequency2

Spatially weighted 

aquifer-scale 

proportion1,2

Number of 

wells

High 

(percent)

Moderate 

(percent) 

Low 

(percent) 

Number 

of wells

Number of 

wells with 

high values

Raw 

detection 

frequency 

(percent)

Number 

of cells

High

(percent) 

Major and minor elements (SMCLs)Total dissolved solids (TDS) SMCL-CA 1,000 mg/L 29 13.7 31.2 55.1 119 15 12.6 34 13.0

Manganese SMCL-CA 50 μg/L 28 13.7 3.4 82.9 120 32 26.7 32 21.2

Iron SMCL-CA 300 μg/L 28 6.9 0.0 93.1 120 10 8.3 32 6.5

Chloride SMCL-CA 500 mg/L 28 3.4 10.3 86.3 117 5 4.3 32 2.7

Sulfate SMCL-CA 500 mg/L 28 3.4 6.9 89.7 117 2 1.7 32 3.7

Zinc SMCL-CA 5,000 μg/L 29 0.0 0.0 100.0 114 0 0.0 32 0.0

Gasoline componentsMTBE MCL-CA 13 μg/L 37 3.0 0.0 97.0 113 2 1.8 39 1.4

Benzene MCL-CA 1 μg/L 37 0.0 0.0 100.0 113 0 0.0 39 0.0

TrihalomethanesChloroform MCL-US 480 μg/L 37 0.0 0.0 100.0 112 0 0.0 38 0.0

SolventsTetrachloroethylene MCL-US 5 μg/L 37 0.0 0.0 100.0 113 0 0.0 39 0.0

Carbon tetrachloride MCL-CA 0.5 μg/L 37 0.0 0.0 100.0 113 0 0.0 39 0.0

Trichloroethylene MCL-US 5 μg/L 37 0.0 0.0 100.0 113 0 0.0 39 0.0

1,2-Dichloropropane MCL-US 5 μg/L 37 0.0 3.0 97.0 113 0 0.0 39 0.0

HerbicidesPrometon HAL-US 100 μg/L 37 0.0 0.0 100.0 38 0 0.0 37 0.0

Simazine MCL-US 4 μg/L 37 0.0 0.0 100.0 101 0 0.0 37 0.0

Atrazine MCL-CA 1 μg/L 37 0.0 0.0 100.0 101 0 0.0 37 0.0
24 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table 4. Aquifer proportions from grid-based and spatially weighted methods for constituents detected in the Alluvial Fill study areas (Temecula Valley, Warner Valley, and Alluvial Basins) (1) with high relative-concentrations reported in the California Department of Public Health (CDPH) database during July 30, 2001–July 29 2004, or (2) with moderate or high relative-concentrations in samples collected from grid wells during May–July 2004, or (3) with organic or special-interest constituents detected in more than 10 percent of samples collected from grid wells during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.—Continued. [Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 47 grid wells during May–July 2004. Spatially weighted aquifer-scale proportions are based on CDPH data for July 30, 2001–July 29, 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. HAL-US, USEPA lifetime health advisory level; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health; μg/L, microgram per liter; ns, not sampled; mg/L, milligram per liter; pCi/L, picocurie per liter] Constituent Threshold type Threshold value Threshold units Grid-based aquifer-scale proportion1 Raw detection frequency2 Spatially weighted aquifer-scale proportion1,2 Number of wells High (percent) Moderate (percent) Low (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High (percent) Constituent of special interest Perchlorate MCL-CA 6 μg/L 32 30.2 36.3 63.7 103 1 0.9 38 0.2

1Alluvial Fill study area aquifer-scale proportion is calculated by summing the area weighted average for each individual study area except the Hard Rock. Area-weighted values for each study area are: 

Temecula Valley = 0.41, Warner Valley = 0.11, Alluvial Basins = 0.48. Aquifer-scale proportions will not sum to 100 if a spatially-weighted value is used.

2Based on most recent analysis for each CDPH well during July 30, 2001–July 29, 2004, combined with GAMA grid-based data.3Spatially-weighted value. Aquifer-scale proportions will not sum to 100 if a spatially-weighted value is used.4The MCL-US threshold for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
Methods 25

Table 5. Number of constituents analyzed and detected by benchmark and constituent type, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit grid wells, California, May 17 to July 29, 2004.[VOCs, volatile organic compounds; NWQL, National Water Quality Laboratory; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health; MCL, USEPA or CDPH Maximum Contaminant Level; HAL, USEPA Health Advisory Level; NL, CDPH Notification Level; RSD5, USEPA Risk Specific Dose at 10-5; AL, USEPA Action Level; SMCL, USEPA or CDPH Secondary 

Maximum Contaminant Level]

Benchmark

type

Groups of organic constituents

Sum organic and specialinterest constituents VOCs + gasoline oxygenates Pesticides and degradates (NWQL Schedule 2003)1 Polar pesticides and degradates (NWQL Schedule 2060)1 Special-interest 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 46 16 32 12 3 2 10 1 1 1

HAL 31 6 7 0 14 5 10 1 0 0

NL 16 0 14 0 0 0 0 0 2 0

RSD5 8 1 4 0 3 0 1 1 0 0

AL 0 0 0 0 0 0 0 0 0 0

SMCL 0 0 0 0 0 0 0 0 0 0

None 117 11 31 0 44 10 42 1 0 0

Total 218 34 88 12 64 17 63 4 3 1

Benchmark

type

Groups of inorganic constituents

Sum of inorganic Major and minor ion 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 21 1 1 2 2 12 10 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 6 6 3 3 3 3 1 1

Total 50 48 10 10 6 6 25 23 9 9

Organic, 

special 

interest, 

inorganic 

total

268 82

1There are four overlapping compounds between NWQL Schedules 2003 and 2060.
26 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Calculation of Aquifer-Scale Proportions

The status assessment is intended to characterize 

the quality of groundwater resources within the primary 

aquifers of the San Diego 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 a discrete aquifer unit. 

In most groundwater basins, municipal and community 

supply wells generally are perforated at greater depths than 

are domestic wells. Thus, because domestic wells are not 

listed in the CDPH database, the primary aquifers generally 

correspond to the portion of the aquifer system tapped by 

municipal and community supply wells. All wells used in the 

status assessment in the San Diego study unit are listed in the 

CDPH database, and are therefore classified as municipal and 

community drinking-water supply wells. 

Two statistical approaches, grid-based and spatially 

weighted (Belitz and others, 2010), were applied to evaluate 

the proportions of the primary aquifers in the San Diego 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 aquifer-scale proportions were 

made for individual constituents meeting the criteria for 

additional evaluation in the status assessment and for classes 

of constituents. Classes of constituents with health-based 

benchmarks included: trace elements, radioactive constituents, 

nutrients, VOCs, and pesticides. Aquifer-scale proportions 

were also calculated for the following constituents having 

aesthetic (SMCL) benchmarks: manganese, total dissolved 

solids, iron, chloride, sulfate, and zinc. 

The grid-based calculation uses the grid-well dataset 

assembled from the USGS- and CDPH-grid wells. For each 

constituent the high aquifer-scale proportion for a study 

area was calculated by dividing the number of cells (wells) 

represented by a high value for that constituent by the total 

number of grid cells with data for that constituent. The high 

aquifer-scale proportions at the study-unit scale were then 

calculated by first multiplying the study-area aquifer-scale 

proportion by an area-weighted correction factor, and then 

summing the high aquifer-scale proportions for all the study 

areas. An area-weighted correction factor was needed because 

the study areas are not the same size (fig. 6A–C). Moderate 

and low aquifer-scale proportions were calculated using the 

same approach as the calculations for the high aquifer-scale 

proportions. A more detailed discussion of the calculation used 

for aquifer-scale proportion is located in appendix B. 

The grid-based estimate is spatially unbiased; however, 

this approach may not detect constituents that are present 

at high relative-concentrations in small proportions of the 

primary aquifers. The spatially weighted calculation uses 

all CDPH wells in the study unit (most recent analysis 

during the current period from July 30, 2001–July 29, 

2004), USGS-grid wells, and USGS-understanding wells to 

represent the primary aquifers. By using the spatially weighted 

approach, the proportion of high relative-concentrations for 

the primary aquifers for each constituent was computed by 

(1) computing the proportion of wells with high relativeconcentrations in each grid cell and (2) averaging together 

the grid-cell proportions computed in step (1) (Isaaks and 

Srivastava, 1989; Belitz and others, 2010). Similar procedures 

were used to calculate the proportions of the aquifer with 

moderate and low relative-concentrations of constituents. 

The resulting proportions are spatially unbiased (Isaaks 

and Srivastava, 1989; Belitz and others, 2010). Confidence 

intervals for spatially weighted detection frequencies of high 

relative-concentrations are not described in this report. 

Table 6. Constituents with one or more concentrations above health-based benchmarks for the period of May 1983 to June 2001, 

based on the California Department of Public Health data for public-supply wells in the San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California.

Constituent Number of wells 

with analyses

Total number of 

analyses

Total number of 

analyses above 

threshold

Number of wells 

with at least one 

high analysis

Date of most recent 

concentration above 

a health-based 

benchmark

Trace elements

Chromium 230 843 1 1 11-29-1989

Cadmium 246 843 3 3 05-22-1990

Mercury 248 859 1 1 02-26-1992

Radioactive constituents

Gross-beta radioactivity 77 80 1 1 07-05-1995

Radium 226 41 109 4 3 06-26-1996

Solvents

Tetrachloroethylene 269 1,173 19 2 10-10-2000

Trichloroethylene 270 1,173 25 2 07-10-2000

1,2-Dichloropropane 243 1,108 2 1 03-27-1995
Methods 27

In addition, for each constituent, the raw detection 

frequencies of high and moderate values for individual 

constituents were calculated by using the same dataset as 

used for the spatially weighted calculations. However, raw 

detection frequencies are not spatially unbiased because the 

wells in the CDPH database are not uniformly distributed. 

For example, if a constituent were present at high relativeconcentrations in a small region of the aquifer with a high 

density of wells, then the raw detection frequency of high 

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

Raw detection frequencies are provided for reference but were 

not used to assess aquifer-scale proportions (see appendix B

for details of statistical methods). 

The grid-based high aquifer-scale proportions were 

used to represent proportions in the primary aquifers unless 

the grid-base high aquifer-scale proportion was zero and 

the spatially weighted proportion was non-zero, and then 

the spatially weighted result was used. This situation can 

arise when the relative-concentration of a constituent is high 

in a small fraction of the primary aquifers. 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 non-zero, then 

the spatially weighed value was used..

Understanding-Assessment Methods

Explanatory factors, including land use, well depth, 

depth to the top of the uppermost open interval, classified 

groundwater age, and redox conditions (see appendix C 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. Statistical tests 

were used to identify significant correlations between the 

constituents of interest and potential explanatory factors. 

Significant correlations for explanatory factors influencing 

water quality are shown in the figures. 

The wells included in the understanding 

assessment include USGS-grid and CDPH-grid well and 

USGS-understanding wells. CDPH-other wells were not 

used in the understanding assessment because age tracer, 

dissolved oxygen, and sometimes well construction data 

were not available. For different potential explanatory 

variables, correlations were tested by 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 

variables (land use) to avoid areal-clustering bias. However, 

USGS-understanding wells were included in analyses of 

relations between constituents and the vertically distributed 

explanatory variables depth, classified groundwater age, 

and oxidation-reduction characteristics in order to have data 

spanning a sufficient range of variables to identify relations. 

For inorganic constituents to be discussed in the 

understanding assessment, they must have been detected 

at high relative-concentrations in greater than or equal 

to 2 percent of the aquifer (based on non area-weighted 

detections for all study areas) For organic and special-interest 

constituents to be discussed in the understanding assessment, 

a constituent needs to be detected at a high or moderate 

relative-concentration, or detected in greater than or equal to 

10 percent of grid wells (based on detections that were not 

area-weighted) regardless of concentration

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 generally are not affected by 

outliers and do not require that the data follow any particular 

distribution (Helsel and Hirsch, 2002). The significance level 

(p) used to test hypotheses for this report was compared to a 

threshold value (α) of 5 percent (α = 0.05) to evaluate whether 

the relation was statistically significant (p < α). 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.
28 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Potential Explanatory Factors

Explanatory factors that potentially affect water quality 

include land use, depth (well depth and the depth to the 

top of the uppermost open interval), groundwater age, and 

geochemical conditions. Sources and methodologies for 

obtaining data for these factors are discussed in the following 

sections. Potential correlations within these factors also 

were evaluated to identify which factors are likely to relate 

directly to water quality and could result in higher relativeconcentrations or detection frequencies, and which factors 

may be coincidental and not directly affecting water-quality.

Land Use

Land use around wells sampled in the San Diego study 

unit generally indicated the composition of land use in the 

respective study areas as a whole. This also was true of the 

land use around PSWs in the CDPH database that was used 

in this study. The majority of land use around PSWs used 

in this study was natural, with lesser amounts of urban and 

agricultural (fig. 3A–B). The most urbanized areas around 

PSWs was in the Alluvial Basins study area (28 percent), 

followed by the Temecula Valley (23 percent), and then 

the Hard Rock study areas (8 percent). The Warner Valley 

study unit did not have wells located in any urban land-use 

settings. Agricultural land-use around PSWs most often was 

in the Temecula Valley (29 percent) study area, followed by 

the Alluvial Basins (17 percent), Hard Rock (1 percent) and 

Warner Valley (1 percent) study areas.

Well Depth

Well-construction information, including well depths, 

depths to the tops of the uppermost open interval, and lengths 

of the perforated intervals, where available, is reported in 

table A1. Depths for the PSWs sampled in the San Diego study 

unit (grid and understanding) ranged from 46 to 2,500 ft, with 

a median of 450 ft (fig. 7). Depth to the top of the uppermost 

open interval ranged from 20 to 690 feet, with a median of 

96 feet. The open length ranged from 23 to 1913 feet with a 

median of 325 feet. These values represent different sets of 

wells because the total well depth was not known for as many 

wells as depth to the top of the uppermost open interval. 

Figure 7. Boxplots of construction attributes for grid and understanding wells, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, 

May–July 2004.

53 0

1,000

DEPTH TO THE TOP OF THE UPPERMOST OPEN INTERVAL,

IN FEET BELOW LAND SURFACE

55 51

Perforation

length

Top of

Perforation

Well

depth

WELL CONSTRUCTION ATTRIBUTES

3,000

2,000

53 Number of wells with data

EXPLANATION

75th PERCENTILE

MEDIAN

25th PERCENTILE

Nearest data point that is within

 1.5*IQR of the 25th PERCENTILE

Nearest data point that is within 

 1.5*IQR of the 75th PERCENTILE

Data point that is outside of 1.5*IQR

 of the 75th PERCENTILE

Data point that is outside of 1.5*IQR

 of the 25th PERCENTILE

INTERQUARTILE

RANGE (IQR)
Methods 29

Groundwater Age Classification

Of the 58 groundwater samples collected by the USGS 

in the San Diego study unit, 8 were modern, 29 were mixed, 

and 19 were pre-modern (see table C1). Samples from two 

wells could not be classified because the age-tracer data was 

incomplete or did not meet all quality-assurance checks. 

Classified groundwater ages generally were older with 

increased depth to the top of the uppermost open interval 

(fig. 8A). The depth to the top of uppermost open interval 

was significantly less for wells with modern and mixed age 

distributions than for wells with pre-modern age distributions. 

Relative to well depth, wells classified as modern and mixed 

were significantly shallower than wells classified as premodern (fig. 8B). 

Figure 8A–B. Boxplots of relation of classified groundwater age to (A) depth to top of the uppermost 

open interval below land surface and (B) well depth below land surface, San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

0

100

200

DEPTH TO TOP THE OF THE UPPER-MOST OPEN INTERVAL,

IN FEET BELOW LAND SURFACE

300

400

6 19

Modern Mixed Pre-modern

GROUNDWATER AGE MIXTURE CLASSIFICATION

0 20

200

400

WELL DEPTH, IN FEET BELOW LAND SURFACE

500

800

1,000

1,100

500

600

700

14

Modern Mixed Pre-modern

GROUNDWATER AGE MIXTURE CLASSIFICATION

27

6

A

B

19 Number of wells with data

EXPLANATION

75th PERCENTILE

MEDIAN

INTERQUARTILE

RANGE (IQR)

25th PERCENTILE

Nearest data point that is within

 1.5*IQR of the 25th PERCENTILE

Nearest data point that is within 

 1.5*IQR of the 75th PERCENTILE

Data point that is outside of 1.5*IQR

 of the 75th PERCENTILE

Data point that is outside of 1.5*IQR

 of the 25th PERCENTILE
30 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Geochemical Condition

Geochemical information collected for the San Diego 

study unit included pH, dissolved oxygen (DO), and 

concentrations of nitrate, manganese, and iron. Concentrations 

of DO, nitrate, manganese, and iron were used to determine 

the “redox” (oxidation-reduction) condition for the wells, 

using techniques described in appendix C. In the San Diego 

study unit, data was sufficient to classify the redox condition 

for 45 grid and understanding wells. Wells were either 

classified as oxic or anoxic; wells tapping groundwater 

with a mixed redox condition were not used this analysis. 

Sixty-two percent of the wells were classified as anoxic and 

38 percent as oxic. pH values in the study unit ranged from 6.6 

to 9.5 with a median value of 7.4.

Correlations between Explanatory Variables

Apparent correlations between an explanatory variable 

and a water-quality constituent actually could indicate 

correlations between explanatory factors. For example, 

detections of VOCs may be inversely correlated to urban 

land-use in a given area because the uppermost open interval 

of wells tend to be deep, and the water being tapped is 

pre-modern, not because VOCs are not used in urban settings. 

Therefore, it is important to identify statistically significant 

correlations between explanatory variables

The majority of explanatory variables used in this report 

are not significantly related (table 7). The strongest correlation 

is between well depth and depth to the top of the uppermost 

open interval. Because of the significance of this correlation 

only depth to the top of the uppermost open interval will 

be used in this report. Positive correlations of well depth to 

groundwater classified as pre-modern and pH were significant. 

The only other significant correlations were positive 

correlations between pH and groundwater classified as premodern and between anoxic groundwater and urban land-use; 

there was a negative correlation between natural land-use and 

depth to the top of the uppermost open interval.

Table 7. Results of non-parametric analysis of correlations between selected potential explanatory variables, San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

[Results are shown only for those correlations with a ρ-value ≤ 0.1. Results with ρ-values ≤ 0.05 are shown in bold. Only results with ρ-values ≤ 0.05 are 

considered significant in this study. ρ, Spearman’s correlation statistic; Z, test statistic for Wilcoxon test; negative number is inverse relation between variables; 

–, p >0.1; <, less than; ≤, less than or equal to]

Wells included 

in analysis Explanatory factor

ρ: Spearman’s correlation statistic Z: Wilcoxon test statistic

Depth to 

top of the 

uppermost open 

interval, 

feet below 

land 

surface

Depth 

of well 

below land 

surface, 

feet

pH, 

pH units

Anoxic 

versus 

oxic

Mixed 

versus 

modern 

age class

Modern 

versus 

pre-modern 

age class

Mixed 

versus 

pre-modern 

age class

Grid wells

Percentage urban land use – – – 2.02 – – –

Percentage agricultural land use 0.27 – – – 1.40 – –

Percentage natural land use -0.39 – – –1.65 – – –

Grid and 

understanding 

wells

Depth to the top of uppermost open 

interval below land surface, feet 0.73 – – – –3.10 –4.10

Depth of well below land surface, feet 0.54 – – –2.93 –4.13

pH, pH units – – –2.77 –3.40
Status and Understanding of Water Quality 31

Status and Understanding of 

Water Quality 

As a starting point for summarizing the results 

of approximately 16,000 individual analytical 

measurements in the San Diego study unit, the maximum 

relative-concentrations of the individual constituents 

and constituent groups were calculated for all four study 

areas (fig. 9). Health-based benchmarks are established 

for all constituents shown, except for those in the group 

inorganic-SMCL, for which non-health-based aesthetic 

benchmarks are established. Aquifer proportions calculated 

by the grid-based approach were considered the most reliable 

and are used in the subsequent discussions, except where 

otherwise noted. In some instances, the spatially weighted 

approach identified constituents that could be present at 

moderate or high relative-concentrations in small proportions 

of the primary aquifers that were not identified using the 

grid-based approach. Results from the spatially weighted 

approach were used only in cases for which the grid-based 

approach was found to have this limitation. Non-significant 

relations generally are not discussed; selected significant 

correlations are shown graphically. 

Thirty-four of the 218 organic and special-interest 

constituents analyzed for were detected in samples collected at 

grid wells (table 5). Some type of health-based benchmark has 

been established for most of the organic and special interest 

constituents detected (23 of the 34). Five of the constituents 

with no health-based benchmarks are pesticide degradates. 

Some of the parent compounds (atrazine, diuron) of these 

degradates with health-based benchmarks were detected 

in samples. In contrast to organic and special-interest 

constituents, inorganic constituents were nearly always 

detected (48 of 50, table 5). Health-based or aesthetic 

benchmarks were not established for just over one-quarter 

of inorganic constituents (13 of 48). Most of the constituents 

without benchmarks are major or minor ions that are naturally 

present in groundwater. 

Table 4 shows the area-weighted aquifer-scale 

proportions for the Temecula Valley, Warner Valley and 

Alluvial Basins study areas (hereinafter referred to as the 

Alluvial Fill study areas because they are composed of 

alluvial fill aquifers), and tables B1A–D show aquifer-scale 

proportions for the individual study areas. Aquifer-scale 

proportions in these tables are calculated by using both 

the grid-based and spatially weighted methods, and show 

constituents with high relative-concentrations under the 

following criteria: (1) high relative-concentrations detected 

during sampling for the GAMA Priority Basin Project, (2) 

high relative-concentrations in the CDPH database during 

the current period (July 30, 2001–July 29, 2004), and (3) 

historically high relative-concentrations in the CDPH 

database. 

Inorganic Constituents

Sixteen inorganic constituents qualified as constituents 

of interest because their relative-concentrations were greater 

than 0.5 in the grid-based assessment (fig. 10). Inorganic 

constituents with health-based benchmarks (nutrients, 

trace elements, and radioactive constituents) were high in 

17.6 percent of the primary aquifers in the Alluvial Fill 

study areas (table 8). The greatest proportion of the primary 

aquifers with high relative-concentrations is in the Temecula 

Valley (27.3 percent) and Alluvial Basins (13.3 percent) 

study areas, whereas no high relative-concentrations were 

detected in the Warner Valley study area (tables B2A–C). High 

relative-concentrations were observed in 25.0 percent of the 

primary aquifers in the Hard Rock study area (table B2D).

Trace Elements

The relative-concentrations of trace elements meeting 

the selection criteria (relative-concentration ≥ 0.5) are shown 

in figure 10. Trace elements were detected at high relativeconcentrations in 14.4 percent of the primary aquifers in the 

Alluvial Fill study areas (table 8). The greatest proportion of 

the primary aquifers with high relative-concentrations was 

in the Temecula Valley (27.3 percent) and Alluvial Basins 

(6.7 percent) study areas (tables B2A and C). High relativeconcentrations (based on spatially weighted calculations) were 

detected in 1.2 of the primary aquifers of the Hard Rock study 

area (table B2D). The three trace elements that were detected 

at high relative-concentrations in greater than or equal to 

2 percent of the primary aquifers (based on aquifer-scale 

proportion that were not area-weighted for all study areas) 

were vanadium (2.8 percent), arsenic (2.0 percent), and boron 

(2.0 percent); the distribution and factors affecting distribution 

of these trace elements are discussed in more detail below.

The location and distribution of V, As, and B in the San 

Diego study unit are displayed on figures 11A–C. Of the high 

relative-concentrations detected for these trace elements, 

only a single high detection (V) was observed outside of the 

Temecula Valley study area. Moderate relative-concentrations 

for these trace elements also were most frequently detected in 

the Temecula Valley study area.
32 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 9. Maximum relative-concentration in grid wells for constituents detected by type of constituent in the San 

Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004. 

VOCs Pesticides Special Interest Nutrients Trace Elements Major and minor

ions and trace

elements-SMCL

Radioactive

1e-005

0.0001

0.001

0.01

0.1

1

10

20

MAXIMUM RELATIVE-CONCENTRATION

Organic and specialinterest constituents Inorganic constituents

17-0006 Wright_Figure 9. 

Zinc Constituents collected at all grid wells-Name and center of symbol is location of data unless indicated

by following location line: 

EXPLANATION

Constituents collected at a subset of grid wells-Name and center of symbol is location of data unless

indicated by following location line:

SMCL, secondary maximum contaminant level

Diuron

Moderate

Low

Moderate

High

Moderate

Low

Gross-alpha

radioactivity,

72 hr

Gross-beta

radioactivity,

72 hr

Copper

Lead

Molybdenum

Strontium

Fluoride

Barium

Nickel

Aluminum

Chromium

Uranium

Arsenic

Selenium

Cadmium

Antimony

Thallium

Boron

Vanadium

Chloride

Sulfate

Total dissolved

solids (TDS)

Zinc

Manganese

Iron

Nitrate plus nitrite,

as nitrogen

Ammonia,

as nitrogen

Nitrite,

as nitrogen

Simazine

Bentazon

Diuron

Bromacil

Prometon

Atrazine

Metolachlor

Tebuthiuron

Hexazinone

Radium-226

Radium-228

Radon-222 Perchlorate

Chloroform 

Bromodichloromethane

Methyl tertbutyl ether 

Tetrachloroethylene

Toluene

1,1,1-Trichloroethane 

cis-1,2-Dichloroethylene

Carbon tetrachloride 

Dichloromethane 

1,1-Dichloroethylene

1,2-Dichloropropane 

Trichloroethylene

Silver
Status and Understanding of Water Quality 33

Figure 10. Dot plots of relative-concentrations of selected trace elements, radioactive constituents, nutrients, and 

major and minor elements in grid wells, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit, California, May–July 2004.

0.001

0.01

0.01

1

10

0.002

0.003

0.004

0.005

0.007

0.009

0.02

0.03

0.04

0.05

0.07

0.2

0.3

0.4

0.5

0.7

2

3

4

5

7

RELATIVE-CONCENTRATION, DIMENSIONLESS

Antimony

Arsenic

Boron

Chloride

Fluoride

Gross Alpha

Iron

Manganese

Nitrate as N

Radon 222

Selenium

Sulfate

TDS

Thallium

Uranium

Vanadium

17-0006 Wright_Figure 10. Relative concentration of compounds.

Low

Moderate

High

Compounds with Health-Based Thresholds

(Maximum Contaminant Level, Health Advisory Level, Notification Level) 

Compounds with Secondary

Maximum Contaminant Levels

Alluvial Basins

Hard Rock Temecula Valley

Warner Valley

Study areas

EXPLANATION
34 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Table 8. Grid-based aquifer-scale proportions for constituent classes in the Alluvial Fill study areas, (Temecula Valley, Warner Valley, 

and Alluvial Basins), San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.

[Values are grid based unless otherwise noted]

Constituent class Aquifer-scale proportion1 (percent)

High values Moderate values Low values

Inorganics with health-based benchmarks

Trace elements 14.4 27.8 57.8

Radioactive 3.2 13.7 83.1

Nutrients 3.4 6.8 89.8

Any inorganic with health-based benchmarks 17.6 32.3 50.1

Inorganics with aesthetic benchmarks

Total dissolved solids and (or) chloride and (or) sulfate 13.7 31.2 55.1

Manganese and (or) iron 13.7 3.4 82.9

Organics with health-based benchmarks

Trihalomethanes 0.0 0.0 100.0

Solvents 0.0 3.0 97.0

Gasoline components 3.0 0.0 97.0

Pesticides 0.0 0.0 100.0

Any organic with health-based benchmarks 3.0 3.0 94.0

Constituents of special interest

Perchlorate 20.2 36.3 63.7

1

 Alluvial Fill study areas aquifer-scale proportion is calculated by summing the area-weighted average for each individual study area except the Hard Rock. 

Area-weighted values for each study area are: Temecula Valley = 0.41, Warner Valley = 0.11, Alluvial Basins = 0.48. Aquifer-scale proportions will not sum to 

100 if a spatially weighted value is used.

2Spatially weighted value.
Status and Understanding of Water Quality 35

Figure 11A–C. Values of selected inorganic constituents in USGS-grid and -understanding wells representative of the primary 

aquifers and the most recent analysis (July 30, 2001–July 29, 2004) for CDPH wells, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004: (A) vanadium, (B) arsenic, and (C) boron. 

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Study areas

17-0006 Wright_Figure 11a Vanadium.

EXPLANATION

PENINSULAR 

RANGES

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

A. Vanadium

PACIFIC OCEAN

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
36 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 11A–C.—Continued

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 11b Arsenic.

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

B. Arsenic

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
Status and Understanding of Water Quality 37

Figure 11A–C.—Continued

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 11c Boron.

PENINSULAR 

RANGES

PACIFIC OCEAN

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

C. Boron

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
38 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Factors Affecting Vanadium Distribution 

Potential sources of V to groundwater are both natural 

and anthropogenic. Natural sources can be attributed to the 

dissolution of V-rich rocks, which include mafic rocks such 

as basalts and gabbros (Nriagu, 1998), and sedimentary rocks 

such as shale (Vine and Tourtelet, 1970; McKelvey and others, 

1986). Anthropogenic sources of V can come from waste 

streams associated with the ferrous metallurgy industry (World 

Health Organization, 1988) and through the combustion of 

V-enriched fossil fuels, primarily in the form of residual 

crude oil and coal (Duce and Hoffman, 1976; Hope, 1997). 

Atmospheric V can be deposited to the land surface through 

wet and dry deposition and transported into to the subsurface 

by infiltrating surface water.

The results of a previous study by Wright and Belitz 

(2010) indicated that the source of moderate and high 

relative-concentrations of V (> 25 μg/L) in California, and in 

particular the Temecula Valley, likely is mafic and andesitic 

rock. In the San Diego study unit, correlations between land 

use and V concentrations in samples collected for this study 

did not indicate that anthropogenic activities were significant 

contributing sources (table 9), which implies that V-rich 

rocks are likely the significant contributing source of V to 

groundwater in the San Diego study unit.

The redox conditions of the system under considerations 

will influence V concentrations in groundwater. This is 

because V is a redox sensitive element that exists in three 

oxidation states in the environment: V (III), V (IV), and V (V). 

Thermodynamically speaking, the predominant oxidation state 

of V is dependent on the Eh and pH conditions of the aqueous 

system under consideration. Vanadium (V) and V (IV) are the 

most important species in natural waters, with V (V) likely the 

most abundant under environmental conditions (Hem, 1985). 

The solubility of V in groundwater is likely to be largely 

controlled by adsorption/desorption processes on mineral 

surfaces (Wehrli and Stumm, 1989; Wanty and others, 1990; 

Wanty and Goldhaber, 1992). Vanadium (V), an oxyanion, 

and V (IV), an oxycation, both adsorb to mineral surfaces. 

However, under most environmental conditions V is expected 

to be most mobile under oxic and alkaline conditions.

Vanadium concentrations were significantly higher 

in samples collected from oxic and alkaline (high pH) 

groundwater than in samples collected from anoxic 

groundwater (fig. 12A; table 9). Vanadium was detected at 

high or moderate relative-concentrations only in samples 

collected from oxic groundwater; concentrations were less 

than or equal to 10 μg/L for all samples collected from anoxic 

groundwater. Additionally, the four samples with the highest 

concentrations were collected from groundwater with a pH of 

at least 7.9 (fig. 12A). These results indicate that V is indeed 

being desorbed from, or being inhibited from adsorbing to, 

mineral surfaces under oxic and alkaline conditions.

The highest V concentrations tended to be detected in 

samples collected from deep wells with mixed and pre-modern 

groundwater age classifications (fig. 12B; table 9). This 

relation most likely is a result due in part to the fact that pH 

values of pre-modern groundwater generally were higher 

than pH values of either modern or mixed waters (table 7). In 

addition, 73 percent of the samples with redox indicator data 

that were classified as pre-modern were classified as oxic. 

Again, these relations highlight the relation between high V 

concentrations and oxic and alkaline groundwater conditions.

Factors Affecting Arsenic Distribution

Like V, potential sources of As to groundwater are 

both natural and anthropogenic. Natural sources may be 

attributed to the dissolution of relatively As-rich igneous 

rocks like basalts and gabbros and sedimentary marine rocks, 

such as shale and phosphorites (Welch and others, 1988). 

Anthropogenic uses of As are varied, but the dominant uses 

in the United States are agricultural applications, wood 

preservation, and glass production (Welch and others, 2000). 

In the San Diego study unit, the positive correlation of arsenic 

concentrations in groundwater samples to any land-use 

type was not significant, which suggests that As-rich rocks 

are the most significant source of arsenic concentrations to 

groundwater.

Arsenic also is a redox sensitive element with a behavior 

affected by the redox and pH conditions of the groundwater 

system under consideration. Arsenic is stable in two oxidation 

states in the environment: As (III) and As (V). Over a wide pH 

range and oxic conditions, the oxyanion As (V) is predicted 

to be the predominant species, whereas under more reducing 

(anoxic) conditions the oxyanion As (III) likely would be 

the predominant species (Welch and others, 1988). Previous 

investigations of As in groundwater (Belitz and others, 2003; 

Welch and others, 2006) and literature reviews (Welch and 

others, 2000; Stollenwerk, 2003) have attributed elevated As 

in groundwater to two mechanisms: (1) the release of As from 

the dissolution of iron or manganese oxyhydroxides under 

anoxic conditions; (2) the desorption from, or inhibition of 

sorption to, mineral surfaces at alkaline pH.

The distribution of sample As concentrations was not 

significantly correlated to either redox or pH conditions 

of groundwater in the San Diego study unit (fig. 13A; 

table 9), although concentrations were correlated to pH at 

the 90 percent confidence level. These results suggest that 

different processes, or a combination thereof, are influencing 

As concentrations in groundwater. Release of As from iron 

and (or) manganese oxyhydroxides in anoxic groundwater 

conditions, and (or) the desorption of As from mineral surfaces 

under alkaline groundwater conditions may be influencing 

As concentrations detected in groundwater in the San Diego 

study unit. Even though the statistical correlation was not 

significant, sample concentrations generally did increase with 

increasing pH, indicating that As is more available in alkaline 

groundwater.
Status and Understanding of Water Quality 39

Table 9. Results of non-parametric analysis of correlations between selected water-quality constituents and potential explanatory factors, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.[Results are shown only for those correlations with a ρ-value ≤ 0.1. Results with ρ-values ≤ 0.05 are shown in bold. Only results with ρ-values ≤ 0.05 are considered significant in this study. A positive value indicates positive correlations; negative values indicate negative correlations. ρ, Spearman’s correlation statistic; Z, test statistic for Wilcoxon test; –, ρ-value > 0.1; THMs, trihalomethanes; TDS, total dissolved solids; MCL-US; U.S. Environmental Protection Agency maximum contaminant level; NL-CA, CDPH notification level; Proposed AMCL-US, U.S. Environmental Protection Agency alternative maximum contaminant level; SMCL-CA, CDPH secondary maximum contaminant level; na, not applicable; <, less than; ≤, less than or equal to; >, greater than; ≥, greater than or equal to]

Constituent Benchmark type High aquifer proportion (percent)

ρ: Spearman’s 

correlation 

statistic

Z, Wilcoxon test statistic ρ: Spearman’s correlation statistic

Data analyzed: Grid and understanding wells Data analyzed: Grid wells

Depth to the 

top of the 

upper-most 

open interval

Mixed 

versus 

modern 

age class

Modern 

versus 

pre-modern 

age class

Mixed 

versus 

pre-modern 

age class

Anoxic 

versus

 oxic

pH

Percent 

urban

 land use1

Percent 

agricultural 

land use1

Percent 

natural

 land use1

Inorganic constituents2Vanadium NL-CA 2.8 0.33 2.10 – – -2.50 0.39 – -0.53 –

Arsenic MCL-US 2.0 0.36 2.57 – – – 0.40 – – –

Boron NL-CA 2.0 – 1.72 – – – 0.34 0.35 – –

Manganese SMCL-CA 20.8 – – – – na -0.28 – – –

TDS SMCL-CA 17.2 -0.31 – 1.73 4.10 – -0.17 – 0.26 –

Iron SMCL-CA 2.0 – – – – na -0.38 – – –

Radon-222 Proposed

AMCL-US

5.3 – – – – – – – – –

Organic constituents and constituent classes3Trihalomethanes, sum of concentrations MCL-US 0.0 – -1.99 1.94 – – – 0.40 – -0.26

Solvents, sum of concentrations variable 0.0 – – – – – – 0.40 – -0.30

Gasoline, sum of concentrations MCL-US 0.1 – – – – – – – – –

Pesticides, sum of concentrations variable 0.0 0.23 – 2.71 2.64 -2.60 -0.40 – – –

Constituents of special interest3

Perchlorate MCL-US 0.0 – – – 1.70 – – – 0.30 -0.36

1Land-use percentages are within a circle with a radius of 500 meters around each well included in analysis.2Constituents with ≥ 2 percent high aquifer-scale proportions based on non-area-weighted calculations for all study areas in the San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit. 

3Classes of compounds that include constituents with high and (or) moderate values or detection frequencies at any concentration ≥ 10 percent.
40 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 12A–B. Relation of vanadium to explanatory variables, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California. (A) Relation of vanadium to redox conditions 

and pH and (B) relation of vanadium concentration to top of the uppermost open interval and 

groundwater age classification.

DEPTH TO THE TOP OF THE UPPERMOST

OPEN INTERVAL, IN FEET BELOW LAND SURFACE

0

100

200

300

400

500

6.0 6.5 7 7.5 8.0 8.5 9.0

VANADIUM, IN MICROGRAMS PER LITER

80

70

0

60

50

40

30

20

10

0 20 40 60

VANADIUM, IN MICROGRAMS PER LITER

80 100 120 140

pH, UNITLESS

Anoxic

Oxic

0.5 x NL

Notification

Level (NL)

Mixed

Modern

Pre-modern

0.5 x NL

Notification

Level (NL)

17-0006 Wright_Figure 12ab pH and Vanadium.

A

B
Status and Understanding of Water Quality 41

Figure 13A–B. Relation of arsenic to explanatory variables in the San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–July 2004. (A) Relation of arsenic to 

redox conditions and pH and (B) relation of arsenic concentration to depth to the top of the uppermost 

open interval and groundwater age classification.

17-0006 Wright_Figure 13ab pH and Vanadium.

A

B

2 

1 

0 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

ARSENIC, IN MICROGRAMS PER LITER 

6.5 7 7.5 8 8.5 9 

pH, UNITLESS 

Anoxic

Oxic

0.5 x MCL

Maximum Contamination

 Level (MCL)

0.5 x MCL

Maximum

 Contamination

 Level (MCL)

0 

100 

200 

300 

400 

500 

600 

700 

800 

1 0 2 3 4 5 6 7 8 9 10 11 12 13 14 

DEPTH TO THE TOP OF THE UPPERMOST

OPEN INTERVAL, IN FEET BELOW LAND SURFACE 

ARSENIC, IN MICROGRAMS PER LITER 

Mixed

Modern

Pre-modern
42 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Correlations of samples with the highest As 

concentrations to depth to the top of the uppermost open 

interval and to mixed rather than to modern aged groundwater 

were significant (fig. 13B; table 9). Although the statistical 

correlation between pre-modern water and As concentrations 

was not significant, 83 percent of the samples with moderate 

and high concentrations (≥ 5 μg/L) came from samples 

consisting of pre-modern groundwater. The reason As 

concentrations tend to be highest in deep wells that are tapping 

mixed and pre-modern groundwater likely is a result in part 

that older groundwater tends to have an alkaline pH. The 

median pH values for samples classified as modern, mixed. 

and pre-modern were 7.0, 7.2, and 8.3, respectively.

Factors Affecting Boron Distribution

Natural sources of B concentrations in groundwater 

include the dissolution of igneous rocks like granite and 

pegmatites, and evaporite minerals such as kernite and 

colemanite (Hem, 1985; Reimann and Caritat, 1998). Borax, 

a B-containing evaporate mineral, is used as a cleaning agent 

and therefore may be present in sewage and industrial wastes. 

In the San Diego study unit, there was a positive correlation 

of B concentrations to urban land-use (table 9), indicating that 

anthropogenic activities may be a source of B in groundwater. 

Background B concentrations are higher in seawater than 

in freshwater (World Health Organization 1998); therefore 

seawater intrusion in coastal aquifers also may increase B 

concentrations. Seawater intrusion does not seem to be a 

significant source of B in this study however, because of the 

relatively low concentrations of B in samples collected from 

the coastal alluvial aquifers (fig 11C). 

Unlike V and As, B is not a redox sensitive element, 

and thus is not greatly affected by the redox conditions of 

groundwater. The molecular configuration of B in groundwater 

is dependent on pH, salinity, and specific cation content 

(Dotsika and others, 2006). The uncharged form of B, B(OH)3, 

is predominant at pH less than 9.2, whereas the anionic form, 

B(OH)-

4 , is predominant at pH greater than 9.2. Most solid 

phases of B, for which data is available, are fairly soluble 

which suggests that adsorption and desorption reactions 

largely control the distribution of B in groundwater systems. 

In the San Diego study unit, the positive correlation between 

B concentrations and pH was significant (fig. 14; table 9), 

indicating that B is being desorbed, or inhibited from being 

adsorbed, to mineral surfaces under alkaline conditions. 

The correlations between boron and any other explanatory 

variables were not significant.

Figure 14. Relation of boron concentrations to redox conditions and pH, San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

100 

0 

200 

300 

400 

500 

1,000

1,100

1,200

1,300

BORON, IN MICROGRAMS PER LITER 

6.5 7 7.5 8 8.5 9 

pH, UNITLESS 

Anoxic

EXPLANATION

Oxic

0.5 x NL

Notification Level (NL)
Status and Understanding of Water Quality 43

Major and Minor Ions

Concentrations of some inorganic constituents can affect 

aesthetic properties of water, such as taste, color, and odor, and 

technical properties, such as scaling and staining. Although 

no adverse health effects are associated with these properties, 

consumer satisfaction with the water may be reduced or 

economic effects may result. For some constituents, CDPH 

has established non-enforceable benchmarks (SMCL-CAs) 

that are based on aesthetic or technical properties rather than 

on health-based concerns. For total dissolved solids (TDS) 

and the major ions chloride and sulfate, CDPH defines a 

“recommended” and an “upper” SMCL-CA. In this report, 

the “upper” SMCL-CA benchmarks were used to compute 

relative-concentrations. An SMCL-CA also has been 

established for the minor elements manganese and iron. 

In the Alluvial Fill study areas, relative-concentrations 

of Mn and TDS were high in 13.7 percent of the primary 

aquifers, and relative-concentrations of Fe and fluoride 

(based on spatially weighted calculations) were high in 

6.9 and 0.7 percent, respectively, of the primary aquifers 

(table 4). Manganese, TDS, and Fe were detected at high 

relative-concentrations in the Alluvial Basins study area 

at 28.6, 28.6, and 14.3 percent, respectively, and fluoride 

(F) was detected at high relative-concentrations (spatially 

weighted) in the Temecula Valley study area in 1.7 percent of 

the primary aquifers; major and minor ions were not detected 

at high relative-concentrations in the Warner Valley study 

area (tables B1A–C). In the Hard Rock study area Mn and 

TDS were detected at high relative-concentration in 33.3 

and 16.7 percent of the primary aquifers, respectively, and 

F was detected at high relative-concentrations (spatially 

weighted) in 2.2 percent of the primary aquifers. Manganese 

(20.8 percent), TDS (17.2 percent), and Fe (2.0 percent) were 

the only constituents with an aesthetic benchmark that were 

detected at high relative-concentrations in greater than or 

equal to 2.0 percent of the primary aquifers for all study areas 

in the San Diego study unit (non area-weighted aquifer-scale 

proportions).

High and moderate relative-concentrations of both Mn 

and Fe generally occurred in the same areas of the San Diego 

study unit. The similar distribution of these constituents is a 

result of the similarities in potential sources and geochemical 

behavior in groundwater. High relative-concentrations of 

Mn and Fe were detected in every study area except for 

the Warner Valley (fig. 15A and 15B). High and moderate 

relative-concentrations most frequently were detected in the 

Alluvial Basins study area followed by the Hard Rock study 

area. In the Alluvial Basins study area, high and moderate 

relative-concentrations were most frequently detected in 

the coastal areas, whereas in the Hard Rock study area 

relative-concentrations were frequently highest in the most 

inland portions of the study area.

High relative-concentrations of TDS were detected in 

every study area except for the Warner Valley (fig. 15C). 

High relative-concentrations were most frequently detected 

in the Alluvial Basins study area (28.6 percent), followed by 

the Hard Rock study area (16.7 percent). TDS concentrations 

tended to be highest in the coastal and inland coastal areas of 

the study unit, and lowest in the most interior portions of the 

study unit.

Factors Affecting Manganese and Iron

Potential natural sources of Mn and Fe to groundwater 

include the dissolution of igneous and metamorphic rocks 

as well as dissolution of various secondary minerals (Hem, 

1985). Rocks that contain significant amounts of Mn and Fe 

have a high composition of the minerals olivine, pyroxene, 

and amphibole. Potential anthropogenic sources of these 

constituents to groundwater include effluents associated with 

the steel and mining industries (Reimann and deCaritat, 1998). 

Manganese and Fe concentrations were not significantly 

correlated to either urban or agricultural land use (table 9), 

thus suggesting that natural sources are the significant 

contributing factor of Mn and Fe to groundwater in the San 

Diego study unit.

Redox and pH conditions significantly influence the 

concentrations of Mn and Fe in groundwater. In sediments, 

the oxyhydroxides of Mn and Fe are common as suspended 

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

These oxyhydroxides are stable in oxygenated systems at 

neutral pH. However, under anoxic conditions, the process of 

reductive dissolution destabilizes these minerals which affect 

the mobility of Mn and Fe in aquifer systems (Sparks, 1995). 

Figure 16 shows the relation between DO concentrations/pH 

and Mn and Fe concentrations of samples collected in the San 

Diego study unit. The negative correlation (Spearman’s rho) 

of both constituents to DO (Mn, rho = –0.52; Fe, rho = –0.57) 

and pH (table 9) was significant, indicating that reductive 

dissolution is a significant pathway for the mobilization 

of Mn and Fe in groundwater in the San Diego study unit. 

Manganese and Fe concentrations were not significantly 

correlated with any other explanatory factorss.

Factors Affecting Total Dissolved Solids

Total dissolved solids either were measured directly 

or calculated from specific conductance (see appendix E). 

Potential anthropogenic sources of TDS to groundwater 

in the San Diego study unit include agricultural and urban 

irrigation, disposal of waste water and industrial effluent, and 

leaking water and sewer pipes. The positive correlation of 

total dissolved solid concentrations to agricultural land-use 

in the San Diego study unit was significant (fig. 17; table 9), 

suggesting that agricultural irrigation practices are a significant 

contributing factor of TDS concentrations in groundwater.
44 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 15A-C. Values of selected inorganic constituents in USGS-grid and -understanding wells representative of the primary 

aquifers and the most recent analysis July 30, 2001–July 29, 2004, for CDPH wells, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004: (A) manganese, (B) iron, and (C) total dissolved solids.

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 15a Manganese

PENINSULAR 

RANGES

PACIFIC OCEAN

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

A. Manganese

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
Status and Understanding of Water Quality 45

Figure 15A–C.—Continued

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 15b Iron.

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

B. Iron

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
46 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 15A–C.—Continued

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 15c tds.

PENINSULAR 

RANGES

PACIFIC OCEAN

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

C. Total dissolved solids

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
Status and Understanding of Water Quality 47

Figure 16. Relation of manganese and iron concentrations to redox conditions and pH, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

17-0006 Wright_Figure 16 pH Manganese/Iron. MANGANESE OR IRON CONCENTRATION, IN MICROGRAMS PER LITER 

6.5 7 7.5 8 8.5 9 

pH, UNITLESS 

500

200

100

0

300

400

600

2,000 

2,200

Manganese, dissolved oxygen < 0.5 milligrams per liter

EXPLANATION

Manganese, dissolved oxygen > 0.5 milligrams per liter

Iron, dissolved oxygen < 0.5 milligrams per liter

Iron, dissolved oxygen > 0.5 milligrams per liter 
48 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 17. Boxplots of relation of total dissolved solids (TDS) concentrations to agricultural land use, San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

Position on the flow path also may affect TDS 

concentrations in groundwater. Samples collected from wells 

at high elevations may be tapping groundwater that is located 

at the proximal end of flow paths where dissolution reactions 

with the aquifer matrix have occurred to a lesser extent than 

in groundwater located at the distal ends of the flow paths. 

Additionally, as groundwater moves down the flow path 

towards discharge areas, evaporation of groundwater near the 

water table can increase TDS concentrations. 

In the San Diego study unit, the negative correlation 

of TDS concentrations to depth to the top of the uppermost 

open interval was significant as was the positive correlation 

of TDS to groundwater with a component of modern recharge 

(fig. 18; table 9). The high TDS concentrations associated 

with shallow wells and modern groundwater recharge implies 

greater loading of dissolved constituents to groundwater in 

recent decades which could indicate several anthropogenic 

factors including agricultural and urban irrigation practices, 

and changes in soil chemistry as a result of historical changes 

in land use. The negative correlation of TDS concentrations 

to pH also was significant (table 9) and most likely is not the 

result of any geochemical processes but instead is the result of 

the correlation between well depth and pH (table 7).

Seawater intrusion also can cause TDS concentrations 

in coastal areas to increase. However, in the coastal alluvial 

aquifers of the San Diego study unit, previous and current 

studies have indicated that seawater intrusion is not a 

significant contributing factor to TDS concentrations (Izbicki, 

1985; San Diego County Water Authority, 1997; Danskin 

and Church, 2005; Robert Anders, U.S. Geological Survey, 

personal commun., 2011). 

AGRICULTURAL LAND USE, IN PERCENT

< 15 16–50 > 50

0

250

750

500

1,000

1,250

1,500

2,000

1,750

TOTAL DISSOLVED SOLIDS, IN MILLIGRAMS PER LITER

35 18 8

17-0006 Wright_Figure 17. Boxplot; Urban Landuse

8 Number of wells with data

EXPLANATION

75th PERCENTILE

MEDIAN

25th PERCENTILE

Nearest data point that is within

 1.5*IQR of the 25th PERCENTILE

Nearest data point that is within 

 1.5*IQR of the 75th PERCENTILE

Data point that is outside of 1.5*IQR

 of the 75th PERCENTILE

Data point that is outside of 1.5*IQR

 of the 25th PERCENTILE

INTERQUARTILE

RANGE (IQR)
Status and Understanding of Water Quality 49

Figure 18. Relation of TDS to depth to the top of the uppermost open interval and to groundwater 

age classification, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, 

California, May–July 2004.

Radioactive Constituents

The relative-concentrations of individual radioactive 

constituents meeting the selection criteria (relativeconcentration ≥ 0.5) are shown in figure 10. As a 

class, radioactive constituents were detected at high 

relative-concentrations in 3.2 percent of the primary aquifers 

in the Alluvial Fill study areas (table 8). These constituents 

most frequently were detected at high relative-concentrations 

in the Alluvial Basins (6.7 percent) study area, and were not 

detected at high relative-concentrations either in the Temecula 

Valley or in the Warner Valley study areas (tables B2A–C). 

In the Hard Rock study area, radioactive constituents were 

detected at high relative-concentrations in 25.0 percent of 

the primary aquifers (table B2D). Radon-222 (5.3 percent) 

was the only radioactive constituent detected at high 

relative-concentrations in greater than or equal to 2.0 percent 

of the primary aquifers for all study areas in the San Diego 

study unit (non area-weighted aquifer-scale proportions).

The location and distribution of radon-222 in the San 

Diego study unit is displayed in figure 19. All but one of the 

radon-222 detections were at low relative-concentrations. The 

one detection at a high relative-concentration was in the Hard 

Rock study area.

Factors Affecting Radon-222

Radon-222 is a radioactive gas that occurs naturally 

in groundwater in the decay of uranium-238 to lead-206. 

Uranium-238 decays in multiples steps to radium-226, 

which decays to radon-222 in aquifer materials. In the San 

Diego study unit, the correlation between radon-222 and 

radium-226 activities in groundwater is not significant. This 

insignificant correlation in part may be a result of groundwater 

in crystalline rocks that generally has low radium activities 

because radium sorbs strongly to mineral surfaces, particularly 

to altered feldspars (Zapecza and Szabo, 1988; Thomas and 

others, 1993). Radon, however, is an inert gas that readily 

diffuses out of the aquifer materials and into the groundwater. 

Ayotte and others (2007) detected greater activities of 

radon-222 in groundwater from crystalline bedrock aquifers 

in the northern United States than in aquifers comprised of 

glacial sediments derived from the crystalline bedrock. The 

greater radon-222 activities in the crystalline bedrock aquifers 

was attributed to concentration of sorbed radium on fracture 

surfaces. The highest activities of radon-222 in this study also 

were detected in the crystalline rock aquifers of the Hard Rock 

study area. Radon-222 concentrations were not significantly 

correlated to any of the potential explanatory variables listed 

in table 9.

17-0006 Wright_Figure 18 Depth/Dissolved solids.

Mixed

Modern

Pre-modern

0.5* Upper MCL

Upper Maximum

 Contaminant Level (MCL)

0 250 500 750 1,000 1,250 1,500 1,750 2,000

TOTAL DISSOLVED SOLIDS, IN MILLIGRAMS PER LITER

0

100

200

300

400

500

600

700

DEPTH TO THE TOP OF THE UPPERMOST

OPEN INTERVAL, IN FEET BELOW LAND SURFACE
50 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 19. Values of radon-222 in USGS-grid and -understanding wells representative of the primary aquifers and the most recent 

analysis July 30, 2001–July 29, 2004, for CDPH wells, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit, California, May–July 2004.

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 19. Radon-222.

PENINSULAR 

RANGES

PACIFIC OCEAN

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Low or not

detected

Moderate

High
Status and Understanding of Water Quality 51

Organic and Special-Interest Constituents

Volatile organic compounds can be in paints, solvents, 

fuels, refrigerants, can be byproducts of water disinfection, 

and are characterized by their tendency to evaporate. In this 

report, VOCs are categorized as trihalomethanes, solvents, and 

gasoline components. Pesticides are used to control weeds, 

insects, or fungi in agricultural, urban, and suburban settings. 

In this report, pesticides are discussed only in terms of 

herbicides because those were only class of pesticides detected 

in grid wells in the San Diego study unit. 

Maximum relative-concentration and detection frequency 

(in grid wells not area-weighted), at any concentration, were 

used as selection criteria for organic and special-interest 

constituents and are shown in figure 20. Seven organic 

and special-interest constituents met the selection criteria: 

chloroform, 1,2-Dichloropropane, methyl tert-butyl ether 

(MTBE), atrazine, simazine, prometon, and perchlorate 

(fig. 21). Overall, organic constituents were detected in 

62 percent of the 47 grid wells (not area-weighted) in the San 

Diego study unit.

Of the 12 VOCs detected with a health-based benchmark, 

10 were detected only at low relative-concentrations. One 

VOC, 1,2-Dichloropropane, was detected at moderate 

relative-concentration (fig. 21). MTBE was the only VOC 

detected at a concentration greater than a health-based 

benchmark. The trihalomethane (THM) chloroform was the 

only VOC detected in more than 10 percent of the grid wells 

(fig. 21). Overall, the detection frequency for VOCs in the 

47 grid wells (not area-weighted) was 34 percent.

Of the 123 pesticides and pesticide degradates analyzed, 

9 pesticides were detected in grid wells with a health-based 

benchmark (fig. 20). In addition to these pesticides, five 

pesticide degradation products (daughter compounds) were 

detected in grid wells. All concentrations of pesticides were 

less than health-based benchmarks. Three pesticides—

simazine, prometon, and atrazine—were detected in greater 

than 10 percent of the grid wells sampled (fig. 21). Overall, the 

detection frequency (non-area-weighted) for pesticides with 

health based benchmarks was 45 percent, and for any pesticide 

or pesticide degradate the detection frequency was 53 percent.

Overall, organic constituents with health based 

benchmarks were detected at high relative-concentrations 

in 3.0 percent of the primary aquifers in the Alluvial Fill 

study areas (table 8). The Alluvial Basins was the only 

study area in which these constituents were detected at 

a high relative-concentration (tables B2A–C). No high 

relative-concentrations were detected in the Hard Rock study 

area (table B2D).

Trihalomethanes 

The THMs chloroform and bromodichloromethane were 

detected in multiple wells in the Alluvial Fill study areas. 

All THMs were detected at low relative-concentrations. The 

detection frequencies for THMs in grid wells was highest 

in the Alluvial Basins study area (33.3 percent), and then in 

the Temecula Valley studies area (25 percent), but were not 

detected in grid wells in the Warner Valley or Hard Rock 

study areas (fig. 22). THMs were the most frequently detected 

class of VOCs in aquifers based on national assessments by 

the USGS National Water-Quality Assessment (NAWQA) 

Program (Zogorski and others, 2006). 

Factors Affecting Trihalomethane Distribution

Potential sources of THMs include recharge from 

landscape irrigation with disinfected water, leakage from 

distribution or sewer systems, and various industrial and 

commercial sources (Ivahnenko and Barbash, 2004). On 

a national scale, the detection of THMs in groundwater is 

correlated to urban land use (Zogorski and others, 2006). In 

the San Diego study unit, the positive correlation of THMs 

to urban land use was significant (table 9). Figure 23A

shows detection frequency and concentration of THMs 

in groundwater samples as a function of urban land use. 

Although THMs are most frequently detected in samples 

collected from wells located in areas where urban landuse is 

greater than 50 percent, the samples with the highest THM 

concentrations came from wells located in a non-urban area. 

These wells (SDTEM-10 and SDTEMFP-04) are tapping 

imported water used for engineered recharge, which may be 

the source of THMs in this area (fig. 23B).

Trihalomethane concentrations were significantly 

higher in wells with modern groundwater age classification 

than in wells with mixed and pre-modern groundwater age 

classifications (fig. 23B; table 9); significant differences 

between wells classified as mixed and pre-modern were not 

detected. Trihalomethanes were detected in 57 percent of the 

samples classified as modern; THM concentrations also were 

highest in samples collected from wells with a component 

of modern recharge. Although THM concentrations were not 

significantly correlated with depth to the top of the uppermost 

open interval, samples with the highest concentration were 

collected at wells with a depth of less than 100 ft (fig. 23B). 
52 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 20. Detection frequency (non-area-weighted) and maximum relative-concentration for organic and specialinterest constituents detected in grid wells, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California, May–July 2004.

17-0006 Wright_Figure 20. 

Simazine

Chloroform

Low

Moderate

High

Prometon

Atrazine

Bromodichloromethane

Methyl tert-butyl ether

(MTBE)

Tetrachloroethylene

Metolachlor

Tebuthiuron

Bentazon

Diuron

Diuron

Bromacil

Toluene

1,1,1-Trichloroethane

cis-1,2-Dichloroethylene

Hexazinone

Carbon tetrachloride

Dichloromethane

1,1-Dichloroethylene

1,2-Dichloropropane

(1,2-DCP)

Trichloroethylene

Perchlorate

Hexazinone Constituents collected at all grid wells-Name and center of symbol is location of data unless indicated

by following location line: 

EXPLANATION

Constituents collected at a subset of grid wells-Name and center of symbol is location of data unless

indicated by following location line:

0.00001

0.0001

0.001

0.01

0.1

1

10

 RELATIVE-CONCENTRATION

DETECTION FREQUENCY, IN PERCENT

0 10 20 30 40
Status and Understanding of Water Quality 53

Figure 21. Detection frequency (non-area-weighted) and relative-concentrations in grid wells of selected organic and specialinterest constituents, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

0.0001 0.001 0.01 0.1 1

(2)

(4)

(2)

(2)

(2)

(2)

(4)

10

RELATIVE-CONCENTRATION, DIMENSIONLESS

EXPLANATION

Study areas

Solvents

Herbicides

Constituent

of special

interest

Gasoline

component

Trihalomethane

1,2-Dichloropropane 

0 10 20 30 40 50

DETECTION FREQUENCY, IN PERCENT

MTBE 

Simazine 

Prometon 

Atrazine 

Chloroform 

Perchlorate

17-0006 Wright_Figure 21. Detection Freq/Concentration.

Alluvial Basins

Hard Rock

Number of values at

 that concentration

Temecula Valley

Warner Valley
54 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 22. Sum of trihalomethanes in USGS-grid and -understanding wells representative of the primary aquifers, and CDPH data 

from the prior 3-year period of study (July 30, 2001 to July 29, 2004), San Diego Groundwater Ambient Monitoring and Assessment

(GAMA) study unit, California, May–July 2004.

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 22. Trihalomethanes.

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Not detected

Low
Status and Understanding of Water Quality 55

Figure 23. (A) Detection frequency and concentration of the trihalomethanes in relation to urban 

land use, and (B) concentration of trihalomethanes in relation to depth to the top of the uppermost 

open interval and groundwater age classification, San Diego Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit, California, May–July 2004.

0

100

200

300

400

500

600

700

800

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

DEPTH TO THE TOP OF THE UPPERMOST

OPEN INTERVAL, IN FEET BELOW LAND SURFACE

SUM OF TRIHALOMETHANES, IN MICROGRAMS PER LITER

Source of trihalomethanes

in these samples

may be imported water

used for recharge

in Temecula Valley 

Mixed

Modern

Pre-modern

10

0

0-50 51-100

20

30

40

50

60

70

DETECTION FREQUENCY, IN PERCENT

Number of

samples: 39

Number of

samples: 8

URBAN LAND USE, IN PERCENT

0

0.1

0.2

0.3

0.5

0.4

0.6

17-0006 Wright_Figure 23ab Land use and chloroform.

A

B

SUM OF TRIHALOMETHANES, IN MICROGRAMS PER LITER

Two samples at

0.03 micrograms

per liter 
56 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Solvents

The only solvent detected that met the selection 

criteria in the San Diego study unit was 1, 2-dichlororpane 

(fig. 20). In the CDPH database, 1, 2-dichloropropane was 

detected at high-relative concentrations in one well, but 

not during the current period (July30, 2001–July 29, 2004) 

(table 6). Two other solvents—tetrachloroethylene (PCE) and 

trichloroethylene (TCE)—were also detected at high-relative 

concentrations in the CDPH database, but not during the 

current period. Solvents were not detected at high relativeconcentrations in the Alluvial Fill study areas, but were 

detected at moderate relative-concentrations in 3.0 percent 

of the primary aquifers (table 8). The detection frequency 

for solvents in grid wells was highest in the Temecula Valley 

study area (16.7 percent), and then the Alluvial Basins study 

area (13.3 percent), but was not detected in grid wells either in 

the Warner Valley or in the Hard Rock study areas (fig. 24).

Factors Affecting Solvent Distribution

Solvents are used for a variety of industrial, commercial, 

and domestic purposes (Zogorski and others, 2006). Solvents 

can be introduced into the subsurface through leaking storage 

tanks and disposal of waste streams from industrial and 

commercial processes. Nationally, solvent concentrations have 

been correlated with urban land-use (Zogorski and others, 

2006; Moran and others, 2007). Like THM concentrations, 

a positive correlation of the sum of solvent concentrations 

to urban land-use was significant in the San Diego study 

unit (table 9). The sum of solvents was calculated from the 

summation of concentrations of all four solvents detected—

PCE, TCE, 1,2-dichloropropane, and carbon tetrachloride. 

Figure 25 shows detection frequency and sum of the 

detected solvent concentrations in groundwater samples as 

a function of urban land-use. The detection frequency for 

solvents in samples collected from wells in areas with urban 

land-use greater than 50 percent was 38 percent compared 

to the detection frequency of just 5 percent for solvents in 

samples collected from wells in areas where the urban land 

use was 50 percent or less. In addition, the sum of solvent 

concentrations was higher in samples collected in urbanized 

areas than in non-urbanized areas.

The sum of solvent concentrations was not significantly 

different between groundwater age classes, or between 

wells with varying depth to the top of the uppermost open 

interval (table 9). Solvents were more frequently detected 

in pre-modern water (16 percent) than either in modern 

(11 percent) or in mixed (12 percent) waters. It is expected 

that solvents would be more prevalent in younger rather than 

older groundwater because these compounds most likely 

were used more in the last 60 years or so. However, because 

some solvents were used before 1950, it is plausible that 

these compounds could be present in pre-modern water. 

Additionally, solvents in pre-modern water could indicate 

short-circuit mechanisms resulting from well construction, 

well operation processes, or other non-advective transport 

processes.

Methyl Tert-Butyl Ether (MTBE) and 

Gasoline Components

MTBE was the only gasoline component detected that 

met the selection criteria in the San Diego study unit (fig. 26). 

In the CDPH database, MTBE was detected at high-relative 

concentrations in two wells; one detection was during the 

current period (July30, 2001–July 29, 2004) (table 4). The 

only other gasoline component detected was benzene; a 

single detection, high-relative concentration of this compound 

was recorded in the CDPH database during the current 

period. Gasoline components were detected at high relativeconcentrations in 3.0 percent of the primary aquifers in the 

Alluvial Fill study areas (table 8). The detection frequency 

at any concentration for gasoline components in grid wells 

was highest in the Alluvial Basins study area (18.8 percent), 

followed by the Warner Valley (11.1 percent) and then by the 

Temecula Valley (8.3 percent) study areas (fig. 26). Gasoline 

components were not detected in USGS-grid wells in the Hard 

Rock study area, but were detected in five wells listed in the 

CDPH.
Status and Understanding of Water Quality 57

Figure 24. Sum of the solvents 1,2-dichloropropane, tetrachloroethylene (PCE), trichloroethylene (TCE), and carbon tetrachloride 

in USGS-grid wells representative of the primary aquifers, and CDPH data from the current period (July 30, 2001–July 29, 2004), San 

Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 24 SOL.

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Not detected

Low

Moderate
58 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 25. Detection frequency and sum of solvents 

concentration in relation to urban land use, San Diego 

Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California, May–July 2004.

0

10

20

30

40

DETECTION FREQENCY, IN PERCENT

0–50 51–100

URBAN LAND USE, IN PERCENT

0

0.2

0.4

0.6

0.8

1.0

1.2

SUM OF SOLVENTS, IN MICROGRAMS PER LITER

Number of

samples: 39

Number of

samples: 8

17-0006 Wright_Figure 25 Solvents.
Status and Understanding of Water Quality 59

Figure 26. Sum of the gasoline components methyl tert-butyl ether (MTBE) and benzene in USGS-grid wells and from CDPH data 

for the prior 3-year period of study (July 30, 2001–July 29, 2004), San Diego Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit, California.

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 26 Gasoline components.

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Not detected

Low

High
60 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 27. Relation of methyl tert-butyl ether (MTBE) concentrations to distance from nearest leaking underground fuel 

tank (LUFT), San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

Factors Affecting the Distribution of Methyl Tert-Butyl 

Ether (MTBE) and other Gasoline Components

Gasoline components, in particular MTBE, can 

be introduced to the sub-surface environment through 

several pathways. These components may be released into 

groundwater from point sources, such as leaking underground 

fuel tanks (LUFT), (Zogorski and others, 2006; Moran and 

others, 2007), or non-point sources, such as urban precipitation 

and storm water runoff (Pankow and others, 1997; Moran and 

others, 1999). Previous water-quality studies in California 

have indicated both point and non-point sources as the source 

of MTBE in groundwater (Happel and others, 1998; Belitz and 

others, 2003; Moran and others, 2004).

The sum of gasoline components was not significantly 

correlated to any of the explanatory factors listed in table 9. 

An additional analysis was done by comparing MTBE 

concentrations in groundwater samples to the distance from 

the nearest LUFT. Data for the LUFTs were obtained from the 

California State Water Resource Control Board’s Geotracker 

Database (State Water Resources Control Board, 2011). The 

negative correlation of MTBE concentrations to distance 

from the nearest LUFT was significant (ρ = -0.54) (fig. 27). 

Of the 13 wells sampled within 500 m of a LUFT, MTBE 

was detected at 62 percent; MTBE was not detected in any 

sample collected greater than 500 m from a well. These results 

suggest that LUFTs are the primary source of MTBE detected 

in groundwater in the San Diego study unit. It must be noted 

however, that MTBEs were not detected in five wells sampled 

within 500 m of a LUFT. Non-detections in these wells may 

be a result of several factors, such as MTBE not being a 

component of the liquid “contained” within the LUFT, severity 

of the leak, well pumping intensity, dilution with unaffected 

water, and (or) rates of biodegradation. 

Methyl tert-butyl ether detected 

Methyl tert-butyl ether not detected 

Maximum Contaminant Level (MCL)

MCL 

0.1*MCL 

0.1*MCL 

100 1,000 10,000 

DISTANCE TO NEAREST LEAKING UNDERGROUND FUEL TANKS (LUFT), IN METERS 

EXPLANATION

0 

5 

10 

15 

20 

25 

30 

METHYL TERT-BUTYL ETHER (MTBE),

IN MICROGRAMS PER LITER 
Status and Understanding of Water Quality 61

Pesticides

The only pesticides sampled for in the San Diego study 

unit that met the selection criteria were simazine, atrazine, 

prometon. Results from a study of major aquifers across 

the United States showed that these three compounds were 

frequently detected in groundwater (Gilliom and others, 2006). 

A groundwater study conducted in California showed that 

simazine was the most frequently detected triazine herbicide in 

groundwater (Troiano and others, 2001). Figure 28 shows the 

distribution of the sum of herbicide concentrations detected 

in the San Diego study unit. All detections of herbicides were 

observed at low relative-concentrations in the Alluvial Fill 

study areas (table 8). Herbicides most frequently were detected 

in grid wells in the Temecula Valley study area (66.7 percent), 

followed by grid wells in the Alluvial Basins (62.5 percent), 

in the Hard Rock (40 percent), and in the Warner Valley 

(22.2 percent) study areas. 

Factors Affecting Pesticide Distribution

Simazine and prometon frequently are used for 

nonagricultural applications including weed control on bare 

ground, around buildings, along roadsides, and in other rightof-ways. Simazine also is used on a variety of crops including 

citrus and vineyards, whereas prometon has no registered 

agricultural uses (Gilliom and others, 2006). On the other 

hand, atrazine mostly is used for agricultural purposes in 

the control of weeds in row crops; some use is reported for 

the control of weeds in right of ways. The sum of herbicide 

concentrations was not significantly correlated to any land-use 

type in the San Diego study unit (table 9).

Concentrations of herbicides were highest in shallow 

wells with modern and mixed groundwater ages (fig. 29); the 

correlation between depth to the top of the uppermost open 

interval (at the 90 percent confidence level) and groundwater age classification are significant (table 9). Herbicides 

primarily were detected in wells with depths to the top of the 

open interval less than 200 ft. The reason that the correlation 

of herbicides to shallow well depths is stronger than are 

that of THMs, solvents, and gasoline components is likely 

because the soil organic carbon/water partition coefficient 

(Koc) values for herbicides are higher than those values for 

VOCs. Compounds with relatively high Koc are hydrophobic 

and are more likely to accumulate in soil and sediment than 

compounds with a low Koc which are more likely to be 

dissolved in water. Therefore, herbicides as a result of a high 

Koc tend to be less readily transported through soil and into 

groundwater.

Some herbicides were detected in relatively deep wells 

that are tapping pre-modern water. Detections of herbicides 

in relatively deep wells with pre-modern groundwater ages 

potentially could be influenced by short-circuiting mechanisms 

that allow small quantities of modern water with dissolved 

herbicides to mix with herbicide-free pre-modern water. 

Results from a USGS study in the San Joaquin Valley suggests 

that inter-borehole flow may cause mixing of shallow and 

deep groundwater during non-pumping conditions (Jurgens 

and others, 2008). 

Herbicide concentrations also were significantly 

correlated with redox conditions (fig. 29B; table 9). 

Concentrations were higher in samples collected from 

wells tapping oxic waters with relatively low pH. Although 

geochemical parameters such as oxygen content and pH of 

groundwater may possibly affect the distribution of herbicides, 

this may not be the cause for the distribution of herbicides 

reported in this study. The correlation observed for oxic 

water and pH may result from shallow wells that tend to tap 

groundwater with a lower pH (table 7) and higher oxygen 

content than deeper wells

Perchlorate and Special-Interest Constituents

Constituents of special interest analyzed for in the San 

Diego study unit were NDMA, 1, 4-dioxane, and perchlorate. 

These constituents were selected because they recently 

were detected in, or are considered to have the potential to 

reach, drinking-water supplies (California Department of 

Public Health, 2008 b,c,d). NDMA and 1-4-dioxane were not 

detected in any wells (Wright and others, 2005). However, 

perchlorate was detected in eight grid wells and three CDPH 

wells (fig. 30). In the CDPH database perchlorate was 

detected as high in one well during the current period (July 30, 

2001–July 29, 2004) (table 4). Perchlorate was detected at 

high relative-concentrations in 0.2 percent (calculated using 

spatially weighted approach) of the primary aquifers in the 

Alluvial Fill study areas (table 8). Perchlorate most frequently 

was detected in grid wells in the Temecula Valley study area 

(33.3 percent), followed by grid wells in the Warner Valley 

(22.2 percent) and in the Alluvial Basins (18.8) study areas; 

perchlorate was not detected in grid wells in the Hard Rock 

study area.
62 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 28. Sum of herbicides in USGS-grid wells and from CDPH data for the prior 3-year period of study (July 30, 2001–July 29, 

2004), San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

17-0006 Wright_Figure 28 Sum of Herbicides.

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

USGS-grid or

-understanding

well

RelativeconcentrationCDPH

wells

Not detected

Low
Status and Understanding of Water Quality 63

Figure 29. Sum of herbicide concentrations in relation to (A) depth to the top of the uppermost open 

interval and groundwater age classification, and in relation to (B) redox condition of groundwater, 

San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–

July 2004.

0

100

200

300 

400

500

600

700

800 

0.00 

0.01 

0.02 

0.03 

0.04 

0.00 0.05 0.10 0.15 0.20

DEPTH TO THE TOP OF THE UPPERMOST

OPEN INTERVAL, IN FEET BELOW LAND SURFACE

SUM OF HERBICIDE CONCENTRATIONS, IN MICROGRAMS PER LITER 

6.5 7 7.5 8 8.5 9 9.5 

SUM OF HERBICIDE CONCENTRATIONS,

IN MICROGRAMS PER LITER 

pH, UNITLESS 

Anoxic 

Oxic 

17-0006 Wright_Figure 29ab Herbicides.

A

B

Mixed

Modern

Pre-modern
64 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure 30. Perchlorate in USGS-grid wells and from CDPH data for the prior 3-year period of study (July 30, 2001–July 29, 2004), 

San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

PACIFIC OCEAN

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

PENINSULAR 

RANGES

Study areas

EXPLANATION

Alluvial Basins

Hard Rock

Temecula Valley

Warner Valley

USGS grid or

understanding

well

RelativeconcentrationCDPH

wells

Not detected

Low

Moderate

High

0 10 20 MILES

0 10 20 KILOMETERS
Summary 65

Factors Affecting Perchlorate Distribution

Potential anthropogenic sources of perchlorate include 

nitrate fertilizers (Dasgupta and others, 2006) and the 

production and use of explosives, road flares, and automobile 

air-bag systems (Parker and others, 2008). In addition, 

Colorado River water, which is imported into the San Diego 

study unit for public supply and agricultural irrigation, is 

known to contain perchlorate (California Department of 

Public Health, 2008b). Perchlorate also has been detected in 

groundwater in some highly arid desert environments as a 

result of natural atmospheric and soil processes (Dasgupta 

and others, 2005; Plummer and others, 2006); However, it 

is unclear whether these processes are occurring, or have 

occurred, in the San Diego study unit. 

Perchlorate concentrations were only significantly 

correlated (negatively) to natural land-use in this study 

(table 9). Perchlorate concentrations, however, were 

positively correlated to agricultural areas at the 90 percent 

confidence level indicating that the use of nitrate fertilizers, 

and (or) Colorado River water that is used for irrigation, are 

contributing sources of perchlorate in groundwater of the 

San Diego study unit. Additionally, a detection of perchlorate 

(which is an order of magnitude below the MCL-CA) may be 

the result of a sample collected from a well (SDTEM-10) that 

is located down-gradient from an engineered recharge facility 

that uses Colorado River water.

Summary 

The Groundwater Ambient Monitoring and Assessment 

(GAMA) Program was created by the California State Water 

Resources Control Board (State Water Board) to provide a 

comprehensive groundwater-quality baseline for the State 

of California. The program is a comprehensive assessment 

of statewide groundwater quality designed to improve 

ambient groundwater-quality monitoring and to increase 

the availability of information about groundwater quality to 

the public. The GAMA program includes the Priority Basin 

Project, conducted by the U.S. Geological Survey (USGS) 

in collaboration with the State Water Board and Lawrence 

Livermore National Laboratory (LLNL). This report is one of 

a series of reports presenting the status and understanding of 

current groundwater-quality conditions in study units of the 

GAMA Priority Basin Project.

The approximately 3,900-square mile (mi2) San Diego 

study unit lies in the southwestern-most corner of California 

and is composed of four study areas—Temecula Valley 

(140 mi2), Warner Valley (37 mi2), Alluvial Basins (166 mi2), 

and the Hard Rock (850 mi2). The purposes of this report are 

(1) to describe briefly the hydrogeologic setting of the San 

Diego study unit, (2) to assess the current status of untreated 

groundwater quality in the primary aquifers in the San Diego 

study unit, and (3) to assess the relations between water 

quality and selected potential explanatory factors.

 The GAMA San Diego study was designed to provide 

a statistically robust assessment of untreated groundwater 

quality within the primary aquifer systems. The primary 

aquifers were defined by the depth interval of the wells 

listed in the California Department of Public Health (CDPH) 

database for the San Diego study unit. Forty-seven grid wells 

were selected randomly within spatially distributed grid cells 

across the San Diego study unit. Grid wells were selected for 

sampling in each study area: 12 in the Temecula Valley, 9 in 

the Warner Valley, 16 in the Alluvial Basins, and 10 in the 

Hard Rock. In addition, 23 CDPH wells with data from the 

prior 3-year period (July 30, 2001–July 29, 2004) were used 

to complement USGS data. Grid-based and spatially weighted 

approaches were used to assess aquifer-scale proportions of 

constituents at high, moderate, and low relative-concentrations 

in the primary aquifers in order to characterize the quality 

of untreated groundwater in the study unit. Area weighting 

was used to account for the disparate size of the study areas 

relative to each other.

Given the large number of analytes, an objective 

algorithm was used to select those constituents of greatest 

importance to water quality in the primary aquifers for 

discussion in the report. To provide context, concentrations 

of constituents measured in the untreated groundwater were 

compared with regulatory and non-regulatory human-health 

and aesthetic benchmarks. Relative-concentrations (sample 

concentration divided by benchmark concentration) were used 

as the primary metric for evaluating groundwater quality. 

Constituents were classified into those whose maximum 

relative-concentrations were high, moderate, or low. Inorganic 

constituents with high relative-concentrations in greater than 

or equal to 2 percent of the grid wells (based on non areaweighted detections for all study areas in the San Diego study 

unit) were tested for relations to a set of potential explanatory 

factors that included land use, classified groundwater age, and 

geochemical-condition indicators. For organic and specialinterest constituents to be tested for relations with potential 

explanatory variables detection frequencies had to be greater 

than 10 percent in grid wells (non area-weighted) at any 

concentration, or be detected at least once in a grid well at a 

moderate or high relative-concentration. 

Inorganic constituents with human-health benchmarks 

were high in 17.6 percent of the primary aquifers in the 

Alluvial Fill study areas. Aquifer-scale high proportions of 

inorganic constituents with human-health based benchmarks 

indicated high relative-concentrations of trace elements, 

nutrients, and radioactive constituents. Trace elements with 

high concentrations in greater than or equal to 2 percent of the 

primary aquifers in the San Diego study unit were vanadium 

(V), arsenic (As), and boron (B). Inorganic constituents with 

non-health based benchmarks at high relative-concentrations 

in greater than or equal to 2 percent in the primary aquifers in 

the San Diego study unit were manganese (Mn), iron (Fe), and 

total dissolved solids (TDS). 
66 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

The relation between the concentrations of V, As, Mn, 

and Fe in groundwater and in urban or agricultural land-use 

was not significant. This result suggests that natural sources 

(dissolution of rocks) are the significant contributing factor 

of these constituents in groundwater. Concentrations of B and 

TDS had a significant positive correlation to land use. Boron 

was correlated with urban land use and TDS with agricultural 

land-use, thus indicating anthropogenic activities as significant 

sources of these constituents in groundwater in the San Diego 

study unit.

pH and redox conditions of groundwater were important 

factors that affect the concentrations of inorganic constituents 

in groundwater. Generally, concentrations of the trace 

elements V, As, and B were high in alkaline groundwater 

which suggests that these trace elements are being desorbed 

from, or are inhibited from adsorbing onto, mineral surfaces. 

Vanadium and As are redox-sensitive species, but only the 

correlation of V to redox conditions was significant. Vanadium 

concentrations were higher in oxic groundwater than in 

anoxic groundwater, indicating that in the San Diego study 

unit V is most mobile under oxic and alkaline conditions. 

Concentrations of V and As also were higher in deep wells 

rather than in shallow wells and in samples that were partially 

or entirely composed of pre-modern (> 50 yrs) groundwater. 

The correlations between concentrations and depth and 

groundwater age likely are because of the significant positive 

correlation of deep wells and pre-modern groundwaters to pH.

The negative correlation of Mn and Fe to pH and 

dissolved oxygen (DO) was statistically significant, a result of 

the differing mechanisms that enhance the mobility of Mn and 

Fe in groundwater. The negative correlation of TDS to pH also 

was significant; however, this relation likely is not the result 

of any geochemical chemical processes, but because the TDS 

concentrations were highest in shallow wells where the pH of 

groundwater is significantly lower than the pH of groundwater 

being tapped by deeper wells.

Organic and special-interest constituents were detected 

at high relative-concentrations in a smaller proportion 

of the primary aquifers in the Alluvial Fill study areas 

(3.0 percent) than were inorganic constituents (17.6 percent). 

Relative-concentrations of organic constituents were moderate 

in 3.0 percent of the primary aquifers in the Alluvial Fill 

study areas. The proportion of the primary aquifers with high 

relative-concentrations of organic constituents was due to a 

detection of the discontinued gasoline oxygenate MTBE. One 

organic and one special-interest constituent were each detected 

at moderate relative-concentrations in grid wells—1,2-

dichloropropane and perchlorate. 

The status assessment for organic constituents indicated 

that 12 of the 88 VOCs and 14 of the 123 pesticides 

and pesticide degradates analyzed in grid wells were 

detected. Of the 12 VOCs detected, 10 were detected at 

low relative-concentration, one at a moderate relativeconcentration, and one at a high relative-concentration. Only 

one VOC, chloroform, was detected in greater than or equal 

to 10 percent of the grid wells. Of the nine herbicides with 

health-based benchmarks that were detected, all had low 

relative-concentrations. Detection frequencies for simazine, 

atrazine, and prometon were greater than or equal to 10 

percent. Perchlorate was the only special interest constituent 

detected in groundwater samples; it was detected in 22 percent 

of samples.

The positive correlation of the sum of THMs and solvent 

concentrations to urban land-use was significant, indicating 

that in the San Diego study unit groundwater located 

underneath urbanized areas is more likely to have detections 

of these anthropogenic constituents. MTBE concentrations 

were negatively correlated to the distance from the nearest 

leaking underground fuel tank, indicating that these point 

sources are the most significant contributing factor for MTBE 

concentrations to groundwater in the San Diego study unit.

The positive correlation of concentrations of THMs 

and herbicides to modern groundwater was significant. 

The negative correlation of herbicides to pH and anoxic 

groundwater also was significant. Although pH and redox 

conditions may affect the distribution of herbicides in 

groundwater, the correlations observed in this study likely are 

because herbicides were detected more frequently and at high 

concentrations in shallow wells where groundwater conditions 

tend to be oxic with relatively low pH.

Acknowledgments

The authors thank the California State Water Board, 

Lawrence Livermore National Laboratory, the California 

Department of Public Health, and the California Department 

of Water Resources. We especially thank the well owners 

and water purveyors for their cooperation and 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 

California State Water Board.

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72 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

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 and was collected within three years 

prior to the end of sampling in the San Diego study unit 

(July 30, 2001 to July 29, 2004). The most recent CDPH data 

from the well were evaluated to determine whether the cation/

anion balance for the CDPH data less than 10 percent. If so, 

the CDPH inorganic data from the well were selected for use 

as grid-well data for inorganic constituents. It was assumed 

that analyses using major ion data with a cation-anion balance 

less than 10 percent also resulted in high-quality data for trace 

elements, nutrients, and radiochemical constituents. This 

step resulted in the selection of inorganic data from CDPH at 

16 wells that also were USGS-grid wells. For identification 

purposes, data from the CDPH for these grid wells were 

assigned GAMA identifications numbers equivalent to the 

GAMA USGS-grid well but with DG inserted between the 

study area prefix and sequence number (for example, CDPHgrid well SDTEM-DG-08 is the same well as USGS-grid well 

SDTEM-08) (table A1).

If the first step did not yield CDPH inorganic data for 

a grid cell, then the second step was to search the CDPH 

database to identify the highest randomly ranked well within 

a cell with a cation/anion imbalance less than 10 percent. This 

step did not result in the selection of any wells.

If no CDPH wells in a grid cell met the charge-balance 

criteria or if there was insufficient data to evaluate charge 

balance, then the third choice for the CDPH-grid well was to 

select the highest randomly ranked CDPH well with any of 

the targeted inorganic data. This step resulted in selection of 

seven grid wells from which CDPH inorganic data were used. 

For identification purposes, data from the CDPH for these 

grid wells not collocated with USGS-grid wells were assigned 

GAMA identifications numbers equivalent to the GAMA 

USGS-grid well for the cell but with DPH inserted between 

the study area prefix and sequence number (for example, 

CDPH-grid well SDTEM-DPH-08 in a grid cell with no 

USGS wells).

Inorganic data from the CDPH database were used at 

23 grid wells (table 2). In combination with USGS-grid well 

inorganic data (19 wells), inorganic data was available for 

42 of the 60 grid cells. Analysis of the combined data sets 

to evaluate the occurrence of relatively high or moderate 

concentrations was not affected by differences in laboratory 

reporting levels (LRLs) between GAMA-collected and 

CDPH data because concentrations greater than one-half of 

water-quality benchmarks generally were substantially higher 

than the highest LRLs. The locations, GAMA identification 

numbers of grid and understanding wells (fig. A1A-A1C), 

and attributes of CDPH-grid wells are located table A1. 

Comparisons between USGS-collected and CDPH data are 

described in appendix D.

Appendix A. Selection of CDHP-Well Data 
Appendix A 73

Table A1. Identification and attributes of grid and understanding wells sampled during May 17–July 29, 2004, and grid wells using data 

for inorganic constituents from the California Department of Public Health (CDPH), San Diego Ground-Water Ambient Monitoring and 

Assessment (GAMA) study unit, California.

[SDALLV, Alluvial Basins study area; SDALLVU, Alluvial Basins study area understanding well; SDHDRK, Hard Rock study area; SDHDRKU, Hard Rock 

study area understanding well; SDTEM, Temecula Valley study area; SDTEMFP, Temecula Valley study area flow path well; SDWARN, Warner Valley study 

area; DG, CDPH data from well sampled by GAMA; DPH, CDPH data from well not sampled by GAMA; ft, feet; m, meter; LSD, land surface datum; USGS, 

U.S. Geological Survey; PSW, public-supply well; –, no data]

USGS GAMA

 well 

identification 

number

CDPH GAMA 

well 

identification 

number

Well 

type

Agricultural 

land use

(percent)

Natural 

land use

(percent)

Urban 

land use

(percent)

Construction information

Well 

depth

Top of uppermost open 

interval

Bottom of 

lowermost 

open interval

Length from top of 

uppermost open 

interval to bottom of 

the lowermost 

open interval

Grid wells

SDALLV-01 – PSW 62 37 1 200 100 180 80

SDALLV-02 SDALLV-DG-12 PSW 5 34 60 130 94 117 23

SDALLV-03 – PSW 0 34 65 606 222 566 344

SDALLV-04 – PSW 71 19 10 180 80 180 100

SDALLV-05 SDALLV-DG-05 PSW 58 42 0 582 234 513 279

SDALLV-06 – PSW 2 33 65 200 100 142 42

SDALLV-071 SDALLV-DG-07 PSW 2 84 14 200 39 – –

SDALLV-08 SDALLV-DG-08 PSW 38 60 2 87 50 78 28

SDALLV-09 – PSW 0 7 93 810 690 800 110

SDALLV-10 SDALLV-DG-10 PSW 26 35 39 135 65 130 65

SDALLV-111 SDALLV-DG-11 PSW 0 46 54 148 50 148 98

SDALLV-12 – PSW 0 58 42 230 60 220 160

SDALLV-131 – PSW 0 94 6 181 96 176 80

SDALLV-14 – PSW 38 62 0 80 40 80 40

SDALLV-15 – PSW 2 71 27 107 54 107 53

SDALLV-161 SDALLV-DG-16 PSW 0 97 3 120 48 120 72

SDHDRK-04 – PSW 0 100 0 315 – – –

SDHDRK-05 – PSW 0 86 14 450 50 450 400

SDHDRK-06 – PSW 1 45 55 1,000 52 1000 948

SDHDRK-07 – PSW 6 80 14 400 97 400 303

SDHDRK-08 – PSW 5 95 0 500 60 500 440

SDHDRK-09 – PSW 0 100 0 400 75 400 325

SDHDRK-10 SDHDRK-DG-10 PSW 0 100 0 – – – –

SDHDRK-11 SDHDRK-DG-11 PSW 0 100 0 455 20 455 435

SDHDRK-12 – PSW 0 100 0 186 60 186 126

SDHDRK-13 – PSW 0 100 0 46 41 – –

SDTEM-01 – PSW 57 36 7 1,000 150 1,000 850

SDTEM-03 SDTEM-DG-03 PSW 5 22 74 – 466 909 443

SDTEM-041 SDTEM-DG-04 PSW 72 25 3 252 95 295 200

SDTEM-05 – PSW 23 51 26 960 200 900 700

SDTEM-06 – PSW 23 28 49 – 170 470 300

SDTEM-07 – PSW 17 18 66 307 60 307 247

SDTEM-08 SDTEM-DG-08 PSW 52 44 4 – 114 426 312

SDTEM-091 SDTEM-DG-09 PSW 43 48 9 970 450 950 500

SDTEM-101 – PSW 0 100 0 250 50 210 160

SDTEM-11 SDTEM-DG-11 PSW 0 51 49 1,000 340 980 640

SDTEM-121 – PSW 39 58 3 546 96 542 446

SDTEM-13 – PSW 35 46 19 860 235 860 625

SDWARN-01 – PSW 0 100 0 473 113 473 360

SDWARN-021 – PSW 1 99 0 585 100 575 475

SDWARN-03 – PSW 0 100 0 550 118 550 432

SDWARN-04 – PSW 18 82 0 438 170 438 268
74 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Table A1. Identification and attributes of grid and understanding wells sampled during May 17–July 29 2004, and grid wells using data 

for inorganic constituents from the California Department of Public Health (CDPH), San Diego Ground-Water Ambient Monitoring and 

Assessment (GAMA) study unit, California.—Continued

[SDALLV, Alluvial Basins study area; SDALLVU, Alluvial Basins study area understanding well; SDHDRK, Hard Rock study area; SDHDRKU, Hard Rock 

study area understanding well; SDTEM, Temecula Valley study area; SDTEMFP, Temecula Valley study area flow path well; SDWARN, Warner Valley study 

area; DG, CDPH data from well sampled by GAMA; DPH, CDPH data from well not sampled by GAMA; ft, feet; m, meter; LSD, land surface datum; USGS, 

U.S. Geological Survey; PSW, public-supply well; –, no data]

USGS GAMA

 well 

identification 

number

CDPH GAMA 

well 

identification 

number

Well 

type

Agricultural 

land use

(percent)

Natural 

land use

(percent)

Urban 

land use

(percent)

Construction information

Well 

depth

Top of uppermost open 

interval

Bottom of 

lowermost 

open interval

Length from top of 

uppermost open 

interval to bottom of 

the lowermost 

open interval

Grid wells—Continued

SDWARN-05 – PSW 0 100 0 743 130 743 613

SDWARN-06 – PSW 0 100 0 730 190 730 540

SDWARN-07 – PSW 0 99 1 295 70 165 95

SDWARN-08 SDWARN-DG-08 PSW 0 100 0 700 280 600 320

SDWARN-09 SDWARN-DG-09 PSW 0 100 0 642 60 642 582

– SDALLV-DPH-17 PSW 0 88 12 – – – –

– SDALLV-DPH-18 PSW 27 54 19 – – – –

– SDALLV-DPH-19 PSW 0 98 2 – – – –

– SDTEM-DPH-14 PSW 40 26 34 – – – –

– SDTEM-DPH-15 PSW 49 43 8 – – – –

– SDTEM-DPH-16 PSW 4 81 15 – – – –

– SDTEM-DPH-17 PSW 0 98 2 – – – –

Understanding wells

SDALLVU-012 – PSW 67 33 0 – – – –

SDHDRKU-012 – PSW 0 9 91 906 110 906 796

SDHDRKU-022 – PSW 0 100 0 92 52 92 40

SDHDRKU-032 – PSW 25 45 30 510 80 510 430

SDTEMFP-01 – PSW 35 50 16 2,500 234 2,147 1,913

SDTEMFP-02 – PSW 30 68 3 858 378 838 460

SDTEMFP-03 – PSW 32 67 1 865 305 845 540

SDTEMFP-04 – PSW 29 67 4 482 75 465 390

SDTEMFP-05 – PSW 19 76 5 280 80 270 190

SDTEMFP-062 – PSW 20 33 47 – 320 1,110 790

SDTEMFP-072 – PSW 65 21 13 – 270 1,000 730

1

Well construction information has been updated subsequent to the publication of the San Diego Ground-Water Ambient Monitoring and Assessment (GAMA) 

study unit data report (Wright and others, 2005).

2

Well has been reclassified from grid to understanding subsequent to the publication of the San Diego Ground-Water Ambient Monitoring and Assessment 

(GAMA) study unit data report (Wright and others, 2005).
Appendix A 75

Figure A1A–A1C. Identifiers and locations of grid and understanding wells sampled during May–July 2004, and grid wells at 

which data for inorganic constituents from the California Department of Public Health were used in the study areas of San Diego 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.

Santa

Margarita

Tucalota Cr

River

Temecula Creek

Murrieta

Creek

Lake Skinner

Vail Lake

Railroad

Canyon

Reservoir

EXPLANATION

Shaded relief derived from U.S. Geological Survey

National Elevation digital Dataset, 2006,

Albers Equal Area Conic Projection

117 15' 117 10' 117 05' 117 00' 116 55' 116 50'

33

30'

33

35'

Temecula

33

40'

RIVERSIDE COUNTY

SAN DIEGO COUNTY

Study cell (Temecula Valley)

Study area boundary

0 5 MILES

0 5 KILOMETERS

Grid well (USGS GAMA)

Grid well (CDHP)

Understanding well (USGS GAMA)

Study unit boundary

SDTEM-05

SDTEM-08

SDTEM-11

SDTEM-03

SDTEM-01

SDTEM-09

SDTEM-06

SDTEM-07 SDTEM-13 SDTEM-12

SDTEM-12

SDTEM-10

SDTEM-DG-08

SDTEM-DG-11

SDTEM-DG-03 SDTEMDG-04

SDTEM-DG-09

SDTEM-DG-09

SDTEM-DPH-15

SDTEM-DPH-16

SDTEM-DPH-17

SDTEMFP-07

SDTEMFP-06

SDTEMFP-01

SDTEMFP-05

SDTEMFP-02

SDTEMFP-03

SDTEMFP-04

SDTEMFP-02

SDTEM-DPH-14

A
76 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure A1A–A1C.—Continued

Lake 

Henshaw

San Luis Rey River

San Luis Rey

River

Agua Caliente Creek

Buena Vista Creek

Matagual Creek

Carrista

Creek

0 2 4 MILES

0 2 4 KILOMETERS

EXPLANATION

116 45' 116 40' 116 35'

33

20'

33

15'

PENINSULAR 

RANGES

Study cell (Warner Valley)

Study area boundary

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Study unit boundary

SDWARN-02 SDWARN-07

SDWARN-08

SDWARN-DG-08

SDWARN-09

SDWARN-06

SDWARN-05

SDWARN-03

SDWARN-01

SDWARN-04

SDWARN-06

SDWARN-DG-08

SDWARN-DG-09

B

Grid well (USGS GAMA)

Grid well (CDHP)
Appendix A 77

Figure A1A–A1C.—Continued

117 30' 117 00' 116 30'

SAN DIEGO COUNTY

RIVERSIDE COUNTY

Desert Province

Transverse Ranges and Selected

Peninsular Ranges Province

33

30'

33

00'

UNITED STATES

MEXICO

ORANGE

COUNTY

Dana Point

San Diego

0 10 20 MILES

0 10 20 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Study areas

Alluvial Basins

Hard Rock

EXPLANATION

PENINSULAR 

RANGES

PACIFIC OCEAN

Grid well (USGS GAMA)

Grid well (CDHP)

Understanding well

 (USGS GAMA)

Study unit

 boundary

0 10 20 MILES

0 10 20 KILOMETERS

SDALLV-02

SDALLV-DPH-17

SDHDRK-DG-10

SDHDRK-DG-11

SDHDRK-DPH-18

SDALLV-DG-08

SDALLV-DG-07

SDALLV-08

SDALLV-07

SDALLV-DG-16

SDALLV-DG-05

SDALLV-DPH-19

SDALLV-DPH-19

SDALLV-DG-11

SDALLV-16

SDALLV-04

SDALLV-15

SDALLV-11

SDALLV-14

SDALLV-01

SDALLV-05

SDALLV-10 SDALLV-DG-10

SDALLV-06

SDALLV-09

SDALLV-03

SDALLV-13

SDHDRK-10

SDHDRK-04

SDHDRK-05

SDHDRK-06

SDHDRK-08

SDHDRK-09

SDHDRK-09

SDHDRK-11

SDHDRK-12

SDHDRK-07

SDHDRK-13

SDHDRKU-03

SDHDRKU-01

SDHDRKU-01

SDALLVU-01

C
78 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Three approaches—grid-based, raw detection frequency, 

and spatially weighted—were selected to evaluate the 

proportion of the primary supply aquifer in the San Diego 

study unit with concentrations of constituents greater than 

water-quality benchmarks (high relative-concentrations).

1. Grid-based: One well in each grid cell was randomly 

selected to represent the primary aquifers. The 

relative-concentration for each constituent (concentration 

relative to its benchmark), or class of constituents, was 

then evaluated for each grid well. The proportion of 

the primary aquifers with high relative-concentrations 

was calculated by taking the number of cells 

with concentrations greater than the benchmark 

(relative‐concentration > 1), and dividing that number 

by the total number of grid wells in each of the study 

areas (Belitz and others, 2010). The proportion for each 

study area is calculated individually because grid-cell 

sizes are not uniform across study areas. The proportion 

for the study unit then is determined by calculating the 

area-weighted sum using the following equation:

,

where

is the aquifer proportion for the study unit,

is the aquifer proportion for a study area, and

is the fraction of the total study unit area

occupied by the study area.

su SA SA

su

SA

SA

AQP AQP F

AQP

AQP

F

= ∑ (A1)

The FSA Alluvial Fill study areas are: Temecula Valley 0.41, 

Warner Valley 0.11, and Alluvial Basins 0.48. The proportions 

of moderate and low relative-concentrations were calculated 

similarly. Aquifer-scale proportions for individual constituents 

for each study area (including Hard Rock) are shown in 

tables B1A-D and for classes of constituents in tables B2A-D. 

Confidence intervals for grid-based proportions shown in 

tables B1A-D were computed using the Jeffreys interval for 

the binomial distribution (Brown and others, 2001). The gridbased estimate is spatially unbiased. However, the grid-based 

approach may not detect constituents that are present at high 

relative-concentrations in small proportions of the primary 

aquifers. 

2. Raw detection frequency: Within selected time criteria 

(July 30 2001–July 29 2004), all available data from the 

following sources were used to calculate the percentage 

(frequency) of wells with high relative-concentrations: 

USGS-grid, CDPH data (most recent analysis per well), 

and USGS-understanding wells with open intervals 

representative of the primary aquifers. However, this 

approach is not spatially unbiased because the CDPH and 

USGS-understanding wells are not uniformly distributed. 

Consequently, high relative-concentrations in wells 

clustering in a particular area represent a small part of the 

primary aquifers and could be given a disproportionately 

high weight compared to spatially unbiased methods. Raw 

detection frequencies of high relative-concentrations are 

provided for reference in this report but were not used to 

assess aquifer-scale proportions. 

3. Spatially weighted: Similar to the detection frequency 

approach, the USGS-grid, CDPH data (one analysis 

per well), and USGS-understanding well data are 

considered for the spatially weighted approach (Belitz 

and others, 2010). However for the spatially weighted 

approach, proportions are computed on a cell-by-cell 

basis (Isaaks and Srivistava, 1989) rather than as an 

average of all the wells. Using the spatially weighted 

approach, the proportion of high values for each study 

area was computed by (step 1) computing the proportion 

of high wells in each grid cell and (step 2) averaging the 

grid-cell values computed in step The spatially weighted 

high proportions for the study unit was calculated by 

summing the area-weight values for each study area in 

the same manner as was used in the grid-based approach. 

Calculations for individual constituents for each study 

area are shown in tables B1A-D and for classes of 

constituents in tables B2A-D. The resulting proportions 

are spatially unbiased (Isaaks and Srivistava, 1989). 

Confidence intervals for spatially weighted detection 

frequencies of high relative-concentrations are not 

described in this report. Results are based on the most 

recent analyses for each CDPH well during July 30, 

2001–July 29, 2004 (3-year period of study).

The grid-based and spatially weighted estimation 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 aquifer at depths that 

are typically used for public supply. These approaches assign 

weights to wells based upon a single well per cell (grid-based) 

or the number of wells per cells (spatially weighted). Another 

possible approach is to assign weights to wells based on water 

use (withdrawal rate). However, water use data for publicsupply and other wells are not readily available. Moreover, 

this approach, even if withdrawal data were available for all 

wells, would characterize the volume of groundwater currently 

used for public supply, which likely would be weighted 

towards fewer wells and smaller areas than the approaches 

used, which were based on spatially distributed grid cells 

across the study unit.

Appendix B. Aquifer Proportion
Appendix B 79

Table B1A. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Temecula Valley study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquiferscale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 12 grid wells in the Temecula Valley study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.[High, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based1 Raw detection frequency1 Spatially weighted1

90 percent 

confidence interval 

for grid-based high 

proportion2

Number 

of wells

High 

aquifer 

proportion

(percent)

Moderate 

aquifer 

proportion 

(percent)

Low 

aquifer 

proportion

(percent)

Number 

of wells

Number 

of wells 

with high 

values

Raw 

detection 

frequency

(percent)

Number 

of cells

High 

aquifer 

proportion

(percent)

Lower 

limit

(percent)

Upper 

limit

(percent)

Trace elementsVanadium NL-CA 50 μg/L 11 18.2 54.5 27.3 46 11 23.9 13 19.5 5.4 41.9

Arsenic MCL-US 10 μg/L 10 10.0 0.0 90.0 45 4 8.9 12 9.3 1.8 33.1

Boron NL-CA 1,000 μg/L 10 10.0 0.0 90.0 42 2 4.8 12 3.1 1.8 33.1

Fluoride MCL-CA 2 mg/L 10 0.0 0.0 100.0 45 1 2.2 12 1.7 0.0 17.1Antimony MCL-US 6 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Selenium MCL-US 50 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Thallium MCL-US 2 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Chromium MCL-CA 50 μg/L 10 0.0 0.0 100.0 46 0 0.0 12 0.0 0.0 17.1

Lead AL-US 15 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Nickel MCL-CA 100 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Aluminum MCL-CA 1,000 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Cadmium MCL-US 5 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Mercury MCL-US 2 μg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1

Radioactive constituentsGross-alpha MCL-US 15 pCi/L 10 0.0 10.0 90.0 46 0 0.0 12 0.0 0.0 17.1

Radon-222 Proposed AMCL-US 4,000 pCi/L 6 0.0 0.0 100.0 11 0 0.0 8 0.0 0.0 26.4

Uranium MCL-US 30 μg/L 8 0.0 0.0 100.0 18 0 0.0 9 0.0 0.0 20.8

Radium-228 MCL-US 5 pCi/L 6 0.0 0.0 100.0 11 0 0.0 8 0.0 0.0 26.4

Gross-beta radioactivity MCL-CA 50 pCi/L 6 0.0 0.0 100.0 12 0 0.0 8 0.0 0.0 26.4

NutrientsNitrate, as nitrogen MCL-US 10 mg/L 12 0.0 8.3 91.7 53 0 0.0 15 0.0 0.0 14.5

Nitrite, as nitrogen MCL-US 1 mg/L 9 0.0 0.0 100.0 46 0 0.0 12 0.0 0.0 18.7
80 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table B1A. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Temecula Valley study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquifer- scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 12 grid wells in the Temecula Valley study area during May– July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued [High, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter] Constituent Threshold type Threshold value Threshold units Grid-based1 Raw detection frequency1 Spatially weighted1 90 percent confidence interval for grid-based high proportion2 Number of wells High aquifer proportion (percent) Moderate aquifer proportion (percent) Low aquifer proportion (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High aquifer proportion (percent) Lower limit (percent) Upper limit (percent) Major and minor elements (SMCLs) Total dissolved solids SMCL-CA 1,000 mg/L 11 0.0 9.1 90.9 47 0 0.0 13 0.0 0.0 15.7 Manganese SMCL-CA 50 μg/L 10 0.0 0.0 100.0 47 6 12.8 12 7.6 0.0 17.1 Iron SMCL-CA 300 μg/L 10 0.0 0.0 100.0 47 1 2.1 12 3.0 0.0 17.1 Chloride SMCL-CA 500 mg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1 Sulfate SMCL-CA 500 mg/L 10 0.0 0.0 100.0 45 0 0.0 12 0.0 0.0 17.1 Zinc SMCL-CA 5,000 μg/L 10 0.0 0.0 100.0 44 0 0.0 12 0.0 0.0 17.1 Gasoline components Benzene MCL-CA 1 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 MTBE MCL-CA 13 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 Trihalomethanes Chloroform MCL-US 380 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 Solvents Tetrachloroethylene MCL-US 5 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 Carbon tetrachloride MCL-CA 0.5 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 Trichloroethylene MCL-US 5 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 1,2-Dichloropropane MCL-US 5 μg/L 12 0.0 0.0 100.0 42 0 0.0 13 0.0 0.0 14.5 Herbicides Prometon HAL-US 100 μg/L 12 0.0 0.0 100.0 12 0 0.0 12 0.0 0.0 14.5 Simazine MCL-US 4 μg/L 12 0.0 0.0 100.0 39 0 0.0 12 0.0 0.0 14.5 Atrazine MCL-CA 1 μg/L 12 0.0 0.0 100.0 39 0 0.0 12 0.0 0.0 14.5
Appendix B 81

Table B1A. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Temecula Valley study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquiferscale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 12 grid wells in the Temecula Valley study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued[High, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based1 Raw detection frequency1 Spatially weighted1

90 percent 

confidence interval 

for grid-based high 

proportion2

Number 

of wells

High 

aquifer 

proportion

(percent)

Moderate 

aquifer 

proportion 

(percent)

Low 

aquifer 

proportion

(percent)

Number 

of wells

Number 

of wells 

with high 

values

Raw 

detection 

frequency

(percent)

Number 

of cells

High 

aquifer 

proportion

(percent)

Lower 

limit

(percent)

Upper 

limit

(percent)

Constituent of special interestPerchlorate MCL-CA 6 μg/L 7 0.0 66.7 33.3 26 0 0.0 12 0.0 0.0 23.2

1Based on most recent analysis for each CDPH well during July 30, 2001–July 29, 2004, combined with GAMA grid-based data.2Based on the Jeffrey’s interval for the binomial distribution (Brown and others, 2001).3The MCL-US threshold for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
82 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table B1B. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Warner Valley study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquifer- scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from nine grid wells in the Warner Valley study area during May– July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data. [High, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter] Constituent Threshold type Threshold value Threshold units Grid-based1 Raw detection frequency1 Spatially weighted1 90 percent confidence interval for grid-based high proportion2 Number of wells High aquifer proportion (percent) Moderate aquifer proportion (percent) Low aquifer proportion (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High aquifer proportion (percent) Lower limit (percent) Upper limit (percent) Trace elements Vanadium NL-CA 50 μg/L 4 0.0 25.0 75.0 5 0 0.0 4 0.0 0.0 36.2 Arsenic MCL-US 10 μg/L 4 0.0 25.0 75.0 5 0 0.0 4 0.0 0.0 36.2 Fluoride MCL-CA 2 mg/L 4 0.0 25.0 75.0 5 0 0.0 4 0.0 0.0 36.2 Boron NL-CA 1,000 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Antimony MCL-US 6 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Selenium MCL-US 50 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Thallium MCL-US 2 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Chromium MCL-CA 50 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Lead AL-US 15 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Nickel MCL-CA 100 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Aluminum MCL-CA 1,000 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Cadmium MCL-US 5 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Mercury MCL-US 2 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2 Radioactive constituents Radon-222 Proposed AMCL-US 4,000 pCi/L 3 0.0 0.0 100.0 3 0 0.0 3 0.0 0.0 44.4 Gross-alpha MCL-US 15 pCi/L 4 0.0 0.0 100.0 3 0 0.0 4 0.0 0.0 36.2 Uranium MCL-US 30 μg/L 3 0.0 0.0 100.0 3 0 0.0 3 0.0 0.0 44.4 Radium-228 MCL-US 5 pCi/L 3 0.0 0.0 100.0 3 0 0.0 3 0.0 0.0 44.4 Gross-beta radioactivity MCL-CA 50 pCi/L 3 0.0 0.0 100.0 3 0 0.0 3 0.0 0.0 44.4 Nutrients Nitrate, as nitrogen MCL-US 10 mg/L 5 0.0 0.0 100.0 6 0 0.0 5 0.0 0.0 30.6 Nitrite, as nitrogen MCL-US 1 mg/L 5 0.0 0.0 100.0 6 0 0.0 5 0.0 0.0 30.6
Appendix B 83

Table B1B. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Warner Valley study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquiferscale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from nine grid wells in the Warner Valley study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued[High, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based1 Raw detection frequency1 Spatially weighted1

90 percent 

confidence interval 

for grid-based high 

proportion2

Number 

of wells

High 

aquifer 

proportion 

(percent)

Moderate 

aquifer 

proportion 

(percent)

Low 

aquifer 

proportion 

(percent)

Number 

of wells

Number

 of wells 

with high 

values

Raw 

detection 

frequency 

(percent)

Number 

of cells

High 

aquifer 

proportion 

(percent)

Lower 

limit

(percent)

Upper 

limit

(percent)

Major and minor elements (SMCLs)Manganese SMCL-CA 50 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2Total dissolved solids SMCL-CA 1,000 mg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2Iron SMCL-CA 300 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2Chloride SMCL-CA 500 mg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2Sulfate SMCL-CA 500 mg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2Zinc SMCL-CA 5,000 μg/L 4 0.0 0.0 100.0 5 0 0.0 4 0.0 0.0 36.2

Gasoline componentsBenzene MCL-CA 1 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1MTBE MCL-CA 13 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1TrihalomethanesChloroform MCL-US 380 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1

SolventsTetrachloroethylene MCL-US 5 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1

Carbon tetrachloride MCL-CA 0.5 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1

Trichloroethylene MCL-US 5 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1

1,2-Dichloropropane MCL-US 5 μg/L 9 0.0 0.0 100.0 10 0 0.0 9 0.0 0.0 17.1

HerbicidesPrometon HAL-US 100 μg/L 9 0.0 0.0 100.0 9 0 0.0 9 0.0 0.0 18.7

Simazine MCL-US 4 μg/L 9 0.0 0.0 100.0 9 0 0.0 9 0.0 0.0 18.7Atrazine MCL-CA 1 μg/L 9 0.0 0.0 100.0 9 0 0.0 9 0.0 0.0 18.7
84 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table B1B. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Warner Valley study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquifer- scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from nine grid wells in the Warner Valley study area during May– July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued [High, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter] Constituent Threshold type Threshold value Threshold units Grid-based1 Raw detection frequency1 Spatially weighted1 90 percent confidence interval for grid-based high proportion2 Number of wells High aquifer proportion (percent) Moderate aquifer proportion (percent) Low aquifer proportion (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High aquifer proportion (percent) Lower limit (percent) Upper limit (percent) Constituent of special interest Perchlorate MCL-CA 6 μg/L 9 0.0 0.0 100.0 9 0 0.0 9 0.0 0.0 18.7

1Based on most recent analysis for each CDPH well during July 30, 2001–July 29, 2004, combined with GAMA grid-based data.2Based on the Jeffrey’s interval for the binomial distribution (Brown and others, 2001).3The MCL-US threshold for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
Appendix B 85

Table B1C. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Alluvial Basins study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquiferscale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 16 grid wells in the Alluvial Basins study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.[high, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based1 Raw detection frequency1 Spatially weighted1

90 percent 

confidence interval 

for grid-based high 

proportion2

Number 

of wells

High 

aquifer 

proportion 

(percent)

Moderate 

aquifer 

proportion 

(percent)

Low 

aquifer 

proportion 

(percent)

Number 

of wells

Number 

of wells 

with high 

values

Raw 

detection 

frequency 

(percent)

Number 

of cells

High 

aquifer 

proportion 

(percent)

Lower 

limit 

(percent)

Upper 

limit 

(percent)

Trace elementsAntimony MCL-US 6 μg/L 14 7.1 0.0 92.9 56 1 1.8 15 6.7 1.3 24.8

Vanadium NL-CA 50 μg/L 13 0.0 7.1 92.9 66 1 1.5 15 3.3 0.0 13.5

Selenium MCL-US 50 μg/L 15 0.0 6.7 93.3 67 0 0.0 16 0.0 0.0 11.8

Arsenic MCL-US 10 μg/L 15 0.0 0.0 100.0 67 0 0.0 16 0.0 0.0 11.8

Boron NL-CA 1,000 μg/L 14 0.0 0.0 100.0 70 0 0.0 16 0.0 0.0 12.6

Fluoride MCL-CA 2 mg/L 14 0.0 0.0 100.0 65 0 0.0 15 0.0 0.0 12.6

Thallium MCL-US 2 μg/L 14 0.0 0.0 100.0 56 0 0.0 16 0.0 0.0 12.6

Chromium MCL-CA 50 μg/L 14 0.0 0.0 100.0 58 0 0.0 16 0.0 0.0 12.6

Lead AL-US 15 μg/L 14 0.0 0.0 100.0 64 0 0.0 15 0.0 0.0 12.6

Nickel MCL-CA 100 μg/L 14 0.0 0.0 100.0 56 0 0.0 16 0.0 0.0 12.6

Aluminum MCL-CA 1,000 μg/L 15 0.0 0.0 100.0 67 0 0.0 16 0.0 0.0 11.8

Cadmium MCL-US 5 μg/L 15 0.0 0.0 100.0 67 0 0.0 16 0.0 0.0 11.8

Mercury MCL-US 2 μg/L 15 0.0 0.0 100.0 67 0 0.0 16 0.0 0.0 11.8

Radioactive constituentsGross-alpha MCL-US 15 pCi/L 15 6.7 13.3 80.0 64 4 6.3 15 7.1 1.2 23.3

Uranium MCL-US 30 μg/L 11 0.0 27.3 72.7 32 1 3.1 14 1.8 0.0 15.7

Radon-222 Proposed AMCL-US 4,000 pCi/L 6 0.0 0.0 100.0 7 0 0.0 7 0.0 0.0 26.4

Radium-228 MCL-US 5 pCi/L 7 0.0 0.0 100.0 20 0 0.0 9 0.0 0.0 23.2

Gross-beta radioactivity MCL-CA 50 pCi/L 10 0.0 0.0 100.0 32 0 0.0 11 0.0 0.0 17.1

NutrientsNitrate, as nitrogen MCL-US 10 mg/L 14 7.1 7.1 85.8 89 7 7.9 16 7.1 1.3 24.8

Nitrite, as nitrogen MCL-US 1 mg/L 14 0.0 0.0 0.0 69 0 0.0 16 0.0 0.0 12.6
86 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table B1C. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Alluvial Basins study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquifer- scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 16 grid wells in the Alluvial Basins study area during May– July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH ata collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued [high, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; % , percent; mg/L, milligrams per liter; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; pCi/L, picocuries per liter] Constituent Threshold type Threshold value Threshold units Grid-based1 Raw detection frequency1 Spatially weighted1 90 percent confidence interval for grid-based high proportion2 Number of wells High aquifer proportion (percent) Moderate aquifer proportion (percent) Low aquifer proportion (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High aquifer proportion (percent) Lower limit (percent) Upper limit (percent) Major and minor elements (SMCLs) Manganese SMCL-CA 50 μg/L 14 28.6 7.1 64.3 68 26 38.2 16 37.7 12.8 50.3 Total dissolved solids SMCL-CA 1,000 μg/L 14 28.6 57.1 14.3 67 15 22.4 16 27.1 12.8 50.3 Iron SMCL-CA 300 μg/L 14 14.3 0.0 85.7 68 9 13.2 16 11.0 4.2 34.2 Chloride SMCL-CA 500 μg/L 14 7.1 21.4 71.5 67 5 7.5 16 5.7 1.3 24.8 Sulfate SMCL-CA 500 μg/L 14 7.1 14.3 78.6 67 2 3.0 16 7.8 1.3 24.8 Zinc SMCL-CA 5,000 μg/L 15 0.0 0.0 100.0 65 0 0.0 16 0.0 0.0 11.8 Gasoline components MTBE MCL-CA 13 μg/L 16 6.3 0.0 93.7 61 2 3.3 17 2.9 1.1 22.0 Benzene MCL-CA 1 μg/L 16 0.0 0.0 100.0 61 0 0.0 17 0.0 0.0 11.1 Trihalomethanes Chloroform MCL-US 380 μg/L 16 0.0 0.0 100.0 60 0 0.0 16 0.0 0.0 11.1 Solvents 1,2-Dichloropropane MCL-US 5 μg/L 16 0.0 6.3 93.7 61 0 0.0 17 0.0 0.0 11.1 Tetrachloroethylene MCL-US 5 μg/L 16 0.0 0.0 100.0 61 0 0.0 17 0.0 0.0 11.1 Carbon tetrachloride MCL-CA 0.5 μg/L 16 0.0 0.0 100.0 61 0 0.0 17 0.0 0.0 11.1 Trichloroethylene MCL-US 5 μg/L 16 0.0 0.0 100.0 61 0 0.0 17 0.0 0.0 11.1 Herbicides Prometon HAL-US 100 μg/L 16 0.0 0.0 100.0 17 0 0.0 16 0.0 0.0 11.1 Simazine MCL-US 4 μg/L 16 0.0 0.0 100.0 53 0 0.0 16 0.0 0.0 11.1 Atrazine MCL-CA 1 μg/L 16 0.0 0.0 100.0 53 0 0.0 16 0.0 0.0 11.1
Appendix B 87

Table B1C. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Alluvial Basins study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquiferscale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 16 grid wells in the Alluvial Basins study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued[high, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; % , percent; mg/L, milligrams per liter; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; pCi/L, picocuries per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based1 Raw detection frequency1 Spatially weighted1

90 percent 

confidence interval 

for grid-based high 

proportion2

Number 

of wells

High 

aquifer 

proportion 

(percent)

Moderate 

aquifer 

proportion 

(percent)

Low 

aquifer 

proportion 

(percent)

Number 

of wells

Number 

of wells 

with high 

values

Raw 

detection 

frequency 

(percent)

Number 

of cells

High 

aquifer 

proportion 

(percent)

Lower 

limit 

(percent)

Upper 

limit 

(percent)

Constituent of special interestPerchlorate MCL-CA 6 μg/L 16 0.0 18.8 81.2 68 1 1.5 17 0.4 0.0 11.1

1Based on most recent analysis for each CDPH well during July 30, 2001–July 29, 2004, combined with GAMA grid-based data.2Based on the Jeffrey’s interval for the binomial distribution (Brown and others, 2001).3The MCL-US threshold for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
88 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table B1D. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Hard Rock study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquifer- scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 10 grid wells in the Hard Rock study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data. [high, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; mg/L, milligrams per liter; pCi/L, picocuries per liter] Constituent Threshold type Threshold value Threshold units Grid-based1 Raw detection frequency1 Spatially weighted1 90 percent confidence interval for grid-based high proportion2 Number of wells High aquifer proportion (percent) Moderate aquifer proportion (percent) Low aquifer proportion (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High aquifer proportion (percent) Lower limit (percent) Upper limit (percent) Trace elements Arsenic MCL-US 10 μg/L 5 0.0 20.0 80.0 45 0 0.0 9 0.0 0.0 30.6 Fluoride MCL-CA 2 mg/L 5 0.0 0.0 100.0 50 2 4.0 9 2.2 0.0 30.6 Aluminum MCL-CA 1,000 μg/L 5 0.0 0.0 100.0 47 1 2.1 9 1.2 0.0 30.6 Vanadium NL-CA 50 μg/L 5 0.0 0.0 100.0 34 0 0.0 9 0.0 0.0 30.6 Boron NL-CA 1,000 μg/L 5 0.0 0.0 100.0 30 0 0.0 9 0.0 0.0 30.6 Antimony MCL-US 6 μg/L 5 0.0 0.0 100.0 41 0 0.0 9 0.0 0.0 30.6 Selenium MCL-US 50 μg/L 5 0.0 0.0 100.0 46 0 0.0 9 0.0 0.0 30.6 Thallium MCL-US 2 μg/L 5 0.0 0.0 100.0 39 0 0.0 9 0.0 0.0 30.6 Chromium MCL-CA 50 μg/L 5 0.0 0.0 100.0 42 0 0.0 9 0.0 0.0 30.6 Lead AL-US 15 μg/L 5 0.0 0.0 100.0 45 0 0.0 9 0.0 0.0 30.6 Nickel MCL-CA 100 μg/L 5 0.0 0.0 100.0 39 0 0.0 9 0.0 0.0 30.6 Cadmium MCL-US 5 μg/L 5 0.0 0.0 100.0 46 0 0.0 9 0.0 0.0 30.6 Mercury MCL-US 2 μg/L 5 0.0 0.0 100.0 46 0 0.0 9 0.0 0.0 30.6 Radioactive constituents Radon-222 Proposed AMCL-US 4,000 pCi/L 4 25.0 0.0 75.0 4 1 25.0 4 25.0 4.6 65.1 Gross-alpha MCL-US 15 pCi/L 6 0.0 50.0 50.0 42 7 16.7 9 18.5 0.0 26.4 Uranium MCL-US 30 μg/L 5 0.0 20.0 80.0 23 3 13.0 7 7.3 0.0 30.6 Radium-228 MCL-US 5 pCi/L 5 0.0 0.0 100.0 19 2 10.5 7 9.5 0.0 30.6 Gross-beta radioactivity MCL-CA 50 pCi/L 4 0.0 0.0 100.0 17 0 0.0 8 0.0 0.0 36.2 Nutrients Nitrate, as nitrogen MCL-US 10 mg/L 6 0.0 0.0 100.0 99 1 1.0 10 1.2 0.0 26.4 Nitrite, as nitrogen MCL-US 1 mg/L 6 0.0 0.0 100.0 69 0 0.0 10 0.0 0.0 26.4
Appendix B 89

Table B1D. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Hard Rock study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquiferscale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 10 grid wells in the Hard Rock study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued[high, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; % , percent; mg/L, milligrams per liter; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; pCi/L, picocuries per liter]

Constituent Threshold type Threshold value Threshold units

Grid-based1 Raw detection frequency1 Spatially weighted1

90 percent 

confidence interval 

for grid-based high 

proportion2

Number 

of wells

High 

aquifer 

proportion 

(percent)

Moderate 

aquifer 

proportion 

(percent)

Low 

aquifer 

proportion 

(percent)

Number 

of wells

Number of 

wells with 

high values

Raw 

detection 

frequency 

(percent)

Number 

of cells

High 

aquifer 

proportion 

(percent)

Lower 

limit

(percent)

Upper 

limit 

(percent)

Major and minor elements (SMCLs)Manganese SMCL-CA 50 μg/L 6 33.3 16.7 50.0 48 15 31.3 9 27.5 10.4 65.9

Total dissolved solids SMCL-CA 1,000 mg/L 6 16.7 0.0 83.3 45 2 4.4 10 10.0 3.0 49.5

Iron SMCL-CA 300 μg/L 6 0.0 33.0 67.0 50 14 28.0 9 29.9 0.0 26.4

Sulfate SMCL-CA 500 mg/L 5 0.0 20.0 80.0 45 1 2.2 9 1.6 0.0 30.6

Chloride SMCL-CA 500 mg/L 5 0.0 0.0 100.0 46 1 2.2 10 5.0 0.0 30.6

Zinc SMCL-CA 5,000 μg/L 5 0.0 0.0 100.0 44 1 2.3 10 1.4 0.0 30.6

Gasoline componentsBenzene MCL-CA 1 μg/L 10 0.0 0.0 100.0 40 1 2.5 10 1.1 0.0 17.1

MTBE MCL-CA 13 μg/L 10 0.0 0.0 100.0 46 0 0.0 10 0.0 0.0 17.1

TrihalomethanesChloroform MCL-US 380 μg/L 8 0.0 0.0 100.0 38 0 0.0 9 0.0 0.0 20.8

SolventsTetrachloroethylene MCL-US 5 μg/L 10 0.0 0.0 100.0 40 0 0.0 10 0.0 0.0 17.1

Carbon tetrachloride MCL-CA 0.5 μg/L 10 0.0 0.0 100.0 40 0 0.0 10 0.0 0.0 17.1

Trichloroethylene MCL-US 5 μg/L 10 0.0 0.0 100.0 40 0 0.0 10 0.0 0.0 17.1

1,2-Dichloropropane MCL-US 5 μg/L 10 0.0 0.0 100.0 40 0 0.0 10 0.0 0.0 17.1

HerbicidesPrometon HAL-US 100 μg/L 10 0.0 0.0 100.0 11 0 0.0 10 0.0 0.0 17.1

Simazine MCL-US 4 μg/L 10 0.0 0.0 100.0 36 0 0.0 10 0.0 0.0 17.1

Atrazine MCL-CA 1 μg/L 10 0.0 0.0 100.0 36 0 0.0 10 0.0 0.0 17.1
90 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project Table B1D. Current aquifer-scale proportions using grid-based and spatially weighted methods for constituents detected in Hard Rock study area (1) with concentrations greater than water-quality benchmarks during the period from July 30, 2001 to July 29, 2004 in the California Department of Public Health (CDPH) database, or (2) with high or moderate concentrations in samples collected from grid wells, or (3) with organic constituents detected in more than 10 percent of the grid wells sampled. Grid-based aquifer- scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from 10 grid wells in the Hard Rock study area during May–July 2004, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California. Spatially weighted aquifer-scale proportions are based on 3 years of CDPH data collected from July 30, 2001–July 29, 2004 in combination with grid and understanding well data.—Continued [high, concentrations greater than water-quality benchmark; moderate, concentrations greater than or equal to 0.1 of benchmark but less than or equal to benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); low, concentrations less than or equal to 0.1 of benchmark for organic constituents (threshold for inorganic constituents is 0.5 of benchmark); MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; % , percent; mg/L, milligrams per liter; AMCL-US, U.S. Environmental Protection Agency alternate maximum contaminant level; μg/L, micrograms per liter; pCi/L, picocuries per liter] Constituent Threshold type Threshold value Threshold units Grid-based1 Raw detection frequency1 Spatially weighted1 90 percent confidence interval for grid-based high proportion2 Number of wells High aquifer proportion (percent) Moderate aquifer proportion (percent) Low aquifer proportion (percent) Number of wells Number of wells with high values Raw detection frequency (percent) Number of cells High aquifer proportion (percent) Lower limit (percent) Upper limit (percent) Constituent of special interest Perchlorate MCL-CA 6 μg/L 10 0.0 0.0 100.0 21 0 0.0 10 0.0 0.0 17.1

2Based on most recent analysis for each CDPH well during July 30, 2001–July 29, 2004, combined with GAMA grid-based data.1Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001).3The MCL-US threshold for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
Appendix B 91

Table B2A. Grid-based aquifer-scale proportions for constituent classes, Temecula Valley study area, San Diego Groundwater 

Ambient Monitoring and Assessment (GAMA) Program study unit, California.

[SMCL, secondary maximum contaminant level; values are grid based unless otherwise noted]

Constituent class Aquifer proportion values (percent)

High Moderate Low

Inorganics with health-based benchmarks

Trace elements 27.3 45.5 27.3

Radioactive 0.0 10.0 90.0

Nutrients 0.0 8.3 91.7

Any inorganic with health-based benchmarks 27.3 50.0 22.7

Inorganics with aesthetic benchmarks

Total dissolved solids and (or) chloride and (or) sulfate 0.0 10.0 90.0

Manganese and (or) iron 17.6 0.0 100.0

Organics with health-based benchmarks

Trihalomethanes 0.0 0.0 100.0

Solvents 0.0 0.0 100.0

Gasoline components 0.0 0.0 100.0

Pesticides 0.0 0.0 100.0

Any organic with health-based benchmarks 0.0 0.0 100.0

Constituent of special interest

Perchlorate 0.0 66.7 33.3

1

Spatially weighted value. Aquifer-scale proportions will not sum to 100 if a spatially weighted value is used.

Table B2B. Grid-based aquifer-scale proportions for constituent classes, Warner Valley study area, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) Program study unit, California.

[SMCL, secondary maximum contaminant level; values are grid based unless otherwise noted]

Aquifer proportion values (percent)

Constituent class High Moderate Low 

Inorganics with health-based benchmarks

Trace elements 0.0 20.0 80.0

Radioactive 0.0 0.0 100.0

Nutrients 0.0 0.0 100.0

Any inorganic with health-based benchmarks 0.0 20.0 80.0

Inorganics with aesthetic benchmarks

Total dissolved solids and (or) chloride and (or) sulfate 0.0 0.0 100.0

Manganese and (or) iron 0.0 0.0 100.0

Organics with health-based benchmarks

Trihalomethanes 0.0 0.0 100.0

Solvents 0.0 0.0 100.0

Gasoline components 0.0 0.0 100.0

Pesticides 0.0 0.0 100.0

Any organic with health-based benchmarks 0.0 0.0 100.0

Constituent of special interest

Perchlorate 0.0 0.0 100.0
92 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Table B2C. Grid-based aquifer-scale proportions for constituent classes, Alluvial Basins study area, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) Program study unit, California.

[SMCL, secondary maximum contaminant level; values are grid based unless otherwise noted]

Constituent class Aquifer proportion values (percent)

High Moderate Low

Inorganics with health-based benchmarks

Trace elements 6.7 13.3 80.0

Radioactive 6.7 13.3 80.0

Nutrients 7.1 7.1 85.8

Any inorganic with health-based benchmarks 13.3 20.0 66.7

Inorganics with aesthetic benchmarks

Total dissolved solids and (or) chloride and (or) sulfate 28.6 57.1 14.3

Manganese and (or) iron 28.6 7.1 64.3

Organics with health-based benchmarks

Trihalomethanes 0.0 0.0 100.0

Solvents 0.0 6.3 93.7

Gasoline components 6.3 0.0 93.7

Pesticides 0.0 0.0 100.0

Any organic with health-based benchmarks 6.3 6.3 87.4

Constituents of special interest

Perchlorate 10.4 18.8 81.2

1

Spatially weighted value. Aquifer-scale proportions will not sum to 100 if a spatially weighted value is used.

Table B2D. Grid-based aquifer-scale proportions for constituent classes, Hard Rock study area, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) Program study unit, California.

[SMCL, secondary maximum contaminant level; values are grid based unless otherwise noted]

Constituent class Aquifer proportion values (percent)

High Moderate Low

Inorganics with health-based benchmarks

Trace elements 11.2 20.0 80.0

Radioactive 25.0 25.0 50.0

Nutrients 11.2 0.0 100.0

Any inorganic with health-based benchmarks 25.0 25.0 50.0

Inorganics with aesthetic benchmarks

Total dissolved solids and (or) chloride and (or) sulfate 16.7 0.0 83.3

Manganese and (or) iron 33.3 16.7 50.0

Organics with health-based benchmarks

Trihalomethanes 0.0 0.0 100.0

Solvents 0.0 0.0 100.0

Gasoline components 11.1 0.0 100.0

Pesticides 0.0 0.0 100.0

Any organic with health-based benchmarks 0.0 0.0 100.0

Constituents of special interest

Perchlorate 0.0 0.0 100.0

1

Spatially weighted value. Aquifer-scale proportions will not sum to 100 if a spatially weighted value is used.
Appendix C 93

Land-use classifications and percentages, well 

construction information, groundwater age data and 

classifications, and redox classifications are listed in 

tables A1, C1, and C2. 

Land-Use Classification

Land use was classified using an “enhanced” version 

of the satellite derived (30 m pixel resolution), nationwide 

USGS National Land Cover Dataset (Volgeman 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 data represent land use during approximately the 

early 1990s. The imagery is classified into 25 land-cover 

classifications (Nakagaki and Wolock, 2005). These 25 landcover classifications were condensed into 3 principal land-use 

categories: urban, agricultural, and natural. Land-use statistics 

for the study unit, study areas, and for circles with a radius of 

500-m around each study well were calculated for classified 

datasets using ArcGIS (Johnson and Belitz, 2009). 

Well Construction Information

Well construction data primarily were determined 

from driller’s logs. On occasion, well construction data was 

obtained from ancillary records of well owners or from the 

USGS National Water Information System database. Well 

identification verification procedures are described by Wright 

and others (2005).

Groundwater Age Classification

Groundwater dating techniques provide a measure of 

the time since the groundwater was last in contact with the 

atmosphere. Techniques aimed at estimating groundwater 

residence times or ‘age’ include those based on tritium (for 

example,Tolstikhin and Kamensskiy, 1969; Torgersen and 

others, 1979) and tritium in combination with its decay 

product helium-3 (Schlosser and others, 1988, 1989; Solomon 

and others, 1992), carbon-14 activities (for example, Vogel 

and Ehhalt, 1963; Plummer and others, 1993; Kalin, 2000), 

and dissolved noble gases, particularly helium-4 accumulation 

(for example, Andrews and Lee, 1979; Davis and DeWiest, 

1966; 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). 3H is produced naturally in the atmosphere 

by the interaction of cosmogenic radiation with nitrogen, 

by above-ground nuclear explosions, and by the operation 

of nuclear reactors. Tritium enters the hydrological cycle 

Appendix C. Ancillary Datasets

following oxidation to tritiated water. 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 using tritium 

alone for water (Solomon and others, 1992)). 

The widely used carbon-14 chronometer relies on 

evaluating 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, dissolving in precipitation and entering the 

hydrologic cycle. 14C activity in groundwater, expressed 

as percent modern carbon (pmc), indicates exposure to the 

atmospheric 14C source, and is governed by the decay constant 

of 14C (with a half-life of 5,730 yrs). 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 based on the 

decrease in 14C activity as a result of radioactive decay with 

time since groundwater recharge, relative to an assumed initial 

14C concentration (Clarke and Fritz, 1997). A mean initial 14C 

activity of 99 pmc is assumed for this study, with estimated 

errors on calculated groundwater ages 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 groundwater He concentrations represent 

the sum of several He components including air-equilibrated 

He (Heeq), 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 resulting from 

air-equilibration [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 
94 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

entrainment). Samples with greater than, or equal to, 5 percent 

terrigenic He represent groundwater with a residence time of 

more than 100 yrs.

Recharge temperatures were calculated from dissolved 

neon, argon, krypton, and xenon using methods described in 

Aeschbach-Hertig and others (1999). Only modeled recharge 

temperatures having a probability greater than 1 percent were 

accepted (Aeschbach-Hertig and others, 2000), and the sample 

with the highest probability was used in this report. 

3H /3He apparent ages were computed as described in 

Solomon and Cook (2000). The uncertainty for computed 

3H/3He apparent ages is greater in samples with terrigenic He 

greater than 5 percent because of sensitivity to the 3He/4He 

ratio of the terrigenic He (Plummer and others, 2000). The 

3He/4He ratio of samples was determined by linear regression 

of the percent of terrigenic He against the δ3He ([δ3He = Rmeas/

Ratm –1] × 100) of samples with less than 1 tritium unit (TU).

In this study, the age distributions of samples are 

classified as pre-modern, modern, and mixed. Groundwater 

with tritium activity less than 1 TU, percent terrigenic He 

greater than, or equal to, 5 percent, and 14C less than 90 pmc 

is designated as pre-modern—defined as having recharged 

prior to 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 recharged during the last 50 years. Samples with both 

pre-modern and modern components are designated as mixed 

groundwater, which includes substantial fractions of both 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 others, 2005). By using a tritium value of 

1.0 TU, at the upper end of the range used in the literature, for 

the threshold in this study, the age classification scheme allows 

a slightly larger fraction of modern water to be present in a 

classified pre-modern age distribution than if a lower threshold 

were used. A lower threshold for tritium would result in fewer 

wells classified as having a pre-modern rather than a mixed 

age distribution, when other tracers, carbon-14 and terrigenic 

helium, suggested that they were primarily pre-modern water. 

This higher threshold was considered more appropriate for this 

study because many of the wells are long-screened production 

wells and some mixing of at least some waters of different 

ages is likely to occur. 

Tritium, pmc, and percent terrigenic helium, along with 

sample age classifications are reported in appendix table C1. 

Because of uncertainties in age distributions, in particular 

caused by mixing of waters of different ages in wells with 

long open interval intervals and high withdrawal rates, these 

more precise age estimates were not specifically used for 

quantifying the relation between age and water quality in this 

report. Although more sophisticated lumped parameter models 

for analyzing age distributions that incorporate mixing are 

available (Cook and Bohlke, 2000), use of these alternative 

models to understand age mixtures was beyond the scope of 

this report. Rather, classification into modern, mixed, and premodern categories was considered to provide an appropriate 

and useful characterization for the purposes of examining 

groundwater quality.

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidationreduction (redox) characteristics. Microorganisms affect the 

redox conditions of groundwater by utilizing terminal electron 

acceptors during the degradation of organic carbon. The order 

of terminal electron acceptor utilization is: O2 > NO3– > Mn 

(IV) > Fe (III) > SO4

2– > CO2 (McMahon and Chapelle, 

2008). With the successive utilization and subsequent 

depletion of terminal electron acceptors, the redox condition 

of groundwater progresses from oxidizing (positive Eh values) 

to reducing (negative Eh values). Oxidation-reduction (redox) 

conditions affect the mobility of many organic and inorganic 

constituents (McMahon and Chapelle, 2008), and thus 

constituent concentrations were compared to redox conditions 

of the system. 

Classification of redox conditions was done using a 

modification of the framework of McMahon and Chapelle 

(2008), and is shown in table C2. Redox conditions were 

classified as either oxic or anoxic. This study utilizes both 

filtered (USGS data) and unfiltered (CDPH data) samples to 

infer redox conditions of groundwater. For filtered samples, 

concentration thresholds as outlined by McMahon and 

Chapelle (2008) were used. However, because unfiltered 

samples may overestimate the concentration of dissolved 

constituents (see discussion in appendix D), a second set of 

raised concentration thresholds were used for CDPH data. 

The difference in concentration between unfiltered CDPH 

samples and filtered USGS samples collected at wells sampled 

by both methods were used to create a raised threshold for 

unfiltered samples. The amount that the thresholds were raised 

by was calculated by taking the median value of the range 

of concentration differences between filtered and unfiltered 

samples and adding that value to the concentration threshold 

used for filtered samples. For example, if the differences in 

concentration between unfiltered and filtered Mn samples 

collected at the same wells within one year of each other 

yielded a range in differences of 0.05 to 0.17 mg/L with a 

median value of 0.07 mg/L, then the raised threshold for 

unfiltered Mn would be: filtered threshold (0.05 mg/L) + 

median difference (0.07 mg/L) = raised threshold (0.12 mg/L). 

Available data did not allow for classifications to be made 

based on SO4

2 and CO2 reducing conditions. 
Appendix C 95

Table C1. Summary of groundwater age data and classification of samples into modern, mixed, and pre-modern age distributions, San 

Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

[°C, Degrees Celsius; TU, tritium units; –, no data]

GAMA_ID

Recharge temperature Tritium

Terrigenic helium, 

percent of total 

helium

Modern carbon

Age 

classification (°C) Error 

(°C) (TU) Error (TU) (percent)

Counting 

error

(percent)

Grid Wells

SDALLV-01 16.2 0.0 3.20 0.09 5.0 99 0.4 Mixed

SDALLV-02 15.9 0.0 4.08 0.18 0.5 99 0.4 Modern

SDALLV-03 17.0 0.0 0.00 0.07 90.2 59 0.3 Pre-Modern

SDALLV-04 19.2 0.1 2.28 0.11 7.6 – – Mixed

SDALLV-06 18.9 0.5 5.83 0.41 46.2 101 0.4 Mixed

SDALLV-07 16.0 0.0 4.31 0.18 7.4 – – Mixed

SDALLV-08 23.7 0.0 3.80 0.17 39.0 – – Mixed

SDALLV-09 20.4 0.2 0.01 0.03 81.6 22 0.2 Pre-Modern

SDALLV-10 15.7 0.0 5.81 0.22 18.8 – – Mixed

SDALLV-11 17.3 0.1 6.93 0.41 54.8 – – Mixed

SDALLV-12 18.3 0.0 2.65 0.12 83.6 – – Mixed

SDALLV-13 17.4 0.0 3.53 0.25 53.8 95 0.4 Mixed

SDALLV-14 15.9 0.0 2.16 0.10 28.2 – – Mixed

SDALLV-15 17.5 0.1 1.91 0.10 69.6 – – Mixed

SDALLV-16 21.0 0.8 4.92 0.41 0.0 – – Modern

SDHDRK-04 9.2 0.2 1.88 0.19 3.1 97 0.4 Modern

SDHDRK-05 18.3 0.0 3.32 0.31 63.7 102 0.4 Mixed

SDHDRK-06 12.7 1.3 0.41 0.05 96.4 76 0.4 Pre-Modern

SDHDRK-07 16.8 2.7 0.38 0.05 39.2 64 0.3 Pre-Modern

SDHDRK-08 14.0 0.3 1.92 0.11 72.3 – – Mixed

SDHDRK-09 14.7 0.6 1.93 0.10 30.2 – – Mixed

SDHDRK-10 15.1 0.7 2.82 0.13 10.4 – – Mixed

SDHDRK-11 17.4 0.0 1.00 0.19 84.4 – – Mixed

SDHDRK-12 14.1 15.1 0.50 0.19 1.2 – – Mixed

SDHDRK-13 15.5 1.5 2.82 0.31 21.4 – – Mixed

SDTEM-01 19.0 0.0 0.09 0.21 95.2 51 0.3 Pre-Modern

SDTEM-03 17.8 0.5 0.09 0.31 90.5 – – Pre-Modern

SDTEM-04 17.4 0.0 3.32 0.31 15.1 – – Mixed

SDTEM-05 20.9 0.0 0.00 0.15 58.3 84 0.4 Pre-Modern

SDTEM-06 16.3 0.0 1.50 0.09 22.0 96 0.4 Mixed

SDTEM-07 15.7 0.0 3.11 0.14 0.0 – – Modern

SDTEM-08 19.3 0.0 1.00 0.19 88.1 – – Mixed

SDTEM-09 13.8 0.2 0.03 0.04 99.1 – – Pre-Modern

SDTEM-10 17.1 0.1 6.43 0.41 0.0 91 0.4 Modern

SDTEM-11 17.6 0.0 0.04 0.04 82.6 – – Pre-Modern

SDTEM-12 16.9 0.0 0.91 0.07 94.0 98 0.4 Mixed

SDTEM-13 18.4 0.0 0.28 0.05 99.3 58 0.3 Pre-Modern

SDWARN-01 13.9 0.8 0.09 0.31 79.1 – – Pre-Modern

SDWARN-02 13.0 0.1 1.91 0.31 0.0 – – Modern

SDWARN-03 13.0 0.0 1.69 0.19 0.0 – – Modern
96 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Table C1. Summary of groundwater age data and classification of samples into modern, mixed, and pre-modern age distributions, San 

Diego Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California, May–July 2004.—Continued

[°C, Degrees Celsius; TU, tritium units; –, no data]

GAMA_ID

Recharge temperature Tritium

Terrigenic helium, 

percent of total 

helium

Modern carbon

Age 

classification (°C) Error 

(°C) (TU) Error (TU) (percent)

Counting 

error

(percent)

SDWARN-04 13.1 0.0 0.19 0.19 94.3 78 0.4 Pre-Modern

SDWARN-05 12.8 0.0 0.09 0.19 23.7 85 0.4 Pre-Modern

SDWARN-06 16.0 0.4 0.06 0.05 5.8 81 0.4 Pre-Modern

SDWARN-08 11.9 0.1 0.21 0.05 98.8 – – Pre-Modern

SDWARN-09 12.0 0.0 0.04 0.05 98.7 – – Pre-Modern

Understanding Wells

SDALLVU-1 17.1 0.5 1.84 0.09 99.2 – – Mixed

SDHDRKU-01 20.4 0.0 2.60 0.31 86.5 – – Mixed

SDHDRKU-02 16.1 0.0 0.32 0.06 0.0 – – Mixed

SDHDRKU-03 19.0 0.0 3.00 0.14 62.5 – – Mixed

SDTEMFP-01 17.6 0.1 0.28 0.04 93.5 74 0.4 Pre-Modern

SDTEMFP-02 14.2 0.0 1.10 0.19 95.1 70 0.3 Mixed

SDTEMFP-03 15.2 0.0 0.89 0.06 90.9 75 0.4 Pre-Modern

SDTEMFP-04 13.6 0.0 6.83 0.41 0.0 92 0.4 Modern

SDTEMFP-05 17.0 0.0 2.92 0.31 8.6 – – Mixed

SDTEMFP-06 14.4 0.0 1.25 0.19 95.1 – – Mixed

SDTEMFP-07 15.9 0.1 0.62 0.06 96.6 60 0.3 Pre-Modern

Table C2. Concentration thresholds in milligrams per liter 

for dissolved redox indicator constituents used to infer redox 

conditions in groundwater, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–

July 2004.

[Modified from McMahon and Chapelle, 2008. Numbers in parenthesis are 

raised thresholds used for unfiltered samples. ≥, greater than or equal to; <, 

less than; –, data not available]

Redox 

condition O2 NO3

– as N Mn Fe 

Oxic ≥ 0.5 ≥ 0.5 (0.6) < 0.05 (0.12) < 0.1 (0.23)

Oxic ≥ 0.5 < 0.5 (0.6) < 0.05 (0.12) < 0.1 (0.23)

Anoxic < 0.5 < 0.5 (0.6) ≥ 0.05 (0.12) ≥ 0.1 (0.23)

Anoxic < 0.5 < 0.5 (0.6) ≥ 0.05 (0.12) < 0.1 (0.23)

Anoxic < 0.5 < 0.5 (0.6) < 0.05 (0.12) ≥ 0.1 (0.23)

Anoxic – < 0.5 (0.6) ≥ 0.05 (0.12) ≥ 0.1 (0.23)

Anoxic – < 0.5 (0.6) ≥ 0.05 (0.12) < 0.1 (0.23)

Anoxic – < 0.5 (0.6) < 0.05 (0.12) ≥ 0.1 (0.23)
Appendix D 97

Comparisons of CDPH and GAMA data were done to 

assess the validity integrating these datasets for the purpose 

of assessing water quality in the San Diego study unit. 

Because reporting levels for most organic constituents were 

substantially lower for data collected by the USGS than for 

data from the CDPH database (table 3), it generally was not 

possible to meaningfully compare concentrations of these 

constituent types in individual wells. However, because 

concentrations of inorganic constituents generally are detected 

at concentrations substantially above LRLs, a comparison 

of the two datasets was possible. Qualitative comparisons 

were done by plotting constituent concentrations on a oneto-one line graph and quantitative comparisons were done by 

calculating the relative percent difference (RPD) for each data 

pair (fig. D1). Only constituents with at least seven data pairs 

were examined. 

Sample mass was sufficient for 15 inorganic constituents 

to allow comparisons to be made. Of these 15 constituents, 

the median RPD for 11 constituents was less than 15, for 3 

constituents median RPD was either 23 or 24, and for 1 (iron) 

median RPD was 100 (fig. D1). Iron concentrations reported 

in the CDPH database were higher than the concentrations 

reported for samples collected by the USGS in five of seven 

Appendix D. Comparison of CDPH and USGS-GAMA Data

replicate pairs. The differences in the sample collection 

procedures between the USGS and CDPH (filtered versus 

non-filtered) may be the reason for the higher concentrations 

observed in the CDPH samples than in the USGS samples. 

The results of this comparison show that inorganic data for 

most constituents from the CDPH database can be reasonably 

integrated with USGS data for the purposes of examining 

water quality in the San Diego study unit. 

Major ion data for grid wells (USGS and CDPH data) 

were plotted on Piper diagrams (Piper, 1944) with 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. 

Piper diagrams are often used to define groundwater type 

(Hem, 1985). 

The similarity of water types represented by CPDH data 

to water types represented by GAMA data indicate that the 

GAMA sampling design indeed did collect a representative 

sample of groundwater that is used for public supply in the 

San Diego study unit (fig. D2).
98 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Figure D1. Paired inorganic concentrations from wells sampled by the Groundwater Ambient Monitoring and 

Assessment (GAMA) Program and the California Department of Public Health, San Diego Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit, California, May–July 2004.

Nitrate (median RPD = 10; n = 10) 

Potassium (median RPD = 13; n = 17) 

Sodium (median RPD = 5; n = 17) 

Sulfate (median RPD = 5; n = 18) 

Total dissolved solids (median RPD = 5; n = 18) 

Vanadium (median RPD = 24; n = 10) 

One-to-one line

Arsenic (median RPD = 23; n = 12) 

EXPLANATION

1.0E + 02

1.0E + 02

1.0E + 01

CDPH DATA, IN MILLIGRAMS PER LITER

USGS GAMA DATA, IN MILLIGRAMS PER LITER 1.0E + 01

1.0E + 00

1.0E + 00

1.0E - 01

1.0E - 01

1.0E - 02

1.0E - 02

1.0E - 03

1.0E - 03

1.0E - 04

1.0E - 04

1.0E - 05

1.0E - 05

1.0E + 03

1.0E + 03

1.0E + 04

1.0E + 04

Barium (median RPD = 11; n = 8) 

Boron (median RPD = 13; n = 12) 

Calcium (median RPD = 4; n = 18) 

Chloride (median RPD = 5; n = 18) 

Fluoride (median RPD = 14; n = 17) 

Iron (median RPD = 100; n = 7) 

Magnesium (median RPD = 7; n = 17) 

Manganese (median RPD = 23; n = 23) 

17-0006 Wright_Appendix D1 Data.
Appendix D 99

Figure D2. Piper diagram for grid wells and all wells in the California Department of Public Health database with 

a charge imbalance of less than 10 percent, San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California, May–July 2004.

PERCENT

CALCIUM

100 

80 

60 

40 

20 

0 

0 

20 

40 

60 

80 

100 

MAGNESIUM

0 

20 

40 

60 

80 

100 

SODIUM PLUS POTASSIUM

CHLORIDE PLUS FLUORIDE

0 

20 

40 

60 

80 

100 

100 

80 

60 

40 

20 

0 

BICARBONATE PLUS CARBONATE

100 

80 

60 

40 

20 

0 

SULFATE

0 

20 

40 

60 

80 

100 

CHLORIDE PLUS FLUORIDE PLUS SULFATE

0 

20 

40 

60 

80 

100 

CALCIUM PLUS MAGNESIUM

100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

0 

PERCENT

PERCENT

GAMA data

CDPH data

EXPLANATION

17-0006 Wright_Appendix D2 Piper diagram GAMA/DHS data.
100 Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004: California GAMA Priority Basin Project

Specific conductance, an electrical measure of TDS, was 

available in all 58 grid and understanding wells sampled by 

the USGS, whereas measured TDS data only were available 

for 24 of these wells. TDS values for the other 34 wells were 

calculated from specific conductance (SC) values using a 

linear regression equation (TDS = 0.628*SC –15.34) so 

Appendix E. Calculating Total Dissolved Solids

Figure E1. Regression of total dissolved solids versus specific conductance for samples collected 

by the U.S. Geological Survey. San Diego Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit, California, May–July 2004.

that all grid wells would have TDS values. The predicted 

TDS values using the regression equation closely matched 

measured TDS values (r2 = 0.98). TDS values from CDPH 

were combined with USGS measured and calculated TDS 

values. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

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

SPECIFIC CONDUCTANCE,

IN MICROSIEMENS PER CENTIMETER

TOTAL DISSOLVED SOLIDS, IN MILLIGRAMS PER LITER

y = 0.628x - 15.34 

R = 0.9843 2
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Wright and Belitz—Status and Understanding of Groundwater Quality in the San Diego Drainages Hydrogeologic Province, 2004:

California GAMA Priority Basin Project—SIR 2011–5154

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