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MULTIVARIATE ANALYSIS OF LEAD IN URBAN SOIL IN SACRAMENTO,
CALIFORNIA
Michael J. Solt
B.S., West Virginia University, 2002
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
GEOLOGY
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2010
MULTIVARIATE ANALYSIS OF LEAD IN URBAN SOIL IN SACRAMENTO,
CALIFORNIA
A Thesis
by
Michael J. Solt
Approved by:
__________________________________, Committee Chair
Dr. Daniel Deocampo
__________________________________, Second Reader
Dr. Michelle Norris
__________________________________, Third Reader
Dr. David Evans
____________________________
Date
ii
Student: Michael J. Solt
I certify that this student has met the requirements for format contained in the University format
manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for
the thesis.
__________________________, Department Chair
Dr. David Evans
Department of Geology
iii
___________________
Date
Abstract
of
MULTIVARIATE ANALYSIS OF LEAD IN URBAN SOIL IN SACRAMENTO,
CALIFORNIA
by
Michael J. Solt
Lead contamination in soil is a common problem in urban areas. Sacramento is no
exception. Seventy soil samples were collected in Sacramento and analyzed by 4-acid
digestion followed by inductively coupled plasma- atomic emission spectrometry and
inductively coupled plasma-mass spectrometry for 43 elements. In addition to the soil
samples collected for this study, 43 soil samples collected and analyzed by the same
methods supplemented the data. Twenty-eight additional soil samples collected in central
Sacramento were analyzed by hand-held X-ray fluorescence spectrometry for Pb and Zn.
Two-hundred and fifty-seven samples collected within the area of Sacramento County
from the late 1970s to 1980 by the National Uranium Resource Evaluation and
Hydrogeochemical Stream Sediment Reconnaissance Program were analyzed by the
United States Geological Survey by 4-acid digestion followed by inductively coupled
plasma- atomic emission spectrometry and inductively coupled plasma-mass
spectrometry for 42 elements. A prediction map of the lead concentrations in soil from
the recent data collected in Sacramento was generated by ordinary kriging. The
prediction map shows elevated lead concentrations in soil located in the central, older
area of Sacramento where traffic density and industrial activity are spatially and
iv
temporally persistent. The historic data collected by the NURE program and a subset of
recent data collected for this study, were analyzed independently by factor analysis. Both
independent analyses identified three lithogenic factors. One such factor includes
correlations among Co, Cr, Fe, Mg, and Ni, which are associated with mafic and
ultramafic rocks. Another factor identified by both independent analyses includes
correlations among Rare Earth Elements, K, and Rb, which are associated with felsic
rocks. The last factor identified by both independent analyses includes correlations
among Ca, Na, and Sr, which are associated with felsic rocks enriched in these elements.
An additional factor was identified by the recent data, which includes correlations among
Pb, Cd, Cu, and Zn associated with anthropogenic contamination from vehicle emissions.
The presence of the anthropogenic factor in the new data and its subsequent absence from
the NURE data is explained by the greater density of recent soil samples collected within
the city of Sacramento where anthropogenic contamination is present.
_______________________, Committee Chair
Dr. Daniel Deocampo
_______________________
Date
v
ACKNOWLEDGMENTS
Dr. Daniel Deocampo, Dr. Michelle Norris, Dr. David Evans, Wendy Oor, Barb Dalgish,
Sacramento County, Georgia State University, Charlie Alpers, CSUS Geology
Department, U. S. Geological Survey
vi
TABLE OF CONTENTS
Page
Acknowledgments.................................................................................................................... vi
List of Tables ........................................................................................................................... ix
List of Figures .......................................................................................................................... xi
Chapter
1. INTRODUCTION.……………..………………………………………………………… 1
2. BACKGROUND ................................................................................................................ 5
Lead Uses..................................................................................................................... 5
Adverse Health Effects of Lead Ingestion ................................................................... 5
Natural and Anthropogenic sources of Lead................................................................ 6
Soil Limits for Lead ..................................................................................................... 8
Naturally Occurring Sacramento and Yolo County Soils ............................................ 9
Sacramento Geology .................................................................................................. 10
Previous Work ........................................................................................................... 14
Factor Analysis of Geochemical Variability.............................................................. 18
Ordinary Kriging of Geospatial Data ......................................................................... 24
3. METHODS ....................................................................................................................... 28
Soil Sample Site Selection ......................................................................................... 28
Soil Sample Collection .............................................................................................. 31
Soil Sample Preparation............................................................................................. 33
Laboratory Analysis ................................................................................................... 33
Spatial Analysis ......................................................................................................... 34
Statistical Analysis ..................................................................................................... 37
4. RESULTS ......................................................................................................................... 43
Environmental Analysis ............................................................................................. 43
Replicate Analysis ..................................................................................................... 45
Lead Concentrations and Spatial Analysis................................................................. 47
Ordinary Kriging........................................................................................................ 51
vii
Factor Analysis: Maximum Likelihood Estimation ................................................... 53
Factor Analysis: Principal Component Estimation .................................................... 60
5. DISCUSSION ................................................................................................................... 67
Proximity to Roads .................................................................................................... 67
Ordinary Kriging Discussion ..................................................................................... 68
Interpretation of Factors............................................................................................. 72
6. CONCLUSIONS............................................................................................................... 85
Appendix A. Tables ............................................................................................................... 88
Appendix B. Figures ............................................................................................................ 122
Appendix C. The Geochemical Procedure for Ultra-Trace Level Using ICP-AES and ICP-MS
.................................................................................................................................. 183
References ............................................................................................................................. 189
viii
LIST OF TABLES
Page
1.
Table 1 Non-regulated soil limits for lead ...................................................................... 88
2.
Table 2 Soil attributes of Sacramento and Yolo Counties .............................................. 88
3.
Table 3 Geologic attributes of units occurring in the study area ............................... 89
4.
Table 4 Elemental concentrations of Sacramento soil analyzed by method MEMS -61 89
5.
Table 5 Summary statistics of elemental concentrations analyzed by method MEMS-61 ..
..................................................................................................................... 104
6.
Table 6 Elements with skewed distributions, which have been normalized ................. 105
7.
Table 7 a) Concentrations of lead in Sacramento soil analyzed by hand- held XRF ..... 105
8.
Table 7 b) Concentrations of zinc in Sacramento soil analyzed by hand- held XRF..... 105
9.
Table 8 NURE HSSR summary statistics for Sacramento County ............................... 107
10. Table 9 Replicate and environmental results, analysis by MEMS-61 method of soil samples
collected in Sacramento, CA.................................................................................... 108
11. Table 10 Summary Statistics of lead concentrations for replicates analyzed by the MEMS-61
method for soil samples collected in Sacramento, CA ............................................ 111
12. Table 11 Percent difference of lead concentrations for replicates analyzed by the MEMS-61
method for soil samples collected in Sacramento, CA ............................................ 111
13. Table 12 Comparison of MEMS-61 method and XRF method for lead and zinc
concentrations in Sacramento soils .......................................................................... 112
14. Table 13 List of historic industry using heavy metals .................................................. 112
15. Table 14 Summary statistics of predicted and observed lead concentrations in parts per
million .................................................................................................................... 112
16. Table 15 Results and summary statistics for leave-one-out cross validation ................ 112
17. Table 16 MEMS-61 Factor loadings for maximum likelihood estimation of elemental
analysis of surface soils form Sacramento, CA ....................................................... 115
18. Table 17 Uniqueness values for maximum likelihood and principal component estimation
methods .................................................................................................................... 116
ix
19. Table 18 NURE Factor loadings for maximum likelihood estimation of elemental analysis of
surface soils form Sacramento, CA ......................................................................... 117
20. Table 19 NURE Uniqueness values for maximum likelihood and principal component
estimation methods .................................................................................................. 118
21. Table 20. MLE and PCE summary statistics from residual matrices of factor analysis of soil
samples from Sacramento, CA ................................................................................ 119
22. Table 21 MLE and PCE summary statistics from residual matrices of factor analysis of soil
samples from Sacramento, CA ................................................................................ 119
23. Table 22 NURE Factor loadings for principal component estimation of elemental analysis of
surface soils form Sacramento, CA ......................................................................... 122
x
LIST OF FIGURES
Page
1.
Figure 1 Study area ...................................................................................................... 122
2.
Figure 2 Lead poisoning cases by zip code in Sacramento County .............................. 123
3.
Figure 3 Historic lead-use in paint and gasoline in the United States ........................... 124
4.
Figure 4 Sierra Nevada rock composition ..................................................................... 125
5.
Figure 5 Quaternary geology of the southern Sacramento Valley map ........................ 126
6.
Figure 6 Prediction map of lead concentrations in Pueblo, CO .................................... 127
7.
Figure 7 Sacramento and Yolo County land use map ................................................... 128
8.
Figure 8 Sample sites (2008) location map ................................................................... 129
9.
Figure 9 XRF sample location map .............................................................................. 130
10. Figure 10 MEMS-61 sample site locations Oor & Deocampo ..................................... 131
11. Figure 11 MEMS-61 sample site location map............................................................. 132
12. Figure 12 Scree plot ...................................................................................................... 133
13. Figure 13 a) Histogram and b) Box plot of lead concentrations for MEMS-61............ 134
14. Figure 14 MEMS-61 lead concentration map .............................................................. 135
15. Figure 15 a) Histogram of non-normally distributed elements ..................................... 136
16. Figure 15 b) Histogram of non-normally distributed elements ..................................... 137
17. Figure 15 c) Histogram of non-normally distributed elements ..................................... 138
18. Figure 15 d) Histogram of non-normally distributed elements ..................................... 139
19. Figure 15 e) Histogram of non-normally distributed elements ..................................... 140
20. Figure 15 f) Histogram of non-normally distributed elements ..................................... 141
21. Figure 15 g) Histogram of non-normally distributed elements ..................................... 142
22. Figure 16 XRF lead concentration map ........................................................................ 143
xi
23. Figure 17 a) Histogram, b) Box plot of lead concentrations for XRF data ................... 144
24. Figure 18 NURE lead concentration map ..................................................................... 145
25. Figure 19 a) Histogram and b) Box plot of lead concentrations for NURE data .......... 146
26. Figure 20 MEMS-61 and XRF replicate comparison ................................................... 147
27. Figure 21 MEMS-61 & XRF lead concentration and soil map..................................... 148
28. Figure 22 Distribution of lead concentrations and clay percentage in soil ................... 149
29. Figure 23 MEMS-61 & XRF lead concentration and Quaternary geology map ........... 150
30. Figure 24 Distribution of lead concentrations within Quaternary geology ................... 151
31. Figure 25 MEMS-61 & XRF lead concentration and land use map ............................. 152
32. Figure 26 Distribution of lead concentrations within land use areas ............................ 153
33. Figure 27 Location of metal-working industry map (1952) .......................................... 154
34. Figure 28 Rose diagrams of wind direction from a) Sacramento Executive Airport and b)
Natomas during year-round and dry-month intervals .............................................. 155
35. Figure 29 Scatter plot of lead concentrations and distances to roads............................ 156
36. Figure 30 MEMS-61 lead concentrations and prediction map ..................................... 157
37. Figure 31 Semivariogram of MEMS-61 data................................................................ 158
38. Figure 32 MEMS-61 lead concentrations and variance prediction map ....................... 159
39. Figure 33 a) MEMS-61 factor loadings 1 & 4 .............................................................. 160
40. Figure 33 b) MEMS-61 factor loadings 2 & 3 ............................................................... 161
41. Figure 34 a) NURE factor loadings 1 & 3 .................................................................... 162
42. Figure 34 b) NURE factor loadings 2 & 3 .................................................................... 163
43. Figure 35 MEMS-61 factor 1 score and prediction map ............................................... 164
44. Figure 36 MEMS-61 factor 2 score and prediction map ............................................... 165
45. Figure 37 MEMS-61 factor 3 score and prediction map ............................................... 166
xii
46. Figure 38 MEMS-61 factor 4 score and prediction map ............................................... 167
47. Figure 39 NURE factor 1 score and prediction map ..................................................... 168
48. Figure 40 NURE factor 2 score and prediction map ..................................................... 169
49. Figure 41 NURE factor 3 score and prediction map ..................................................... 170
50. Figure 42 Historic roadmap of Sacramento (1933) ....................................................... 171
51. Figure 43 Coal-burning smoke stack (1939) ................................................................. 172
52. Figure 44 Histogram of a) Observed and b) Predicted lead concentrations .................. 173
53. Figure 45 a) MEMS-61 and NURE Factor 1 loadings .................................................. 174
54. Figure 45 b) MEMS-61 and NURE Factor 2 loadings.................................................. 175
55. Figure 45 c) MEMS-61 and NURE Factor 3 & 4 loadings ........................................... 176
56. Figure 46 Occurrence of mafic rocks in relation to the study area .............................. 177
57. Figure 47 Occurrence of marine sedimentary rocks in relation to the study area. ........ 178
58. Figure 48 Occurrence of granitic rocks in relation to the study area ............................ 179
59. Figure 49 Coincidence of factor 2 scores with the Lower Riverbank formation .......... 180
60. Figure 50 Distribution of factor 2 scores among Quaternary geology .......................... 181
61. Figure 51 Residual matrix bar chart .............................................................................. 182
xiii
1
Chapter 1
INTRODUCTION
Lead contamination in soil is a common problem in urban areas. While lead
occurs naturally, no significant sources of lead are found in Sacramento or the
surrounding areas. This implies that the origin of elevated concentrations of lead found
in the soils of Sacramento is mainly anthropogenic. The purpose of this study is to assess
the extent of lead contamination in the soils of Sacramento, California and to identify
potential sources (Figure 1). In order to achieve this objective, soil samples collected in
Sacramento were analyzed for lead and other trace metals. Lead concentration data was
spatially compared to land use, geology, and soil type to determine if a relationship was
present. In addition, factor analysis was applied to metal concentrations to identify
correlations among these metals. Kriging was used to generate a prediction map of lead
concentrations at unsampled locations.
Lead has served various purposes throughout human history because of its
malleability, density, and ease of extraction. However, its usefulness is overshadowed by
its toxicity, especially in young children. The link between lead in soil and lead in blood
is the subject of much research, which has identified a link between elevated loadings of
lead in house dust and elevated concentrations of lead in soil tracked in homes from
outside (Laidlaw et al., 2008).
Although lead has been phased out of several products in recent decades, its
persistence in the environment remains a serious problem. Urban areas have shown to be
especially vulnerable to elevated levels of lead in soil (Mielke et al., 2007; Yassoglou et
2
al., 1987; Filippelli et al., 2005; Francek, 1992). These areas of elevated lead levels
reflect a diffuse pattern of redistributed lead from their original point sources. Filippelli
et al., 2005, suggest lead particles from automobile exhaust emissions, originally
deposited in roadside soils, can be re-suspended and transported greater distances
eventually leaving a city-wide “bulls-eye” pattern of lead contamination.
Elevated levels of lead in soil have been indirectly linked to elevated blood-lead
levels through dust lead loadings in residences (Tsuji & Serl, 1996; Johnson & Bretsch,
2002; Laidlaw et al., 2008; Lanphear et al., 1998). Lead loading of dust is defined as
mass of lead per unit area (Laidlaw et al., 2008). In residences, an appreciable portion of
dust lead loadings originates from re-suspension of dust as well as outside dust and soil
tracked in from lead contaminated soil (Laidlaw et al., 2008). House dust lead
concentrations increase as a function of traffic density indicating a large portion of lead in
house dust is due to gasoline combustion (Mielke et al., 1998). Lanphear et al., (1998)
and Filippelli et al., (2005) state that lead-contaminated house dust is a major source for
lead exposure for children. According to the U.S. Environmental Protection Agency
(EPA), lead-based paint, urban soil and dust, and lead in drinking water are three major
sources of elevated blood-lead levels (Mielke et al, 1998).
Children between the ages of 6 and 36 months are at great risk of lead exposure
because of increased hand-mouth behavior (Lanphere et al, 1998). Lead from soil, air,
and paint accumulate in house dust. The dust is picked up by children’s hands and then
transferred to their mouths. The lead particles are then transferred through biological
processes into the bloodstream (Mielke et al., 1998). According to Lanphere et al. (1998)
3
children residing in urban areas have higher blood-lead levels and exposure to lead than
children living in suburban or rural areas or in towns with nearby lead-related industries.
Lead deposited from anthropogenic activities is usually partitioned into more highly
bioavailable carbonate, iron, and manganese hydroxide soil fractions (Filippelli et al.,
2005). Bioavailability of lead species refers to the amount of lead that is available to be
incorporated into biological processes. Particle size and chemical species are important
factors in human absorption and retention of lead (Mielke et al., 1998). Smaller particles
are more easily absorbed by the digestive system allowing lead in soil and dust to be up
to three times more bioavailable than lead from paint (Mielke et al., 1998). In addition,
research shows that an upper limit of lead absorption limits the transfer of high doses of
lead from paint chips into the body (Mielke et al., 1998). However, low levels of lead
can pose significant risks. Recent studies reveal significant cognitive impairment can
occur at blood-lead levels below the current post-abatement clearance standards of 10
μg/dL (Laidlaw et al., 2008).
Mostly, lead poisoning occurs among children. Huffman, (2010) reported the rare
occurrence of adult cases (1-2 per year) among men working in construction or the
automotive industry. Orr, (2005) reports 257 reported cases of lead poisoning between
the years of 1989 and 2004 in Sacramento County. The Center for Disease Control
(CDC) lists 92 of 118 children having blood lead levels of 10 μg/dL or greater between
2002 and 2006 in Sacramento (CDC, 2006). A study published in 1992, identifying
Sacramento as one of three high-risk cities in California, reports 1 percent of children in
4
the 232 households participating have blood lead levels over 20 μg/dL (CDC, 1992).
Between January 1st, 2008 and December 1st, 2008 15,457 intravenous and capillary tests
were conducted in Sacramento County. Of those, 15,350 cases had blood-lead levels
below 10 μg/dL; forty cases had blood-lead levels between 10 and 14.9 μg/dL; sixtyseven cases (including false positives) had blood-lead levels above 15 μg/dL (Huffman,
2010). Blood-lead concentrations of 5-10 μg/dL result in a telephone call to the residence
to discuss possible sources of contamination (Huffman, 2010). Blood-lead
concentrations above 15 μg/dL result in further investigation by the County of
Sacramento. (Figure 2) shows a map of Sacramento County zip codes and the number of
lead poisoning cases between July 1st, 2008 and June 30th, 2009. Twenty-eight cases of
lead poisoning occurred within this time with 9 false positives.
The U.S. EPA developed a model to predict health risk associated with lead in
soil. The Integrated Exposure Uptake Biokinetic (IEUBK) Model assesses the routes of
environmental exposure to lead and determines the distribution of lead in human tissues
(EPA, 2010). Environmental exposure pathways for lead considered by the model
include air, diet, water, soil, and paint (Tsuji & Serl, 1996). The model assumes the
behavior of lead in the body and predicts mean blood-lead levels and the percent risk of
exceeding 10 μg/dL (Tsuji & Serl, 1996).
5
Chapter 2
BACKGROUND
Lead Uses
Lead (Pb) is a soft gray metal that has had many applications throughout human
history. Lead was used in Turkey as early as 6500 BC and used for indoor plumbing by
the Romans (Stanford University, 2010). It has been used historically in goblets, ceramic
glaze, water pipes, and as a Pb-salt preservative in wine. More recently, applications of
lead include bullets, solder, pesticides, car batteries, and x-ray shielding, many of which
are no longer in use. Two of the most common uses of lead in our industrial society were
as paint and gasoline additives. Approximately the same amount of lead was used in
paint from 1884 to 1989 as was used as a gasoline additive from 1929 to 1989 in the
United States (Mielke et al., 1998) (Figure 3.). As a result, a residue of 4 to 5 million
metric tons was dispersed into the environment from the use of leaded gasoline (Mielke
et al., 1998).
Adverse Health Effects of Lead Ingestion
Despite the recognition of lead as an environmental contaminant and its
subsequent removal or reduction in usage, it remains a serious and persistent health
concern. Children younger than six years are in the highest health risk group. Lead,
which is normally ingested via soil on fingers and toys, is taken up by the child’s
underdeveloped gastrointestinal pathway (Filippelli et al., 2005). Because of similar
charge and ionic radii, lead replaces calcium in neural signal processing and bone
6
formation (Filippelli et al., 2005; Laidlaw et al., 2008). Placement of lead instead of
calcium in neurons causes permanent neural differentiation defects resulting in mental
retardation, learning disorders, and attention deficit hyper-activity disorder (Filippelli et
al., 2005; Laidlaw et al., 2008). Lead accumulation in bone acts as a long-term source to
blood levels (Filippelli et al., 2005; Laidlaw et al., 2008).
Natural and Anthropogenic Sources of Lead
Lead is found naturally in hydrothermal sulfide deposits. Its most common form
occurs as the mineral galena (PbS). Other mineral forms include anglesite (PbSO4),
cerussite (PbCO3), crocoite (PbCrO4), Wulfenite (PbMoO4), pyromorphite (Pb5(PO4)3Cl,
vanadinite (Pb5(VO4)3Cl and jamesonite (Pb4FeSb6S14) (Perkins, 1998).
Sacramento soils are derived mainly from the Sierra Nevada Mountain Range to
the east and the Coast Range Mountains to the west, which are not substantial sources of
naturally occurring lead.
Therefore, high concentrations of lead in soil in Sacramento are of anthropogenic
origin. The most common sources of anthropogenic lead contamination are automobile
exhaust (prior to 1996 when leaded gasoline was phased out of production), industrial
smelting, and lead used in paint in high quantities prior to the 1920s. Facchinelli et al.,
(2000) list less common sources of anthropogenic lead, which include manure, sewage
sludge, lead-arsenate pesticides, industrial fumes, and coal burning. Wang et al., (2005)
also attribute addition of lead into the environment to the combustion of coal.
7
Automobile exhaust
The addition of lead into gasoline began in the 1920s in an attempt to control the
“knocking” of cylinders in a combustion engine (Filippelli et. al., 2005). Tetra-ethyl lead
or TEL (CH3CH2)4Pb is combined with 1,2-dibromoethane (BrCH2CH2Br), 1,2dichloroethane (C2H4Cl2), and red dye to produce ethyl fluid, which is then added to
gasoline (Seyferth, 2003). TEL produces water, carbon dioxide, and lead when burned.
(CH3CH2)4Pb + 13 O2 → 8 CO2 + 10 H2O + Pb
1,2-dibromoethane and 1,2-dichloroethane are added to prevent lead oxide accumulation
(Seyferth, 2003). This results in expulsion of lead (II) bromide and lead (II) chloride
from the exhaust (Seyferth, 2003). In 1973 the Environmental Protection Agency
proposed a gradual phasedown of leaded gasoline (EPA, 1996). Beginning in 1975 lighter
trucks and cars were manufactured with a catalytic converter in the exhaust system which
required unleaded fuel (EPA, 1996). Finally on January 1, 1996 the Clean Air Act banned
the sale of leaded gasoline with the exception of aircraft, racing cars, farm equipment,
and marine engines (EPA, 1996).
Industrial smelting
Industrial production of metals from ore can result in the release of lead and other
trace metals associated with the ore into the environment. Industrial smelting has been
identified as the cause of lead contamination in Pueblo, Colorado (Diawara et al., 2006).
Particulate matter and sulfur dioxide are the main air pollutants emitted from lead and
zinc smelting processes (World Bank Group, 1998).
8
Lead in paint
Lead added to paint is used mainly as a pigment in the form of lead (II) chromate
(PbCrO4), and lead (II) carbonate (PbCO3), but is also used to speed drying, increase
durability, and resist moisture. Lead in exterior paints contributes to the occurrence of
lead in the environment. Improper removal of such exterior paint poses the greatest risk
of dispersion into the environment. In 1978, the U.S. Consumer Product Safety
Commission banned the use of lead paint in consumer products, where lead content is
0.06 percent (600 ppm) of the total weight of dried paint, including paints accessible to
consumers, furniture, and toys (USCPSC, 1977).
Soil Limits for Lead
There are several non-regulated limits for soil concentrations of lead published in
California and the United States (Table 1.). The US Environmental Protection Agency
(USEPA) publishes Regional Screening Levels (RSLs) for Chemical Contaminants at
Superfund Sites. These RSLs are risk-based concentrations that take into account human
exposure and toxicity but are not necessarily used for regulatory purposes. For lead in
soil located in the upper 3 meters, the residential Screening Level is 400 mg/kg and the
industrial Screening Level is 800 mg/kg (USEPA Regional Screening Levels, 2010). The
California Environmental Protection Agency (CalEPA) publishes Human Health
Screening Levels for California (CHHSLs) for contaminated Properties that indicate
concentrations of chemicals that are considered to be below thresholds of concern for
9
risks to human health. For lead in residential land use, the California Human Health
Screening Level is 150 mg/kg and the CHHSL for commercial/industrial land use is
3,500 mg/kg (CalEPA, 2005). The California Department of Toxic Substances Control
(CDTSC) publishes Total Threshold Limit Concentrations (TTLCs) to help characterize
hazardous waste. If a substance in a waste equals or exceeds the TTLC level, it is
considered a hazardous toxic waste. For lead in soil, the TTLC is 1,000 mg/kg
(CADTSC, 2005). The California Regional Water Quality Control Board for the San
Francisco Bay Region publishes Environmental Screening Levels (ESLs) for sites with
contaminated soil and groundwater. Generally, the presence of a chemical in soil at
concentrations below the corresponding ESL can be assumed to not pose a significant,
long-term (chronic) threat to human health and the environment (in particular
groundwater used for public drinking water supply). In a residential area, the ESL for
lead in shallow soil (<3m) is 200 mg/kg, whereas in a commercial/industrial land use area
the ESL for lead in shallow soil (<3m) is 750 mg/kg (SFBRWQCB, 2008).
Naturally Occurring Sacramento and Yolo County Soils
The U. S. Department of Agriculture’s Natural Resources Conservation Service
(NRCS), the Soil Conservation Service, and the University of California, Agricultural
Experiment Station, in a joint effort for the National Cooperative Soil Survey (NCSS)
produced reports to characterize the soils of Sacramento and Yolo counties (NRCS,
1993). These reports characterize the soil based on observations of physical and
chemical properties. All of these characteristics are incorporated into the soil type and are
10
named accordingly. The naming convention used by the NRCS and other agencies
conveys the location of the soil unit, some descriptive characteristics, and/or a degree of
mixing. For example: ‘Cosumnes silt loam’ refers to a location where soils exhibit
properties typical of this soil, Cosumnes, and a descriptive property of the soil, silty
loam; ‘San Joaquin urban land complex’ refers to a location where soils exhibit
properties typical of this soil, ‘San Joaquin’, and ‘urban land complex’ refers to areas
covered by impervious surfaces such as roads, sidewalks, and buildings.
The classification of the soils collected for the purpose of this study is based on
location within the boundaries specified by the NRCS for each soil unit. A table of soil
units describes characteristics of soils from which samples were collected (Table 2).
Sacramento Geology
The geology of the Sacramento Valley is dominated by the Sierra Nevada
Mountains flanking it to the east and the Coast Ranges to the west. The Sierra Nevada
Mountains are mainly comprised of a granitic pluton of Mesozoic age with some
metamorphic and marine sedimentary rocks as roof pendants. The rocks that make up the
granitic pluton of the Sierra Nevada Mountains are mainly granite, granodiorite, tonalite,
and some diorite (Figure 4). The granitic rocks, which extend far beneath the surface, are
bordered by a metamorphic belt that crops out at the surface on the west side of the
Sierran foothills. These accreted oceanic terraces of the Sierran foothills include the 500million-year-old Shoo Fly Complex consisting of oceanic metamorphic, metasedimentary, and meta-volcanic rocks, late Paleozoic and Mesozoic subduction
11
complexes of submarine volcanic, plutonic, metamorphic, and sedimentary origin known
as the Calaveras Complex, and the foothills terrane of middle to late Jurassic age
consisting of meta-volcanic and meta-sedimentary rocks (Harden, 2004). The Foothills
Terrane contains assemblages of blue and green schist, unweathered meta-volcanics high
in iron content known as greenstone and slate as well as schist metamorphosed from
marine sandstone and shale (Harden, 2004). It also contains the Mariposa Formation
consisting of gabbro and ultramafic rocks as well as Mariposite schist containing minor
amounts of chromium (Harden, 2004).
The Coast Ranges to the west of the Sacramento Valley formed after the Sierra
Nevada batholith near the close of the late Cretaceous (Page, 1986). These mountains
consist of a complex assemblage of marine sediments, metamorphic rock, and volcanic
rock that make up the Franciscan Complex (Harden 1998). The rocks that are found
within the Franciscan Complex include sedimentary rocks comprised of sandstone, shale
and some limestone, slightly metamorphosed volcanic basalt such as greenstone pillows,
and metamorphic rocks including blueschist, eclogite, and perodotic serpentenite
(Harden, 2004). Minerals like glaucophane, and jadeite can be found in the metamorphic
assemblages (Harden, 2004).
Following the formation of the Coast Ranges, sediment began to accumulate in
the Sacramento Valley. The Sacramento Valley is the northern section of the Central
Valley, which is a large asymmetrical structural trough filled with as much as 10 vertical
miles of pre-Tertiary marine sediment, Tertiary marine and continental deposits, and
Quaternary continental sediment eroded primarily from the Sierra Nevada Mountains
12
(Page 1986). The Great Valley Sequence crops out on either side of the Sacramento
Valley. This sequence consists of various episodes of deposition from transgressions and
regressions of Lake Clyde, the ancient lake that once filled the Great Valley (Harden,
2004). The older, lower sequence of Jurassic and Cretaceous age sedimentary rocks
consists of mainly sandstone. Volcanic rock fragments can also be found from the Sierran
arc (Harden, 2004). The younger sandstone from the great valley sequence is rich in
feldspar and quartz.
Overlying the older sequences, the Quaternary sediments in the sample area
consist of alluvium, basin deposits, stream channel deposits, and tailings of Holocene
age, upper and lower members of the Modesto Formation, upper and lower members of
the Riverbank Formation, and Turlock Lake Formation sediments of Pleistocene age, and
Laguna Formation sediments of Pliocene age (Table 3). The following descriptions are
adapted from Helly & Harwood, (1986) and can be seen in Figure 5.
The Stream Channel deposits (Qsc) can be found along active stream channels
under constantly changing conditions at the surface to a depth of 0 – 25 meters (Helly &
Harwood, 1986). Presumably, the natural levees formed by the stream channel deposits
of the Sacramento River restrict sediment transport across the Sacramento River allowing
the identification of chemically distinct valley soils derived from the Sierra Nevada and
Coast Ranges observed by Goldhaber et al., (2009). Alluvium (Qa) found in the study
area consists of un-weathered gravel, sand, and silt derived from the Sierra Nevada,
Coastal, and Klamath mountains. It can be found extending beyond stream channel
13
deposits and ranges from a few centimeters to as much as 10 meters depth (Helly &
Harwood, 1986).
The Modesto formation of Pleistocene age consists of upper and lower members,
which are both present in the study area. The upper and lower formations are found in
alluvial terraces, alluvial fans, and some abandoned channel ridges. These low-lying
sediments are found along streams and in valleys at higher elevations than younger
Holocene sediments, which have been put in place by stream incision then deposition.
The sediments are typically tan and light gray gravely sand, silt, and clay, except where
derived from volcanic rocks of the Tuscan formation. These sediments are found to be
red and black with minor brown clasts. The upper member for the Modesto formation
(Qmu) is typically comprised of unconsolidated, un-weathered gravel, sand, silt, and clay
only a few meters thick. The soils of the upper Modesto formation exhibit A and Chorizons but lack an argillic B-horizon. The sediments of the lower Modesto formation
(Qml) consist of unconsolidated, slightly weathered gravel, sand, silt, and clay. These
soils contain an argillic B-horizon and more clays than the upper Modesto member.
The Riverbank formation sediments of Pleistocene age exhibit reddish gravel,
sand, and silt and are found on alluvial terraces and alluvial fans. These sediments are
older than the Modesto formation and generally have thicker argillic horizons in soils and
paleosols. The upper member of the Riverbank formation (Qru) consists of compact,
unconsolidated, dark brown to red gravel, sand, silt, and some clay. The upper member
sediments are found on dissected alluvial fans on the northwest and southeast side of the
Sacramento Valley. The dissected alluvial fans of the lower member of the Riverbank
14
formation (Qrl) consist of red semi-consolidated gravel, sand, and silt. The arkosic
alluvium near Sacramento indicates that the lower member of the Riverbank formation
was derived from the Sierra Nevada Mountains and deposited by the American River.
The sediments of the Laguna and Turlock Lake formations as well as the Basin
deposits appear sparsely in the study area and are not represented by soil samples. The
Laguna formation of Pliocene age consists of interbedded alluvial gravel, sand, and silt
containing pebbles and cobbles of quartz and metamorphic rock as well as arkosic
gravelly units and finer sediments. The deeply weathered and dissected arkosic gravels
of the Turlock Lake formation of Pleistocene age contain small amounts of metamorphic
rock fragments and quartz pebbles. Sand and silt are present along the south and east
sides of the Sacramento Valley. The plutonic rocks of the Sierra Nevada Mountains are
the primary source for these sediments of eroded alluvial fans. The undivided Basin
deposits of Holocene age consist of fine grain silt and clay derived from the same sources
as modern alluvium.
Previous Work
Anthropogenic contamination of soils by lead has been the focus of research
around the world. Researchers have found that the spatial distribution of anthropogenic
lead contamination can be correlated to land-use. Areas of land-use where contamination
of lead in soil has been documented include roads, industrial areas, and residential areas.
Statistical and geochemical tools are used to delineate and decipher the extent and
sources of contamination. These tools are often employed in conjunction with one
15
another to help fully understand the origins and extent of metal contamination. Generally
a map is constructed to portray the extent and intensity of such contamination.
Research of the soils and streambed sediments of the southern Sacramento Valley
has been conducted using data collected in the late 1970s and early 1980s by the U. S.
Department of Energy’s National Uranium Resource Evaluation Hydrogeochemical
Stream Sediment Reconnaissance (NURE HSSR) Program and further analyzed by the U.
S. Geological Survey (Goldhaber et al., 2009; Wanty et al., 2009; Morrison et al., 2009).
These studies examined the chemical composition of southern Sacramento Valley soils.
Goldhaber et al., (2009), shows three distinctive groupings of elements, indicating the
geologic, hydrologic, geomorphic, and anthropogenic factors relating to their deposition.
The three groups identified by Goldhaber et al., (2009), are mafic, silicic, and
anthropogenic. Morrison et al., (2009), focus on chromium and nickel concentrations of
soil and groundwater in order to identify soils derived from ultramafic rocks in the Coast
Ranges and Sierra Nevada. This research has shown that weathering of ultramafic
material and subsequent transport results in enrichment of chromium and nickel. Wanty
et al., (2009), compare surface- and groundwater chemistry of the east and west side of
the southern Sacramento Valley to soil chemistry of the NURE data. This research
reveals differences between the groundwater of the east and west side of the southern
Sacramento Valley as well as similarities between groundwater concentrations and soil
concentrations.
In addition, the U. S. Geological Survey has conducted research of metal loading
in the Sacramento River (Alpers et al., 2000). This research has shown Spring Creek, a
16
northern tributary of the Sacramento River System, to be a source for metals such as
cadmium, copper, lead, and zinc. These metals are present because of the historic West
Shasta Cu-mining of sulfide deposits. Dunlap et al., (2008), used lead concentrations and
isotope ratios of water colloids and streambed samples to show that the extent of lead
from the sulfide deposits is limited to 60 km downstream and suggest that leaded gasoline
emissions and hydraulic Au-mining are dominant sources for lead downstream.
Oor, (2005) has conducted work pertaining to lead concentrations in soil of
Sacramento, CA under the advisement of Dr. Daniel Deocampo at California State
University, Sacramento. Oor, (2005), examined the lead concentrations in soil coupled
with the location of blood lead levels by zip code in Sacramento and surrounding areas.
Findings reveal correlations between lead and cadmium as well as lead and zinc. In
addition, reported lead poisoning coincided with high lead concentrations in soil.
Zhang’s senior thesis focused on a transect across a major road and the concentrations of
lead compared to the distance from the road. Dr. Daniel Deocampo collected multiple
soil samples in the Sacramento and surrounding areas and collaborated with Sacramento
County to fund this project. Their work and sampling efforts laid the foundation for this
research project.
Land use
Diawara et al., 2006 used ArcGIS to generate a prediction map to estimate the
concentration of arsenic, cadmium, lead, and mercury in Pueblo, CO (Figure 6) The
source of lead contamination is attributed to a local smelter operating since the early
17
1900s. Diawara et al., (2006) also explored the socioeconomic status in relation to the
spatial distribution of lead. Filippelli et al., (2005) examined the occurrence of lead
toxicity in blood in urban children and its spatial relationship to metropolitan roadways in
Indianapolis, Indiana. Mielke et al., (2007) evaluated the association between
concentrations of lead in soil and lead levels in blood of children in urban New Orleans,
LA and developed an index for the potential for blood level exposure. Yassoglou et al.,
(1987) examined the heavy metal contamination of roadside soils in the greater Athens
area, Greece. This paper evaluated the relationship between heavy metal contamination
and the distance from roadways with heavy traffic loads. In addition, metals that are
associated with automobiles such as cadmium and zinc were correlated with lead and
each other. Francek, (1992) examined lead pollution in a small town environment of Mt.
Pleasant, Michigan. Results indicate that lead loading of small cities is less than that of
major urban areas.
Geochemical mapping
Lax and Selinus, (2005) constructed geochemical maps of Sweden, some for the
purpose of displaying contamination, others for the purpose of preliminary bedrock
investigation. Fordyce et al, (2005) mapped urban geochemistry of soils in Great Britain
for the purpose of providing an overview of the urban geochemical signature. Bityukova
et al, (2000) mapped the elemental distributions from a major industrial area of Tallinn,
Estonia. The resulting analysis showed that the major chemical elements of the soil
depended mainly upon the composition of the underlying rocks.
18
Factor analysis
Researchers employ factor analysis and principal component analysis to
determine the origin of chemical element concentrations in various regions across the
world. These statistical techniques are used in lead studies to show the connection
between lead and other contaminants associated with human activities (Bityukova et al.,
2000; Cicchella et al., 2008; Davies and Wixson, 1986; Facchinelli et al., 2001; Garcia
and Millan, 1998; Ratha and Sahu, 1992; Wang et al., 2005). In addition, these studies
reveal the origin of the sediments in which samples were collected by virtue of separating
out anthropogenic inputs.
Bityukova et al., (2000) applied factor analysis to chemical data from the largest
industrial region of Estonia. Two sources of industrial contamination were detected based
on correlations among elements as well as elemental signatures reflecting the
composition of underlying rock
Factor Analysis of Geochemical Variability
Factor analysis was developed by Karl Pearson, Charles Spearman, and others in
the field of psychology to define and measure intelligence. In addition to applications in
the field of psychology, factor analysis is applied in the fields of marketing, ecology, and
geochemistry.
Multivariate data occur when several variables are measured on each sample item.
Reading comprehension, vocabulary, and mathematics are examples of measured
19
variables associated with intelligence testing for each student. In the case of geochemical
modeling, elemental concentrations serve as variables at individual sample sites. If a
multivariate data set contains a few groups of highly correlated variables, it may be
possible to explain the data’s correlation structure more simply by using a few underlying
factors. These factors are unobserved variables that manifest themselves throughout the
observed correlations among measured variables. Verbal intelligence may manifest itself
on different tests like reading comprehension and vocabulary. Where as mathematical
ability may manifest itself on tests related to mathematics. In the case of urban soils of
Sacramento, parent material of soil may be inferred from correlations derived from
elemental concentrations. Factor analysis is a statistical technique for quantifying the
relationship between unobserved factors and the observed data. Highly correlated
variables exist in groups. Less correlated variables from one group may be correlated
together in a relatively independent factor group (Tabachnick & Fidell, 1983). Since the
factors are unobserved, they must be inferred by the analyst based on correlations among
variables in a data set and the analyst’s scientific knowledge of the data.
Model description
A factor analysis model assumes that each response variable is a linear
combination of the latent factors plus an error term. The error term accounts for the
variability in the response variable that is not explained by the factors.
Mathematically, the model may be explained as follows
20
X 1i  1  l11 F1i  l12 F2i  ...  l1m Fmi   1i
X 2i   2  l 21 F1i  l 22 F2i  ...  l 2 m Fmi   2i

X pi  p  l p1 F1i  l p 2 F2i  ...  l pm Fmi   pi
X1i = the observed data for the 1st variable on the ith subject
1 = the mean of the 1st variable.
l11 = the loading of the 1st variable on the 1st factor
F1i = the 1st common factor on the ith subject.
1i = the 1st specific factor on the ith subject.
(Johnson & Wichern, 1998). The common factor (F) represents the latent variable that is
not directly observed but inferred for each group of variables. The loading l11, quantifies
the relationship between the ith observed variable and the jth unobserved factor. Since
the factors are not observed, some assumptions are needed to make this model
identifiable. Therefore we assume the random vectors F and ɛ satisfy the following
conditions.
E(F) = 0, Cov(F) = I
E() = 0, Cov() = Ψ
Where the specific variance, Ψ, is a diagonal matrix and F and  are independant.
Specific variance represents a random component of each variable not associated with
any of the factors. It can be shown that this model leads to Cor ( X )  LL'  . This
21
demonstrates how the factor loadings and uniqueness may be used to reproduce,
approximately, the correlation matrix of the multivariate data.
Specifically for this study, the observed data for a sample site, (X1i, X2i,…Xpi), are
the elemental concentrations of the soil. The unobserved factors (F) are inferred and
represent the postulated parent material of the soil based on correlations of chemical data
identified by the loadings (l). The random variation of elemental concentrations among
sample sites is accounted for in the error term (ε).
Parameter estimation
Maximum Likelihood Estimation (MLE) and Principal Component Estimation
(PCE) are two of several methods used to estimate the parameters of this model. MLE
estimates the loadings and uniquenesses as the values which maximize the probability of
reproducing the observed data. The data likelihood depends on the loading matrix and
specific variance matrix. Although the estimated covariance matrix is unique, the factor
loadings are not unique unless a uniqueness condition is imposed (Johnson & Wichern,
1998). The uniqueness condition defines the matrix loadings (L) below.
L'  1 L  
Where  is a diagonal matrix (Johnson & Wichern, 1998).
PCE proceeds by computing the spectral decomposition of the observed
correlation matrix to obtain the eigenvalue-eigenvector pairs. The eigenvalue and
eigenvectors are then used for estimating the loading matrix in PCE. The factor loadings
are the scaled coefficients of the first few sample principal components where the first
22
few principal component eigenvalues are arranged in decreasing value from greatest to
least (Johnson & Wischern, 1998). The factor loading matrix times its transpose is
exactly equal to the observed correlation matrix when the number of factors equals the
number of variables. However the purpose of factor analysis is to reduce a large number
of observed variables to a small number of factors (Korre, 1999), so it is necessary to
choose the lowest number of eigenvalues with the greatest value that sufficiently explain
the variability of the data. The exclusion of the smaller eigenvalues prevents the
observed correlation matrix from being exactly reproduced by the loading matrix times
its transpose. Now, the specific variances are estimated by subtracting LL’ from the
sample correlation matrix, then taking the diagonal of the result:
ˆ  ˆ  LL'

(Johnson & Wichern, 1998). MLE and PCE methods of parameter estimation should
yield similar results if the factor model is a suitable solution to the problem (Johnson &
Wichern, 1998).
Factor rotation
For a data set, the factor loadings are not unique. Since for any orthogonal
matrix, T, we have that TT’ is the p x p identity matrix. Therefore,
^ ^
^
^
^
^
^
^
^
L L'   L TT ' L   L* L *' 
(Johnson & Wichern, 1998). Thus, both the loadings L and L* will result in the same
estimated correlation matrix. Factor rotation, applied to the loading matrix, assists the
23
analyst in finding the loading matrix that leads to the most useful interpretation of the
data at hand. Orthogonal factor rotation helps interpret factor loading through a rigid
rotation of the factor axes without changing the estimated correlation matrix. Varimax
rotation, a type of orthogonal rotation, maximized the variance of the loadings for all
variables in each factor (Tabachnick & Fidell, 1983). This accentuates the higher
magnitude loadings and diminishes the lower magnitude loadings (Korre, 1999). Oblique
rotations are best suited to common factors that are correlated (Tabachnick & Fidell,
1983; Johnson & Wichern, 1998). If the factors are uncorrelated both the orthogonal and
oblique rotations produce similar results (Costello and Osborne, 2005).
Model adequacy
One way to determine how well the factor model accounts for the observed
correlation structure in the data is to compare the sample correlation matrix and the
correlation matrix estimated using the loadings and the uniquenesses. A residual
correlation matrix can be calculated by subtracting the estimated correlation matrix from
the observed correlation matrix. Low values in the off-diagonal positions of the matrix
indicate near reproduction of the observed correlation matrix. A factor model that
adequately accounts for the correlation structure in the data nearly reproduces the
observed correlation matrix.
24
Prediction of factor scores
Once the factor analysis calculations are complete and the model
sufficiently explains the variability of the data, factor scores are calculated. Factor scores
are predictions of latent variables as if they were measured directly (Tabachnick & Fidell,
1983). Several methods are available for the prediction of factor scores, for example, the
weighted least squares method. Weighted least squares calculation entails obtaining
predicted factor scores by minimizing the sum of the squares of the errors weighted by
the reciprocal of their variance (Johnson & Wichern, 1998). Factor scores are useful
because they assess how strongly or weakly a factor is associated with a particular
subject. In the case of the soils of urban Sacramento, the estimates of latent variables can
be assigned to the locations from which soil samples were collected. Assigning a
location to the estimates of latent variables allow the factors to be assessed spatially.
This is especially useful when attempting to postulate the location of the origin of these
unobserved variables.
Ordinary Kriging of Geospatial Data
Lead concentrations can be predicted at unsampled locations using data from
sampled locations by the statistical technique called kriging. Kriging was named for
Daniel Gerhardus Krige who employed distance-weighted averages of data from sampled
locations to predict gold grade in South Africa. Georges Matheron refined the technique.
The statistical properties of a kriging model minimize error to produce the best
prediction. The prediction is optimized if the observed data exhibits spatial correlation.
25
A semivariogram links the spatial correlation of the observed data to the kriging
estimates.
Kriging is a method of interpolating a value such as elevation at an unobserved
location based on a weighted average of observed values at near-by locations. Kriging is
used in predicting ore concentrations and metal contamination in the respective fields of
geology and geochemistry (Diawara et al., 2006; McGrath et al., 2003; Fordyce et al.,
2005; Markus and McBratney, 2001). If data are collected at spatial locations x1, x2,…, xn
and the data are denoted V(x1), V(x2),…, V(xn) then the predicted value at an unobserved
location x0 is

n
V ( xo )   wi  V ( xi )
i 1
(Isaaks & Srivastava, 1989). Where the wi are weights that sum to one, which are
calculated so that the resulting prediction has desirable statistical properties.
A random function model underlies kriging and enables one to obtain estimated
margins of error for interpolated values. In addition, ordinary kriging produces the best
linear, unbiased estimator by minimizing the variance of modeled errors. The weights, wi
are selected to minimize the kriging error.
w  C 1  D
Where C-1 is the inverse of the correlation matrix and D represents the ordinary kriging
system (Isaaks & Srivastava, 1989).
26
Kriging uses the spatial correlation of the data to improve interpolation. In
kriging applications, spatially correlated data refers to data in which data values resemble
values of nearby data. The data must exhibit spatial correlation for kriging to result in an
improved interpolation over simpler methods. A semivariogram quantifies spatial
correlation as a function of distance between points and is used to calculate the weights.
The semivariogram values are computed for each distance, h. First all points with
distance h between them are identified. Second, for each such pair of points, the squared
difference in observed values at the two locations is calculated. Third, the semivariogram
value at h is one-half the average of the values computed in the second step. More
Succinctly,
 ( h) 
1 N ( h)
( xi  y i ) 2

2 N (h) i 1
N(h) = number of pairs separated by distance h.
(Isaaks & Srivastava, 1989).
This distance, h, is called the lag distance. Since few pairs of sites will be exactly h units
apart, each lag distance has a tolerance so the semivariogram estimate for distance, h,
includes the distances that lie between before and beyond h according to the tolerance.
Terminology related to the semivariogram includes the range, sill, and nugget
effect. The range describes the distance at which the maximum variation among spatially
correlated samples is achieved. The sill describes the maximum variation, which occurs
at the range. A nugget effect accounts for the jump of values associated with the
27
discontinuity at the origin. Typically an idealized model is fit to the semivariogram
estimated from the data. Commonly used idealized models include the spherical,
Gaussian, and linear models. This idealized curve is plotted against the semivariogram
values calculated from the data. This permits the analyst to determine how well the data
fits the model. Once the semivariogram model is fit, the kriging weights and interpolated
values can be calculated.
28
Chapter 3
METHODS
Soil Sample Site Selection
A few factors determined the specific sample site selection. Soil samples were
collected from public land in the City of Sacramento and nearby areas. Public parks,
public schools, and verges between sidewalks and roads yielded a majority of the sample
site locations. Physical access to each site was necessary to obtain each sample. Fences
and other obstacles prevented access to potential sample sites. Another consideration in
sample site location was the presence of vegetation. Because lead and other metals are
taken-up by plant roots, soil collected from a heavily vegetated area may contain less
metals than actually deposited in the area (Wang et al., 2005), thus negatively biasing the
reported concentration in the area. Finally, sound sample site selection depends upon the
likelihood that the soil has been relatively undisturbed.
When collecting soil samples representative of the accumulated anthropogenic
contaminants in a geomorphologically passive setting it is important to consider land use.
Unacceptable collection sites include areas where soil may have recently accumulated or
eroded rapidly. Riverbanks and steep slopes are particular examples of areas where soil
was avoided. Construction areas where massive amounts of earth may have been moved
are also unacceptable for this type of study. Fields where farmers may have tilled
represent areas where an undisturbed soil sample cannot be collected. All of these
examples present the problem of introducing soil from below a depth of 5 cm, and thus
negatively biasing the sample.
29
MEMS-61 site selection
Seventy soil sample sites selected for heavy metal analysis by MEMS-61 method
in Sacramento, CA were visited. The MEMS-61 method consists of acid digestion and
chemical analysis by inductively coupled plasma-atomic emission spectroscopy (ICPAES) and inductively coupled plasma-mass spectroscopy (ICP-MS). MEMS-61 sites
were used to produce a prediction map for lead concentrations in soil in Sacramento,
California. The MEMS-61 sample sites were chosen based on their proximity to major
roads, industrial areas, and their proximity to each other. Various maps were used for the
site selection process. Land-use maps from the county of Sacramento displayed the
industrial areas of the city proper (Figure 7). Sample sites were located in these areas in
order to ascertain the contribution of lead in Sacramento soils form the industrial areas.
Potential sample sites were chosen from aerial photos obtained from Google Earth. The
location of major roads and highways, Interstate 5, Interstate 80, and Highway 50, were
considered when selecting sample sites to include possible contributions of lead to
Sacramento soils from lead additives in gasoline (Filippelli et al., 2005; Yassoglou et al.,
1987; Garcia & Millan, 1998; Mielke et al., 2005). The location of potential sites selected
along a transect were modified according to conditions dictated in the field. These
conditions included site accessibility, vegetation, and the appearance that the soil has
been undisturbed. The majority of the samples fall along two northwest/southeast
trending transects (Figure 8). These two parallel transects span from the industrial area
of West Sacramento to the industrial area of southeast Sacramento. The sample sites
along each transect were spaced approximately 1 km apart to assure a relatively dense
30
sampling frequency with respect to the total size of the field area. The remaining sites
supplement transects to the northeast and southwest, ultimately forming a rectangular
shape.
XRF site selection
Thirty-four soil samples were collected for analysis by hand-held X-Ray
Fluorescence spectrometry (XRF) (Figure 9). These samples were collected for the
purpose of verifying the results of the prediction map and establishing a baseline for
comparison between the analytical methods. The elevated lead concentration contours
generated by kriging the ME-MS61 sites are oriented in an elliptical shape. The XRF
samples were collected along the major and minor axes of the ellipse at a distance of 0.25
kilometers. Replicate splits of samples analyzed by MEMS-61 method were analyzed by
XRF.
Previous sample sites
In addition to the 70 sites and 8 replicates along and around transects, 43 sites had
been sampled in the greater Sacramento area in previous preliminary studies (Figure 10).
Wendy Oor, Dr. Daniel Deocampo, and Brian Zhou have compiled these 43 samples
using the same field techniques and analytical methods as the MEMS-61 samples
collected in 2008 (Orr, 2005; Deocampo & Oor, 2006). The locations of these sites
include: Elk Grove, North highlands, Woodland, Rio Linda, West Sacramento, and
Sacramento. Some of these sites will serves as duplicate samples. Other samples with
31
low levels of lead concentrations provide an estimate of the background concentrations of
lead in the Sacramento and surrounding soils. Other sites will be omitted from the factor
analysis and prediction map because of their distance from the main sample cluster. The
MEMS-61 sample sites are combined with the previously sampled sites (Oor, 2005) to
complete the data set and will be referred to as MEMS-61_XRF data (Figure 11).
Soil Sample Collection
Soil samples were collected with a stainless steel hand spade. Stainless steel,
because of its toughness is ideal for sample collection. One square foot of soil collected
to a depth of 5 cm. Studies have shown the upper 5 cm of soil represents the
accumulation of lead from anthropogenic activities (Yassoglou et al., 1987; Filippelli et
al., 2005). Because of uptake of heavy metals to roots (Wang, 2005), samples were
collected in sparsely vegetated areas. Ziploc plastic bags contained the samples until
baking. The samples collected for hand-held XRF analysis were not baked. A Garmin
Etrex GPS unit catalogued the latitude and longitude coordinates with respect to the
North American Datum (1983). Photographs were taken of the sample site and in the
four cardinal directions (north, south, east, and west) to assess land-use. Site maps were
drawn to identify the locations of the sample sites from streets. Characteristics of the soil
such as compaction, field estimate of grain size, and the presence of vegetation were
noted.
In addition to the main sample set, three replicate splits and five duplicate samples
from the ME-MS61 collection event were sent to ALS Chemex lab for analysis. A
32
duplicate sample was collected at five sample sites in different areas of the same general
site location to measure the variability of metal concentrations within that site. In
addition to measuring the variability of metal concentrations within a sample location,
variation within the samples themselves is measured. Replicate splits, derived from a
single location at a sample site, divide the sample from each collection vessel to measure
variations in metal concentrations of the sub samples. The purpose of duplicate analyses
was to test the variability of metal concentrations in soil at the sample site. Ideally, a
replicate sample collected 10 – 20 feet from the environmental sample would show
similar concentrations. Each sample was assigned the site name corresponding to the
environmental sample plus a letter. The letter “a” is used to denote a split from a single
sample, where the letter “b” is used to signify that the sample was collected as a duplicate
sample separate from the environmental sample. The three split samples and their
associated environmental samples include Lead 1 & Lead 1a, Lead 2 & Lead 2a, and
Lead 5 & Lead 5a. The five duplicate samples and their associated environmental
samples include Lead 14 & Lead 14b, Lead 31 & Lead 31b, Lead 38 & Lead 38b, Lead
52 & Lead 52b, and Lead 61 & Lead 61b. In addition to the replicate samples analyzed
by the MEMS-61 method, ten replicate splits were also analyzed by XRF. The XRF
replicate samples consisted of sample splits previously analyzed by the MEMS-61
method. The comparison of the XRF and MEMS-61 allows their combined use in spatial
and statistical analyses. The split samples include Lead 3, Lead 4, Lead 5, Lead 6, lead 7,
Lead 8, Lead 24, Lead 29, Lead 47, and Lead 61b.
33
Soil Sample Preparation
Samples collected for the MEMS-61 method were prepared before shipping to
ALS Chemex. In order to claim a representative sample, soil samples were mixed
thoroughly in the collection bag. Sub-samples collected from the bag were placed in glass
Pyrex dishes. Pyrex was selected because of its resistance to heat and relatively simple
composition, whereas other materials may leach trace metals when heated. Some gravel
and vegetation, if present, were removed from various samples. Residual moisture was
removed by baking overnight at 80º C. The baked samples were then sieved at 500 μm to
remove pebbles and debris. Approximately 250 grams of baked and sieved samples were
collected into zip-lock bags and labeled according to their sample site. The dishes were
washed with soap and water, then rinsed with tap water, then dried, and reused. The bags
of samples were stored in a temperature-controlled lab. The sub samples were shipped to
ALS Chemex Lab.
Laboratory Analysis
MEMS-61
The soil samples were analyzed by ALS Chemex laboratory in Reno, NV using a
ME-MS61 geochemical method. Samples were pulverized so that 85% of the grains
passed through a 75 micron (200 mesh) sieve. The samples were decomposed with a 4acid digestion prior to the (ICP-AES) and ICP-MS) of the ME-MS61 method. The fouracid digestion includes perchloric, nitric, hydrofluoric, and hydrochloric acids. The
samples are analyzed by ICP-AES. In addition, samples containing high concentrations
34
of bismuth, mercury, molybdenum, silver and tungsten are analyzed by ICP-MS.
Additional information can be found in the appendix ‘The Geochemical Procedure for
Ultra-Trace Level Using ICP-AES and ICP-MS’.
XRF
Thirty soil samples were analyzed using a hand-held, Innov-X, α-4000 X-Ray
Fluorescence (XRF) spectrometer at Georgia State University. This instrument uses a
tungsten x-ray tube and an energy-dispersive spectrometer. A trial of three analyses is
conducted per sample and the concentrations are reported with a standard deviation.
NURE HSSR
The U. S. Geological Survey analyzed 257 soil samples collected by the NURE
HSSR program in Sacramento County (USGS, 1997). The analysis of these samples
yielded 42 major, minor, and trace elements were determined by digestion of perchloric,
nitric, hydrofluoric, and hydrochloric acids, followed by a combination of ICP-AES and
ICP-MS (Goldhaber et al., 2009).
Spatial Analysis
Four individuals collected the samples and noted the location of the soil samples
at different stages of the study. The location for data from previous collection events was
recorded as a description of cross streets. These sample sites were located on aerial
photographs from Google Earth and assigned a degree, minute, second coordinate in the
35
North American Datum (1983) based on their location description. The final stage of
sample collection for this study effort employed a GPS unit to locate the sample sites.
Google Earth and GPS coordinates collected in the NAD83 system as degrees, minutes,
and seconds were converted into decimal degrees. These coordinates added into Arc GIS
as sample site locations serve as place markers for the sample sites.
The concentrations were matched with the site locations at each of the sampling
points. The metal concentrations at each point display the amount of metal in the soil
collected at each site. Factor scores, which display the magnitude of influence of each
factor, were predicted for each location.
Other spatial features such as soil type, geology, and land-use are considered in
order to determine their relationship with elevated lead concentrations in Sacramento.
The combination of these features coupled with statistical analyses allow a better
understanding of the processes that lead to elevated lead concentrations.
GIS analysis
Various map layers have been added to locate sample sites, examine potential
contamination sources, and understand the relationship between elevated levels of lead
and the map layers. Land use and topography were assessed from aerial photos obtained
from Google Earth and USA images (USGS). These map features are especially useful
in obtaining additional distances not collected from the field. Sanborne fire insurance
maps were used to understand local historic land use and pinpoint possible point sources
that may contribute to anomalous data or locate trends within downtown Sacramento.
Geologic, soil, and land use maps were examined in conjunction with the distribution of
36
lead concentration and sampling frequency to explore possible links between lead
concentration and surface geology, soil, and land use. Street maps were used to estimate
distances from roads to sample sites in order to establish a direct connection between lead
concentrations and distance to roads. Prediction maps generated from interpolated lead
concentrations and factor scores were visually compared to geology, soil, and land use
map layers. A visual comparison provides a qualitative assessment of the spatial
orientation of these variables.
Wind directions and magnitudes measured at two stations around the Sacramento
area provide an estimate of possible avenues of particulate transport. The University of
California Statewide Integrated Pest Management Program compiled the information
between 1995 and 2009. These stations provide daily maximum wind direction and
magnitude. The direction of the wind was compiled and reported as degrees on the
azimuth scale. Rose diagrams generated in Rockworks were used to compare the
frequency of maximum and or average wind direction. A subset of the data collected
between the months May and October was extracted from the analysis. These data have
been extracted to only account for wind direction and magnitude during dry months when
particulates are most likely transported from the ground (Filippelli et al., 2005). Rose
diagrams were used to compare the frequency and magnitude of maximum and/or
average wind to its direction for the subset.
37
Statistical Analysis
Statistics is useful in presenting data and communicating the key features of data
sets. In addition, statistics are useful for making inferences based on sampled data and
evaluating the accuracy of the inferences about relationships between variables.
Summary statistics display the maximum, minimum, and average values in data but often
overlook the more subtle aspects of data. In order to define relationships and explain the
variability among data further evaluation is necessary. Analyses of multiple variables
allow patterns to emerge that are otherwise unnoticed. Factor analysis allows the
relationship among variables to be explained by grouping them into fewer unobserved
variables. The groups of these variables are interpreted as underlying factors. Since it is
impractical to measure the lead concentrations in soil throughout an entire area, discrete
measurements were taken and geostatistics was employed to interpolate values between
measurements. Spatial statistics such as kriging help visualize emerging patterns through
smooth interpolation of trends. Statistical tests were also used to assess the accuracy of
the resulting prediction. A semivariogram measured the spatial correlation of data to
assess the propriety of kriging.
Summary statistics for the environmental and replicate data were calculated.
These include the minimum, maximum, median, and mean of metal concentrations.
Standard deviation was computed for the environmental data set. Calculation of
summary statistics aids in the detection of anomalous data.
38
Factor Analysis
Maximum likelihood estimation
The statistical program R version 2.8.1(The R Foundation for Statistical
Computing, 2008) computed factor analysis model parameters employing the maximum
likelihood method for 43 elemental concentrations collected from 93 sites for the MEMS61 data. In addition, 29 elemental concentrations from the 257 NURE data sites across
Sacramento County underwent an independent factor analysis employing the maximum
likelihood method. The purpose of these separate analyses was to test two independent
data sets. The model for factor analysis assumes a normal distribution for the input data
(Johnson & Wichern, 1998). Elements that exhibited a skewed distribution were log
transformed then standardized prior to factor analysis (Davies & Wixson, 1986; Ratha
and Sahu, 1993; Garcia & Millan, 1998; Bityukova et al., 2000; Costello & Osborne,
2005; Cicchella et al., 2008). Standardization allows elements on different scales to be
compared to each other. Standardization of data is common practice among researchers
applying factor analysis and principal component methods (Bityukova et al., 2000;
Costello & Osborne, 2005). Four underlying factors were identified for the factor
analysis under the maximum likelihood model estimation, which used the MEMS-61
data. Three underlying factors were identified for the NURE data using the maximum
likelihood method of parameter estimation. These factors were iteratively determined
based on the amount of total variability explained by the model and each factor’s
stability. Factor stability is measured by counting the number of variables with
significant loadings, 0.50 or greater (Cicchella et al., 2005), in a single factor. Less than
39
three significant loadings and the factor is said to be unstable (Korre, 1999). A varimax
rotation was applied to the factors for ease of interpretation. Varimax minimizes the
number of variables that have high loadings on each factor (Cicchella et al., 2005).
Varimax is a type of orthogonal rotation intended for uncorrelated factors, which has
proved a reliable analytical technique (Ratha & Sahu et al., 1993; Bityukova et al., 2000).
A residual matrix displays the difference between the data correlation matrix and an
estimated matrix from the factor analysis model. This comparison serves as a test to
determine model fit. Generating a kriged prediction map of factor scores using ArcGIS
allows the visual assessment of the influence of underlying factors.
Principal component estimation
Principal component estimation (PCE) provides an additional tool for identifying
communality among the elemental variables. It also serves as a method to check against
other estimators to determine if the use of factor analysis is appropriate. Although PCE
and MLE are different estimation methods they should yield similar parameter estimates
if factor analysis is appropriate (Johnson & Wischern, 1998). PCE was used in this study
to verify the adequacy of MLE for parameter estimation. Since parameter estimates were
similar under PCE and MLE, PCE was not used to calculate factor scores or for
subsequent calculations. MLE was used instead as the primary mode of factor analysis.
The same data analyzed by MLE was also analyzed using PCE. The
normalization and standardization of the data mentioned above were applied prior to
principal component estimation. A spectral decomposition, performed by the computing
40
program “R”, provided the eigenvalue/eigenvector pairs. The first four eigenvalues were
chosen to represent the number of factors. This determination was based on three aspects
of the analysis. First, the Scree test provides an initial inspection of the eigenvalues that
represent the majority of the communality (Figure 12). The proportion of cumulative
variance explained by each factor determined the number of factors chosen in PCE
extraction. If a sufficient portion of the variability is explained additional factors may not
be necessary. Second, an iterative process of varying the number of eigenvalues selected
examined the stability of the resulting factor loadings. A stable factor, according to
Korre, 1999, displays significant loadings, 0.50 or greater (Cicchella et al., 2005), for
three or more variables. Third, near reproduction of the observed correlation matrix was
an indication that a sufficient number of factors have been selected (Johnson & Wichern,
1998). A poor reproduction of the original matrix may indicate the need for addition or
subtraction of factors. The factor loadings were rotated using an orthogonal varimax
rotation. This rotation simply accentuates high loadings on each factor while diminishing
the lower loadings (Korre, 1999). The subsequent construction of a specific variance
matrix displays the uniqueness of each element and allows computation of an estimation
matrix. The estimation matrix was subtracted element-wise from the observed
correlation matrix, R, yielding the residual matrix. The resulting residual matrix enables
the researcher to examine how closely the parameter estimates of the PCE method
reproduced the observed correlation matrix (R). This was done as a final step to decide if
PCE or MLE adequately explained the variability of the data. Inspection of the residual
matrix may also disclose individual variables not well modeled by factor analysis.
41
Ordinary kriging
Ordinary kriging was applied to 103 sites sampled for lead concentrations in the
Sacramento area. The mean concentration of five samples located in essentially the same
location was calculated and input as one site reducing the number of sites used in the
kriging analysis to 99.
Predictions of lead concentration in Sacramento were calculated by ordinary
kriging with the statistical software program R. Specifically, the “gstat” computing
package (The R Foundation for Statistical Computing, 2008) provides the tools necessary
to complete the kriging exercises. In this case the gstat package assumes the data are
projected so the location coordinates are converted from latitude/longitude to Universal
Transverse Mercator (UTM) coordinates. The latitude/longitude coordinates were
converted to UTM in WGS 84 datum. In R the data were converted from a data frame
format to a spatial points data frame format in order for the program to perform the
kriging operation. Once the spatial data were in the proper format a semivariogram of the
log-transformed lead concentrations was constructed. Model parameters, such as range
and lag size, were specified and a model was fit to the available data. An ideal model of
the semivariogram was plotted with the semivariogram estimated of the data. A spherical
model was fit to the estimated semivariogram equation. A lag size of 500 and a maximum
range of 10,000 were used. The error is the departure of the data from the ideal model.
An omni-directional semivariogram model was used to since spatial correlation is
assumed to be independent of direction. A directional semivariogram can model a
42
preferential pattern in anisotropic data. Next, the kriging calculations were applied to a
predefined grid using the log of the lead concentrations and the semivariogram
parameters discussed earlier. The ordinary kriging map exhibits the predicted lead values
based on the linear, weighted combination of nearby observed lead concentrations. A
variance map displays the uncertainty in the kriging prediction across the region being
analyzed. Regions of high variation indicate where the prediction may diverge
substantially from the observed values. An additional method used to assess the accuracy
of predicted concentrations is leave-one-out cross validation. This method extracts a
single observed value and generates a prediction map without the use of the extracted
value. The predicted value at the location of the extracted datum is compared to the
value of the observed datum that was extracted. The difference indicates how well the
model predicts the values.
43
Chapter 4
RESULTS
Environmental Analysis
MEMS-61 environmental analysis
The results for elemental analysis of soils by the MEMS-61 method are reported
in parts per million (ppm) and presented in (Table 4). Summary statistics are presented
for the metals analyzed by the ME-MS61 method in (Table 5).
The maximum concentration of lead in the 103 soil samples collected for this
project is 1540ppm. This value is almost twice the next highest value of 863ppm. The
minimum is 10.6ppm. The mean is 127.99ppm, the median is 52.6ppm, and the standard
deviation is 204.9ppm. Lead concentrations in soils of Sacramento follow a positively
skewed distribution (Figure 13 a & b). Seventy-three percent of the lead concentrations
lie within the first 10% of the range of concentrations. The locations of sample sites
displayed with lead concentrations are presented in Figure 14. The distribution of lead
concentrations is divided into four categories. Green indicates the concentration value is
between 10 and 100 ppm, yellow represents concentrations of less than 100 to 300 ppm,
orange represents concentrations of 300 to 863 ppm, and red represents concentrations
greater than 863 ppm. It is evident from the map that the lowest concentrations of lead in
the soils of Sacramento occur outside the central portion of the city. Conversely, the
higher concentrations of lead occur within the central portion of the city. While the
majority of elevated concentrations occur in the central portion of the city, some of the
highest concentrations do occur beyond this central area.
44
Histograms and box plots were used to evaluate the distribution of each elemental
concentration. The MLE method requires normally distributed data in order to properly
run the factor analysis model. In addition, Korre, 1999, Wang et al., 2005 cite non-normal
distributions as an indication of pollution. The elemental concentrations displaying nonnormal distributions are presented in (Table 6 and figure 15 a-g).
XRF environmental analysis
The results for the analysis of Pb and Zn of soils by the XRF method are reported
in ppm and presented in Table 7 (a & b). The results are reported as an average of three
readings measured by the instrument for each sample site. The XRF lead concentration
data are represented spatially in Figure 16. The distribution of lead concentrations is
divided into four categories. Green symbols indicate the concentration value is between
10 and 100 ppm, yellow represents concentrations of less than 100 to 300 ppm, orange
represents concentrations of 300 to 863 ppm, and red represents concentrations greater
than 863 ppm. None of the XRF data fall into the last category, and the same symbol
convention is used between data sets for ease of comparison. The maximum lead
concentration in soil measured using a hand-held XRF is 746ppm. The minimum
concentration of lead in soil analyzed by this method is 7ppm. The mean, median, and
standard deviation are 181, 147, and 154 respectively (Table 7 a). The Pb concentration
of the XRF data is graphically displayed by a histogram and box plot (Figure 17).
45
NURE HSSR environmental analysis
Two hundred and fifty seven samples of soil collected by the NURE program in
Sacramento County were analyzed for lead and other elemental concentrations (Figure
18). The results for elemental analysis of soils determined by 4-acid digestion followed
by analysis by ICP-AES and ICP-MS are reported in parts per million. Summary
statistics are presented for the NURE samples in (Table 8).
The maximum concentration of lead in the 257 soil samples collected by the
NURE program is 1039ppm. The minimum is non-detect reported as 0.0. The mean is
49ppm, the median is 22ppm, and the standard deviation is 91ppm. Lead concentrations
of the NURE data set follow a slightly positively skewed distribution, but are tightly
clustered relative to the samples of the MEMS-61 data (Figure 19). The distribution of
lead concentrations is presented in the same four categories as described above for the
MEMS-61 and XRF mentioned data. Most elevated lead concentrations are proximal to
the city of Sacramento, although some elevated lead concentrations occur outside of the
city.
Histograms and box plots were used to evaluate the distribution of each elemental
concentration. Korre, 1999; Wang et al., 2005 cite non-normal distributions as an
indication of pollution.
Replicate Analysis
MEMS-61 replicate analysis
46
Eight replicate samples were collected and analyzed with the MEMS-61 method
(Table 9). These samples are compared with the corresponding environmental samples to
help determine variability in collection and analysis processes. Three of the eight
replicate samples were taken as sample splits from single locations at randomly selected
sample sites. The purpose of analyzing sample splits from a single sample is to test the
variability of metal concentrations within the sample split selected for analysis at the
laboratory. The remaining 5 replicate samples were collected separately as duplicate
samples at randomly selected sites at different locations within the same site as their
corresponding environmental sample.
Summary statistics were applied to the replicates, corresponding environmental
samples, and both replicate and environmental samples. In addition, the split samples
and duplicate samples, collected at different locations within the same site, were
evaluated separately and together (Table 10).
Percent difference was calculated between the replicate samples and associated
environmental samples (Table 11). The 5 duplicate samples, collected at different
locations within the same site from the associated environmental samples, when
compared to their corresponding environmental sample, all exhibited percent differences
less than 5%. Two of the three split samples, split form the original collection bag,
exhibit a percent difference greater than 5% in lead concentrations. The environmental
sample Lead 2 and its corresponding replicate Lead 2a exhibit a 22% difference of lead
concentrations. The lead concentration measured in the environmental sample Lead 2 is
216 ppm, much higher than the associated replicate split Lead 2a with a concentration of
47
83.9 ppm. The environmental sample Lead 5 and its corresponding replicate split Lead
5a exhibit a 5.5% difference in lead concentrations. The lead concentration measured in
the environmental sample Lead 5 is 509 ppm, greater than the associated replicate split
Lead 5a with a concentration of 408 ppm.
XRF replicate analysis
Ten replicate split samples analyzed by MEMS-61 methods were also analyzed
by hand-held XRF for zinc and lead concentrations. Soil samples analyzed by method
MEMS-61 and by XRF using a hand-held instrument exhibit comparable concentrations
(Table 12 and Figure 20). A scatter plot of these samples yields a linear trend with an rsquared value of 0.95. The maximum difference between the average XRF
concentrations and the MEMS-61 concentrations is 76ppm. Percent difference between
the concentration of lead between the XRF and MEMS methods ranges from 1.1 to 111
percent with a standard deviation of 32.
Lead Concentrations and Spatial Analysis
Soil type
The distribution of lead concentrations from MEMS_XRF data in soils of
Sacramento compared to varying types of geology, land use, and soil type that occur in
the study area reveal relationships with various aspects of the region. Analysis of the
distribution of lead concentrations compared to ranges of the percentage of clay in soil as
described by the NRCS soil report of Sacramento County does not reveal any significant
48
relationship (Figure 21). Category 1 represents a range of 5 to 18 percent clay and
includes 15 percent of the data. Category 2 represents a range of 10 to 25 percent clay
and includes 62 percent of the data. Category 3 represents a range of 15 to 27 percent
clay and includes 17 percent of the data. Category 4 represents a range from 27 to 60
percent clay and includes only six percent of the lead concentrations (Figure 22).
Twenty-two samples were excluded from the analysis because no information
corresponding to the percentage of clay in the soils was available. Average lead
concentrations do not vary significantly between the ranges of clay percent in soils.
Category 4 exhibits a lower average lead concentration but does not represent a
significant portion of the data. Category 2 contains the highest values of the data set,
however the average values and upper and lower quartiles are similar among the plots. It
is important to note that actual clay content of urban areas may vary more than indicated
by the NRCS.
Geology
The distribution of lead concentrations from MEMS_XRF data is also compared
to geologic units in which the samples were collected (Figure 23). The samples were
collected from four geologic units present in the study area. These units include
alluvium, Qa, which encompasses 42 percent of the sites sampled for lead concentrations;
undivided basin deposits, Qb, which encompass only six percent of the sites; Upper
Modesto Formation, Qmu, with only one sample comprising just under one percent of the
sites; and the Lower Riverbank Formation, Qrl, encompassing 51 percent of the sites
49
(Figure 24). Although the highest concentrations occur in the Lower Riverbank
Formation, the average lead concentration is slightly higher in the Alluvium. The upper
quartile of data within the alluvium is also greater than that of the Riverbank Formation.
The samples contained in the undivided basin deposits and Upper Modesto Formation are
too few to be significant. It is important to realize that the study area of Sacramento is
located mostly within the bounds of the Riverbank Formation, which does not appear to
be related to the distribution of lead.
Land use
The lead concentrations from MEMS_XRF data are more evenly distributed
among the categories of land use in the study area (Figure 25). The categories of land use
include agriculture, encompassing four percent of the sites sampled for lead
concentrations; commercial, encompassing 17 percent of the sample sites; industrial,
encompassing 21 percent of the sample sites; park, encompassing 17 percent of the
sample sites; and residential, encompassing 41 percent of the sample sites (Figure 26).
The average value of lead concentrations is higher for the commercial and recreational
land uses. The industrial and residential land use categories exhibit lower average lead
concentrations, although the residential category exhibits the largest variation between
the 25th and 75th percentile. Some of the highest lead concentrations occur in the
residential land use category.
50
Historic industry
Sanborne fire insurance maps of downtown Sacramento from 1952 list locations
of potential heavy metal sources (Figure 27). A list of companies and their associated
industry is presented in Table 13.
Wind direction
Historical data from two weather stations in Natomas and the Sacramento Airport
were compiled in order to ascertain the prevailing wind direction within the study area.
Rose diagrams display the frequency of the direction of maximum wind currents for each
area for year-round and dry-month conditions (Figure 28 a & b). Dry-month intervals
were subtracted from yearly data by removing data occurring during the months of
November through March. Minimal discrepancies are observed between wind direction
frequencies plotted for “dry” months and year-round observations with the exception of
the Sacramento Airport site. The Sacramento airport site exhibits a clearly dominant
pattern of wind coming from a southwest and minor influence from the northwest
directions during year-round observations. This trend is present during “dry month”
observations with an increased frequency of wind vectors from the northwest and south.
The Natomas site shows wind direction predominantly from the southwest for year-round
conditions. In addition, wind from the north and northeast directions increases during
“dry months”. The persistent wind direction for both of these sites originates from the
southwest.
51
Distance to roads
The distance from sample sites to roads was measured by field observation as
well as in Google Earth and Arc GIS. A scatter plot of lead concentrations, from MEMS61 data, and distances to roads for each sample indicates the relationship between lead
concentration and distance to road with an R-squared value of 0.02 is not strong (Figure
29). High lead concentrations occur with higher probability close to roads. However,
other unobserved factors are likely to affect lead concentration. Since some sites close to
the road have low lead concentrations, factors might include vehicle traffic density and
proximity to older areas of town.
Ordinary Kriging
Ordinary kriging was used to interpolate lead values at unsampled locations based
on measured lead concentrations at sampled sites. Kriging was used to produce a
prediction map, which displays sample sites and prediction contours of lead
concentrations (Figure 30). The lead concentration contours reveal downtown/midtown
Sacramento located in the central portion of the study area and the northeast portion of
the study area to contain the highest concentration of lead in soil. Elevated
concentrations predicted by kriging of lead in soils are also present just south of
downtown, continuing to the south central portion of the study area. The lowest
concentrations predicted by the kriging calculation occur in the southeast portion of the
study area below the American River, as well as the southwest, west, and northwest
portions of the study area. It is evident that high predicted values for lead in the soils of
52
Sacramento are concentrated in the most developed areas. The maximum concentration
of lead in soils of Sacramento predicted by the kriging analysis does not exceed 173 ppm.
The minimum value predicted for lead in Sacramento soils is 23.7 ppm. The mean,
median, and standard deviation are 58.9 ppm, 45.6 ppm, and 31.6 ppm respectively. The
summary statistics of the kriging interpolation are compared to the summary statistics of
the observed lead concentrations (Table 14).
The semivariogram for Pb is shown in Figure 31. The lag distances are displayed
in meters. Each lag distance is 500 meters with a tolerance of approximately +/- 250
meters. Each point on the semivariogram corresponds to a particular lag distance and is
calculated with a tolerance. The tolerance enables sites of approximately similar
distances apart to be grouped together. At the range the distance between sample sites
becomes too great to be spatially correlated. The range is reached at a distance of
approximately 8,000 meters where the semivariance approaches the sill at around 1.5 γ.
Semivariance, γ, equals one-half the average squared difference of values among
locations. The nugget effect is evident at a gamma value of approximately 0.7. The
greatest departure from the model occurs at a distance of approximately 1,000 meters.
An increase in the semivariance (γ), at a distance of approximately 1,000 m, indicates
high and low lead concentrations at a specific distance apart. Conversely, a decrease in γ,
at a distance of 3,300 m, indicates lead concentrations are similar for the specified
distance.
A variance map of the ordinary kriging exercise displays the variation of
predicted lead concentrations (Figure 32). The variation was calculated from lead
53
concentrations in ppm that have been log transformed and are reported as the difference
between observed lead concentrations that have been log transformed and predicted
concentrations from those values. The central region of the study area exhibits the least
amount of variance. This indicates a small prediction error for the kriging model. The
fringes of the study area exhibit the greatest amount of variation with the exception of the
northeast corner. The northeast corner displays a moderate amount of variance.
The leave-one-out cross-validation results display the deviation of predicted
values from observed values at locations where the prediction is made from nearby
variables excluding the observed variable at that location (Table 15). The cross
validation analysis reports predicted values at locations of observed concentrations. The
maximum value predicted by the cross validation analysis is 165 ppm, much lower than
the corresponding observed lead concentration of 1,540 ppm. The minimum, mean,
median, and standard deviation are 18, 75, 67, and 41 ppm respectively. The minimum,
mean, median, and standard deviation of the observed values are 10.6, 127.9, 52.5, and
204.9 ppm respectively. The difference between observed concentrations and predicted
concentrations generated by the cross validation analysis are referred to as residuals. The
maximum residual value is 1,467 ppm, which reflects a large underestimation of the
predicted value from the observed value. The greatest overestimation occurs with a
residual of –144 ppm. The median values of the observed data and the predicted data are
similar.
54
Factor Analysis: Maximum Likelihood Estimation
Factor loadings
Factor loadings quantify the relationship between observed elemental
concentrations and the unobserved factors. Variables with high to moderate factor
loading values, in the range of 0.80 to 0.40, can be considered well correlated to other
variables with high to moderate loadings in that factor and are selected for factor
interpretation (Costello & Osborne, 2005). Dominant variables within a single factor
may exhibit negative loadings. Negative values observed within a factor contrast positive
values, but do not affect the analysis among other factors (Johnson & Wichern, 1998).
Low factor loading values, below 0.40, indicate that the variable may not be related to
other variables (Costello & Osborne, 2005). Tabachinick and Fidell (1983) cite 0.32 as a
minimum loading for related values.
MEMS-61 data
The following results represent rotated factor loadings for a model with four
factors using a subset of MEMS-61 data that has been standardized and transformed by
log base 10 (Bityukova et al., 2000; Costello & Osborne, 2005) when its distribution is
skewed (Figure 15 a-g) (Table 16). The cumulative variation explained by these four
factors is 69.5 percent. The cumulative variation represents the percentage of data
variation accounted for by the latent factors. A graphical representation of the factor
loadings of MEMS-61 data by MLE is presented in Figure 33 a & b.
Factor I shows high to moderate loadings for the elements Al, Co, Cs, Fe, Ga, Li,
Mg, Mn, Ni, Ti, V, and Y. No high negative loadings are present in this factor. All other
55
variables show low correlations and loadings. Factor I explains 23.1% of the cumulative
variance.
Factor II shows high to moderate loadings for the elements Al, Ba, Be, Ce, Ga,
Hf, K, La, Nb, Rb, Ta, Th, Tl, and U. All other variables exhibit little correlation within
the factor. Factor II explains 20.4% of the total variance explained.
Factor III shows high to moderate loadings for the elements Ag, Bi, Cd, Cu, Mo,
P, Pb, S, Sb, Sn, W, and Zn. No high negative loadings are present in this factor. All
other variables show low correlations and loadings. Factor II explains 18.4% of the
cumulative variance.
Factor IV shows high loadings for the elements Ca, Na, and Sr. Factor IV also
shows high to moderate negative loadings for the elements Cs, Li, and Rb. All other
elements exhibit low loadings, which correspond to little or no correlation within this
factor. Factor IV explains 7.6% of the cumulative variance.
Crossloading of factors occurs when the variables are moderately loaded on two
or more factors. Costello & Osborne, (2005), cite any variable with a loading of 0.32 or
higher on two or more factors as an example of crossloading. Since most factors of the
MEMS-61 data exhibit strong loadings, the criteria for crossloading includes a loading of
at least 0.40 for two or more factors and the absence of a strong loading on other factors.
Factor crossloadings are observed between factors I and II for the elements Al, Be, Cs,
Ga, Ti, U, and Y. Factor crossloadings are observed between factors I and III for the
elements As, Cu, and In.
56
The variation of each variable is described by its loading and uniqueness values
(Table 17). Uniqueness represents the random aspect of each variable that is not
explained by the latent factors. Variables with uniqueness values above 0.33 are not well
defined by the underlying factors and include Ag, As, Cr, Ge, In, K, Mn, Mo, P, S, Th,
Tl, U, W and Zn
NURE data
The following results represent rotated factor loadings for a model with three
factors using data from NURE elemental concentrations that have been standardized and
transformed by log base 10 (Bityukova et al., 2000; Costello et al., 2005) when their
distribution is skewed (Table 18). These four factors explain 52.7% of the cumulative
variation. A graphical representation of the factor loadings of NURE data by MLE is
presented in Figure 34 a & b.
Factor I of the NURE data shows high to moderate loadings for the elements Al,
Co, Cr, Cu, Fe, Li, Mg, Mn, Ni, P, Sc, V, Y, and Zn. Low factor I loadings exist for Ca,
Ce, and La. The elements B, Be, Pb, Sr, Th, U, and Zr have zero loadings. Negative
loadings include Ba, K, Na, Nb, and Ti. Factor I explains 27.1% of the cumulative
variance of the NURE data. This factor corresponds to the Factor I of the MEMS-61
data.
Factor II of the NURE data shows high to moderate loadings for the elements Al,
Ba, Be, Ce, La, Nb, Th, Ti, Y, and Zr. Low factor II loadings exist for the elements B,
Be, Co, Fe, K, Li, Mn, Sr, and U indicating low correlation of these elements within
57
factor II. The elements Ca, Cr, Cu, Na, Ni, P, Pb, Sc, V, and Zn show zero factor II
loadings. Mg is negatively loaded for factor II. Factor II explains 14.2% of the total
variance explained of the NURE data. This factor corresponds to factor II of the MEMS61 data.
Factor III of the NURE data shows high to moderate loadings for the elements Al,
Ca, Na, and Sr. Low correlations are indicated by low factor III loadings for Ba, Be, Fe,
Mg, Mn, Nb, Pb, Sc, V, and Y. Elements B, Cu, Li, Ni, U, and Zr have zero loadings for
factor III. The elements that exhibit negative loadings for factor III of the NURE data
include B, Cu, Li, Ni, U, and Zr. Factor III explains 11.4% of the cumulative variance of
the NURE data. Factor III extracted from the factor analysis of the NURE data
corresponds to factor IV of the MEMS-61 data.
Less variability is explained for the NURE data than the MEMS-61 data,
therefore loadings of 0.32 or higher on two or more factors for the NURE data will be
considered crossloaded (Costello & Osborne, 2005). Crossloadings exist between all
three factors of the NURE data for Al. Yttrium is crossloaded between factors I and II.
Crossloading between factor I and III, although weak, exist between the elements Mg and
Ca.
Variables with uniqueness values above 0.33 are not well defined by the
underlying factors for the NURE data include Al, B, Ba, Be, K, Li, Mn, Nb, P, Pb, Th, Ti,
V, Y, Zn, and Zr (Table 19).
58
Residual matrix
A residual matrix displays how closely the estimated factor analysis model
reproduces the observed correlation matrix. Recall that the observed correlation matrix is
simply a correlation matrix of the elemental concentrations. The difference between the
observed matrix and the estimated matrix produced by factor analysis produces the
residual matrix. The residual matrix from the MLE that nearly reproduces the observed
matrix indicates a sufficient number of factors chosen to represent the underlying
variables. An exact reproduction of the observed correlation matrix would result in a
residual matrix consisting of all zeros. The maximum and minimum values of the
residual matrix represent the greatest variation from the reproduction of the observed
matrix.
MEMS-61 data
A summary of the residual matrix for the MEMS-61 data shows the overall
deviation of the estimated matrix from the observed matrix (Table 19). The estimated
matrix exhibits a maximum difference of 0.312 above zero and 0.163 below zero from
the observed correlation matrix. The average variation of all elements from zero is
0.0005, with a median of zero and an average standard deviation of 0.0489.
NURE data
A summary of the residual matrix for the NURE data shows the overall deviation
of the estimated matrix from the observed matrix (Table 20). The MLE estimation
matrix generated from the NURE data exhibits a maximum difference of 0.413 above
zero and –0.266 below zero from the observed correlation matrix. The average variation
59
of all elements from zero is 0.00898, with a median of zero and an average standard
deviation of 0.0798.
Factor scores
Factor scores calculated by the weighted least squares method were displayed for
each factor of the MEMS-61 and NURE data sets by means of a prediction map. The
factor scores were used to assess the spatial distribution of each factor. The prediction
map was generated by the Geostatistical Analyst package in Arc GIS 9.3.1.
MEMS-61 data
Factor I scores (Figure 35) are shown to be the highest in the northwest and
western portion of the study area. Moderate values of factor I occur in isolated areas of
the southeast and central east portion of the study area. Low and negative values of
factor I scores occur primarily in the eastern portion of the study area dropping sharply
from west to east in the north and more gradually from west to east in the south.
Factor II scores (Figure 36) occur in greatest concentration in the northern and
south-central and southeastern portions of the study area. The American River bisects
these two areas of high concentration. Moderate concentrations of factor II scores are
present in the central-east side of the area. Negative factor II scores are concentrated to
the west of the Sacramento River in the west-central portion of the area. The location of
the American and Sacramento Rivers coincide with concentration isopleths.
Factor III scores (Figure 37) occur in greatest concentration in the central portion
of the study area coinciding with downtown Sacramento and decrease outward. Negative
60
values occur mostly in the eastern portion of the study area with isolated regions in the
west-central and southwest study area. The northern and southwestern portions of the
prediction map display erratic concentration isopleths.
Factor IV scores (Figure 38) occur in greatest concentration in the northeast and
eastern portion of the map. Negative values occur west and northeast of the confluence
of the Sacramento and American Rivers.
NURE data
Factor I scores from the NURE data (Figure 39) are elevated along reaches of the
Sacramento River and in the northeast area of Sacramento County, south of Folsom Lake.
Moderate factor I scores occur in sediments near the American and Cosumnes Rivers.
Negative values of factor I scores for the NURE data are generally concentrated midway
between the Sacramento River to the west and the foothills to the east.
Factor II scores from the NURE data (Figure 40) show higher values in the
southeast and south areas of Sacramento County, with the exception of the limb of
samples that extend southwest toward the delta. These samples exhibit moderate factor
scores with some intermittent negative scores along with samples collected within the
remainder of the county. Negative factor II scores occur most densely in the northern
half of Sacramento County.
Factor III scores from the NURE data (Figure 41) exhibit high values along the
northern boarder of Sacramento County, as well as along the American and Cosumnes
Rivers. Negative factor III values occur in the areas south of the American and
61
Cosumnes Rivers. A few negative factor III score values are observed along the
Sacramento River in the southwest area of the county.
Factor Analysis: Principal Component Estimation
Results of the principal component estimation (PCE) of factor analysis parameters
with a varimax rotation are presented. All data have been standardized and those data
with concentration distributions that deviate from a normal distribution have been
transformed to log base 10 prior to standardization (Bityukova et al, 2000; Costello et al,
2005).
Factor loadings
MEMS-61 data
Factor loadings calculated by PCE exhibit a slightly higher percent of cumulative
variance explained, 72.1%, than those calculated by MLE (69.5%) (Table 21).
Factor I of the rotated principal component analysis reveals high to moderate
loadings for the elements Al, As, Co, Cr, Cs, Cu, Fe, Ga, Ge, In, Li, Mg, Mn, Ni, Ti, V,
and Y. The remaining elements show little or no correlation with each other or the high
to moderately loaded elements of factor I. Factor I explains 22.2% of the cumulative
variance and corresponds to factor I extracted by MLE for the NURE and MEMS-61
data.
Factor II of the rotated principal component analysis displays high to moderate
negative loadings for the elements Al, Ba, Be, Ce, Cs, Ga, Hf, K, La, Nb, Rb, Ta, Th, Ti,
62
Tl, U, and Y. Cr exhibits a moderate positive loading reflecting a negative correlation to
the elements of this factor. The remaining elements show little or no correlation with
each other or the high to moderately loaded elements of factor I. Factor II explains
22.5% of the cumulative variation and corresponds to factor II of the NURE and MEMS61 data when MLE is used.
Factor III of the rotated principal component analysis reveals high to moderate
negative loadings for the elements Ag, As, Bi, Cd, Cu, In, Mo, P, Pb, S, Sb, Sn, W, and
Zn. The remaining elements show little or no correlation with each other or the high to
moderately loaded elements of factor III. Factor III explains 19.1% of the cumulative
variability and corresponds to factor III of the MEMS-61 data when MLE is used to
extract the factors. Factor analysis of the NURE data did not produce a corresponding
factor.
Factor IV of the rotated principal component analysis, which corresponds to the
MLE extraction of factor IV from MEMS-61 data and factor III of NURE data, reveals
high to moderate positive loadings for the elements Ca, Na, and Sr. Moderate negative
loadings are associated with the elements Cs and Li for Factor IV. The remaining
elements show little or no correlation with each other or the high to moderately loaded
elements of factor IV. Factor IV explains 8.2% of the cumulative variability.
Factor crossloadings for PCE are observed between factors I and II and occur
among the elements As, Cu, and In. Cross loadings are observed between factors I and III
for the elements Al, Cs, Ga, Ti, and Y.
63
The variables with specific variance values above 0.33 are not explained well by
these similar factors. These include Ag, As, Cr, Ge, In, Mn, Mo, P, S, Th, Tl, U, W and
Zn (Table 16). These are similar elements not well explained by MLE with the exception
of K.
NURE data
Factor loadings extracted by PCE for the NURE data exhibit a higher percent of
cumulative variance explained, 56.9%, than those calculated by MLE (52.7%) (Table 22).
These factors correspond to the resulting extractions from the previous extraction
methods with the exception of Factor III, which corresponds to factor IV of the MLE and
PCE extractions from MEMS-61 data. Factor III extracted from the MEMS-61 data is
not accounted for in the NURE analysis.
Factor I of the rotated principal component analysis of the NURE data displays
high to moderate loadings (>0.40) for the elements Al, Co, Cr, Cu, Fe, Li, Mg, Mn, Ni, P,
Sc, V, Y, and Zn. The remaining elements show little or no correlation with each other
or the high to moderately loaded elements of factor I. The elements with loadings less
than 0.40 include B, Ca, Ce, La, Th, and Ti. The elements U, Be, Pb, Sr, and Zr exhibit a
zero loading. Elements with negative loadings include Ba, k, Na, and Nb. Factor I of the
NURE data represents 28.3 percent of the variability explained by the PCE.
Factor II of the rotated principal component analysis of the NURE data reveals
high to moderate negative loadings for the elements Al, Ba, Ce, K, La, Nb, Th, Ti, U, and
Zr. These negatively loaded elements represent the dominant elements of the NURE data
64
for factor II. The remaining elements show little or no correlation with each other or the
high to moderately loaded elements of factor II. These elements include low negative
loadings of B, Be, Co, Fe, Li, Mn, Sr, and Y. Elements exhibiting zero loadings include
Ca, Cr, Cu, Na, Ni, P, Pb, Sc, V, and Zn. Magnesium is the only positively loaded
element in this factor. Factor II of the NURE data represents 16.0 percent of the
variability explained by the PCE.
Factor III of the rotated principal component analysis of NURE data reveals high
to moderate negative loadings for the elements Al, Ca, Na, and Sr. These negatively
loaded elements represent the dominant elements of factor III for the NURE data. The
remaining elements show little or no correlation with each other or the high to moderately
loaded elements of factor III. These elements include low negative loadings of Ba, Be,
Fe, Mg, Mn, Nb, Sc, Ti, V, and Y. Elements exhibiting zero loadings include Ce, Co, Cr,
K, La, Pb, Th, and Zn. Positively loaded elements include B, Cu, Li, Ni, P, U, and Zr.
Factor III of the NURE data represents 12.6 percent of the variability explained by the
PCE.
Less variability is explained for the NURE data than the MEMS-61 data,
therefore loadings of 0.32 or higher on two or more factors for the NURE data will be
considered crossloaded (Costello & Osborne, 2005). Crossloadings exist between all
three factors of the NURE data for Al and Y.
Variables with specific variance values above 0.33 are not well defined by PCE
for the NURE data include B, Ba, Be, K, Li, Mg, Nb, P, Pb, Th, Ti, V, Y, Zn, and Zr
(Table 18).
65
Residual matrix
MEMS-61 data
The summary statistics of the residual matrix of the MEMS-61 data computed by
PCE is presented in (Table 19). The residual matrix is the difference between the
correlation matrix from the observed elemental concentrations and the estimation matrix
calculated by factor analysis. The PCE estimation matrix computed from the MEMS-61
data exhibits a maximum difference of 0.279 above zero and 0.166 below zero from the
observed correlation matrix. The average variation of all elements from zero is -0.006,
with an average median of –0.004 and an average standard deviation of 0.050.
NURE data
The residual matrix computed from the NURE data by PCE is displayed in (Table
19). The estimation matrix calculated from the PCE of NURE data yields a maximum
difference of 0.372 above zero and –0.342 below zero from the observed correlation
matrix. The average variation of all elements from zero is –0.008, with an average
median value calculated for all elements of –0.012, and an average standard deviation
calculated for all elements of 0.084.
A comparison between the summary statistics of the residual matrices of MLE
and PCE for the MEMS-61 and NURE data is presented in (Table 24). In both data sets,
(MEMS-61 and NURE) the MLE matrix produces a higher maximum value. However, it
exhibits a lower average variation from the observed correlation matrix than the PCE
66
matrix. This lower average variation indicates that MLE estimates the observed
correlation matrix better than PCE in both data sets. The similarity between parameter
estimates under PCE and MLE indicate the factor analysis model is appropriate.
67
Chapter 5
DISCUSSION
Proximity to Roads
The nature of collecting soil samples in a heavily populated area restricts the
samples to areas where collection is possible. This includes verge between sidewalks and
roads. Although it has been shown roadside soils can be a source of lead due to
automobile exhausts predating the ban on the use leaded gasoline, the proximity to roads
does not exclude other potential sources of lead contamination in soils. Many of the
sample sites for this study are within a close distance to a road, which introduces bias to
the study. A plot of lead concentrations and distances to the nearest road (figure 28) does
not show a strong correlation between these variables. While some of the highest
concentrations occur at a short distance from roads, many low concentrations are also
present at close proximity to roads. Perhaps unobserved variables such as traffic density
or proxy to central, older areas of Sacramento influence lead concentrations near roads.
It is important to understand that the samples were collected at various locations around
Sacramento and not along transects as in other studies. The comparison of lead
concentrations to distance to roads in this study was for the purpose of identifying
possible bias introduced by collecting soil near roadsides. In studies by Filippelli et al.,
(2005) and Yassoglou et al., (1987) samples were collected along transects perpendicular
to a single road to ascertain the relationship between lead concentrations and distance to a
specific road. These studies observed an exponential relationship between lead
concentrations and distance to road.
68
Ordinary Kriging Discussion
A prediction map generated by ordinary kriging interpolation displays a
concentration of elevated lead values in Sacramento soils in the central, south central, and
northeastern portions of the study area (Figure 30). The lead predictions in the central
portion of the study area are substantiated by the occurrence of multiple observations of
elevated lead concentrations. Compared to soil samples collected in the outskirts of the
study area, these observations contain a considerable amount of lead. The connection
between developed areas of Sacramento and elevated lead concentrations indicates an
anthropogenic origin of the lead. Korre, (1999) and Wang et al., (2005) cite statistical
characteristics of concentration data, such as non-normally distributed data and the
combination of high concentrations and high standard deviations, as an indicator for
anthropogenic input. Distributions of lead concentration for MEMS-61 data samples
displayed by a histogram and box plot support this claim (Figure 13 a & b). In addition,
soil samples analyzed by XRF were collected in the central portion of the study area and
exhibit elevated lead concentrations (Figure 16). In general, because the XRF samples
were collected in a localized central portion of the study area known to have greater
concentrations than the outskirts, their concentration distribution is higher than the
MEMS-61 sample set (Figure 17 a & b).
The elevated lead concentrations coincide with the older areas of Sacramento.
Brown et al., (2008) observe a similar case in Lubbock, Texas where elevated lead
concentrations decrease with distance from the central, older sections of town. In the case
69
of Lubbock, Texas, the anthropogenic input of lead was attributed to lead additives in
gasoline emitted by vehicle traffic. Lead additives in gasoline were officially banned in
the 1990s and may be a contributor to elevated lead concentrations in Sacramento soils.
Historic road maps from 1933 and 1967 show the downtown area of Sacramento to be
well established as an urban area (Figure 42). In addition to vehicle traffic, Sanborne fire
insurance maps drafted in 1952 of the western downtown area of Sacramento list industry
involved with processes utilizing heavy metals (Figure 27). In particular, the Southern
Pacific Railroad (SPRR) locomotive shops operating from the 1860s to 1999 maintained,
repaired, and constructed railroad equipment (CPRR, 2009). This site, located southeast
of the confluence of the American and Sacramento Rivers, eventually covered 200 acres
and contained a coal-burning smokestack (Figure 43) that may have emitted lead into the
environment (Wang et al., 2005). The frequency of the direction of the daily maximum
wind vectors for sites located at Natomas and the Sacramento Executive Airport were
evaluated to assess the impact of the railroad shop on the distribution of lead in
Sacramento. Rose diagrams display a strong northeast trend as well as moderate southsoutheast and northwest trends in air current for the study area (Figure 28 a & b). The
frequency of direction for maximum wind currents coupled with the location of the
historic railroad shop does not indicate an apparent connection to the distribution of lead
in Sacramento and the SPRR locomotive shops. This qualitative assessment is based
upon the location of the railroad shops at the northwest fringe of the elevated lead
concentrations predicted by ordinary kriging and the frequency of the maximum direction
70
of wind vectors at nearby locations trending in a direction other than southeast, toward
elevated lead concentrations.
Several studies have demonstrated lead contamination in urban areas is a function
of traffic density and age (Mielke et al., 1998; Filippelli et al., 2005; Laidlaw et al., 2008;
Brown et al., 2008). Elevated lead concentrations in soil were observed in the central,
older sections of Sacramento and are consistent with previous findings in other urban
areas. A bull’s-eye of lead concentrations is evident when predicted lead concentrations
are contoured (Figure 30). This is an indication of lead deposition, redistribution, and
smearing of original point sources (Filippelli et al., 2005).
A prediction map of the variance of predicted lead concentrations generated in
Arc-GIS displays moderate variance in the southern area of the study (Figure 32). The
southern predictions represent interpolations based on observed lead concentrations,
which exhibit a few very high concentration values. With the exception of the few high
values, the remaining lead concentrations are generally low with very few moderate
concentration values. The occurrence of observed lead concentration of high values
nested within lower-valued observations indicates localized contributions of lead at these
locations. Lead in paint from point sources such as homes can generate localized spikes
in lead concentrations. Grinding and improper removal of paint containing lead is likely
to distribute lead locally. Elevated predictions generated from the northeast portion of
the study area exhibiting high lead concentrations are suspect. Only two observed points,
both exhibiting lead concentrations greater than 300ppm, are present in the section of the
study area identified to have elevated lead concentrations. In addition, distance between
71
observed data points in this section of the study area is larger than that of other sections.
The lack of data in this portion of the study area indicates the instability of the prediction
map in the area and is exemplified by the higher value of variance contours found in the
northern section of the map (Figure 32).
The semivariogram measuring the spatial correlation of the data displays a good
fit to the spherical semivariogram model (Figure 31). The distance to the range is much
greater than the sampling interval indicating spatial structure will be adequately displayed
on the prediction map (McGrath et al., 2004). Departure of the semivariogram data from
the ideal semivariogram occurs above and below the model line. The values above the
line indicate greater variation of data values at a particular distance. Variation in data is
caused by the observation of lead values with high concentrations near lead values with
low concentrations. The values below the model line indicate that more similar values
occur at that particular distance than calculated by the model.
Comparison of the summary statistics of the observed lead concentrations and the
summary statistics from the predicted lead concentrations generated by kriging reveals
the degree of smoothing the data has undergone. The maximum value of the predicted
lead concentrations, 173 ppm, is far below the highest observed value of lead
concentrations, 1540 ppm. The average lead concentration of the observed values is
slightly more than double the predicted values. The standard deviation of the observed
values is almost greater than the predicted values of lead concentration by a factor of
seven. The median value, however, is only slightly higher for the observed lead
concentrations than for the predicted lead concentrations. The minimum values are also
72
similar between the observed lead concentrations and the predicted lead concentrations.
The large difference among the average and standard deviation values coupled with the
low difference between the median values of the observed and predicted lead
concentrations implies the presence of localized point sources that are not easily modeled
at this level of spatial resolution. Histogram plots of the observed values and the
predicted values exhibit similar shapes with the exception of outliers (Figure 44 a & b).
Interpretation of Factors
Factor analysis was applied separately to two independent data sets from the same
general area. The two data sets consist of the MEMS-61 data collected and analyzed for
the purpose of this study and the NURE data collected by the NURE HSSR program and
analyzed by the U. S. Geological Survey. The NURE data was collected in a much larger
area encompassing Sacramento County, while the MEMS-61 data was collected in the
Sacramento city area, within the bounds of the NURE data area. The comparison of these
two independent data sets serves to validate the findings of each other. Factor analysis
extracted the same lithogenic factors from each set. A comparison of factor loadings is
displayed in Figure 45 (a-c). These include a mafic factor with elemental loadings
indicating associations with mafic and ultramafic rocks; a pegmatite factor with
elemental loadings associated with pegmatite dikes; and a base cation factor with
elemental loadings indicating the weathering of plagioclase. An additional,
anthropogenic factor was extracted from the MEMS-61 data that did not manifest itself in
the factor analysis of the NURE data. Four factors extracted from the MEMS-61 data by
the MLE method explained 69.5% of the cumulative variance. Three factors extracted
73
from the NURE data by the MLE method explain 52.7% of the variability. The absence
of the anthropogenic factor from the factor analysis of the NURE data is attributed to the
distribution of the NURE samples throughout Sacramento County rather than being
focused on the city of Sacramento where the anthropogenic inputs are shown to be
greatest.
Goldhaber et al., (2009), cite the Sacramento River, rivers and streams draining
the Sierra Nevada on the east, and rivers and streams draining the Coast Ranges on the
west as three major transport pathways for surficial materials residing in the southern
Sacramento Valley. In addition, the maintenance of natural and artificial levees acts to
spatially segregate west and east sediment sources (Goldhaber et al., 2009). Factor scores
used to assess the influence of each factor at a site location indicate an association
between geologic materials and location. Ideally, the location of elevated factor scores
coupled with evidence of a transport mechanism can indicate a source for the deposited
sediment. Although the depositional sources for the southern Sacramento Valley are well
defined, mixing and redistribution of soil in the process of urbanization can mask
provenance.
Factor I lithogenic, mafic
Factor I derived from the MEMS-61 data is dominated by Al, As, Be, Co, Cs, Cu,
Fe, Ga, Ge, Hf, In, Li, Mg, Mn, Ni, Ti, U, V, and Y and explains 23.1% of the cumulative
variability. Factor I derived from the NURE data is dominated by Al, Co, Cr, Cu, Fe, Li,
Mg, Ni, P, Sc, V, Y, and Zn and explains 27.1 % of the cumulative variability. The
74
dominant elements of factor I indicate the source to be weathering of mafic and
ultramafic rocks. Goldhaber et al., (2009), observes elemental groupings of Cr, Ni, V,
Co, Cu, and Mg in the soil on the west side of the Sacramento Valley and suggests
sediment contributions are dominated by the Coast Ranges. Morrison et al., (2009),
observed elevated concentrations of Cr and Ni in soils of the Sacramento Valley west of
the Sacramento River reflecting contribution from ultramafic rocks of the Coast Range
Ophiolite (CRO). In addition, Morrison et al., (2009), observed lower concentrations of
Cr and Ni in Sacramento Valley soils east of the Sacramento River reflecting contribution
from ultramafic rocks of the Western Metamorphic Belt (WMB) of the Sierra Nevada
diluted by granitic material. Figure 46 shows occurrence of mafic rocks on the east and
west side of the southern Sacramento Valley in relation to the study area. Similar
associations found in other published multivariate analyses indicate mafic parent rocks as
the source of dominant elements. Wang et al., (2005) cite Al, Ga, Li, Mn, Ti, and V
distributions as being controlled by the parent rock. D.S. Ratha & Sahu et al., (1993)
observe through factor analysis Co, Cr, Cu, Fe, Mn, and Ni to be weathering products of
basaltic source rocks, although they do not all occur in the same factor. Facchinelli et al.,
(2000) state Co, Cr, and Ni appear to be derived from ultramafic parent rock and suggest
serpentinized ocean floor peridotites to be the source. Cicchella et al., (2008) associate
the location of Al, Co, Ga, and Ti, among other elements, with Mt. Somma-Vesuvius
volcanics cropping out on the eastern part of Napoli, Italy. Davies & Wixson, (1986)
state Cr is associated with Mg and Ni during magmatic fractionation and accumulates in
ultra basic rocks such as serpentinite. Since Al is common in the earth’s crust, it is no
75
surprise to observe its presence among other elements derived from the crust. Goldhaber
et al., (2009), observe elevated concentrations of Li in the sediments of Cache Creek
located on the west side of the southern Sacramento Valley associated with marine
sedimentary rocks of the Great Valley Group. Figure 47 shows the occurrence of marine
rock of the Great Valley Sequence in the southern Sacramento Valley.
Factor I scores (figure 35) from the MEMS-61 data are highest in the west side of
the study area with sporadic occurrences of elevated factor I scores to the east.
Goldhaber et al., (2009) cite the Coast Ranges as the major sediment source for the
western Valley with Cache and Putah Creeks being the largest tributaries to the
Sacramento River system in the western study area. Harden, (2004) notes that sediments
eroded from the Coast Ranges are deposited on the floodplains and bottomlands of the
central valley. In addition to the sedimentary rocks of the Great Valley Group found in
the Coast Ranges, slightly metamorphosed volcanic basalt such as greenstone pillows,
metamorphic rocks like blueschist, eclogite, and sepentinized peridotite, which include
minerals such as glaucophane and jadeite (Harden, 2004), and ultramafic rocks of
Mesozoic age mostly serpentine with minor amounts of gabbro, peridotite, and diabase
(Jennings et al., 1977) are present. The composition of these mafic rocks are consistent
with the elements identified by factor I (Blatt & Tracy, 1996).
The factor I scores
generated from the NURE data appear to be elevated along the Sacramento, American,
and Cosumnes Rivers (Figure 39), which all have ultramafic sources near their
headwaters. Goldhaber et al., (2009), hypothesizes that in addition to the contribution to
Sacramento River levee and floodplain deposits by Cache and Putah Creeks, Sacramento
76
River levee and floodplain deposits contain a component of material derived from the
Klamath Mountains, which are comprised of accreted oceanic terrains and contain mafic
rocks similar to those of the WMB. As noted in Morrison et al., (2009), the occurrence
of ultramafic rocks in the WMB of the Sierra Nevada foothills contribute to Cr and Ni in
sediment east of the Sacramento River, but are diluted by granitic material of the Sierra
Nevada. The dilution of the ultramafic signature in the soil east of the Sacramento River
is supported by the sporadic occurrence of factor I anomalies generated by the factor I
score prediction map of the MEMS-61 data. The presence of natural and artificial levees
segregating soil geochemistry across the Sacramento River (Goldhaber et al., 2009)
supports the CRO and WMB as separate sources for soils derived from mafic and
ultramafic rocks.
As noted in studies by Korre, (1999) and Wang et al., (2005) loading of elemental
concentrations by anthropogenic activities tends to skew distributions of elemental
concentrations. Since the distributions of the dominant elements of factor I are not
skewed (Figure 32), they are not likely due to anthropogenic activity indicating natural
deposition. As a result, Factor I is designated as a lithogenic factor derived from mafic
rocks.
Factor II lithogenic, felsic
Factor II of the MEMS-61 data is dominated by the elements Al, Ba, Be, Ce, Cs,
Ga, Hf, K, La, Nb, Rb, Ta, Th, Ti, Tl, and U and comprises 20.4% of the cumulative
variability. Factor II, generated from the NURE data is dominated by Al, Ba, Ce, La, Nb,
77
Th, Ti, Y, and Zr and accounts for 14.2 % of the cumulative variability. The dominant
elements of this factor indicate felsic rocks with K and incompatible elements as a
source. These elements are sometimes associated with pegmatite dikes (Perkins, 1998).
A pegmatite is an intrusive igneous rock, typically forming as masses in dikes and veins
along the margins of batholiths (USGS, 2009). After partial crystallization, the
pegmatite intrusion consists of slowly cooling magmas containing water and
incompatible elements such as K, Rb, Li, Be, B, and Rare Earth Elements (REE)
(Perkins, 1998). Pegmatite intrusions also include minerals like gold Au, tantalite
(Fe,Mn)(Nb,Ta)2 O6, monazite (Ce,La,Th,Y) PO4, and uraninite UO2 (Perkins, 1998).
Many of these heavier minerals can be found concentrated in stream bottoms of placer
deposits, which occur from weathered vein deposits in the Sierra Nevada Mountains
(Perkins, 1998). Goldhaber et al., (2009), observe the dominance of silicic soils enriched
with REE (typified by La and Ce), as well as Ti, Th, and U on the east side of the
Sacramento Valley. Helly & Harwood, (1986) also note the arkosic nature of these
sediments in the Sacramento area and the probability that they are derived from the
western slope of the Sierra Nevada. Figure 48 shows the occurrence of granitic rocks of
the Sierra Nevada batholith, east of the study area.
A prediction map generated from factor II scores generated from the MEMS-61
data shows two regions of elevated values on the east portion of the study area bisected
by the American River, which runs east to west (Figure 40). The geology of the eastern
side of the study area is defined mostly by the American River including geologic units
such as Stream Channel Deposits, Alluvium, and Undivided Basin Deposits of Holocene
78
age and Upper and Lower Riverbank Deposits, Upper Modesto Formation, and Turlock
Lake Formation of Pleistocene age (Helly & Harwood, 1986). Goldhaber et al., (2009),
attributes the dominance of the more silicic alluvium on the east side of the Sacramento
Valley to sediment derived from the Sierra Nevada glaciation and the hydraulic mining of
gold in the northern Sierra Nevada. The geomorphic setting of Sacramento sediments is
such that the American River is incising the older sediments and depositing younger
sediments along the riverbanks (Helly & Harwood, 1986). The prediction map of factor
II scores from the MEMS-61 data follows a similar pattern where elevated values of
factor II coincide with Lower Riverbank Formation sediments north and south of the
American River (Figure 49). This observation is supported by the elevated distribution of
positive factor II scores within the lower Riverbank Formation among the samples
collected within the Quaternary geology (Figure 50). The factor II scores generated from
the NURE data show elevated areas in the eastern and southern Sacramento County
(Figure 40). The elevated factor II scores from the NURE data occur in moderate
amounts north of the American River, west of Folsom Lake and on the eastern edge of
Sacramento County proceeding down along the Cosumnes River and to the south where
they become more clustered. The presence of elevated factor II scores along the
Cosumnes River indicate the parent rock for soils dominated by factor II elements is
present in locations other than the sediment sources for the study area.
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Factor III anthropogenic
Factor III, generated from the MEMS-61 data, is dominated by the elements Ag,
As, Bi, Cd, Cu, In, Mo, P, Pb, S, Sb, Sn, W, and Zn, which explain 18.4% of the
variability. Factor analysis of the NURE data did not yield a comparable factor to the
anthropogenic factor III of the MEMS-61 data. The dominant elements in factor III
indicate a combination of anthropogenic activities. Korre (1999) states that data
generated from potentially polluted areas are likely to include variables that are not
normally distributed. Wang et al., (2005) report that high concentrations coupled with
high standard deviations suggest anthropogenic sources. Box plots and histograms of the
distribution of elemental concentrations for Ag, As, Bi, Cd, Cu, Mo, Pb, S, Sb, Sn, and
Zn display a positive skew, which indicates contamination (Figure 15 a-g).
The correlation of Pb, Cd, Cu, Mo, and Zn suggest contamination from vehicular
traffic. Wang et al., (2005) note the influence of traffic on Zn, Cd, and Pb concentrations
in their evaluation of an urban soil in China using Principal Component Analysis (PCA).
The presence of Zn is attributed to corrosion of engine parts; tire wear, and its use in
lubricating oils. Cadmium and Cu are also associated with wear of engine parts. In
addition, Wang et al., (2005) reports the use of Ni, Cu, and Mo in automobile oil pumps
and their eventual release into the urban environment. Goldhaber et al., (2009),
recognize that Pb and Zn concentrations in northern California are impacted by
anthropogenic inputs such as leaded gasoline and tire wear, respectively. In an isotope
analysis of river colloid and stream bed sediments, Dunlap et al., (2008), conclude past
leaded gasoline emissions and hydraulic Au- mining dominate lead inputs to the
80
Sacramento River within the study area. In a factor analysis of urban soils in Bombay,
India, Ratha & Sahu et al., (1993) cite anthropogenic sources such as industrial chimneys
and vehicular exhausts to be the source of Cd, and Pb enrichment. Cicchella et al.,
(2008) report Pb and Zn to be enriched from leaded gasoline and tire wear, respectively,
based on factor analysis of Napoli, Italy soils. Using factor analysis Garcia & Millan,
(1998) cite traffic activity as the cause for metal contamination of roadside soils in
Gipuzkoa, Spain, since Pb, Cd, Cu, and Zn are associated with gasoline, engines, tires,
lubricant oils, and galvanized parts of vehicles. Bityukova et al., (2000) noted Fe, Mn,
Pb, and Zn near gasoline pumps and industrial railway stations in the soils of Tallinn,
Estonia. Vehicular traffic, however, does not account for all of the dominant elements of
factor III indicating there are some other anthropogenic sources to consider.
Elements with significant correlations such as Bi, S, Sb, Sn, and W occur in factor
III. Other combinations of some of the dominant elemental concentrations and proposed
sources of factor III include ore mining (Cu, Ni, Co, Pb, Zn, and Mn (Davies, 1987)),
coal burning (Bi, Cr, As, Hg, Sb and S (Wang et al, 2005)), and industries working and
building ferrous metal products (Cd, Cr, Fe, Pb, Sn, and Zn (Bityukova et al., 2000)).
Davies & Wixson, (1987) determined mining operations in Madison County, Missouri,
U.S.A. to be the cause of enrichment of Co, Cu, Ni, Pb, Zn, and to a lesser extent Mn in
soils through PCA. Wang et al., (2005) identify Bi, Cr, As, Hg, and Sb as tracers of
anthropogenic pollution linked with coal burning. Bityukova et al., (2000) identified,
through PCA, the locations of factories of metal-working and ferrous metal building
industries associated with Cd, Cr, Fe, Pb, Sn, and Zn.
81
A prediction map of factor scores of factor III indicate that the highest
concentrations of this factor occur in the downtown area near the confluence of the
American and Sacramento Rivers (Figure 37). It is conceivable that a portion of the
metal contamination can be attributed to vehicular traffic. This area of Sacramento
happens to be the oldest and most central area of the city. The duration of the historical
vehicle traffic and the traffic density support this result. Another portion of the
contamination could be attributed to historical industry in the downtown/old-town area.
Box plots comparing the distribution of factor III scores to land use observed at sample
sites reveal the highest factor III scores to coincide with industrial land use (Figure 26).
Sanborne fire insurance maps provide a historical snapshot of the location of business
and industry in the city of Sacramento. A record of the industry of Sacramento in 1952
describes the location of metal working industries in and near downtown Sacramento
(Table 14). The Southern Pacific Railroad locomotive shop, located southeast of the
confluence of the American and Sacramento Rivers, featured various shops for
constructing, maintaining, and repairing railroad equipment as well as a coal-burning
powerhouse and smokestack. Steam engine and boiler repair, which require metalworking, are some of the tasks performed at the railroad shops and may have contributed
to the correlated elements of factor III.
The absence of an anthropogenic factor among the factors extracted from the
NURE data is best explained by the fact that the countywide NURE samples were not
collected at a sampling frequency within the city of Sacramento great enough to detect
the metal contamination. It should be noted that some of the highest concentrations for
82
lead of the data collected for the NURE program were located within the bounds of the
city.
Factor IV lithogenic, felsic
Factor IV generated from the MEMS-61 data is dominated by the elements Ca,
Na, and Sr, which explain 7.6% of the variability. Moderate negative loadings are
observed in the elements Cs and Li indicating that as the influence of factor IV increases
these elements have lower concentrations. Factor analysis of NURE data yielded a
comparable factor to the felsic factor IV of the MEMS-61 data. The elements Al, Ca, Na,
and Sr dominate this factor and account for 11.4% of the variability of the NURE data.
Similarly, Li is found to be negatively loaded among the elements of this lithogenic,
felsic factor. The dominant elements in factor IV indicate the soils dominated by this
factor are derived from parent rocks with strong sodic and calcic associations. Several
rocks and minerals contain Ca and Na, including granodiorite.
A prediction map of factor IV scores associated with the MEMS-61 data displays
the greatest concentration in the northeast portion of the study area (Figure 38). The
eastern shore of the Sacramento River south of the confluence of the Sacramento and
American Rivers mark the boundaries of the positive factor scores displayed by the
prediction map of factor IV scores to the west. Another prominent feature of the
prediction map of the factor IV scores is the swath of negative values dominating the
western portion of the study area. The distinct pattern of low factor IV scores west of the
Sacramento River confirms Goldhaber et al., (2009), statement that the Sacramento River
83
serves as an east-west sediment divide and indicates sedimentary input from the east.
Goldhaber et al., (2009) cite the Sierra Nevada glaciation and Au-hydraulic mining as a
source for silicic sediments on the east side of the Sacramento Valley. Factor IV scores
generated from the NURE data show elevated scores in the north and northeast areas of
Sacramento County near Folsom Lake as well as along the American and Cosumnes
Rivers. Stream sediment samples evaluated in Goldhaber et al., (2009), show higher
concentrations of Ca, Na, and Sr presumably reflecting the composition of rocks
weathering from higher elevations. Golddich’s weathering series of silicate rocks
explained in terms of bond strength (Railback, 2007), identifies Na and Ca as cations that
are susceptible to preferential weathering from silicate rocks. Figure 47 shows the
occurrence of the granitic rocks of the Sierra Nevada batholith.
Cross loading
Cross loadings between factor I and factor II of the elements Al, Be, Cs, Ga, Ti,
U, and Y is explained by the fact that both of these factors group elements derived from
lithogenic material. Al is among the dominant elements of all three factors extracted by
the factor analysis of NURE data. In addition, factor I and factor II computed from the
NURE data exhibit crossloading of Y. Many of these elements are ubiquitous in the
environment. Aluminum is a common element in the earth’s crust and should be
expected to show up in factors related to sediments with lithogenic origins. Titanium is
also common in the earth’s crust and can be expected to be present in lithogenic factors.
Cross loading also occurs between factor I and factor III in the factor analysis of the
84
MEMS-61 data for the elements As, Cu, and In. This represents their presence in both
lithogenic and anthropogenic factors. Cu is a major constituent of ore deposits in the
Sacramento Valley drainage basin with As as an accessory mineral (Goldhaber et al.,
2009). Both elements have been identified in Sacramento River levee deposits and Cu
has also been associated with mafic elements (Goldhaber et al., 2009). The
anthropogenic factor III list As and Cu among the contaminants associated with
urbanization and human activity. The occurrence of these elements in both lithogenic and
anthropogenic factors simply demonstrates that they are derived from both natural and
human sources.
Residual matrix
The residual matrix of the MEMS-61 data generated by MLE nearly reproduces
the observed correlation matrix indicating the factor analysis is adequate for explaining
the variability of the data (Table 24). The correlations between lead and other elements
are explained well by the factor analysis model using MLE. The elements with residuals
most frequently occurring above 0.08 include Cr, Ti, Na, K, and S (Figure 51). Other
elemental pairs with relatively high residuals include Th and U and Ba and K. Little
guidance is offered in the literature about acceptable residual values. Since the
correlation coefficient is on a scale from 0-1, the fact that most residuals are below 0.08
suggests an acceptable model fit. Aside from Ti, the other elements in this group are
fairly mobile, so perhaps weathering and transport of these elements complicates the
analysis of these elements.
85
Chapter 6
CONCLUSIONS
Analysis of lead and other elemental concentrations from soil samples collected in
Sacramento, CA partially reveal the origin and location of lead contamination in
Sacramento soils. Factor analysis makes correlations among elements more evident and
can indicate associations with potential source rocks. Ordinary kriging predicts the
location of elevated lead concentrations and factor scores.
Factor analysis indicates that elevated lead concentrations in Sacramento are of
anthropogenic origin. Although lead is ubiquitous in the environment, the portion of the
Sierra Nevada and Coast Ranges, which provide sediments to Sacramento, do not contain
any significant sources of lead. In addition, values observed in urban areas exceed values
measured outside of developed areas, thought to represent background values, by a factor
of ten in some cases. This trend is evident in the distribution of lead concentrations
among soil samples, which show a greater density of low concentrations (background)
compared to a lower density of elevated concentrations (pollution). The pattern of
distribution exhibited by lead concentrations is also evident among other elements
thought to be of anthropogenic origin, which are grouped by statistical analysis.
Elevated lead concentrations in Sacramento are due to a combination of industrial
and vehicular pollutant sources. Factor analysis reveals a correlation among lead and
other elements including antimony, arsenic, bismuth, molybdenum, silver, sulfur, tin, and
86
tungsten, indicating industrial processes as a source for contamination. Within the same
factor, factor analysis also groups lead with cadmium, zinc, and copper, which are
elements known to be related to vehicle emissions.
Lead concentration in Sacramento soils range from 10 ppm to 1,540ppm,
averaging 128 ppm. The greatest concentrations of lead in the surface soils of
Sacramento are focused in the downtown area. In addition, prediction maps of lead
concentrations and anthropogenic factor scores indicate the greatest concentrations of
lead and anthropogenic elements occur in the downtown area of Sacramento. Since this
area of Sacramento was one of the first to be developed, it is likely that the occurrence of
elevated lead concentrations in Sacramento is linked to the density and duration of traffic
and industry over the years.
Independent factor analysis of NURE data extracted similar factor groupings to those
extracted from the MEMS-61 data. Three of the four factors extracted from the MEMS61 data manifested themselves in the NURE data. The exception in the case of the
NURE data was the anthropogenic factor observed from the MEMS-61 data. The
presence of the anthropogenic factor in the MEMS-61 data and its subsequent absence
from the NURE data is explained by the greater density of MEMS-61 soil samples
collected within the city of Sacramento where anthropogenic contamination is present.
The extraction of the other three lithogenic factors for both independent analyses was
successful because the elemental groupings for each factor were present in the soil at both
sampling scales.
87
APPENDICES
88
APPENDIX A
Tables
Table 1 Non-regulated soil limits for lead.
USEPA
residential
industrial
CalEPA
residential
industrial
SFBRWQCB
residential
industrial
CDTSC
hazardous waste
RSLs (mg/kg)
400
800
CHHSLs (mg/kg)
150
3500
ESLs (mg/kg)
200
750
TTLCs (mg/kg)
1000
USEPA - U. S. Environmental Protection Agency; CalEPA - California Environmental
Protection Agency; SFBRWQCB - San Francisco Bay Regional Water Quality Control Board;
CDTSC - California Department of Toxic Substances Control. RSL - Regional Screening
Level; CHHSL - California Human Health Screening Level; ESL - Environmental Screening
Level; TTLC - Total Threshold Limit Concentration.
Table 2 Soil attributes of Sacramento and Yolo Counties.
Map Unit Name
Depth (in) Clay (%)
LANG SANDY LOAM
0-6
8-18
LANG SANDY LOAM DEEP
0-6
15-25
LANG SILT LOAM
0-6
15-25
SACRAMENTO SILTY CLAY LOAM
0-16
30-40
SYCAMORE SILT LOAM
0-14
15-27
YOLO SILTY CLAY LOAM
0-26
27-35
AMERICANOS-URBAN LAND COMPLEX
0-8
20-20
COLUMBIA SANDY LOAM
0-11
8-18
COLUMBIA-URBAN LAND COMPLEX
0-11
8-18
COSUMNES SILT LOAM
0-8
20-27
COSUMNES
0-8
20-27
COSUMNES-URBAN LAND COMPLEX
0-8
20-27
DUMPS
N/A
N/A
EGBERT-URBAN LAND COMPLEX
0-18
40-55
HEDGE LOAM
0-14
12-20
JACKTONE
0-11
40-60
KIMBALL-URBAN LAND COMPLEX
0-24
15-25
LAUGENOUR LOAM
0-16
10-20
LAUGENOUR-URBAN LAND COMPLEX
0-16
10-20
ORTHENTS-URBAN LAND COMPLEX
N/A
N/A
Texture Description
sandy loam
sandy loam
silt loam
silty clay loam
silt loam
silty clay loam
silt loam
sandy loam
sandy loam
silt loam
silt loam
silt loam
variable
clay
loam
clay
silt loam
loam
loam
variable
89
ROSSMOOR-URBAN LAND COMPLEX
SAILBOAT SILT LOAM, PARTIALLY DRAINED
SAILBOAT-URBAN LAND COMPLEX
SAN JOAQUIN FINE SANDY LOAM
SAN JOAQUIN SILT LOAM
SAN JOAQUIN SILT LOAM
SAN JOAQUIN-URBAN LAND COMPLEX
SAN JOAQUIN-URBAN LAND COMPLEX
URBAN LAND
URBAN LAND-NATOMAS COMPLEX
URBAN LAND-XERARENTS-FIDDYMENT
COMPLEX
VALPAC LOAM
VALPAC-URBAN LAND COMPLEX
XERARENTS-URBAN LAND-SAN JOAQUIN
COMPLEX
0-6
0-16
0-16
0-13
0-23
0-23
0-23
0-13
N/A
0-17
5-15
15-27
15-27
10-20
15-25
15-25
15-25
10-20
N/A
15-25
0-8
0-10
0-10
10-18 fine sandy loam
18-27 loam
18-27 variable
0-13
10-20 fine sandy loam
N/A, not available; %, percent.
fine sandy loam
silt loam
silt loam
fine sandy loam
silt loam
silt loam
silt loam
fine sandy loam
variable
variable
adapted from NRCS
Table 3 Geologic attributes of units occurring in the study area
Lithology Lithology Name
Age
Qa*
Alluvium
Holocene
Qb*
Basin Deposits, Undivided
Holocene
Qml
Lower Member, Modesto Formation
Pleistocene
Qmu*
Upper Member, Modesto Formation
Pleistocene
Qrl*
Lower Member, Riverbank Formation
Pleistocene
Qru
Upper Member, Riverbank Formation
Pleistocene
Qsc
Stream Channel Deposits
Holocene
Qtl
Turlock Lake Formation
Pleistocene
t
Tailings
Holocene
Tla
Laguna Formation
Pliocene
Helly &
Harwood
* sample collected within the geologic unit
(1986).
Table 4 Elemental concentrations of Sacramento soil analyzed by method MEMS-61.
ppm, parts per million; %, percent; na, no analysis.
Site Name
Ag_ppm Al_% As_ppm
Ba_ppm
Be_ppm Bi_ppm Ca_%
Lead_1
0.09
4.75
7.20
460.00
0.68
0.10
1.32
Lead_2
0.14
4.52
6.70
570.00
0.81
0.14
1.49
Lead_3
0.34
6.49
27.90
680.00
1.27
0.49
1.97
Lead_4
0.24
6.56
9.70
630.00
1.26
0.28
2.87
Lead_5
0.25
5.77
8.80
570.00
1.25
0.67
2.06
Lead_6
0.22
5.85
7.80
500.00
1.10
0.19
1.98
Lead_7
0.16
5.70
7.20
580.00
1.19
0.19
1.54
90
Lead_8
Lead_9
Lead_10
Lead_11
Lead_12
Lead_13
Lead_14
Lead_15
Lead_16
Lead_17
Lead_18
Lead_19
Lead_20
Lead_21
Lead_22
Lead_23
Lead_24
Lead_25
Lead_26
Lead_27
Lead_28
Lead_29
Lead_30
Lead_31
Lead_32
Lead_33
Lead_34
Lead_35
Lead_36
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
Lead_49
Lead_50
Lead_51
Lead_52
Lead_53
0.14
0.08
0.09
0.09
0.06
0.09
0.11
0.05
0.10
0.11
0.10
0.14
0.14
0.10
0.13
0.09
0.18
0.10
0.13
0.12
0.14
0.27
0.10
0.12
0.13
0.08
0.08
0.11
0.09
0.14
0.15
0.10
0.25
0.15
0.38
0.07
0.11
0.12
0.17
0.18
0.14
0.81
0.09
0.09
0.12
0.10
6.40
5.60
6.56
6.22
6.79
6.37
6.29
5.97
6.91
5.71
5.95
5.51
7.33
5.40
6.09
6.93
6.57
6.57
5.47
6.87
5.39
6.21
7.12
6.25
5.47
6.50
5.87
7.81
7.55
6.66
6.76
7.05
6.06
6.67
5.72
5.80
4.77
6.06
5.62
6.99
7.12
6.30
6.10
5.94
5.14
7.13
6.70
7.00
8.10
6.40
6.10
5.10
5.90
5.70
6.60
7.00
7.40
6.20
12.60
4.50
6.50
6.20
13.50
8.00
5.50
10.20
9.20
4.90
6.80
5.60
6.20
6.30
5.30
8.70
6.90
7.50
7.50
7.80
11.30
15.20
8.00
3.60
11.10
15.20
8.80
16.30
15.30
8.50
4.60
20.80
8.00
10.40
650.00
600.00
680.00
590.00
660.00
670.00
610.00
620.00
660.00
590.00
510.00
450.00
550.00
490.00
600.00
600.00
620.00
650.00
580.00
660.00
540.00
720.00
700.00
660.00
620.00
660.00
670.00
710.00
650.00
610.00
550.00
520.00
590.00
660.00
680.00
580.00
490.00
580.00
580.00
710.00
680.00
620.00
550.00
560.00
500.00
650.00
1.60
0.97
1.50
1.27
1.22
1.29
1.22
1.26
1.49
1.01
0.89
0.80
1.24
0.90
1.09
1.33
1.33
1.17
1.11
1.49
0.98
1.38
1.41
1.27
0.89
1.21
1.06
1.79
1.59
1.38
1.10
1.13
1.21
1.39
1.09
0.97
1.00
1.27
0.97
1.46
1.35
1.20
1.34
1.15
1.12
1.25
0.18
0.11
0.14
0.13
0.16
0.13
0.20
0.12
0.15
0.11
0.12
0.08
0.16
0.11
0.16
0.14
0.21
0.11
0.13
0.20
0.15
0.45
0.20
0.15
0.18
0.14
0.11
0.18
0.17
0.12
0.19
0.13
0.14
0.15
0.40
0.07
0.09
0.16
0.18
0.15
0.16
0.19
0.12
0.13
0.11
0.19
2.14
1.62
1.44
1.49
1.29
1.64
1.64
1.13
1.54
5.10
1.74
2.05
1.13
2.20
2.30
2.23
1.68
2.09
2.18
1.50
1.52
1.89
1.70
1.45
2.01
1.36
1.61
1.50
1.15
1.47
1.65
2.15
1.30
1.44
3.25
2.45
1.19
1.89
2.50
1.84
1.96
1.93
1.58
1.76
1.46
1.60
91
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
Lead_63
Lead_64
Lead_65
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
SACSECT16
SACSAC17
SACSAC18
SACSAC19
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
SACSA-2
SACSA-3
SACSA-4
SACSA-5
SACSA-6
0.07
0.08
0.15
0.12
0.11
0.13
0.37
0.15
0.15
0.09
0.09
0.03
0.11
0.12
0.21
0.09
0.12
0.14
0.11
0.16
0.10
0.09
0.09
0.07
0.06
0.11
0.12
0.10
0.07
0.07
0.26
0.14
0.12
0.15
0.11
0.13
0.08
0.09
0.18
0.06
na
na
na
na
na
na
5.86
4.53
8.85
8.24
7.94
6.07
5.89
6.36
5.56
7.02
6.99
5.75
7.83
6.33
7.04
6.03
6.59
7.02
7.03
6.84
5.20
7.03
6.22
6.25
6.17
6.20
7.70
6.78
7.29
7.13
7.43
5.85
5.77
6.24
6.65
6.51
6.56
6.51
9.59
6.80
na
na
na
na
na
na
3.60
4.20
20.50
10.00
14.20
7.30
8.10
12.10
27.20
9.80
7.90
2.70
16.00
8.80
9.00
15.20
5.40
6.50
5.70
10.00
10.90
6.00
5.00
4.90
4.80
6.00
6.90
7.90
5.50
8.80
16.10
7.00
4.90
12.70
8.90
13.40
7.10
4.60
19.10
3.30
na
na
na
na
na
na
630.00
530.00
590.00
600.00
580.00
550.00
580.00
570.00
600.00
630.00
700.00
590.00
610.00
510.00
650.00
540.00
700.00
710.00
670.00
610.00
620.00
580.00
550.00
570.00
580.00
560.00
620.00
640.00
580.00
650.00
660.00
580.00
470.00
550.00
610.00
570.00
570.00
550.00
640.00
620.00
na
na
na
na
na
na
1.20
0.79
1.57
1.68
1.48
1.22
1.02
1.18
1.16
1.27
1.28
1.13
1.33
1.00
1.51
1.25
1.23
1.18
1.44
1.19
0.83
1.18
1.15
1.08
1.10
1.14
1.21
1.25
1.24
1.21
1.37
1.00
0.82
1.07
1.24
1.05
1.07
0.98
1.56
1.34
na
na
na
na
na
na
0.09
0.09
0.20
0.16
0.17
0.13
0.16
0.15
0.28
0.15
0.13
0.09
0.16
0.10
0.16
0.20
0.27
0.19
0.14
0.15
0.15
0.14
0.11
0.10
0.10
0.11
0.12
0.13
0.12
0.09
0.22
0.24
0.12
0.16
0.13
0.17
0.09
0.10
0.21
0.11
na
na
na
na
na
na
1.24
1.23
0.78
1.85
1.30
1.72
2.36
2.01
2.02
1.89
2.02
1.41
1.31
1.50
2.09
1.98
1.64
1.96
1.54
1.84
1.66
1.73
1.39
1.31
1.13
1.64
2.20
1.98
1.63
2.11
1.70
2.60
2.25
1.54
1.32
2.30
2.48
2.86
1.16
1.58
na
na
na
na
na
na
92
SACSA-7
SACSA-8
SACSA-9
SACSA-10
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
Table 4 (continued) Elemental concentrations of Sacramento soil analyzed by method
MEMS-61.
ppm, parts per million; %, percent; na, no analysis.
Site Name
Cd_ppm Ce_ppm Co_ppm Cr_ppm Cs_ppm Cu_ppm Fe_%
Lead_1
0.23
25.90
12.90
164.00
1.65
27.10
2.60
Lead_2
0.39
29.20
15.00
235.00
1.36
39.90
3.13
Lead_3
0.74
45.60
18.80
180.00
2.32
84.10
4.21
Lead_4
0.99
50.40
19.20
146.00
2.28
49.30
3.79
Lead_5
0.97
42.70
17.10
193.00
1.92
65.80
3.48
Lead_6
0.46
40.30
15.90
156.00
1.82
45.90
3.12
Lead_7
0.62
51.50
16.10
123.00
1.91
40.40
3.00
Lead_8
0.91
48.00
17.20
185.00
2.15
45.00
3.40
Lead_9
0.34
31.00
12.20
108.00
1.60
28.80
2.74
Lead_10
0.30
52.90
16.90
133.00
2.40
33.60
3.13
Lead_11
0.23
45.80
15.40
149.00
2.01
30.30
3.16
Lead_12
0.24
53.80
19.60
124.00
2.40
33.90
3.44
Lead_13
0.20
44.50
13.70
86.00
1.96
29.60
2.77
Lead_14
0.34
51.60
18.30
144.00
2.19
34.60
3.39
Lead_15
0.17
56.40
18.70
148.00
2.32
33.20
2.92
Lead_16
0.71
55.00
19.50
148.00
2.61
40.90
3.81
Lead_17
0.23
35.20
14.10
172.00
1.48
31.70
3.21
Lead_18
0.36
33.00
18.40
177.00
2.08
97.00
3.32
Lead_19
0.46
24.70
17.00
202.00
1.76
32.90
3.04
Lead_20
0.34
42.10
20.30
166.00
2.64
48.60
3.67
Lead_21
0.44
30.00
15.30
264.00
1.38
37.60
3.19
Lead_22
0.43
37.90
18.30
160.00
1.76
51.40
3.59
Lead_23
0.24
48.50
19.60
135.00
2.16
42.80
3.98
Lead_24
0.41
48.60
18.90
141.00
2.31
44.80
3.65
Lead_25
0.19
34.30
14.20
120.00
1.77
35.30
3.01
Lead_26
0.86
38.80
14.10
163.00
1.54
32.90
2.89
Lead_27
0.34
54.70
19.90
136.00
2.49
47.40
3.65
Lead_28
0.72
42.00
14.50
206.00
1.91
40.60
2.90
Lead_29
0.55
36.40
12.20
114.00
1.74
35.60
2.61
Lead_30
0.24
48.00
17.40
101.00
2.65
36.50
3.39
Lead_31
0.16
53.10
17.80
129.00
2.29
30.10
2.95
Lead_32
1.37
38.90
13.20
127.00
1.46
52.60
2.89
Lead_33
0.31
45.50
16.80
126.00
2.23
33.70
3.22
Lead_34
0.25
40.50
12.40
125.00
1.84
33.00
2.54
Lead_35
0.27
56.00
20.50
128.00
2.69
52.90
4.19
Lead_36
0.14
56.50
20.60
123.00
2.85
39.30
3.91
93
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
Lead_49
Lead_50
Lead_51
Lead_52
Lead_53
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
Lead_63
Lead_64
Lead_65
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
SACSECT16
SACSAC17
SACSAC18
SACSAC19
0.17
0.37
0.25
0.50
0.31
2.04
0.20
0.31
0.38
0.57
0.50
0.38
1.15
0.26
0.27
0.27
0.34
0.15
0.15
0.36
0.23
0.31
0.25
0.63
0.29
2.26
0.34
0.24
0.15
0.30
0.24
0.34
0.24
0.16
0.24
0.53
0.48
0.58
0.24
0.15
0.10
0.08
0.21
0.20
0.20
0.10
50.20
34.40
32.80
38.70
44.40
37.00
38.00
25.50
42.80
31.20
44.00
50.90
40.20
45.30
41.40
37.40
48.90
41.70
29.00
45.40
43.20
45.60
37.40
36.60
43.60
37.90
48.50
49.30
36.60
42.10
34.80
45.90
44.00
48.50
50.60
46.10
45.60
34.70
55.80
50.00
49.00
61.60
47.80
43.40
44.90
41.80
17.30
19.40
22.20
16.20
18.70
17.20
14.40
15.40
17.90
12.80
16.60
20.90
16.80
16.40
15.10
13.90
18.30
13.70
10.90
23.10
25.50
22.00
18.10
15.10
17.00
14.80
20.70
17.40
12.20
23.60
19.90
20.20
18.00
11.10
12.60
15.50
22.30
12.10
16.30
14.60
14.60
15.60
16.40
22.20
16.50
16.30
107.00
154.00
179.00
172.00
152.00
230.00
308.00
168.00
144.00
144.00
109.00
156.00
132.00
170.00
139.00
176.00
165.00
108.00
157.00
159.00
162.00
156.00
181.00
143.00
185.00
157.00
170.00
140.00
173.00
182.00
181.00
153.00
173.00
89.00
87.00
99.00
174.00
136.00
127.00
122.00
130.00
126.00
143.00
188.00
116.00
118.00
2.01
2.74
2.67
2.39
2.42
1.76
1.18
1.74
2.02
1.57
2.25
2.90
2.13
1.94
1.95
1.67
2.59
1.59
1.33
3.33
3.06
3.02
2.02
1.57
1.80
1.75
2.26
2.10
1.37
3.31
2.30
2.33
1.75
2.04
2.13
2.15
2.51
1.26
2.02
1.93
1.91
2.02
1.87
2.48
2.20
2.37
32.40
50.50
50.40
43.60
43.50
104.50
22.80
27.40
70.50
40.90
44.60
55.40
65.70
30.90
31.20
31.70
55.00
57.80
18.90
64.40
58.80
55.60
39.80
49.30
32.60
44.90
43.00
36.00
14.70
55.20
35.80
41.30
33.40
20.80
33.00
27.00
42.70
29.00
35.60
26.60
26.60
22.50
31.70
41.20
32.30
25.90
3.29
3.79
4.05
3.46
3.69
3.84
3.72
2.66
3.72
2.76
3.50
3.91
3.49
3.12
3.33
3.00
3.71
2.40
2.18
4.36
4.48
4.05
3.46
3.24
3.39
2.98
4.00
3.72
2.32
4.33
3.65
3.79
3.71
2.44
3.59
3.22
4.10
2.67
3.93
3.12
3.11
2.92
3.28
4.67
3.30
3.48
94
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
SACSA-2
SACSA-3
SACSA-4
SACSA-5
SACSA-6
SACSA-7
SACSA-8
SACSA-9
SACSA-10
0.11
1.24
0.72
1.06
1.75
0.38
0.86
0.23
0.51
0.29
0.11
0.56
0.23
1.98
0.66
0.20
1.24
0.15
1.29
2.63
0.40
64.30
53.50
35.60
32.10
41.50
44.10
45.40
39.00
38.20
51.10
47.10
na
na
na
na
na
na
na
na
na
na
23.20
21.70
17.90
18.40
16.80
16.20
22.30
17.60
14.10
25.50
12.20
na
na
na
na
na
na
na
na
na
na
372.00
163.00
138.00
166.00
146.00
114.00
209.00
139.00
184.00
154.00
67.00
na
na
na
na
na
na
na
na
na
na
1.72
2.61
1.57
2.14
1.95
2.30
1.65
1.53
1.24
3.58
1.77
na
na
na
na
na
na
na
na
na
na
33.20
76.90
53.30
51.00
50.50
37.60
54.40
32.30
29.70
63.60
17.60
na
na
na
na
na
na
na
na
na
na
4.76
4.31
3.57
3.40
3.44
3.06
4.51
4.16
3.76
4.82
2.72
na
na
na
na
na
na
na
na
na
na
Table 4 (continued) Elemental concentrations of Sacramento soil analyzed by method
MEMS-61.
ppm, parts per million; %, percent; na, no analysis.
Site Name
Ga_ppm
Ge_ppm Hf_ppm In_ppm K_% La_ppm Li_ppm
Lead_1
9.70
0.08
1.30
0.03
0.97
14.00
13.20
Lead_2
9.36
0.09
1.10
0.06
0.96
14.70
12.90
Lead_3
14.65
0.13
1.90
0.05
1.31
24.80
19.70
Lead_4
15.95
0.13
1.90
0.05
1.27
26.00
21.20
Lead_5
13.10
0.13
1.70
0.10
1.09
21.10
18.60
Lead_6
13.00
0.15
1.50
0.04
1.10
20.40
17.00
Lead_7
13.40
0.14
1.90
0.04
1.17
25.20
16.50
Lead_8
14.90
0.14
1.90
0.04
1.34
24.20
21.70
Lead_9
12.60
0.13
1.30
0.03
1.33
16.10
14.10
Lead_10
15.65
0.14
2.00
0.05
1.51
26.80
18.80
Lead_11
15.05
0.13
1.90
0.04
1.30
23.40
18.90
Lead_12
16.50
0.14
2.20
0.05
1.34
26.20
20.60
Lead_13
15.10
0.13
1.80
0.04
1.40
22.80
15.30
Lead_14
14.95
0.13
2.20
0.04
1.17
24.70
18.30
Lead_15
14.40
0.15
2.20
0.04
1.38
25.80
17.30
Lead_16
17.00
0.15
2.20
0.05
1.38
26.10
19.70
Lead_17
12.35
0.13
1.60
0.04
1.17
18.60
14.60
Lead_18
13.80
0.14
1.60
0.04
1.01
16.50
22.00
Lead_19
12.10
0.12
1.30
0.04
0.95
12.50
21.10
95
Lead_20
Lead_21
Lead_22
Lead_23
Lead_24
Lead_25
Lead_26
Lead_27
Lead_28
Lead_29
Lead_30
Lead_31
Lead_32
Lead_33
Lead_34
Lead_35
Lead_36
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
Lead_49
Lead_50
Lead_51
Lead_52
Lead_53
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
Lead_63
Lead_64
Lead_65
17.70
12.10
14.05
16.70
16.05
15.25
12.90
17.05
12.60
14.70
18.00
15.35
12.15
15.60
13.65
20.00
19.15
15.40
15.55
16.20
14.20
15.85
12.80
14.00
11.05
14.10
12.45
16.85
17.20
14.85
14.75
14.40
12.75
17.20
13.45
10.55
23.20
20.30
19.50
14.20
13.30
15.25
12.95
17.55
16.90
12.45
0.14
0.13
0.13
0.14
0.15
0.13
0.13
0.15
0.14
0.15
0.13
0.14
0.13
0.15
0.13
0.15
0.16
0.12
0.15
0.15
0.13
0.13
0.15
0.14
0.13
0.15
0.13
0.15
0.15
0.15
0.14
0.14
0.13
0.16
0.14
0.12
0.15
0.16
0.18
0.13
0.14
0.14
0.13
0.16
0.16
0.14
2.10
1.50
1.70
2.10
2.00
1.70
1.60
2.20
1.70
1.70
1.90
2.10
2.30
1.90
1.70
2.50
2.50
2.10
1.80
2.00
1.80
1.90
1.60
1.40
1.30
1.70
1.30
1.60
2.00
1.70
1.70
1.80
1.50
2.10
1.70
1.30
2.40
2.30
2.10
1.60
1.60
1.80
1.60
1.90
1.80
1.70
0.06
0.04
0.04
0.05
0.05
0.05
0.04
0.05
0.04
0.04
0.05
0.05
0.04
0.05
0.04
0.06
0.05
0.04
0.05
0.05
0.05
0.04
0.05
0.03
0.03
0.07
0.04
0.05
0.05
0.04
0.05
0.04
0.04
0.07
0.03
0.03
0.07
0.06
0.06
0.04
0.05
0.05
0.05
0.05
0.04
0.03
1.03
1.02
1.12
1.14
1.23
1.39
1.19
1.25
1.05
1.51
1.51
1.46
1.16
1.51
1.42
1.37
1.32
1.15
1.05
0.90
1.26
1.30
1.13
1.30
1.04
1.19
1.11
1.50
1.32
1.25
1.22
1.15
1.07
1.47
1.37
1.16
1.14
1.10
1.06
1.12
1.12
1.21
1.21
1.29
1.49
1.41
21.20
15.60
19.20
24.30
25.10
17.90
20.60
26.80
20.80
19.40
23.90
26.20
18.00
22.80
20.10
28.00
27.40
24.70
16.60
16.10
19.90
23.00
18.90
19.60
12.70
22.00
15.90
22.40
26.20
20.60
22.50
21.00
18.80
25.40
21.50
14.60
22.80
21.20
22.90
19.00
18.80
22.00
19.30
25.10
25.50
19.30
27.50
17.10
19.40
21.00
21.60
18.20
16.20
24.10
17.70
14.40
20.90
16.50
13.40
18.60
14.80
25.60
25.10
17.20
33.50
32.00
22.80
20.30
18.00
10.50
17.40
17.90
16.60
21.10
28.70
19.80
16.30
16.20
14.10
24.00
12.90
11.00
35.30
32.40
31.30
20.80
14.90
16.80
14.90
21.70
18.40
11.60
96
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
SACSECT16
SACSAC17
SACSAC18
SACSAC19
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
SACSA-2
SACSA-3
SACSA-4
SACSA-5
SACSA-6
SACSA-7
SACSA-8
SACSA-9
SACSA-10
19.05
15.30
18.15
15.00
14.90
15.90
17.10
17.10
11.50
17.50
15.10
14.75
14.75
14.55
17.65
16.20
17.45
17.20
17.75
13.65
12.40
14.65
15.80
15.60
14.80
14.75
24.50
15.50
na
na
na
na
na
na
na
na
na
na
0.16
0.14
0.15
0.15
0.06
0.08
0.16
0.13
0.12
0.13
0.12
0.12
0.12
0.12
0.14
0.12
0.11
0.14
0.14
0.12
0.11
0.11
0.12
0.12
0.12
0.11
0.12
0.11
na
na
na
na
na
na
na
na
na
na
2.10
1.70
1.90
1.80
1.80
2.00
1.80
1.80
1.40
2.00
2.00
1.80
2.00
1.80
1.80
1.80
1.80
1.60
1.90
1.60
1.40
1.50
1.80
1.70
1.50
1.40
2.20
1.90
na
na
na
na
na
na
na
na
na
na
0.06
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.03
0.04
0.04
0.04
0.04
0.04
0.05
0.04
0.04
0.05
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.06
0.03
na
na
na
na
na
na
na
na
na
na
1.19
1.06
1.36
1.20
1.59
1.48
1.52
1.29
1.36
1.16
1.12
1.17
1.22
1.20
1.22
1.26
1.12
0.99
1.35
1.10
0.90
1.15
1.24
1.16
1.16
1.28
1.32
1.32
na
na
na
na
na
na
na
na
na
na
21.30
17.50
23.40
22.00
24.90
24.70
24.10
23.90
16.50
26.10
25.70
25.80
27.60
24.30
23.50
23.10
22.50
28.60
28.60
18.50
19.90
21.80
22.90
22.00
21.20
18.70
26.60
25.10
na
na
na
na
na
na
na
na
na
na
42.60
22.70
24.90
16.80
12.50
17.00
16.80
23.40
9.80
18.40
14.80
14.00
13.80
14.90
24.80
19.70
19.10
16.90
25.10
17.10
26.90
16.80
18.20
16.60
13.80
10.60
33.80
12.20
na
na
na
na
na
na
na
na
na
na
Table 4 (continued) Elemental concentrations of Sacramento soil analyzed by method
MEMS-61.
ppm, parts per million; %, percent; na, no analysis.
Site Name
Mg_% Mn_ppm Mo_ppm Na_% Nb_ppm Ni_ppm
P_ppm
Lead_1
0.77
521.00
1.97
0.74
4.60
44.10
600.00
Lead_2
0.90
539.00
2.82
0.88
4.40
53.80
440.00
97
Lead_3
Lead_4
Lead_5
Lead_6
Lead_7
Lead_8
Lead_9
Lead_10
Lead_11
Lead_12
Lead_13
Lead_14
Lead_15
Lead_16
Lead_17
Lead_18
Lead_19
Lead_20
Lead_21
Lead_22
Lead_23
Lead_24
Lead_25
Lead_26
Lead_27
Lead_28
Lead_29
Lead_30
Lead_31
Lead_32
Lead_33
Lead_34
Lead_35
Lead_36
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
1.21
1.37
1.20
1.10
0.72
1.04
0.76
0.74
0.82
0.71
0.65
0.77
0.46
0.83
1.09
1.25
1.32
0.99
1.27
1.37
1.13
1.22
0.93
1.03
0.92
0.73
0.63
0.75
0.65
0.88
0.75
0.61
1.07
0.90
0.69
1.47
1.46
1.10
1.15
1.09
0.98
0.88
1.16
0.97
1.07
1.53
735.00
684.00
660.00
712.00
663.00
705.00
614.00
697.00
622.00
837.00
615.00
759.00
902.00
807.00
661.00
626.00
604.00
583.00
588.00
655.00
729.00
661.00
550.00
531.00
772.00
585.00
559.00
706.00
745.00
545.00
656.00
503.00
769.00
768.00
816.00
656.00
789.00
552.00
698.00
670.00
605.00
505.00
660.00
633.00
643.00
786.00
2.61
1.17
2.54
1.95
1.27
2.03
1.08
1.38
0.98
1.42
1.03
1.42
1.66
1.52
1.86
1.28
1.67
2.32
1.76
1.83
1.70
1.49
2.62
1.25
1.40
1.26
1.17
1.03
1.08
2.75
1.25
1.56
1.19
1.28
0.95
1.56
1.41
1.62
1.88
4.13
0.76
1.83
3.30
2.04
1.48
1.48
0.94
1.14
0.99
1.12
1.05
1.15
1.14
1.08
1.05
0.99
1.29
1.07
0.93
1.03
1.24
1.04
1.16
0.65
1.23
1.22
1.16
0.98
1.47
1.11
1.04
0.95
1.40
1.21
1.11
1.22
1.12
1.18
1.04
0.95
1.02
0.98
1.25
0.92
0.81
1.06
1.38
0.78
0.91
1.17
1.17
1.08
7.10
7.80
6.70
6.10
7.80
7.80
5.40
8.50
7.70
8.40
7.60
8.40
9.00
9.20
5.80
5.60
4.60
7.50
5.30
6.20
7.70
7.90
5.80
5.80
8.50
6.80
6.20
8.40
9.30
6.00
8.20
7.40
9.70
9.50
7.30
5.60
5.30
6.30
7.00
6.00
6.10
4.40
6.70
4.60
6.70
7.70
73.20
77.70
67.20
59.70
56.10
74.10
44.90
58.00
62.00
62.70
41.00
57.30
49.20
64.40
52.10
86.70
84.70
76.80
68.30
71.50
72.10
76.70
51.50
58.10
74.50
70.90
37.60
52.80
55.40
50.20
56.20
40.90
87.10
75.30
57.10
98.50
111.00
76.40
75.20
70.30
39.30
56.20
67.40
50.90
65.80
97.20
1110.00
1210.00
1180.00
1190.00
630.00
870.00
490.00
580.00
370.00
690.00
410.00
430.00
830.00
490.00
460.00
920.00
1300.00
760.00
730.00
650.00
650.00
680.00
410.00
920.00
570.00
780.00
650.00
440.00
420.00
650.00
550.00
420.00
450.00
420.00
400.00
670.00
550.00
680.00
820.00
800.00
430.00
570.00
1030.00
630.00
980.00
930.00
98
Lead_49
Lead_50
Lead_51
Lead_52
Lead_53
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
Lead_63
Lead_64
Lead_65
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
SACSECT16
SACSAC17
SACSAC18
SACSAC19
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
1.09
0.78
0.93
0.92
1.05
0.47
0.54
1.13
1.47
1.19
1.09
1.09
1.14
0.87
1.32
1.11
0.41
1.47
1.22
1.40
1.21
0.52
0.77
0.66
1.42
0.63
0.93
0.64
0.62
0.52
0.84
1.54
0.91
0.70
1.04
1.34
1.28
1.31
1.07
0.74
1.41
1.35
1.28
1.36
0.50
na
651.00
634.00
602.00
500.00
679.00
449.00
422.00
625.00
831.00
589.00
634.00
595.00
684.00
728.00
749.00
721.00
436.00
665.00
612.00
660.00
697.00
537.00
561.00
623.00
755.00
491.00
625.00
547.00
571.00
626.00
612.00
935.00
601.00
659.00
790.00
710.00
646.00
729.00
587.00
596.00
786.00
695.00
634.00
723.00
460.00
na
1.26
1.60
1.44
2.12
3.79
0.73
1.64
1.72
1.36
1.54
2.06
1.62
1.96
1.94
1.28
2.13
0.65
1.59
1.36
1.23
1.20
1.26
1.45
0.73
1.07
1.50
1.38
0.99
0.89
0.80
0.94
0.83
1.16
0.68
0.80
1.65
1.66
1.72
3.65
1.77
1.97
1.24
1.42
1.86
0.57
na
1.13
1.09
1.11
0.87
1.07
1.09
0.86
0.57
1.09
0.76
0.99
1.17
1.19
1.06
1.17
1.26
1.17
0.82
1.04
1.16
1.13
1.41
1.36
1.23
0.99
1.08
1.04
0.97
0.97
1.01
1.03
1.25
1.03
1.11
1.15
0.91
1.08
0.93
0.83
0.81
1.26
1.30
1.53
0.71
1.32
na
6.30
7.00
6.80
5.80
7.90
6.30
5.10
8.00
7.20
7.30
5.80
5.70
7.10
6.00
7.40
7.50
6.20
6.80
5.90
7.40
6.80
7.50
7.20
7.50
7.50
5.90
7.40
7.90
7.50
8.10
7.30
7.20
7.20
8.10
9.10
7.70
5.90
4.90
6.40
7.10
6.20
5.70
6.70
8.30
7.40
na
62.50
59.10
52.60
49.40
80.60
52.90
33.30
97.50
107.00
92.60
72.70
55.80
62.10
50.30
93.70
60.80
54.10
112.50
83.50
87.40
62.30
25.50
33.70
43.30
86.80
35.30
62.60
46.50
45.10
41.60
51.50
108.50
58.90
49.10
66.30
87.80
58.40
86.10
62.80
57.20
74.30
51.70
40.30
104.00
23.50
84.30
1040.00
600.00
880.00
700.00
920.00
360.00
420.00
630.00
540.00
670.00
640.00
590.00
620.00
1310.00
620.00
850.00
300.00
700.00
620.00
420.00
660.00
470.00
410.00
600.00
1340.00
490.00
460.00
380.00
330.00
230.00
520.00
540.00
490.00
280.00
340.00
1020.00
780.00
860.00
950.00
760.00
610.00
540.00
590.00
810.00
240.00
na
99
SACSA-2
SACSA-3
SACSA-4
SACSA-5
SACSA-6
SACSA-7
SACSA-8
SACSA-9
SACSA-10
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
66.40
88.90
96.00
53.80
59.40
75.20
46.80
46.90
74.80
na
na
na
na
na
na
na
na
na
Table 4 (continued) Elemental concentrations of Sacramento soil analyzed by method MEMS-61.
ppm, parts per million; %, percent; na, no analysis.
Site Name
Pb_ppm Rb_ppm S_% Sb_ppm Sc_ppm Sn_ppm Sr_ppm Ta_ppm
Lead_1
15.80
42.80
0.02
0.97
10.10
1.10
167.50
0.38
Lead_2
216.00
41.40
0.04
2.42
9.90
4.20
180.50
0.36
Lead_3
441.00
58.90
0.03
8.17
14.20
20.50
232.00
0.56
Lead_4
189.50
60.20
0.02
1.47
15.20
8.10
260.00
0.58
Lead_5
509.00
51.10
0.06
3.63
13.90
35.10
239.00
0.50
Lead_6
141.50
51.60
0.04
1.33
12.70
5.50
253.00
0.44
Lead_7
178.50
63.60
0.05
1.69
11.90
4.90
213.00
0.57
Lead_8
174.00
63.40
0.04
1.64
14.10
3.80
258.00
0.62
Lead_9
129.00
51.10
0.02
1.49
10.70
2.50
230.00
0.42
Lead_10
62.10
75.60
0.02
0.93
13.40
4.10
214.00
0.62
Lead_11
49.10
60.80
0.02
0.88
13.30
1.80
218.00
0.58
Lead_12
38.00
67.50
0.03
0.88
14.30
2.70
201.00
0.63
Lead_13
23.00
62.40
0.02
0.89
12.50
2.20
253.00
0.57
Lead_14
32.70
61.40
0.02
1.00
14.30
3.10
222.00
0.60
Lead_15
18.20
71.80
0.03
0.73
12.50
1.40
174.00
0.64
Lead_16
56.40
78.00
0.02
1.20
15.30
2.30
219.00
0.64
Lead_17
20.00
45.70
0.09
1.13
12.60
1.60
343.00
0.46
Lead_18
87.20
56.60
0.04
1.00
14.90
1.80
201.00
0.40
Lead_19
33.10
38.90
0.03
1.01
13.30
2.90
208.00
0.32
Lead_20
40.70
50.90
0.03
1.69
19.80
2.30
144.00
0.56
Lead_21
120.00
39.70
0.03
1.09
13.90
3.80
250.00
0.38
Lead_22
56.00
49.80
0.04
2.13
15.70
3.70
300.00
0.44
Lead_23
17.10
54.90
0.04
0.90
16.70
1.60
292.00
0.53
Lead_24
148.00
68.30
0.03
1.89
16.00
4.00
228.00
0.56
Lead_25
38.70
52.60
0.02
1.71
13.30
3.00
278.00
0.44
Lead_26
113.50
52.20
0.05
1.21
12.60
3.60
283.00
0.42
Lead_27
217.00
65.70
0.03
1.21
16.00
3.60
223.00
0.61
Lead_28
75.50
59.50
0.07
1.38
12.50
2.40
189.00
0.51
Lead_29
229.00
59.60
0.02
1.87
11.20
8.20
282.00
0.49
Lead_30
83.70
72.90
0.02
1.09
15.00
3.60
237.00
0.64
Lead_31
35.40
75.70
0.02
0.87
14.20
2.20
227.00
0.66
Lead_32
260.00
48.70
0.06
2.37
12.30
3.20
276.00
0.45
100
Lead_33
Lead_34
Lead_35
Lead_36
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
Lead_49
Lead_50
Lead_51
Lead_52
Lead_53
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
Lead_63
Lead_64
Lead_65
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
79.50
50.60
26.50
31.50
16.20
30.70
17.30
48.90
66.60
475.00
16.90
52.60
81.10
261.00
109.50
58.40
217.00
38.40
87.00
21.20
53.90
35.60
26.30
25.20
17.00
35.80
52.90
143.00
48.30
200.00
47.90
58.00
18.20
26.90
12.70
20.30
33.70
57.70
40.80
24.80
61.10
524.00
29.50
20.80
18.00
16.60
71.50
63.40
72.90
75.80
50.40
52.00
43.70
58.10
63.80
49.00
44.00
45.90
56.30
43.90
70.00
93.20
63.10
57.10
54.00
47.30
72.70
51.50
44.50
64.50
59.30
64.20
51.50
45.60
59.30
56.00
61.10
65.50
49.10
63.90
52.20
64.90
55.40
67.50
56.60
64.80
58.90
47.10
52.30
55.90
56.80
62.30
0.03
0.03
0.01
0.01
0.02
0.05
0.02
0.02
0.02
0.08
0.01
0.02
0.04
0.04
0.03
0.04
0.05
0.03
0.04
0.02
0.05
0.01
0.01
0.02
0.02
0.03
0.05
0.05
0.03
0.08
0.01
0.03
0.01
0.02
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.06
0.03
0.03
0.02
0.01
0.94
0.98
0.80
0.75
0.77
1.28
1.01
2.03
1.66
6.86
0.89
1.10
5.93
1.38
1.85
1.66
2.15
1.89
1.29
1.11
2.03
0.79
0.98
1.59
1.29
1.60
1.10
3.18
1.08
3.11
0.98
0.85
0.60
1.16
0.93
0.83
1.02
1.84
2.13
0.90
1.06
1.68
1.15
0.86
0.70
0.68
13.80
11.70
17.50
16.80
13.20
17.90
18.70
14.00
15.90
13.90
14.70
11.50
14.70
11.70
15.00
17.70
14.90
14.40
14.10
12.80
16.70
9.70
9.20
24.70
23.00
21.60
15.70
14.30
15.20
12.50
17.60
15.50
9.40
21.80
17.50
17.90
15.80
10.40
12.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
2.30
3.20
1.70
1.60
1.90
1.50
7.90
2.10
64.10
1.90
1.20
13.70
4.20
10.90
3.00
8.00
3.10
1.90
1.30
3.40
1.50
1.90
1.80
1.50
1.80
2.80
6.50
2.10
5.10
1.70
1.50
1.10
2.00
1.60
1.90
1.50
1.70
1.90
1.90
3.40
2.40
1.80
1.50
1.40
1.30
210.00
232.00
248.00
196.50
225.00
196.50
206.00
188.50
193.50
279.00
314.00
156.00
242.00
290.00
261.00
253.00
237.00
216.00
246.00
199.00
215.00
218.00
182.50
106.00
203.00
153.50
209.00
280.00
268.00
239.00
258.00
300.00
235.00
170.00
183.50
253.00
261.00
272.00
334.00
255.00
238.00
241.00
269.00
221.00
211.00
204.00
0.59
0.58
0.66
0.66
0.61
0.57
0.40
0.51
0.55
0.48
0.58
0.34
0.54
0.39
0.54
0.60
0.52
0.57
0.55
0.48
0.63
0.54
0.41
0.62
0.54
0.55
0.46
0.47
0.58
0.50
0.58
0.59
0.58
0.52
0.46
0.59
0.55
0.58
0.60
0.55
0.59
0.44
0.54
0.59
0.53
0.57
101
SACSECT16
SACSAC17
SACSAC18
SACSAC19
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
SACSA-2
SACSA-3
SACSA-4
SACSA-5
SACSA-6
SACSA-7
SACSA-8
SACSA-9
SACSA-10
19.80
25.50
46.90
16.40
10.60
420.00
171.00
78.80
375.00
82.60
666.00
15.80
154.50
24.40
23.00
114.50
18.60
289.00
269.00
29.00
863.00
21.40
342.00
1540.00
283.00
57.00
51.00
58.40
52.10
37.60
65.60
45.50
43.00
57.10
59.50
46.90
43.60
40.50
70.80
51.80
na
na
na
na
na
na
na
na
na
na
0.03
0.02
0.03
0.01
0.01
0.05
0.08
0.13
0.05
0.06
0.04
0.02
0.03
0.03
0.02
na
na
na
na
na
na
na
na
na
na
0.84
0.83
0.95
0.79
0.83
1.87
2.99
1.20
2.12
1.30
1.84
0.98
1.90
1.54
0.77
na
na
na
na
na
na
na
na
na
na
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
na
na
na
na
na
na
na
na
na
na
1.40
1.70
1.80
1.40
1.30
4.90
9.50
3.10
3.50
2.20
2.70
1.10
2.30
2.00
1.40
na
na
na
na
na
na
na
na
na
na
235.00
268.00
239.00
229.00
276.00
221.00
303.00
193.50
214.00
183.50
378.00
326.00
378.00
172.00
275.00
na
na
na
na
na
na
na
na
na
na
0.53
0.50
0.52
0.58
0.65
0.54
0.41
0.33
0.45
0.55
0.45
0.42
0.52
0.57
0.58
na
na
na
na
na
na
na
na
na
na
Table 4 (continued) Elemental concentrations of Sacramento soil analyzed by method MEMS-61.
ppm, parts per million; %, percent; na, no analysis.
Site Name
Th_ppm Ti_% Tl_ppm U_ppm V_ppm W_ppm Y_ppm Zn_ppm
Lead_1
3.90
0.29
0.23
1.20
86.00
1.50
10.70
73.00
Lead_2
4.40
0.27
0.23
1.20
87.00
1.40
12.50
211.00
Lead_3
6.90
0.36
0.79
1.80
114.00
1.90
15.30
354.00
Lead_4
7.10
0.38
0.33
2.80
112.00
1.40
15.70
210.00
Lead_5
5.90
0.33
0.29
1.80
100.00
2.00
13.40
261.00
Lead_6
5.40
0.31
0.27
1.50
85.00
1.40
12.60
232.00
Lead_7
7.20
0.33
0.32
1.60
89.00
1.50
13.90
163.00
Lead_8
7.20
0.35
0.33
1.80
101.00
2.30
14.40
198.00
Lead_9
4.60
0.26
0.27
1.30
78.00
1.10
10.70
117.00
Lead_10
7.90
0.35
0.39
2.00
96.00
1.40
14.70
112.00
Lead_11
6.90
0.34
0.34
1.80
100.00
1.20
14.50
101.00
Lead_12
7.80
0.36
0.36
2.00
104.00
1.60
15.60
86.00
Lead_13
6.60
0.32
0.31
1.60
87.00
1.30
13.60
83.00
Lead_14
7.50
0.38
0.33
1.80
106.00
1.30
14.10
134.00
Lead_15
7.60
0.38
0.36
2.00
95.00
1.30
13.50
87.00
Lead_16
8.10
0.40
0.41
1.90
120.00
1.30
14.40
110.00
102
Lead_17
Lead_18
Lead_19
Lead_20
Lead_21
Lead_22
Lead_23
Lead_24
Lead_25
Lead_26
Lead_27
Lead_28
Lead_29
Lead_30
Lead_31
Lead_32
Lead_33
Lead_34
Lead_35
Lead_36
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
Lead_49
Lead_50
Lead_51
Lead_52
Lead_53
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
5.80
4.50
3.20
6.10
4.60
6.20
6.60
7.30
5.20
4.90
7.70
5.70
5.50
8.70
7.30
5.40
6.60
6.10
9.00
9.30
7.40
5.00
4.30
6.10
7.00
5.60
8.40
3.90
7.10
4.90
6.70
7.80
6.30
6.40
6.40
5.50
7.70
6.80
4.60
7.50
6.30
7.00
5.90
5.60
6.80
5.80
0.29
0.31
0.28
0.42
0.29
0.32
0.39
0.36
0.30
0.27
0.37
0.31
0.27
0.36
0.40
0.31
0.36
0.32
0.40
0.41
0.36
0.34
0.37
0.33
0.36
0.31
0.38
0.26
0.36
0.25
0.31
0.37
0.33
0.37
0.34
0.31
0.38
0.27
0.27
0.47
0.43
0.41
0.32
0.31
0.36
0.29
0.24
0.25
0.20
0.32
0.22
0.29
0.30
0.37
0.30
0.26
0.36
0.28
0.31
0.41
0.38
0.26
0.35
0.32
0.40
0.42
0.31
0.30
0.26
0.35
0.39
0.26
0.25
0.25
0.34
0.24
0.36
0.40
0.33
0.32
0.30
0.28
0.40
0.29
0.25
0.43
0.34
0.38
0.29
0.26
0.30
0.31
1.50
1.80
1.10
2.20
1.20
1.70
1.80
1.90
1.50
1.50
1.70
1.40
1.60
2.00
2.20
1.30
1.80
1.50
1.80
2.00
1.70
1.70
1.40
1.80
2.20
1.50
1.80
1.20
1.80
1.40
2.50
2.20
1.90
1.50
1.80
1.80
1.80
1.60
1.20
2.70
2.10
2.60
1.70
1.80
2.10
1.60
107.00
99.00
91.00
139.00
99.00
105.00
124.00
114.00
98.00
84.00
101.00
82.00
78.00
109.00
104.00
84.00
102.00
79.00
124.00
122.00
100.00
123.00
131.00
107.00
123.00
99.00
137.00
82.00
104.00
79.00
99.00
118.00
97.00
99.00
100.00
98.00
111.00
70.00
74.00
173.00
147.00
139.00
108.00
98.00
105.00
89.00
1.40
1.30
1.40
1.80
1.40
1.80
1.20
1.30
1.10
0.90
1.50
2.20
1.50
1.80
1.30
1.80
1.40
2.30
1.30
1.40
1.10
1.50
1.10
1.30
1.60
2.90
0.70
1.30
1.50
1.60
1.40
1.70
1.60
1.40
1.20
1.40
1.80
0.90
1.10
2.20
1.60
1.90
2.30
1.50
1.50
1.70
12.40
13.80
12.10
17.60
12.00
13.20
15.80
15.40
13.00
11.40
16.70
12.50
12.20
15.20
14.60
11.30
13.60
12.20
17.10
15.20
13.80
15.40
17.30
13.30
14.60
12.50
12.70
10.40
13.10
10.90
14.10
16.40
13.90
14.10
12.40
11.40
16.70
12.90
8.90
20.40
19.70
18.50
13.40
12.70
14.10
12.60
88.00
166.00
148.00
110.00
130.00
224.00
91.00
126.00
121.00
317.00
171.00
690.00
178.00
140.00
76.00
224.00
103.00
102.00
90.00
86.00
70.00
174.00
104.00
148.00
108.00
529.00
73.00
91.00
183.00
176.00
187.00
130.00
240.00
94.00
124.00
86.00
162.00
55.00
60.00
109.00
101.00
110.00
103.00
136.00
85.00
6010.00
103
Lead_63
Lead_64
Lead_65
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
SACSECT16
SACSAC17
SACSAC18
SACSAC19
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
SACSA-2
SACSA-3
SACSA-4
SACSA-5
SACSA-6
SACSA-7
SACSA-8
SACSA-9
SACSA-10
7.00
8.00
7.70
6.50
5.00
7.20
6.90
7.10
8.30
7.30
6.90
5.40
8.60
11.90
25.50
8.70
7.40
6.70
7.00
6.70
7.40
8.10
6.10
3.80
6.40
6.90
6.30
5.90
4.20
8.30
7.30
na
na
na
na
na
na
na
na
na
na
0.38
0.35
0.27
0.39
0.36
0.36
0.37
0.32
0.32
0.40
0.41
0.34
0.40
0.40
0.40
0.42
0.38
0.42
0.37
0.45
0.60
0.40
0.33
0.32
0.35
0.35
0.39
0.36
0.40
0.48
0.37
na
na
na
na
na
na
na
na
na
na
0.36
0.36
0.24
0.39
0.27
0.37
0.29
0.33
0.33
0.31
0.35
0.20
0.26
0.29
0.27
0.30
0.28
0.31
0.32
0.29
0.22
0.37
0.23
0.20
0.27
0.31
0.26
0.23
0.19
0.42
0.27
na
na
na
na
na
na
na
na
na
na
1.80
2.20
1.70
2.10
1.70
1.50
1.90
1.80
2.00
1.90
2.50
1.30
1.70
2.00
3.00
1.80
1.70
1.60
1.50
1.60
1.60
2.60
1.50
1.20
1.90
1.80
1.60
1.50
1.30
2.90
1.70
na
na
na
na
na
na
na
na
na
na
125.00
113.00
67.00
148.00
121.00
118.00
114.00
81.00
101.00
108.00
134.00
88.00
124.00
104.00
106.00
104.00
110.00
144.00
110.00
118.00
174.00
136.00
119.00
108.00
116.00
110.00
147.00
146.00
142.00
186.00
97.00
na
na
na
na
na
na
na
na
na
na
1.30
1.90
0.80
1.90
1.10
1.40
1.10
1.50
1.30
1.10
1.10
1.10
1.30
0.90
0.90
0.90
1.00
1.50
2.10
1.00
1.60
1.80
1.70
1.30
1.40
1.50
1.00
0.90
1.10
2.00
0.90
na
na
na
na
na
na
na
na
na
na
15.90
14.80
11.40
18.00
14.90
16.20
13.80
13.40
14.10
13.20
15.00
8.60
13.70
12.00
11.50
11.90
11.70
15.60
12.70
15.50
19.00
15.90
11.60
12.60
12.00
13.60
11.60
12.40
13.00
19.00
13.40
na
na
na
na
na
na
na
na
na
na
Table 5 Summary statistics of elemental concentrations analyzed by method MEMS-61.
155.00
163.00
123.00
114.00
76.00
90.00
104.00
73.00
116.00
193.00
147.00
189.00
120.00
78.00
66.00
52.00
77.00
105.00
112.00
62.00
81.00
350.00
333.00
301.00
280.00
108.00
241.00
73.00
159.00
108.00
54.00
150.00
101.00
344.00
206.00
82.00
402.00
92.00
204.00
783.00
199.00
104
inorganic
maximum minimum
mean
median
standard deviation
constituent
(ppm)
(ppm)
(ppm)
(ppm)
(ppm)
Ag
0.81
0.03
0.14
0.11
0.09
Al%
9.59
4.52
6.42
6.36
0.85
As
27.9
2.7
8.91
7.5
4.72
Ba
720
450
602.04
600
60.69
Be
1.79
0.68
1.20
1.21
0.21
Bi
0.67
0.07
0.16
0.15
0.09
Ca%
5.1
0.78
1.80
1.68
0.55
Cd*
2.63
0.08
0.49
0.34
0.47
Ce
64.3
24.7
43.29
44
8.08
Co
25.5
10.9
17.16
17
3.24
Cr
372
67
154.08
149
43.33
Cs
3.58
1.18
2.08
2.02
0.48
Cu
104.5
14.7
41.90
39.3
15.68
Fe%
4.82
2.18
3.43
3.4
0.57
Ga
24.5
9.36
15.21
15
2.53
Ge
0.18
0.06
0.13
0.13
0.02
Hf
2.5
1.1
1.80
1.8
0.29
In
0.098
0.026
0.04
0.043
0.01
K%
1.59
0.9
1.23
1.21
0.16
La
28.6
12.5
21.90
22.4
3.77
Li
42.6
9.8
19.31
18.2
5.95
Mg%
1.54
0.41
0.99
1.03
0.29
Mn
935
422
651.00
651
99.90
Mo
4.13
0.57
1.58
1.48
0.66
Na%
1.53
0.57
1.07
1.08
0.18
Nb
9.7
4.4
6.93
7.1
1.20
Ni*
112.5
23.5
64.91
62.1
19.33
P
1340
230
655.59
620
251.25
Pb*
1540
10.6
127.99
52.6
204.93
Rb
93.2
37.6
57.12
56.8
10.34
S%
0.13
0.01
0.03
0.03
0.02
Sb
8.17
0.6
1.54
1.16
1.18
Sn
64.1
1.1
4.19
2.2
7.65
Sr
378
106
236.07
232
47.83
Ta
0.66
0.32
0.53
0.55
0.08
Th
25.5
3.2
6.77
6.7
2.41
Ti%
0.596
0.251
0.35
0.36
0.05
Tl
0.79
0.19
0.31
0.31
0.08
U
3
1.1
1.78
1.8
0.39
V
186
67
108.97
105
22.52
W
2.9
0.7
1.44
1.4
0.39
Y
20.4
8.6
13.88
13.6
2.24
Zn*
6010
52
215.7961
120
588.22
* includes additional samples collected for these constituents (Cd, Ni, Pb, Zn)
na, information not reported; mg, milligram; kg, kilogram; ppm, parts per million
105
Table 6 Elements with skewed distributions,
which have been normalized.
Elements
Ag
As
Bi
Cd
Cr
Cu
Li
Mo
Pb
S
Sb
Sn
Zn
Table 7 a) Concentrations of lead in Sacramento soil analyzed by handheld XRF.
error
Pb 1
error
Pb 2
error
Pb3
mean
standard
Site
(ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) deviation (ppm)
XRF 1
(+/-) 11
172
(+/-) 12
193
(+/-) 11
168
177.67
13.43
XRF 2
(+/-) 6
25
(+/-) 6
30
(+/-) 6
23
26.00
3.61
XRF 3
(+/-) 10
149
(+/-) 10
157
(+/-) 11
179
161.67
15.53
XRF 4
(+/-) 16
389
(+/-) 15
380
(+/-) 17
485
418.00
58.20
XRF 4a
(+/-) 11
203
(+/-) 12
235
(+/-) 14
301
246.33
49.97
XRF 5
(+/-) 17
0
(+/-) 6
21
(+/-) 17
0
7.00
12.12
XRF 6
(+/-) 11
185
(+/-) 10
169
(+/-) 10
161
171.67
12.22
XRF 7
(+/-) 14
308
(+/-) 14
334
(+/-) 15
354
332.00
23.07
XRF 8
(+/-) 9
92
(+/-) 9
103
(+/-) 10
105
100.00
7.00
XRF 8a
(+/-) 8
74
(+/-) 17
0
(+/-) 7
47
40.33
37.45
XRF 9
(+/-) 11
172
(+/-) 10
142
(+/-) 11
181
165.00
20.42
XRF 10
(+/-) 12
233
(+/-) 12
221
(+/-) 13
295
249.67
39.72
XRF 11
(+/-) 15
375
(+/-) 16
419
(+/-) 15
356
383.33
32.32
XRF 12
(+/-) 8
83
(+/-) 8
81
(+/-) 8
79
81.00
2.00
XRF 12a (+/-) 7
26
(+/-) 7
32
(+/-) 7
31
29.67
3.21
XRF 13
(+/-) 7
36
(+/-) 17
0
(+/-) 6
26
20.67
18.58
XRF 14
(+/-) 10
150
(+/-) 10
138
(+/-) 10
153
147.00
7.94
XRF 15
(+/-) 9
93
(+/-) 8
83
(+/-) 8
90
88.67
5.13
XRF 16
(+/-) 9
101
(+/-) 9
105
(+/-) 9
109
105.00
4.00
XRF 16a (+/-) 12
236
(+/-) 13
231
(+/-) 14
243
236.67
6.03
XRF 17
(+/-) 11
196
(+/-) 10
164
(+/-) 11
189
183.00
16.82
XRF 18
(+/-) 11
183
(+/-) 12
207
(+/-) 11
189
193.00
12.49
XRF 17
(+/-) 7
68
(+/-) 7
72
(+/-) 8
77
72.33
4.51
106
XRF 20
XRF 20a
XRF 22
XRF 23
XRF 24
XRF 25
XRF 26
XRF 27
XRF 28
XRF 29
XRF 30
max
min
mean
median
standard
deviation
(+/-) 10
(+/-) 10
(+/-) 8
(+/-) 8
(+/-) 9
(+/-) 12
(+/-) 13
(+/-) 10
(+/-) 9
(+/-) 11
(+/-) 24
130
142
61
55
95
224
261
137
133
203
777
(+/-) 9
(+/-) 10
(+/-) 7
(+/-) 7
(+/-) 9
(+/-) 13
(+/-) 12
(+/-) 9
(+/-) 10
(+/-) 12
(+/-) 23
106
154
55
51
105
240
253
130
139
241
750
(+/-) 9
(+/-) 12
(+/-) 8
(+/-) 7
(+/-) 9
(+/-) 12
(+/-) 12
(+/-) 10
(+/-) 9
(+/-) 13
(+/-) 22
104
180
69
39
96
242
245
140
121
280
713
113.33
158.67
61.67
48.33
98.67
235.33
253.00
135.67
131.00
241.33
746.67
746.67
20.67
172.26
135.67
14.47
19.43
7.02
8.33
5.51
9.87
8.00
5.13
9.17
38.50
32.13
147.038
Table 7 b) Concentrations of zinc in Sacramento soil analyzed by hand-held XRF.
Site
XRF 1
XRF 2
XRF 3
XRF 4
XRF 4a
XRF 5
XRF 6
XRF 7
XRF 8
error
(ppm)
(+/-) 15
(+/-) 9
(+/-) 11
(+/-) 17
(+/-) 12
(+/-) 9
(+/-) 12
(+/-) 13
(+/-) 12
Zn1
(ppm)
294
70
140
342
185
76
174
217
152
error
(ppm)
(+/-) 16
(+/-) 9
(+/-) 11
(+/-) 15
(+/-) 12
(+/-) 9
(+/-) 12
(+/-) 13
(+/-) 13
XRF 8a
XRF 9
XRF 10
XRF 11
XRF 12
XRF 12a
XRF 13
XRF 14
XRF 15
XRF 16
XRF 16a
XRF 17
XRF 18
(+/-) 12
(+/-) 10
(+/-) 11
(+/-) 12
(+/-) 10
(+/-) 8
(+/-) 8
(+/-) 12
(+/-) 11
(+/-) 14
(+/-) 15
(+/-) 11
(+/-) 12
148
107
151
158
123
54
61
157
123
238
274
133
179
(+/-) 0
(+/-) 11
(+/-) 11
(+/-) 12
(+/-) 11
(+/-) 9
(+/-) 9
(+/-) 12
(+/-) 10
(+/-) 15
(+/-) 15
(+/-) 11
(+/-) 14
Zn2
(ppm)
305
81
151
290
175
75
175
198
171
non
detect
119
146
182
136
68
68
148
103
266
258
145
203
error
(ppm)
(+/-) 18
(+/-) 9
(+/-) 12
(+/-) 16
(+/-) 13
(+/-) 10
(+/-) 12
(+/-) 14
(+/-) 15
Zn3
(ppm)
413
64
163
333
198
100
173
235
213
mean
(ppm)
337.33
71.67
151.33
321.67
186.00
83.67
174.00
216.67
178.67
standard
deviation
(ppm)
65.76
8.62
11.50
27.79
11.53
14.15
1.00
18.50
31.21
(+/-) 12
(+/-) 10
(+/-) 11
(+/-) 12
(+/-) 10
(+/-) 8
(+/-) 9
(+/-) 12
(+/-) 10
(+/-) 14
(+/-) 18
(+/-) 11
(+/-) 12
164
103
149
172
129
67
69
164
114
239
334
145
155
156.00
109.67
148.67
170.67
129.33
63.00
66.00
156.33
113.33
247.67
288.67
141.00
179.00
11.31
8.33
2.52
12.06
6.51
7.81
4.36
8.02
10.02
15.89
40.07
6.93
24.00
107
XRF 17
XRF 20
XRF 20a
XRF 22
XRF 23
XRF 24
XRF 25
XRF 26
XRF 27
XRF 28
XRF 29
XRF 30
max
min
mean
median
standard
deviation
(+/-) 10
(+/-) 12
(+/-) 13
(+/-) 10
(+/-) 9
(+/-) 15
(+/-) 14
(+/-) 12
(+/-) 11
(+/-) 14
(+/-) 12
(+/-) 15
129
158
187
113
70
253
234
150
135
241
183
251
(+/-) 11
(+/-) 12
(+/-) 12
(+/-) 11
(+/-) 10
(+/-) 13
(+/-) 14
(+/-) 12
(+/-) 11
(+/-) 12
(+/-) 13
(+/-) 14
189
150
166
126
104
191
215
183
147
146
217
242
(+/-) 11
(+/-) 12
(+/-) 14
(+/-) 10
(+/-) 9
(+/-) 13
(+/-) 13
(+/-) 13
(+/-) 12
(+/-) 12
(+/-) 12
(+/-) 15
152
163
213
107
85
200
196
197
166
159
193
267
156.67
157.00
188.67
115.33
86.33
214.67
215.00
176.67
149.33
182.00
197.67
253.33
288.67
63.00
165.09
157.00
30.27
6.56
23.54
9.71
17.04
33.50
19.00
24.13
15.63
51.51
17.47
12.66
54.51696
Table 8 NURE HSSR summary statistics for Sacramento County.
Sample ID U ppm Al ppm B ppm Ba ppm Be ppm Ca ppm Ce ppm Co ppm
max
6.50 93500 597.00 1052.00
2.00
38500
58.00
38.00
min
0.00 27800 0.00
106.00
0.00
1400
0.00
4.00
mean
2.46 61613 22.55 509.94
1.04
15521
27.08
12.79
median
2.40 62300 14.00 509.00
1.00
15400
27.00
13.00
stdev
0.79 11671 44.88 108.01
0.43
6788
8.36
4.33
Table 8 (continued) NURE HSSR summary statistics for Sacramento County.
Sample ID Cr ppm Cu ppm Fe ppm
K ppm La ppm Li ppm Mg ppm Mn ppm
max
206.00 125.00 59300 23100.00 31.00 56.00 20100 1570.00
min
16.00
7.00
12000 2400.00
2.00
6.00
1300
189.00
mean
79.42
31.09
32021 9293.77
13.58 17.62
7517
624.00
median
79.00
28.00
32200 9300.00
14.00 15.00
7200
606.00
stdev
30.65
15.51
8810
2289.57
3.94
8.01
3793
181.01
Table 8 (continued) NURE HSSR summary statistics for Sacramento County.
Sample ID
Na ppm Nb ppm Ni ppm P ppm Pb ppm Sc ppm Sr ppm Th ppm
max
23600
18.00 110.00 1801.00 1039.00 24.00 675.00 272.00
min
1200
0.00
8.00
68.00
0.00
3.00
41.00
0.00
mean
11824
7.27
42.74 434.11
48.76
11.11 220.97
5.80
median
12300
7.00
38.00 375.00
22.00
11.00 213.00
4.00
stdev
3900
2.52
22.73 262.94
90.62
3.42
87.45
17.05
108
Table 8 (continued) NURE HSSR summary statistics
for Sacramento County.
Sample ID Ti ppm V ppm Y ppm Zn ppm Zr ppm
max
10776 295.00 49.00 858.00 151.00
min
1868 43.00 4.00
25.00 11.00
mean
4069 125.08 11.25 84.55 42.16
median
3860 123.00 11.00 70.00 40.00
stdev
1217 38.76 3.84
75.78 13.14
Table 9 Replicate and environmental results, analysis by MEMS-61 method of soil
samples collected in Sacramento, CA.
ppm, parts per million; %, percent
Site Name
Ag
Al
As
Ba
Be
Bi
Ca
ppm
%
ppm
ppm
ppm
ppm
%
Lead 1
0.09
4.75
7.2
460
0.68
0.1
1.32
Lead 1a
0.08
4.8
7
460
0.63
0.08
1.36
Lead 2
0.14
4.52
6.7
570
0.81
0.14
1.49
Lead 2a
0.14
4.37
6.3
530
0.76
0.11
1.46
Lead 5
0.25
5.77
8.8
570
1.25
0.67
2.06
Lead 5a
0.25
5.83
8.6
570
1.22
0.45
2.08
Lead 14
0.11
6.29
5.9
610
1.22
0.2
1.64
Lead 14b
0.1
6.41
5.9
600
1.35
0.14
1.85
Lead 31
0.12
6.25
5.6
660
1.27
0.15
1.45
Lead 31b
0.08
6.17
6.2
610
1.15
0.13
1.56
Lead 38
0.15
6.76
7.5
550
1.1
0.19
1.65
Lead 38b
0.23
6.98
7.9
560
1.22
0.22
1.7
Lead 52
0.12
5.14
8
500
1.12
0.11
1.46
Lead 52b
0.11
5.72
16
510
1.06
0.13
1.54
Lead 61b
0.15
6.36
12.1
570
1.18
0.15
2.01
Lead 61bR
0.08
5.95
11.2
530
1.21
0.12
2.06
Table 9 (continued) Replicate and environmental results, analysis by MEMS-61
method of soil samples collected in Sacramento, CA.
ppm, parts per million; %, percent
Site Name
Cd
Ce
Co
Cr
Cs
Cu
ppm
ppm
ppm
ppm
ppm
ppm
Lead 1
0.23
25.9
12.9
164
1.65
27.1
Lead 1a
0.27
25.7
13
138
1.59
26.5
Lead 2
0.39
29.2
15
235
1.36
39.9
Lead 2a
0.43
26.8
14.2
181
1.34
39.3
Lead 5
0.97
42.7
17.1
193
1.92
65.8
Lead 5a
0.93
42.8
17.1
151
1.94
121
Lead 14
0.34
51.6
18.3
144
2.19
34.6
Lead 14b
0.38
50
17.9
137
2.16
35.1
Lead 31
0.16
53.1
17.8
129
2.29
30.1
Fe
%
2.6
3.04
3.13
3.2
3.48
3.67
3.39
3.59
2.95
109
Lead 31b
Lead 38
Lead 38b
Lead 52
Lead 52b
Lead 61b
Lead 61bR
0.23
0.37
0.4
0.27
0.33
0.29
0.26
52.4
34.4
34.8
37.4
42.6
43.6
41.5
17.9
19.4
19.9
13.9
14
17
16
136
154
157
176
144
185
196
2.25
2.74
2.79
1.67
1.92
1.8
1.58
45.9
50.5
52.9
31.7
32.7
32.6
28
Table 9 (continued) Replicate and environmental results, analysis by MEMS-61
method of soil samples collected in Sacramento, CA.
ppm, parts per million; %, percent
Site Name
Ga
Ge
Hf
In
K
La
ppm
ppm
ppm
ppm
%
ppm
Lead 1
9.7
0.08
1.3
0.033
0.97
14
Lead 1a
9.8
0.09
1.4
0.03
0.98
13.9
Lead 2
9.36
0.09
1.1
0.06
0.96
14.7
Lead 2a
8.97
0.1
1.2
0.055
0.98
13.5
Lead 5
13.1
0.13
1.7
0.098
1.09
21.1
Lead 5a
13.6
0.13
1.8
0.071
1.1
20.9
Lead 14
14.95
0.13
2.2
0.043
1.17
24.7
Lead 14b
15.35
0.16
2.1
0.04
1.18
24.3
Lead 31
15.35
0.14
2.1
0.046
1.46
26.2
Lead 31b
15.3
0.16
2
0.045
1.3
25.6
Lead 38
15.55
0.15
1.8
0.049
1.05
16.6
Lead 38b
16
0.13
1.8
0.048
1.03
17
Lead 52
12.75
0.13
1.5
0.035
1.07
18.8
Lead 52b
14
0.16
1.7
0.044
1.11
21.9
Lead 61b
15.25
0.14
1.8
0.045
1.21
22
Lead 61bR
14.25
0.16
1.7
0.04
1.14
20.9
3.21
3.79
3.99
3
3.54
3.39
3.48
Li
ppm
13.2
13.3
12.9
12.7
18.6
18.9
18.3
17.8
16.5
18
33.5
32.9
14.1
15.7
16.8
14.8
Table 9 (continued) Replicate and environmental results, analysis by MEMS-61
method of soil samples collected in Sacramento, CA.
ppm, parts per million; %, percent
Site Name
Mg
Mn
Mo
Na
Nb
Ni
P
%
ppm
ppm
%
ppm
ppm
ppm
Lead 1
0.77
521
1.97
0.74
4.6
44.1
600
Lead 1a
0.78
549
1.05
0.77
4.6
43.6
580
Lead 2
0.9
539
2.82
0.88
4.4
53.8
440
Lead 2a
0.89
547
1.75
0.86
4.5
51.8
430
Lead 5
1.2
660
2.54
0.99
6.7
67.2
1180
Lead 5a
1.21
681
1.39
1
6.7
66.3
1170
Lead 14
0.77
759
1.42
1.07
8.4
57.3
430
Lead 14b
0.93
732
1.28
1.15
8.3
58.9
480
Lead 31
0.65
745
1.08
1.11
9.3
55.4
420
110
Lead 31b
Lead 38
Lead 38b
Lead 52
Lead 52b
Lead 61b
Lead 61bR
0.76
1.47
1.45
0.92
1.02
1.14
1.11
712
656
661
500
538
684
669
0.9
1.56
1.22
2.12
1.47
1.96
1.07
1.06
0.98
0.94
0.87
0.95
1.19
1.18
9
5.6
5.6
5.8
6.6
7.1
6.8
57.2
98.5
94.5
49.4
50.4
62.1
55.6
480
670
630
700
1170
620
600
Table 9 (continued) Replicate and environmental results, analysis by MEMS-61
method of soil samples collected in Sacramento, CA.
ppm, parts per million; %, percent
Site Name
Pb
Rb
S
Sb
Sc
Sn
Sr
ppm
ppm
%
ppm
ppm
ppm
ppm
Lead 1
15.8
42.8
0.02
0.97
10.1
1.1
167.5
Lead 1a
15.2
42.9
0.02
0.99
10.1
1
172.5
Lead 2
216
41.4
0.04
2.42
9.9
4.2
180.5
Lead 2a
83.9
41.1
0.03
2.48
9.5
4.8
176.5
Lead 5
509
51.1
0.06
3.63
13.9
35.1
239
Lead 5a
408
50.8
0.06
2.59
14.1
41.3
241
Lead 14
32.7
61.4
0.02
1
14.3
3.1
222
Lead 14b
30.8
60.4
0.03
0.99
15.3
1.7
235
Lead 31
35.4
75.7
0.02
0.87
14.2
2.2
227
Lead 31b
40
70.2
0.03
1
14.6
1.9
225
Lead 38
30.7
52
0.05
1.28
17.9
1.9
196.5
Lead 38b
33.3
52.7
0.05
1.61
18.6
2.1
198.5
Lead 52
21.2
47.3
0.02
1.11
12.8
1.3
199
Lead 52b
22.7
52
0.03
0.99
14.3
1.4
211
Lead 61b
48.3
59.3
0.03
1.08
15.2
2.1
268
Lead 61bR
52
56.8
0.03
1.07
14.6
2.3
267
Table 9 (continued) Replicate and environmental results, analysis by MEMS-61
method of soil samples collected in Sacramento, CA.
ppm, parts per million; %, percent
Site Name
Ta
Th
Ti
Tl
U
V
W
Y
Zn
ppm ppm
%
ppm ppm ppm ppm ppm ppm
Lead 1
0.38
3.9
0.285 0.23
1.2
86
1.5
10.7
73
Lead 1a
0.36
3.8
0.285 0.22
1.2
85
1
10.8
73
Lead 2
0.36
4.4
0.271 0.23
1.2
87
1.4
12.5 211
Lead 2a
0.36
3.9
0.268 0.22
1.2
85
0.8
11.4 204
Lead 5
0.5
5.9
0.328 0.29
1.8
100
2
13.4 261
Lead 5a
0.49
5.7
0.332 0.28
1.8
99
1.4
13.4 252
Lead 14
0.6
7.5
0.376 0.33
1.8
106
1.3
14.1 134
Lead 14b
0.6
7.3
0.376 0.34
1.7
110
1.3
14.3
97
Lead 31
0.66
7.3
0.402 0.38
2.2
104
1.3
14.6
76
111
Lead 31b
Lead 38
Lead 38b
Lead 52
Lead 52b
Lead 61b
Lead 61bR
0.65
0.57
0.45
0.48
0.51
0.58
0.54
7.5
5
5.4
5.5
6.6
6.8
6.1
0.387
0.339
0.346
0.314
0.338
0.36
0.359
0.37
0.3
0.3
0.28
0.3
0.3
0.26
2.4
1.7
1.8
1.8
2.5
2.1
1.7
99
123
129
98
105
105
107
1.2
1.5
1.5
1.4
1.2
1.5
1
14.6
15.4
15.5
11.4
13.2
14.1
13.4
97
174
165
86
78
85
80
Table 10 Summary Statistics of lead concentrations for replicates analyzed by the
MEMS-61 method for soil samples collected in Sacramento, CA.
Split
Duplicate
Both
Env Rep Env+Rep Env Rep Env+Rep Env Rep Env+Rep
min
15.8 15.2
15.2 21.2 22.7 21.2
15.8 15.2
15.2
max
509.0 408.0 509.0 48.3 52.0 52.0 509.0 408.0 509.0
mean
246.9 169.0 208.0 33.7 35.8 34.7 113.6 85.7
99.7
median
216.0 83.9 150.0 32.7 33.3 33.0
34.1 36.7
34.4
standard deviation 248.1 209.8 209.8 9.8 11.0
9.9
172.7 131.9 149.1
Table 11 Percent difference of lead concentrations
for replicates analyzed by the MEMS-61 method
for soil samples collected in Sacramento, CA.
Split Samples
Env
Rep
Lead 1
Lead 1a
Lead 2
Lead 2a
Lead 5
Lead 5a
percent difference
10%
22%
6.00%
Duplicate Samples
Env
Rep
Lead 14
Lead 14b
Lead 31
Lead 31b
Lead 38
Lead 38b
Lead 52
Lead 52b
Lead 61b
Lead 61bR
percent difference
<5%
<5%
<5%
<5%
<5%
Table 12 Comparison of MEMS-61 method and
XRF method for lead and zinc concentrations in
Sacramento soils.
112
ppm, parts per million; avg, average
MEMS-61 XRF
Site Name
Pb_ppm Pb_avg Pb % diff
Lead_3
441.00 365.00
18.9
Lead_4
189.50 187.33
1.1
Lead_5
509.00 437.67
15.1
Lead_6
141.50 149.33
5.4
Lead_7
178.50 149.33
17.8
Lead_8
174.00 167.33
3.9
Lead_24
148.00 111.33
28.3
Lead_29
229.00 267.33
15.4
Lead_47
109.50
90.00
19.5
Lead_61b
48.30
13.67
111.8
standard deviation
145
129
32
Table 13 List of historic industry using heavy metals
Company
Palm Iron Works
Alling Iron Works
Union Auto Freight Depot, Iron Works
Southern Pacific Co., Railroad Shop & Roundhouse
Table 14 Summary statistics of predicted and observed
lead concentrations in parts per million.
lead (ppm)
lead (ppm)
predicted
observed
Max
173.1
1540.0
Min
23.7
10.6
Mean
58.9
128.0
Median
45.6
52.6
StndDev
31.7
204.9
Table 15 Results and summary statistics for
leave-one-out cross validation.
lead (ppm) lead (ppm) (ppm)
Site Name
predicted observed Residual
Lead_1
66.8
15.8
-51.0
Lead_2
86.8
216.0
129.2
Lead_3
105.7
441.0
335.3
Lead_4
108.0
189.5
81.5
Lead_5
107.3
509.0
401.7
Lead_6
139.4
141.5
2.1
113
Lead_7
Lead_8
Lead_9
Lead_10
Lead_11
Lead_12
Lead_13
Lead_14
Lead_15
Lead_16
Lead_17
Lead_18
Lead_19
Lead_20
Lead_21
Lead_22
Lead_23
Lead_24
Lead_25
Lead_26
Lead_27
Lead_28
Lead_29
Lead_30
Lead_31
Lead_32
Lead_33
Lead_34
Lead_35
Lead_36
Lead_37
Lead_38
Lead_39
Lead_40
Lead_41
Lead_42
Lead_43
Lead_44
Lead_45
Lead_46
Lead_47
Lead_48
Lead_49
Lead_50
Lead_51
Lead_52
132.7
133.1
120.4
94.9
68.0
63.3
61.9
35.1
37.0
25.4
27.1
27.8
38.3
42.9
63.0
96.9
140.9
107.8
157.0
152.5
158.8
163.1
111.0
117.5
85.0
62.4
63.0
55.2
48.5
32.2
27.6
42.1
42.5
101.0
106.7
98.4
73.5
39.9
125.1
116.7
116.6
46.6
76.9
156.9
102.2
38.6
178.5
174.0
129.0
62.1
49.1
38.0
23.0
32.7
18.2
56.4
20.0
87.2
33.1
40.7
120.0
56.0
17.1
148.0
38.7
113.5
217.0
75.5
229.0
83.7
35.4
260.0
79.5
50.6
26.5
31.5
16.2
30.7
17.3
48.9
66.6
475.0
16.9
52.6
81.1
261.0
109.5
58.4
217.0
38.4
87.0
21.2
45.8
40.9
8.6
-32.8
-18.9
-25.3
-38.9
-2.4
-18.8
31.0
-7.1
59.4
-5.2
-2.2
57.0
-40.9
-123.8
40.2
-118.3
-39.0
58.2
-87.6
118.0
-33.8
-49.6
197.6
16.5
-4.6
-22.0
-0.7
-11.4
-11.4
-25.2
-52.1
-40.1
376.6
-56.6
12.7
-44.0
144.3
-7.1
11.8
140.1
-118.5
-15.2
-17.4
114
Lead_53
Lead_54
Lead_55
Lead_56
Lead_57
Lead_58
Lead_59
Lead_60
Lead_61b
Lead_62
Lead_63
Lead_64
Lead_65
Lead_66
Lead_67
Lead_68a
Lead_68b
Lead_69
Lead_70
SACSAC6
SACSAC8
SACSAC11
SACSECT12
SACSECT13
SACSECT14
SACSECT15
SACSECT16
SACSAC17
SACSAC18
SACSAC19
SACSAC20
SACSAC21
SACSAC22
YOLWS23
SACSAC24
SACSAC25
SACSAC26
SACSAC27
SACSAC28
SACNUT29
SACNH30
SACSA-1
SACSA-2
SACSA-3
SACSA-4
SACSA-5
59.9
39.3
49.9
29.8
32.6
46.9
106.4
95.0
128.8
79.7
59.0
25.6
50.7
37.1
34.0
32.9
22.7
73.3
73.1
27.9
52.7
52.1
18.2
18.4
19.2
19.8
18.6
31.8
44.3
36.0
73.7
114.5
113.6
35.6
54.6
79.4
34.9
97.8
37.7
36.3
73.4
127.5
155.2
105.2
112.3
102.3
53.9
35.6
26.3
25.2
17.0
35.8
52.9
143.0
48.3
200.0
47.9
58.0
18.2
26.9
12.7
20.3
33.7
57.7
40.8
24.8
61.1
524.0
29.5
20.8
18.0
16.6
19.8
25.5
46.9
16.4
10.6
420.0
171.0
78.8
375.0
82.6
666.0
15.8
154.5
24.4
23.0
114.5
18.6
289.0
269.0
29.0
-6.0
-3.7
-23.6
-4.6
-15.6
-11.1
-53.5
48.0
-80.5
120.3
-11.1
32.4
-32.5
-10.2
-21.3
-12.6
11.0
-15.6
-32.3
-3.1
8.4
471.9
11.3
2.4
-1.2
-3.2
1.2
-6.3
2.6
-19.6
-63.1
305.5
57.4
43.2
320.4
3.2
631.1
-82.0
116.8
-11.9
-50.4
-13.0
-136.6
183.8
156.7
-73.3
115
SACSA-6
SACSA-7
SACSA-8
SACSA-9
SACSA-10
Max
Min
Mean
Median
StndDev
61.3
165.3
80.7
73.4
150.3
165.3
18.2
75.2
66.8
41.4
863.0
21.4
342.0
1540.0
283.0
1540.0
10.6
128.0
52.6
204.9
801.7
-143.9
261.3
1466.6
132.7
1466.6
-143.9
52.8
-4.6
203.0
Table 16 MEMS-61 Factor loadings for maximum
likelihood estimation of elemental analysis of surface soils
form Sacramento, CA.
Element
Ag
Al
As
Ba
Be
Bi
Ca
Cd
Ce
Co
Cr
Cs
Cu
Fe
Ga
Ge
Hf
In
K
La
Li
Mg
Mn
Mo
Na
Nb
Ni
P
Pb
Factor1
0.167
0.759
0.468
0.447
0.114
0.268
0.931
0.347
0.708
0.527
0.92
0.745
0.417
0.44
0.496
-0.226
0.292
0.822
0.784
0.62
-0.214
0.272
0.865
0.201
-0.209
Factor2
0.508
0.799
0.734
0.351
-0.272
-0.17
0.842
0.121
-0.496
0.425
0.114
0.548
0.206
0.702
0.227
0.738
0.85
-0.364
0.292
-0.242
0.88
-0.13
-0.162
Factor3 Factor4
0.757
-0.123
0.506
-0.2
0.166
0.21
-0.109
0.763
0.26
0.733
0.843
-0.166
-0.156
0.129
-0.453
0.705
0.112
0.171
-0.139
0.134
-0.134
-0.199
0.434
-0.192
0.181
-0.125
0.19
-0.354
0.337
0.222
0.127
0.657
-0.101
-0.117
0.806
-0.196
0.211
-0.244
0.687
0.817
0.151
116
Rb
S
Sb
Sn
Sr
Ta
Th
Ti
Tl
U
V
W
Y
Zn
Proportion
Cumulative
0.182
-0.105
0.191
0.653
0.303
0.431
0.874
0.217
0.805
0.775
-0.221
0.106
0.639
0.839
0.857
-0.265
0.876
0.495
0.428
0.607
0.534
-0.194
-0.253
-0.321
0.272
0.989
0.111
0.389
-0.112
-0.14
0.572
-0.266
-0.197
-0.21
-0.195
0.752
Factor1 Factor2 Factor3 Factor4
0.231
0.204
0.184
0.076
0.231
0.435
0.619
0.695
Table 17 Uniqueness values for maximum likelihood and
principal component estimation methods.
MLE
PCE
Element
Uniqueness
Uniqueness
Ag
0.397
0.39
Al
0.149
0.161
As
0.483
0.423
Ba
0.29
0.214
Be
0.248
0.221
Bi
0.281
0.259
Ca
0.317
0.249
Cd
0.253
0.222
Ce
0.191
0.215
Co
0.089
0.071
Cr
0.616
0.496
Cs
0.111
0.105
Cu
0.214
0.211
Fe
0.099
0.079
Ga
0.118
0.121
Ge
0.748
0.726
Hf
0.266
0.255
In
0.477
0.45
117
K
0.372
0.267
La
0.175
0.184
Li
0.154
0.17
Mg
0.09
0.14
Mn
0.504
0.441
Mo
0.495
0.42
Na
0.285
0.225
Nb
0.108
0.135
Ni
0.131
0.151
P
0.454
0.418
Pb
0.265
0.244
Rb
0.284
0.223
S
0.531
0.471
Sb
0.284
0.249
Sn
0.25
0.241
Sr
0.005
0.068
Ta
0.156
0.171
Th
0.688
0.656
Ti
0.287
0.262
Tl
0.395
0.343
U
0.489
0.444
V
0.201
0.178
W
0.569
0.52
Y
0.162
0.153
Zn
0.414
0.367
Table 18 NURE Factor loadings for maximum likelihood
estimation of elemental analysis of surface soils form
Sacramento, CA.
NURE
Factor1
Factor2
Factor3
U_ppm
AL_ppm
B_ppm
Ba_ppm
0.52
0.33
0.426
-0.126
0.47
-0.151
0.116
0.551
-0.135
0.183
0.35
0.141
Be_ppm
Ca_ppm
0.337
0.884
Ce_ppm
0.196
0.96
Co_ppm
0.829
0.212
118
Cr_ppm
0.846
Cu_ppm
Fe_ppm
0.834
0.793
0.29
K_ppm
-0.358
0.298
La_ppm
Li_ppm
Mg_ppm
Mn_ppm
0.213
0.79
0.825
0.393
0.955
0.158
-0.115
0.3
-0.25
0.324
0.168
Na_ppm
Nb_ppm
-0.11
-0.19
0.641
0.844
0.161
Ni_ppm
0.847
P_ppm
0.679
-0.152
Pb_ppm
Sc_ppm
-0.175
0.181
0.147
0.863
0.223
Sr_ppm
0.122
Th_ppm
0.48
0.961
Ti_ppm
0.136
0.444
V_ppm
Y_ppm
0.766
0.621
0.425
0.146
0.34
Zn_ppm
0.47
0.584
-0.236
0.142
0.413
0.114
0.527
Zr_ppm
Proportion
Cumulative
0.271
0.271
Table 19 NURE Uniqueness values for maximum
likelihood and principal component estimation
methods.
MLE
PCE
Element
Uniqueness
Uniqueness
U_ppm
0.892
0.739
AL_ppm
0.346
0.265
B_ppm
0.961
0.871
Ba_ppm
0.64
0.468
Be_ppm
0.881
0.790
Ca_ppm
0.102
0.119
Ce_ppm
0.141
0.279
Co_ppm
0.267
0.253
119
Cr_ppm
Cu_ppm
Fe_ppm
K_ppm
La_ppm
Li_ppm
Mg_ppm
Mn_ppm
Na_ppm
Nb_ppm
Ni_ppm
P_ppm
Pb_ppm
Sc_ppm
Sr_ppm
Th_ppm
Ti_ppm
V_ppm
Y_ppm
Zn_ppm
Zr_ppm
0.283
0.273
0.295
0.78
0.043
0.41
0.201
0.727
0.276
0.527
0.258
0.535
0.977
0.205
0.061
0.76
0.775
0.392
0.34
0.774
0.604
0.278
0.232
0.229
0.665
0.184
0.336
0.235
0.657
0.274
0.397
0.240
0.460
0.993
0.168
0.148
0.757
0.673
0.345
0.334
0.721
0.390
Table 20 MLE and PCE summary statistics from residual
matrices of factor analysis of soil samples from Sacramento,
CA.
MEMS-61
NURE
MLE
PCE
MLE
PCE
max
0.3120
0.2785
0.4130
0.3717
min
-0.1630
-0.1663 -0.2660 -0.3424
mean
0.0005
-0.0055
0.0090 -0.0080
median
-0.0002
-0.0040
0.0000 -0.0116
mean (stdev)
0.0489
0.0499
0.0799
0.0836
Table 21 MLE and PCE summary statistics from residual
matrices of factor analysis of soil samples from
Sacramento, CA.
Element
Ag
Al
As
Ba
Be
Bi
Ca
Factor1 Factor2 Factor3 Factor4
-0.166
-0.763
-0.696
-0.58
0.119
-0.461
-0.527
-0.281
-0.835
-0.188
0.226
-0.382
-0.785
-0.126
-0.355
-0.778
-0.12
0.285
-0.278
0.76
120
Cd
Ce
Co
Cr
Cs
Cu
Fe
Ga
Ge
Hf
In
K
La
Li
Mg
Mn
Mo
Na
Nb
Ni
P
Pb
Rb
S
Sb
Sn
Sr
Ta
Th
Ti
Tl
U
V
W
Y
Zn
Proportion
Cumulative
-0.237
-0.931
-0.46
-0.641
-0.534
-0.926
-0.682
-0.427
-0.385
-0.515
0.32
-0.247
-0.785
-0.799
-0.643
0.217
-0.234
-0.86
-0.223
0.202
0.19
-0.837
-0.182
0.522
-0.5
-0.169
-0.617
-0.245
-0.734
-0.242
-0.772
-0.859
-0.172
0.297
-0.32
0.262
-0.879
0.156
-0.821
0.263
-0.151
-0.664
-0.237
-0.376
-0.882
-0.223
-0.784
-0.878
-0.525
-0.44
-0.661
-0.597
-0.136
-0.105
-0.444
0.139
-0.856
0.159
-0.161
-0.117
-0.697
-0.107
0.136
-0.122
-0.44
0.118
-0.174
-0.324
-0.686
0.106
0.189
-0.184
-0.704
-0.83
-0.114
-0.675
-0.861
-0.861
0.183
0.26
0.321
-0.277
0.14
-0.606
-0.482
-0.134
0.15
-0.12
-0.13
-0.224
-0.183
0.185
-0.394
0.168
0.185
-0.177
0.843
-0.267
-0.111
0.151
-0.285
0.114
0.957
-0.295
-0.239
-0.227
-0.187
-0.778
Factor1 Factor2 Factor3 Factor4
0.222
0.225
0.191
0.082
0.222
0.447
0.638
0.721
121
Table 22 NURE Factor loadings for principal
component estimation of elemental analysis of
surface soils form Sacramento, CA.
Element
U_ppm
AL_ppm
B_ppm
Ba_ppm
Be_ppm
Ca_ppm
Ce_ppm
Co_ppm
Cr_ppm
Cu_ppm
Fe_ppm
K_ppm
La_ppm
Li_ppm
Mg_ppm
Mn_ppm
Na_ppm
Nb_ppm
Ni_ppm
P_ppm
Pb_ppm
Sc_ppm
Sr_ppm
Th_ppm
Ti_ppm
V_ppm
Y_ppm
Zn_ppm
Zr_ppm
Proportion
Cumulative
Factor1
0.493
0.102
-0.149
0.308
0.22
0.85
0.847
0.859
0.816
-0.384
0.242
0.741
0.81
0.408
-0.153
-0.197
0.855
0.721
Factor2
-0.448
-0.451
-0.207
-0.702
-0.393
-0.818
-0.158
-0.232
-0.433
-0.869
-0.198
0.121
-0.292
-0.728
0.856
0.103
0.162
0.783
0.631
0.524
0.283
0.283
Factor3
0.245
-0.538
0.274
-0.131
-0.23
-0.882
-0.124
-0.471
-0.52
0.173
-0.228
0.276
-0.307
-0.301
-0.837
-0.185
0.165
0.121
-0.312
-0.914
-0.346
-0.173
-0.204
-0.384
-0.725
0.287
0.16
0.443
0.126
0.569
122
APPENDIX B
Figures
Figure 1 Study area
123
Distribution of lead poisoning cases by zip code in Sacramento
County between July 1, 2008 and June 30, 2009.
n=28
(Lea Huffman, Sacramento County)
Figure 2 Lead poisoning cases by zip code in Sacramento County
124
(Laidlaw et al., 2008)
Figure 3 Historic lead-use in paint and gasoline in the United States
125
(Harden, 2004)
Granitic rock of the
Sierra Nevada
Figure 4 Sierra Nevada rock composition.
126
(Helly &
Harwood, 1986)
Figure 5 Quaternary geology of southern Sacramento Valley map
127
(Diawara et al., 2006)
Figure 6 Prediction map of lead concentrations in Pueblo, CO
128
(Sacramento & Yolo Counties, 2005)
Figure 7 Sacramento and Yolo County land use map
129
Figure 8 Sample sites (2008) location map
130
Figure 9 XRF sample location map
131
Figure 10 MEMS-61 sample site location map Oor & Deocampo
132
Figure 11 MEMS-61 sample site location map
133
Scree test
Eigenvalues selected for principal
component estimation loadings
16
14
eigenvalues
12
10
8
6
4
2
0
0
5
10
15
20
25
factors
Figure 12 Scree plot
30
35
40
45
50
134
a)
Percent
75
60
45
30
15
0
10
160
320
470
620
780x 10
930
-3 1008
Pb ppm
Pb (ppm)
1230 1390 1540
b)
(ppm)
Figure 13 a) Histogram and b) box plot of lead concentrations for MEMS-61 data
135
Figure 14 MEMS-61 lead concentration map
136
a)
percent
43
Ag
34
26
17
9
0
0.03 0.11 0.18
0.26
0.34
0.42
0.50
0.57
0.65 0.73 0.81
(ppm)
33
percent
As
26
20
13
7
0
2.7
5.2
7.7
10.3
12.8
15.3
17.8
20.3
22.9
(ppm)
Figure 15 a) Histograms of non-normally distributed elements
25.4
27.9
137
b)
38
percent
Bi
30
28
15
8
0
0.07 0.13
0.19 0.25
0.31
0.37
0.43
0.49
0.55 0.61
0.67
(ppm)
54
percent
Cd
43
32
22
11
0
0.1
0.35 0.61 0.86
1.11 1.37
1.62
1.87
2.12
2.38 2.63
(ppm)
Figure 15 b) Histograms of elemental concentrations displaying non-normal distributions
138
c)
31
percent
Cr
25
19
12
6
0
67
27
97
128
159
189
219 250
(ppm)
281
311
342
372
96
104
percent
Cu
22
16
11
5
0
15
24
33
42
51
60
69
78
87
(ppm)
Figure 15 c) Histograms of elemental concentrations displaying non-normal distributions
139
d)
29
percent
Li
23
17
12
6
0
9.8
13.1
16.4
19.6
22.9
26.2
29.5
32.8
36
39.3
42.6
(ppm)
percent
24
Mo
19
14
10
5
0
0.57 0.93 1.28
1.64 1.99
2.35
2.71
3.06
3.42
3.77 4.13
(ppm)
Figure 15 d Histograms of elemental concentrations displaying non-normal distributions
140
e)
percent
75
Pb
60
45
30
15
0
10
160
320
470
620
780
930
1008
1230 1390 1540
(ppm)
percent
40
S
32
24
16
8
0
100
220
340
460
580
700
820
940
1060
1180 1300
(ppm)
Figure 15 e Histograms of elemental concentrations displaying non-normal distributions
141
f)
percent
Sb
52
42
32
21
10
0
0.6
1.36
2.11
2.87
3.63
4.38 5.14
(ppm)
5.9
6.66 7.41
8.17
percent
Sn
79
63
47
32
16
0
1.1
7.4
13.7
20
26.3
32.6 38.9
(ppm)
45.2
51.5 57.8 64.1
Figure 15 f Histograms of elemental concentrations displaying non-normal distributions
142
g)
percent
96
Zn
77
58
38
19
0
50
650
1250
1840 2440 3030 3630
4220 4820
5410 6010
(ppm)
Figure 15 g Histograms of elemental concentrations displaying non-normal distributions
143
Figure 16 XRF lead concentration map
144
a)
7
81
155
229
303 377
(ppm)
451 525
599
673 747
b)
(ppm)
Figure 17 a) Histogram and b) box plot of lead Concentrations for XRF data
145
Figure 18 NURE lead concentrations map
146
a)
23
percent
18
14
9
4.5
0
10
90
200
300
410
510
620
720
830
930 1040
Pb (ppm)
b)
Pb (ppm)
Figure 19. a) Histogram and b) box plot of lead concentrations for NURE data
147
MEMS-61 & XRF Pb replicates
(ppm)
500
R2 = 0.95
XRF
400
300
200
100
0
0
200
400
MEMS-61
Figure 20 MEMS-61 and XRF replicate comparison
600
148
Figure 21 MEMS and XRF lead concentrations and soil map
149
Pb (ppm)
1 = 5-18 % clay
2 = 10-25 % clay
3 = 15-27 % clay
4 = 27-60% clay
Figure 22 Distribution of lead concentrations compared to clay percentage in soil
150
Figure 23 MEMS-61 and XRF lead concentrations and Quaternary geology map
151
Pb (ppm)
Figure 24 Distribution of lead concentrations within Quaternary geology
152
Figure 25 MEMS-61 and XRF lead concentrations and land use map
153
Pb
(ppm)
Figure 26 Distribution of lead concentrations within land use areas
154
Figure 27 Locations of metal-working industry map (1952)
155
a.)
b.)
Year-round
Sacramento Executive Airport
Year-round
Dry-month
Dry-month
Natomas
(UC Davis Integrated Pest Management, 2009)
Figure 28 Rose diagrams of wind directions from: a) Sacramento Executive Airport and
b) Natomas during year-round and dry-month intervals.
156
Pb vs distance to road
1800.00
1600.00
R2 = 0.02
Pb concentration
1400.00
1200.00
1000.00
800.00
600.00
400.00
200.00
0.00
0
100
200
300
400
500
600
700
-200.00
Feet
Figure 29 Scatter plot of lead concentrations and distance to roads
800
900
157
Figure 30 MEMS-61 lead concentrations and prediction map
158
(γ)
Model
Data
(meters)
Figure 31 Semivariogram of MEMS-61 data
159
Figure 32 MEMS-61 lead concentrations and variance prediction map
160
Factor 1
0
Factor 4
0
Figure 33 a) MEMS-61 factor loadings 1 & 4
161
Factor 3
0
Factor 2
0
Figure 33 b) MEMS-61 factor loadings 2 & 3
162
Factor 1
Factor 3
Figure 34 a) NURE factor loadings 1 & 3
163
Factor 2
Factor 3
Figure 34 b) NURE factor loadings 2 & 3
164
Figure 35 MEMS-61 factor 1 scores and prediction map
165
Figure 36 MEMS-61 factor 2 scores and prediction map
166
Figure 37 MEMS-61 factor 3 scores and prediction map
167
Figure 38 MEMS-61 factor 4 scores and prediction map
168
Figure 39 NURE factor 1 scores and prediction map
169
Figure 40 NURE factor 2 scores and prediction map
170
Figure 41 NURE factor 3 scores and prediction map
171
(Historic Photos and Maps of California Highways, 2009)
Figure 42 Historic roadmaps of Sacramento (1933)
172
(Sacramento History Online, 2009)
Figure 43 Coal-burning smoke stack (1939)
173
a)
Percent
75
60
45
30
15
0
10
160
320
470
54
69
620
780
930-3 1008
Pb ppm Pb
x 10
Observed
(ppm)
1230
1390 1540
b)
percent
53
43
32
21
11
0
24
39
83
98
113
Predicted Pb (ppm)
128
143
158
173
Figure 44 Histogram of a) observed and b) predicted lead concentrations
174
Figure 45 a) MEMS-61 and NURE factor 1 loadings
175
Figure 45 b) MEMS-61 and NURE factor 2 loadings
176
Figure 45 c) MEMS-61 Factor 4 and NURE factor 3 loadings
177
Figure 46 Occurrence of mafic Rocks in relation to the study area
178
Figure 47 Occurrence of marine sedimentary rocks in relation to the study area
179
Figure 48 Occurrence of granitic rocks in relation to the study area
180
Figure 49 Coincidence of factor 2 scores with the Lower Riverbank formation
181
Figure 50 Distribution of factor 2 scores among Quaternary geology
Figure 51 Residual matrix bar chart
Zn
_
pp
m
pm
m
S
W
_p
Cr
_p
p
pp
m
pp
m
16
U
Ti
_
m
In
Ta
_
Mn
Sn
_p
p
S_
pp
m
m
pm
Pb
_p
p
i_
p
m
pm
14
N
a_
p
K
N
pm
n_
pp
Ce
M
Ge
Li
_p
8
K_
pp
m
f_
pp
m
pp
m
pm
m
12
H
G
a_
u_
p
18
C
pm
pm
m
r_
pp
e_
p
a_
p
10
C
C
C
m
pp
m
Be
_p
p
As
_
Ag
_p
p
182
Frequency above 0.08
Ti
Na
V
Sb
6
4
2
0
183
APPENDIX C
The Geochemical Procedure for Ultra-Trace Level Using ICP-AES and ICP-MS
184
185
186
187
188
189
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