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 South Coast 

Range–Coastal Study Unit, 2008: California GAMA Priority Basin Project

Photo placement

Scientific Investigations Report 2013–5053
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

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

Cover photographs:

Front cover: Marigold farm near Lompoc, California. (Photograph taken by Cathy Munday,

U.S. Geological Survey.)

Back cover: Irrigation well, Lompoc, California. (Photograph taken by Dara Goldrath,

U.S. Geological Survey.) 

Sampling priority

Study area

Low-use basins

Priority basins

Areas that are outside CDWR-defined

 groundwater basins
Status and Understanding of Groundwater Quality 

in the South Coast Range–Coastal Study Unit, 2008: 

California GAMA Priority Basin Project

By Carmen A. Burton, Michael T. Land, 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 2013–5053

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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Suggested citation:

Burton, C.A., Land, M.T., and Belitz, Kenneth, 2013, Status and understanding of groundwater quality in the South 

Coast Range–Coastal study unit, 2008—California GAMA Priority Basin Project: U.S. Geological Survey Scientific 

Investigations Report 2013–5053, 86 p.
iii

Contents

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

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

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

Description of South Coast Range–Coastal Study Unit...........................................................................5

Methods.........................................................................................................................................................11

Status Assessment Methods............................................................................................................11

Relative-Concentrations and Water-Quality Benchmarks .................................................11

Design of Sampling Networks for Status Assessment........................................................12

California Department of Public Health Grid Well Selection..............................................13

Selection of Constituents for Additional Evaluation ............................................................13

Calculation of Aquifer-Scale Proportions..............................................................................16

Calculation of Gross Alpha Radioactivity, Uranium, and Total Dissolved Solids ............18

Understanding Assessment Methods.............................................................................................18

U.S. Geological Survey Understanding Wells.......................................................................19

Statistical Analysis ....................................................................................................................19

Potential Explanatory Factors....................................................................................................................19

Land Use...............................................................................................................................................19

Depth ..................................................................................................................................................21

Septic-System Density.......................................................................................................................21

Formerly Leaking Underground Fuel Tanks ....................................................................................21

Groundwater Age................................................................................................................................21

Geochemical Conditions....................................................................................................................21

Correlations Between Explanatory Variables ................................................................................24

Status and Understanding of Water Quality............................................................................................28

Inorganic Constituents ......................................................................................................................30

Trace Elements and Minor Ions...............................................................................................33

Arsenic................................................................................................................................33

Molybdenum......................................................................................................................37

Nutrients......................................................................................................................................42

Nitrate ................................................................................................................................42

Uranium and Radioactive Constituents..................................................................................42

Inorganic Constituents with Aesthetic Benchmarks...........................................................44

Manganese and Iron........................................................................................................44

Total Dissolved Solids ......................................................................................................44

Sulfate ................................................................................................................................47

Organic Constituents..........................................................................................................................47

Volatile Organic Compounds....................................................................................................47

Trihalomethanes (Chloroform)........................................................................................52

Perchloroethene (PCE) and Other Solvents .................................................................52

Dichlorodifluoromethane (CFC-12) and Other Volatile Organic Compounds ..........54

Pesticides....................................................................................................................................54
iv

Constituents of Special Interest.......................................................................................................54

Perchlorate .................................................................................................................................54

Summary........................................................................................................................................................56

Acknowledgments.......................................................................................................................................58

References Cited..........................................................................................................................................58

Appendix A. Selection of California Department of Public Health Grid Wells ..................................65

Appendix B. Comparison of CDPH and GAMA Priority Basin Data ....................................................70

Appendix C. Calculation of Aquifer-Scale Proportions .........................................................................73

Appendix D. Attribution of Potential Explanatory Factors ....................................................................74

Appendix E. Data not Published in the USGS Data-Series Report ......................................................83

Contents—Continued

Figures

1. Map showing location of the South Coast Range–Coastal study unit and California 

hydrogeologic provinces (modified from Belitz and others, 2003), California GAMA 

Priority Basin Project...................................................................................................................4

2. Map showing study areas, groundwater basins, and geographic features of the 

South Coast Range–Coastal study unit, California GAMA Priority Basin Project .............6

3. Map showing land use in the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project......................................................................................................7

4. Map showing geology and study areas in the South Coast Range–Coastal study 

unit, California GAMA Priority Basin Project...........................................................................8

5. Map showing grid cells, USGS-grid and USGS-understanding wells sampled during 

May–December 2008, CDPH-grid wells sampled for inorganic constituents, and 

generalized groundwater flow for the South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project..................................................................................10

6. Ternary diagram with proportions of natural, agricultural, and urban land use 

for wells and the study unit and study areas, South Coast Range–Coastal study 

unit, California GAMA Priority Basin Project.........................................................................20

7. Graphs showing construction characteristics for USGS- and CDPH-grid wells and 

USGS-understanding wells and grid wells in the Basins and Uplands study areas, 

South Coast Range–Coastal study unit, California GAMA Priority Basin Project ...........22

8. Graphs showing groundwater age classification in relation to study areas, well 

depth, and depth to top-of-perforations, South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project..................................................................................23

9. Graph showing relation of groundwater age classification to numbers of wells 

in each of the three depth categories, South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project..................................................................................28

10. Maps showing dissolved oxygen concentrations and pH values for wells in the 

South Coast Range–Coastal study unit, California GAMA Priority Basin Project ...........29
v

11. Graph showing maximum relative-concentration in USGS- and CDPH-grid wells 

for detected constituents by constituent class, South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project ..............................................................31

12. Graphs showing relative-concentrations of inorganic constituents having humanhealth-based or SMCL benchmarks and high or moderate maximum relativeconcentrations in USGS- and CDPH-grid wells, South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project ..............................................................34

13. Maps showing relative-concentrations of arsenic, molybdenum, and nitrate in 

USGS-grid and CDPH-grid wells and CDPH-other wells in the South Coast 

Range–Coastal study unit, California GAMA Priority Basin Project..................................36

14. Graphs showing relation of arsenic concentrations to manganese 

concentrations and groundwater age in the Basins study area and dissolved 

oxygen concentrations in the Uplands study area, South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project ..............................................................39

15. Graphs showing relation of molybdenum concentrations to well depth and 

groundwater age and to arsenic and dissolved oxygen concentrations and pH 

in wells with disolved oxygen greater than 0.5 milligram per liter, South Coast 

Range–Coastal study unit, California GAMA Priority Basin Project..................................40

16. Graph showing relation of nitrate concentrations to dissolved oxygen 

concentrations, groundwater age, and well perforation intervals in the South 

Coast Range–Coastal study unit, California GAMA Priority Basin Project.......................43

17. Maps showing relative-concentrations of manganese, iron, total dissolved 

solids, and sulfate in USGS-grid and CDPH-grid wells, USGS-understanding 

wells, and CDPH-other wells in the South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project..................................................................................45

18. Graph showing detection frequencies and maximum relative-concentrations of

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

South Coast Range–Coastal study unit, California GAMA Priority Basin Project ...........48

19. Graphs showing detection frequencies and relative-concentrations of selected 

organic and special-interest constituents in USGS-grid wells in the South Coast 

Range–Coastal study unit, California GAMA Priority Basin Project..................................49

20. Maps showing number of volatile organic compound (VOC) detections, 

trihalomethane (chloroform) concentrations, perchloroethene concentrations, 

number of other VOC detections, and number of pesticide detections in USGS-grid and 

USGS-understanding wells, and CDPH-wells in the South Coast Range–

Coastal study unit, California GAMA Priority Basin Project................................................50

21. Graph showing relation of the number of pesticide detections per well with 

well depth and groundwater age in the South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project..................................................................................55

22. Map showing perchlorate concentrations in USGS-grid wells, USGSunderstanding wells, and CDPH-wells in the South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project ..............................................................55

23. Graph showing relation of perchlorate concentrations to dissolved oxygen 

concentrations in the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project....................................................................................................56

Figures—Continued
vi

Tables

1. Analytes and wells sampled for each analytical schedule for the South Coast 

Range–Coastal study unit, California GAMA Priority Basin Project, 

May–November 2008 .................................................................................................................14

2. Inorganic constituents, associated benchmark information, and number of grid 

wells per constituent, South Coast Range–Coastal study unit, California GAMA 

Priority Basin Project.................................................................................................................15

3. Comparison of the number of compounds and median method detection levels or 

laboratory reporting levels by constituent class for data stored in the CDPH 

database and for data collected by the U.S. Geological Survey in the South 

Coast Range–Coastal study unit, California GAMA Priority Basin Project, 

May–November 2008 .................................................................................................................16

4. Constituents in CDPH wells with historically high concentrations but not high 

during the current period (May 20, 2005, to May 19, 2008) in the South Coast 

Range–Coastal study unit, California GAMA Priority Basin Project..................................17

5. Results of nonparametric statistical tests (Kruskal-Wallis or Wilcoxon) for 

differences in values of potential categorical and continuous explanatory factors 

and differences in values of potential categorical explanatory factors and selected 

water-quality constituents for the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project....................................................................................................25

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

selected potential explanatory factors in grid and understanding wells, South 

Coast Range–Coastal study unit, California GAMA Priority Basin Project.......................27

7. Number of constituents analyzed and detected in USGS-grid wells, listed by 

human-health-based or aesthetic benchmarks and constituent class, in the 

South Coast Range–Coastal study unit, California GAMA Priority Basin Project ...........30

8. Raw detection frequencies and aquifer-scale proportions using spatially 

weighted and grid-based methods for constituents detected at concentrations 

greater than water-quality benchmarks during the most recent 3 years of data 

available (May 20, 2005, to May 19, 2008) from the California Department of Public 

Health database, or detected at high or moderate relative-concentrations in 

samples collected from USGS-grid wells, or organic compounds detected at 

frequencies greater than 10 percent in samples collected from USGS-grid wells, 

May–November 2008 in the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project....................................................................................................32

9. Aquifer-scale proportions for constituent classes and constituents of special 

interest for the South Coast Range–Coastal study unit, California GAMA Priority 

Basin Project...............................................................................................................................33

10. Results of nonparametric (Spearman’s method) correlation analysis between 

selected inorganic water-quality constituents and potential explanatory factors 

in the South Coast Range–Coastal study unit, California GAMA Priority Basin 

Project ..........................................................................................................................................38

11. Results of nonparametric correlation analysis (Spearman’s method) between 

selected organic and special-interest water-quality constituents and potential 

explanatory factors in the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project....................................................................................................53
vii

Conversion Factors, Datums, and Abbreviations and 

Acronyms

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square foot (ft2

) 0.09290 square meter (m2)

square mile (mi2

) 2.590 square kilometer (km2) 

Radioactivity

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

picocurie per liter (pCi/L) 0.313 tritium units (TU) 

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 10.76 square foot (ft2) 

square kilometer (km2

) 0.3861 square mile (mi2

)

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). One milligram per liter is equivalent to 1 part per million (ppm); 

1 microgram per liter is equivalent to 1 part per billion (ppb).

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

Abbreviations and Acronyms

AB Assembly Bill (through the California State Assembly)

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

bls below land surface

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

LUFT leaking underground fuel tank

MCL-CA California Department of Public Health maximum contaminant level 

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

MDL method detection limit

NL-CA California Department of Public Health notification level 

QC quality control

RPD relative percent difference

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

SC specific conductance

SCRC South Coast Range–Coastal study unit

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

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

TEAP terminal electron acceptor process

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

Organizations

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

July 1, 2007)

CDPR California Department of Pesticide Regulation

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water-Quality Assessment Program (USGS)

SWRCB State Water Resources Control Board (California)

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
ix

Selected chemical names

CFC-12 dichlorodifluoromethane

DO dissolved oxygen

NDMA N-nitrosodimethylamine

Nitrate nitrite plus nitrate as nitrogen

PCE perchloroethene (tetrachloroethene)

TCE trichloroethene

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound

Units of measure

δ iE delta notation, the ratio of a heavier isotope of an element (iE) to the more common 

lighter isotope of an element, relative to a standard reference material, expressed 

as per mil

per mil parts per thousand

pmc percent modern carbon

TU tritium unit

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
x

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Abstract

Groundwater quality in the South Coast Range–Coastal 

(SCRC) study unit was investigated from May through 

November 2008 as part of the Priority Basin Project of the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program. The study unit is located in the Southern Coast 

Range hydrologic province and includes parts of Santa 

Barbara and San Luis Obispo Counties. The GAMA Priority 

Basin Project is conducted by the U.S. Geological Survey 

(USGS) in collaboration with the California State Water 

Resources Control Board and the Lawrence Livermore 

National Laboratory. 

The GAMA Priority Basin Project was designed 

to provide a statistically unbiased, spatially distributed 

assessment of untreated groundwater quality within the 

primary aquifer system. The primary aquifer system is defined 

as that part of the aquifer corresponding to the perforation 

interval of wells listed in the California Department of Public 

Health (CDPH) database for the SCRC study unit. 

The assessments for the SCRC study unit were based 

on water-quality and ancillary data collected in 2008 by 

the USGS from 55 wells on a spatially distributed grid, and 

water-quality data from the CDPH database. Two types of 

assessments were made: (1) status, assessment of the current 

quality of the groundwater resource, and (2) understanding, 

identification of the natural and human factors affecting 

groundwater quality. Water-quality and ancillary data were 

collected from an additional 15 wells for the understanding 

assessment. The assessments characterize untreated 

groundwater quality, not the quality of treated drinking water 

delivered to consumers by water purveyors.

The first component of this study, the status assessment 

of groundwater quality, used data from samples analyzed 

for anthropogenic constituents such as volatile organic 

compounds (VOCs) and pesticides, as well as naturally 

occurring inorganic constituents such as major ions and trace 

elements. Although the status assessment applies to untreated 

groundwater, Federal and California regulatory and nonregulatory water-quality benchmarks that apply to drinking 

water are used to provide context for the results. Relativeconcentrations (sample concentration divided by benchmark 

concentration) were used for evaluating groundwater. 

A relative-concentration greater than (>) 1.0 indicates a 

concentration greater than the benchmark and is classified 

as high. Inorganic constituents are classified as moderate if 

relative-concentrations are >0.5 and less than or equal to (≤) 

1.0, or low if relative-concentrations are ≤0.5. For organic 

constituents, the boundary between moderate and low relativeconcentrations was set at 0.1.

Aquifer-scale proportion was used in the status 

assessment as the primary metric for evaluating regional-scale 

groundwater quality. High aquifer-scale proportion is defined 

as the areal percentage of the primary aquifer system with a 

high relative-concentration for a particular constituent or class 

of constituents. Moderate and low aquifer-scale proportions 

were defined as the areal percentage of the primary aquifer 

system with moderate and low relative-concentrations, 

respectively. Two statistical approaches—grid-based and 

spatially weighted—were used to evaluate aquifer-scale 

proportions for individual constituents and classes of 

constituents. Grid-based and spatially weighted estimates 

were comparable for the study (within 90 percent confidence 

intervals).

For inorganic constituents with human-health 

benchmarks, relative-concentrations were high for at least 

one constituent for 33 percent of the primary aquifer system 

in the SCRC study unit. Arsenic, molybdenum, and nitrate 

were the primary inorganic constituents with human-health 

benchmarks that were detected at high relative-concentrations. 

Inorganic constituents with aesthetic benchmarks, referred to 

as secondary maximum contaminant levels (SMCLs), had high 

relative-concentrations for 35 percent of the primary aquifer 

system. Iron, manganese, total dissolved solids (TDS), and 

sulfate were the inorganic constituents with SMCLs detected 

at high relative-concentrations.

Status and Understanding of Groundwater Quality in the 

South Coast Range–Coastal Study Unit, 2008: California 

GAMA Priority Basin Project

By Carmen A. Burton, Michael T. Land, and Kenneth Belitz
2 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

In contrast to inorganic constituents, organic 

constituents with human-health benchmarks were not 

detected at high relative-concentrations in the primary 

aquifer system in the SCRC study unit. Of the 205 organic 

constituents analyzed, 21 were detected—13 with humanhealth benchmarks. Perchloroethene (PCE) was the only 

VOC detected at moderate relative-concentrations. PCE, 

dichlorodifluoromethane (CFC-12), and chloroform were 

detected in more than 10 percent of the primary aquifer 

system. Of the two special-interest constituents, one was 

detected; perchlorate, which has a human-health benchmark, 

was detected at moderate relative-concentrations in 29 percent 

of the primary aquifer system and had a detection frequency of 

60 percent in the SCRC study unit.

The second component of this study, the understanding 

assessment, identified the natural and human factors that 

may have affected groundwater quality in the SCRC study 

unit by evaluating statistical correlations between waterquality constituents and potential explanatory factors. 

The potential explanatory factors evaluated were land 

use, septic tank density, well depth and depth to top-ofperforations, groundwater age, density and distance to the 

nearest formerly leaking underground fuel tank (LUFT), pH, 

and dissolved oxygen (DO) concentration. Results of the 

statistical evaluations were used to explain the occurrence and 

distribution of constituents in the study unit.

DO was the primary explanatory factor influencing 

the concentrations of many inorganic constituents. Arsenic, 

iron, and manganese concentrations increased as DO 

concentrations decreased, consistent with patterns expected 

as a result of reductive dissolution of iron and (or) manganese 

oxides in aquifer sediments. Molybdenum concentrations 

increased in anoxic conditions and in oxic conditions with 

high pH, reflecting two mechanisms for the mobilization 

of molybdenum—reductive dissolution and pH-dependent 

desorption under oxic conditions from aquifer sediments. 

Nitrate concentrations decreased as DO concentrations 

decreased which would be consistent with degradation of 

nitrate under anoxic conditions (denitrification). It also is 

possible that nitrate concentrations decreased in relation to 

increasing depth and groundwater age and not as a result of 

denitrification.

Groundwater age was another explanatory factor 

frequently correlated to several inorganic constituents. Iron 

and manganese concentrations were higher in pre-modern 

(water recharged before 1952) or mixed-age groundwater. This 

correlation is one indication that iron and manganese are from 

natural sources. Nitrate, TDS, and sulfate concentrations were 

higher in modern groundwater (water recharged since 1952) 

and may indicate that human activities increase concentrations 

of nitrate, TDS, and sulfate.

Land use was a third explanatory factor frequently 

correlated with inorganic constituents. Nitrate, TDS, and 

sulfate concentrations were higher in agricultural land-use 

areas than in natural land-use areas, indicating that increased 

concentrations may be a result of agricultural practices.

Organic constituents usually were detected at low 

relative-concentrations; therefore, statistical analyses of 

relations to explanatory factors usually were done for classes 

of constituents (for example, pesticides or solvents) as well 

as for selected constituents. The number of VOCs detected 

in a well was not correlated to any of the explanatory factors 

evaluated. The number of pesticide and solvent detections 

and PCE and CFC-12 concentrations were higher in modern 

groundwater than in pre-modern groundwater. PCE and CFC12 also were positively correlated to the density of LUFTs. 

PCE was negatively correlated to natural land use. Chloroform 

concentrations were positively correlated to the density of 

septic systems.

Perchlorate concentrations were greater in agricultural 

areas than in urban or natural areas. Correlation of perchlorate 

with DO may indicate that perchlorate biodegradation under 

anoxic conditions may occur. Anthropogenic sources have 

contributed perchlorate to groundwater in the SCRC study 

unit, although low levels of perchlorate may occur naturally.

Introduction

Groundwater composes nearly half of the water used 

for public supply in California (Hutson and others, 2004). 

To assess the quality of ambient groundwater in aquifers 

used for drinking-water supply and to establish a baseline 

groundwater-quality monitoring program, the California State 

Water Resources Control Board (SWRCB), in collaboration 

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

Livermore National Laboratory (LLNL), implemented the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program (California State Water Resources Control Board, 

2011, website at http://www.waterboards.ca.gov/water_issues/

programs/gama). The statewide GAMA Program currently 

consists of four projects: (1) the GAMA Priority Basin 

Project, conducted by the USGS; (2) the GAMA Domestic 

Well Project, conducted by the SWRCB; (3) the GAMA 

Special Studies, conducted by LLNL; and (4) GeoTracker 

GAMA, conducted by the SWRCB. On a statewide basis, 

the GAMA Priority Basin Project primarily focused on the 

deep portion of the groundwater resource (primary aquifer 

system), and the SWRCB Domestic Well Project generally 

focused on the shallow aquifer systems. The primary aquifer 

system may be at less risk of contamination than the shallow 

wells, such as private domestic or environmental monitoring 

wells, that are closer to surficial sources of contaminants. 

As a result, concentrations of contaminants, such as volatile 

organic compounds (VOCs) and nitrate, in wells screened 

in the deeper primary aquifer system may be lower than 

concentrations of contaminants in shallow wells (Kulongoski 

and others, 2010; Landon and others, 2010; Burton and others, 

2012).
Introduction 3

The SWRCB initiated the GAMA Program in 2000 in 

response to Legislative mandates (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 

the understanding of and to identify risks to groundwater 

resources and to increase the availability of information 

about groundwater quality to the public. For the GAMA 

Priority Basin Project, the USGS, in collaboration with the 

SWRCB, developed a monitoring plan to assess groundwater 

basins through direct and other statistically reliable sampling 

approaches (Belitz and others, 2003; California State Water 

Resources Control Board, 2003). Additional partners in 

the GAMA Priority Basin Project include the California 

Department of Public Health (CDPH), the California 

Department of Pesticide Regulation (CDPR), the California 

Department of Water Resources (CDWR), and local water 

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

The range of hydrologic, geologic, and climatic 

conditions in California should be considered in an 

assessment of groundwater quality. Belitz and others (2003) 

partitioned the State into 10 hydrogeologic provinces, 

each with distinctive hydrologic, geologic, and climatic 

characteristics (fig. 1). These hydrogeologic provinces 

include 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 (California Department of Water Resources, 2003). 

Eighty percent of California’s approximately 16,000 drinkingwater wells listed in the statewide database maintained by the 

CDPH (hereinafter referred to as CDPH wells) are located 

in designated groundwater basins within these hydrologic 

provinces. Groundwater basins and subbasins were prioritized 

for sampling on the basis of the number of CDPH wells in 

the basin or subbasin, with secondary consideration given 

to municipal groundwater use, agricultural pumping, the 

number of historical leaking underground fuel tanks, and 

registered pesticide applications (Belitz and others, 2003). 

Of the 472 basins and subbasins designated by the CDWR, 

116 priority basins, as well as additional areas outside defined 

groundwater basins, were grouped into 35 study units, 

which include approximately 95 percent of CDPH wells 

in California. The South Coast Range–Coastal study unit 

(hereinafter referred to as the SCRC study unit) is located 

along the California coast in the southwestern part of the 

Southern Coast Ranges hydrogeologic province (fig. 1).

Purpose and Scope

This report is one of a series of GAMA Priority 

Basin Project assessment reports presenting the status and 

understanding of current water-quality conditions in GAMA 

Priority Basin Project study units. The purposes of this report 

are to provide (1) a study unit description: description of the 

hydrogeologic setting of the SCRC study unit, (2) a status 

assessment: assessment of the status of the current quality of 

groundwater in the primary aquifer system in the SCRC study 

unit, and (3) an understanding assessment: identification of 

natural and human factors affecting groundwater quality and 

the relations between water quality and selected explanatory 

factors.

This report describes methods used to design the 

sampling network, identify CDPH data for use in the 

status assessment, estimate aquifer-scale proportions of 

relative-concentrations, analyze ancillary datasets, classify 

groundwater age, and assess the status and understanding of 

groundwater quality by statistical and graphical approaches. 

Water-quality data for samples collected by the USGS for the 

GAMA Program in the SCRC study unit and details of sample 

collection, analysis, and quality-assurance procedures are 

reported by Mathany and others (2010).

The status assessment includes analyses of water-quality 

data for 55 wells selected for sampling by the USGS within 

spatially distributed grid cells across the SCRC study unit 

(hereinafter referred to as USGS-grid wells). Samples were 

collected for analysis of anthropogenic constituents, such 

as volatile organic compounds (VOCs) and pesticides, and 

naturally occurring inorganic constituents, such as major 

ions and trace elements. Water-quality data from the CDPH 

database were used to supplement data collected by the USGS 

for the GAMA Priority Basin Project. The resulting set of 

water-quality data from USGS-grid wells and selected CDPH 

wells was considered to be representative of the primary 

aquifer system in the SCRC study unit; the primary aquifer 

system is defined by the depth intervals of the wells listed in 

the CDPH database for the SCRC study unit. GAMA status 

assessments were designed to provide a statistically robust 

characterization of groundwater quality in the primary aquifer 

system at the basin-scale (Belitz and others, 2003), which 

allows for comparisons between basins and allows results to 

be synthesized regionally and statewide.

The understanding assessment uses data from the 

55 USGS-grid wells and selected CDPH wells plus an 

additional 15 wells sampled by the USGS (hereinafter referred 

to as USGS-understanding wells) to identify the natural and 

human factors affecting groundwater quality and to help 

explain the relations between water quality and selected 

potential explanatory factors. Potential explanatory factors 

examined included land use, well depth and depth to top-ofperforations, distance to formerly leaking underground fuel 

tanks (LUFTs), septic tank density, groundwater age, and 

geochemical conditions. 
4 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 1. Location of the South Coast Range–Coastal study unit and California hydrogeologic provinces (modified from Belitz and 

others, 2003), California GAMA Priority Basin Project.

Basin and Range

Central

Valley

Klamath

Mountains

Desert

Cascades and

Modoc Plateau

Transverse Ranges and

selected Peninsular Ranges

Southern Coast

Ranges

Sierra

Nevada

Northern

Coast

Ranges

San Diego

Drainages

South Coast Range–Coastal

study unit

(figure 2)

Bakersfield

San

Francisco

Sacramento

OREGON

NEVADA

MEXICO

ARIZONA

Redding

Los Angeles

San Diego

PA C I F I C O C E A N

0 200 MILES

0 200 KILOMETERS

100

100

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Provinces from Belitz and others, 2003

sac13-0497_Figure 01
Description of South Coast Range–Coastal Study Unit 5

To provide context, the water-quality data discussed in 

this report were compared to California and Federal regulatory 

and non-regulatory benchmarks for treated drinking water. 

The assessments in this report are intended to characterize 

the quality of untreated groundwater resources in the primary 

aquifer system within the study unit, not the treated drinking 

water delivered to consumers by water purveyors. This study 

does not attempt to evaluate the quality of water delivered to 

consumers; after withdrawal from the ground, water typically 

is treated, disinfected, and (or) blended with other waters to 

maintain acceptable water quality. Regulatory benchmarks 

apply to treated water that is delivered to the consumer, not to 

untreated groundwater. 

Description of South Coast Range–

Coastal Study Unit

The SCRC study unit covers approximately 766 square 

miles (mi2) (1,980 square kilometers [km2]) in Santa Barbara 

and San Luis Obispo Counties, California, and lies in 

the southwestern part of the Southern Coast Ranges 

hydrogeologic province (fig. 1) (Belitz and others, 2003). The 

SCRC study unit includes five groundwater basins (Los Osos 

Valley, San Luis Obispo Valley, Santa Maria River Valley, 

San Antonio Creek Valley, and Santa Ynez River Valley 

[California Department of Water Resources, 2003]), as well 

as upland areas that consist of Plio-Pleistocene non-marine 

and Pliocene marine sediments that are adjacent to the 

groundwater basins (fig. 2). The SCRC is bordered on the 

north by the Santa Lucia and San Luis Ranges, on the east 

by the San Rafael Mountains, on the south by the Santa Ynez 

Mountains, and on the west by the Pacific Ocean. Altitudes 

in the SCRC range from sea level, where the study unit 

boundary touches the Pacific Ocean, to about 6,500 feet (ft) 

(1,980 m) above sea level in the San Rafael Mountains. The 

major surface-drainage features of the SCRC study unit are 

the Santa Maria River, Santa Ynez River, San Antonio Creek, 

and their tributaries, all of which terminate in the Pacific 

Ocean (fig. 2). The SCRC study unit is divided into two study 

areas—the Basins and Uplands study areas.

The climate in the SCRC study unit is influenced in large 

part by the Pacific Ocean as well as by the topography of the 

surrounding areas. Areas located along the coastal lowlands 

and (or) valleys near the Pacific Ocean have a coastal climate 

characterized by warm, dry summers and cool, wet winters. 

Coastal fog is common throughout the year (Bright and 

others, 1992). Areas in the SCRC located farther from the 

Pacific Ocean, on the slopes of the coastal valleys or within 

the upland areas, have a Mediterranean climate with hot, dry 

summers and cool, wet winters (Hamlin, 1985). Practically 

all precipitation in the SCRC occurs from October through 

April, with average annual precipitation ranging from about 

13 inches (in.) (33 centimeters [cm]) along the coastal 

lowlands in the Santa Maria River Valley groundwater basin, 

to more than 20 in. (51 cm) in the uplands of the San Luis 

Obispo Valley groundwater basin (California Department of 

Water Resources, 2004b, c).

Land use in the SCRC study unit primarily is natural. 

Land use is approximately 61 percent natural, 29 percent 

agricultural, and 10 percent urban, based on the classification 

of USGS National Land Cover Data (Nakagaki and others, 

2007; fig. 3). The natural land use mostly consists of 

grasslands, shrubs, and sand. Agricultural land use consists of 

row crops (such as vegetables and flowers), pasture and hay, 

and vineyards. The largest urban areas in the SCRC study unit 

are the cities of Santa Maria, San Luis Obispo, Lompoc, and 

Arroyo Grande.

The SCRC study unit was separated into the two study 

areas (Basins and Uplands) by the non-marine and marine 

geologic formations in the five groundwater basins. The 

Basins study area is defined by the presence of the Quaternary 

alluvium and Quaternary sand formations in the Los Osos 

Valley, Santa Maria River Valley, San Antonio Creek Valley, 

and Santa Ynez River Valley groundwater basins (California 

Department of Water Resources, 2004a, c, d, e) (fig. 4). The 

Uplands study area is delineated largely by the location of 

the terrace formations of Plio-Pleistocene non-marine and 

Pliocene marine sediment in the San Luis Obispo, Santa Maria 

River Valley, San Antonio Creek Valley, and Santa Ynez River 

Valley groundwater basins (California Department of Water 

Resources, 2004b, c, d, e) and areas adjacent to the basins 

(fig. 4).

The Quaternary alluvium and sand formations that 

were used to delineate the Basins study area are the alluvial 

deposits located along river channels of Holocene age, the 

unnamed dune sand formations of Holocene and Pleistocene 

age, and some terrace deposits of Pleistocene age (Upson 

and Thomasson, 1951; Bright and others, 1992, 1997; 

California Department of Water Resources, 2002; California 

Department of Water Resources, 2004a, b, c, d, e). The 

alluvium of Holocene age consists of unconsolidated gravel, 

clay, silt, and sand of fluvial origin (California Department 

of Water Resources, 2004a, b, c, d, e). Dune sand formations 

of Pleistocene age occur in the Los Osos Valley and San 

Antonio Creek Valley groundwater basins, and dune sand of 

Holocene and Pleistocene age are present in the Santa Maria 

River Valley groundwater basin (California Department of 

Water Resources, 2004a, c, d). Terrace deposits of the Orcutt 

Formation are present in the lower Santa Ynez River Valley. 

The Paso Robles Formation and the Careaga Sand, which 

outcrop in the Uplands study area, underlie much of the 

alluvium and sand dunes in the Basins study area (Wilson, 

1959; Yates and Wiese, 1988; California Department of Water 

Resources, 2004a, b, c, d, e). 
6 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 2. Study areas, groundwater basins, and geographic features of the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41

1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 02 area map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Groundwater basins

Santa Ynez River Valley

San Antonio Creek Valley

Santa Maria River Valley

San Luis Obispo Valley

Los Osos Valley

Study areas County line

City or town Basins

Uplands

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Pozo Creek

San Juan Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

Pacific Ocean
Description of South Coast Range–Coastal Study Unit 7

Figure 3. Land use in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41

1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 03 Land-use

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Land-use classification Study areas

Basins

Uplands

Urban

Agricultural

Natural

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Pozo Creek

San Juan Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

Pacific Ocean

County line

City or town
8 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 4. Geology and study areas in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

Geology from Jennings (1977),

Saucedo and others (2000)

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

Santa Ynez River fault

Santa Maria fault zone

Edna fault zone

Los Osos fault zone

Sac13-0497_figure 04 geology

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National elevation dataset, 2006, 

Albers equal area conic projection EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study areas

Basins

Uplands

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Pozo Creek

San Juan Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

Faults

Approximately

 located

Fault, certain

Fault, concealed

Plio-Pleistocene

 sediment

Quaternary

 alluvium

Quaternary sand

 deposits

Other Tertiary

 marine sediment

Tertiary non-

 marine sediment

Pliocene

 marine

 sediment

Miocene marine

 sediment

Ultramafic/

 mafic

Tertiary

 volcanics

Franciscan

 Complex

Granitic rocks

Metasediment

Metamorphic

 other

Sedimentary rocks Other

Pacific Ocean
Description of South Coast Range–Coastal Study Unit 9

The geologic formations that were used to delineate the 

Uplands study area are the non-marine sediments of PlioPleistocene age and marine sediments of Pliocene age. These 

formations include the alluvial terrace deposits (including the 

Orcutt Formation), the Paso Robles Formation, and Careaga 

Sand. The alluvial terrace deposits are of Pleistocene age, are 

composed of unconsolidated sand, gravel, silt, and clay, and 

are located in the San Luis Obispo Valley and the Santa Ynez 

River Valley groundwater basins and in the uplands located 

just outside of the Santa Maria River Valley groundwater 

basin (California Department of Water Resources, 2004b, c, 

e). The Orcutt Formation of Pleistocene age, which may be 

classified as a terrace deposit, is primarily of fluvial origin and 

is composed of unconsolidated clay and sand interbedded with 

gravel (California Department of Water Resources, 2004c, 

d, e). The Paso Robles Formation is of Plio-Pleistocene age 

and consists of unconsolidated to poorly consolidated coarse 

sand and gravel, as well as finer sand, silt, and clay and some 

limestone that formed from deposition in floodplains and 

small lakes. The Careaga Sand is of Pliocene age and consists 

of unconsolidated fine- to medium-grained marine sand. The 

Careaga Sand generally underlies the Paso Robles Formation 

but is exposed in the San Luis Obispo Valley, the northern 

part of the Santa Maria River Valley, and the central part of 

the Santa Ynez River Valley groundwater basins (Upson and 

Thomasson, 1951; California Department of Water Resources, 

2002, 2004b).

The area surrounding the study unit mostly consists of 

marine sediment of Miocene age (fig. 4). The area around the 

Los Osos Valley and San Luis Obispo Valley groundwater 

basins mostly consists of the Franciscan complex which may 

include some rocks of volcanic origin. Some volcanics of 

Tertiary age exist in the San Luis Range near Nipomo, and 

ultramafic and mafic rocks border the Uplands study area 

northeast of Solvang.

The SCRC study unit has three major faults zones (the 

Los Osos, Edna, and Santa Maria) that act as barriers to 

the lateral movement of groundwater from the surrounding 

mountains. A fourth fault, the Santa Ynez River Fault, does 

not restrict groundwater flows into the basins (Sylvester and 

Darrow, 1979; California Department of Water Resources, 

2004a, b, c, d, e) (fig. 4). There also is a subsurface restrictive 

structure within the San Antonio Creek Valley groundwater 

basin that creates the wetland known as Barka Slough 

(California Department of Water Resources, 2004c).

The water-bearing formations that serve as the primary 

aquifer system in the SCRC study unit are the alluvial deposits 

of non-marine and fluvial origin and of Holocene age, the Paso 

Robles Formation, and the Careaga Sand. The water-bearing 

units are underlain by much less permeable bedrock and 

granite of Tertiary and Cretaceous age (Valentine and others, 

2001). 

Groundwater recharge in the SCRC study unit 

occurs from a mixture of ambient recharge (percolation of 

precipitation, irrigation waters, and seepage from streams and 

rivers), subsurface inflow, and engineered recharge (urban 

and agricultural return water, treated wastewater, and lakes) 

(California Department of Water Resources, 2004a, b, c, d, e). 

Groundwater flow generally is westward toward the Pacific 

Ocean for all groundwater basins except San Luis Obispo 

Valley (fig. 5; Bright and others, 1992; California Department 

of Water Resources, 2002, 2004a, c, d, e; http://www.water.

ca.gov/waterdatalibrary/groundwater/contour/index.cfm [water 

levels for February–April 2008, accessed January 6, 2012]). 

Groundwater flow in the San Luis Obispo Valley is eastward 

or westward but always toward San Luis Obispo Creek. 

The main source of groundwater discharge in the SCRC 

study unit is from groundwater pumping, used primarily for 

irrigation and secondarily for public water supply (California 

Department of Water Resources, 2004a, b, c, d, e). The cities 

of Arroyo Grande, Lompoc, San Luis Obispo, and Santa Maria 

get all or part of their water supply from groundwater (Water 

Education Foundation, 2006). Pumping depressions occur near 

Nipomo and west of Lompoc (California Department of Water 

Resources, 2002; http://www.water.ca.gov/waterdatalibrary/

groundwater/contour/index.cfm [water levels for February–

April 2008, accessed January 6, 2012]). 
10 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 5. Grid cells, USGS-grid and USGS-understanding wells sampled during May–December 2008, California Department of 

Public Health (CDPH)-grid wells sampled for inorganic constituents, and generalized groundwater flow for the South Coast Range–

Coastal study unit, California GAMA Priority Basin Project.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41

1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 05 wells

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study areas Wells

Generalized direction

 of groundwater flow Basins

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Pozo Creek

San Juan Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

USGS-grid well

USGS- and CDPH-grid well

USGS-understanding well

CDPH-grid well

Other CDPH well

Pacific Ocean

Uplands/Grid

Grid cell

County line

City or town
Methods 11

Methods

The status assessment was designed to provide a 

spatially unbiased evaluation of the areal proportions of 

individual constituents and classes of constituents possibly 

affecting groundwater quality of the primary aquifer system. 

The understanding assessment was designed to identify the 

relation between observed groundwater quality and potential 

natural and human explanatory factors. This section describes 

the methods used for (1) defining groundwater quality, 

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

(3) determining which constituents warranted assessment, 

(4) calculating aquifer-scale proportions, and (5) analyzing 

explanatory factors and water-quality datasets as part of the 

understanding assessment. Methods used for compilation 

of data on potential explanatory factors are described in 

appendix A.

The primary metric for defining groundwater quality 

is relative-concentration, which compares concentrations 

of constituents measured in groundwater to regulatory and 

non-regulatory benchmarks used to evaluate drinking-water 

quality. Constituents were selected for additional evaluation 

in the assessment based on objective criteria by using their 

relative-concentrations. Groundwater-quality data collected by 

the GAMA Priority Basin Project (Mathany and others, 2010) 

and data compiled in the CDPH database were used in the 

status assessment. Two statistical methods based on spatially 

unbiased equal-area grids were used to calculate aquifer-scale 

proportions of low, moderate, or high relative-concentrations: 

the “grid-based” method uses one value per cell to represent 

groundwater quality, and the “spatially weighted” method uses 

many values per cell (Belitz and others, 2010).

Priority Basin Project understanding assessments were 

designed to evaluate the natural and human factors that affect 

groundwater quality at the study-unit level. A finite set of 

potential explanatory factors was analyzed in relation to 

constituents of interest to place the observed water quality 

within the context of physical and chemical processes. 

Nonparametric statistical tests were used to identify significant 

correlations between the constituents of interest and potential 

explanatory factors.

Status Assessment Methods

The status assessment included the following two steps. 

(1) Water-quality data were normalized to their respective 

water-quality benchmarks by calculating their relativeconcentrations (Toccalino and others, 2004; Toccalino and 

Norman, 2006), and (2) aquifer-scale proportions were 

determined for categories of “high,” “moderate,” and “low” 

relative-concentrations by using two methods: grid-based 

and spatially weighted. Results for the two approaches were 

compared, and results from the preferred approach were used 

to identify constituents of interest for further discussion.

Relative-Concentrations and Water-Quality 

Benchmarks

To provide context for water-quality data, measured 

concentrations of constituents may be compared to waterquality benchmarks that are typically applied to finished 

drinking water. Concentrations of constituents are presented as 

relative-concentrations in the status assessment section of this 

report:

Relative-concentration Sample concentration

Water-quality b = enchmark concentration .

Toccalino and others (2004), Toccalino and Norman 

(2006), and Rowe and others (2007) used the ratio of 

measured concentration to a benchmark (either maximum 

contaminant levels [MCLs] or health-based screening levels 

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

Benchmark quotients are relative-concentrations. Relativeconcentrations less than 1.0 indicate sample concentrations 

less than the benchmark, and values greater than 1.0 indicate 

sample concentrations greater than the benchmark. The use of 

relative-concentrations permits comparison of a wide range of 

concentrations for different constituents on a single scale.

In this report, the ratio of measured concentration to a 

water-quality benchmark is called a relative-concentration 

rather than a benchmark quotient because the benchmarks 

used to calculate relative-concentrations are not the same 

as the benchmarks used to calculate benchmark quotients 

for all constituents. Benchmark quotients are calculated 

using U.S. Environmental Protection Agency (USEPA) 

maximum contaminant levels (MCL-US) or USGS-USEPA 

HBSLs. HBSLs are determined using USEPA methodologies 

for establishing drinking-water guidelines, and the most 

recent USEPA peer-reviewed, publicly available humanhealth toxicity information (Toccalino, 2007). Relativeconcentrations are calculated using benchmarks established 

by the USEPA and CDPH (U.S. Environmental Protection 

Agency, 2006; California Department of Health Services, 

2007). HBSLs were not used in this report because HBSLs are 

not currently used as benchmarks by California drinking-water 

regulatory agencies. 

Regulatory and non-regulatory benchmarks apply 

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

groundwater. The benchmarks used for each constituent were 

selected in the following order of priority:

1. Regulatory, in the order of health-based USEPA and 

CDPH maximum contaminant levels (MCL-US and 

MCL-CA), USEPA action levels (AL-US), and USEPA 

treatment technique levels (TT-US). Federal benchmarks 

were used unless the California levels were lower.

2. Non-regulatory USEPA and CDPH secondary maximum 

contaminant levels (SMCL-US and SMCL-CA). For 

constituents with recommended and upper SMCL-CA 

levels, the values for the upper levels were used.
12 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

3. Non-regulatory, in the order of health-based CDPH 

notification levels (NL-CA), USEPA lifetime health 

advisory levels (HAL-US) and USEPA risk-specific dose 

(1 in 100,000 lifetime risk of cancer, RSD5-US).

Note that for constituents with multiple types of 

benchmarks, this hierarchy may not result in selection of 

the benchmark with the lowest concentration. Additional 

information on the types of benchmarks and the benchmarks 

for all constituents analyzed is provided by Mathany and 

others (2010).

Relative-concentrations were classified into high, 

moderate, and low categories:

Category Relative-concentrations for 

organic constituents

Relative-concentrations for 

inorganic constituents

High > 1 > 1

Moderate > 0.1 and ≤ 1 > 0.5 and ≤ 1

Low ≤ 0.1 ≤ 0.5

A relative-concentration greater than 1.0 is classified 

as high, indicating the concentration is greater than the 

benchmark concentration. A relative-concentration of 0.1 

was used as a boundary between moderate and low values 

of organic and special-interest constituents for consistency 

with other studies and reporting requirements (Toccalino 

and others, 2004). The USEPA also established a relativeconcentration of 0.1 of the regulatory benchmark as a 

threshold concentration so that the agency would be notified 

if the presence of a pesticide in surface water or groundwater 

is greater than or equal to that threshold (U.S. Environmental 

Protection Agency, 1997). In addition, organic and specialinterest constituents, which generally are anthropogenic, 

usually are less prevalent and have smaller maximum 

relative-concentrations than inorganic constituents. In 

contrast, inorganic constituents are typically naturally 

occurring at concentrations that could be greater than 0.1 of 

regulatory benchmarks; consequently, it would be difficult, or 

potentially misleading, to identify inorganic constituents that 

may have elevated concentrations greater than background 

levels if a relative-concentration of 0.1 was used as the 

threshold between moderate and low relative-concentrations. 

Therefore, the boundary between moderate and low relativeconcentrations was set at 0.5 of the regulatory benchmark for 

inorganic constituents. 

Design of Sampling Networks for Status 

Assessment

The wells selected for sampling by the USGS in the 

SCRC study provide a statistically unbiased, spatially 

distributed set of wells for the assessment of the quality 

of groundwater in the primary aquifer system (USGS-grid 

wells). Water-quality data from the USGS-grid wells were 

supplemented with data from selected wells in the CDPH 

database (CDPH-grid wells; see the “California Department 

of Public Health Grid Well Selection” section) to obtain more 

complete spatial coverage and to include constituents that 

were not analyzed for in every USGS-grid well. These data 

were used to assess proportions of the primary aquifer system 

with high, moderate, and low relative-concentrations. 

The primary data used for the grid-based calculations 

of aquifer-scale proportions were data from wells sampled 

by the GAMA Priority Basin Project. Detailed descriptions 

of the methods used to identify wells for sampling are given 

in Mathany and others (2010). USGS-grid wells (39 wells 

in the Basins study area and 16 wells in the Uplands study 

area) were selected to provide a statistically unbiased and 

spatially distributed set of wells for the assessment of the 

quality of groundwater in the primary aquifer system (Scott, 

1990). The SCRC study unit was divided into equal-area 

grid cells of approximately 10 mi2

 (25 km2). The objective of 

the grid design was to sample one CDPH well in each cell. 

If a grid cell did not contain accessible CDPH wells, then 

commercial, irrigation, or domestic wells were considered for 

sampling. The USGS-grid wells were sampled by the USGS 

for the GAMA Priority Basin Project, but are owned by other 

organizations or individuals.

One USGS-grid well was sampled in 55 of the 61 grid 

cells in the SCRC study unit, including 39 of the 45 grid cells 

in the Basins study area and all 16 grid cells in the Uplands 

study area (fig. 5). The grid cells from which samples were 

not collected had few, if any, wells, or permission to sample 

was not granted for wells in those cells. The 55 USGS-grid 

wells sampled in the SCRC study unit included 31 CDPH 

wells, 20 irrigation wells, 2 industrial wells, 1 domestic well, 

and 1 monitoring well. The irrigation, industrial, domestic, 

and monitoring wells had depth of perforations within the 

range of the depth of perforations for CDPH wells located 

in the SCRC study unit. The CDPH wells, irrigation wells, 

industrial wells, and domestic well are considered production 

wells for this report. USGS-grid wells in the SCRC study 

unit were numbered in the order of sample collection with the 

prefix varying by study area: Basins study area, SCRC-B and 

Uplands study area, SCRC-H (fig. A1, table A1). 
Methods 13

Samples collected from USGS-grid wells were analyzed 

for 220 to 289 constituents, depending on the particular 

sampling schedule(s) used for the well (table 1). The fast 

and slow analytical schedules refer to the amount of time 

required for a field crew to complete all work at a well. 

Samples from all wells were analyzed for VOCs, pesticides, 

perchlorate, noble gases, tritium, and stable isotopes of 

hydrogen and oxygen. Samples from 32 USGS-grid wells and 

7 understanding wells were analyzed for pharmaceuticals, 

N-nitrosodimethylamine (NDMA), major and minor ions, 

trace elements, nutrients, dissolved organic carbon, gross 

alpha and beta radioactivity, carbon isotopes, and arsenic 

and iron species. In addition, samples from six USGS-grid 

wells and six USGS-understanding wells were analyzed for 

dissolved gases, stable isotopes of nitrogen and oxygen in 

nitrate, and stable isotopes of nitrogen in dissolved nitrogen 

gas. The collection, analysis, and quality-control data for the 

analytes listed in table 1 are described by Mathany and others 

(2010) except for stable isotopes of nitrogen in dissolved 

nitrogen gas which are described in appendix E. 

California Department of Public Health Grid 

Well Selection

Samples for analysis of inorganic constituents on the 

slow or special study schedules were collected from 32 

to 36 of the 55 USGS-grid wells (table 2). Because the 

GAMA Priority Basin Project did not collect samples for 

analysis of a complete suite of inorganic constituents for 

all grid cells, the CDPH database was used to provide data 

for inorganic constituents for the cells without these data 

(table 2). In addition, the GAMA Priority Basin Project 

was not able to sample wells in six of the grid cells. CDPH 

wells were selected to represent as many of these grid cells 

as possible. CDPH wells that were selected to supplement 

USGS-grid wells are referred to as “CDPH-grid” wells. The 

approach used to identify suitable CDPH wells is described 

in appendix A. Briefly, the first choice was to use CDPH data 

from the same well as the USGS-grid well (“DG” CDPH-grid 

wells; table A1). If the DG well did not have all needed data, a 

second well was randomly selected from the subset of CDPH 

wells in the same cell with data (“DPH” CDPH-grid wells; 

table A1). Combining data from CDPH-grid wells with data 

from USGS-grid wells produced inorganic data for 44 cells 

(56 cells for total dissolved solids). All other CDPH wells with 

data from the current period (May 20, 2005, through May 19, 

2008) not selected to be CDPH-grid wells are referred to as 

“CDPH-other” wells. Comparisons of data from USGS and 

CDPH wells to assess the validity of using these different 

sources in combination are presented in appendix B. 

Samples for analysis of VOCs, pesticides, and perchlorate 

were collected at all USGS-grid wells. More VOCs and 

pesticides were analyzed by the GAMA Priority Basin Project 

than were available in the CDPH database (table 3). Where 

data were available from both databases, organic constituents 

analyzed in samples collected as part of the GAMA Priority 

Basin Project were selected for data interpretation largely 

because of lower laboratory reporting levels of the GAMA 

Priority Basin Project data, typically by one or two orders of 

magnitude, relative to the method detection limits (MDLs) 

used for analyses compiled by the CDPH (table 3). 

Low-level analyses of VOCs and pesticides may be 

used as tracers of groundwater that has recharged since these 

compounds began to be used for industrial and commercial 

purposes. Low-level analyses provide an early awareness 

of constituents whose presence in groundwater at low 

concentrations may be important for the prioritization of 

monitoring water quality in the future.

Selection of Constituents for Additional 

Evaluation

The GAMA Priority Basin Project used available 

monitoring data along with newly collected data for 

characterization of the groundwater resource. The statewide 

CDPH database contains data for regulated constituents with 

water-quality benchmarks. Although other organizations in 

California also collect water-quality data, the CDPH has the 

only statewide database of public-supply well data available 

for comprehensive analysis. Data for some constituents, 

including VOCs, pesticides, inorganic constituents, and 

radioactive constituents, are available from the GAMA 

Priority Basin Project and the CDPH databases. Both datasets 

are used in the status and understanding assessments. The 

CDPH database contains more than 200,000 records from 

more than 500 wells in the SCRC study unit, necessitating 

targeted retrievals to access water-quality data effectively. 

CDPH data were used with USGS-grid data to identify 

constituents in the study unit at concentrations greater than 

water-quality benchmarks at any time during the period of 

record (January 18, 1984, through May 19, 2008). 

More than 300 constituents were analyzed in the SCRC 

study unit; however, only a subset of these constituents was 

selected for additional evaluation in this report. Three criteria 

were used to identify constituents for additional evaluation in 

the status assessment:

1. Constituents with concentrations at high or moderate 

relative-concentrations in the CDPH database during the 

current 3-year period (May 20, 2005, to May 19, 2008),

2. Constituents with concentrations at high or moderate 

relative-concentrations in the USGS-grid wells or USGSunderstanding wells, or

3. Organic constituents having detection frequencies greater 

than 10 percent in the USGS-grid well dataset for a given 

study unit, even if relative-concentrations were low.
14 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Table 1. Analytes and wells sampled for each analytical schedule for the South Coast Range–Coastal study unit, California GAMA 

Priority Basin Project, May–November 2008.

[GAMA, Groundwater Ambient Monitoring and Assessment Program; —, no data]

Number of wells Sampling schedule1

Fast Slow Special study2

Total number of wells 31 39 13

Number of grid wells sampled 23 32 6

Number of understanding wells sampled 8 7 7

Analyte groups Number of constituents

Water-quality indicators (field parameters)

Specific conductance and temperature 2 2 —

Dissolved oxygen and pH 2 2 —

Field alkalinity, bicarbonate, and carbonate — 3 —

Organic constituents

Volatile organic compounds (VOCs) 3 85 85 —

Pesticides and pesticide degradates 63 63 —

Polar pesticides and degradates 57 57 —

Pharmaceuticals — 13 —

Dissolved organic carbon — 1 —

Constituents of special interest

Perchlorate 1 1 —

N-Nitrosodimethylamine (NDMA) — 1 —

Inorganic constituents

Trace elements, major and minor ions, and total dissolved solids (TDS) — 36 —

Nutrients — 5 5

Arsenic and iron species — 4 —

Isotopes

Stable isotopes of hydrogen and oxygen 2 2 —

Carbon-13 and carbon-14 — 2 —

Stable isotopes of nitrogen and oxygen in nitrate — — 2

Stable isotope of nitrogen in dissoved nitrogen gas — — 1

Radioactivity and dissolved gases

Dissolved gases — — 5

Tritium 1 1 —

Noble gases and tritium 7 7 —

Gross alpha and beta radioactivity 4 — 4 —

Total 220 289 13

1

 Fast and slow analytical schedules refer to the amount of time required for a field crew to complete all work at a well.

2

 Special study analytes were added to 10 wells on the slow schedule and 3 wells on the fast schedule. 

3

 Includes nine constituents classified as fumigants or fumigant synthesis byproducts.

4

 Both gross alpha and gross beta particle activities were measured after 72-hour and 30-day holding times; the 30-day results are used in this report.
Methods 15

Table 2. Inorganic constituents, associated benchmark information, and number of grid wells per constituent, South Coast Range–

Coastal study unit, California GAMA Priority Basin Project.

[CDPH, California Department of Public Health; GAMA, Groundwater Ambient Monitoring and Assessment Program; MCL-CA, California Department of 

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

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

USGS, U.S. Geological Survey; mg/L, milligrams per liter; μg/L, micrograms per liter; pCi/L, picocuries per liter]

Constituent Benchmark 

type

Benchmark 

value

Benchmark 

unit

Number of grid 

wells sampled by 

USGS GAMA

Number of grid wells 

with data selected 

from CDPH

Nutrients with health-based benchmarks

Ammonia, as nitrogen HAL-US 1

 24.7 mg/L 2 36 0

Nitrate plus nitrite, as nitrogen MCL-US 10 mg/L 2

 36 13

Nitrite, as nitrogen MCL-US 1 mg/L 2

 36 10

Trace elements and minor ions with health-based benchmarks

Aluminum MCL-CA 1,000 μg/L 32 12

Antimony MCL-US 6 μg/L 32 12

Arsenic MCL-US 10 μg/L 32 12

Barium MCL-CA 1,000 μg/L 32 12

Beryllium MCL-US 4 μg/L 32 12

Boron NL-CA 1,000 μg/L 35 9

Cadmium MCL-US 5 μg/L 32 12

Chromium MCL-CA 50 μg/L 32 12

Copper AL-US 1,300 μg/L 32 12

Lead AL-US 15 μg/L 32 12

Mercury MCL-US 2 μg/L 30 12

Molybdenum HAL-US 40 μg/L 32 0

Nickel MCL-CA 100 μg/L 32 12

Selenium MCL-US 50 μg/L 32 12

Strontium HAL-US 4,000 μg/L 32 0

Thallium MCL-US 2 μg/L 32 12

Vanadium NL-CA 50 μg/L 32 8

Fluoride MCL-CA 2 mg/L 35 12

Trace elements and major ions with aesthetic-based benchmarks

Iron SMCL-CA 300 μg/L 35 12

Manganese SMCL-CA 50 μg/L 35 12

Silver SMCL-CA 100 μg/L 32 12

Zinc SMCL-CA 5,000 μg/L 32 12

Chloride SMCL-CA 500 mg/L 35 12

Sulfate SMCL-CA 500 mg/L 35 12

Total dissolved solids (TDS) SMCL-CA 1,000 mg/L 3

 43 12

Radioactive constituents with health-based benchmarks

Gross alpha radioactivity MCL-US 15 pCi/L 32 8

Gross beta radioactivity MCL-CA 50 pCi/L 32 0

Uranium MCL-US 30 μg/L 32 4

1

 The HAL-US is 30 mg/L “as ammonia.” To facilitate comparison to the analytical results, we have converted and reported this HAL-US as 24.7 mg/L “as 

nitrogen.”

2

 One well was a CDPH grid well (SCRC-DPH-B25) and a USGS-understanding well (SCRC-U04).

3

 TDS concentrations for eight wells were calculated from specific conductance data.
16 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

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

class for data stored in the California Department of Public Health (CDPH) database and for data collected by the U.S. Geological Survey 

in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project, May–November 2008.

[GAMA, Groundwater Ambient Monitoring and Assessment Program; MDL, method detection limit; LRL, laboratory reporting level; mg/L, milligrams per 

liter; μg/L, micrograms per liter; pCi/L, picocuries per liter; ssLc, sample-specific critical level; nc, not collected]

Constituent 

type

CDPH GAMA Concentration 

or activity 

units

Number of 

compounds

Median 

MDL

Number of 

compounds

Median 

LRL

Volatile organic compounds plus gasoline oxygenates (including fumigants) 73 0.5 85 0.06 μg/L

Pesticides plus degradates 70 1 121 0.040 μg/L

Pharmaceuticals nc nc 13 0.030 μg/L

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

Perchlorate 1 4 1 0.5 μg/L

Trace elements 21 6 25 0.12 μg/L

Radioactive constituents (ssLc

) 6 1 4 1 1.10 pCi/L

Nutrients, dissolved organic carbon 6 0.4 6 0.05 mg/L

Major and minor ions 10 unknown 11 0.10 mg/L

1

 Value reported is a median sample-specific critical level (ssLc) for four radioactive constituents collected and analyzed by GAMA.

A complete list of the constituents investigated by the 

GAMA Priority Basin Project in the SCRC study unit may be 

found in the data report (Mathany and others, 2010).

The CDPH database was used to identify constituents that 

have been reported at high relative-concentrations historically 

but not currently (table 4). The historical period was defined 

as the period starting with the earliest record maintained in 

the CDPH electronic database and ending just prior to the 

3-year interval used for the status assessment (January 18, 

1984, to May 19, 2005). Constituent concentrations retrieved 

from the CDPH database for samples in the study unit were 

identified as “historically high” (table 4) if concentrations 

were high (greater than benchmarks) before the current period 

but not high during the current period or in USGS-grid data. 

Historically high constituents that do not otherwise meet 

the criteria for additional evaluation in the status assessment 

were not considered representative of potential groundwaterquality concerns in the study unit during the current period. 

Constituents may be historically high but not currently 

high because of improvement of groundwater quality with 

time or abandonment of wells with high concentrations of 

constituents. 

Calculation of Aquifer-Scale Proportions

The status assessment is intended to characterize the 

quality of groundwater resources in the primary aquifer 

system of the SCRC study unit. The primary aquifer system 

is defined by the depth intervals over which wells listed in the 

CDPH database are perforated. The use of the term “primary 

aquifer system” does not imply that there is a discrete aquifer 

unit. In most groundwater basins, municipal and community 

supply wells generally are perforated at greater depths than are 

domestic wells. Most of the wells used in the status assessment 

are listed in the CDPH databases. Thus, because domestic 

wells are not listed in the CDPH database, the primary aquifer 

system generally corresponds to the part of the aquifer system 

tapped by municipal and community supply wells. Depths of 

CDPH wells in the SCRC study unit typically range from 265 

to 630 ft below land surface (bls); top-of-perforations typically 

range from 115 to 350 ft bls.

Water quality in the primary aquifer system can differ 

from water quality in shallow or deep parts of the aquifer 

system. Previous investigations in the study unit have shown 

that groundwater in shallow parts of the aquifer in the Santa 

Ynez River Valley groundwater basin generally is of poorer 

quality than groundwater at greater depths in the aquifer 

(Hamlin, 1985; Bright and others, 1992). A report for the 

northern part of the Santa Maria River Valley groundwater 

basin indicates that groundwater quality varies with depth in 

some areas, but not in others (California Department of Water 

Resources, 2002). An earlier study in the Santa Maria River 

Valley groundwater basin did not demonstrate differences 

in water quality with depth (Worts, 1951). The aquifer-scale 

proportions discussed in this report do not characterize water 

quality in parts of the aquifer system that are more shallow 

than or deeper than the primary aquifer system.

Two statistical methods—grid-based and spatially 

weighted—were applied to evaluate the proportions of the 

primary aquifer system in the SCRC 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 

trihalomethanes (THMs), solvents, other VOCs, pesticides, 
Methods 17

Table 4. Constituents in California Department of Public Health (CDPH) wells with historically high concentrations but not high during 

the current period (May 20, 2005, to May 19, 2008) in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

[A high analysis is defined as a concentration that is greater than the human-health benchmark for that constituent. Abbreviations: CDPH, California 

Department of Public Health; GAMA, Groundwater Ambient Monitoring and Assessment Program; MCL-US; U.S. Environmental Protection Agency (USEPA) 

maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; pCi/L, picocuries per liter; μg/L, micrograms 

per liter]

Constituent Number of wells 

with analyses

Benchmark 

type

Benchmark 

value Unit

Number of wells 

with at least one 

historically high 

analysis

Date of most 

recent high 

value 

Trace Elements

Aluminum 348 MCL-CA 1,000 μg/L 1 3/5/2003

Boron 285 NL-CA 1,000 μg/L 2 8/28/2002

Cadmium 356 MCL-US 5 μg/L 6 12/4/2002

Chromium 361 MCL-CA 50 μg/L 1 5/8/2002

Vanadium 276 NL-CA 50 μg/L 4 5/28/2003

Organic compounds

Carbon tetrachloride 347 MCL-CA 0.5 μg/L 3 2/21/2005

Dichloromethane (methylene chloride) 347 MCL-US 5 μg/L 1 5/8/1990

Di(2-ethylhexyl) phthalate 92 MCL-CA 4 μg/L 2 6/27/1989

Heptachlor 205 MCL-CA 0.01 μg/L 2 6/27/1989

trace elements and minor ions, radioactive constituents, and 

nutrients. Among constituents with aesthetic benchmarks 

(SMCLs), aquifer-scale proportions were calculated for major 

ions (total dissolved solids, chloride, and sulfate) in addition to 

manganese and iron.

The grid-based calculation uses the grid-well dataset 

assembled from the USGS- and CDPH-grid wells (Belitz and 

others, 2010). The areal proportion of the primary aquifer 

system with high relative-concentrations of a constituent was 

calculated by dividing the number of grid cells represented 

by a high value for that constituent by the total number of 

grid cells with data for that constituent (see appendix C for 

details of methods). Areal proportions of moderate and low 

relative-concentrations were calculated similarly. Confidence 

intervals for grid-based detection frequencies of high relativeconcentrations were computed using the Jeffreys interval for 

the binomial distribution (Brown and others, 2001). Although 

the grid-based estimate is spatially unbiased, the grid-based 

approach may not detect constituents that are present at 

high relative-concentrations in small proportions of the 

primary aquifer system. For calculation of high aquifer-scale 

proportion for a class of constituents, cells were considered 

high if the value for any of the constituents in that particular 

class was high. Cells were considered moderate if the relativeconcentration for any of the constituents was moderate, but 

none of the relative-concentrations were high.

The spatially weighted calculation used all available 

data from the following sources to calculate the aquifer-scale 

proportions—(1) all CDPH wells in the study unit (most 

recent analysis from each well with data for the constituent 

available during the current period, May 20, 2005, to May 19, 

2008, (2) USGS-grid wells, and (3) USGS-understanding 

wells with perforation intervals representative of the primary 

aquifer system. USGS-understanding wells that were 

monitoring wells were excluded because these wells were 

perforated at shallower depths than is typical for wells in 

the CDPH database. For the spatially weighted approach, 

areal proportions were computed on a cell-by-cell basis 

(Isaaks and Srivastava, 1989; Belitz and others, 2010). The 

areal proportion of high relative-concentrations for each 

constituent for the primary aquifer system was computed 

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

average of the grid-cell proportions computed in step (1) (see 

appendix C for details of methods). Similar procedures were 

used to calculate the aquifer-scale proportions of moderate 

and low relative-concentrations. The resulting proportions are 

spatially unbiased (Isaaks and Srivastava, 1989).

Raw detection frequencies of constituents with high 

relative-concentrations, calculated using the same data used 

for the spatially weighted approach, are provided for reference 

in this report, but were not used to assess aquifer-scale 

proportions. Wells are not uniformly distributed throughout 

the study area; therefore, the frequency of encountering a 

well with high relative-concentrations is spatially biased. For 

example, high relative-concentrations in spatially clustered 

wells in a particular area representing a small part of the 

primary aquifer system could be given a disproportionately 

higher weight compared to spatially unbiased methods.

The grid-based aquifer-scale proportions were used to 

represent proportions in the primary aquifer system unless 

the spatially weighted proportions were significantly different 

from the grid-based values. Significantly different results were 

defined as follows:
18 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

1. If the grid-based high aquifer-scale proportion was zero 

and spatially weighted aquifer-scale proportion was nonzero, then the spatially weighted result was used. This 

situation can arise when the concentration of a constituent 

is high in a small fraction of the aquifer.

2. If the grid-based high aquifer-scale proportion was nonzero, then the 90 percent confidence interval (based on the 

Jeffreys interval for the binomial distribution, Brown and 

others, 2001) was used to evaluate the difference. If the 

spatially weighted proportion was outside the 90 percent 

confidence interval, then the spatially weighted proportion 

was used.

The grid-based method to evaluate the aquifer-scale 

proportion of moderate and low relative-concentrations was 

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 as an estimate for the moderate proportion. 

Calculation of Gross Alpha Radioactivity, 

Uranium, and Total Dissolved Solids

The MCL-US (15 picocuries per liter [pCi/L]) for gross 

alpha particle activity applies to adjusted gross alpha activity, 

which is equal to the measured gross alpha activity minus 

uranium activity (U.S. Environmental Protection Agency, 

2009). Gross alpha is used a screening tool to determine 

whether other radioactive constituents must be analyzed. For 

regulatory purposes, analysis of uranium is only required 

if gross alpha activity is greater than 15 pCi/L; as a result, 

it is not always possible to calculate adjusted gross alpha 

activity. For this reason, gross alpha data without correction 

for uranium were the primary data used in this report. 

Examination of data from samples having USGS-GAMA data 

for uranium and gross alpha indicated that, in the absence of 

data for uranium, uncorrected gross alpha data likely provide 

a more accurate estimate of the aquifer-scale proportions for 

uranium and radioactive constituents as a class than does 

adjusted gross alpha (Miranda Fram, USGS California Water 

Science Center, written commun., 2012). 

USGS-GAMA reports data for gross alpha particle 

activity counted at 72 hours and at 30 days after 

sample collection. Regulatory sampling for gross alpha 

activity permits use of quarterly composite samples 

(U.S. Environmental Protection Agency, 2000; California 

Department of Public Health, 2012); thus, the USGS-GAMA 

gross alpha 30-day count data may be more appropriate to use 

when combining USGS-GAMA and CDPH datasets. Gross 

alpha activity in a groundwater sample may change with time 

after sample collection due to radioactive decay and ingrowth 

(activity may increase or decrease depending on sample 

composition and holding time) (Arndt, 2010).

Most data for uranium in the CDPH database are reported 

as activities in units of picocuries per liter, and the majority 

of uranium data gathered by USGS-GAMA are reported as 

concentrations in units of micrograms per liter. The factor 

used to convert uranium mass concentration to uranium 

activity depends on the isotopic composition of the uranium 

(U.S. Environmental Protection Agency, 2000). This report 

uses a conversion factor of 0.79 (Wong and others, 1999).

For USGS- and CDPH-grid wells without measured total 

dissolved solids (TDS) concentrations, TDS was estimated 

from specific conductance (SC) by using a linear regression 

equation, which was developed from measured SC and TDS 

data for 35 USGS-grid and understanding wells. Generally, 

SC follows a linear relation with TDS, and SC data were 

available for all 55 USGS-grid and 15 USGS-understanding 

wells, whereas laboratory-measured TDS data (as residue 

on evaporation) were available for only 35 of these wells. 

The predicted TDS using the regression equation (TDS = 

[0.748*SC]–54.12) closely matched measured TDS (R2 > 

0.97). 

Understanding Assessment Methods

The understanding assessment was based on USGS- and 

CDPH-grid wells and USGS-understanding wells (figs. 5

and A1). Constituents discussed in the status assessment 

were selected for the understanding assessment if relativeconcentrations were considered high in more than 2 percent 

of the primary aquifer system, or, for organics, were detected 

in more than 10 percent of the USGS-grid well dataset. 

These constituents were selected to focus the assessment for 

understanding on those constituents that have the greatest 

effect on groundwater quality. CDPH-other wells were not 

used in the understanding assessment because data for many 

of the potential explanatory factors were not available (for 

example, tritium, dissolved oxygen, and well construction 

information). 

The potential explanatory factors—land use, well depth, 

depth to the top-of-perforations, classified groundwater 

age, and geochemical condition (see appendix D for more 

details)—were analyzed in relation to constituents selected 

for additional evaluation for the understanding assessment to 

establish context for physical and chemical processes within 

the groundwater system. Statistical tests were used to identify 

significant correlations between the constituents of interest and 

potential explanatory factors. Graphs, bar charts, and maps 

were used to improve the understanding of factors affecting 

water quality for selected constituents with correlations to 

explanatory factors. 
Potential Explanatory Factors 19

U.S. Geological Survey Understanding Wells

Fifteen wells (USGS-understanding wells) were selected 

for sampling by the USGS to increase the data density in 

some areas to assess spatial changes in water quality. The 

USGS-understanding wells sampled in the SCRC study unit 

were identified with a prefix modified from those used for the 

USGS-grid wells (SCRC-U) and numbered 01 through 15.

The USGS-understanding wells were selected on the 

basis of two design objectives: (1) to assess changes in water 

quality along groundwater flow paths across parts of the Santa 

Maria River Valley groundwater basin and the Santa Ynez 

River Valley groundwater basin, and (2) to assess the water 

quality in the near-ocean aquifer in the Santa Ynez River 

Valley and San Antonio Creek Valley groundwater basins. 

The understanding wells included eight irrigation wells, six 

monitoring wells, and one CDPH well. 

Statistical Analysis

Nonparametric statistical methods were used to test the 

significance of correlations between water-quality parameters 

and potential explanatory variables. 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 for hypothesis testing 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). Two 

different types of statistical tests were used because the set 

of potential explanatory factors included both continuous 

and categorical variables. Relations between categorical 

variables (for example, classified groundwater age or land-use 

class) and water-quality variables were evaluated using the 

Wilcoxon rank-sum or Kruskal-Wallis nonparametric tests. 

Correlations between continuous variables were evaluated 

by using Spearman’s method. Correlations between potential 

explanatory factors, between water-quality parameters, and 

between potential explanatory factors and water-quality 

constituents were tested for significance. 

Correlations between explanatory factors and 

groundwater constituents were tested by using either the set 

of USGS- and CDPH-grid wells plus USGS-understanding 

wells or USGS- and CDPH-grid wells only. Because the 

USGS-understanding wells represented relatively shallow 

groundwater or were in agricultural areas that were not 

randomly selected on a spatially distributed grid, they were 

excluded from analysis of relations between water quality and 

areally distributed explanatory variables (such as land use and 

study area) to avoid areal-clustering bias. However, USGSunderstanding wells were included in analysis of relations 

between water-quality constituents and vertically distributed 

explanatory factors (such as well depth, classified groundwater 

age, and oxidation-reduction characteristics). 

Potential Explanatory Factors

A brief description of potential explanatory factors 

including land use, well depth, septic system density, formerly 

leaking underground fuel tanks (LUFTs), groundwater age, 

and geochemical conditions is given in this section. The 

data sources and methodology used for assigning values for 

potential explanatory factors are described in appendix D.

Land Use

Land use was quantified as the percentage of three 

land-use types: natural, agricultural, and urban. Land-use 

percentages were calculated for study units, study area, and 

within a 500-meter (m) radius around wells (Johnson and 

Belitz, 2009).

Land use in the SCRC study unit is 61 percent natural, 

29 percent agricultural, and 10 percent urban (figs. 3 and 6B; 

Nakagaki and others, 2007). Land use in the areas surrounding 

the SCRC primarily is natural (fig. 3). In contrast, within the 

500-m (1,640-ft) radius around each grid well, the average 

land use was 47 percent natural, 32 percent agricultural, and 

21 percent urban (fig. 6). The average natural land use was 

lower and the average urban land use was higher within the 

500-m radius around each grid well than for the study unit 

(fig. 6B). Within the 500-m (1,640-ft) radius around each 

CDPH well, the average agricultural land use was lower than 

that for the SCRC study unit or for the grid wells (25 percent), 

and urban land-use was higher than that for the study unit 

or for the grid wells (33 percent) (fig. 6B). In general, the 

land use around the grid wells in the SCRC study unit overrepresent the urban land use and under-represent the natural 

land use but are closer to the average land use of the study unit 

than land use around the CDPH wells.

Land use in the Uplands study area is 85 percent 

natural, 11 percent agricultural, and 4 percent urban; land 

use in the Basins study area is 52 percent natural, 36 percent 

agricultural, and 12 percent urban (fig. 6B). Within the 500-m 

radius around each well, the average land use around Upland 

grid wells was about the same as in the Uplands area, but 

the average land use around Basins grid wells had higher 

agricultural (41 percent) and urban (28 percent) land use than 

the Basins study area. In general, the Basins study area was 

more developed than the Uplands study area on the basis of 

the percentages of agricultural and urban land use.
20 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 6. Ternary diagram with proportions of natural, agricultural, and urban land use for (A) wells and (B) the study unit and 

study areas, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

B

20

40

60

80

100

0

Urban land use, in percent

100 80 60 40 20 0

Agricultural land use, in percent

100

80

60

40

20

0

Natural land use, in percent

Basins

Uplands

SOUTH COAST RANGE–

COASTAL STUDY UNIT

Data point:

Land use

based on

satellite

imagery

Data point:

Land use

based on a

500-meter-radius

area around

grid wells

STUDY AREA

STUDY UNIT

All CDPH wells in South Coast

 Range–Coastal study unit

CDPH wells in Basins

 study area

CDPH wells in Uplands

 study area

A

20

40

60

80

100

0

Urban land use, in percent

100 80 60 40 20 0

Agricultural land use, in percent

100

80

60

40

20

0

Natural land use, in percent

Data point:

Land use

based on a

500-meter-radius

area around

grid wells

SOUTH COAST RANGE–

COASTAL STUDY UNIT

Basins grid well

Uplands grid well

STUDY AREA

Understanding well

IP026177_Figure 06ab
Potential Explanatory Factors 21

Depth

The median well depth for grid wells in the SCRC 

study unit was 460 ft (140 m) bls; well depths ranged from 

68 to 1,263 ft bls (21–385 m bls; fig. 7A, table A1). The 

median depth to top-of-perforations for grid wells was 207 ft 

(63 m) bls. The median well depth for the Basins study area 

grid wells was 418 ft (127 m) bls; well depths ranged from 68 

to 1,120 ft bls (21–341 m bls; fig. 7B). The median well depth 

for Uplands study area grid wells was 575 ft (175 m) bls; 

well depths ranged from 260 to 1,263 ft (79–385 m) bls. The 

median depth to the top-of-perforations for the Basins study 

area grid wells was 188 ft (57 m) bls; depth to the top-ofperforations ranged from 33 to 650 ft bls (10–198 m bls; 

fig. 7B). The median depth to the top-of-perforations for 

Uplands study area grid wells was 252 ft (77 m) bls; depth to 

the top-of-perforations ranged from 75 to 535 ft (23–163 m) 

bls. (fig. 7B). These values represent a subset of the grid wells 

because well depth and depth to the top-of-perforations were 

not known for several wells. 

The median depth of USGS-understanding wells (130 ft 

[40 m] bls) in the SCRC study unit was shallower than the 

median depth for grid wells (fig. 7A). This was expected 

because 6 of the 15 understanding wells were monitoring 

wells that tap shallow groundwater. The median depth to 

top-of-perforations for USGS-understanding wells was 53 ft 

(16 m) bls. 

Septic-System Density

The number of septic tanks or cesspools in the 500-m 

(1,640-ft) radius around each USGS-grid and understanding 

well in the SCRC study unit ranged from 0 to 799 septic tanks 

per square kilometer (tanks/km2), with a median of 1.1 septic 

tank/km2 (table D1). The number of septic tanks or cesspools 

ranged from 0 to 799 septic tanks/km2 in the Basins study area 

and ranged from 0 to 9.4 septic tanks/km2 in the Uplands study 

area (table D1). 

Formerly Leaking Underground Fuel Tanks

The density of LUFTs located within the Thiessen 

polygon (a description of a Thiessen polygon can be found 

in appendix D in the section Formerly Leaking Underground 

Fuel Tanks) around each USGS- and CDPH-grid and USGSunderstanding well in the SCRC study unit ranged from <0.01 

to 13.04 tanks/km2, with a median of 0.02 tank/km2 (table D1). 

The median distance to the nearest LUFT was 9,012 ft 

(2,747 m). The LUFT density for most of the wells in the 

SCRC study unit was very low—usually less than 

0.05 tank/km2

The density of LUFTs around grid and understanding 

wells in the Basins study area also ranged from <0.01 to 

13.04 tanks/km2, with a median of 0.02 tank/km2 (table D1). 

However, the median distance to the nearest LUFT was only 

6,263 ft (1,909 m). 

The density of LUFTs around grid and understanding 

wells was less in the Uplands study area (<0.01 to 

0.37 tank/km2) than in the Basins study area, with a median of 

0.01 tank/km2 (table D1). The median distance to the nearest 

LUFT in the Uplands study area was 18,441 ft (5,621 m).

Groundwater Age

Groundwater samples were assigned age classifications 

based on the tritium, carbon-14 (14C), and terrigenic helium 

content of the samples (appendix D). Of the 70 USGS-grid 

and understanding wells in the SCRC study unit evaluated by 

the Priority Basin Project for groundwater age, groundwater 

samples were classified as modern for 21 wells, mixedage for 4 wells (evidence of both modern and pre-modern 

groundwater in the same sample), and pre-modern for 

28 wells (table D2). Samples from nine wells were classified 

as pre-modern or mixed. For statistical purposes, these 

samples were combined with samples of pre-modern age. 

Samples from eight wells were classified as modern or mixedage because the age-tracer data were incomplete. For statistical 

purposes, these samples were combined with samples of 

mixed-age. Groundwater age varied between the two study 

areas (fig. 8A). Samples from all but one well in the Uplands 

study area were classified as pre-modern groundwater, 

whereas the wells in the Basins study area included a mixture 

of modern, mixed-age, and pre-modern groundwater.

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory factors in this report include oxidation-reduction 

characteristics, including dissolved oxygen (DO), and pH. 

An abridged classification of oxidation-reduction (redox) 

conditions adapted from the framework presented by 

McMahon and Chapelle (2008) for USGS-grid and USGSunderstanding wells in the SCRC study unit is given in 

appendix D (table D3). The classification “indeterminate” was 

added to the framework for groundwater samples that did not 

have sufficient data available to be classified as oxic, anoxic/

suboxic, or mixed anoxic/oxic (Jurgens and others, 2009). 

pH data were available for all grid and understanding wells, 

but DO concentration data were not available for some grid 

wells. Groundwater in the SCRC study unit was oxic (redox 

category oxic, or DO greater than or equal to 0.5 milligram per 

liter [mg/L]) in 84 percent of USGS-grid wells and 79 percent 

of USGS-understanding wells. pH ranged from 5.8 to 7.9 in 

USGS-grid and understanding wells.
22 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 7. Construction characteristics for (A) USGS- and CDPH-grid wells and USGS-understanding wells and (B) grid wells 

in the Basins and Uplands study areas, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

Well

depth

Top-ofperforationsPerforation

length

Well

depth

Top-ofperforationsPerforation

length

Grid wells

59

Understanding wells

48 48 15 10 10

1,000

1,200

1,400

800

600

400

200

0

Depth below land surface or length of perforated interval, in feet

48 Number of wells

 with data

75th percentile

Median

25th percentile

Data point that

 is outside

 10th percentile

90th percentile

 10th percentile

Data point that

 is outside

 90th percentile

EXPLANATION

sac13-0497_Figure 07a_box plot

A

43 16 34 14

1,000

1,200

1,400

800

600

400

200

0

Depth below land surface, in feet

B

Basins Uplands Basins Uplands

Well depth Depth to top-of-perforations
Potential Explanatory Factors 23

Figure 8. Groundwater age classification in relation to (A) study areas, (B) well depth, and (C) depth to 

top-of-perforations, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

Groundwater age classification

Groundwater age classification

Number of wells

0

5

10

15

20

25

Modern Mixed Pre-modern

Modern Mixed Pre-modern

Groundwater age classification

Modern Mixed Pre-modern

Uplands study area

Basins study area

Well depth, in feet

0

200

400

600

800

1,000

1,200

1,400

Depth to top-of-perforations, in feet

0

100

200

300

400

500

600

700 14 10 30

21 12 36

A

A

B

B

A

A,B

A

B

C

48 Number of wells

 with data

EXPLANATION

EXPLANATION

Boxes with different letters

are significantly different

75th percentile

Median

25th percentile

Data point that

 is outside

 10th percentile

90th percentile

 10th percentile

Data point that

 is outside

 90th percentile

sac13-0497_Figure 08abc
24 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Correlations Between Explanatory Variables

Apparent correlations between an explanatory variable 

and a water-quality constituent could indicate relations 

between two explanatory variables and not between an 

explanatory variable and a water-quality constituent. 

Therefore, significant correlations between explanatory 

variables are important to identify. Significant correlations 

between categorical explanatory variables that were obtained 

by using the Wilcoxon or Kruskal-Wallis statistical tests 

are given in table 5 for the SCRC study unit. Significant 

correlations between continuous explanatory variables that 

were obtained by using the Spearman’s method are given in 

table 6. 

Relations were observed between well depth and 

groundwater age. The median depth of USGS-grid and 

understanding wells in the SCRC study unit with groundwater 

classified as pre-modern was deeper than the depths of wells 

with groundwater classified as modern or mixed ages (table 5; 

fig. 8B). The median depth to the top-of-perforations of 

wells with groundwater classified as pre-modern also was 

deeper than the depth to the top-of-perforations of wells with 

groundwater classified as modern or mixed ages (table 5; 

fig. 8C). Well depths or depths to top-of-perforations in wells 

with groundwater samples classified as modern and mixed 

ages were not significantly different.

Groundwater ages for most SCRC wells perforated 

entirely at depths less than 400 ft (122 m) bls (26 of 37 wells, 

70 percent) were modern, mixed, or modern or mixed 

(fig. 9). Groundwater ages for most of the wells with the 

top-of-perforations less than 400 ft but with the bottom-ofperforations greater than or equal to 400 ft were pre-modern, 

or mixed or pre-modern (13 of 19 wells). Groundwater ages 

for wells perforated entirely at depths greater than or equal to 

400 ft were all pre-modern except for one well that had mixedage groundwater. 

Each of the three depth categories included wells from 

more than one of the groundwater age classifications (fig. 9). 

The presence of pre-modern groundwater from wells less than 

400 ft (122 m) deep and mixed-age groundwater from wells 

greater than or equal to 400 ft deep (fig. 9) indicates that there 

are local variations in the general groundwater age-depth 

relations. These variations may indicate the position of the 

well relative to the regional groundwater flow system. 

Wells with pre-modern groundwater were more likely 

to be in areas of natural land use than wells with modern 

groundwater (table 5). Urban and natural land uses were 

correlated with LUFTs. The density of LUFTs increased as 

the percentage of urban land use increased; in contrast, the 

LUFT density decreased as the percentage of natural land use 

increased (table 6). The distance to the nearest LUFT was less 

for wells in urban land-use areas than for wells in natural landuse areas (tables 5 and 6).

DO concentrations were greater for modern groundwater 

than for pre-modern groundwater and were positively 

correlated with agricultural land use, pH, and the number of 

septic systems within 500 m (1,640 ft) of the well (tables 5

and 6). The lack of a correlation between DO and well depth 

was unexpected because DO concentrations were lower in 

pre-modern and mixed-age groundwater, and groundwater age 

increased with well depth (table 5; fig. 8B, C). Previous studies 

in other aquifers have reported a negative correlation of DO 

with depth (McMahon and Chapelle, 2008; Burton and others, 

2011); however, some other Priority Basin Project study units 

located in coastal basins also did not have a correlation of DO 

with depth (Kulongoski and others, 2010; Kulongoski and 

Belitz, 2011). DO was correlated with agricultural land use. 

This correlation was not found in other Priority Basin Project 

study units (Kulongoski and Belitz, 2010; Landon and others, 

2010; Burton and others, 2011, 2012). The correlation of DO 

with agriculture may be a result of irrigation practices which 

may accelerate the percolation of aerated water to the primary 

aquifer system. DO also was positively correlated with pH. 

This correlation was different from findings for other study 

units in the GAMA Priority Basin Project that reported DO to 

be negatively correlated or not correlated to pH (Kulongoski 

and others, 2010; Landon and others, 2010; Kulongoski and 

Belitz, 2011; Burton and others, 2012; Fram and Belitz, 2012). 

Areas with low DO include parts of the Santa Ynez River 

Valley and San Antonio Creek Valley groundwater basins and 

the northern edge of the Santa Maria River Valley groundwater 

basin (fig. 10A).

pH was positively correlated with the depth to top-ofperforations and the density of septic systems within 500 m 

(1,640 ft) of the well. Wells with groundwater having a pH 

of less than 7 are located in the area near Arroyo Grande, 

as well as near the San Antonio Creek Valley and the Santa 

Ynez River Valley groundwater basins (fig. 10B). Wells 

with groundwater having a pH greater than 7.5 primarily are 

located south of the city of Santa Maria and in the eastern 

portion of the Santa Ynez River Valley groundwater basin.
Potential Explanatory Factors 25

Table 5. Results of nonparametric statistical tests (Kruskal-Wallis or Wilcoxon) for differences in values of potential categorical 

and continuous explanatory factors and differences in values of potential categorical explanatory factors and selected water-quality 

constituents for the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

[Groundwater age class: Mod, modern water; Mix, mixture of modern and pre-modern water; preM, pre-modern water (see appendix D for explanation of 

groundwater age classes). p-values less than 0.05 calculated using the Kruskal-Wallis (for comparing three or more groups) or Wilcoxon (for comparing 

two groups). Nonparametric tests indicate significant differences. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; 

Ag, agricultural; Nat, natural; m, meter; mg/L, milligrams per liter; na, not applicable; ns, not significant; VOC, volatile organic compound; >, greater than; 

<, less than; tanks/km2, tanks per square kilometer]

Explanatory factor or 

water-quality constituent

Groundwater age class 

(Mod, Mix, preM) 

p-value 

significant differences 1

Study area 2 

(Basins, Uplands) 

p-value 

significant differences 1

Land-use categories 2

(Ag, Urban, Nat) 

p-value 

significant differences 1

Potential explanatory factors

Well depth <0.001 

preM > Mod, Mix

ns ns

Depth to top-of-perforations <0.001

preM > Mod

ns ns

Agricultural land use (percent) 2 ns 0.021

Basins > Uplands

na

Urban land use (percent) 2 ns <0.001

Basins > Uplands

na

Natural land use (percent) 2 0.035

preM > Mod

<0.001

Uplands > Basins

na

Septic tanks or cesspools (number of tanks/km2

) ns ns ns

Distance to nearest formerly leaking underground fuel tank (m) ns <0.001

Uplands > Basins

<0.001

Nat > Ag > Urban

Formerly leaking underground fuel tanks (number of tanks/km2

) ns ns 0.025

Urban > Nat

Dissolved oxygen (mg/L) 0.005

Mod > preM

ns ns

pH ns ns ns

Selected inorganic water-quality constituents

Arsenic 0.031

preM, Mix > Mod

0.016

Uplands > Basins

ns

Barium 0.024

Mix > Mod

ns ns

Copper 0.023

Mod > Mix, preM

ns ns

Molybdenum ns 0.007

Uplands > Basins

ns

Selenium 0.023

Mod > Mix, preM

ns ns

Nitrate <0.001

Mod > Mix, PreM

ns 0.011

Ag > Nat, Urban

Manganese 0.002

PreM, Mix > Mod

ns ns

Iron 0.008

PreM, Mix > Mod

ns ns

TDS 0.041

Mod > preM

<0.001

Basins > Uplands

0.003

Ag, Urban > Nat

Sulfate 0.012

Mod > preM

0.003

Basins > Uplands

0.018

Ag > Nat

Chloride 0.034

Mix > Mod

ns ns
26 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Potential explanatory factors

Groundwater age class 

(Mod, Mix, preM) 

p-value 

significant differences1

Study area2 

(Basins, Uplands) 

p-value 

significant differences1

Land-use categories2

(Ag, Urban, Nat) 

p-value 

significant differences1

Selected organic and special-interest water-quality constituents

Number of VOC detections 3 ns ns ns

Chloroform ns ns ns

Number of solvent detections 0.027

Mod > PreM

ns ns

Perchloroethene (PCE) 0.016

Mod > preM

ns ns

Number of other VOC detections ns ns ns

Dichlorodifluoromethane (CFC-12) 0.038

Mod > preM

ns ns

Number of pesticide detections 4 0.035

Mod > preM

ns ns

Perchlorate ns 0.037

Basins > Uplands

0.027

Ag > Urban

1

 Only significant differences are shown. For example, PreM>Mod for well depth means that wells with pre-modern-age water are significantly deeper than 

wells with modern-age water, but wells with mixed-age water are not significantly different from wells with pre-modern-age or modern-age water.

2

 Grid wells only.

3

 Does not include VOCs classified as fumigants.

4

 Includes VOCs classified as fumigants.

Table 5. Results of nonparametric statistical tests (Kruskal-Wallis or Wilcoxon) for differences in values of potential categorical 

and continuous explanatory factors and differences in values of potential categorical explanatory factors and selected water-quality 

constituents for the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.—Continued

[Groundwater age class: Mod, modern water; Mix, mixture of modern and pre-modern water; preM, pre-modern water (see appendix D for explanation of 

groundwater age classes). p-values less than 0.05 calculated using the Kruskal-Wallis (for comparing three or more groups) or Wilcoxon (for comparing 

two groups). Nonparametric tests indicate significant differences. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; 

Ag, agricultural; Nat, natural; m, meter; mg/L, milligrams per liter; na, not applicable; ns, not significant; VOC, volatile organic compound; >, greater than; 

<, less than; tanks/km2, tanks per square kilometer]
Potential Explanatory Factors 27

Table 6. Results of nonparametric (Spearman’s method) analysis of correlations between selected potential explanatory factors in grid 

and understanding wells, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

[Spearman’s correlation statistic (ρ) and p-values are shown when correlations between selected potential explanatory factors are significant (p < 0.05). Number 

of septic tanks or cesspools in 500-meter radius around each well (U.S. Census Bureau, 1990). Number of formerly leaking underground fuel tanks within a 

Thiessen polygon, in square kilometers, data from Geographic Information Management System GeoTracker (California Environmental Protection Agency, 

2001). Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; p, significance level of Spearman’s test based on a threshold value 

(a) of 0.05; LSD, land surface datum; m, meter; mg/L, milligrams per liter; ns, not significant; tanks/km2, tanks per square kilometer]

Explanatory 

factor

ρ/p

Well depth 

(in feet below 

LSD)

Depth to 

top-ofperforations 

(in feet below 

LSD)

Dissolved 

oxygen 

(mg/L)

pH

Number of 

septic tanks 

(number of 

tanks/km2

)

Distance 

to nearest 

formerly 

leaking 

underground 

fuel tank 

(m)

Number of 

formerly 

leaking 

underground 

fuel tanks 

(number of 

tanks/km2

)

Grid wells used in analysis

Agricultural land use (percent) ns ns 0.284

0.022

ns ns ns ns

Natural land use (percent) ns ns ns ns –0.249

0.033

0.523

<0.001

–0.419

0.008

Urban land use (percent) ns ns ns ns 0.330

0.004

–0.696

<0.001

0.395

<0.001

Grid and understanding wells used in analysis

Well depth (in feet below LSD) 0.847

<0.001

ns 0.240

0.042

ns ns ns

Depth to top-of-perforations 

(in feet below LSD)

ns 0.273

0.042

ns ns ns

Dissolved oxygen (mg/L) 0.281

0.023

0.305

0.014

ns ns

pH 0.243

0.040

ns ns

Number of septic tanks or cesspools 

(number of tanks/km2

)

–0.294

0.014

ns

Distance to nearest formerly leaking 

underground fuel tank (m)

–0.557

<0.001
28 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 9. Relation of groundwater age classification to numbers of wells in each of the three depth 

categories, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

Number of wells

Depth of perforated interval below land surface

Entire perforation

interval is within

400 ft of land surface

Top-of-perforation

interval is less than 400 ft; 

Bottom-of-perforation

interval is equal to or

greater than 400 ft 

Entire perforation

interval is equal to or

greater than 400 ft 

0 2 4 6 8 10 12 14 16 18 20

sac13-0497_Figure 09

Modern

Mixed

Pre-modern

GROUNDWATER AGE

 CLASSIFICATION

EXPLANATION

Status and Understanding of Water 

Quality

The status assessment was designed to identify 

the constituents or classes of constituents most likely 

to be water-quality concerns because of high relativeconcentrations or prevalence. The assessment applies 

only to constituents with regulatory (MCL-US, MCL-CA, 

and AL-US) or non-regulatory (HAL-US, RSD5-US, or 

NL-CA) human-health benchmarks or aesthetic benchmarks 

(SMCL-US or SMCL-CA) established by the USEPA or 

the CDPH (California Department of Public Health, 2008a; 

U.S. Environmental Protection Agency, 2008a, b). The 

spatially distributed, randomized approach to well selection 

and data analysis yields a view of groundwater quality in 

which all areas of the primary aquifer system are weighted 

equally.

The understanding assessment was designed to help 

answer the question of why specific constituents are, or are 

not, detected in groundwater. The understanding assessment

addresses a subset of the constituents discussed in the status 

assessment and is based on statistical correlations between 

water quality and a selected set of potential explanatory 

factors. This assessment may improve our understanding 

of how human and natural sources of contaminants affect 

groundwater quality in the SCRC study unit; however, it was 

not designed to identify specific sources of constituents to 

specific wells. 

Samples from USGS-grid wells in the SCRC study 

unit were analyzed for 205 organic constituents (85 VOCs, 

63 pesticides, and 57 polar pesticides) and 2 special-interest 

constituents. In USGS-grid wells, about one-tenth of organic 

and special-interest constituents analyzed for were detected 

(22 of 207). Human-health benchmarks are established 

for about two-thirds of the organic and special-interest 

constituents detected (14 of 22, table 7). Of the 85 VOCs 

analyzed, 9 VOCs were detected—8 with human-health 

benchmarks. Of the 120 pesticides and polar pesticides 

analyzed, 8 pesticides and 4 pesticide degradates were 

detected (Mathany and others, 2010). Five of the eight 

pesticides had human-health benchmarks (Mathany and 

others, 2010). The four pesticide degradates [de-ethylatrazine, 

3,4-dichloroanaline, 3-(4-chlorophenyl)-1-methyl urea, and 

fipronil sulfide] did not have benchmarks; however, humanhealth benchmarks are established for two of the three parent 

compounds (atrazine and diuron) of these degradates. Humanhealth benchmarks have not been established for the parent 

compound (fipronil) of the remaining two degradates that were 

detected. Human-health benchmarks are established for the 

constituent of special interest detected (perchlorate). Thus, 

most of the organic and special-interest compounds detected 

in groundwater in the SCRC study unit have established 

benchmarks.
Status and Understanding of Water Quality 29

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41 1

1

154 101

101

101

41 1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 10 DO/pH map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary Study areas

Basins

Uplands/Grid

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

120°30’ 120°

35°

34°

30’

Creek

Creek

Santa Cruz

San Antonio Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

A

B

Dissolved oxygen concentration,

in milligrams per liter

pH,

in standard units

Grid wells

< 0.5

0.5 to < 2

> 2

Understanding wells

< 0.5

0.5 to < 2

> 2

Grid wells

<7

7 to 7.5

>7.5

Understanding wells

<7

7 to 7.5

CDPH wells

<7

7 to 7.5

>7.5 Pacific Ocean

Pacific Ocean

Figure 10. (A) Dissolved oxygen concentrations and (B) pH values for wells in the South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project.
30 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

In contrast to organic constituents, all inorganic 

constituents analyzed for were detected (46 of 46, table 7) 

in USGS- and CDPH-grid wells. Human-health or aesthetic 

benchmarks have been established for more than two-thirds 

of the inorganic constituents detected (32 of 46). Most of the 

14 constituents without benchmarks are major or minor ions 

that are naturally present in nearly all groundwater.

The maximum relative-concentration for each constituent 

with a water-quality benchmark in grid wells is shown in 

figure 11. In the SCRC study unit, seven inorganic constituents 

were detected at high relative-concentrations in one or more 

grid wells, and eight additional inorganic constituents were 

detected at moderate relative-concentrations. In contrast, none 

of the organic and special-interest constituents were detected 

at high relative-concentrations in grid wells, although two 

constituents, one organic and one special-interest constituent, 

were detected at moderate relative-concentrations. 

Aquifer-scale proportions were calculated for each 

inorganic and organic constituent detected at high or moderate 

relative-concentrations and for each organic and specialinterest constituent detected in more than 10 percent of the 

grid wells (table 8). The spatially weighted high aquifer-scale 

proportions for these constituents were within the 90 percent 

confidence intervals for their respective grid-based high 

aquifer-scale proportions, providing evidence that the gridbased approach yields statistically equivalent results to the 

spatially weighted approach. 

Inorganic Constituents 

Inorganic constituents generally occur naturally in 

groundwater, although concentrations of these constituents 

may be affected by human as well as natural factors. 

Inorganic constituents in the SCRC study unit with 

human-health benchmarks, as a class (trace elements and 

minor ions, radioactive constituents, and nutrients), were 

detected at high relative-concentrations in 33 percent, at 

moderate relative-concentrations in 35 percent, and at low 

relative-concentrations or not detected in 31 percent of the 

primary aquifer system (table 9). Inorganic constituents 

with aesthetic benchmarks, as a class, were detected at 

high relative-concentrations in 35 percent, at moderate 

relative-concentrations in 55 percent, and at low relativeconcentrations or not detected in 11 percent of the primary 

aquifer system. All detections are in untreated groundwater 

collected from the primary aquifer system and not from 

drinking water; generally, drinking water is treated before it is 

delivered to consumers by water purveyors.

Table 7. Number of constituents analyzed and detected in 

USGS-grid wells, listed by human-health-based or aesthetic 

benchmarks and constituent class, in the South Coast Range–

Coastal study unit, California GAMA Priority Basin Project.

[Regulatory human-health benchmarks include U.S. Environmental Protection 

Agency (USEPA) maximum contaminant level and California Department of 

Public Health (CDPH) maximum contaminant level. Non-regulatory humanhealth benchmarks include USEPA lifetime health advisory levels and riskspecific dose level at 10–5 lifetime cancer risk and CDPH notification level. 

Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment 

Program; HHB, human-health-based benchmark; NWQL, U.S. Geological 

Survey (USGS) National Water Quality Laboratory; SMCL, USEPA or CDPH 

secondary maximum contaminant level (aesthetic based)]

Benchmark type

Number of 

constituents 

analyzed

Number of 

constituents 

detected

Volatile organic compounds (VOCs)

Regulatory HHB 33 1

 7

Non-regulatory HHB 25 1

None 27 1

Total 85 9

Pesticides and degradates

Regulatory HHB 3 2

Non-regulatory HHB 17 1

 1

None 43 6

Total 63 9

Polar pesticides and degradates

Regulatory HHB 8 0

Non-regulatory HHB 11 2

None 38 1

 1

Total 57 3

Special-interest constituents

Regulatory HHB 1 1

Non-regulatory HHB 1 0

Total 2 1

Inorganic and radioactive constituents

Regulatory HHB 20 20

Non-regulatory HHB 5 5

Aesthetic - SMCL 7 7

None 14 14

Total 46 46

1

 Additional constituent(s) were detected in USGS-understanding wells.
Status and Understanding of Water Quality 31

0.001

0.01

0.1

1

10

100

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

 Unless indicated by following location line:

EXPLANATION

Moderate

Low

Moderate

Low

Moderate

High High

Organic and specialinterest constituentsInorganic

constituents

Maximum relative-concentration, dimensionless

Volatile

organic

compounds

Pesticides Special

interest

Nutrients Trace

elements

with HHB

Major and

minor ions

and trace

elements

with SMCL

Radioactive

constituents

Number of 

compounds

detected

< 0.001

1 1 0 0 0 1 0

sac130497Figure 11MaxRCs

Abbreviations: N, nitrogen; TDS, total dissolved solids; HHB, human-health-based benchmark;

 SMCL, secondary maximum contaminant levels

Chloroform

CFC-11

Toluene

1,2-Dichloropropane

Dichloromethane

Methyl tert-butyl ether

Atrazine

Simazine

Bromacil

Diuron

Nitrate-N

Ammonia-N

Nitrite-N

Gross beta

Gross alpha

Uranium

Molybdenum

Strontium

Fluoride

Boron

Vanadium

Barium

Arsenic

Selenium

Nickel

Chromium

Cadmium

Lead

Copper

Mercury

Antimony

Aluminum

Beryllium

Thallium

Chloride

Sulfate

TDS

Manganese

Zinc

Iron

Perchlorate

Tetrachloroethene

(PCE)

Figure 11. Maximum relative-concentration in USGS- and CDPH-grid wells for detected constituents by constituent 

class, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.
32 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Table 8. Raw detection frequencies and aquifer-scale proportions using spatially weighted and grid-based methods for constituents detected at concentrations greater than water-quality benchmarks during the most recent 3 years of data available (May 20, 2005, to May 19, 2008) from the California Department of Public Health (CDPH) database, or detected at high or moderate relative-concentrations in samples collected from USGS-grid wells, or organic compounds detected at frequencies greater than 10 percent in samples collected from USGS-grid wells, May–November 2008 in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.[High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to benchmark but greater than 0.1 (for organic and special-interest constituents) or 0.5 (for inorganic constituents) of the benchmark; low, concentrations less than or equal to 0.1 (for organic and special-interest constituents) or 0.5 (for inorganic constituents) of the benchmark. Constituents are ordered by class and high aquifer-scale proportion. GAMA, Groundwater Ambient Monitoring and Assessment Program; nc, not collected; %, percent; SMCL, secondary maximum contaminant level]

Constituent

Raw 

detection frequency1

Spatially weighted 

aquifer-scale proportion1

Grid-based 

aquifer-scale proportion

90% confidence interval 

for grid-based high 

aquifer-scale proportion2

Number 

of wells

Moderate values 

(percent)

High values 

(percent)

Number 

of cells

Moderate values 

(percent)

High values 

(percent)

Number 

of cells

Moderate values 

(percent)

High values 

(percent)

Lower limit 

(percent)

Upper limit 

(percent)

Trace elements and minor ions

Molybdenum 3 39 18.0 23.0 32 22 25 32 22 25 14 39

Arsenic 196 8.7 3.1 46 15 4.0 44 18 6.8 2.5 15Selenium 194 2.1 1.5 46 3.4 0.4 44 2.3 0 0 3.0Copper 196 0.5 0.5 46 2.2 0.1 44 2.3 0 0 3.0Barium 193 0.5 0.5 46 0.3 0.1 44 0 0 0 3.0Boron 111 1.8 0 43 3.1 0 44 4.5 0 0 3.2

Cadmium 193 1.0 0 46 2.3 0 44 2.3 0 0 3.0Chromium 193 2.1 0 46 4.3 0 44 2.3 0 0 3.0

Nutrients

Nitrate 263 16 9.9 48 13 8.7 50 14 10 4.9 19

Uranium and radioactive constituents

Gross alpha radioactivity 119 7.6 0 44 4.5 0 40 5.0 0 0 3.7

Uranium 68 2.9 0 40 3.8 0 36 2.9 0 0 3.7

Inorganics with aesthetic benchmarks (SMCLs)

Manganese 194 8.2 19 46 9 24 44 8.5 25 16 37Iron 194 6.6 15 46 6.1 16 44 6.4 15 8.5 26Total dissolved solids (TDS) 193 58 8.3 46 60 13 56 66 16 9.3 25Sulfate 193 21 4.1 46 24 8.4 44 32 11 5.3 21

Chloride 193 2.0 1.0 46 4.8 0.4 44 4.3 0 0 3

Trihalomethanes (THM)

Total THMs (chloroform) 4 181 0.5 0 55 0.1 0 55 0 0 0 2.4

SolventsPerchloroethene (PCE) 180 1.1 0.6 55 2.0 0.3 55 1.8 0 0 2.4

Trichloroethene (TCE) 179 1.1 0.6 55 0.5 0.1 55 0 0 0 2.4

Other VOCs

Dichlorodifluoromethane (CFC-12) 153 0 0 55 0 0 55 0 0 0 2.4

Special interest

Perchlorate 203 9.9 0 55 21 0 55 29 0 0 2.4

1 Based on most recent analysis for each CDPH well during May 20, 2005–May 19, 2008, combined with GAMA grid-based data.2 Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001).3 Number of wells for the raw detection frequency and the spatially weighted aquifer-scale proportion is low because there were no data for molybdenum for CDPH wells.4 Chloroform was the only THM detected.
Status and Understanding of Water Quality 33

Table 9. Aquifer-scale proportions for constituent classes and 

constituents of special interest for the South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project.

[Aquifer-scale proportions are given in percentage of area of the primary 

aquifer. All values greater than 10 percent are rounded to the nearest 1 percent. 

Values less than 10 percent are rounded to the nearest 0.1 percent. Values 

may not add up to 100 precent because of rounding. GAMA, Groundwater 

Ambient Monitoring and Assessment Program; VOCs, volatile organic 

compounds; SMCL, secondary maximum contaminant level; TDS, total 

dissolved solids; SO4, sulfate; Cl, chloride]

Constituent class

Aquifer-scale proportion 

(percent)

High Moderate Low or 

not detected 

Inorganics with human-health benchmarks

Trace elements and minor ions 27 29 44

Uranium and radioactive constituents1 0 5.0 95

Nutrients 10 14 76

Any inorganic with human-health 

benchmarks

33 35 31

Inorganics with aesthetic benchmarks (SMCLs)

Major ions (TDS, SO4

, Cl) 19 65 16

Manganese and iron 34 4.3 62

Any inorganic with an SMCL 35 55 11

Organics with human-health benchmarks

Trihalomethanes (chloroform)2 0 0.1 100

Solvents 0.4 1.8 98

Other VOCs 0 0 100

Pesticides 0 0 100

Any organic constituent 0.4 1.8 98

Constituents of special interest

Perchlorate, NDMA 0 29 71

1

 Aquifer-scale proportions for the class uranium and radioactive 

constituents and all inorganic constituents with health-based benchmarks were 

calculated using unadjusted gross alpha activity.

2

 Chloroform was the only THM detected.

Trace Elements and Minor Ions

In the SCRC study unit, trace elements, as a class, were 

detected at high relative-concentrations in 27 percent, at 

moderate relative-concentrations in 29 percent, and at low 

relative-concentrations or not detected in 44 percent of the 

primary aquifer system (table 9). The only trace elements with 

human-health benchmarks with high relative-concentrations 

in more than 2 percent of the primary aquifer system were 

arsenic and molybdenum (table 8). Barium, copper, and 

selenium were detected at high relative-concentrations in less 

than 2 percent of the primary aquifer system. Boron, cadmium, 

and chromium were detected at moderate concentrations 

(table 8; fig. 12). Only constituents detected at high relativeconcentrations in more than 2 percent of the primary aquifer 

system in the study unit are discussed further in this report.

Arsenic

Arsenic is a naturally occurring semi-metallic trace 

element. The most common source of arsenic is from aquifer 

materials, including dissolution of arsenic-rich minerals such 

as arsenopyrite, a common constituent of shales, and apatite, 

a common constituent of phosphorites. Arsenic in ocean 

waters is often sequestered by iron or manganese hydroxides 

in marine sediment (Maher, 1984) or can be incorporated into 

pyrite (Belzile and Lebel, 1986). Anthropogenic sources of 

arsenic include use as a wood preservative, in paints and dyes, 

in drugs, and in the mining of copper and gold (Welch and 

others, 2000). 

Arsenic was detected at high relative-concentrations 

in 6.8 percent of the primary aquifer system in the SCRC 

study unit (table 8). Arsenic was detected at high relativeconcentrations in USGS- and CDPH-grid wells in the Basins 

and Uplands study areas (fig. 12). The grid well with high 

relative-concentrations of arsenic in the Basins study area 

was located in the Santa Ynez River Valley (fig. 13A). The 

two grid wells with high relative-concentrations of arsenic in 

the Uplands study area are located between the Santa Ynez 

River Valley and Santa Maria River Valley groundwater 

basins. There is one CDPH-other well with high relativeconcentration located just north of the Santa Maria River 

Valley (fig. 13A).

Arsenic concentrations were higher in pre-modern and 

mixed-age groundwater than in modern groundwater (table 5). 

Arsenic concentrations were not significantly correlated to any 

of the other explanatory variables investigated in the SCRC 

study unit (table 10), although arsenic concentrations were 

significantly higher in the Uplands study area (table 5).
34 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

0.001

0.0001

0.01

0.1

1

10

Relative-concentration, dimensionless

Boron Cadmium Chromium Gross alpha Uranium

B

sac13-0497_Figure 12b

Basins

Uplands

Constituents that have health-based thresholds

(Maximum contaminant level, health

advisory level, notification level) 

Study areas Relative-concentration

High

Moderate

Low

Moderate

Low

High

0.001

0.0001

0.01

0.1

1

10

Relative-concentration, dimensionless

Molybdenum Nitrate as

nitrogen

Arsenic Barium Copper Selenium

A

sac13-0497_Figure 12a

Basins

Uplands

Constituents that have health-based thresholds

(Maximum contaminant level, health

advisory level, notification level) 

Study areas Relative-concentration

High

Moderate

Low

Moderate

Low

High

Figure 12. Relative-concentrations of inorganic constituents having (A, B) human-health-based or (C) SMCL benchmarks and 

high or moderate maximum relative-concentrations in USGS- and CDPH-grid wells, South Coast Range–Coastal study unit, 

California GAMA Priority Basin Project.
Status and Understanding of Water Quality 35

0.01

0.001

0.1

1

10

100

Relative-concentration, dimensionless

Sulfate Total

dissolved

solids

Chloride Iron Manganese C

sac13-0497_Figure 12c

Basins

Uplands

Study area Relative-concentration

High

Moderate

Low

Constituents that have secondary

maximum contaminant levels (SMCL)

Moderate

Low

High

Figure 12.—Continued.

Arsenic mobilization and distribution in groundwater is 

affected by the oxidation-reduction (redox) and pH conditions 

of the groundwater system. Arsenic concentrations also can 

be higher in older groundwater because of extended exposure 

to arsenic-bearing minerals under the appropriate redox or pH 

conditions. Previous investigations of arsenic in other aquifers 

(Belitz and others, 2003; Welch and others, 2006; Izbicki 

and others, 2008) and literature reviews (Welch and others, 

2000; Stollenwerk, 2003) have indicated two mechanisms 

for elevated arsenic: (1) the release of arsenic resulting from 

reductive dissolution of iron or manganese oxyhydroxides 

under iron- or manganese-reducing conditions, and (2) the 

pH-dependent desorption of arsenic from aquifer sediments 

or inhibition of arsenic sorption to aquifer sediments under 

alkaline conditions, which tends to occur in groundwater with 

pH greater than 7 (Stollenwerk, 2003).

Evidence for the release of arsenic under reducing 

conditions in the Basins study area in the SCRC study unit 

includes the correlation of arsenic with manganese (rho = 

0.512, p = 0.002). Most of the samples from the Basins study 

area with arsenic greater than 2 μg/L also had manganese 

concentrations greater than 50 μg/L (largely indicative of 

manganese-reducing conditions; fig. 14; table D3). Almost 

all of the arsenic detections greater than 2 μg/L are from 

groundwater samples of pre-modern or mixed ages (fig. 14A). 

This suggests that the accumulation of arsenic from the 

longer exposure to arsenic-bearing minerals under reducing 

conditions also may contribute to the elevated arsenic in the 

Basins study area.

Although reductive dissolution may be a possible 

mechanism for the occurrence of high relative-concentrations 

of arsenic in the Basins area, there is little evidence for this 

mechanism contributing to elevated arsenic concentrations in 

the Uplands study area. Arsenic is not correlated with either 

iron or manganese in the Uplands study area. However, most 

high and moderate concentrations of arsenic (5 of 7) in the 

Uplands study area are associated with DO concentrations less 

than 2 mg/L (fig. 14B), which indicates that some reduction 

dissolution may occur. While arsenic was not correlated to pH 

on the study-unit scale, arsenic was negatively correlated to 

pH in the Uplands (rho = –0.568, p = 0.021). This is different 

from what has been found in other GAMA study units where 

arsenic showed a positive correlation with pH (Kulongoski 

and others, 2010; Landon and others, 2010; Burton and others, 

2012). This indicates that the second mechanism—preferential 

desorption from sediments under alkaline conditions—is 

not a major mechanism for elevated arsenic concentrations 

in the Uplands study area. It is unknown if the aquifer 

sediments in the Uplands study area contain more arsenic 

than the sediments in the Basins study area. It is possible 

that the higher arsenic concentrations in the Uplands study 

area may result from extended exposure of groundwater to 

arsenic-bearing minerals or arsenic adsorbed to minerals in the 

aquifer sediments in areas where DO is relatively low because 

the groundwater in the Uplands study area is generally 

pre-modern.
36 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 13. Relative-concentrations of (A) arsenic, (B) molybdenum, and (C) nitrate in USGS-grid and CDPH-grid wells and 

CDPH-other wells in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41 1

1

154 101

101

101

41 1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 13ab As/Mo map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary Study areas

Basins

Uplands/Grid

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

120°30’ 120°

35°

34°

30’

Creek

Creek

Santa Cruz

San Antonio Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

A

B

Arsenic concentration,

Relative-concentration in micrograms per liter

Molybdenum concentration,

in micrograms per liter

Grid wells

< 5 

>5 to <10 

>10 

< 5 

>5 to <10 

< 5 

> 5 to < 10 

> 10 

Low

Moderate

High

Low

Moderate

Low

Moderate

High

Relative-concentration

Low

Moderate

High

Low

High

Understanding wells

Grid wells

< 20 

>20 to < 40 

>40 

< 20 

>40 

Understanding wells

Other CDPH wells

Pacific Ocean

Pacific Ocean
Status and Understanding of Water Quality 37

Figure 13.—Continued.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range 41 1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 13c NO3 map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

C

Nitrate as nitrogen concentration,

in milligrams per liter

Pacific Ocean

Grid wells

< 5 

> 5 to < 10 

>10 

< 5 

> 5 to < 10 

< 5 

>5 to < 10 

> 10 

Understanding wells

> 10 

Other CDPH wells

Land-use classification

Urban

Agricultural

Natural

Relative-concentration

Low

Moderate

High

Low

Moderate

High

Low

Moderate

High

In summary, data indicate that the occurrences of 

elevated arsenic concentrations likely result from the release 

of naturally occurring arsenic from dissolution of manganese 

oxides under manganese-reducing conditions in the Basins 

study area and possible accumulation during relatively long 

residence times in the Uplands study area.

Molybdenum

Molybdenum is a metallic trace element used in highstrength steel alloys. The main natural source of molybdenum 

to groundwater is dissolution of molybdenum-bearing 

minerals in aquifer materials, the most common of which 

is the sulfide mineral molybdenite (MoS2). Molybdenite 

generally forms in high-temperature environments and 

therefore occurs in many igneous and contact metamorphic 

rocks and may be present in fossil fuels. Molybdenum is more 

soluble under oxic conditions than anoxic conditions (Drever, 

1997; Reimann and de Caritat, 1998). Potential anthropogenic 

sources include manufacture and use of molybdenum steel 

alloys, dry lubricants, and other industrial products (Hem, 

1985). Molybdenum can readily accumulate in vegetation, 

especially for nitrogen-fixing species such as legumes. High 

levels of molybdenum in plants may be harmful to grazing 

animals (Reisenauer and others, 1973, as cited by Goldberg, 

2009).

Molybdenum was detected at high relative-concentrations 

in 25 percent, and at moderate relative-concentrations in 

22 percent of the primary aquifer system (table 8). High 

relative-concentrations of molybdenum occurred in the 

Uplands study area (figs. 12A and 13B). Moderate relativeconcentrations of molybdenum were detected in some USGSgrid wells in the Santa Ynez River Valley groundwater basin 

in the Basins study area. There were no data for molybdenum 

in the CDPH database for the SCRC study unit.

Molybdenum was positively correlated to well depth 

(table 10, fig. 15A) and positively correlated with arsenic 

(rho=0.738, p<0.001; fig. 15B). This indicates molybdenum 

may be released during reductive dissolution of the oxides, 

similar to arsenic. Molybdenum was not significantly 

correlated to groundwater age; however, almost all of the high 

and moderate relative-concentrations of molybdenum were 

found in wells with pre-modern groundwater (fig. 15A). 
38 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008 Table 10. Results of nonparametric (Spearman’s method) correlation analysis between selected inorganic water-quality constituents and potential explanatory factors in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project. [Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value 

(α) of 0.05. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; MCL-US, U.S. Environmental Protection Agency (USEPA) maximum contaminant level; AL-US, USEPA 

action level; HAL-US, USEPA health advisory level; SMCL-CA, California Department of Public Health secondary maximum contaminant level; ns, no significant correlation; TDS, total dissolved solids; 

<, less than; mg/L, milligrams per liter; LSD, land surface datum; tanks/km2, tanks per square kilometer; m, meter; N, nitrogen; μg/L, micrograms per liter]

Constituent Benchmark 

type

Benchmark 

value

High 

aquifer 

proportion 

(percent)

Grid and understanding wells used in analysis Grid wells used in analysis

Well 

depth 

(in feet 

below 

LSD)

Depth to 

top-ofperforations 

(in feet 

below LSD)

Dissolved 

oxygen 

(mg/L)

pH

Distance 

to nearest 

formerly leaking 

underground 

fuel tank 

(m)

Number of 

formerly 

leaking 

underground 

fuel tanks 

(tanks/km2

)

Number 

of septic 

tanks or 

cesspools1

(tanks/

km2

)

Agricultural 

land use1

(percent)

Urban 

land use1

(percent)

Natural 

land use1

(percent)

ρ/p

Arsenic MCL-US 10 μg/L 6.8 ns ns ns ns ns ns ns ns ns ns

Barium MCL-US 1,000 μg/L 2 0.1 ns ns ns ns ns ns ns ns ns ns

Copper AL-US 1,300 μg/L 2 0.1 ns ns 0.388

0.010

ns ns ns ns ns ns ns

Molybdenum HAL-US 40 μg/L 25 0.336

0.037

ns ns ns 0.340

0.030

ns ns ns –0.379

0.018

ns

Selenium MCL-US 50 μg/L 2 0.4 ns ns 0.357

0.018

ns 0.459

0.001

ns ns ns ns ns

Nitrate MCL-US 10 mg/L as N 10 ns ns 0.653

<0.001

ns ns ns ns 0.482

<0.001

ns –0.375

0.006

Chloride SMCL-CA 500 mg/L 2 0.4 ns ns –0.564

<0.001

–0.269

0.047

ns ns ns ns ns ns

Iron SMCL-CA 300 μg/L 15 ns ns –0.732

<0.001

–0.269

0.047

ns ns ns ns ns ns

Manganese SMCL-CA 50 μg/L 25 ns ns –0.652

<0.001

–0.325

0.016

ns ns ns ns ns ns

Sulfate SMCL-CA 500 mg/L 11 ns ns ns ns ns ns ns 0.344

0.010

ns –0.490

<0.001

TDS SMCL-CA 1,000 mg/L 16 –0.279

0.026

ns ns ns ns ns ns 0.315

0.011

ns –0.493

<0.001

1 Land-use percentages and number of septic tanks are within a circle with a radius of 500 meters centered around each well included in analysis.2 Based on the spatially weighted approach.
Status and Understanding of Water Quality 39

Figure 14. Relation of arsenic concentrations to (A) manganese concentrations and groundwater age in the 

Basins study area and (B) dissolved oxygen concentrations in the Uplands study area, South Coast Range–

Coastal study unit, California GAMA Priority Basin Project.

Manganese concentration, in micrograms per liter

Arsenic concentration, in micrograms per liter Arsenic concentration, in micrograms per liter

0

0.01 0.1 1 10 100 1,000 10,000

2

4

6

8

10

12

14

16

18

20

Modern

Mixed

Pre-modern

Age unknown

Dissolved oxygen concentration, in milligrams per liter

0

0 1 2 3 4 5 6 7 8

2

4

6

8

10

12

14

16

18

20

High

Moderate

Low

sac13-0497_Figure 14ab

A

B
40 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008 Well depth, in feet below land surface

Molybdenum concentration, in micrograms per liter 

0

200

400

600

800

1,000

1,200

1,400

0.1

0.1

1

10

100

1,000

1 10 100

0.1 1 10 100

1,000

High

Moderate

Low

Modern

Mixed

Pre-modern

> 2

0.5 to 2

< 0.5

Unknown

Molybdenum concentration, in micrograms per liter 

Arsenic concentration, in micrograms per liter

High

Moderate

Low

Dissolved oxygen, in

 milligrams per liter

sac13-0497_Figure 15ab

A

B

Figure 15. Relation of molybdenum concentrations to (A) well depth and groundwater age and to (B) arsenic 

and dissolved oxygen concentrations and (C) pH in wells with disolved oxygen greater than 0.5 milligram per 

liter, South Coast Range–Coastal study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 41

0.1

1

10

100

1,000

6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8

> 2

0.5 to 2

Molybdenum concentration, in micrograms per liter

High

Moderate

Low

Dissolved oxygen, in

 milligrams per liter

pH, in standard units

sac13-0497_Figure 15c

C

Figure 15.—Continued.

Molybdenum was not correlated with DO; however, 

most of the high and moderate relative-concentrations were 

in samples with DO less than 2 mg/L (fig. 15B). In addition, 

most of the high and moderate relative-concentrations of 

molybdenum were in groundwater that was suboxic or 

manganese-reducing. This observation is similar to what was 

found in some groundwater basins in the Sierras (Fram and 

Belitz, 2012). This observation indicates that the molybdate 

oxyanion, which is the major soluble species of molybdenum 

at pH greater than 5, adsorbed to manganese oxide minerals is 

released during reductive dissolution of the oxides, similar to 

the release of arsenic resulting from reductive dissolution of 

iron or manganese oxides.

Dissolved molybdenum also can increase from 

pH-dependent desorption of molybdenum from aquifer 

sediments under oxic conditions; this tends to occur in 

groundwater with pH greater than 4 (Goldberg, 2009), 

especially when groundwater is neutral or alkaline (Reimann 

and de Caritat, 1998). Molybdenum was not correlated with 

pH in the SCRC study unit (table 10). However, molybdenum 

concentrations tended to increase with pH when the DO 

concentration was greater than 0.5 mg/L (fig. 15C). 

Molybdenum was negatively correlated to urban land 

use and positively correlated to the distance to the nearest 

LUFT (table 10). Molybdenum concentrations decreased as 

urban land use increased and the distance to the nearest LUFT 

increased. These correlations suggest that the primary source 

of elevated molybdenum is not from anthropogenic sources. 

The positive correlation of molybdenum with the distance to 

the nearest LUFT may reflect the correlation of LUFTs with 

urban land use rather than a direct relation of molybdenum 

with LUFTs or that the location of high and moderate 

molybdenum concentrations is in the Uplands study area 

where there is less urban and agricultural development than in 

the Basins study area. 

In summary, molybdenum is from natural sources. 

High relative-concentrations of molybdenum occurred in 

the Uplands study area. Similar to arsenic, molybdenum 

concentrations may be influenced by the release of adsorbed 

molybdenum during reductive dissolution of manganese 

or iron oxides in reducing conditions. In oxic conditions, 

molybdenum concentrations may be influenced by 

pH-dependent desorption.
42 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Nutrients

The only nutrient with human-health benchmarks 

detected at high relative-concentrations in the study unit was 

nitrate plus nitrite (table 8). Nitrate plus nitrite was detected 

at high relative-concentrations in 10 percent of the primary 

aquifer system (table 9). Nitrite concentrations were negligible 

for wells sampled in the SCRC study unit (Mathany and 

others, 2010); therefore, nitrate plus nitrite is referred to as 

nitrate in this report. 

Nitrate

Nitrogen in groundwater occurs in the forms of dissolved 

nitrate, nitrite, or ammonia. Certain bacteria and algae 

naturally convert nitrogen from the atmosphere to nitrate, 

which is an important nutrient for plants. Nitrate also is 

present in precipitation (Hem, 1970), partly from nitrogen 

oxides (NOx

) released during the combustion of fossil fuels 

(Kendall, 1998). Anthropogenic sources of nitrate include 

its application as an inorganic fertilizer for agriculture and 

production by livestock of nitrogenous waste that can leach 

to groundwater when animals are present in concentrated 

numbers (Hem, 1985; Sparks, 2003). Septic and municipal 

sewage systems also contain nitrogenous waste that may leach 

into groundwater (Sparks, 2003).

Nitrate was detected at high relative-concentrations in 

10 percent of the primary aquifer system in the SCRC study 

unit (table 8). High and moderate relative-concentrations of 

nitrate occurred in grid and understanding wells in the Basins 

study area (fig. 12A); specifically, high and moderate relativeconcentrations of nitrate were located in the Santa Maria River 

Valley groundwater basin (fig. 13C). Some moderate relativeconcentrations of nitrate also were detected in the Los Osos 

Valley and San Luis Obispo Valley groundwater basins to the 

north and in the upper Santa Ynez River Valley groundwater 

basin to the south (fig. 13C). 

Nitrate was positively correlated with agricultural land 

use and negatively correlated with natural land use (table 10); 

nitrate concentrations were greater in areas with agricultural 

land use than in areas with urban or natural land uses (table 5). 

Elevated nitrate concentrations have been attributed to 

agricultural practices in the northern part of the Santa Maria 

River Valley near Arroyo Grande (California Department of 

Water Resources, 2002). 

Nitrate was correlated to groundwater age (table 5) 

and positively correlated with DO in the SCRC study unit 

(table 10). Samples of modern groundwater have significantly 

higher nitrate concentrations than samples of mixed-age or 

pre-modern groundwater (table 5; fig. 16). Almost all of the 

moderate and high relative-concentrations of nitrate had DO 

concentrations greater than 2 mg/L (fig. 16). Almost all of the 

samples with very low (greater than 0.3 mg/L but less than or 

equal to 1 mg/L) and ultra-low (less than or equal to 0.3 mg/L) 

nitrate concentrations had DO concentrations less than 

2 mg/L. The positive correlation of nitrate, a redox-sensitive 

constituent, to DO indicates that nitrate is preserved in aerobic 

environments (Kendall, 1998) or that higher nitrate and DO 

tend to co-occur in modern, shallow groundwater and decrease 

with depth and age. Nitrate also was negatively correlated with 

manganese (rho = –0.681, p <0.001) and iron (rho = –0.649, 

p <0.001), which substantiates the prevalence of nitrate under 

oxidizing conditions. Reducing conditions mostly exist in 

parts of the Santa Ynez River Valley and San Antonio Creek 

Valley groundwater basins. Nitrate concentrations are low in 

those areas. 

Unlike in other Priority Basin Project study units in 

California, nitrate was not correlated with well depth or 

depth to top-of-perforations (table 10) (Bennett and others, 

2010; Landon and others, 2010; Kulongoski and others, 

2010; Kulongoski and Belitz, 2011; Burton and others, 2011, 

2012). Worts (1951) also did not see a relation of nitrate with 

depth in the Santa Maria River Valley area. However, high 

concentrations of nitrate were observed in shallow wells 

(top-of-perforations less than 100 ft) in the northern part of 

the Santa Maria River Valley groundwater basin (California 

Department of Water Resources, 2002). In the SCRC study 

unit, the relation of nitrate with well depth and depth to top-ofperforations may be complicated by the existence of shallow 

wells (well depth less than 400 ft) with low DO concentrations 

and pre-modern groundwater. Some of these shallow wells 

were located in the agricultural area near Lompoc; other wells 

were located in the Uplands study area or near the boundary 

of the Basins study area where there was less agricultural 

and urban development. These shallow wells are depicted 

on the left side of figure 16 where nitrate concentrations are 

categorized as ultra-low. Nitrate concentrations are negatively 

correlated with well depth (rho = –0.406, p = 0.012) if these 

shallow wells are omitted from the analysis. 

In summary, nitrate concentrations in the SCRC study 

unit are influenced by a number of factors. Correlations of 

nitrate with agricultural and natural land use, groundwater 

age, and well depth indicate elevated concentrations result 

from human activities. In the SCRC study unit, nitrate 

concentrations are strongly influenced by redox conditions as 

indicated by relations with DO, manganese, and iron.

Uranium and Radioactive Constituents

Uranium and other radioactive constituents occur 

naturally, primarily in granites, shale, and schist (Drever, 

1997; Reimann and de Caritat, 1998). Uranium-238, 

thorium-232, and uranium-235 are the main sources of natural 

radioactivity in groundwater (Hem, 1985). Uranium-238 is 

the most common. Gross alpha radioactivity usually consists 

of isotopes of radium and radon which are part of the uranium 

and thorium radioactive decay series (Hem, 1985). In this 

study, the method used to analyze gross alpha radioactivity 

removes radon (Arndt, 2010); therefore, gross alpha 

radioactivity reported in this study results from the decay of 

uranium and radium.
Status and Understanding of Water Quality 43

Nitrate concentration, in milligrams per liter

Dissolved oxygen concentration,

in milligrams per liter

Groundwater-age

classification

EXPLANATION 

Well depth, in feet below land surface

0

200

400

600

800

1,000

1,200

1,400

0.01 0.1 1 10 100

?

?

?

?

?

?

?

?

?

?

Ultra-low Very low Low Moderate High

> 2

< 2

Unknown

Modern

Mixed

Pre-modern

Unknown

Top

Bottom

Perforated interval,

 Dashed with ? when

 top-of-perforation

 is unknown

sac13-0497_Figure 16

Figure 16. Relation of nitrate concentrations to dissolved oxygen concentrations, groundwater age, and well perforation 

intervals in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.
44 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Radioactive constituents were detected at moderate 

relative-concentrations in 5.0 percent and at low relativeconcentrations or not detected in 95 percent of the primary 

aquifer system in the SCRC study unit (table 9). Uranium had 

moderate relative-concentrations in 2.9 percent of the primary 

aquifer system, and gross alpha radioactivity had moderate 

relative-concentrations in 5 percent of the primary aquifer 

system (table 8; fig. 12). 

Inorganic Constituents with Aesthetic 

Benchmarks

As a class, inorganic constituents with aesthetic 

benchmarks (SMCLs) were detected at high relativeconcentrations in 35 percent of the primary aquifer system 

and at moderate relative-concentrations in 55 percent (for 

one or more constituents; table 9). Inorganic constituents 

with aesthetic benchmarks that were detected at high relativeconcentrations in more than 2 percent of the primary aquifer 

system were iron, manganese, TDS, and sulfate (table 8). 

Chloride was detected at high relative-concentrations in less 

than 2 percent of the primary aquifer system and, therefore, is 

not discussed further in this report. 

Manganese and Iron

Potential natural sources of manganese and iron 

to groundwater include the dissolution of igneous and 

metamorphic rocks as well as various secondary minerals 

(Hem, 1970) which can be mobilized under reducing 

or low pH conditions (McMahon and Chapelle, 2008). 

Potential anthropogenic sources of these constituents to 

groundwater include effluents associated with the steel and 

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

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

are added to deficient soils to stimulate crop growth.

Manganese was detected at high relative-concentrations 

in 25 percent and at moderate relative-concentrations in 

8.5 percent of the primary aquifer system in the SCRC study 

unit (table 8). High relative-concentrations of manganese 

occurred in USGS- and CDPH-grid wells mostly in the Basins 

study area (fig. 12C). Most of these high concentrations 

are in the Santa Ynez River and San Antonio Creek Valleys 

(fig. 17A). High relative-concentrations of manganese also 

were detected in some CDPH-other wells in the northern part 

of the Santa Maria River Valley (fig. 17A). 

Iron was detected at high relative-concentrations 

in 15 percent and at moderate relative-concentrations in 

6.4 percent of the primary aquifer system in the SCRC study 

unit (table 8). Most high relative-concentrations of iron in 

USGS- and CDPH-grid wells occurred in the Basins study 

area in parts of the Santa Ynez River Valley (fig. 17B). In 

addition, high relative-concentrations of iron were detected in 

CDPH-other wells in the northern part of Santa Maria River 

Valley. Distributions of high relative-concentrations of iron 

were similar to high relative-concentrations of manganese. 

Distributions of manganese and iron concentrations 

are strongly influenced by redox conditions. Under anoxic 

conditions, reductive dissolution of manganese and iron 

oxides that commonly coat sediment particles increases the 

mobility of manganese and iron in aquifer systems (Sparks, 

2003). Both manganese and iron were negatively correlated 

with DO in the SCRC study unit (table 10); these relations are 

expected if reductive dissolution is a significant pathway for 

the mobilization of manganese and iron in the primary aquifer 

system in the SCRC study unit. The negative correlations 

of manganese and iron with pH were not expected based on 

the lack of correlations observed in other coastal study units 

(Kulongoski and others, 2010; Kulongoski and Belitz, 2011; 

Mary Parsons, USGS, written commun., 2012) and may reflect 

that pH in the SCRC study unit was less than 8. 

Manganese and iron concentrations were higher 

in mixed and pre-modern groundwater than in modern 

groundwater (table 5). Manganese and iron concentrations 

were not correlated to land use (table 10). In summary, higher 

manganese and iron concentrations in older and more reduced 

groundwater indicate mobilization from aquifer sediments as 

the primary source of manganese and iron in the SCRC study 

unit.

Total Dissolved Solids

Natural sources of TDS include seawater intrusion, 

mixing of groundwater in the primary aquifer system used 

for public supply with deep saline groundwater affected 

by interactions with deep marine and lacustrine sediments, 

concentration of salts by evaporation in discharge areas, 

and(or) rock/water interaction (Sparks, 2003). Potential 

anthropogenic sources of TDS to groundwater include 

concentration of salts by evaporation from agricultural and 

urban irrigation, disposal of wastewater and industrial effluent, 

and leaking water and sewer pipes (Sparks, 2003). 

TDS was detected at high relative-concentrations 

in 16 percent and at moderate relative-concentrations in 

66 percent of the primary aquifer system in the SCRC study 

unit (table 8). TDS concentrations are significantly higher 

in the Basins study area than in the Uplands study area 

(table 5). The high relative-concentrations of TDS in USGSand CDPH-grid wells occur in the Basins study area in the 

Santa Maria River and Santa Ynez River Valleys (figs. 12C

and 17C). Moderate relative-concentrations of TDS occur 

throughout the study unit. 

TDS concentrations were significantly higher in wells 

with modern groundwater than in wells with pre-modern 

groundwater (table 5). TDS concentrations also were 

higher in areas with predominantly agricultural or urban 

land use than in areas with natural land use. Although TDS 

concentrations were statistically similar in groundwater 

collected from wells categorized as agricultural or urban 

(table D1), TDS concentrations were positively correlated only 

to the percentage of agricultural land use (table 10). Bright 

and others (1992, 1997) observed that TDS concentrations 
Status and Understanding of Water Quality 45

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41 1

1

154 101

101

101

41 1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary Study areas

Basins

Uplands/Grid

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

120°30’ 120°

35°

34°

30’

Creek

Creek

Santa Cruz

San Antonio Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

A

B

Pacific Ocean

Pacific Ocean

Manganese concentration,

in micrograms per liter

Iron concentration,

in micrograms per liter

Grid wells

< 25 

> 25 to < 50 

> 50 

< 25 

< 25 

> 25 to < 50 

> 50 

Understanding wells

>50 

Grid wells

< 150 

>150 to < 300 

> 300 

< 150 

>300 

< 150 

>150 to < 300 

> 300 

Understanding wells

Other CDPH wells

Other CDPH wells

sac13-0497_Figure 17ab Mn/Fo map

Relative-concentration

Low

Moderate

High

Low

High

Low

Moderate

High

Relative-concentration

Low

Moderate

High

Low

Moderate

High

Low

High

Figure 17. Relative-concentrations of (A) manganese, (B) iron, (C) total dissolved solids, and (D) sulfate in USGS-grid and 

CDPH-grid wells, USGS-understanding wells, and CDPH-other wells in the South Coast Range–Coastal study unit, California 

GAMA Priority Basin Project.
46 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41 1

1

154 101

101

101

41 1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure 17cd TDS_SO4 map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

120°30’ 120°

35°

34°

30’

Creek

Creek

Santa Cruz

San Antonio Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

C

D

Pacific Ocean

Pacific Ocean

Land-use classification

Urban

Agricultural

Natural

Total dissolved solids concentration,

in milligrams per liter

Sulfate concentration,

in milligrams per liter

Grid wells

< 500 

> 500 to < 1,000 

> 1,000 

< 500 

> 500 to < 1,000 

< 500 

> 500 to < 1,000 

> 1,000 

Understanding wells

> 1,000 

Other CDPH wells

Grid wells

< 250 

> 250 to < 500 

> 500 

< 250 

> 250 to < 500 

< 250 

> 250 to < 500 

> 500 

Understanding wells

> 500 

Other CDPH wells

Relative-concentration

Low

Moderate

High

High

Low

Moderate

Low

Moderate

High

Relative-concentration

Low

Moderate

High

Low

Moderate

High

Low

Moderate

High

Figure 17.—Continued.
Status and Understanding of Water Quality 47

increased in shallow zones in agricultural areas based on 

samples collected in the 1940s and late 1980s in the Lompoc 

area. Although Worts (1951) did not find a relation with 

well depth in the Santa Maria River Valley, the findings in 

this study were similar to those of Bright and others (1992, 

1997), where TDS concentrations were negatively correlated 

with well depth (table 10). Other studies have shown that 

differences in TDS concentrations were found with depth in 

the alluvium and in the Paso Robles Formation in some areas 

of the SCRC study unit (California Department of Water 

Resources, 2002). In the Lompoc area, groundwater that 

was in contact with the consolidated rock of marine origin 

had higher concentrations of TDS than other groundwater 

(Bright and others, 1997). In summary, TDS concentrations 

are naturally occurring, but correlations with groundwater age, 

agricultural and urban land use, and well depth indicate that 

human activities may elevate TDS concentrations.

Sulfate

Sulfur occurs naturally in both igneous and sedimentary 

rocks as metallic sulfides. Pyrite crystals that occur in many 

sedimentary rocks are a major source of both ferrous iron and 

sulfate in groundwater (Hem, 1985). Sulfate also occurs in 

evaporate minerals such as gypsum (calcium sulfate) and is 

common in rainfall (Hem, 1970). The sulfate in rainfall has 

been attributed to the emission of hydrogen sulfide (H2S) at 

the ocean margins, the combustion of fuels, the solution of 

dust particles, dissolution of gypsum or anhydrite, and the 

oxidation of uplifted fine-grained marine sediments (Hem, 

1970). Sulfur also is applied as an agricultural fertilizer in 

parts of the SCRC study unit (California Department of 

Pesticide Regulation, 2008). 

Sulfate was detected at high relative-concentrations 

in 11 percent and at moderate relative-concentrations in 

32 percent of the primary aquifer system in the SCRC study 

unit (table 8). The distribution of sulfate is similar to the 

distribution of TDS. Sulfate concentrations are significantly 

higher in the Basins study area than in the Uplands study area 

(table 5; fig. 12C). The high relative-concentrations of sulfate 

occur in USGS-grid and understanding wells in the Basins 

study area in the south-central Santa Maria River Valley and 

parts of the Santa Ynez River Valley (fig. 17D).

Similar to TDS concentrations, sulfate concentrations 

were higher in wells with modern groundwater than in wells 

with pre-modern groundwater (table 5). Sulfate concentrations 

were significantly higher in areas with agricultural land 

use than in areas with natural land use (table 5), and were 

positively correlated with agricultural land use and negatively 

with natural land use (table 10). Sulfate concentrations 

were highly correlated to TDS concentrations (rho = 0.855, 

p <0.001). The correlation of sulfate with TDS indicates that 

sulfate contributes significantly to the composition of TDS. 

The correlations of sulfate with groundwater age and land use 

indicate that concentrations of sulfate may be elevated as a 

result of human activities. 

Organic Constituents

Organic constituents (such as VOCs and pesticides), 

unlike inorganic constituents, usually are of anthropogenic 

origin. VOCs may be present in paints, solvents, fuels, and 

refrigerants, can be byproducts of water disinfection, and 

are characterized by their tendency to evaporate. Pesticides 

are used to control weeds, insects, or fungi in agricultural, 

urban, and suburban settings. Classes of organic compounds 

consisted of VOCs—which were further subdivided into 

trihalomethanes (THMs), solvents, and other VOCs—and 

pesticides. 

Organic constituents with human-health benchmarks 

were detected at high relative-concentrations in 0.4 percent 

and at moderate relative-concentrations in 1.8 percent of the 

primary aquifer system in the SCRC study unit (table 9). The 

only class of organic constituents detected at high relativeconcentrations was solvents; both solvents and THMs were 

detected at moderate relative-concentrations. Other VOCs and 

pesticides with human-health benchmarks were detected at 

low relative-concentrations or were not detected (table 9). The 

THM chloroform, the solvent perchloroethene (PCE), and the 

refrigerant dichlorodifluoromethane (CFC-12) were organic 

constituents that were prevalent (detection frequency greater 

than 10 percent in USGS-grid wells) in the primary aquifer 

system (fig. 18). The detection frequencies and relativeconcentrations of selected organic compounds are shown 

in figure 19 in relation to the study area in which they were 

detected.

Volatile Organic Compounds

VOCs discussed in this report are classified as THMs, 

solvents, and other VOCs. More than one VOC was detected 

in 15 percent of the USGS-grid wells with VOC detections. 

Figure 20A shows the number of VOC detections in USGSgrid wells, USGS-understanding wells, and CDPH wells. 

Wells with more than one VOC generally were located in the 

Santa Maria River Valley groundwater basin. The number of 

VOC detections was not correlated with any of the explanatory 

factors evaluated (tables 5 and 11). 
48 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Number of

detections 0.00001

0.0001

0.001

0.01

0.1

1

1 10 100

10

Low

Moderate

High

Detection frequency, in percent

Maximum relative-concentration, dimensionless

Chloroform

PCE

CFC-12

Atrazine

CFC-11 Simazine Bromacil

Diuron

Metolachlor

Toluene

1,2-DCP

Dichloromethane

Perchlorate

sac13-0497_Figure 18

EXPLANATION

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

 Unless indicated by following location line:

Methyl tert-butyl ether

Figure 18. Detection frequencies and maximum relative-concentrations of organic and special-interest constituents 

detected in USGS-grid wells in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 49

Number of

detections

0.00001 0.0001 0.001 0.01 0.1 1 10

Relative-concentration, dimensionless

Low Moderate High

A B

6

0

7

0

6

4

28

5

Detection frequency, in percent

0

10-percent line

20 40 60 80 100

Dichlorodifluoromethane

(CFC-12)

Tetrachloroethene

(PCE)

Perchlorate

Total

trihalomethanes

(Chloroform)

sac13-0497_Figure 19ab

Basins

Uplands

Study areas Relative-concentration

High

Moderate

Low

EXPLANATION

Figure 19. (A) Detection frequencies and (B) relative-concentrations of selected organic and special-interest 

constituents in USGS-grid wells in the South Coast Range–Coastal study unit, California GAMA Priority Basin 

Project.
50 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 20. (A) Number of volatile organic compound (VOC) detections, (B) trihalomethane (chloroform) concentrations, 

(C) perchloroethene (PCE) concentrations, (D) number of other VOC detections, and (E) number of pesticide detections in USGSgrid and USGS-understanding wells, and CDPH-wells in the South Coast Range–Coastal study unit, California GAMA Priority 

Basin Project.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41 1

1

154 101

101

101

41 1

1

154 101

101

101

Morro Bay

Santa Maria

San Luis Obispo

Morro Bay

Santa Maria

San Luis Obispo

sac13-0497_Figure 20ab VOC/TTHM map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

120°30’ 120°

35°

34°

30’

Creek

Creek

Santa Cruz

San Antonio Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

A

B

Volatile organic compound (VOC) detections

USGS-grid wells

1 VOC

2 to 3 VOCs

No detections

No detections

1 VOC

No detections

1 VOC

2 to 3 VOCs

USGS-understanding wells

>5 VOCs

CDPH wells

>3 VOCs

Total trihalomethane (TTHM)

concentrations,

in micrograms per liter

USGS-grid wells

< 8

No detections

No detections

< 8

No detections

< 8

> 8

USGS-understanding wells

Pacific Ocean

CDPH wells

Pacific Ocean

Land-use classification

Urban

Agricultural

Natural

Relative-concentration

Low

Low

Low

Moderate
Status and Understanding of Water Quality 51

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41 1

1

154 101

101

101

41 1

1

154 101

101

101

Morro Bay

Santa Maria

San Luis Obispo

Morro Bay

Santa Maria

San Luis Obispo

sac13-0497_Figure 20cd PCE/OVOC map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

120°30’ 120°

35°

34°

30’

Creek

Creek

Santa Cruz

San Antonio Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

C

D

Perchloroethene (PCE)

concentration,

in micrograms per liter

USGS-grid wells

< 0.5 μg/L

> 0.5 to < 5 μg/L

Not detected

Not detected

Not detected

USGS-understanding wells

CDPH wells

> 5 μg/L

Other volatile organic compound (VOC) detections

USGS-grid wells

2 Other VOCs

No detections

No detections

No detections

1 Other VOC

USGS-understanding wells

2 Other VOCs

CDPH wells

Pacific Ocean

Pacific Ocean

Land-use classification

Urban

Agricultural

Natural

Relative-concentration

Low

Moderate

High

Figure 20.—Continued.
52 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Figure 20.—Continued.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range 41 1

1

154 101

101

Morro Bay 101

Santa Maria

San Luis Obispo

sac13-0497_Figure 20e Pest map

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

E

Pesticide detections

USGS-grid wells

1 Pesticide

2 to 3 Pesticides

4 to 5 Pesticides

Not detected

Not detected

Not detected

USGS-understanding wells

CDPH wells

Pacific Ocean

1 Pesticide

2 Pesticides

Land-use classification

Urban

Agricultural

Natural

Trihalomethanes (Chloroform)

Water used for drinking water and other household 

uses in both domestic and municipal systems commonly is 

disinfected with chlorine solutions (bleach). As a side effect of 

disinfection, the chlorine reacts with organic matter to produce 

THMs and other chlorinated and/or brominated disinfection 

byproducts. The THMs analyzed in this study were 

chloroform, bromodichloromethane, dibromochloromethane, 

and bromoform. Chloroform, the only THM detected, was 

present at low relative-concentrations in the primary aquifer 

system (fig. 19B) with a detection frequency of 18 percent 

(fig. 18). Chloroform was detected in both study areas 

(figs. 19B and 20B). Nationally, chloroform was the most 

frequently detected VOC in aquifers in studies conducted 

by the USGS National Water-Quality Assessment Program 

(NAWQA; Zogorski and others, 2006).

In the SCRC study unit, chloroform concentrations 

were positively correlated with the number of septic tanks 

or cesspools (table 11). Chloroform concentrations were not 

correlated with urban land use or modern groundwater as has 

been found in other GAMA Priority Basin Project study units 

(Kulongoski and others, 2010; Landon and others, 2010; Fram 

and Belitz, 2012). Nationally, chloroform and THMs have 

been strongly correlated with percentage of urban land use 

(Zogorski and others, 2006).

Perchloroethene (PCE) and Other Solvents

Solvents are used for various industrial, commercial, and 

domestic purposes. PCE is a solvent that is primarily used 

for dry cleaning of fabrics and degreasing metal parts and 

is an ingredient in a wide range of products, including paint 

removers, polishes, printing inks, lubricants, and adhesives. 

Trichloroethene (TCE) is a degradate of PCE under reducing 

conditions (Vogel and McCarty, 1985; Russell and others, 

1992). Solvents as a class had a high aquifer-scale proportion 

of 0.4 percent and a moderate aquifer-scale proportion of 

1.8 percent of the primary aquifer system (table 9). The 

solvents PCE and TCE had spatially weighted high aquiferscale proportions of 0.3 and 0.1 percent of the primary aquifer 

system, respectively (table 8). PCE also had a detection 

frequency of 13 percent (fig. 18 and 19A) and was detected at 

moderate relative-concentrations in 2 percent of the primary 

aquifer system (table 8). All of the PCE detections were in 

the Basins study area (fig. 19B), mostly near the city of Santa 

Maria (fig. 20C). 
Status and Understanding of Water Quality 53

Table 11. Results of nonparametric correlation analysis (Spearman’s method) between selected organic and special-interest water-quality constituents and potential explanatory factors in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.[Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value (α) of 0.05. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; MCL-US, U.S. Environmental Protection Agency (USEPA) maximum contaminant level; MCL-CA, California Department of Public Health (CDPH) maximum contaminant level; NL-CA, CDPH notification level; ns, no significant correlation; VOC, volatile organic compound; mg/L, milligrams per liter; LSD, land surface datum; tanks/km2, tanks per square kilometer; m, meter; N, nitrogen; μg/L, micrograms per liter]

Constituent Benchmark 

type

Benchmark 

value 

(μg/L)

High 

aquifer 

proportion 

(percent)

Grid and understanding wells used in analysis Grid wells used in analysis

Well 

depth 

(in feet 

below 

LSD)

Depth to 

top-ofperforations 

(in feet 

below LSD)

Dissolved 

oxygen 

(mg/L)

pH

Distance 

to nearest 

formerly leaking 

underground 

fuel tank 

(m)

Number of 

formerly 

leaking 

underground 

fuel tanks 

(tanks/km2)

Number 

of septic 

tanks or 

cesspools1

(tanks/

km2)

Agricultural 

land use1

(percent)

Urban 

land use1

(percent)

Natural 

land use1

(percent)

ρ/p

Number of VOC detections 2 variable variable 3 0.4 ns ns ns ns ns ns ns ns ns ns

Chloroform concentration MCL-US 80 0.0 ns ns ns ns ns ns 0.289

0.015

ns ns ns

Number of solvent 

detections

variable variable 3 0.4 ns ns ns ns ns ns ns ns ns ns

Perchloroethene (PCE) MCL-US 5 3 0.3 ns ns ns ns ns 0.324

0.006

ns ns ns –0.261

0.029

Number of other VOC 

detections

variable variable 0.0 ns ns ns ns ns ns ns ns ns ns

Dichlorodifluoromethane 

(CFC-12)

NL-CA 1,000 0.0 ns ns ns ns ns 0.293

0.014

ns ns ns ns

Number of pesticide 

detections 4

variable variable 0.0 ns ns ns ns ns ns ns ns ns ns

Perchlorate MCL-CA 6 0.0 ns ns 0.570

<0.001

ns ns ns ns 0.314

0.008

ns –0.281

0.019

1 Land-use percentages and number of septic tanks are within a circle with a radius of 500 meters centered around each well included in analysis.2 Does not include VOCs classified as fumigants.3 Based on the spatially weighted approach.4 Includes VOCs classified as fumigants.
54 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

The number of solvents detected and PCE concentrations 

were higher in modern groundwater than in pre-modern 

groundwater (table 5). PCE was not correlated with urban 

land use, although, nationally, solvent concentrations have 

been correlated with urban land use because most solvents are 

of anthropogenic origin (Zogorski and others, 2006; Moran 

and others, 2007). However, PCE was negatively correlated 

with natural land use and positively correlated to the number 

of LUFTs per square kilometer within 500 m of the sampled 

wells (table 11). Because LUFTs are correlated to urban land 

use, these correlations may relate to the anthropogenic sources 

of PCE.

Dichlorodifluoromethane (CFC-12) and Other Volatile 

Organic Compounds

The organic constituent class referred to as “other VOCs” 

includes organic synthesis reagents, refrigerants, and gasoline 

hydrocarbons. Other VOCs, as a class, were not detected 

at high or moderate relative-concentrations in the primary 

aquifer system in the SCRC study unit (table 9). Detections 

of other VOCs in USGS-grid wells in the SCRC study unit 

consisted of two refrigerants, one gasoline hydrocarbon, 

and one gasoline oxygenate (Mathany and others, 2010). 

Dichlorodifluoromethane (CFC-12), a refrigerant, was 

detected at low relative-concentrations, with a detection 

frequency of 11 percent (figs. 18 and 19; Mathany and others, 

2010). The distribution of the detections of other VOCs was 

similar to the distribution of PCE. Most of the detections were 

near the city of Santa Maria (fig. 20D). CFC-12 concentrations 

were higher in modern groundwater than in pre-modern 

groundwater (table 5). CFC-12 concentrations also were 

correlated to the number of LUFTs per square kilometer 

within 500 m of a well (table 11). The correlation of CFC-12 

to LUFTs may relate to the anthropogenic origin of CFC-12, 

or the relation may be coincidence and not have any causative 

basis.

Pesticides

Pesticides include herbicides, insecticides, and fungicides 

and are used in agricultural and urban settings. Pesticides, 

as a class, were not detected at high or moderate relativeconcentrations in the SCRC study unit (table 9). Pesticides 

were detected at low relative-concentrations and in less 

than 10 percent of the primary aquifer system in the SCRC 

study unit. More than one pesticide (or pesticide degradate) 

was detected in about half of the wells (9 of 21 wells) with 

pesticide detections (fig. 20E). The number of pesticide 

detections was greater in wells with modern groundwater 

than in wells with pre-modern groundwater (table 5; fig. 21). 

The number of pesticide detections was not correlated with 

any of the other explanatory factors evaluated in this report 

(table 11); however, most of the pesticide detections were in 

wells less than 400 ft deep (fig. 21). Atrazine and its degradate, 

deethylatrazine (which does not have a benchmark), were 

the most frequently detected pesticides at 9 and 18 percent, 

respectively (Mathany and others, 2010).

Constituents of Special Interest

Special-interest constituents, similar to organic 

constituents, usually are anthropogenic in origin. The 

special-interest constituents analyzed by the Priority 

Basin Project in the SCRC study unit are perchlorate and 

N-nitrosodimethylamine (NDMA). Constituents of special 

interest, as a class, were not detected at high relativeconcentrations in the SCRC study unit, but were detected at 

moderate relative-concentrations in 29 percent of the primary 

aquifer system in the SCRC study unit (table 9). Perchlorate 

was the only constituent of special interest that was detected.

Perchlorate

Possible anthropogenic sources of perchlorate include 

nitrate fertilizers mined from the Atacama Desert of Chile 

that have been used historically on some orchard crops 

(Dasgupta and others, 2006), or industrial, manufacturing, 

and commercial uses such as explosives, road flares, rocket 

fuel, and other products (California Department of Public 

Health, 2008b; Parker and others, 2008). Perchlorate can 

occur under natural conditions in a variety of climatic 

conditions (Fram and Belitz, 2011) and not just in arid 

climates (Dasgupta and others, 2005; Plummer and others, 

2006). However, perchlorate is more likely to occur naturally 

in the arid and semi-arid environments found further inland 

in the southwestern United States (Fram and Belitz, 2011). 

Perchlorate has been detected recently in, or is considered to 

have the potential to reach, water resources used for drinkingwater supplies (California Department of Public Health, 

2008b). 

Perchlorate had a detection frequency of 60 percent in the 

SCRC study unit (fig. 18). Moderate relative-concentrations 

of perchlorate were located throughout most of the study unit 

(fig. 22), except from the area north of Santa Maria to Morro 

Bay in the northern part of the study unit where perchlorate, 

if detected, had low relative-concentrations. Perchlorate 

concentrations were generally higher in the Basins study area 

than in the Uplands study area (table 5).

Perchlorate was positively correlated with DO in the 

SCRC study unit (table 11; fig. 23). Most of the detections 

of perchlorate had a DO concentration greater than 2 mg/L. 

The positive correlation of perchlorate with DO in the SCRC 

study unit was similar to relations noted in the Central Valley 

(Landon and others, 2010; Burton and others, 2012). The 

positive correlation between perchlorate and DO concentration 

suggests that perchlorate is preserved (or stable) under aerobic 

conditions because perchlorate can biodegrade under anoxic 

conditions (Sturchio and others, 2007).
Status and Understanding of Water Quality 55

sac13_0497_Figure 21_Pesticide_GW-Age

Number of pesticides detected in a well

Well depth, in feet below land surface datum

0

200

400

600

800

1,000

1,200

1,400

0 1 2 3 4 5

Modern

Mixed

Pre-modern

Age unknown

Figure 21. Relation of the number of pesticide detections per well with well depth and groundwater 

age in the South Coast Range–Coastal study unit, California GAMA Priority Basin Project.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN

CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range 41 1

1

154 101

101

Morro Bay 101

Santa Maria

San Luis Obispo

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study unit boundary

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

Perchlorate concentration,

in micrograms per liter

USGS-grid wells

< 0.6

> 0.6 to 6

No detections

No detections

Not detected

USGS-understanding wells

CDPH wells

Pacific Ocean

< 0.6

> 0.6 to 6

Land-use classification

Urban

Agricultural

Natural

Relative-concentration

Low

Moderate

Low

Moderate

Figure 22. Perchlorate concentrations in USGS-grid wells, USGS-understanding wells, and CDPH-wells in the South Coast 

Range–Coastal study unit, California GAMA Priority Basin Project.
56 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

sac13-0497_Figure 23_perchlorate_DO

Dissolved oxygen concentration, in milligrams per liter

Perchlorate concentrations, in micrograms per liter 

0

0 2 4 6 8 10

0.5

1.0

1.5

2.0

2.5

Urban

Agricultural

Natural

Mixed 

Non-detections

Moderate

Low

Figure 23. Relation of perchlorate concentrations to dissolved oxygen concentrations in the South Coast Range–

Coastal study unit, California GAMA Priority Basin Project.

Perchlorate was correlated to land use (tables 5 and 11). 

Perchlorate was positively correlated with agricultural land 

use and negatively correlated with natural land use. Most of 

the samples collected from agricultural areas (15 of 18) had 

detections of perchlorate (fig. 23). About half of the samples 

collected from natural areas (18 of 34) did not have any 

perchlorate detections.

The predicted probability of detecting naturally occurring 

perchlorate at a concentration greater than 0.5 μg/L is 5 

to 10 percent on the basis of the logistic regression model 

developed by Fram and Belitz (2011) for the semi-arid climate 

of the SCRC study unit. This low probability implies that 

anthropogenic sources may have contributed perchlorate to 

groundwater in the study unit. The predicted probability of 

detecting naturally occurring perchlorate at a concentration 

greater than 0.1 μg/L is 50 to 60 percent (Fram and Belitz, 

2011). About half of the detections in the SCRC study unit that 

are less than 0.5 μg/L are in areas with natural land use. It is 

possible that some of the detections between 0.1 and 0.5 μg/L 

are from natural sources. 

In summary, perchlorate concentrations were greater in 

areas with agricultural land use. Perchlorate occurs naturally 

in the SCRC study unit, but human activities may elevate the 

perchlorate concentrations. Correlation of perchlorate with 

DO indicates that biodegradation under anoxic conditions may 

occur.

Summary

Groundwater quality in the 766-mi2 (1,980-km2) South 

Coast Range–Coastal (SCRC) study unit was investigated as 

part of the Priority Basin Project of the Groundwater Ambient 

Monitoring and Assessment (GAMA) Program. The GAMA 

Priority Basin Project is designed to provide a statistically 

unbiased, spatially distributed assessment of untreated 

groundwater quality in the primary aquifer system at the 

basin-scale. The primary aquifer system was defined as that 

part of the aquifer corresponding to the perforation interval 

of wells listed in the California Department of Public Health 

(CDPH) database for the SCRC study unit. 

Wells were randomly selected within spatially distributed 

grid cells across the study unit to assess the quality of the 

groundwater. Samples were collected by the U.S. Geological 

Survey (USGS) from May through November 2008 from 

55 grid wells. An additional 15 wells were sampled to 

improve the understanding of the relation of water quality 

to explanatory factors representing general groundwater 

characteristics and land-use activities. Samples from USGSgrid and USGS-understanding wells were analyzed for up to 

302 constituents. CDPH inorganic data from the 3-year period 

(May 20, 2005, to May 19, 2008) were used to complement 

USGS-grid well data and provide additional information about 

groundwater quality. 
Summary 57

The first component of this study, the status assessment, 

characterized the current quality of groundwater resources 

in the primary aquifer system of the SCRC study unit, 

before it is treated and delivered to consumers by water 

purveyors. This assessment used data from samples analyzed 

for anthropogenic constituents such as volatile organic 

compounds (VOCs) and pesticides, as well as naturally 

occurring inorganic constituents such as major ions and trace 

elements. Relative-concentrations (sample concentration 

divided by the health- or aesthetic-based benchmark 

concentration) were used for evaluating groundwater quality 

for those constituents that have Federal and (or) California 

regulatory or non-regulatory benchmarks for drinking-water 

quality. The relative-concentration threshold for classifying 

inorganic constituents as moderate was 0.5, whereas for 

organic constituents it was 0.1. A relative-concentration of 

0.1 was used as a boundary between low and moderate values 

of organic and special-interest constituents for consistency 

with other studies and reporting requirements.

Aquifer-scale proportion was used as a metric for 

assessing the quality of untreated groundwater within the 

SCRC study unit. High aquifer-scale proportion is defined 

as the areal percentage of the primary aquifer system with 

a relative-concentration greater than 1.0 for a particular 

constituent or class of constituents. Moderate and low aquiferscale proportions were defined as the areal percentage of 

the primary aquifer system with moderate and low relativeconcentrations, respectively. Grid-based and spatially 

weighted statistical approaches were used to assess aquiferscale proportions of constituents at high, moderate, and low 

relative-concentrations in the primary aquifer system.

Inorganic constituents were more prevalent and generally 

had higher relative-concentrations than organic constituents. 

For inorganic constituents with human-health benchmarks, 

relative-concentrations for 33 percent of the primary aquifer 

system were high for at least one constituent in the SCRC 

study unit. The inorganic constituents with human-healthbased benchmarks that were detected at high relativeconcentrations in more than 2 percent of the primary aquifer 

system were arsenic, molybdenum, and nitrate. 

For inorganic constituents with aesthetic benchmarks 

(SMCLs), relative-concentrations were high for at least one 

constituent in 35 percent of the primary aquifer system in 

the SCRC study unit. Inorganic constituents with aesthetic 

benchmarks that were detected at high relative-concentrations 

in more than 2 percent of the primary aquifer system were 

iron, manganese, total dissolved solids (TDS), and sulfate.

In contrast to inorganic constituents, organic constituents 

with human-health benchmarks were not detected at high 

relative-concentrations in the primary aquifer system 

in the SCRC study unit. Of the 85 VOCs analyzed, 

9 were detected—8 with human-health benchmarks. 

Perchloroethene (PCE) was the only VOC detected at 

moderate relative-concentrations in the SCRC study unit. 

PCE, dichlorodifluoromethane (CFC-12), and chloroform 

had detection frequencies greater than 10 percent. Of 

the 120 pesticides and degradates analyzed, 12 were 

detected—5 with human-health benchmarks. All pesticides 

were detected at low relative-concentrations or were not 

detected. The special-interest constituent perchlorate was 

detected at moderate relative-concentrations in 29 percent of 

the primary aquifer system and had a detection frequency of 

60 percent in the SCRC study unit.

The second component of this work, the understanding 

assessment, identified some of the primary natural and 

human factors that may affect groundwater quality by 

evaluating correlations between potential explanatory factors 

and relative-concentrations of constituents. The potential 

explanatory factors evaluated were land use, well depth, 

septic system density, density and distance to nearest formerly 

leaking underground fuel tanks (LUFTs), groundwater age, 

and geochemical conditions (dissolved oxygen [DO] and pH). 

Results from these analyses attempt to explain the occurrence 

and distribution of constituents in the SCRC study unit. 

Selected constituents with high relative-concentrations for any 

constituent or detection frequencies greater than or equal to 

10 percent for organic constituents were selected to focus the 

understanding assessment on those constituents that were of 

greatest concern.

Arsenic concentrations were higher in pre-modern and 

mixed-age groundwater than in modern groundwater. In 

the Basins study area, data indicate that the occurrences of 

elevated arsenic concentrations likely result from the release 

of naturally occurring arsenic from dissolution of aquifer 

sediments under reducing conditions. In the Uplands study 

area, arsenic concentrations may be elevated as a result of 

extended exposure to arsenic-bearing minerals or arsenic 

adsorbed to minerals in the aquifer sediments in areas where 

DO is relatively low.

Molybdenum was negatively correlated with urban land 

use and positively correlated with well depth, the distance 

to the nearest LUFT, and arsenic concentrations. These 

correlations indicate that molybdenum is from natural sources. 

Molybdenum showed similar geochemical characteristics to 

arsenic; molybdenum concentrations may be influenced by the 

release of adsorbed molybdenum during reductive dissolution 

of metal oxides. In other areas, molybdenum concentrations 

may be influenced by pH-dependent desorption from aquifer 

sediments under oxic conditions.

Nitrate was positively correlated to agricultural land 

use and negatively correlated to natural land use. Nitrate 

concentrations are higher in modern groundwater than in 

mixed-age or pre-modern groundwater. The correlations with 

groundwater age and land use are consistent with elevated 

concentrations of nitrate resulting from human activities. 

Nitrate was positively correlated with DO and negatively 

correlated with manganese and iron concentrations, which 

indicates that nitrate may be influenced by redox conditions in 

the SCRC study unit. 

Manganese and iron were negatively correlated to DO 

and pH, and concentrations were higher in older groundwater. 

Manganese and iron concentrations were not correlated to 
58 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

any other explanatory factor. These correlations indicate that 

manganese and iron are from naturally occurring sediments 

and that concentrations likely are influenced by the reductive 

dissolution of manganese and iron oxides in reducing 

environments.

TDS and sulfate are naturally occurring as a result of 

interaction of the groundwater with the sediments that exist 

in the SCRC study unit. TDS and sulfate concentrations 

were higher in the Basins study area than in the Uplands 

study area. Higher concentrations of TDS also were in 

modern groundwater and areas with agricultural or urban 

land use. TDS concentrations decrease with well depth. 

Sulfate concentrations were higher in modern groundwater 

and in areas with agricultural land use. These correlations 

indicate that human activities elevate natural TDS and sulfate 

concentrations.

Organic compounds usually were detected at low 

relative-concentrations; therefore, statistical analyses of 

relations to explanatory factors usually were done for classes 

of constituents. Classes of organic compounds consisted of 

VOCs—which were further subdivided into trihalomethanes 

(THMs), solvents, and other VOCs—and pesticides. The 

number of VOCs detected in any particular well was not 

correlated to any of the explanatory factors evaluated.

The number of solvents and pesticides detected in a well, 

PCE concentrations, and CFC-12 concentrations were higher 

in modern groundwater than in pre-modern groundwater. PCE 

and CFC-12 were also correlated to the number of LUFTs 

located near the well; PCE was negatively correlated to natural 

land use. These VOC compounds appear to be more prevalent 

in urban areas than in agricultural or natural areas in the study 

unit.

Perchlorate was positively correlated with agricultural 

land use and DO and negatively correlated with natural land 

use. Perchlorate concentrations were greater in agricultural 

areas than in urban areas. Correlation of perchlorate with 

DO indicates that perchlorate is more stable under aerobic 

conditions. Perchlorate concentrations generally were high 

in groundwater with high DO concentrations. Anthropogenic 

sources have contributed perchlorate to groundwater in the 

SCRC study unit, although low levels of perchlorate may 

occur naturally.

Low-level analyses of VOCs and pesticides may be used 

as tracers of groundwater that has recharged over the decades 

when these compounds began to be used for industrial and 

commercial purposes. Low-level analyses provide an early 

awareness of constituents whose presence in groundwater at 

low concentrations may be important for the prioritization of 

monitoring water quality in the future.

Acknowledgments

The authors thank the following cooperators for their 

support: the California State Water Resources Control 

Board, Lawrence Livermore National Laboratory, California 

Department of Public Health, and California Department 

of Water Resources. We especially thank the 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 

State Water Resources Control Board. 

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Appendix A 65

California requires samples to be collected regularly from 

public-supply wells under Title 22 (California Department 

of Health Services, 2007). Historical data derived from these 

samples are available from the CDPH database. Assembly 

Bill 599 directs the GAMA Program to use available data 

and to collect new data as needed. The GAMA Priority Basin 

Project used the existing monitoring data along with newly 

collected data to characterize the water quality of the primary 

aquifer system. The CDPH database provided additional 

water-quality data for the spatially weighted and grid-based 

approaches to estimating aquifer-scale proportions for a wide 

range of constituents. CDPH data were not used to provide 

data for grid wells for VOCs, pesticides, or perchlorate 

because reporting limits for these constituents in the CDPH 

database generally were not sufficiently low enough to 

differentiate between “low” and “moderate” concentrations. 

Three approaches were used to select CDPH inorganic 

constituent data for each grid cell where the USGS did not 

sample for inorganic constituents. The first approach was 

to identify CDPH data collected during the current period 

(May 20, 2005, to May 19, 2008) for any USGS-grid well not 

sampled for inorganic constituents by the USGS. Analytical 

results obtained from the CDPH database were reviewed 

for cation-anion charge balance, a commonly used qualitycontrol criterion for water-sample analysis (Hem, 1985). 

Because water is electrically neutral, the total positive charge 

on dissolved cation species in a water sample must equal the 

total negative charge on dissolved anion species. Cation-anion 

imbalance was calculated as the absolute difference between 

the total cations and total anions divided by the sum of the 

cations and anions, expressed as a percentage:

percent difference

cations anions

cations anions = −+









∑∑

∑∑

 







× 100 ,

where 

∑ cations is the sum of calcium, magnesium, sodium, 

and potassium, in milliequivalents per liter 

(meq/L), and

∑ anions is the sum of chloride, sulfate, fluoride, 

nitrate, and bicarbonate, in meq/L.

An imbalance, or percentage difference, greater than or 

equal to 5 percent indicates uncertainty in the quality of the 

data. The most recent CDPH data from the current period 

for the USGS-grid wells with missing data were evaluated to 

determine whether the cation/anion imbalance for CDPH data 

was less than 5 percent. If so, the CDPH inorganic data for the 

well was selected for use as the grid well data for inorganic 

constituents for that well. It was assumed that if major-ion data 

were of acceptable quality, then the data for trace elements, 

nutrients, and radiochemical constituents would also be of 

acceptable quality. This approach resulted in the selection of 

inorganic data from the CDPH database for 10 USGS-grid 

wells in the SCRC study unit. For identification purposes, data 

from the CDPH database for these grid wells were assigned 

GAMA identification numbers equivalent to the GAMA 

USGS-grid well number but with “DG” inserted between the 

study area prefix and sequence number (for example, CDPHgrid well SCRC-DG-B01 is the same well as USGS-grid well 

SCRC-B01, table A1).

If the first approach did not yield CDPH inorganic data 

for a grid cell, the second approach was to search the CDPH 

database to identify the highest ranked well within that cell 

with a cation/anion imbalance of less than 5 percent. This 

approach resulted in selecting CDPH inorganic data for wells 

not sampled by the USGS in five grid cells in the SCRC 

study unit. These five CDPH-grid wells were located within 

the same cell as the USGS-grid well but not necessarily right 

next to the USGS-grid well. To identify these new CDPH-grid 

wells, a well ID was created that added “DPH” after the study 

area prefix (for example, CDPH-grid well SCRC-DPH-B07 

is in the same cell as USGS-grid well SCRC-B07 but is not 

the same well). There was one well, SCRC-B25, where the 

CDPH-grid well, SCRC-DPH-B25, was the same as the 

USGS-understanding well, SCRC-U04. Data collected by the 

USGS from SCRC-U04 were used to supplement the data 

from well SCRC-B25 when available.

If the second approach failed to produce a well from 

the CDPH database having a cation-anion imbalance less 

than 5 percent or there were insufficient data to evaluate a 

charge balance, the third approach was to select the highest 

ranked well in the CDPH database with any of the needed 

inorganic data. This approach resulted in the selection of one 

USGS-grid well in the SCRC study unit from which some 

CDPH inorganic data (usually nutrient data) were available. 

Because the well was a USGS-grid well, a well ID was 

created that added “DG” to the GAMA ID (for example, well 

SCRC-DG-B15). 

The result of these approaches was one grid well per 

cell with data from the USGS database, the CDPH database, 

or both databases. Inorganic data for 16 CDPH-grid wells 

in the CDPH database were used (fig. A1). Data were not 

available for all inorganic constituents from all 16 CDPH-grid 

wells. Table 2 in the report shows the number of USGS- and 

CDPH-grid wells with data for each inorganic constituent. In 

combination with USGS-grid well inorganic data (32 wells), 

some inorganic data were available for 49 of the 61 grid 

cells. Most of the cells without a grid well were located in the 

coastal areas of the Santa Ynez River Valley and San Antonio 

Creek Valley groundwater basins.

Appendix A. Selection of California Department of Public Health Grid Wells 

Appendix A
66 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Table A1. Nomenclature and construction information for USGS-grid and USGS-understanding wells and CDPH-grid wells, South 

Coast Range–Coastal study unit, California GAMA Priority Basin Project.

[CDPH, California Department of Public Health; GAMA, Groundwater Ambient Monitoring and Assessment Program; SCRC-B, South Coast Range–

Coastal study unit Basins study area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; SCRC-U, South Coast Range–Coastal study unit 

understanding well; SCRC-DG, South Coast Range–Coastal study unit grid well with supplemental CDPH data; SCRC-DPH, CDPH grid well; ft, feet; LSD, 

land surface datum; na, not available; USGS, U.S. Geological Survey; —, not applicable]

USGS 

GAMA well 

identification 

number

CDPH GAMA well 

identification 

number

Cell 

number

Well construction information

Well type Well depth

(ft below LSD)

Top of 

perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells

SCRC-B01 SCRC-DG-B01 5 Production 607 305 595 290

SCRC-B02 SCRC-DG-B02 6 Production 140 40 130 90

SCRC-B03 SCRC-DG-B03 23 Production 1,000 260 980 720

SCRC-B04 SCRC-DG-B04 7 Production 550 340 527 187

SCRC-B05 — 35 Production 651 523 na na

SCRC-B06 SCRC-DG-B06 37 Production 800 650 800 150

SCRC-B07 — 38 Production 500 100 500 400

— SCRC-DPH-B07 38 Production 515 426 505 79

SCRC-B08 — 41 Production 1,120 550 1,100 550

SCRC-B09 — 43 Production 349 150 340 190

— SCRC-DPH-B09 43 Production 810 250 798 548

SCRC-B10 — 44 Production 490 190 470 280

SCRC-B11 — 27 Production 68 na na na

SCRC-B12 SCRC-DG-B12 40 Production 103 60 na na

SCRC-B13 SCRC-DG-B13 42 Production 490 130 470 340

SCRC-B14 SCRC-DG-B14 30 Production 946 279 946 667

SCRC-B15 SCRC-DG-B15 22 Production 463 na na na

SCRC-B16 — 31 Production 370 185 365 180

SCRC-B17 — 33 Production 191 135 na na

SCRC-B18 — 34 Production 210 98 na na

SCRC-B19 — 36 Production 512 125 507 382

SCRC-B20 — 45 Production 115 95 na na

SCRC-B21 — 14 Production 190 na na na

SCRC-B22 — 17 Production 350 na na na

SCRC-B23 — 19 Production 620 375 600 225

SCRC-B24 — 24 Production 600 na na na

SCRC-B25 — 32 Production 401 160 400 240

— SCRC-DPH-B25 1 32 Production 125 115 125 10

SCRC-B26 — 12 Production 180 na na na

SCRC-B27 — 8 Production 420 260 420 160

SCRC-B28 — 21 Production 390 300 380 80

SCRC-B29 SCRC-DG-B29 20 Production 225 33 120 87

SCRC-B30 — 9 Production 418 338 na na

SCRC-B31 — 3 Production na na na na

SCRC-B32 — 10 Production 300 102 na 198

SCRC-B33 — 39 Production 830 605 825 220

— SCRC-DPH-B33 39 Production 145 99 126 28

SCRC-B34 — 28 Production 707 542 705 163

SCRC-B35 SCRC-DG-B35 26 Production 140 80 140 60
Appendix A 67

USGS 

GAMA well 

identification 

number

CDPH GAMA well 

identification 

number

Cell 

number

Well construction information

Well type Well depth

(ft below LSD)

Top of 

perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells—Continued

SCRC-B36 — 18 Production 200 na na na

SCRC-B37 — 25 Production 232 112 212 100

SCRC-B38 — 29 Production 362 na na na

— SCRC-DPH-B38 29 Production 1,050 na na na

SCRC-B39 — 4 Monitoring 615 527 615 88

SCRC-H01 — 2 Production 460 160 440 280

SCRC-H02 — 12 Production 239 na na na

SCRC-H03 — 7 Production 550 350 550 200

SCRC-H04 — 8 Production 620 410 610 200

SCRC-H05 — 13 Production 720 310 720 410

SCRC-H06 — 5 Production 345 215 335 120

SCRC-H07 — 1 Production 390 200 na na

SCRC-H08 — 14 Production 920 535 910 375

SCRC-H09 — 9 Production 750 na na na

SCRC-H10 — 11 Production 600 420 600 180

SCRC-H11 — 3 Production 260 200 260 60

SCRC-H12 — 6 Production 645 105 645 540

SCRC-H13 — 15 Production 980 290 960 670

SCRC-H14 — 4 Production 300 150 290 140

SCRC-H15 — 10 Production 1,263 448 1,253 805

SCRC-H16 — 16 Production 280 75 270 195

Understanding wells

SCRC-U01 — 14 Production 130 na na na

SCRC-U02 — 26 Production 174 134 174 40

SCRC-U03 — 28 Production 600 300 590 290

SCRC-U04 SCRC-DPH-B25 32 Production 125 115 125 10

SCRC-U05 — 8 Monitoring 55 35 55 20

SCRC-U06 — 19 Production 582 na na na

SCRC-U07 — 21 Production 203 110 179 69

SCRC-U08 — 21 Production 335 na na na

SCRC-U09 — 21 Production 186 na na na

SCRC-U10 — 21 Production 265 na na na

SCRC-U11 — 1 Monitoring 19 10 19 9

SCRC-U12 — 15 Monitoring 77 72 77 5

SCRC-U13 — 11 Monitoring 23 8 23 15

SCRC-U14 — 16 Monitoring 18 8 18 10

SCRC-U15 — 2 Monitoring 38 8 38 30

1

 This well was also sampled as USGS-understanding well SCRC-U04.

Table A1. Nomenclature and construction information for USGS-grid and USGS-understanding wells and CDPH-grid wells, South 

Coast Range–Coastal study unit, California GAMA Priority Basin Project.—Continued

[CDPH, California Department of Public Health; GAMA, Groundwater Ambient Monitoring and Assessment Program; SCRC-B, South Coast Range–

Coastal study unit Basins study area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; SCRC-U, South Coast Range–Coastal study unit 

understanding well; SCRC-DG, South Coast Range–Coastal study unit grid well with supplemental CDPH data; SCRC-DPH, CDPH grid well; ft, feet; LSD, 

land surface datum; na, not available; USGS, U.S. Geological Survey; —, not applicable]
68 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41

1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure A1a USGS wells

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study areas

Basins

Uplands

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Pozo Creek

San Juan Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

Pacific Ocean

Grid cell USGS-grid well and identifier

U12 USGS-understanding well and identifier

B33

A

“SCRC” has been omitted from

 figure due to spatial concerns

H16

H15

H14

H13

H12

H11

H10

H09

H08

H07

H06

H05

H04

H03

H02

H01

B39

B38

B37

B36

B35

B34

B33

B32

B31

B30

B29

B28

B26

B25

B24

B23

B22

B21

B20

B19

B18

B17

B16 B15

B14

B13

B12

B11

B10

B09

B08

B07

B06

B05

B04

B03

B02

B01

B27

U15 U14

U13

U12

U11

U10

U06 U05

U03

U02

U01

U07 U09 U08

Figure A1. Identifiers and locations of (A) USGS-grid and USGS-understanding wells sampled during May through 

November 2008 and (B) CDPH-grid wells with data for inorganic constituents from the California Department of Public Health 

(CDPH), South Coast Range–Coastal study unit, California GAMA Priority Basin Project.
Appendix A 69

Figure A1.—Continued.

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

San Rafael Mountains

Santa Ynez Mountains

San Luis Range

San Lucia Range

41

1

1

154 101

101

101

Nipomo

Lompoc

Solvang

Buellton

Morro Bay

Santa Maria

Arroyo Grande

San Luis Obispo

sac13-0497_Figure A1b CDPH wells

120°30’ 120°

35°

34°

30’

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

5 10 Miles

0 10 20 Kilometers

0

Study areas

Basins

Uplands

Creek

Creek

Santa Cruz

San Antonio

Barka Slough

Sisquoc River

Cuyama River

Salinas River

Santa Maria River

Alamo Creek

Pozo Creek

San Juan Creek

Los Osos Creek

San Luis

Osbispo

Creek

Santa Ynez River 

Pacific Ocean

Grid cell

“SCRC” has been omitted from

 figure due to spatial concerns

B

USGS- and CDPH-grid well and identifier

CDPH-grid well and identifier

DPH-B09

DPH-B33

DPH-B07

DPH-B07

DPH-B25

DPH-B38

DG-B35

DG-B29

DG-B15

DG-B14

DG-B13

DG-B06

DG-B04

DG-B03

DG-B02

DG-B01

DG-B12

DG-B12
70 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

CDPH and USGS-GAMA data were compared to 

assess the validity of combining data from these different 

sources. Because laboratory reporting levels for most 

organic constituents and trace elements were substantially 

lower for USGS-GAMA data than for CDPH data (table 2), 

only relatively high concentrations of constituents could be 

compared; as a result, there were insufficient data from which 

to evaluate agreement between CDPH and USGS-GAMA 

data. However, concentrations of major ions and nitrate, which 

generally are prevalent and have concentrations substantially 

above reporting levels, could be compared for each well using 

data from both sources. 

Comparisons were made for inorganic constituents that 

were analyzed by the USGS-GAMA Priority Basin Project and 

for which CDPH data were available within the most recent 

3-year interval. Major-ion and nitrate data were available from 

11 to 13 wells from the SCRC study unit in the USGS and the 

CDPH databases for 10 constituents (calcium, magnesium, 

sodium, potassium, alkalinity, chloride, sulfate, total dissolved 

solids, nitrate, and fluoride). A nonparametric signed-rank 

test indicated no significant differences between the paired 

USGS-GAMA and CDPH data for seven of these constituents. 

The three constituents that showed significant differences 

were sodium, chloride, and alkalinity. However, the median 

relative percent difference (RPD, absolute difference of the 

two values divided by the average of the two values and 

multiplied by 100) calculated for these three data pairs was 

less than 20 percent, similar to the RPDs for the other seven 

constituents. This suggests that the observed differences for 

sodium, chloride, and alkalinity are negligible as pertains to 

the needs of this report. Trace element data were available 

for four to eight wells in the SCRC study unit in the USGS 

and CDPH databases for eight constituents (arsenic, barium, 

boron, cadmium, chromium, iron, manganese, and vanadium). 

A nonparametric signed-rank test indicated no significant 

differences between the paired USGS-GAMA and CDPH data 

for all eight trace elements. Although differences between 

the paired datasets occurred for a few wells, most sample 

pairs plotted close to a 1-to-1 line (fig. B1). These direct 

comparisons indicated that the GAMA and CDPH inorganic 

data were not significantly different.

Piper diagrams show the relative abundance of major 

cations and anions (on a charge equivalent basis) as a 

percentage of the total ion content of the water (fig. B2). Piper 

diagrams often are used to define groundwater type (Hem, 

1985). Combined GAMA Priority Basin Project and CDPH 

major-ion data for grid wells were plotted on Piper diagrams 

(Piper, 1944) along with all CDPH major-ion data from the 

current period to determine whether the groundwater types 

in grid wells were similar to groundwater types observed 

historically in the study unit. All cation/anion data in the 

CDPH database with a cation/anion balance of less than 

5 percent were retrieved and plotted on these Piper diagrams 

for comparison with grid well data.

The range of water types for grid wells and other wells 

from the CDPH database for the current period were similar 

(fig. B2). Most wells in the SCRC study unit were classified 

as mixed cation–mixed anion type waters, indicating that 

no single cation accounted for more than 50 percent of the 

total cations, and no single anion accounted for more than 

50 percent of the total anions. The most common cations 

were calcium and magnesium, although some samples also 

contained a high percentage of sodium. Bicarbonate and 

sulfate were the dominant anions in these waters, although a 

few samples contained a high percentage of chloride. Waters 

in a minority of wells were classified as sodium-bicarbonate,

magnesium-bicarbonate, mixed cation-bicarbonate, mixed 

cation-chloride, mixed cation-sulfate, magnesium-mixed 

anion, or sodium-mixed anion type waters, indicating sodium, 

magnesium, bicarbonate, chloride, or sulfate accounted for 

more than 50 percent of the total cations or anions.

The determination that the range of relative abundance 

of major cations and anions in grid wells (34 wells) is similar 

to the range of those in the selected CDPH-other wells 

(32 wells) indicates that the grid wells represent the diversity 

of water types present within the SCRC study unit. However, 

there was one minor difference between grid well data and 

CDPH-other well data. Two CDPH-other wells with a sodiumbicarbonate water type are located in the Uplands study area 

just north of the Santa Maria River Valley. The USGS-grid 

well (SCRC-H01) located just west of these wells has a mixed 

cation–mixed anion water type.

Appendix B. Comparison of CDPH and GAMA Priority Basin Data 

Appendix B
Appendix B 71

Concentration, USGS data, in milligrams per liter

Concentration, CDPH data, in milligrams per liter

0.1

0.1 1 10 100 1,000 10,000

1

10

100

1,000

10,000

Calcium

Magnesium

Potassium

Sodium

Alkalinity

Chloride

Fluoride

Sulfate

Total dissolved solids

Nitrate

1:1 correlation line

A

Concentration, USGS data, in micrograms per liter

Concentration, CDPH data, in micrograms per liter

0.1

0.1 1 10 100 1,000 10,000

1

10

100

1,000

10,000

1:1 correlation line

B

Arsenic

Barium

Boron

Cadmium

Chromium

Iron

Manganese

Vanadium

sac13-0497_Figure B1_concentrations Figure B1. Paired inorganic concentrations from wells sampled in the South Coast 

Range–Coastal study unit (May through November 2008) and the most recent available 

analysis in the California Department of Health Services (May 20, 2005, to May 19, 2008) for 

(A) major ions and nitrate and (B) trace elements, California GAMA Priority Basin Project.
72 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Percent

Calcium

 100 

 80 

 60 

 40 

 20 

 0 

 0 

 20 

 40 

 60 

 80 

 100 

Magnesium

 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrite plus Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

Sulfate

 0 

 20 

 40 

 60 

 80 

 100 

Sulfate plus Chloride

 0 

 20 

 40 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 20 

 0 

Percent

Percent

EXPLANATION

USGS-grid well

CDPH well

Figure B2. USGS- and CDPH-grid wells and all other wells in the California Department of Public Health database with 

a charge imbalance of less than 5 percent in the South Coast Range–Coastal study unit, California GAMA Priority Basin 

Project.
Appendix C 73

Two statistical approaches—grid-based and spatially 

weighted—were selected to evaluate the aquifer-scale 

proportions of the primary aquifer system in the SCRC study 

unit with high, moderate, or low relative-concentrations 

(concentration relative to its water-quality benchmark) of 

constituents. Raw detection frequencies also were calculated 

for individual constituents, but were not used for estimating 

aquifer-scale proportions because this method creates spatial 

bias towards regions with large numbers of wells. 

Grid-Based Calculation

One well in each grid cell, a “grid well,” was used 

to represent the primary aquifer system. Most grid wells 

sampled for this study were USGS-grid wells. The relativeconcentration for each constituent (concentration relative 

to its water-quality benchmark) was evaluated for each grid 

well. The areal proportion of the primary aquifer system 

with high relative-concentrations was calculated by dividing 

the number of cells with concentrations greater than the 

benchmark (relative-concentration greater than 1) by the total 

number of grid wells in the study unit (Belitz and others, 

2010). Proportions containing moderate and low relativeconcentrations were calculated similarly. Confidence intervals 

for grid-based aquifer-scale proportions were computed using 

the Jeffreys interval for the binomial distribution (Brown and 

others, 2001). The grid-based estimate is spatially unbiased. 

However, the grid-based approach may not detect constituents 

that are present at high relative-concentrations in small 

proportions of the primary aquifer system.

The grid-based aquifer-scale proportions for constituent 

classes also were calculated on a one-value-per-cell basis. A 

cell with a high relative-concentration for any constituent in 

the class is defined as a high cell, and the high proportion is 

the number of high cells divided by the number of cells with 

data for any of the constituents in that class. The moderate 

proportion for the constituent class is calculated similarly, 

except that a cell already defined as high cannot also be 

defined as moderate. The grid-based aquifer-scale proportion 

for the low category was calculated similarly, such that a cell 

could only be low if the relative-concentration was neither 

moderate nor high for any constituent in the class. The 

proportions for the high, moderate, and low categories were 

expected to total 100 percent, except for small differences as a 

result of rounding.

Spatially Weighted Calculation

The spatially weighted calculation of aquifer-scale 

proportions uses the most recent value for a constituent from 

all wells in the CDPH database with data in the 3-year interval 

prior to USGS-GAMA sampling (May 20, 2005, to May 19, 

2008) in the SCRC study unit, from all USGS-grid well 

data, and from selected USGS-understanding well data. The 

spatially weighted approach computes the aquifer-scale areal 

proportion by using the percentage of wells with high relativeconcentrations from all the wells in each cell, instead of using 

data from only one well (Belitz and others, 2010). For each 

constituent, the high aquifer-scale proportion was computed 

by calculating the proportion of wells with high relativeconcentrations in each grid cell and dividing by the number of 

cells (Belitz and others, 2010): 

P

W

W i

high

total

= , (C1)

P

P

n

SU

i

i

n

= =

∑

1 , (C2)

where 

Pi is the proportion of wells in the i

th cell with 

high relative-concentrations,

Wtotal is the number of wells in the i

th cell with data 

for the constituent,

Whigh is the number of wells in the i

th cell with high 

relative-concentrations,

PSU is the aquifer-scale proportion for the study 

unit, and

n is the number of cells with data for the 

constituent.

Similar procedures were used to calculate the proportions 

of moderate and low relative-concentrations. The resulting 

proportions were spatially unbiased (Isaaks and Srivastava, 

1989). 

Raw Detection Frequencies

The raw detection frequencies of wells with high relativeconcentrations for constituents were calculated using the 

same data used for the spatially weighted approach. Raw 

detection frequency is the percentage (frequency) of wells in 

the study unit with high relative-concentrations. However, 

raw detection frequencies are spatially biased because the 

wells in the CDPH database and USGS-understanding wells 

are not uniformly distributed. Consequently, high relativeconcentrations for wells clustered in a particular area represent 

a small part of the primary aquifer system and could be given 

a disproportionately high weight compared to that given by 

spatially unbiased approaches. Raw detection frequencies of 

high relative-concentrations are provided for reference in this 

report but were not used to assess aquifer-scale proportions.

Appendix C. Calculation of Aquifer-Scale Proportions

Appendix C
74 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Appendix D. Attribution of Potential Explanatory Factors

Appendix D

Well Construction Information

Well construction data were from driller’s logs or from 

information provided by the well owner. Well identification 

verification procedures are described by Mathany and others 

(2010). Well depths and depths to the top and bottom of the 

perforated interval for USGS-grid, USGS-understanding, 

and CDPH-grid wells are listed for the SCRC study unit 

in table A1. Wells were classified as production wells or 

monitoring wells. Production wells pump groundwater from 

the aquifer to a distribution system and generally are screened 

over long intervals. Monitoring wells are short-screened wells 

installed exclusively for monitoring purposes. 

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 (Nakagaki and others, 2007). 

This dataset has been used in previous national and regional 

studies relating land use to water quality (Gilliom and others, 

2006; Paul and others, 2007). The dataset characterizes 

land cover during the early 1990s. The imagery is classified 

into 25 land-cover classifications (Nakagaki and Wolock, 

2005). These 25 land-cover classifications were assigned to 

3 general land-use classifications—urban, agricultural, and 

natural. Land-use statistics for the SCRC study unit and for 

circles with a radius of 500 m around each well (table D1) 

were assigned using the USGS National Land Cover Dataset 

(Johnson and Belitz, 2009).

Septic Systems

Septic tank density was determined from housing 

characteristics data from the 1990 U.S. Census (U.S. Census 

Bureau, 1990). The density of septic tanks in each housing 

census block was calculated from the number of tanks and 

block area. The density of septic tanks around each well was 

calculated from the area-weighted mean of the block densities 

for blocks within a 500-m buffer around the well location 

(Tyler Johnson, U.S. Geological Survey, written commun., 

2009) (table D1).

Formerly Leaking Underground Fuel Tanks

Density for formerly leaking underground fuel tanks 

(LUFTs) was determined from data obtained from the 

Geographic Information Management System GeoTracker 

(California Environmental Protection Agency, 2001). The 

density is a measure of the number of tanks in a Thiessen 

polygon in square kilometers. The boundaries of the Thiessen 

polygons are created by bisecting the distance between all 

surrounding LUFTs. For instance, if a tank is surrounded by 

four tanks each 1,000 m away, then the Thiessen polygon 

will be drawn exactly one-half of the distance to each tank 

(500 m), resulting in a polygon that is relatively small and 

therefore of high density. The density is calculated by dividing 

the number of tanks at a single location (usually one) and 

dividing it by the total area of the polygon. If the nearest 

tanks are many kilometers away, then the polygon will be 

large, and therefore the density will be relatively low. This 

measure was added because two wells could each be 100 m 

away from a LUFT, but one could be surrounded by 10 nearby 

tanks and the other secluded without another tank for 100 km. 

The Thiessen polygon method is a non-interpolated measure 

of density that has the added value of being able to handle 

extreme high and low densities equally well. LUFT density 

data and distance to nearest LUFT for each USGS-grid and 

understanding well and CDPH-grid well are in table D1.

Groundwater Age Classification

Groundwater dating techniques indicate the time since 

the groundwater was last in contact with the atmosphere. 

Techniques used to estimate groundwater residence times or 

‘age’ include those based on tritium (for example, Tolstikhin 

and Kamenskiy, 1969), tritium combined with its decay 

product helium-3 (for example, Takaoka and Mizutani, 

1987; Poreda and others, 1988; Schlosser and others, 1988), 

carbon-14 (14C) content (for example, Vogel and Ehhalt, 

1963; Plummer and others, 1993), and dissolved noble gases, 

particularly helium-4 accumulation (for example, Davis and 

DeWiest, 1966; Andrews and Lee, 1979; Cey and others, 

2008; Kulongoski and others, 2008). 

Tritium is a short-lived radioactive isotope of hydrogen 

with a half-life of 12.32 years (Lucas and Unterweger, 2000). 

Tritium is produced naturally in the atmosphere from the 

interaction of cosmogenic radiation with nitrogen (Craig and 

Lal, 1961), by above-ground nuclear explosions (Michel, 

1989), and by the operation of nuclear reactors. Tritium enters 

the hydrologic cycle following oxidation to tritiated water. 

Natural background levels of tritium in precipitation are 

approximately 3 to 15 tritium units (TU) (Craig and Lal, 1961; 

Clark and Fritz, 1997). Above-ground nuclear explosions 

resulted in a large increase in tritium values in precipitation, 

beginning in about 1952 and peaking in 1963 at values over 

1,000 TU in the northern hemisphere (Michel, 1989; Solomon 

and Cook, 2000). Radioactive decay over a period of 50 years 

would decrease tritium values of 10 TU to 0.6 TU. 
Appendix D 75

Table D1. Land-use classification, septic systems, and formerly leaking underground fuel tank information for USGS-grid and 

USGS-understanding wells, and CDPH-grid wells for inorganic constituents, South Coast Range–Coastal study unit, California GAMA 

Priority Basin Project.

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks in 500-meter radius around each 

well (U.S. Census Bureau, 1990). Number of formerly leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). GAMA, Groundwater Ambient Monitoring and 

Assessment Program; SCRC-B, South Coast Range–Coastal study unit Basins study area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; 

SCRC-U, South Coast Range–Coastal study unit understanding well; DG, South Coast Range–Coastal study unit grid well with supplemental CDPH data; DPH, 

CDPH-grid well; km2, square kilometer; CDPH, California Department of Public Health; tanks/km2, tanks per square kilometer]

GAMA well 

identification number 

(USGS ID/CDPH ID)

Land use (in percent)

Land-use 

classification

Septic tank density 

(number of 

`tanks/km2

)

Distance to nearest 

formerly leaking 

underground fuel tank 

(meter)

Formerly leaking 

underground 

fuel tank density

(number of tanks/km2)

Agricultural Natural Urban 

Grid wells

SCRC-B01/DG-B01 5.0 3.1 91.9 Urban 18.9 819 0.0245

SCRC-B02/DG-B02 53.8 10.7 35.5 Agricultural 1.0 467 0.7876

SCRC-B03/DG-B03 38.0 62.0 0.0 Natural 1.1 1,620 0.0266

SCRC-B04/DG-B04 0.1 28.5 71.4 Urban 799 738 0.0151

SCRC-B05 5.8 84.3 9.9 Natural 5.4 2,140 0.0094

SCRC-B06/DG-B06 0.0 73.9 26.1 Natural 5.4 2,898 0.0142

SCRC-B07 92.7 6.6 0.7 Agricultural 1.2 1,259 0.0217

SCRC-B08 10.8 8.9 80.3 Urban 12.6 368 0.0839

SCRC-B09/DG-B09 81.3 10.4 8.2 Agricultural 19.5 2,889 0.0328

SCRC-B10 35.7 29.0 35.3 Mixed 0.6 711 0.0292

SCRC-B11 31.0 8.1 60.8 Urban 1.8 233 0.1278

SCRC-B12/DG-B12 50.9 19.0 30.1 Agricultural 16.7 1,281 0.0250

SCRC-B13/DG-B13 45.8 14.2 40.0 Mixed 25.2 980 0.0328

SCRC-B14/DG-B14 0.1 65.9 34.0 Natural 1.0 875 0.3899

SCRC-B15/DG-B15 95.5 0.9 3.6 Agricultural 1.7 1,692 0.0310

SCRC-B16 32.9 11.1 56.0 Urban 0.5 880 0.5780

SCRC-B17 5.4 30.8 63.8 Urban 0.2 507 0.0049

SCRC-B18 95.0 5.0 0.0 Agricultural 5.4 1,492 0.0094

SCRC-B19 80.6 6.9 12.5 Agricultural 0.9 3,887 0.0025

SCRC-B20 35.5 62.3 2.2 Natural 0.6 6,215 0.0025

SCRC-B21 95.3 4.7 0.0 Agricultural 0.7 3,220 0.0148

SCRC-B22 99.7 0.3 0.0 Agricultural 0.8 2,829 0.1966

SCRC-B23 26.5 69.4 4.1 Natural 7.2 1,506 0.0163

SCRC-B24 69.3 27.4 3.3 Agricultural 0.8 629 0.0144

SCRC-B25 50.3 1.6 48.1 Agricultural 1.9 441 0.0531

SCRC-B26 8.2 91.8 0.0 Natural 0.1 5,296 0.0148

SCRC-B27 2.1 60.3 37.7 Natural 7.2 5,685 0.0163

SCRC-B28 0.2 58.0 41.8 Natural 89.4 1,504 0.0506

SCRC-B29/DG-B29 53.2 11.8 35.1 Agricultural 40.9 294 0.0031

SCRC-B30 80.4 8.7 10.9 Agricultural 1.1 2,912 0.0342

SCRC-B31 0.0 99.5 0.5 Natural 0.6 4,941 0.0114

SCRC-B32 97.4 1.3 1.4 Agricultural 0.8 2,126 0.0480

SCRC-B33 46.3 49.7 4.0 Mixed 1.4 2,453 0.0059

SCRC-B34 0.0 16.7 83.3 Urban 0.0 1,030 0.0242

SCRC-B35/DG-B35 0.0 0.1 99.9 Urban 2.7 665 3.4495

SCRC-B36 90.4 8.9 0.7 Agricultural 4.3 3,007 0.0163

SCRC-B37 63.9 32.3 3.8 Agricultural 0.2 684 0.0749
76 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

GAMA well 

identification number 

(USGS ID/CDPH ID)

Land-use (in percent)

Land-use 

classification

Septic tank density 

(number of 

tanks/km2

)

Distance to nearest 

formerly leaking 

underground fuel tank 

(meter)

Formerly leaking 

underground 

fuel tank density 

(number of tanks/km2

)

Agricultural Natural Urban 

Grid wells—Continued

SCRC-B38 0.5 34.4 65.2 Urban 0.1 410 0.5314

SCRC-B39 0.0 99.8 0.2 Natural 1.1 6,045 0.0114

SCRC-DPH-B07 4.8 91.6 3.6 Natural 1.4 — —

SCRC-DPH-B09 81.9 13.1 5.0 Agricultural 19.5 — —

SCRC-DPH-B25 30.0 63.6 6.4 Natural 1.0 — —

SCRC-DPH-B33 16.4 13.4 70.2 Urban 7.4 — —

SCRC-DPH-B38 0.2 21.1 78.7 Urban 3.9 — —

SCRC-H01 0.9 65.3 33.8 Natural 4.9 1,689 0.3729

SCRC-H02 0.1 99.9 0.0 Natural 1.8 9,119 0.0055

SCRC-H03 12.3 80.3 7.4 Natural 2.6 2,128 0.0345

SCRC-H04 2.9 97.0 0.1 Natural 0.7 1,706 0.0055

SCRC-H05 0.8 99.2 0.0 Natural 1.8 5,745 0.0055

SCRC-H06 46.7 53.3 0.0 Natural 0.8 5,498 0.0761

SCRC-H07 9.4 90.6 0.0 Natural 0.2 6,724 0.0197

SCRC-H08 0.2 98.9 0.9 Natural 1.8 3,271 0.0055

SCRC-H09 83.8 15.9 0.2 Agricultural 0.6 6,122 0.0055

SCRC-H10 6.1 93.8 0.1 Natural 0.6 11,872 0.0055

SCRC-H11 1.4 97.9 0.7 Natural 9.4 4,416 0.0186

SCRC-H12 0.0 100.0 0.0 Natural 0.6 4,294 0.0292

SCRC-H13 0.1 99.9 0.0 Natural 0.8 7,593 0.0142

SCRC-H14 2.5 92.1 5.4 Natural 8.1 844 0.0690

SCRC-H15 24.4 75.6 0.0 Natural 0.6 10,278 0.0055

SCRC-H16 0.3 99.5 0.1 Natural 0.1 6,905 0.0015

Understanding wells

SCRC-U01 33.6 66.3 0.1 Natural 0.4 3,544 0.0148

SCRC-U02 89.8 9.6 0.6 Agricultural 0.7 1,234 0.0749

SCRC-U03 89.8 10.2 0.0 Agricultural 1.4 7,433 0.0242

SCRC-U04 30.0 63.6 6.4 Natural 1.0 2,665 0.0531

SCRC-U05 8.8 89.2 1.9 Natural 7.2 5,272 0.0163

SCRC-U06 17.8 82.0 0.2 Natural 7.2 1,418 0.0163

SCRC-U07 94.3 5.3 0.5 Agricultural 1.1 3,522 0.0163

SCRC-U08 51.3 48.3 0.3 Agricultural 2.6 4,019 0.0310

SCRC-U09 93.9 5.7 0.3 Agricultural 1.1 3,800 0.0163

SCRC-U10 94.6 5.3 0.1 Agricultural 1.1 3,890 0.0163

SCRC-U11 0.0 98.2 1.8 Natural 0.1 10,656 0.0038

SCRC-U12 0.0 62.7 37.3 Natural 0.1 1,084 13.0428

SCRC-U13 0.0 97.0 3.0 Natural 0.1 7,881 0.0085

SCRC-U14 0.6 99.0 0.5 Natural 0.1 4,559 0.0085

SCRC-U15 1.0 98.3 0.7 Natural 0.1 8,248 0.0085

Table D1. Land-use classification, septic systems, and formerly leaking underground fuel tank information for USGS-grid and 

USGS-understanding wells, and CDPH-grid wells for inorganic constituents, South Coast Range–Coastal study unit, California GAMA 

Priority Basin Project.—Continued

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks in 500-meter radius around each 

well (U.S. Census Bureau, 1990). Number of formerly leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). GAMA, Groundwater Ambient Monitoring and 

Assessment Program; SCRC-B, South Coast Range–Coastal study unit Basins study area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; 

SCRC-U, South Coast Range–Coastal study unit understanding well; DG, South Coast Range–Coastal study unit grid well with supplemental CDPH data; DPH, 

CDPH-grid well; km2, square kilometer; CDPH, California Department of Public Health; tanks/km2, tanks per square kilometer]
Appendix D 77

Previous investigations have used a range of tritium 

values from 0.3 to 1.0 TU as thresholds for indicating 

presence of water that has exchanged with the atmosphere 

since 1952 (Michel, 1989; Plummer and others, 1993; Michel 

and Schroeder, 1994; Manning and others, 2005; Kulongoski 

and others, 2010). Tritium values greater than or equal to a 

threshold of 1.0 TU were defined as indicating the presence of 

groundwater recharged since 1952 for samples collected for 

the SCRC study unit. Using a tritium concentration of 1.0 TU 

as the threshold in this study allows a slightly larger fraction 

of modern water to be present in pre-modern groundwater 

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

higher threshold was used for this study because most of the 

wells were long-screened production wells and some mixing 

of waters of different ages likely occurred. Water recharged 

since 1952 is defined as “modern” groundwater. 14C is a widely used chronometer based on the 

radiocarbon content of organic and inorganic carbon. 

Dissolved inorganic carbon species, carbonic acid, 

bicarbonate, and carbonate typically are used for 14C dating 

of groundwater. 14C is formed in the atmosphere by the 

interaction of cosmic-ray neutrons with nitrogen and, to a 

lesser degree, with oxygen and carbon, and by above-ground 

nuclear explosions. 14C is incorporated into carbon dioxide and 

mixed throughout the atmosphere. The carbon dioxide enters 

the hydrologic cycle because it dissolves in precipitation and 

surface water in contact with the atmosphere. The 14C content 

in groundwater reflects the time since the water was last 

exposed to the atmospheric 14C source. 14C has a half-life of 

about 5,700 years and can be used to estimate groundwater 

ages ranging from 1,000 to approximately 30,000 years before 

present (Clark and Fritz, 1997). 14C data may be reported in units of percent modern (pM) 

or in units of percent modern carbon (pmc). 14C data for the 

SCRC study unit (Mathany and others, 2010) are given in pM 

units as reported by the analyzing laboratory. 14C data in pM 

units have been normalized for carbon isotopic fractionation 

based on a d13C value of –25‰ to account for biological 

process and exchange of carbon dioxide between the air and 

surface water that fractionate both 13C and 14C (Mook and Van 

Der Plicht, 1999). However, most of the variation of d13C in 

the dissolved inorganic carbon in groundwater is caused by 

water-rock interaction rather than by biological or gaseous 

exchange processes; thus, use of normalized 14C data may 

not be appropriate. Geochemical calculations to correct for 

changes in the isotopic composition of dissolved inorganic 

carbon by water-rock interaction must be made using 14C data 

that have not been normalized (Plummer and others, 2004). 

Un-normalized 14C data are reported in pmc units. Data were 

converted from pM to pmc using following equation derived 

from Plummer and others (2004):

pmc

pM C

=

+





 



 1

1 000

0 975

13 2

2



,

. , (D1)

where 

pM is the 14C value in units of pM, and

d13C is the measured 13C composition in units of 

per mil.

Calculated radiocarbon ages in this study are referred 

to as “uncorrected” because they have not been adjusted to 

consider water-rock interactions, such as exchanges with 

sedimentary sources of carbon (Fontes and Garnier, 1979). 

Estimated errors in radiocarbon ages are up to ± 20 percent. 

Groundwater with a 14C activity of greater than 90 pmc is 

reported as having an age of less than 1,000 years; no attempt 

was made to refine 14C ages less than 1,000 years. Measured 

values of pmc can be greater than 100 because the definition 

of the 14C content in “modern” carbon does not include the 

excess 14C produced in the atmosphere by above-ground 

nuclear weapons testing (Clark and Fritz, 1997). For the 

SCRC study unit, 14C activity less than 90 pmc was defined 

as indicating the presence of groundwater recharged before 

1952 (Kulongoski and others, 2010; Landon and others, 2010). 

Water recharged before 1952 is defined as “pre-modern” 

groundwater. 

Helium (He) is a naturally occurring inert gas initially 

included during the accretion of the Earth and later produced 

by radioactive decay of lithium, uranium, and thorium 

in the planet’s crust and mantle. Helium (3He plus 4He) 

concentrations in groundwater often exceed the expected 

solubility equilibrium values as a result of air entrainment, or 

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). Measured He 

concentrations in groundwater are the sum of several He 

components including

He He He He He total e = +

q a

+ + trit terr , (D2)

where 

Hetotal is the total amount of helium measured in the 

groundwater sample;

Heeq is the helium derived from equilibration with 

the atmosphere at the time of recharge;

Hea is the helium derived from entrained air 

bubbles (“excess” air);

Hetrit is the helium produced by radioactive decay 

of tritium in the sample; and

Heterr is the helium produced by radioactive 

decay of uranium and thorium in aquifer 

materials or emanating from deeper in the 

Earth’s crust or mantle.
78 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Heeq, Hea, and Heterr all consist of helium-3 (3He) and helium-4 

(

4

He); however, Hetrit consists only of 3He.

The presence of Heterr in groundwater, from its production 

in aquifer material or deeper in the crust, is indicative of 

long groundwater residence times (Andrews and Lee, 1979; 

Kulongoski and others, 2008). For the purpose of estimating 

groundwater residence times, the amount of Heterr is defined 

as the concentration of the total measured helium minus the 

fraction as a result of air equilibration (Heeq) and entrained air 

bubbles (Hea). For the purposes of this study, the percentage 

of Heterr (%Heterr) is used to identify groundwater with 

residence times greater than 100 years. %Heterr is defined as 

the concentration of Heterr divided by the Hetotal in the sample 

(corrected for air-bubble entrainment):

%He He

He He terr

terr

total a

= − × 100 . (D3).

(Hetrit is neglected in calculation of %Heterr because it typically 

is very small.) For the SCRC study unit, values of %Heterr 

>5 were defined as indicating the presence of pre-modern 

groundwater similar to the procedures for other GAMA 

Priority Basin Project study units (Kulongoski and others, 

2010; Landon and others, 2010). 

Tritium concentrations, %Heterr, carbon-14 (as pmc), 

carbon-14 ages, and age classifications are reported in 

table D2. While more sophisticated lumped parameter models 

for analyzing age distributions that incorporate mixing are 

available (Cook and Böhlke, 2000), use of these alternative 

models to understand age mixtures was not needed for the 

assessments in this report. Classification into modern, mixed, 

and pre-modern categories was sufficient to provide an 

appropriate and useful characterization for the purposes of 

examining groundwater quality.

For the SCRC, groundwater ages were classified as 

follows: 

Classification Tritium (TU) Carbon-14 (pmc) %Heterr

Modern ≥ 1.0 ≥ 90 or no data < 5

Modern or Mixed ≥ 1.0 no data ≥ 5 or no data

Mixed ≥ 1.0 < 90 any value

Pre-modern or Mixed < 1.0 no data < 5 or no data

Pre-modern < 1.0 < 90 any value

Pre-modern < 1.0 no data ≥5

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidation-reduction 

characteristics, DO concentrations, and pH (table D3). 

Oxidation-reduction (redox) conditions and pH influence the 

mobility of many organic and inorganic constituents (Hem, 

1985; McMahon and Chapelle, 2008). Along groundwater 

flow paths, redox conditions commonly proceed along a 

well-documented sequence of Terminal Electron Acceptor 

Processes (TEAP); one TEAP typically dominates at a 

particular time and aquifer location (Chapelle and others, 

1995; Chapelle, 2001). The predominant TEAPs are oxygenreducing (oxic), nitrate-reducing, manganese-reducing, ironreducing, sulfate-reducing, and methanogenesis. The presence 

of redox-sensitive chemical species indicating more than one 

TEAP may indicate (1) the discharge from the well includes 

mixed waters from different redox zones upgradient of the 

well, (2) the well is screened across more than one redox 

zone, or (3) there is spatial heterogeneity in microbial activity 

in the aquifer. In addition, different redox couples often are 

not consistent, indicating electrochemical disequilibrium 

in groundwater (Lindburg and Runnells, 1984) that further 

complicates the assessments of redox conditions. 

In this report, redox conditions were represented in two 

ways: dissolved oxygen concentration and classified redox 

state. Dissolved oxygen concentrations were measured at 

USGS-grid and understanding wells (Mathany and others, 

2010), but are not reported in the CDPH database (table D3). 

Redox conditions were classified based on dissolved oxygen, 

nitrate, manganese, iron, and sulfate concentrations by using 

the classification scheme of McMahon and Chapelle (2008) 

(table D3). An automated workbook program was used to 

assign the redox classification to each sample (Jurgens and 

others, 2009). For wells without USGS inorganic constituent 

data, the most recent data within the current period (May 20, 

2005, to May 19, 2008) for that well in the CDPH database 

were used.
Appendix D 79

Table D2. Groundwater age classification information for USGS-grid and USGS-understanding wells, South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project, May–November 2008.

[Sample classified as pre-modern if recharged prior to 1952. Samples classified as modern if recharged after 1952. Sample is classified as mixed if sample 

contains both modern and pre-modern water. GAMA, Groundwater Ambient Monitoring and Assessment Program; SCRC-B, South Coast Range–Coastal study 

unit Basins study area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; SCRC-U, South Coast Range–Coastal study unit understanding 

well; TU, tritium units; <, less than; >, greater than; nc, not collected; —, no data; USGS, U.S. Geological Survey]

USGS GAMA well 

identification number

Tritium activity 

(TU)

Terrigenic helium, 

(percent of total helium)

Carbon-141

(percent modern carbon)

Carbon-14 age 

(uncorrected), years

Groundwater age 

classification

Grid wells

SCRC-B01 0.2 nc nc — Pre-modern or Mixed

SCRC-B02 2.0 nc nc — Modern or Mixed

SCRC-B03 2.0 0.0 nc — Modern

SCRC-B04 0.3 4.9 nc — Pre-modern or Mixed

SCRC-B05 1.6 0.0 81.6 1,580 Mixed

SCRC-B06 0.4 0.0 nc — Pre-modern or Mixed

SCRC-B07 0.4 nc nc — Pre-modern or Mixed

SCRC-B08 <0.2 nc 73.0 2,470 Pre-modern

SCRC-B09 0.2 16.2 nc — Pre-modern

SCRC-B10 0.4 1.8 78.2 1,920 Pre-modern

SCRC-B11 1.7 0.0 92.1 <1,000 Modern

SCRC-B12 1.6 0.0 nc — Modern

SCRC-B13 0.6 8.9 nc — Pre-modern

SCRC-B14 2.0 26.4 nc — Modern or Mixed

SCRC-B15 2.9 30.5 nc — Modern or Mixed

SCRC-B16 2.9 0.0 105.5 <1,000 Modern

SCRC-B17 2.4 0.0 nc — Modern

SCRC-B18 2.3 0.1 nc — Modern

SCRC-B19 1.7 13.4 nc — Modern or Mixed

SCRC-B20 1.8 9.2 nc — Modern or Mixed

SCRC-B21 0.6 3.5 71.7 2,620 Pre-modern

SCRC-B22 1.5 0.0 96.4 <1,000 Modern

SCRC-B23 0.2 0.0 67.3 3,120 Pre-modern

SCRC-B24 0.2 0.5 nc — Pre-modern or Mixed

SCRC-B25 2.2 0.0 nc — Modern

SCRC-B26 1.0 nc 82.3 1,510 Mixed

SCRC-B27 <0.2 49.3 63.3 3,620 Pre-modern

SCRC-B28 1.6 0.0 77.9 1,950 Mixed

SCRC-B29 0.8 23.8 nc — Pre-modern

SCRC-B30 2.3 0.0 96.2 <1,000 Modern

SCRC-B31 0.2 26.0 nc — Pre-modern

SCRC-B32 1.1 28.3 nc — Modern or Mixed

SCRC-B33 <0.2 0.0 nc — Pre-modern or Mixed

SCRC-B34 <0.2 10.2 16.0 14,670 Pre-modern

SCRC-B35 0.9 2.2 nc — Pre-modern or Mixed

SCRC-B36 2.0 0.0 98.8 <1,000 Modern

SCRC-B37 1.1 15.9 62.9 3,670 Mixed

SCRC-B38 <0.2 0.0 nc — Pre-modern or Mixed

SCRC-B39 <0.2 0.0 56.1 4,590 Pre-modern

SCRC-H01 <0.2 57.6 11.1 17,570 Pre-modern

SCRC-H02 1.8 nc 91.0 <1,000 Modern
80 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

USGS GAMA well 

identification number

Tritium activity 

(TU)

Terrigenic helium, 

(percent of total helium)

Carbon-141

(percent modern carbon)

Carbon-14 age 

(uncorrected), years

Groundwater age 

classification

Grid wells—Continued

SCRC-H03 0.3 49.0 17.5 13,940 Pre-modern

SCRC-H04 <0.2 nc 1.7 32,710 Pre-modern

SCRC-H05 0.1 17.7 27.5 10,320 Pre-modern

SCRC-H06 0.5 0.0 57.4 4,410 Pre-modern

SCRC-H07 0.3 6.0 50.0 5,510 Pre-modern

SCRC-H08 <0.2 87.5 2.3 30,340 Pre-modern

SCRC-H09 0.3 3.2 64.2 3,500 Pre-modern

SCRC-H10 <0.2 8.7 15.4 15,000 Pre-modern

SCRC-H11 0.3 17.6 24.3 11,290 Pre-modern

SCRC-H12 0.2 48.6 42.0 6,920 Pre-modern

SCRC-H13 <0.2 0.9 61.1 3,900 Pre-modern

SCRC-H14 <0.2 85.8 24.9 11,100 Pre-modern

SCRC-H15 <0.2 11.7 34.8 8,420 Pre-modern

SCRC-H16 0.2 61.7 9.1 19,200 Pre-modern

Understanding wells

SCRC-U01 0.6 nc nc — Pre-modern or Mixed

SCRC-U02 1.3 12.6 nc — Modern or Mixed

SCRC-U03 0.4 0.0 80.5 1,690 Pre-modern

SCRC-U04 1.6 nc nc — Modern or Mixed

SCRC-U05 2.0 0.0 nc — Modern

SCRC-U06 <0.2 1.3 74.1 2,350 Pre-modern

SCRC-U07 1.7 9.8 106.9 <1,000 Modern

SCRC-U08 1.7 0.0 99.7 <1,000 Modern

SCRC-U09 1.6 1.6 104.5 <1,000 Modern

SCRC-U10 1.5 0.0 103.5 <1,000 Modern

SCRC-U11 1.7 0.0 nc — Modern

SCRC-U12 1.7 0.0 nc — Modern

SCRC-U13 2.0 0.0 nc — Modern

SCRC-U14 2.1 0.0 nc — Modern

SCRC-U15 2.0 0.0 96.1 <1,000 Modern

1

 Carbon-14 data shown in this table are different from data provided in the data series report by Mathany and others (2010) because the carbon-14 data have 

not been normalized by the associated carbon-13 data.

Table D2. Groundwater age classification information for USGS-grid and USGS-understanding wells, South Coast Range–Coastal 

study unit, California GAMA Priority Basin Project, May–November 2008.—Continued

[Sample classified as pre-modern if recharged prior to 1952. Samples classified as modern if recharged after 1952. Sample is classified as mixed if sample 

contains both modern and pre-modern water. GAMA, Groundwater Ambient Monitoring and Assessment Program; SCRC-B, South Coast Range–Coastal study 

unit Basins study area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; SCRC-U, South Coast Range–Coastal study unit understanding 

well; TU, tritium units; <, less than; >, greater than; nc, not collected; —, no data; USGS, U.S. Geological Survey]
Appendix D 81

Table D3. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, South Coast Range–Coastal study 

unit, California GAMA Priority Basin Project.

[Redox category and redox process determined using the algorithm from Jurgens and others (2009); Oxic, dissolved oxygen greater than or equal to (≥) 

0.5 mg/L; anoxic/suboxic, dissolved oxygen < 0.5 mg/L; indeterminate, insufficient data to determine redox classification; mixed, oxic and anoxic processes are 

present. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; SCRC-B, South Coast Range–Coastal study unit Basins study 

area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; SCRC-U, South Coast Range–Coastal study unit understanding well; DG, South 

Coast Range–Coastal study unit grid well with supplemental CDPH data; DPH, CDPH grid well; redox, oxidation reduction; mg/L, milligrams per liter; 

μg/L, micrograms per liter; <, less than; nc, not collected; Fe, iron; SO4, sulfate; Mn, manganese]

GAMA/CDPH 

well identification 

number

pH

Oxidizing and reducing constituents

Redox 

category

Redox 

process

Dissolved 

oxygen 

(mg/L)

Nitrate plus nitrite 

as nitrogen

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Grid wells

SCRC-B01/DG-B01 7.1 0.2 0.00 25 1,200 140 Anoxic Fe/SO4

-reducing

SCRC-B02/DG-B02 7.8 2.5 1.67 <10 50 23 Oxic Oxic

SCRC-B03/DG-B03 7.9 9.4 16.9 0 0 270 Oxic Oxic

SCRC-B04/DG-B04 7.1 0.8 0.41 <5 8 33 Oxic Oxic

SCRC-B05 7.4 nc 8.38 0 9 300 Indeterminate

SCRC-B06/DG-B06 7.2 9.8 2.08 nc nc nc Oxic Oxic

SCRC-B07 1 6.6 6.6 1.60 124 <8 108 Mixed oxic/Mn-reducing

SCRC-B08 7.2 0.7 0.50 156 48 251 Mixed oxic/Mn-reducing

SCRC-B09/DG-B09 7.5 8.7 nc nc nc nc Oxic Oxic

SCRC-B10 6.2 1.9 1.75 28 13 128 Oxic Oxic

SCRC-B11 7.1 2.1 0.02 948 1,600 383 Mixed oxic/Mn/Fe/SO4

-reducing

SCRC-B12/DG-B12 7.2 5.1 8.81 nc nc nc Oxic Oxic

SCRC-B13/DG-B13 7.4 5.2 3.39 0 0 97 Oxic Oxic

SCRC-B14/DG-B14 7.3 nc 17.2 0 0 520 Indeterminate

SCRC-B15/DG-B15 7.4 nc 6.10 nc nc nc Indeterminate

SCRC-B16 7.1 nc 35.6 <0.2 9 514 Indeterminate

SCRC-B17 7.3 nc nc nc nc nc Indeterminate

SCRC-B18 7.2 5.4 nc nc nc nc Oxic Oxic

SCRC-B19 1 7.2 5.6 1.83 0 <8 313 Oxic Oxic

SCRC-B20 7.0 2.8 nc nc nc nc Oxic Oxic

SCRC-B21 7.1 0.2 0.04 1,030 3,170 673 Anoxic Mn/Fe/SO4

-reducing

SCRC-B22 7.1 3.6 25.9 0 16 514 Oxic Oxic

SCRC-B23 7.2 4.8 2.81 0 8 288 Oxic Oxic

SCRC-B24 7.3 5.8 nc nc nc nc Oxic Oxic

SCRC-B25 1 7.2 0.5 2.09 <0.4 <8 325 Oxic Oxic

SCRC-DPH-B25/U-04 7.0 1.7 5.08 0 0 12 Oxic Oxic

SCRC-B26 7.2 0.1 <0.04 559 4,450 373 Anoxic Mn/Fe/SO4

-reducing

SCRC-B27 7.2 4.3 1.67 1 8 311 Oxic Oxic

SCRC-B28 6.5 6.5 7.86 1 7 38 Oxic Oxic

SCRC-B29/DG-B29 5.8 0.1 <0.04 40 590 217 Anoxic Fe/SO4

-reducing

SCRC-B30 7.3 8.0 12.0 <0.2 <8 314 Oxic Oxic

SCRC-B31 7.2 2.0 nc nc nc nc Oxic Oxic

SCRC-B32 7.1 4.2 nc nc nc nc Oxic Oxic

SCRC-B33 6.9 3.7 nc nc nc nc Oxic Oxic

SCRC-DPH-B33 7.5 nc 6.55 0 0 270 Indeterminate

SCRC-B34 7.1 0.2 0.03 254 130 136 Anoxic Mn/Fe/SO4

-reducing

SCRC-B35/DG-B35 7.0 0.7 <0.09 1,300 270 550 Mixed Fe/SO4

-reducing
82 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

GAMA/CDPH 

well identification 

number

pH

Oxidizing and reducing constituents

Redox 

category

Redox 

process

Dissolved 

oxygen 

(mg/L)

Nitrate plus nitrite 

as nitrogen

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Grid wells—Continued

SCRC-B36 7.3 8.5 6.75 0 7 309 Oxic Oxic

SCRC-B37 7.2 0.3 <0.04 422 156 142 Anoxic Mn/Fe/SO4

-reducing

SCRC-B38 7.3 7.1 nc nc nc nc Oxic Oxic

SCRC-DPH-B38 7.5 nc 0.88 3 51 250 Indeterminate

SCRC-B39 7.2 4.1 0.46 <0.2 <4 312 Oxic Oxic

SCRC-H01 7.6 0.2 <0.04 142 105 44 Anoxic Mn/Fe/SO4

-reducing

SCRC-H02 7.5 6.2 0.06 7 19 264 Oxic Oxic

SCRC-H03 6.6 0.5 0.58 24 430 72 Mixed Oxic/Fe/SO4

-reducing

SCRC-H04 7.2 0.3 0.06 52 45 93 Anoxic Mn-red

SCRC-H05 7.6 4.6 2.70 1 <8 13 Oxic Oxic

SCRC-H06 6.8 4.4 2.65 14 11 24 Oxic Oxic

SCRC-H07 6.7 0.6 0.03 52 244 78 Mixed oxic/Fe/SO4

-reducing

SCRC-H08 7.4 1.1 0.37 17 11 66 Oxic Oxic

SCRC-H09 7.1 1.8 <0.04 30 577 166 Mixed oxic/Fe/SO4

-reducing

SCRC-H10 7.5 1.3 1.85 12 8 234 Oxic Oxic

SCRC-H11 7.2 0.9 0.14 1 8 30 Oxic Oxic

SCRC-H12 6.9 0.5 0.32 11 15 261 Oxic Oxic

SCRC-H13 6.8 2.7 2.28 10 71 23 Oxic Oxic

SCRC-H14 7.2 0.7 0.14 25 29 51 Oxic Oxic

SCRC-H15 7.0 1.0 0.36 29 54 218 Oxic Oxic

SCRC-H16 7.2 nc 0.10 11 7 65 Indeterminate

Understanding wells

SCRC-U01 1 7.3 0.3 <0.06 595 80 310 Anoxic Mn-reducing

SCRC-U02 1 7.2 0.6 <0.04 648 2,930 624 Mixed oxic/Fe/SO4

-reducing

SCRC-U03 7.1 0.8 5.73 136 <8 240 Mixed oxic/Mn-reducing

SCRC-U05 6.7 9.1 2.19 nc nc nc Oxic Oxic

SCRC-U06 7.3 5.2 2.23 0 <8 277 Oxic Oxic

SCRC-U07 7.2 7.5 77.3 <0.2 <8 474 Oxic Oxic

SCRC-U08 7.4 7.2 3.17 0 <8 186 Oxic Oxic

SCRC-U09 7.2 5.9 67.8 0 <8 381 Oxic Oxic

SCRC-U10 7.1 7.6 73.3 0 <8 364 Oxic Oxic

SCRC-U11 6.2 0.5 nc nc nc nc Oxic Oxic

SCRC-U12 6.0 4.6 nc nc nc nc Oxic Oxic

SCRC-U13 7.0 0.1 nc nc nc nc Anoxic/suboxic Indeterminate

SCRC-U14 7.0 0.3 nc nc nc nc Anoxic/suboxic Indeterminate

SCRC-U15 6.1 1.3 1.99 8 <8 22 Oxic Oxic

1

 Data for nitrate plus nitrite, manganese, iron, and sulfate were not collected by the GAMA Priority Basin Project but were collected by the USGS for another 

study during July–August 2008 except for SCRC-U01 data which were collected during August 2007.

Table D3. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, South Coast Range–Coastal study 

unit, California GAMA Priority Basin Project.—Continued

[Redox category and redox process determined using the algorithm from Jurgens and others (2009); Oxic, dissolved oxygen greater than or equal to (≥) 

0.5 mg/L; anoxic/suboxic, dissolved oxygen < 0.5 mg/L; indeterminate, insufficient data to determine redox classification; mixed, oxic and anoxic processes are 

present. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; SCRC-B, South Coast Range–Coastal study unit Basins study 

area; SCRC-H, South Coast Range–Coastal study unit Uplands study area; SCRC-U, South Coast Range–Coastal study unit understanding well; DG, South 

Coast Range–Coastal study unit grid well with supplemental CDPH data; DPH, CDPH grid well; redox, oxidation reduction; mg/L, milligrams per liter; 

μg/L, micrograms per liter; <, less than; nc, not collected; Fe, iron; SO4, sulfate; Mn, manganese]
Appendix E 83

Data for noble gases and stable isotopes of nitrogen in 

dissolved nitrogen gas were not available in time for inclusion 

in the report by Mathany and others (2010). A brief discussion 

of the collection procedures and the associated data are given 

here.

Samples were collected from 61 wells in the SCRC 

study unit for analysis of dissolved noble gases. Dissolved 

noble gases were collected in 3⁄8-inch-diameter copper tubes 

using reinforced nylon tubing connected to the hose bib at 

the wellhead. Groundwater was flushed through the tubing 

to dislodge bubbles before flow was restricted with a back 

pressure valve. Clamps on either end of the copper tube then 

were tightened, trapping a sample of groundwater for analyses 

of dissolved noble gases (Weiss, 1968). Samples were 

analyzed by the Lawrence Livermore National Laboratory 

using methods described by Moran and others (2002) and 

Eaton and others (2003). Data for noble gases are given in 

table E1.

Stable isotopes of nitrogen in dissolved nitrogen gas 

were determined using the samples collected for analysis of 

dissolved gases. Samples were collected from 12 wells in the 

SCRC study unit for analysis of dissolved gases. Dissolved 

gases were collected by bottom filling two 150-milliliter 

glass serum bottles that were first filled with groundwater, 

then submerged in a stainless container (at least 2 liters in 

size or larger) filled with groundwater. The discharge tube 

was slowly removed with water still flowing, and a rubber 

stopper was inserted to cap the bottle while the bottle was 

submerged in the water. These samples had no headspace or 

air bubbles inside the bottles and were sealed underwater to 

avoid atmospheric contamination. Samples were analyzed by 

the USGS Stable Isotope Laboratory in Reston, Virginia, using 

methods described in Revesz and others (1999), Tobias and 

others, (2007), and Green and others (2008). Stable isotope 

data for nitrogen in dissolved gases are given in table E1.

Appendix E. Data not Published in the USGS Data-Series Report

Appendix E Stable isotopic compositions of nitrogen are reported as 

relative isotope ratios using the standard delta notation, 

δ15N/‰ (Coplen, 2011):

 i

i j

P

i j

std

E

R E E

R E E = ( )

( )

















− /

/

1, (E1)

where 

i is the atomic mass of the heavier isotope of 

the element,

j is the atomic mass of the lighter isotope of the 

element,

E is the element (for this report, N for nitrogen),

Rp is the isotope ratio of the heavier isotope of 

the element (iE ) to the lighter isotope of 

the element (jE) in the sample P, and

Rstd is the isotope ratio of the heavier isotope of 

the element (iE ) to the lighter isotope of 

the element (jE) in the reference material.

The reference material nitrogen is atmospheric nitrogen 

gas, which is assigned a d15N value of 0. Positive values 

indicate enrichment of the heavier isotope, and negative values 

indicate depletion of the heavier isotope, compared to the 

ratios observed in the standard reference material.
84 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

Table E1. Results for analyses of helium isotope ratios, noble gases, and stable isotope ratios of nitrogen gas derived for samples 

collected for the South Coast Range–Coastal study unit, California GAMA Priority Basin Project, May–November 2008.

[Stable isotope nitrogen ratios are reported in the standard delta notation (δ), the ratio of the heavier isotope to the more common lighter isotope relative to a 

standard reference material. Abbreviations: GAMA, Groundwater Ambient and Monitoring; cm3 STP/g–1 H2O, cubic centimeters at standard temperature and 

pressure per gram of water; mg/L, milligram per liter; N, nitrogen; nc, not collected; ‰, per mil]

USGS 

GAMA well 

identification 

number

Helium-3/

helium-4 

(61040)

(x 10–6)

atom ratio

Helium-4 

(85561)

(x 10–8)

Argon 

(85563)

(x 10–4)

Krypton 

(85565)

(x 10–8)

Neon 

(61046)

(x 10–7)

Xenon 

(85567)

(x 10–8)

Dissolved 

nitrogen gas 

(00597)

(mg/L)

δ15N of 

dissolved 

nitrogen gas

(82698)

(‰) (cm3

 STP/g–1 H2O)

Grid wells

SCRC-B01 1.31 4.11 nc nc 1.94 nc nc nc

SCRC-B02 1.43 6.06 nc nc 2.63 nc nc nc

SCRC-B03 1.44 9.20 4.64 9.21 3.69 1.24 nc nc

SCRC-B04 1.28 7.17 4.05 8.58 2.78 1.14 nc nc

SCRC-B05 1.49 7.56 4.12 8.29 3.01 1.26 nc nc

SCRC-B06 1.35 9.63 4.54 8.70 3.93 1.25 nc nc

SCRC-B09 1.39 10.14 4.59 8.95 4.26 1.09 nc nc

SCRC-B10 1.36 7.14 4.03 8.54 2.86 1.09 nc nc

SCRC-B11 1.40 7.79 4.11 8.75 3.38 1.16 nc nc

SCRC-B12 1.37 5.12 3.23 7.08 2.23 0.98 nc nc

SCRC-B13 1.35 9.15 4.22 7.88 3.17 0.82 nc nc

SCRC-B14 1.51 8.78 4.38 9.05 3.49 1.13 nc nc

SCRC-B15 1.67 10.52 4.95 10.25 4.19 1.20 nc nc

SCRC-B16 1.41 6.36 3.95 8.73 2.82 1.17 nc nc

SCRC-B17 1.38 5.95 4.16 8.94 2.65 1.09 nc nc

SCRC-B18 1.41 11.82 4.76 8.76 4.51 1.28 nc nc

SCRC-B19 1.37 9.70 4.00 8.06 3.38 1.19 nc nc

SCRC-B20 1.34 5.55 3.36 7.23 2.13 1.10 nc nc

SCRC-B21 1.29 5.14 3.31 7.60 2.07 1.12 nc nc

SCRC-B22 1.52 9.04 4.41 8.73 3.57 1.18 nc nc

SCRC-B23 1.29 10.04 4.67 9.15 3.65 1.50 30.27 0.53

SCRC-B24 1.33 7.90 4.11 8.37 3.09 1.23 nc nc

SCRC-B25 1.42 7.44 4.43 8.88 3.06 1.19 nc nc

SCRC-B27 1.32 6.56 4.53 8.85 3.79 1.21 29.84 0.46

SCRC-B28 1.39 5.15 3.57 7.56 2.34 0.99 21.78 1.01

SCRC-B29 1.12 13.29 4.57 8.78 3.94 1.15 30.36 1.86

SCRC-B30 1.50 11.00 5.25 10.16 4.45 1.25 37.23 0.43

SCRC-B31 1.34 7.84 3.94 8.33 2.92 1.04 nc nc

SCRC-B32 1.41 9.55 4.56 9.14 3.80 1.14 nc nc

SCRC-B33 1.29 5.63 3.25 7.02 2.35 0.95 nc nc

SCRC-B34 1.03 7.26 3.49 7.21 2.57 1.04 nc nc

SCRC-B35 1.34 6.71 4.01 8.62 2.70 1.11 nc nc

SCRC-B36 1.49 11.55 4.98 9.02 5.09 1.11 35.46 0.56

SCRC-B37 1.31 7.25 3.72 7.74 2.46 1.15 nc nc

SCRC-B38 1.37 6.60 3.47 7.21 2.83 0.98 nc nc

SCRC-B39 1.27 8.14 3.99 8.36 3.02 1.29 nc nc

SCRC-H01 0.49 14.64 3.67 7.82 2.31 1.27 nc nc

SCRC-H03 0.64 11.36 3.78 8.08 2.37 1.21 nc nc

SCRC-H05 1.06 7.35 3.43 7.25 2.39 1.00 nc nc
Appendix E 85

USGS 

GAMA well 

identification 

number

Helium-3/

helium-4 

(61040)

(x 10–6)

atom ratio

Helium-4 

(85561)

(x 10–8)

Argon 

(85563)

(x 10–4)

Krypton 

(85565)

(x 10–8)

Neon 

(61046)

(x 10–7)

Xenon 

(85567)

(x 10–8)

Dissolved 

nitrogen gas 

(00597)

(mg/L)

δ15N of 

dissolved 

nitrogen gas

(82698)

(‰) (cm3

 STP/g–1 H2O)

Grid wells—Continued

SCRC-H06 1.16 5.51 3.08 6.63 2.44 0.90 nc nc

SCRC-H07 1.17 5.84 3.26 7.29 2.22 0.96 nc nc

SCRC-H08 0.20 53.35 3.80 8.34 2.66 1.15 nc nc

SCRC-H09 1.22 5.77 3.37 7.44 2.30 1.04 nc nc

SCRC-H10 1.15 8.99 3.97 7.96 3.10 1.10 nc nc

SCRC-H11 1.08 7.92 3.65 7.74 2.58 1.05 nc nc

SCRC-H12 0.84 11.57 3.50 7.65 2.40 1.09 nc nc

SCRC-H13 1.32 5.42 3.14 6.49 2.16 0.97 nc nc

SCRC-H14 0.36 45.02 4.11 8.02 2.64 1.16 nc nc

SCRC-H15 1.13 5.72 3.32 7.37 2.07 1.05 nc nc

SCRC-H16 0.74 15.82 4.39 8.72 3.58 1.38 nc nc

Understanding wells

SCRC-U01 0.76 10.14 3.50 7.73 2.30 1.05 nc nc

SCRC-U02 1.31 5.25 3.14 7.06 1.91 1.02 nc nc

SCRC-U03 1.38 5.70 3.63 7.78 2.38 1.17 nc nc

SCRC-U04 nc nc nc nc nc nc 23.29 0.48

SCRC-U05 1.41 4.73 3.21 7.14 2.10 0.98 nc nc

SCRC-U06 1.36 8.45 4.32 8.90 3.31 1.22 28.93 0.45

SCRC-U07 1.41 17.93 6.37 10.95 6.24 1.31 51.11 0.40

SCRC-U08 1.41 8.98 4.77 9.37 3.67 1.35 27.96 0.65

SCRC-U09 1.44 11.02 4.37 8.30 4.11 1.06 27.76 0.60

SCRC-U10 1.45 8.29 3.67 7.45 3.20 0.99 23.65 0.48

SCRC-U11 1.38 4.37 3.62 8.21 2.03 1.12 nc nc

SCRC-U12 1.41 6.96 4.54 9.06 3.16 1.25 nc nc

SCRC-U13 0.96 5.37 2.89 6.26 1.60 0.88 nc nc

SCRC-U14 1.37 4.31 3.04 6.75 1.85 0.89 nc nc

SCRC-U15 1.35 5.44 3.48 7.48 2.22 0.93 nc nc

Table E1. Results for analyses of helium isotope ratios, noble gases, and stable isotope ratios of nitrogen gas derived for samples 

collected for the South Coast Range–Coastal study unit, California GAMA Priority Basin Project, May–November 2008.—Continued

[Stable isotope nitrogen ratios are reported in the standard delta notation (δ), the ratio of the heavier isotope to the more common lighter isotope relative to a 

standard reference material. Abbreviations: GAMA, Groundwater Ambient and Monitoring; cm3 STP/g–1 H2O, cubic centimeters at standard temperature and 

pressure per gram of water; mg/L, milligram per liter; N, nitrogen; nc, not collected; ‰, per mil]

Appendix E
86 Status and Understanding of Groundwater Quality in the South Coast Range–Coastal Study Unit, 2008

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