Thesis for the Master’s degree in chemistry
Mahsa Haei
Trace Metals in Forest Soils in Southwestern China
60 study points
DEPARTMENT OF CHEMISTRY Faculty of mathematics and natural sciences UNIVERSITY OF OSLO 11/2006
2
Trace Metals in Forest Soils in
Southwestern China
Thesis for the Master’s degree in Chemistry
Mahsa Haei
Department of Chemistry
Faculty of Mathematics and Natural Sciences
University of Oslo
November 2006
3
Acknowledgment
This Master thesis has been carried out at the Department of Chemistry, University of Oslo,
since autumn 2004.
First and foremost, I would like to express my deepest gratitude to my supervisors Professor
Hans Martin Seip and Associate professors Thorjørn Larssen and Grethe Wibetoe for their
generous contribution of knowledge and experience, valuable comments and unique patience. I
am also grateful for the support and encouragement I got in attending the 15th Seminar on
Hydrology and Environmental Geochemistry in Trondheim, February 2006; the 7th
International Symposium on Environmental Geochemistry, Beijing, September 2006; and the
4th International Conference of Young Chemists in Pultusk, Poland, October 2006.
I would specially like to thank Professor Rolf D. Vogt for his supportive attitude and fruitful
discussions. Senior Engineer Anne-Marie Skramstad is appreciated for her great help in fixing
the instruments. Special thanks go to Dr. Alemayehu Asfaw for his assistance with the use of
ICP-MS.
I am grateful to all the former and present members of the Environmental Chemistry Group,
especially Marianne, Kine, Polina and Therese for the useful discussions and social activities
we had together.
My profound gratitude goes to the Universal House of Justice, the Baha’i World Centre, for
their generous financial support of my master studies. The National Spiritual Assembly of the
Baha’is of Norway, especially secretary Britt Strandlie Thoresen, are heartily appreciated for
their kind efforts regarding practical issues of attendance at the University of Oslo.
My wholehearted love goes to my family for their lasting support and encouragement, even
over long distances. Fereshteh, Neda, Alex, Meetali and all my dear friends have likewise
offered great camaraderie and support.
Oslo, 15 November 2006
Mahsa Haei
Table of contents Acknowledgment............................................................................................................... 3
Table of contents ............................................................................................................... 5
List of figures..................................................................................................................... 9
List of tables..................................................................................................................... 11
Symbols and Abbreviations ........................................................................................... 13
Abstract............................................................................................................................ 15
1 Introduction.................................................................................................................. 17
1.1 Trace metals ............................................................................................................ 17
1.1.1 Definition ......................................................................................................... 17
1.1.2 Trace metal release to the environment ........................................................... 17
1.1.3 Long range atmospheric transportation of trace metals ................................... 18
1.1.4 Trace metals in terrestrial ecosystems ............................................................. 19
1.2 Trace metal pollution in China ............................................................................... 21
1.2.1 History and background................................................................................... 21
1.2.2 Sources............................................................................................................. 22
1.2.3 Studies on trace metals..................................................................................... 23
1.3 Aim of this study..................................................................................................... 25
2 Theory ........................................................................................................................... 27
2.1 Physical- chemical forms of trace metals in the atmosphere .................................. 27
2.2 Behavior of trace metals in soils ............................................................................. 27
2.2.1 Soil composition .............................................................................................. 27
2.2.2 Soil profile ....................................................................................................... 28
2.2.3 Soil chemical parameters ................................................................................. 29
2.2.4 Adsorption of trace metals in soils................................................................... 31
2.3 Trace metals’ ecotoxicology ................................................................................... 32
2.3.1 Plants................................................................................................................ 32
2.3.2 Microbial biomass............................................................................................ 33
2.4 Theory for experimental methods used................................................................... 34
2.4.1 Closed vessel microwave digestion ................................................................. 34
6
2.4.2 Inductively Couples Plasma Atomic Emission Spectroscopy ......................... 35
(ICP-AES)................................................................................................................. 35
2.4.3 Inductively Coupled Plasma- Mass Spectrometry (ICP-MS).......................... 36
2.4.4 Direct Mercury Analyzer (DMA) .................................................................... 37
2.5 Principal Component Analysis (PCA) .................................................................... 38
3 Experimental Section................................................................................................... 39
3.1 Equipment and Instrumentation.............................................................................. 39
3.2 Reagents .................................................................................................................. 41
3.2.1 Chemicals......................................................................................................... 41
3.2.2 Gases ................................................................................................................ 42
3.2.3 Water qualities ................................................................................................. 42
3.2.4 Single and multi-element standard solutions ................................................... 43
3.3 Samples ................................................................................................................... 43
3.3.1 Site description................................................................................................. 43
3.3.2 Soil samples ..................................................................................................... 45
3.3.3 Soil reference materials.................................................................................... 48
3.4 Procedures............................................................................................................... 48
3.4.1 Pre-cleaning ..................................................................................................... 48
3.4.2 Sample preparation .......................................................................................... 50
3.4.3 Analyses........................................................................................................... 52
3.5 Quality control ........................................................................................................ 56
3.5.1 Method validation ............................................................................................ 56
3.5.2 Operation conditions and instrumental drift .................................................... 57
3.6 Statistical methods .................................................................................................. 58
4 Results and Discussion................................................................................................. 59
4.1 Development and validation of the methods .......................................................... 59
4.1.1 Digestion procedures ....................................................................................... 59
4.1.2 Adaptation of analytical methods .................................................................... 60
4.1.3 Accuracy and precision.................................................................................... 60
4.1.4 Limits of detection ........................................................................................... 65
4.2 Operation conditions and instrumental drift ........................................................... 67
7
4.2.1 Sample references and control solutions.......................................................... 67
4.2.2 Memory effect.................................................................................................. 69
4.3 Data analyses .......................................................................................................... 69
4.3.1 Concentrations assessed in relation to the background and standard values ... 69
4.3.2 Comparison with similar studies...................................................................... 79
4.3.3 Comparison among the studied sites................................................................ 81
4.3.4 Variations among and within macroplots ........................................................ 81
4.3.5 Principal Component Analysis (PCA) ............................................................. 85
4.3.6 Assessment of the trace metals behavior in the soil profile............................. 92
5 Conclusion and Further work..................................................................................... 97
References........................................................................................................................ 99
List of Appendices......................................................................................................... 107
Appendices..................................................................................................................... 109
List of figures Figure 1.1 China primary energy consumption, 1980-2004 ………………... 21
Figure 1.2 Primary Chinese energy sources in 2003 ……………………….. 22
Figure 1.3 Map of China with province borders and places mentioned in the
text ……………………………………………………………….
24
Figure 2.1 Different horizons in a podzol profile …………………………... 28
Figure 2.2 Examples of isomorphic substitution in the silicate lattice ……... 30
Figure 2.3 Major components of a typical ICP-AES ……………………….. 35
Figure 2.4 Major components of a typical ICP-MS ………………………... 36
Figure 2.5 Schematic drawing of DMA-80 ………………………………… 37
Figure 3.1 MM mixer mill, 10-mL grinding cups and 12-mm beads ………. 39
Figure 3.2 Milestone 1600 microwave oven ……………………………….. 40
Figure 3.3 Accessories of Milestone ETHOS 1600 microwave oven ……… 40
Figure 3.4 Varian Vista AX CCD ICP-AES ……………………………….. 40
Figure 3.5 Perkin-Elmer ICP-MS …………………………………………... 40
Figure 3.6 Direct Mercury Analyzer (DMA-80) …………………………… 41
Figure 3.7 Nickel and quartz sampling boats used in DMA-80 …………… 41
Figure 3.8 Location of the sampling sites …………………………………... 44
Figure 3.9 Maps of the sampling sites ……………………………………… 47
Figure 3.10 Coning and quartering of the soil samples ……………………… 50
Figure 4.1 Trace metals recoveries for Montana Soil analyzed by ICP-AES 61
Figure 4.2a,b Trace metals recoveries for Montana- and San Joaquin Soil
analyzed by ICP-MS …………………………………………….
61-2
Figure 4.3 Hg recoveries for San Joaquin Soil analyzed by DMA-80 ……... 62
Figures 4.4a,b As distributions in the A- and B horizons at the studied sites,
background value and Chinese Class I standard ………………...
71
10
Figures 4.5a,b Cd distributions in the A- and B horizons at the studied sites,
background, Chinese Class I- and European standards …………
72
Figures 4.6a,b Co distributions in the A- and B horizons at the studied sites,
background value and European standard ……………………….
72
Figures 4.7a,b Cr distributions in the A- and B horizons at the studied sites,
Chinese Class I- and European standards ……………………….
73
Figures 4.8a,b Cu distributions in the A- and B horizons at the studied sites and
background value ………………………………………………..
74
Figures 4.9a,b Hg distributions in the A- and B horizons at the studied sites,
background, Chinese Class I- and European standards ………….
75
Figures 4.10a,b Mn distributions in the A- and B horizons at the studied sites and
background value ………………………………………………..
75
Figures 4.11a,b Ni distributions in the A- and B horizons at the studied sites …. 76
Figures 4.12a,b Pb distributions in the A- and B horizons at the studied sites,
background, Chinese Class I- and European standards ………….
77
Figures 4.13a,b V distributions in the A- and B horizons at the studied sites and
background value ………………………………………………..
77
Figures 4.14a,b Zn distributions in the A- and B horizons at the studied sites,
background, Chinese Class I- and European standards ………….
78
Figures 4.15a,b Fe distributions in the A- and B horizons at the studied sites …... 79
Figures 4.16a,b Sn distributions in the A- and B horizons at the studied sites …... 79
Figure 4.17 PCA; Scree Plot for the A horizon ……………………………… 86
Figure 4.18 Loading Plot of PC2 vs PC1 for the A horizon …………………. 89
Figure 4.19 Score Plot of PC2 vs PC1 for the A horizon ……………………. 89
Figure 4.20 PCA; Scree Plot for the B horizon ……………………………… 90
Figure 4.21 Loading Plot of PC2 vs PC1 for the B horizon …………………. 92
List of tables
Table 1.1 Percent reduction in emission and deposition of Cd, Hg and Pb in
Europe and contribution of various sources to deposition in
Europe ……………………………………………………………
19
Table 3.1 Precipitation amount, sulfur deposition and soil quality data in the
sampling sites ……………………………………………………...
45
Table 3.2 Number of samples taken from the selected macroplots in each
horizon (A and B) ………………………………………………….
46
Table 3.3 Microwave procedure used to clean the PFA vessels …………… 49
Table 3.4 Microwave programs tried to digest the soil samples …………….. 51
Table 3.5 ICP-AES instrumental parameters and operating conditions …… 52
Table 3.6 ICP-MS operating parameters …………………………………….. 54
Table 3.7 Technical specifications of DMA-80 ……………………………... 55
Table 3.8 Experimental parameters for Hg analysis ………………………… 55
Table 4.1 Results and remarks for different digestion trials ………………… 59
Table 4.2a ICP-AES analyses of trace metals in Montana Soil; final selected
wavelengths, RSD% and accuracy test data (based on
uncertainties) ………………………………………………………
63
Table 4.2b ICP-MS analyses of trace metals in Montana Soil; final selected
isotopes, RSD% and accuracy test data (based on uncertainties)
63
Table 4.2c ICP-MS analyses of trace metals in San Joaquin Soil; final
selected isotopes, RSD% and accuracy test data ICP (based on
uncertainties)
64
Table 4.2d Mercury analyses in San Joaquin Soil; RSD% and accuracy test
data (based on uncertainties) ………………………………………
64
Table 4.3 Limits of detection for ICP-AES analyses ………………………... 65
12
Table 4.4 Limits of detection for ICP-MS analyses …………………………. 66
Table 4.5 Limits of detection for DMA-80 analysis ………………………… 67
Table 4.6a Trace metal concentrations in the sample reference (LCG)
analyzed together with each series of the samples ………………...
68
Table 4.6b Trace metal concentrations in the sample reference (LGS)
analyzed together with each series of the samples ………………... 68
Table 4.7 Trace metal levels in China (present and previous study) and
similar studies in Europe (mean and range) ………………………. 80
Table 4.8 RSD% for the samples from the A and B horizons within each site 82
Table 4.9 RSD% for the samples from the A and B horizons within each
Macroplot ………………………………………………………….
84
Table 4.10 Eigenanalysis of the Correlation Matrix for A horizon …………… 85
Table 4.11 Loadings of the first five Principal Components for the A horizon . 87
Table 4.12 Eigenanalysis of the Correlation Matrix for B horizon …………… 90
Table 4.13 Loadings of the first four Principal Components for the B horizon . 91
Table 4.14 Quantification of the difference between metal concentrations in
the A and B horizons ………………………………………………
93
Symbols and Abbreviations ADP Adenosine diphosphate
Ag Silver
Ar Argon
As Arsenic
ATP Adenosine triphosphate
Be Beryllium
BS% Base saturation (%)
Cd Cadmium
CEC Cation Exchange Capacity
CLRTAP Convention on Long-Range Transboundary Air Pollution
C/N Carbon/Nitrogen
Co Cobalt
Cr Chromium
CRM Certified Reference Material
CRV Certified Reference Value
Cu Copper
DMA Direct Mercury Analyzer
Detr. V. Determined value
∆m Absolute difference between mean measured value and the certified value
Fe Iron
FGD Flue Gas Desulfurization
ha Hectare
HCl Hydrochloric acid
HClO4 Perchloric acid
HF Hydrofluoric acid
Hg Mercury
HNO3 Nitric acid
H2O2 Hydrogen peroxide
14
ICP-AES Inductively Coupled Plasma Atomic Emission Spectroscopy
ICP-forests International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests
ICP-MS Inductively Coupled Plasma Mass Spectrometry
LCG Liu Chong Guan
LGS Lei Gong Shan
LOD Limit Of Detection
LOI Loss On Ignition
MDL Method Detection Limit
Mn Manganese
Ni Nickel
NEPA National Environmental Protection Agency (China), now SEPA: State Environmental Protection Agency
Pb Lead
Pers. Comm. Personal Communication
PC Principal Component
PCA Principal Component Analysis
PFA perfluoroalkoxy resin
RSD Relative Standard Deviation
Sb Antimony
SD Standard Deviation
Se Selenium
-SH Sulphydryl
Sn Tin
std. Standard
Tl Thallium
TSP Tie Shan Ping
U∆ Expanded uncertainty
UNECE United Nations Economic Commission for Europe
V Vanadium
yr Year
Zn Zinc
Abstract
Industrial development in China has accelerated rapidly in the last few decades. This has
led to a range of environmental problems. Deposition of trace metals to forest ecosystems
via the atmosphere is a potential regional concern.
In this study, levels of trace metals (As, Cd, Co, Cr, Cu, Fe, Hg, Ni, Mn, Pb, Sn, V and
Zn) have been measured in the soil samples from A and B horizons in acid-sensitive
forest soils at 3 different sites in China. Two of these sites, Tie Shan Ping (TSP) and Liu
Chong Guan (LCG), are located close to the large cosmopolitan city of Chongqing and
the less developed city of Guiyang, respectively. The third site, Lei Gong Shan (LGS), is
situated in a remote region of Guizhou province with no large local emission source.
Total Hg concentrations have been determined using a Direct Mercury Analyzer. For the
other trace metals, total content of each element has been determined by Inductively
Coupled Plasma Mass Spectrometry after microwave digestion. With a few exceptions,
the metal concentrations can be characterized as moderate. Compared with the soil
environmental background values and Chinese Class I standards, Cd, Pb, Hg, and to some
extent As, show higher concentrations in the upper horizon; Cd, Pb and Hg with median
values 0.25, 47 and 0.28 mg kg-1; 0.17, 30 and 0.39 mg kg-1; and 0.42, 28 and 0.37 mg
kg-1 at TSP, LCG and LGS respectively, and As with median concentrations 14 and 16
mg kg-1 at TSP and LGS respectively. Average metal concentrations in this study are
generally similar to results available from studies in China and Europe. Significantly
higher concentration of Cd (0.59 mg kg-1) in a previous study in the same area may be
due to poor accuracy of the semi-quantitative analytical method used in the previous
study.
Metal concentrations are rather similar among the studied sites. High contents of Cd and
Pb at TSP and Pb and V (in a couple of samples) at LCG may be related to proximity to
the big cities Chongqing and Guiyang and large emission sources. At the remote site
16
(LGS), metal levels are fairly similar or even higher than at the other sites; this can be
mostly associated with higher metal content in the minerals (shale). In addition, there
may be some contributions from long-range transportation of trace metals and high
deposition flux due to high precipitation at this site.
Evaluation of variations among and within the macroplots (10 m x 10 m area) generally
shows the lowest variations for As and Cd with RSD% < 25% and the largest variations
for Fe, Mn and V with RSD% > 25%.
Investigation of the behavior of metals in the soil profile and association with soil
characteristics using statistical approaches (Principal Component Analysis and t-test)
indicates accumulation of Hg, Pb and Cd at TSP and Hg at LCG in the A horizons.
Accumulation of metals in the upper horizon can be related to high atmospheric
deposition due to proximity to urban areas. Since Pb and Hg are strongly complexed to
organic matter, their accumulation might be associated with higher organic content of A
horizons. However, the PCA shows only a weak correlation between Hg and Pb and soil
organic content. This could be affected by the significantly different depositions of these
metals among the studied sites which are not considered in the PCA. Accumulation of
Co, Mn and Ni in the B horizon is found at TSP and may be the result of mobilization at
low pH-values in the A horizon. At the remote site (LGS), most of the investigated metals
(As, Cd, Co, Cu, Ni, Pb, Sn and Zn) show higher concentrations in the A horizon than in
the B horizon. Except for Cd, the differences are rather small.
Increased mobilization of Cd due to acidification in combination with high Cd levels in
the studied area can be a potential concern as mobilization of Cd may lead to
ecotoxicological impacts.
1 Introduction
1.1 Trace metals
1.1.1 Definition
“Trace metals” or “heavy metals” are terms applied to a large group of elements which
are biologically and industrially important. Trace metals occur in concentrations of less
than 1% of dry mass (frequently below 0.01% or 100 mg kg-1) in the rocks of the earth’s
crust [Alloway, 1995c]. According to Phipps (1981) heavy metals are metals with atomic
density greater than 6 g cm-3 [Alloway, 1995b]. However, somewhat different definitions
can be found in different references; in Article 1 of the Protocol on Heavy Metals under
the Convention on Long-Range Transboundary Air Pollution, it has been stated that
“heavy metals means those metals or, in some cases, metalloids which are stable and
have a density greater than 4.5 g cm-3 and their compounds” [UNECE, 2004]. Heavy
metals are also defined as the elements with metallic properties and atomic number
greater than 20 [Lasat, 1999]. In the following the term “trace metals” is used.
1.1.2 Trace metal release to the environment
In recent decades, there has been growing concern about trace metal contamination of the
environment. Trace metals are released to the environment from natural as well as
anthropogenic sources. Natural inputs come from wind-blown dust, volcanic activities,
forest fires, sea-salt emissions and biogenic sources [Pacyna et al., 1991].
Trace metals are released from a wide range of anthropogenic sources, including fossil
fuel combustion, agricultural chemicals such as fungicides and fertilizers, waste disposal
Trace Metals in Forest Soils in Southwestern China
18
such as farm manure, sewage sludge and mine wastes, chemicals, electronical and
metallurgical industries, metalliferous mining and smelting and incidental accumulation
because of warfare and military trainings. Different sources can contribute to
environmental contamination in several forms; for example, trace metal content of coal or
petroleum can be emitted as part of air-borne particles or accumulate in residue ash which
may cause water and soil contamination. Trace metals from metallurgical industries can
also be emitted to the atmosphere as aerosols or dust or accumulate in liquid effluents or
solid wastes [Alloway, 1995d].
Anthropogenic emissions of trace metals may have significant temporal cycles; e.g. trace
metal emissions from heat production sources are highest during the winter and emissions
from electrical power production and road transport are lowest during the night
[Travnikov and Ilyin, 2005].
1.1.3 Long range atmospheric transportation of trace metals
The atmosphere is an important medium for transportation of trace metals and other air
pollutants. Most of the metals are usually present in air as aerosol particles with relatively
short residence times. However, it has been indicated that air pollutants, including trace
metals, can be transported via the atmosphere over long distances, up to several hundred
kilometers away from the emission sources [Alloway, 1995d]. As a consequence, natural
cycles of trace metals in the environment can be altered not only in the vicinity of
emission sources but also a few hundred kilometers from the sources [Pacyna et al.,
1991].
In 1998, The Protocol on Heavy Metals was signed under the Convention on Long-Range
Transboundary Air Pollution (CLRTAP) in Aarhus, Denmark. The aim of the protocol is
reduction of heavy metal emissions to the environment through restrictions on use and
emission. It targets the three metals Cd, Pb and Hg in the context of long range
atmospheric transport [UNECE, 2006].
Introduction
19
Emission and deposition of Cd, Hg and Pb have declined in Europe since 1990 [Ilyin and
Travnikov, 2003; Ilyin et al., 2005]. European anthropogenic sources as well as re-
emission, natural emissions and sources located outside the studied region (Europe)
contribute to the total deposition to the European countries [Ilyin et al., 2005]. Table 1.1
shows the amount of reduction in emission and deposition of Cd, Hg and Pb in Europe
and contribution of various sources to deposition.
Table 1.1. Percent reduction in emission and deposition of Cd, Hg and Pb in Europe and contribution of various sources to deposition in Europe Emission
reduction during 1990-2001 (%) 1
Deposition reduction during 1990-2003 (%) 2
Contribution of the European
anthropogenic sources in deposition in 2003
(%) 2
Contribution of re-emission, natural
emissions and sources located outside the
region in 2003 (%) 2
Cd 46 59 78 22
Hg 53 41 45 65
Pb 70 59 87 13 1) Ilyin and Travnikov, 2003 2) Ilyin et al., 2005
The decrease of Pb emission (Table 1.1) is mainly related to restrictions on leaded
gasoline usage in European countries [Ilyin and Travnikov, 2003]. Reduction in Cd
emission is associated with the broad implementation of emission control equipment in
Europe [Pacyna et al., 2002]. Introduction of flue gas desulfurization (FGD) in the
European power plants has mostly contributed to European emission decline of Hg. FGD
is used to remove sulfur dioxide as well as gaseous Hg [Pacyna et al., 2002]. Hg exists
mainly in gaseous form and can be transported in the atmosphere all around the world, to
a much greater extent than Cd and Pb. Therefore Hg pollution in Europe is strongly
affected by the emission sources outside Europe [Ilyin et al., 2005].
1.1.4 Trace metals in terrestrial ecosystems
The elemental composition of soil generally reflects the local geology and
geomorphology, but surface soils may be considerably influenced by atmospheric metal
Trace Metals in Forest Soils in Southwestern China
20
deposition. Hence, the soil may be both a source and a sink of trace metals; the
concentration of trace metals inherited from soil parent materials is modified by natural
as well as anthropogenic inputs [Steinnes et al., 1997].
Forest soils are normally not exposed to direct discharges of pollutants and the main
pollution pathway is via atmospheric dry- and wet deposition [Bowen, 1979]. Trace
metals from the atmospheric input typically react with the functional groups on the
surface of soil particles. They may form surface complexes and accumulate in the soil
[Alloway, 1995c].
In general, the mobility and bioavailability of a large number of trace metals increase
under acidic conditions. Particularly the more mobile trace metals such as Cd and Zn can
be easily taken up by plants, microbial biomass and soil fauna; this may also lead to
increasing surface water and ground water pollution [Rademacher, 2001]. Less mobile
trace metals such as Cu and Pb are more strongly complexed with organic matter and
may accumulate in top soil [Berthelsen and Steinnes, 1995 and references therein].
Some of the trace metals, such as Co, Cu, Mn and Zn are essential in low concentrations
for the normal healthy growth of animals and plants, but excess amounts of all trace
elements are toxic to living organisms [Alloway, 1995b]. In plants, various biochemical
and physiological symptoms of trace metal toxicity have been observed, including
reduced growth of roots and shoots, decreased enzymatic activity and declined nutrient
concentrations in foliar tissues [Balsberg Påhlsson, 1989]. High content of trace metals
can also damage microorganisms and disturb the microbial soil processes such as litter
decomposition and soil respiration [Bååth, 1989].
Agricultural soils may be contaminated from irrigation and use of fertilizers. This may
lead to ecotoxicological and possibly human toxicological impacts that occur as a result
of crop consumption or ground water use for drinking. Blood disorder and effects on
liver, kidneys and nervous system are some of the trace metal effects on human health
[Bringmark and Lundin, 2004]. High concentration of trace metals in agricultural soils
Introduction
21
affects the diversity and abundance of soil fauna such as nematodes and earth worms or
damages the essential organs of terrestrial fauna such as birds and mammals [de Vries et
al., 2002 and references therein].
1.2 Trace metal pollution in China
1.2.1 History and background
Economic growth and industrial development in China have accelerated rapidly during
the last decades. This has led to a range of environmental problems, including severe
pollution of water and air. Regarding air pollution, most attention in China has been on
local urban problems, particularly related to human health [Aunan et al., 2006]. In
addition, acid rain and possible impacts on regional scale have received increasing
attention. In southern and southwestern China sulfur deposition levels have exceeded the
highest observed levels in Europe and North America [Larssen et al., 2006]. Long range
transported trace metals, however, has so far received little attention in China and few
studies exist. It is reported, however, that trace metal pollution has become a serious
problem as a consequence of industrialization and expanded energy consumption also in
China (see figure 1.1) [Cheng, 2003 and references therein].
Figure 1.1. China primary energy consumption, 1980-2004 [EIA, 2006]
0.000
10.000
20.000
30.000
40.000
50.000
60.000
70.000
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
Year
Ene
rgy
cons
umpt
ion
(Qua
drill
ion
Btu
)
Trace Metals in Forest Soils in Southwestern China
22
1.2.2 Sources
Industrial emission is the main source of trace metal pollution in China. Development of
mining, metal smelting and metallurgical industries have increased the atmospheric trace
metal concentrations [Cheng, 2003 and references therein]. Burning of coal and oil for
energy production (see Figure 1.2) are other sources of atmospheric pollution by trace
metals particularly in the cities [Li et al., 2000]. Consumption of leaded gasoline resulted
in increased trace metal levels in the atmosphere especially in the big cities [Cheng,
2003]. Since 2000, however, the use of leaded gasoline for cars has been completely
phased out in the whole country [Tang Dagang, Pers. Comm., 2006]. In some cases,
natural and meteorological factors such as dust-sand storm and local winds affect the
atmospheric concentration of trace metals [Fan, 1999 and Zhang and Gao, 2003].
Figure 1.2. Primary Chinese energy sources in 2003 [BP, 2005]
Natural gas
3% Oil 23%
Coal 68%
Nuclear 1%
Hydroelectric 5%
Introduction
23
The main sources of soil and crop pollution by trace metals are waste water, waste
residual and airborne trace metals from mining, coal burning, smelting, zincification,
stabilized compounds of dyes and plastics, colorants in oil paint and the tire
manufacturing industry [Guo, 1994 and Chen et al., 1999]. Irrigation of cultivated lands
with untreated waste waters has been common in China for a long time, especially in
areas with the lack of water resources such as northwestern China [Cheng, 2003 and
references therein]. Irrigation with waste water and usage of chemical fertilizers and
agrochemicals has also increased heavy metal levels in farmlands [He and Hu, 1991].
1.2.3 Studies on trace metals
Atmospheric as well as soil trace metal contents have been investigated in various
industrial and agricultural lands and a few forested areas in China. Some of the studies
are summarized here.
Studies in China have indicated that atmospheric trace metal levels in industrial areas are
higher than in rural and non-industrial regions. Xu and Yang (1995) have shown that
atmospheric cadmium concentration in rural areas is usually less than 1.0 pg L-1 but it
reaches up to 100 pg L-1 in some urban areas. A dust study in Zhuzhou City, Hunan
province (see Figure 1.3) has shown particularly high content of As, Cd, Cu, Hg, Pb and
Zn in atmospheric particulate matter [Haiyan and Stuanes, 2003]. Analysis of aerosol
samples from northern and southern regions of Nanling Mountains in South China
indicates that the elevated levels of Pb can be related to long-range transportation from
northern China. The same study also reports severe trace metal contamination in the
Pearl River Delta in Guangdong province (see Figure 1.3) [Lee et al., 2005]. Xie and
coworkers (2006) found high concentrations of As in aerosols in the heavy industrialized
city Taiyuan in Shanxi Province (See Figure 1.3).
High levels of atmospheric trace metals have been reported in big cities such as Shanghai
[Zhou et al., 1994a] and Chongqing [Chen et al., 1997] (See Figure 1.3). Yang et al.
Trace Metals in Forest Soils in Southwestern China
24
(1987) studied the atmospheric Pb pollution in Tianjing in eastern China and showed that
leaded gasoline had been an important source at that time.
Increased levels of Cr, Fe, Mn and Zn under dust-sand storm conditions were reported in
a case study in northwestern China, indicating the importance of natural sources for these
metals [Fan et al., 1999].
Figure 1.3. Map of China with province borders and places mentioned in the text
Atmospheric trace metals will necessarily end up on the ground through wet- or dry
deposition. Zhang (2001) has studied trace metals in the atmosphere and their
accumulation in soils in Taiyuan city and reported that Cd, Hg and Pb inputs to the soil
were 5.8, 4.5 and 347 g ha-1 yr-1, respectively. Wong et al. (2003) has studied the trace
metal deposition in the Pearl River Delta (PRD) located in the southern part of
Guangdong Province (Figure 1.3) and made a comparison with data from the Great Lakes
in North America and the North Sea in Europe, surrounded by urban and industrial areas;
elevated atmospheric deposition of Cu, Cr and Zn and significantly higher atmospheric
1 1000 km
Introduction
25
Pb loading have been reported in the PRD. However, the annual atmospheric deposition
of Pb was comparable to the data from N. W. Indiana and W. Pennsylvania (1975-1980)
while leaded petrol was still in use in the United states.
Most studies of trace metals in soils in China have been carried out in areas close to
pollution sources such as sewage irrigated and fertilized agricultural soils [Cheng, 2003
and references therein], refinery areas and close to highways [Zhang et al., 1999]. Very
little is known about the trace metal contents in forest soils. In a pilot study, Hansen et al.,
2001, determined the trace metal levels in four different forested areas in Southern China.
Investigation of As, Cd, Co, Cr, Cu, Ni, Pb, Sn, V and Zn indicated generally low
concentrations. Except for Cd, the trace metal concentrations were lower than Chinese
Class I Standards1.
1.3 Aim of this study
The rapid industrial development in China has caused a range of environmental problems.
Along with emissions of “conventional” air pollutants, such as sulfur and nitrogen oxides,
there is also a fast increase in emissions of trace metals to the atmosphere.
Little information exists regarding metal contamination of forest soils in China. A pilot
study was carried out by Hansen et al. (2001) but only a semi-quantitative analytical
method was used. In order to get an overview of the potential extent of the problem it is
necessary to conduct analyses of the metal content in different areas.
The aim of this study is to estimate the concentrations of a range of trace metals in
selected Chinese acid-sensitive forest soils with different topographies and depositions in
order to get an idea about potential concerns related to long-range atmospheric transport
of trace metals in China. Emphasis was on metals with ecotoxicological importance
1 The most strict soil standards in China based on the background values [Chen et al., 1999]
Trace Metals in Forest Soils in Southwestern China
26
such as As, Cd, Co, Cr, Cu, Hg2, Ni, Pb, Sn, V and Zn [Bringmark and Lundin, 2004]. In
addition, Fe and Mn concentrations were determined; Fe and Mn possess high
background levels and are not generally considered as soil pollutants, but their toxicity
may be a concern under certain conditions [Chen et al., 1999].
Further, we wanted to evaluate the data by comparing results among the studied sites,
with available background and standard levels as well as with values from similar studies
and to see how far the concentrations and variations could be explained on the basis of
soil characteristics.
2 Hg analysis was not included in the original plan. Since a Direct Mercury Analyzer became available, it was decided to include Hg analysis.
2 Theory
2.1 Physical- chemical forms of trace metals in the atmosphere
Trace metals such as As, Cd, Cr, Ni and Pb and their compounds are usually present in
the atmosphere associated with particles. Atmospheric particle sizes can be in the range 5
nm-20 µm, although a majority of atmospheric particles are often 0.1-10 µm in diameter
and have an average residence time in the atmosphere of 10-30 days [Bowen, 1979]. As,
mostly trivalent, is emitted both as gaseous species and associated with particulates.
Elemental Cd and its oxides are the predominant forms of this element in different
emissions [Pacyna et al., 1991]. Aerosols containing Cr (III) or Cr (VI) are generated
from specific industries. Ni species present in the atmospheric deposition probably
include sulphates and oxides [McGrath, 1995 and references therein]. Hg is emitted
mostly in gaseous elemental and oxidized forms with much longer residence time in the
atmosphere (1-2 years) [MSC-E, 2006]. Vehicles using leaded gasoline emit lead halides
primarily in gaseous form which condense to PbCl2, PbBr2 and PbClBr particles. It has
been shown that lead is usually emitted from smelters in the form of Pb and PbO. Tetra-
alkyl lead has also been observed in the gaseous phase in the atmosphere [Pacyna et al.,
1991].
2.2 Behavior of trace metals in soils
2.2.1 Soil composition
Soil is an essential substrate for growth of plants and degradation and recycling of
biomass. It is a complex heterogeneous medium which consists of organic and inorganic
(mineral) solids, aqueous and gaseous components. The organic matter includes the living
Trace Metals in Forest Soils in Southwestern China
28
organisms, dead plant material (litter) and humus which is the final product of
microorganisms’ activities on litter. The mineral soil comprises the weathering rock
fragment and secondary minerals such as phyllo-silicates or clay minerals, oxides of Al,
Mn and Fe and sometimes carbonates. Soil solid compartments come together to form
aggregates. Aggregates contain a system of interconnected pores which are filled with air
as well as water [Alloway, 1995c].
2.2.2 Soil profile
A soil profile, the unit of study in pedology1, is a vertical section between the soil surface
at the top and weathered bedrock at the bottom. According to the natural pedogenic
processes, a soil profile is divided into marked horizontal layers which are called
“horizons” [Alloway, 1999]. A typical soil profile is shown in figure 2.1:
1 Study of the origin, occurrence and classification of soils
Figure 2.1. Different horizons in a podzol profile
Theory
29
General distribution of metals in a soil profile (podzol) is as follows [Alloway, 1999]:
• Organic horizon (O horizon): Atmospheric deposits of metals and elements cycled
through plants accumulate in this horizon.
• Organo-mineral horizon (A horizon): Metals accumulate in this horizon after litter
humification.
• Eluvial horizon (E horizon): This horizon contains some of the metals which have
been adsorbed on clay particles and moved down the profile.
• Illuvial horizon (B horizon): The metals adsorbed on clay accumulate in this
horizon.
• Parent material (C horizon): This region comprises either the highly soluble
metals or those which have fallen down the cracks in association with particles.
2.2.3 Soil chemical parameters
pH: Soil pH is the most important physico-chemical parameter which affects the
behavior of ions in soil and plant growth. Mobility and bioavailability of a wide range of
divalent trace metals are higher under acidic conditions so they can be easily taken up by
plants [Alloway, 1999]; for example, various studies in Europe have shown increased
uptake of the more mobile trace metals (e. g. Zn and Cd) by plants for pH values less than
5.5-6 [Rademacher and references therein, 2001]. In addition, bacteria can not tolerate
very acidic conditions; microbial decomposition of organic matter in soil is usually
promoted at pH 6-8 [Alloway, 1999].
Cation Exchange Capacity (CEC): In general, this parameter refers to the maximum
negative surface charge of the soil and indicates the potential capacity of the soil to
exchange the cations with the equivalent amount in the soil solution. The negative surface
of soil could be the result of isomorphic substitution in the silicate lattice (Figure 2.2) or
deprotonation of inorganic and organic functional groups (Equation 1) [Essington, 2004].
Trace Metals in Forest Soils in Southwestern China
30
-X-OHs ↔ -X-O-s + H+
aq (1)
In general, the higher the CEC of soil, the greater the amount of metal a soil can accept
without potential hazards [Adriano, 1986].
Base Saturation (BS%): This parameter indicates the relative equivalent amount of base
cations on the ion exchanger (Equation 2):
“Base cations” is a term that is used for a group of cations associated with strong bases (i.
e. NaOH and KOH) which exchange with H+ on the ion exchanger such as Na+, K+, Ca2+,
Mg2+ and NH4+ [Appelo and Postma, 1993].
Redox conditions: Speciation of metals such as Ag, As, Cr, Cu, Fe, Hg and Pb can be
affected by changes in oxidation-reduction conditions. Redox conditions also reflect the
oxygen supply for soil microorganisms and plant roots. Alteration of redox conditions
and oxygen supply cause changes in products of organic material decomposition, e. g.
100CEC
cations base leexchangeab meq%BS ⋅= (2)
Figure 2.2. Examples of isomorphic substitution in the silicate lattice [Appelo and Postma, 1993]
Theory
31
under anoxic conditions microbial methylation takes place for some of the metallic and
metalloid pollutants such as As, Hg, Sb, Se and Tl (see equation 3). This mechanism is
very important for loss of these elements, especially Hg, from soil and formation of more
toxic and bioavailable species [Alloway, 1999].
Co
CH3
N
NNR
N
_
+ Hg 2+ + H2O Co
N
NNR
N
OH2
+ CH3 Hg+
(3) [Crosby, 1998]
2.2.4 Adsorption of trace metals in soils
The most important processes that affect the behavior and bioavailability of metals in
soils are metal adsorptions from soil solution to the solid phase. The main adsorption
processes are cation exchange (non-specific adsorption), specific adsorption, co-
precipitation and organic complexation [Alloway, 1995c].
Cation exchange (non-specific adsorption) is the adsorption of metal cations by
negatively charged surface of the soil (See 2-2-3 CEC). In this process, exchangeable
ions remain hydrated (Equation 4) and are held at soil surfaces through relatively weak
electrostatic and nonspecific interactions [Essington, 2004].
X-O-+M(H2O)4n+ X-O-····M(H2O)4
n+ (4)
Specific sorption is the metal cations exchange with surface ligands and formation of
inner sphere complexes with partly covalent bonds (Equation 5). This adsorption strongly
Trace Metals in Forest Soils in Southwestern China
32
depends on pH and metals hydrolysis. Specific adsorption takes place for the metals with
high tendency for hydroxyl complex formation [Alloway, 1995c].
-X-O-H + M(H2O)nz+ -X-O-M (z-1)+ + H++ n H2O (5)
Co-precipitation is simultaneous precipitation of an element in combination with the
other elements by any mechanism and at any rate [Alloway, 1995c].
Organic complexation is the adsorption of metals by the humic substances in the soil
solid phase to form chelate complexes. Low molecular weight organic ligands can form
soluble complexes with metals and prevent the adsorption or precipitation of metals
[Alloway, 1995c].
The mechanism and extent of metals adsorption to the soil depend on the metal’s
properties, the soil composition (organic matter content, clay minerals and Fe/Mn oxides)
and the soil pH [Alloway, 1999].
2.3 Trace metals’ ecotoxicology
2.3.1 Plants
Phytotoxicity is caused by excess amount of essential as well as non-essential metals in
plants. Relative toxicity of various metals in plants is different and depends on plant
genotype, pH level and clay and organic content of soil, but the most toxic metals for
higher plants are Cd, Co, Cu, Hg, Ni, Pb and probably Ag, Be and Sn [Alloway, 1995c
and references therein; Bååth, 1989].
Phytotoxicity occurs through various mechanisms, e.g. reaction of trace metals with
sulphydryl (-SH) groups, replacement by essential ions, competition for sites with
essential metabolites and reaction with phosphate groups of adenosine triphosphate
(ATP) and adenosine diphosphate (ADP) [Alloway, 1995c].
Theory
33
Toxic effects of trace metals include disturbance of enzymes and cellular membranes,
essential nutrient deficiency and root-functioning disorder [Ahonen and Finaly, 2001]. In
addition, trace metals can find their way into the plant cells and damage the whole
organism. Various investigations have shown that trace metal contents of more than 5-10
µg Cd/g, 15-20 µg Cu/g, 20-35 µg Pb/g and 200-300 µg Zn/g in plant tissues, specially in
leaves and roots, can cause damage to the whole plant [Rademacher, 2001 and references
therein].
General symptoms of trace metal toxicity are foliar damage, decreased root growth,
chlorosis, discolouration, decreased growth and stem changes [Ahonen and Finaly, 2001].
Plants are made more sensitive to other kinds of stress by trace metal toxicity, e.g. they
can be more easily damaged by insects or fungi [Rademacher, 2001 and references
therein].
2.3.2 Microbial biomass
Microorganisms and microbial soil processes are affected by high concentrations of trace
metals in soil; e.g. high levels will decrease litter decomposition, soil respiration rate and
enzymatic activity [Bååth, 1989]. Low enzymatic activity can be caused through
inactivation of enzymes by masking the active groups or protein denaturation,
competition with activating metal ions and decreased enzyme synthesis [Tyler et al.,
1989]. Nitrogen transformation processes are also affected by trace metal contamination;
nitrification shows the highest sensitivity to heavy metal toxicity. High content of trace
metals causes less nitrate accumulation in soil [Bååth, 1989]. Heavy metal toxicity causes
some changes in the abundance, species composition and diversity of the major groups of
organisms as well as particular species [Duxbury, 1986].
Trace Metals in Forest Soils in Southwestern China
34
2.4 Theory for experimental methods used
2.4.1 Closed vessel microwave digestion In order to analyze various heterogeneous samples with ICP-MS, they should usually be
transferred to homogeneous solutions. In closed vessel microwave digestion, a small
amount of sample is digested using various reagents such as mineral acids, under high
temperature and pressure. Digestion takes place in closed vessels which are transparent to
microwave energy and resistant to acid corrosion [Kingston and Walter, 1998].
The microwave energy is transferred to heat by the electric polarization and ionic
conduction, thus the liquid acid and the vessel in contact with acid is heated. After
increasing the temperature and exceeding the acid(s) boiling point, a large amount of
gaseous acid is produced. The gaseous acids which can not properly absorb the
microwave energy are condensed in contact with the cold vessel walls and release the
energy to the walls. In the next stage which is called “Sustained dynamic thermal non-
equilibrium”, evaporation and condensation of acids continue and the reaction
temperature sustains during the digestion [Kingston and Walter, 1998].
Closed vessel microwave digestion has several advantages; small amount of reagents is
used and the dissolution time is short. The reaction atmosphere is closed and controlled
so there is no contamination from the atmosphere and no loss of volatile elements.
Microwave vessels are usually made of Teflon or quartz, so they are appropriate for trace
analyses [Kingston and Walter, 1998].
Various reagents and mineral acids, usually mixtures, are used to digest various samples.
Nitric acid (HNO3) in concentrated form is a powerful oxidizing acid. It is the most
common acid to oxidize the organic matrices. Aqua regia, a 3+1 mixture of hydrochloric
(HCl) and nitric acid, produces strong oxidizing agents which dissolve even noble metals
that can not be dissolved by nitric or hydrochloric acid individually. Warm and
Theory
35
concentrated (60-72%) perchloric acid is a powerful oxidizing agent and easily
decomposes organic matter. Hydrofluoric acid (HF) is a non-oxidizing agent with high
complexing capacity. It is one of the few acids which dissolve silicates. Perchloric and
hydrofluoric acid are usually mixed with HNO3. Hydrogen peroxide (H2O2) reacts
explosively with many organics especially in its concentrated form. The oxidizing power
of hydrogen peroxide increases at lower pH [Kingston and Walter, 1998].
2.4.2 Inductively Couples Plasma Atomic Emission Spectroscopy
(ICP-AES)
In Inductively Couples Plasma-Atomic Emission Spectroscopy (ICP-AES), samples
mostly in the liquid form, are nebulized to aerosols and carried to the center of the plasma
by argon flow (Figure 2.3). In the high temperature plasma, desolvation, vaporization and
atomization of the aerosol occur. The processes are followed by excitation and ionization
of the atoms. Consequently the characteristic radiations emitted from the excited species
at several wavelengths are separated and converted to the electrical signals in the
spectrometer [Boss and Fredeen, 1999].
Figure 2.3. Major components of a typical ICP-AES
Sample solution
Ar gas
Nebulizer
Spray chamber
Torch
Spectrometer
Read out
Transfer optics
ICP
Trace Metals in Forest Soils in Southwestern China
36
2.4.3 Inductively Coupled Plasma- Mass Spectrometry (ICP-MS)
Inductively Coupled Plasma- Mass Spectrometry (ICP-MS) is a fast, multielement
method which is used to analyze various types of samples. ICP-MS with a wide dynamic
range and very low detection limits, even in ppt (part per trillion) concentration range, is
also capable to measure isotopic ratios [Thomas, 2004].
A sample, usually in liquid form, is pumped by a peristaltic pump to a nebulizer to be
converted to aerosols (Figure 2.4). Fine sample droplets are separated from the big ones
in a spray chamber and transferred to the torch together with argon (Ar) gas. In the torch,
argon gas is partly ionized and a high temperature plasma discharge is produced. The
plasma is at atmospheric pressure (760 Torr) and generates the positively charged ions of
the sample. Positive ions pass through the interface region that is at a vacuum of 1-2 Torr.
This region is located between two metallic cones which are called sampler and skimmer
cone, respectively. Positive ions from the interface region are focused towards the mass
separator via a series of ion lenses, while the photons and different species are removed.
Mass separator operates at approximately 10-6 Torr. It separates the analyte ions with a
particular mass to charge ratio and direct them to the ion detector which converts the ions
to electrical signals [Thomas, 2004].
Figure 2.4. Major components of a typical ICP-MS
Sampler cone
Sample solution
Ar gas
Spray chamber
Torch
Detector
Nebulizer
Skimmer cone
Ion lenses
Mass separator
In vacuum ICP
Theory
37
2.4.4 Direct Mercury Analyzer (DMA)
The Direct Mercury Analyzer used in this work (DMA-80, see Figure 2.5) is based on no
sample pretreatment. In this method, liquid or solid sample is dispensed to a sample boat.
The sample boat is thermally stable and made of non-amalgamating metals or metal
alloys. After the sample boat is inserted in a quartz decomposition tube, the sample will
be dried and thermally decomposed in an oxygen environment. The released mercury
vapor is transported to an amalgamator. The amalgamator is heated in a furnace and the
amalgamated mercury is released as elemental Hg. The mercury vapor passes through
two absorbing cells which are situated in the light path from a mercury vapor lamp and
maintained at about 120°C by a heating unit. To cover a wider concentration range cells
have different lengths; the long cell is appropriate for low concentrations and the short
one is suitable for higher concentrations. Mercury is quantified at 253.7 nm and the
absolute amount, usually in ng, is measured by the detector and the concentration is
calculated by the software [US-EPA, 1998]. The calculation of concentration is based on
the calibration curve.
Figure 2.5 Schematic drawing of DMA-80
Read outDetector
Cell heating block
Hg lamp
Furnace
Sample boat Amalgamator
Short cell
Long cell Oxygen flow
Drying and decomposition
furnace Catalyst furnace
Trace Metals in Forest Soils in Southwestern China
38
2.5 Principal Component Analysis (PCA)
A Principal Component Analysis results in an approximation to a given data matrix, e.g.
an n x p-dimensional matrix which consists of n objects and p variables characterizing the
objects. In this approach, a limited number of new variables are derived from the large
data matrix. These new variables called “Principal Components” are specifically
independent of each other. The number of principal components (PCs) is determined by
the smallest number of objects and variables. The first PC (with the largest eigenvalue)
explains the largest part of the variation in the dataset, the other PCs represent
successively smaller and smaller parts. Therefore, only the first few PCs need to be
considered, the later components can be neglected. The contribution of PCs to the total
variance is usually visualized in “Scree Plot” [Esbensen et al., 1994].
Each PC contains p coefficients which are called “loadings”. Loadings provide
information about the relationship between the original variables and the PCs. A “loading
plot” can be used not only to visualize the relation between the variables and PCs, but
also show the correlation between the variables. Results of the PCA can also be
visualized in a “score plot”. A score plot shows the correlation among the objects as well
as the relation between the objects and PCs [Esbensen et al., 1994].
3 Experimental Section
3.1 Equipment and Instrumentation
In order to wash and dry the equipment, a Miele Mielabor G 7783 Mutitronic washing
machine (Miele, Germany) operating with 5% HNO3 and type 2 water (see Section 3-2-3)
and a TS 8056 Termaks drying oven (Bergen, Norway) were used, respectively. The
Termaks oven was also used to dry the reference materials.
Samples were weighed by means of a Sartorius CP 224S (Goettingen, Germany) balance
with measurement precision of 0.1 mg.
Grinding of soil samples was carried out by means of an MM 2000 mixer mill (Retsch,
Haan, Germany) equipped with two 10-mL grinding cups and two 12-mm beads, all
made of zirconium oxide (Figure 3.1).
Figure 3.1. MM mixer mill, 10-mL grinding cups and
12-mm beads (Photo: Mahsa Haei, 2006)
Trace Metals in Forest Soils in Southwestern China
40
For the dissolution of soil samples, a Milestone ETHOS 1600 (Sorisole, Italy) microwave
digestion system was used together with 100-mL PFA Teflon vessels and HPR-1000/10 S
rotors (Figures 3.2 and 3.3).
The ICP-AES analysis was carried out using a Varian Vista AX CCD Simultaneous axial
view ICP-AES (Varian Ltd., Australia) equipped with a three channel peristaltic pump,
V-groove nebulizer, Sturman-Masters spray chamber and HF resistant torch (Figure 3.4).
Figure 3.2. Milestone 1600 microwave oven Figure 3.3. Accessories of Milestone ETHOS (Photo: Mahsa Haei, 2006) 1600 microwave oven (Photo: Mahsa Haei, 2006)
Figure 3.4. Varian Vista AX CCD ICP-AES Figure 3.5. Perkin-Elmer ICP-MS (Photo: Mahsa Haei, 2006) (Photo: Mahsa Haei, 2006)
Experimental Section 41
ICP-MS analysis was performed using a Perkin-Elmer ICP-MS (Norwalk, CT, USA)
equipped with HF resistant torch, platinum or nickel sampler and skimmer cones, a
Gilson Minipuls 3 peristaltic pump, an HF resistant, cross flow nebulizer, a double pass
spray chamber, an IBM PS/2 77 486 DX2 computer and Elan 5000 software (XENIX
Platform) (Figure 3.5).
A Milestone DMA-80 Direct Mercury Analyzer (Sorisole, Italy) was used to determine
the mercury content. It was equipped with nickel and quartz sample boats (Figures 3.6
and 3.7).
Figure 3.6. DMA-80 Figure 3.7. Nickel and quartz sampling (Photo: Mahsa Haei, 2006) boats used in DMA-80 (Photo: Mahsa Haei, 2006)
3.2 Reagents
3.2.1 Chemicals Several chemicals with the following qualities were used in different stages of the
experimental work:
• Hydrochloric acid (HCl), 30%, Suprapur® (Merck KGaA, Darmstadt,
Germany)
• Hydrofluoric acid (HF), 40%, Suprapur® (Merck KGaA, Darmstadt,
Germany)
Trace Metals in Forest Soils in Southwestern China
42
• Hydrofluoric acid (HF), 40%, Pro analysi (Merck KGaA, Darmstadt,
Germany)1
• Hydrogen peroxide (H2O2) solution, TraceSelectUltra (Fluka Chemie, Buchs,
Switzerland)
• Hydrogen peroxide (H2O2), 35%, Purum p. a. (Fluka Chemie, Buchs,
Switzerland)1
• Nitric acid (HNO3), 65%, Suprapur® (Merck KGaA, Darmstadt, Germany)
• Nitric acid (HNO3), 65%, Pro analysi (Merck KGaA, Darmstadt, Germany)1
• Perchloric acid (HClO4), 70%, Puriss p. a. (Fluka Chemie, Buchs,
Switzerland)
3.2.2 Gases Qualities of the gases used in the instruments were as follows:
• The argon (Ar) gas used in the ICP-MS was “Argon 5.0” (99.999% Ar)
(AGA, Oslo, Norway)
• The oxygen (O2) gas in the Direct Mercury Analyzer was “Oxygen 4.5”
(99.995%) (Yara, Oslo, Norway)
3.2.3 Water qualities The following types of water were used:
• Type 1 water, resistance >18.0 MΩ cm (at 25 °C), Millipore Elix-5/Milli-Q
purification system (Millipore, Billerica, MA USA)
• Type 2 water, resistance > 1.0 MΩ cm (at 25 °C), Millipore Elix-10
(Millipore, Billerica, MA USA)
Unless otherwise stated, type 1 water was used.
1 Used in the Teflon vessels pre-cleaning procedure in the microwave oven
Experimental Section 43
3.2.4 Single and multi-element standard solutions Single and multi-element standard solutions used to make the inter-calibration and
calibration solutions are listed in Appendix B.
3.3 Samples2
3.3.1 Site description
Soil sampling was carried out in several so called macroplots3 at 3 monitoring sites of the
IMPACTS4 project (see Figure 3.8). In IMPACTS project, 5 monitoring sites have been
extensively studied from the viewpoint of acidification. The monitoring sites are located
in the acid control zone in South and Southwest China (see map, Figure 3.8). All of the
sites are forested areas without limestone bedrock. In most of them, the soil parent
material is sedimentary rock such as sandstone and shale. The predominant soil type is
yellow soil according to the Chinese classification system or Acrisol according to the
FAO classification system [Vogt et al., 2006].
As a large part of Chinese forests, the sites were logged during “The Great Leap
Forward” between 1958 and 1962 and planted again in the 1960s. Both coniferous and
deciduous forests cover the monitoring areas with Masson pine (Pinus massoniana) and
Chinese fir (Cunninghamia lanceolata) as prevailing species.
The whole area has monsoonal climate with wet summers and dry winters. The dominant
wind direction is from the southwest in the summer and from the northeast in the winter.
The individual sites are described below and some important characteristics are given in
Table 3.1.
2 Unless otherwise stated, reference for the information in this part is Larssen et al., 2004 and references therein 3 A “macroplot” is a defined area (for example 10 m x 10 m or 30 m x 30 m) in the catchment where intensive investigation and sampling is carried out. In this study, the samples were taken from the central square (10 m x 10 m) in the 30 m x 30 m plots (see Figure 3.9). 4 Integrated Monitoring Program on Acidification of Chinese Terrestrial Systems [Larssen et al., 2006]
Trace Metals in Forest Soils in Southwestern China
44
Figure 3.8. Location of the sampling sites
Tie Shan Ping (TSP)
The Tie Shan Ping (TSP) site in Sichuan basin is located about 25 km north-east from the
centre of the large cosmopolitan Chongqing City (see figure 3.8). The TSP catchment
covers about 16 ha and is part of a national protected forest area. It has a subtropical,
humid climate with much fog and little frost and snow.
Liu Chong Guan (LCG)
The Liu Chong Guan (LCG) site in Guizhou province is located about 10 km north-east
from the less developed Guiyang City (See Figure 3.8). The LCG catchment covers about
7 ha and is part of a protected area in a botanical garden. It has an average of 220 cloudy
days per year.
1 1000 km
Experimental Section 45
Lei Gong Shan (LGS)
Lei Gong Shan (LGS) in Guizhou province is a small remote area which is located 40 km
south-east of Kaili City and 140 km east of Guiyang (See Figure 3.8). The catchment is
part of a nationally protected mountainous area with little human activity. The area is
about 6 ha and the elevation ranges from 1630 m to 1735 m a.s.l. The area is often foggy.
Table 3.1. Precipitation amount, sulfur deposition and soil quality data at the sampling sites1
Site Precipitation amount 3
mm yr-1
Sulfur deposition g m-2 yr-1
Bedrock Hor- izon
Depth limits3
cm
Soil pH
BS %
C/N CEC (meq/100g)
LOI 4 (W%)
TSP 1363 16.0 Sandstone A
B
2-5
30-60
3.5
3.8
26
9
20
12
12.1
3.4
22.8
3.7
LCG 851 4.2 Sandstone A
B
2-5
20-60
3.6
4.0
33
14
19
16
23.5
8.6
36.4
9.7
LGS 1788 2 1.8 Shale A
B
2.5-12
40-60
3.9
4.3
46
31
15
11
15.6
5.0
22.7
14.4 1) Unless otherwise stated, data are taken from Larssen et al., 2004 2) Precipitation has been measured in Kaili City which is located at a considerably lower altitude than the catchment at Lei Gong Shan. 3) Vogt et al., 2006 4) Loss On Ignition
3.3.2 Soil samples Soil sampling had been performed by Chinese colleagues prior to this study. In order to
limit the number of samples, a random selection of samples was conducted among the
macroplots in China. Therefore the available samples to be analyzed in Oslo were as
follows (see Table 3.2):
- Seven soil samples from the A and B horizons collected from four macroplots at
TSP.
- Four soil samples from the A and B horizons taken from four macroplots at LCG.
Each sample was obtained by lumping the samples from 5 locations in each
macroplot.
- Seventeen soil samples from the A and B horizons collected from four macroplots
at LGS.
Trace Metals in Forest Soils in Southwestern China
46
Table 3.2. Number of samples taken from the selected macroplots in each horizon (A and B) Sampling site Selected macroplots No. of samples
TSP Macroplot 1 2
Macroplot 4 1
Macroplot 5 2
Macroplot 6 2
LCG Macroplot 1 1 lumped sample
Macroplot 6 1 lumped sample
Macroplot 7 1 lumped sample
Macroplot 10 1 lumped sample
LGS Macroplot 1 5
Macroplot 2 4
Macroplot 4 4
Macroplot 8 4
Maps of the sampling sites and selected macroplots are given in Figure 3.9.
Experimental Section 47
← Tie Shan Ping (TSP)
↓ Lei Gong Shan (LGS) ↓ Liu Chon Guang
Figure 3.9. Maps of the sampling sites (The selected macroplots are specified by red circles) The marked plots on the maps are 30 x 30 m2 (divided to nine 10 x 10 m2 squares) and samples are taken from the central 10 x 10 m2 plots.
Trace Metals in Forest Soils in Southwestern China
48
3.3.3 Soil reference materials The following reference materials with certified values for most of the studied elements
were analyzed by ICP-MS to validate the method:
1. Standard Reference Material® 2710, Montana Soil, Highly Elevated Trace
Element Concentrations (National Institute of Standards and Technology,
Gaithersburg, MD, USA)
2. Standard Reference Material® 2709, San Joaquin Soil, Baseline Trace Element
Concentrations (National Institute of Standards and Technology, Gaithersburg,
MD, USA)
The first and second reference materials were respectively used to validate the ICP-AES
and DMA analyses.
Certificates of the reference materials are presented in Appendix H.
3.4 Procedures
3.4.1 Pre-cleaning Equipment
All glassware and polypropylene flasks were washed in the washing machine with
temperatures 85 and 60 °C, respectively and kept in clean plastic bags. The poly-
propylene volumetric flasks were filled with 5% HNO3 solution over night. Before use,
all the equipment were rinsed with type 2 and type 1 water, respectively. Glass flasks
used for sample storage were dried for 30 min at 110 °C in the drying oven after rinsing
with 5% HNO3 solution.
Experimental Section 49
Microwave vessels
The microwave procedure used to clean all the PFA vessels before running the digestion
procedure is given in Table 3.3:
Table 3.3. Microwave procedure used to clean the PFA vessels Reagents Microwave program
Name Volume
(mL) Ramping time (min)
Holding time (min)
Ventilation time (min)
Temperature (°C)
Power (watt)
HNO3
HF
H2O2
7
4
2
10
15
10
210
Up to 1000
After the microwave cleaning procedure, the vessels were rinsed with proper amount of
type 2 and 1 water, respectively.
In China, samples had been air dried and passed through a 2-mm stainless steel sieve. In
Oslo they were homogenized by coning and quartering; first the sample was mixed and
shaped as a cone and divided into quadrants, then the final amount of the sample was
achieved by removing the alternate quadrants and mixing the remainders (see Figure
3.10). The homogenized samples were subsequently ground by the mixer mill with
amplitude set to position 40. Each sample was ground for 6 minutes. The ground samples
were stored in the pre-cleaned glass flasks.
Trace Metals in Forest Soils in Southwestern China
50
Figure 3.10. Coning and quartering of the soil samples
3.4.2 Sample preparation
Soil samples were digested using the microwave system. Ground samples were weighed
on Ø110 mm filter papers (Schleicher & Schuell Gmblt, Germany) and transferred to pre-
cleaned PFA Teflon vessels and suitable amounts of reagents were added to them. The
capped vessels were placed inside the rotor bodies, sealed, tightened and submitted to
microwave digestion programs. Each series of digestion included a sample reference (see
Section 3-5-2) and a sample blank which was a pre-cleaned Teflon vessel containing the
same type and amount of the reagents. Several digestion procedures were tried to achieve
complete decomposition and clear solutions. The used microwave digestion programs are
summarized in Table 3.4.
1 2
3 4
Experimental Section 51
Table 3.4. Microwave programs tried to digest the soil samples Reagents Microwave program procedure Sample
size (g) Name Volume (mL)
Ramping time (min)
Holding time (min)
Ventilation time (min)
Temperature (°C)
Power (watt)
Procedure A1
0.25 HNO3 H2O2 HClO4
6 1 1
5 7 10 180 Up to
1000
Procedure B2
0.5 HNO3 HCl
3 9
10 15 10 200 Up to 1000
Procedure C3
0.3 HNO3 HF H2O2
7 4 2
10
20 15 200 Up to 1000
Modified procedure C
0.3 HNO3 HF H2O2
7 4 2
12 20 15 210 Up to 1000
1) From application note 151, Sea Sediment, for Milestone Ethos 1600 [Milestone, 2000] 2) From application note 031, Soils, for Milestone Ethos 1600 [Milestone, 2000]
3) [Nkoane et al., 2006]
The modified procedure C (increased temperature and ramping time compared to
procedure C) was used to digest all the soil samples.
After digestion, the vessels were cooled down in an ice bath for about one hour and the
digested samples were quantitatively transferred to polypropylene volumetric flasks and
diluted to the final volume which was mainly 50 mL. They were kept in the fridge until
analyses.
Trace Metals in Forest Soils in Southwestern China
52
3.4.3 Analyses ICP-AES
In a preliminary operation, the digested soil samples were analyzed by means of ICP-
AES. The instrument was calibrated using five matrix-matched calibration solutions5
ranging 0-2.5 mg L-1 for As, Cd and Ni and 0-20 mg L-1 for Pb, Zn, Cu and Fe. The
calibration solutions were made by dilution of several single-element standards (see
Appendix C.1). ICP-AES instrumental parameters and operating conditions are
summarized in table 3.5.
Table 3.5. ICP-AES instrumental parameters and operating conditions RF power (kW) 1.0
Plasma Ar flow (L min-1) 15
Auxiliary Ar flow (L min-1) 1.5
Nebulizer Ar flow (L min-1) 0.75
Pump rate (rpm) 15
Flow rate of the pump (mL min-1) 1.0
Replicate rate time (s) 1
Sample uptake delay (s) 30
Instrument stabilization delay (s) 15
Rinse time (s) 15
Background correction Fitted background correction
Replicate rate time (s) 1
Selected wavelengths (nm) As 188.980, As 193.696, As 197.1981, As 228.812, As 234.984
Cd 214.439, Cd 226.502, Cd 228.802
Cu 213.598, Cu 223.009, Cu 324.754, Cu 327.395
Fe 234.350, Fe 238.204, Fe 259.940
Ni 221.648, Ni 230.299, Ni 231.604
Pb 182.143, Pb 217.000, Pb 220.353, Pb 283.305
Zn 202.548, Zn 206.200, Zn 213.857
1) Wavelengths in bold are the selected ones in the final analysis
5 The calibration solutions were matched by the same amount of acids used to decompose the CRMs.
Experimental Section 53
ICP-MS
In order to make the calibration curve for each one of the selected elements, 7 calibration
solutions in the range 0-100 µg L-1 were prepared by dilution of single-element standards
in several steps. The calibration solutions were matrix matched by adding proper amounts
of HNO3 and HF and kept in the polypropylene volumetric flasks (see Appendix C.2).
The operating conditions affect the performance of an ICP-MS instrument. The plasma
operating conditions such as RF power, the nebulizer gas flow rate, the torch position and
the ion lens voltages of the instrument were daily optimized while introducing a 10 µg L-1
solution containing Ba, Cd, Ce, Cu, Ge, Mg, Pb, Rh, Sc, Tb and Tl in order to keep the
instrument specification. The cones were also inspected and cleaned if needed. ICP-MS
Operating parameters are listed in Table 3.6.
In the preliminary work, several isotopes were measured for elements with more than one
isotope (Table 3.6). After calibration, the analyte intensities of the “sample blank” and
sample solutions were measured by ICP-MS and the analyte values were corrected by
subtracting the blank values.
Three replicated of each reference materials were digested and analyzed by the same
procedure as the samples. To correct for the moisture content, all reference materials
were dried for 2 hours at 110 °C in the oven (see certificates in Appendix H).
Trace Metals in Forest Soils in Southwestern China
54
Table 3.6. ICP-MS operating parameters ICP-MS parameters
RF power (watt)
1000
Plasma Ar flow (L min-1) 15
Auxiliary Ar flow (L min-1)
Nebulizer Ar flow (L min-1)
1.0
0.81-0.91 (optimized daily)
Sample introduction
Nebulizer sample uptake rate (mL min-1)
1.0
MS data aquisition
Sweeps per reading 1
Points across peak 1
Replicate time (ms) 1000
Scanning mode Peak hopping
Signal measured mode Average
Dwell time (ms) 1000
Number of replicates
Reading per replicate
Isotopes measured 1
3
1 75As1
111Cd, 112 Cd, 114 Cd 53Cr, 52 Cr 208Pb 63Cu, 65 Cu 66Zn, 64 Zn 60 Ni, 58 Ni 59 Co 55 Mn (omni range2: 25) 56 Fe (omni range: 25), 57 Fe (omni range: 25), 51 V 116 Sn,117 Sn, 118 Sn
1) The isotopes in bold are the selected isotopes in the final analyses 2) See Appencix O for “Omni range”
Experimental Section 55
DMA
An empty sample boat and different amounts of Hg solutions were introduced to the
direct mercury analyzer as blank and standards, respectively. Two calibration curves were
obtained which covered the range of 0-503 ng Hg (see Appendix C.3). Technical
specifications of DMA-80 are summarized in Table 3.7.
Table 3.7. Technical specifications of DMA-80 [Milestone, 2002] Hg detection system Single beam spectrophotometer with
sequential flow through of measurement cell
Light source Low pressure Hg vapor lamp
Wavelength (nm) 253.65
Interference filter (nm) / bandwidth (mm) 254 / 9
Detector Silicon UV photodetector
Detection limit (ng Hg) 0.02
Low working range (ng Hg) 0-35
High working range (ng Hg) 35-600
The soil samples were analyzed in the solid phase using DMA to determine the mercury
content. The procedure for determination of Hg in soil SRM using a DMA [Milestone,
2003] was used and modified. The detailed experimental parameters are given in table
3.8.
Table 3.8. Experimental parameters for Hg analysis Sample size (mg) 68.0-85.0 (accurately weighed)
Drying temperature (°C) 160
Drying time (s) 10
Decomposition Temperature (°C) 850
Decomposition time (s) 150
Amalgamation time (s) 12
Waiting time (s) 60
Trace Metals in Forest Soils in Southwestern China
56
3.5 Quality control
3.5.1 Method validation Accuracy and precision
The ICP-AES-, ICP-MS- and DMA-methods were validated analyzing the reference
materials with the same procedures as the samples. The accuracy of the method was
evaluated comparing the mean measured results and certified values. The “recovery
percentage” (Equation 5) was defined as the indicator to check the accuracy:
Recovery% = (Average measured value / Certified value)*100% (5)
In another approach, both uncertainties of the measurement results and the certified
values were taken into account. In this approach, the expanded uncertainty (U∆), achieved
by combining the uncertainties, was compared to the absolute difference between mean
measured values and the certified values (∆m). In the cases that ∆m ≤ U∆, there is no
significant difference (on the 95% level) between the measurement result and the
certified value [Linsinger, 2005]. The formulas are presented in Appendix G.1.
The relative standard deviations of the measured values for several replicates of the
reference materials were used as the indicator to check the method precision in the
analyses using ICP-MS and DMA.
The formulas used to calculate the mean and absolute and relative standard deviations are
given in appendix D.
Limits of detection
In order to determine the detection limit (LOD) in the ICP-MS and ICP-AES analyses,
the calibration blank (0 µg L-1 calibration solution) was analyzed ten times.
In the case of Direct Mercury Analyzer, the detection limit was determined by analyzing
ten empty sample boats.
Experimental Section 57
3.5.2 Operation conditions and instrumental drift Sample references
Two soil samples were selected as “sample references” to control the digestion and ICP-
MS analyses processes. 5 replicates of each of these soils were digested and analyzed by
ICP-MS and the mean values were determined. Together with each series of samples, one
of the sample references was run through the same decomposition and analysis procedure
for quality control. Values in the soil references in the range of expected value ± 10%
were considered as satisfactory.
Control solutions
The 50 µg L-1 calibration solution used to make the calibration curve was analyzed as
“control solution” in the ICP-MS analyses. In order to check the calibration curve, the
control solution was analyzed after introducing the calibration solutions. It was also
analyzed after each 8th sample to check the instrumental drift.
A control solution containing 1mg L-1 mercury was made by dilution of 1000 mg L-1 Hg
stock solution (Appendix C.3.2). Various amounts of the control solution were analyzed
by means of DMA in order to check the calibration curve and instrumental drift.
Both instruments were recalibrated in those cases where the measured values of most of
the metals in the control solutions were out of the range of the expected values ±10%.
Memory effect
In the ICP-MS analyses, in order to avoid memory effect, especially from As, the system
was washed for 100 s with HNO3 (5%) before introducing each sample.6
6After some experience this was not found necessary and was therefore skipped in later analyses.
Trace Metals in Forest Soils in Southwestern China
58
The extreme memory effect of mercury was minimized by running empty sample boats
between the samples.
3.6 Statistical methods
A Principal Component Analysis (PCA) was conducted in order to get ideas about the
correlation among the studied metals and study the effects of soil characteristics on
metals behavior. The PCA was carried out using the software Minitab 14
by introducing a 12 x 17-dimensional matrix: twelve objects (macroplots) and seventeen
variables (metal concentrations and soil properties). The investigated soil properties were
pH, CEC, BS% and LOI. As the variables were measured on different scales, the
correlation matrix was used to standardize the variables. See appendix L for the data
matrix.
A manual F-test (see Appendix M) and a two sample t-test at 95% confidence interval
were carried out using Minitab software (see Appendix N). The ratio (median (or mean)
value for a trace metal in A horizon) to (median (or mean) value in B horizon) was used
to quantify the differences between the concentrations in the upper and lower soil
horizons [Rademacher, 2001]. This approach was applied to assess metals behavior in the
soil profile.
Results and Discussion
59
4 Results and Discussion
4.1 Development and validation of the methods
4.1.1 Digestion procedures Different trace metals have different affinities to various compartments of the soil. It was
therefore necessary to use a digestion procedure which led to complete decomposition of
the soil samples to determine the total trace metals content. After trying the procedures A
(HNO3, H2O2 and HClO4) and B (HNO3 and HCl) with incomplete decomposition,
procedure C containing HF was used. The oxidizing nature of HNO3 and H2O2 together
with the ability of HF to dissolve the silicates led to almost complete decomposition.
Modification of procedure C (see Section 3-4-2) prevented possible clogging of the tubes
in the ICP-MS as well as losing some sample and possible contaminations through
filtration. Therefore the modified procedure C was selected for digesting the soil samples.
Results and remarks of the different digestion procedures (Table 3.4) are summarized in Table 4.1. Table 4.1. Results and remarks for different digestion trials Procedure (reagents) Result and remark
Procedure A (HNO3, H2O2 and HClO4)
Incomplete decomposition and precipitate formation
Procedure B (HNO3 and HCl)
Incomplete decomposition and precipitate formation
Procedure C (HNO3, H2O2 and HF)
Nearly complete decomposition but some fine particles in the solution
Modified procedure C Complete decomposition and clear
solution
Trace Metals in Forest Soils in Southwestern China
60
4.1.2 Adaptation of analytical methods
Preliminary total metal determination using ICP-AES In the preliminary experiments, several wavelengths were used for each element (see
Table 3.5), but because of the interferences from the other components in the samples,
good results were not achieved for all the chosen wavelengths. The final wavelengths to
be used in the analyses of the real samples (Table 4.2a) were selected on the basis of
freedom from interferences and the best recoveries for the reference material.
Total metal determination using ICP-MS
The isotopes for the final analyses were selected on the basis of the best recoveries for the
reference materials (Table 4.2b and 4.2c).
Total mercury determination using DMA-80
The recommended mass of samples in the used application note was 85 mg [Milestone,
2003]. In the analyses conducted slightly less amounts (71-85 mg) of sample were used in
most cases since this was sufficient to give accurate results for the reference material, and
at the same time reduced the memory effect of mercury in the system
4.1.3 Accuracy and precision
Analysis of some selected trace metals in the reference material (Montana Soil) using
ICP-AES resulted in acceptable recoveries for most of them (Figure 4.1).1
1 The moisture content of the reference material was checked and no moisture content was observed.
Results and Discussion
61
As Cd Cu Fe Ni Pb Zn0
20
40
60
80
100
120
Rec
over
y%
Trace metal
Figure 4.1. Trace metals recoveries for Montana soil analyzed by ICP-AES
In the ICP-MS analyses, the recoveries were in the range of 80-105% and 78-101% for
Montana and San Joaquin Soils, respectively (Figures 4.2a and 4.2b), and hence indicated
acceptable accuracy.2
As Cd Co Cr Cu Fe Mn Ni Pb V Zn0
20
40
60
80
100
Rec
over
y%
Trace metal
2 In the case of San Joaquin Soil the moisture content was 3.3% and results were corrected according to the measured water content.
Figure 4.2a. Trace metals recoveries for Montana Soil analyzed by ICP-MS
Trace Metals in Forest Soils in Southwestern China
62
As Cd Co Cr Cu Fe Mn Ni Pb V Zn0
20
40
60
80
100
Rec
over
y%
Trace metal
Figure 4.2b. Trace metals recoveries for San Joaquin Soil analyzed by ICP-MS
DMA-80 analysis provided accurate and precise results with recoveries between 101.8%
and 103.6%. Figure 4.3 shows the recovery of Hg in the reference material (San Joaquin
Soil) for three runs.
10/5-2006 12/5-2006 14/5-20060
20
40
60
80
100
Rec
over
y%
Experiment date
Figure 4.3. Hg recoveries for San Joaquin Soil analyzed by DMA-80
Results and Discussion
63
Evaluation of the results for the CRMs on the basis of uncertainties in the measured and
certified values are presented in Tables 4.2a-d. See Section 3-5-1 and Appendix G for
more details.
Table 4.2a. ICP-AES analyses of trace metals in Montana Soil; Final selected wavelengths, RSD% and accuracy test data (based on uncertainties) Trace metal
Wavelengt (nm)
CRV± uncertainty1
(mg kg-1)
Detr. V.±SD2
(mg kg-1) RSD%3 ∆m4
(mg kg-1) U∆
5 (mg kg-1)
Remarks6
As 197.198 626±38 603±57 9 23 119 No significant difference
Cd 214.439 21.8±0.2 24.4±2.0 8 2.6 4.0 No significant difference
Cu 324.754 2950±130 2851±121 4 99 267 No significant difference
Cu 327.395 2950±130 2718±94 3 232 219 Significant difference
Fe 234.350 33800±1000 21869±695 3 11931 1638 Significant difference
Ni 231.604 14.3±1 15.9±2.5 16 1.6 5.1 No significant difference
Pb 283.305 5532±80 4953±211 4 579 428 Significant difference
Zn 206.200 6952±91 5510±385 7 1442 774 Significant difference 1) Mean Certified Reference Value±Uncertainty (n=9) 2) Determined value: The mean value of three measurements ±Standard deviation (n=3) 3) Relative standard deviation of three measurements (n=3) 4) Absolute difference between mean measured value and certified value : | CRV – Detr. V.| 5) U∆= 2*Combined uncertainty of result and certified value (see Appendix G) 6) 95% confidence level for significance test Table 4.2b. ICP-MS analyses of trace metals in Montana Soil; Final selected isotopes, RSD% and accuracy test data (based on uncertainties)1 Trace Metal
CRV± Uncertainty1
(mg kg-1)
Detr. V.±SD2
(mg kg-1)
RSD%3 ∆m4 (mg kg-1)
U∆5
(mg kg-1)
Remarks6
75 As 626±38 620±15 2 6 45 No significant difference 111 Cd 21.8±0.2 19.4±1.0 5 2.4 2.0 Significant difference 63 Cu 2950±130 3001±22 1 51 121 No significant difference 56 Fe 33800±1000 30140±2146 7 3660 4378 No significant difference 55 Mn 10100±400 10695±548 5 595 1149 No significant difference 60 Ni 14.3±1 11.6±0.4 3 2.7 1.2 Significant difference 208 Pb 5532±80 5826±224 4 294 453 No significant difference
51 V 76.6±2.3 71±1.2 2 5.6 3.1 Significant difference 66 Zn 6952±91 6731±211 3 221 429 No significant difference
1) For explanation of symbols and abbreviations, see footnotes in Table 4.2a.
Trace Metals in Forest Soils in Southwestern China
64
Table 4.2c. ICP-MS analyses of trace metals in San Joaquin Soil; Final selected isotopes, RSD% and accuracy test data (based on uncertainties)1
1) For explanation of symbols and abbreviations, see footnotes in Table 4.2a.
Table 4.2d. Mercury Analyses in San Joaquin Soil; RSD% and accuracy test data (based on uncertainties)1 Experiment Date
CRV± uncertainty1
(mg kg-1)
Detr. V.±SD2
(mg kg-1) RSD%3 ∆m4
(mg kg-1) U∆
5
(mg kg-1) Remarks6
10.mai 1.4±0.08 1.49±0.03 2 0.09 0.09 No significant difference
12.mai 1.4±0.08 1.49±0.01 1 0.09 0.07 Significant difference
14.mai 1.4±0.08 1.47±0.08 5 0.07 0.17 No significant difference 1) For explanation of symbols and abbreviations, see footnotes in Table 4.2a.
As the combined uncertainties of results and certified values [Tables 4.2a-d] are based on
the standard deviation of only three measurements, this may give unreasonably low SDs
in some cases [Linsinger, 2005].
Tables 4.2a-d show that the measurement result and the certified value are significantly
different in some cases. However, this is not of great concern if the recovery is good and
the relative standard deviation is low. In assessment of the concentration level and its
potential environmental impacts, the natural variation within a given geographical area
and hence the uncertainties related to the selection of sampling location is normally the
Trace Metal
CRV± Uncertainty1
(mg kg-1)
Detr. V.±SD2
(mg kg-1)
RSD%3 ∆m4
(mg kg-1)
U∆5
(mg kg-1)
Remarks6
75 As 17.7±0.8 17.9±1.0 6 0.2 2.1 No significant difference 111 Cd 0.38±0.01 0.34±0.03 8 0.04 0.06 No significant difference 59 Co 13.4±0.7 13.5±0.6 4 0.1 1.3 No significant difference 53 Cr 130±4 109±5 5 21 11 Significant difference 63 Cu 34.6±0.7 29.7±2.0 7 4.9 4.0 Significant difference 56 Fe 35000±1100 29360±985 3 5640 2188 Significant difference 55 Mn 538±17 423±18 4 115 39 Significant difference 60 Ni 88±5 73±5 7 15 11 Significant difference 208 Pb 18.9±0.5 17.2±0.5 3 1.7 1.1 Significant difference
51 V 112±5 109±3 3 3 7 No significant difference 66 Zn 106±3 98±8 7 8 16 No significant difference
Results and Discussion
65
largest source of uncertainty [Essington, 2004]. We also note that for the ICP-MS method
the difference was significant for both soil standards only for Ni (Tables 4.2b and 4.2c).
In the ICP-AES analyses, RSD% indicated good precision of the method, although the
value for Ni is relatively high at 15.7% (Table 4.2a).
In the ICP-MS analyses, the RSD% for 3 replicates of the reference materials were in the
range of 1-7% and 3-7.6% for Montana and San Joaquin Soil, respectively, for all the
elements included (Tables 4.2b and 4.2c) and hence showed satisfactory precision of the
analytical method.
Low values of RSD% (max. 5.3%) indicate satisfactory precision also for the Hg analyses
(Table 4.2d).
4.1.4 Limits of detection The calculated limits of detection for ICP-AES analyses are reported in Table 4.3.
Table 4.3. Limits of detection for ICP-AES analysis Trace metal Wavelength
(nm) LOD1 (mg L-1)
MDL2 (µg g-1)
As 197.198 0.58
9.7
Cd 214.439 0.01
0.2
Cu
324.754 0.07
1.2
Fe 327.395 0.04
0.7
Ni 234.350 0.03
0.5
Pb 231.604 0.11
1.8
Zn 283.305 0.06
1.1
1) Limit of Detection (see Appendix E) 2) Method detection Limit (see Appendix E)
Trace Metals in Forest Soils in Southwestern China
66
The limits of detection indicated that ICP-AES was not satisfactory for the selected trace
metals occurring in low concentrations.
Table 4.4. Limits of detection for ICP-MS analysis Trace metal LOD1
(µg L-1) MDL2 (ng g-1)
0.41 68 75 As 111 Cd 0.03 5 59 Co 0.02 3 53 Cr 0.46 77 63 Cu 0.09 15 56 Fe 18 1.3 x 103
55 Mn 0.28 47 60 Ni 0.61 103 208 Pb 0.03 5 117 Sn 0.20 33
51 V 0.06 11 66 Zn 17 2.9 x 103
1) Limit of Detection (see Appendix E) 2) Method Detection Limit (see Appendix E) Values for blank measurements are given in Appendix E.
Based on generally lower detection limits as well as more accurate results for a wide
range of the trace metals in the reference materials (Tables 4.4 and 4.2b and c), it was
decided to use ICP-MS in the analyses of the sample, on the basis of the ICP-MS
procedure used for the reference materials. There was no value for Sn in the used
reference materials certificates (see Appendix H). As acceptable results had been
obtained for the other elements, it was assumed that accurate results can be also obtained
for Sn and the isotope which provided the lowest detection limit ( 117 Sn) was selected for
the final analyses.
Results and Discussion
67
The limits of detection for DMA analysis are given in table 4.5.
Table 4.5. Limits of detection for DMA-80 analysis LOD1 (ng) MDL2 (ng g-1)
Hg 0.16 2.1 1) Limit of Detection (see Appendix E) 2) Method Detection Limit (see Appendix E)
See Appendix E for the individual results for the measurement of the empty sample boats.
4.2 Operation conditions and instrumental drift
4.2.1 Sample references and control solutions Expected values for the sample references analyzed by ICP-MS are given in Appendix I.
The results for two sample references (LCG and LGS) analyzed together with each series
of the samples are summarized in Table 4.6a and 4.6b. As mentioned in 3-5-2, values in
the range of expected values ± 10% are considered as satisfactory. The determined values
of the sample references were mostly satisfactory (Tables 4.6a and 4.6b).
The ICP-MS was recalibrated in the cases that the concentrations of most of the metals
were out of the range of expected value ± 10%. The DMA was stable during the whole
analyses period and recalibration was not needed (see Appendix J).
Trace Metals in Forest Soils in Southwestern China
68
Table 4.6a. Trace metal concentrations in the sample reference (LCG) analyzed together with each series of the samples
Detr. Value2
SD
(µg g-1)
Rec.%3
(µg g-1)
Trace metal
Exp. Value1 (µg g-1 )
No. 1 No. 2 No. 3 No. 4 No. 1 No. 2 No. 3 No. 4
RSD%
As 14.4 17.3 16.2 15.0 17.4 109 108 96 104 1.1 6.9
Cd 0.39 0.41 0.35 0.41 0.34 104 90 106 86 0.04 10.2
Co 3.6 4.2 3.8 3.3 3.7 117 104 92 104 0.4 9.6
Cr 47.9 52.1 52.2 44.6 48.6 109 109 93 101 3.6 7.4
Cu 17.7 19.4 19.3 17.1 18.5 109 108 96 104 1.1 5.7
Fe4 20.5 21.6 21.3 21.7 17.1 105 104 106 83 2.2 10.9
Mn 133 139.3 112.8 126.8 119.5 105 85 95 90 11.3 9.1
Ni 14.2 15.6 15.5 13.4 14.7 110 108 94 103 1.0 6.8
Sn 5 5.2 4.9 5.2 4.5 104 98 105 90 0.3 6.9
V 73.5 78.8 69.0 80.3 64.7 107 94 109 88 7.6 10.3
Zn 50 56.6 47.6 44.1 44.0 113 95 88 88 5.9 12.4
1) Expected value : Mean value of five replicates 2) Determined value 3) Recovery % = (Measured value/ Expected value)*100% 4) Concentrations for Fe are in mg g-1
Table 4.6b. Trace metal concentrations in the sample reference (LGS) analyzed together with each series of the samples1
1) For explanation of abbreviations and extra information, see footnotes in Table 4.6a.
Exp. value1
Detr. Value2
SD
(µg g-1 )
Rec.%3 Trace metal
(µg g-1 )
No. 1 No. 2 No. 3 No. 4 No. 1 No. 2 No. 3 No. 4
(µg g-1)
RSD%
As 17.0 18.8 19.6 15.3 18.9 111 115 90 111 2.6 14.9
Cd 0.39 0.39 0.37 0.34 0.39 100 96 87 99 0.04 10.5
Co 6.2 7.2 6.4 5.6 6.4 115 103 90 103 0.8 12.8
Cr 41.4 41.3 41.8 39.9 42.5 100 101 96 102 3.2 7.9
Cu 15.2 15.5 16.3 13.2 14.2 102 107 86 93 1.7 12.1
Fe4 23.4 24.4 23.5 21.0 21.0 104 100 90 89 2.2 10.3
Mn 760.7 810.2 733.8 682.5 773.4 106 96 90 102 63.4 8.7
Ni 13.1 14.5 13.5 11.0 12.8 110 103 84 97 1.8 14.3
Pb 32.6 31.6 31.4 34.5 34.2 97 96 106 105 1.4 4.3
Sn 2.9 4.1 3.7 3.1 3.7 143 130 106 129 0.5 14.0
V 52.5 56.7 53.3 47.1 51.9 108 101 90 99 5.1 10.1
Zn 58.6 54.3 25.7 36.1 52.6 93 44 61 90 11.9 28.3
Results and Discussion
69
4.2.2 Memory effect
In the ICP-MS analyses, later experiments showed that washing the system was not
needed in the concentration range of the metals in the samples and was therefore stopped.
Running empty boats between the samples resulted in peak heights (absorption) less than
0.008 in all of the DMA analyses. This amount was much less than the peak heights of
the samples.
4.3 Data analyses
The analyses of data start with comparison of the observed trace metal levels with
background and standard values (see below). Then the concentrations will be compared
with the result of the previous study at the same sites as well as similar studies in Europe.
In addition, a comparison of the trace metal levels will be performed among the studied
sites. Finally the variations between and within the macroplots will be carried out
followed by the comparison between trace metal contents in the A and B horizons.
Metal concentrations in the individual samples are given in Appendix K.4. The median
and mean concentrations in the A and B horizons at the studied sites are given in
Appendix K.5 together with standard- and background values.
4.3.1 Concentrations assessed in relation to the background and standard values Medians, means and distributions of the investigated trace metals in the A and B horizons
at the studied sites are shown in box and whisker plots in Figures 4.4a-4.16b. The box
and whisker plots are obtained based on the mean concentrations in four macroplots in
each site. Figures 4.4a-4.16b also show background values and soil standards for most of
the metals.
Trace Metals in Forest Soils in Southwestern China
70
The background ranges used to assess the trace metal levels were obtained from “Yellow
soil” in the Motuo Region situated on the South side of the Himalayas Mountains [NEPA,
1994]. This area is a pristine area with soils similar to those in our study. The available
background ranges are given an Appendix K.5 and the upper values are shown in Figures
4.4a-4.14b.
Two sets of available environmental standards were applied to assess potential harmful
levels of the studied trace metals:
1) Chinese Class I standards which are the most strict soil standards in China based
on the background values [Chen et al., 1999]
2) The lowest standard values for trace metals in soils from European decrees set to
minimize the risks of effects on soil organisms or higher plants [Rademacher,
2001]
In order to perform a risk assessment, the occurrence of higher values is rather more
important than the median values. Therefore the comparison with the background and
standard values was not only conducted for the median values at each site, but also for all
of the individual samples (Appendix K.4).
The median As value for LGS/A is clearly higher and for TSP/A slightly higher than
background values (Figure 4.4a). In the A horizon, four samples from macroplot 1 and all
of the samples from macroplots 2 and 8 at LGS, one sample from each one of the plots 1,
4 and 6 at TSP and also the mixed sample from plot 7/LCG show higher concentrations
than the background values. In the B horizon, just one sample from plot 1/TSP is higher
than the background value (Appendix K.4).
The median As concentration at LGS/A is higher than the Chinese standard (Figure 4.4a).
In the A horizon, all the samples from plots 2 and 8/LGS and two samples from plot
1/LGS have higher concentrations than the Chinese standard. Also one sample from plot
Results and Discussion
71
4/TSP has higher concentrations but all of the samples from LCG are below the standard.
In the B horizon values are all below the Chinese standard (Appendix K.4).
Compared with the background values, the median Cd concentrations are higher in the
studied sites (Figure 4.5a). Compared with the background values, all the LGS/A samples
have higher Cd values. In the A horizon at TSP, samples from plots 1 and 4 and one
sample at each one of plots 5 and 6 are also higher. In addition, A horizons at plots 1 and
7/LCG have also high values. In the B horizon, at LGS one sample in plot 1, all the
samples from plot 2, and two samples from plot 4 have higher Cd concentrations
(Appendix K.4).
The median Cd values for TSP/A and LGS/A are above the Chinese standard but all the
median values are below the European standard (Figures 4.5a,b). All of the samples from
LGS, five samples from four plots at TSP and two plots at LCG have higher values than
the Chinese standard. Among the samples from the B horizon, one sample from plot
2/LGS has almost the same value as the Chinese standard (Appendix K.4).
TSP LCG LGS
10.0
12.5
15.0
17.5
Con
cent
ratio
n (m
g kg
-1)
Site
TSP LCG LGS
7.5
10.0
12.5
15.0
17.5
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.4a . As distribution in the A horizon at the studied sites, background value (…) and Chinese Class I std. (—); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown
Figure 4.4b . As distribution in the B horizon at the studied sites, background value (…) and Chinese Class I std. (—); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown
Trace Metals in Forest Soils in Southwestern China
72
The median Co concentrations are lower than the background and European standard
values in both horizons at the studied sites (Figures 4.6a,b). Just one sample from the A
horizon at plot 2/LGS has higher Co concentration than the background value and
European standard (Appendix K.4).
TSP LCG LGS
0.1
0.2
0.3
0.4
0.5
Con
cent
ratio
n (m
g kg
-1)
SiteTSP LCG LGS
0.1
0.2
0.3
0.4
0.5
Con
cent
ratio
n (m
g kg
-1)
Site
TSP LCG LGS0
5
10
15
20
Con
cent
ratio
n (m
g kg
-1)
SiteTSP LCG LGS
0
5
10
15
20
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.6a . Co distribution in the A horizon at the studied sites, background value and European std. (- -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.6b . Co distribution in the B horizon at the studied sites, background value and European std. (- -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.5a . Cd distribution in the A horizon at the studied sites, background value (…), Chinese Class I std. (—) and European std. (- - -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.5b . Cd distribution in the B horizon at the studied sites, background value (…), Chinese Class I std. (—) and European std. (- - -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Results and Discussion
73
All the Cr concentrations are less than the highest background value (Figures 4.7a,b).
Median values of Cr at TSP/B and LCG/B are higher than European standards (Figure
4.7b). Samples from the A horizon at plot 4/TSP and plot 6/LCG, together with one
sample from the B horizon at plot 1/LGS, have higher Cr concentrations than European
standard (Appendix K.4).
For Cu, medians are less than the upper background limit (Figures 4.8a,b), but all the
samples from the A horizon at plot 2/LGS and two samples from plot 4/LGS are higher
than background values (Appendix K.4). None of the samples had higher Cu levels than
the standard values (Figures 4.8a,b). In the B horizon, Cu concentration in all of the
samples from plot 2/LGS is higher than the background value (Appendix K.4).
TSP LCG LGS
15
30
45
60
75
90
Con
cent
ratio
n (m
g kg
-1)
SiteTSP LCG LGS
15
30
45
60
75
90
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.7a . Cr distribution in the A horizon at the studied sites, Chinese Class I std. (—) and European std. (- -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown. Background value out of scale (118 mg kg-1).
Figure 4.7b . Cr distribution in the B horizon at the studied sites, Chinese Class I std. (—) and European std. (- -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown. Background value out of scale (118 mg kg-1).
Trace Metals in Forest Soils in Southwestern China
74
In the case of Hg, higher values are observed compared with the background and standard
values (Figures 4.9a,b) but it is not very easy to interpret the data in the same way as for
the other studied trace metals. As mentioned before, Hg analysis was not supposed to be
conducted in this project, but because of the opportunity to use DMA-80, the samples
were analyzed for the Hg content. The soil samples were air dried in China; this can lead
to Hg loss or contamination of the samples with Hg. Therefore, although we may
compare results obtained in this study, comparison with other studies and standards is not
reliable.
TSP LCG LGS
5
10
15
20
25
Con
cent
ratio
n (m
g kg
-1)
SiteTSP LCG LGS
5
10
15
20
25
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.8a. Cu distribution in the A horizon at the studied sites and background value (…); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown. Chinese Class I- and European stds. Out of scale (35 and 30 mg kg-1, respectively)
Figure 4.8b. Cu distribution in the B horizon at the studied sites and background value (…); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown. Chinese Class I- and European stds. Out of scale (35 and 30 mg kg-1, respectively)
Results and Discussion
75
For Mn, no standard value was available, but compared with the background values,
median concentration in the A horizon at LGS is higher (Figure 4.10a). In the A horizon
at LGS, one sample from plot 1, three samples from plot 2 and all of the samples from
plots 4 and 8 are higher than the background value (Appendix K.4). In the B horizon at
LGS, two samples in plot 2, two samples in plot 4 and one sample in plot 8 have higher
concentrations than the background values (Figures 4.10b).
TSP LCG LGS0.0
0.1
0.2
0.3
0.4
0.5C
once
ntra
tion
(mg
kg-1)
Site
TSP LCG LGS0.0
0.1
0.2
0.3
0.4
0.5
Con
cent
ratio
n (m
g kg
-1)
Site
TSP LCG LGS0
200
400
600
800
Con
cent
ratio
n (m
g kg
-1)
SiteTSP LCG LGS
0
200
400
600
800
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.9a. Hg distribution in the A horizon at the studied sites, background value and Chinese Class I std. (—) and European std. (- - -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.9b. Hg distribution in the B horizon at the studied sites, background value and Chinese Class I std. (—) and European std. (- - -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.10a . Mn distribution in the A horizon at the studied sites and background value (…); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.10b . Mn distribution in the B horizon at the studied sites and background value (…); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Trace Metals in Forest Soils in Southwestern China
76
Ni concentrations in all of the samples are considerably less than background and
standard values (Figures 4.11a,b).
In the case of Pb, samples from both horizons at LGS and A horizon samples at LCG and
TSP have higher median values than the background value (Figures 4.12a,b). In the A
horizon at LGS, three samples from plot 1, all of the samples from plots 2, 4 and 8 are
higher than background. Also all of the samples from TSP and the samples from plots 1
and 6/LCG are higher (Appendix k.4). In the B horizon, all the samples from plots 2 and
4/LGS and one sample from plot 8 are higher than background value. Also the sample
from plot 4/TSP and plot 6/ LCG are higher than background (Appendix K.4).
Not only the median Pb concentration in the A horizon at TSP is higher than the Chinese
and European standards (Figures 4.12a,b), but also all of the individual values at TSP/A
and one sample from the A horizon at plot 1/LCG are higher than the standards
(Appendix K.4).
TSP LCG LGS
5
10
15
20
25
Con
cent
ratio
n (m
g kg
-1)
SiteTSP LCG LGS
5
10
15
20
25
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.11a. Ni distribution in the A horizon at the studied sites; Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown. Background value, Chinese Class I and European stds. out of scale (>51, 40 and 35 mg kg-1, respectively).
Figure 4.11b. Ni distribution in the B horizon at the studied sites; Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown. Background value, Chinese Class I and European stds. out of scale (>51, 40 and 35 mg kg-1, respectively).
Results and Discussion
77
For V, no standard is available, but all of the median concentrations are less than the
background values (Figures 4.13a,b). Among the individual samples, the sample from the
A horizon at plot 1/LCG is higher than background value. In the B horizon, one sample
from plot 1/LGS, two samples from plot 2 and one sample from plot 8/LGS are also
higher (Appendix K.4).
TSP LCG LGS10
20
30
40
50
60C
once
ntra
tion
(mg
kg-1)
SiteTSP LCG LGS
10
20
30
40
50
60
Con
cent
ratio
n (m
g kg
-1)
Site
TSP LCG LGS
40
60
80
100
120
Con
cent
ratio
n (m
g kg
-1)
S iteTSP LCG LGS
40
60
80
100
120
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.12a . Pb distribution in the A horizon at the studied sites, background value (…), Chinese Class I std. (—) and European std. (- - -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.12b . Pb distribution in the B horizon at the studied sites, background value (…), Chinese Class I std. (—) and European std. (- - -); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.13a. V distribution in the A horizon at the studied sites and background value (…); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.13b. V distribution in the B horizon at the studied sites and background value (…); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Trace Metals in Forest Soils in Southwestern China
78
Compared with the background values, the median Zn concentrations are lower (Figures
4.14a,b). In the A horizon, three samples from plot 4/LGS and one sample from plot 8 are
higher than background value (Appendix K.4). Median Zn concentrations are also less
than the standards (figure 4.14a,b), but one of the samples from the A horizon at plot
4/LGS has higher concentration than both of the standards (Appendix K.4).
In case of Fe and Sn, no background and standard values were available (Figures 4.15a,b
and 4.16a,b).
In summary, the concentrations of the studied trace metals are usually below or close to
the standards. However, in the upper horizon, Cd, Pb and Hg tend to be higher than the
Chinese Class I standard.
TSP LCG LGS
20
40
60
80
100
Con
cent
ratio
n (m
g kg
-1)
S iteTSP LCG LGS
20
40
60
80
100
Con
cent
ratio
n (m
g kg
-1)
S ite
Figure 4.14a. Zn distribution in the A horizon at the studied sites, background value (…), Chinese Class I- and European stds. (—); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.14b. Zn distribution in the B horizon at the studied sites, background value (…), Chinese Class I- and European stds. (—); Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Results and Discussion
79
4.3.2 Comparison with similar studies The concentrations of the studied trace metals are compared with two sets of similar
studies in China and Europe (see Table 4.7):
TSP LCG LGS10000
20000
30000
40000
50000
Con
cent
ratio
n (m
g kg
-1)
Site
TSP LCG LGS1
2
3
4
5
6C
once
ntra
tion
(mg
kg-1)
SiteTSP LCG LGS
1
2
3
4
5
6
Con
cent
ratio
n (m
g kg
-1)
Site
TSP LCG LGS10000
20000
30000
40000
50000
Con
cent
ratio
n (m
g kg
-1)
Site
Figure 4.15a. Fe distribution in the A horizon at the studied sites; Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.15b. Fe distribution in the B horizon at the studied sites; Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.16a. Sn distribution in the A horizon at the studied sites; Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Figure 4.16b. Sn distribution in the B horizon at the studied sites; Box range (25-75th percentile), Whisker range (5-95th percentile); mean ( ) and median ( _ ) values are also shown.
Trace Metals in Forest Soils in Southwestern China
80
1) The previous study of trace metals at the same area by Hansen et al., 2001
2) The Intensive Monitoring Programme (Level II programme) in the ICP forest
monitoring system [Rademacher, 2001]
No large difference exists between the As levels of the Chinese acid sensitive sites in the
previous and present study. The mean Cd concentration in this study is much less than the
previous study and the similar studies in Europe (Table 4.7). It should be taken into
account that a semi-quantitative measurement was performed in the previous study. The
mean Ni concentration at the Chinese sites in this study is higher than in the last study at
the same area and slightly higher than the European mean. Mean Cu is somewhat higher
in our study than the European mean. In the case of Pb and Zn, no marked differences are
observed between the Chinese and European sites (Table 4.7). Soil is a heterogeneous
medium, therefore it is expected that the concentrations of the metals vary.
Table 4.7. Trace metal levels in China (present and previous study) and similar studies in Europe (mean and range) (mg kg -1) Trace China China Europe metal This study 1 Previous
study 2 ICP forest
(Level II)3
Mean Range Mean Range Mean Range As 11.9 7.4-19.6 15.7 2.8-98.4 d.n.a. 4 d.n.a.
Cd 0.18 0.04-0.24 0.59 0.47-1.01 0.56 0.10-4.40
Cu 13.9 4.4-25.5 19.1 8.5-36.9 8.7 1.0-738.0
Ni 13.8 4.1-23.0 5.5 0-22.3 9.3 1.0-55.0
Pb 30.2 15.2-77.2 38.3 19.0-97.4 29.0 1.0-271.0
Zn 38.4 16.9-105.2 38.4 9.3-73.0 31.0 1.0-682.0 1) Elemental content in A and B horizons 2) Hansen et al., 2001: Elemental content in A/O and B horizons 3) Rademacher, 2001: Elemental content in all humus and mineral soil horizons 4) Data not available
Results and Discussion
81
4.3.3 Comparison among the studied sites
According to proximity to cities and industries, higher trace metal deposition was
expected at TSP and LCG than at LGS, but for most of the investigated trace metals,
there are small differences in soil metal concentrations among the studied sites (Figures
4.4a-z). High content of the trace metals in the remote area of LGS can be mainly
explained on the basis of higher concentrations in the parent material. Shale has normally
higher metal content than sandstone [Alloway, 1995a and references therein]. In addition
there could be some contributions of long-range transportation of trace metals enhanced
by high amounts of precipitation [Rolf D. Vogt, Pers. Comm., 2006].
Much higher levels of Pb at TSP (Figure 4.4s) can be associated with the intensive
vehicle traffic in the metropolitan Chongqing City. In spite of the fact that leaded fuel has
been phased out since 2000, Pb can remain in soil over many years [Davies, 1995].
4.3.4 Variations among and within macroplots The relative standard deviation (RSD%) was used as an indicator to characterize the
variations among the individual samples in each macroplot and among the macroplots. It
is not possible to give an objective estimation of expected natural variations, but values
≥25% are considered as indicators of high variation. Thus, in this section RSD is used as
a tool to look at the variations in nature and not to assess the accuracy of the analytical
methods.
Variations among the macroplots
The RSD% in the A and B horizons of the studied sites are summarized in table 4.8. At
LGS, high variations are observed among the macroplots for Co, Hg, Mn and Zn in the
samples from the A horizon. In the B horizon, the concentrations of Cd, Co, Cu, Fe, Hg,
Mn, V and Zn vary markedly between the macroplots in each site (Table 4.8).
Trace Metals in Forest Soils in Southwestern China
82
At TSP, the measured values of Fe, Mn and V in the macroplots have large variations in
the A horizon. In the B horizon, the concentration of Mn varies markedly among the
macroplots (Table 4.8).
At LCG, except for As in the A horizon and As, Cd and Fe in the B horizon, all the
concentrations show high variations among the macroplots (Table 4.8).
Table 4.8. RSD% for the samples from the A and B horizons within each site LGS TSP LCG A horizon B horizon A horizon B horizon A horizon B horizon As 22.9 14.8 17.3 18.4 18.5 15.9
Cd 14.9 33.4 11.1 12.7 57.5 20.4
Co 48.7 42.6 11.3 21.0 85.2 67.1
Cr 4.1 4.9 7.4 16.3 34.7 30.0
Cu 20.7 26.5 9.8 13.4 49.2 46.0
Fe 5.5 36.3 30.1 17.0 39.6 23.0
Hg 28.2 45.0 10.4 24.1 29.5 37.8
Mn 25.8 37.1 25.6 29.9 58.8 43.3
Ni 19.8 15.4 13.3 18.1 52.0 41.3
Pb 10.4 17.6 22.1 7.2 46.3 28.9
Sn 3.8 10.8 14.5 19.5 35.1 28.3
V 3.2 31.7 27.2 21.6 41.2 30.7
Zn 43.9 27.8 11.4 17.3 40.5 23.9
Variations within the macroplots
Results for RDS% are shown in Table 4.9. In the case of LCG, it was not possible to
calculate the standard deviation because only one lumped sample from each macroplot
was available from China. For macroplot 4, TSP, there was just one sample and not
possible to check the standard deviation.
Results and Discussion
83
In the case of As, no large variations waere observed (RSD% = 5.3-16.7% in the A and
1.8-17.9% in the B horizon). For Cd, the variations among the samples were large in the
A horizon sample at macroplots 6, TSP (38.3%). In the case of Co, large variations were
observed among A and B horizon samples at macroplot 2, LGS (RSD% = 29.1 and
27.9%) and A horizon at macroplot 5, TSP (RSD% = 35.7%). In the case of Cr, Cu and
Sn, small variations were observed (RSD%= 1.3-20.8, 3.7-12.0 and 3.8-20.6%,
respectively) (Table 4.9). For Fe, variations are large among the samples from the A
horizon at macroplots 1 and 5, TSP (RSD% = 46.4 and 32.1%, respectively), and B
horizon at macroplots 1,2 and 8, LGS (RSD% = 61.2, 58.6 and 56.9%, respectively).
In the case of Hg, largest variations were observed in the A horizon at macroplot 4, LGS
and macroplot 5, TSP (RSD%= 36.1 and 32.2%). For Mn, large variations were observed
among the A horizon’s samples at macroplots 1 and 8, LGS (RSD% = 44.1 and 28.0%)
and macroplot 1, TSP (RSD% = 39.8%) and the B horizon’s samples from macroplots
1,2 and 8, LGS (RSD% = 53.7, 58.6 and 33.0%). Large variation among the measured Ni
concentrations were only observed in the samples from A horizon at macroplot 5, TSP
(RSD% = 35.0%). For Pb, large variation was only observed among the samples from the
A horizon at macroplot 1, TSP (RSD% = 27.9%). In the case of V, the largest variations
were found in macroplots 1,2 and 8, LGS (B horizon) (RSD% = 59.2, 58.0 and 58.5%)
and macroplot 1, TSP (A horizon) (RSD% = 47.4%). For Zn, there were large variations
in the A horizon at macroplots 1, 2 and 8, LGS (RSD% = 41.0, 33.5 and 54.5%) (Table
4.9).
In summary, As, Cd, Cr, Cu, Hg, Ni, Pb and Sn concentrations showed mainly small
variations while larger variations were found Co, Fe, Mn, V and Zn within the
macroplots.
Trace Metals in Forest Soils in Southwestern China
84
Table 4.9. RSD% for the samples from the A and B horizons within each macroplot As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn
LGS
Macroplot 1
A horizon (n=5) 16.7 24.8 14.8 7.7 5.0 4.4 14.0 44.1 16.5 13.2 12.2 4.6 41.0
B horizon (n=5) 12.9 24.6 17.3 20.8 12.0 61.2 12.8 53.7 19.4 8.7 18.2 59.2 24.6
Macroplot 2
A horizon (n=4) 7.5 7.3 29.1 12.5 6.6 7.9 6.0 4.8 14.9 6.1 5.3 9.8 33.5
B horizon (n=4) 8.0 17.1 27.9 13.8 6.9 58.6 7.9 58.6 21.9 10.0 3.8 58.0 13.2
Macroplot 4
A horizon (n=4) 6.5 12.6 15.4 8.7 7.5 6.8 36.1 5.5 12.2 3.5 20.6 5.2 54.5
B horizon (n=4) 3.1 43.9 9.4 5.4 11.1 8.3 24.4 11.4 10.0 6.2 5.0 8.2 17.4
Macroplot 8
A horizon (n=4) 5.3 16.0 7.3 2.4 4.5 3.0 3.6 28.0 4.7 9.8 11.1 5.2 54.5
B horizon (n=4) 13.1 7.7 9.4 15.3 6.3 56.9 4.5 33.0 3.8 10.0 2.8 58.5 11.5
TSP
Macroplot 1
A horizon (n=2) 10.9 0.8 7.8 1.3 7.4 46.4 4.5 39.8 4.7 27.9 4.7 47.4 5.8
B horizon (n=2) 13.9 2.3 16.0 6.1 11.1 9.8 7.1 8.7 11.1 6.2 8.2 11.4 11.3
Macroplot 5
A horizon (n=2) 8.8 22.9 35.7 19.0 10.5 32.1 32.2 21.1 35.0 0.3 6.4 20.0 19.6
B horizon (n=2) 17.9 14.9 21.5 10.2 3.7 17.7 20.8 8.4 23.1 4.2 10.1 17.3 18.7
Macroplot 6
A horizon (n=2) 12.0 38.3 10.2 11.6 11.0 1.6 19.4 8.7 7.9 8.5 11.9 0.3 16.9
B horizon (n=2) 1.8 5.6 12.4 8.1 5.0 7.8 13.1 15.9 6.2 3.8 7.5 10 7.7
Results and Discussion
85
4.3.5 Principal Component Analysis (PCA) A PCA was conducted in order to investigate the correlation among the studied metals as
well as the effects of soil characteristics on metal behavior.
PCA is described in Section 2-5. The objects are in our case the soil macroplots and the
variables metal concentrations or soil characteristics (see Appendix L).
A horizon
According to eigenvalues (Table 4.10), 44.1% and 24.8% of the total variability can be
represented by the two first principal components (PC1 and PC2) respectively. The next
three components (PC3, PC4 and PC5) explain 24.1% of the total variance. The
remaining principal components account for a very small proportion of the variability and
can be neglected. The Scree plot illustrates this information (Figure 4.17).
Table 4.10. Eigenanalysis of the Correlation Matrix for A horizon 11 cases used, 1 cases contain missing values Eigenvalue 7.5000 4.2091 1.7595 1.3654 0.9767 0.5743 0.3537 0.1499 Proportion 0.441 0.248 0.103 0.080 0.057 0.034 0.021 0.009 Cumulative 0.441 0.689 0.792 0.873 0.930 0.964 0.985 0.993 Eigenvalue 0.0846 0.0267 0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 Proportion 0.005 0.002 0.000 0.000 0.000 -0.000 -0.000 -0.000 Cumulative 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Eigenvalue -0.0000 Proportion -0.000 Cumulative 1.000
Trace Metals in Forest Soils in Southwestern China
86
Component Number
Eige
nval
ue
161412108642
8
7
6
5
4
3
2
1
0
PC1 and PC2
As the two first principal components explain a large part of the variation in the data
(Table 4.10), the discussion of the PCA results is mainly based on these components.
All variables, except Pb and Sn, have positive loadings for PC1; Cd, Co, Cu, Mn, Ni, Zn,
pH, BS% and CEC (Table 4.11) possess the highest loadings. They are accordingly the
most dominant variables. Cr, Sn, V and LOI are the dominant variables in PC2. The
similar loadings for the mentioned variables can indicate that they provide the same kind
of information.
Figure 4.17. PCA; Scree Plot for the A horizon
Results and Discussion
87
Variable PC1 PC2 PC3 PC4 PC5
As 0.004 0.134 -0.622 0.360 -0.106
Cd 0.301 -0.093 -0.310 0.180 0.177
Co 0.339 0.075 0.081 -0.005 -0.264
Cr 0.024 0.347 0.320 0.415 -0.099
Cu 0.332 0.135 0.054 0.121 -0.195
Fe 0.199 0.291 -0.080 -0.311 0.355
Hg 0.103 0.243 -0.491 -0.379 -0.098
Mn 0.336 -0.094 -0.183 0.073 0.013
Ni 0.322 0.146 0.152 -0.027 -0.188
Pb -0.158 0.289 -0.065 0.328 0.527
Sn -0.109 0.375 -0.051 0.284 0.072
V 0.001 0.380 0.172 -0.386 0.329
Zn 0.318 -0.087 0.176 0.193 0.274
pH 0.300 -0.210 0.147 0.111 0.244
CEC 0.274 0.240 0.046 -0.123 -0.042
BS% 0.337 -0.126 -0.022 0.011 0.132
LOI 0.010 0.402 0.096 0.045 -0.353
In the loading plot of PC2 vs PC1 (Figure 4.18), some clusters can be recognized; Cluster
1 shows the similar behaviors of Cd, Zn, Mn, BS% and pH. Their loadings along the PC1
axis are much larger than on the PC2 axis. In cluster 2, close relation among Ni, Cu and
Co can be seen. They are mainly affected by pH, BS% and CEC and somewhat by the
organic content of the soil (LOI). Cluster 3, visually shows the dependence of the
concentration of a large number of trace metals on pH, BS% and CEC. High values of
CEC and BS% provide suitable conditions for adsorption of trace metals to the solid
phase of the soil and higher pH values prevent the mobilization of the metals to the soil
solution. Therefore the metal concentrations increase in the solid part of the soil.
Table 4.11. Loadings of the first five Principal Components for the A horizon
Trace Metals in Forest Soils in Southwestern China
88
In the case of As, Cr, V and LOI, the variations mostly occurred along the PC2 axis.
Cluster 4 indicates the similarity between Cr and V and close relation between their
concentration in soil and LOI (Figure 4.18).
Cluster 5 (Figure 4.18), which covers a large area on the upper part of the plot, indicates
that concentrations of Cr, Fe, Hg, Pb, Sn and V are influenced by LOI. In the case of Hg
and Fe, concentrations are not only affected by LOI, but also by pH and CEC.
The positions of soil properties in the loading plot are consistent with general theories
(Figure 4.18); CEC is affected by both pH and LOI. Higher organic content provides
larger number of organic functional groups and higher pH values in the soil solution lead
to deprotonation of weak acids and adsorption of metal ions to the soil.
Individual results presented in Appendix K.4 explain important features among the
variables (trace metals) and objects (macroplots) seen when comparing loading- and
score plots (Figures 4.18 and 4.19). Macroplot 4 at LGS (LGS 4) contains the maximum
amount of Cd and Mn and high levels of Zn and is found at a high PC1 value (Figure
4.19). Cluster A contains the plots with high levels of Zn, Cd and Mn and has a positive
PC1 value (Figure 4.19). LGS 2 has maximum concentrations of Cu, Co and Ni and is
situated in Figure 4.19 in the equivalent position as cluster 2 in Figure 4.18. LCG 6 has
the highest Cr concentration and is located in the score plot in a position equivalent to Cr
in the loading plot (Figures 4.18 and 4.19). Cluster B in the score plot consists of the
plots with high levels of V and cluster C has plots with high Sn levels. These two clusters
are situated in equivalent positions to V and Sn in the loading plot. Cluster D, containing
samples with high amounts of Pb, is located in Figure 4.19 close to the position of Pb in
Figure 4.18.
Results and Discussion
89
PC1
PC2
0.40.30.20.10.0-0.1-0.2
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
0
0
LOI
BS%
CEC
pH
Zn
VSn
Pb
Ni
Mn
Hg
Fe
Cu
Cr
Co
Cd
As
Figure 4.18. Loading Plot of PC2 vs PC1 for the A horizon
PC1
PC2
6543210-1-2-3
3
2
1
0
-1
-2
-3
-4
LCG 10
LCG 6
LCG 1
TSP 6TSP 5
TSP 4
TSP 1
LGS 8
LGS 4
LGS 2
LGS 1
Figure 4.19. Score Plot of PC2 vs PC1 for the A horizon
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster A
Cluster C Cluster D
Cluster B
Trace Metals in Forest Soils in Southwestern China
90
B horizon
According to eigenvalues (Table 4.12), 42.0% and 24.8% of the total variability can be
represented by the two first principal components (PC1 and PC2) respectively. The next
two components (PC3 and PC4) explain 23.6% of the total variance. The other principal
components explain a small proportion of the variability and can be neglected. The Scree
plot provides this information visually (Figure 4.20).
Table 4.12. Eigenanalysis of the Correlation Matrix for B horizon Eigenvalue 7.1344 4.2110 2.6908 1.3192 0.8082 0.3858 0.1954 0.1417 Proportion 0.420 0.248 0.158 0.078 0.048 0.023 0.011 0.008 Cumulative 0.420 0.667 0.826 0.903 0.951 0.973 0.985 0.993 Eigenvalue 0.0642 0.0274 0.0217 0.0000 0.0000 -0.0000 -0.0000 -0.0000 Proportion 0.004 0.002 0.001 0.000 0.000 -0.000 -0.000 -0.000 Cumulative 0.997 0.999 1.000 1.000 1.000 1.000 1.000 1.000 Eigenvalue -0.0000 Proportion -0.000 Cumulative 1.000
Component Number
Eige
nval
ue
161412108642
8
7
6
5
4
3
2
1
0
Figure 4.20. PCA; Scree plot for the B horizon
Results and Discussion
91
PC2 vs PC1
Except for As and Cr, all of the variables have negative loadings for PC1 and Cd, Co,
Cu, Fe, Mn and LOI present the higher absolute-value loadings and they are accordingly
the most dominant variables. Cr, Ni and Sn are the dominant variables in PC2 (Table
4.13). The similar loadings for the mentioned variables can indicate that they provide the
same kind of information.
Comparison between the loading plots for the A and B horizons (Figures 4.18 and 4.21)
indicates similar behavior of Cd, Mn, Co, Cu , Zn and partly Hg and Fe along the PC1
axis in the studied soil horizons and their correlation with pH and BS%. In contrast to the
A horizon (Figure 4.18), Pb concentration in the B horizon (Figure 4.21) is directly
correlated with pH and BS% along the PC1 axes. Along the PC2 axis, Cr, V and Sn show
Variable PC1 PC2 PC3 PC4
As 0.062 0.068 -0.274 0.651
Cd -0.330 0.177 0.071 0.165
Co -0.316 -0.043 -0.106 0.226
Cr 0.055 -0.467 -0.028 0.090
Cu -0.328 -0.184 -0.068 -0.094
Fe -0.283 0.071 -0.367 -0.071
Hg -0.253 0.215 -0.290 -0.247
Mn -0.289 0.258 -0.003 0.263
Ni -0.195 -0.386 -0.024 -0.123
Pb -0.258 -0.209 0.014 0.281
Sn -0.103 -0.398 -0.033 0.031
V -0.204 -0.273 -0.353 -0.145
Zn -0.236 -0.096 0.345 0.317
CEC -0.114 -0.288 0.410 -0.111
BS -0.247 0.167 0.392 0.036
pH -0.246 0.160 0.323 -0.227
LOI -0.321 0.143 -0.113 -0.253
Table 4.13. Loadings of the first four Principal Components for the B horizon
Trace Metals in Forest Soils in Southwestern China
92
different behaviors in the soil profiles; contrary to the A horizon, they are negatively
correlated to the organic matter (LOI) in the B horizon along the PC2 axis. Cluster E is
the most obvious cluster which can be recognized in the loading plot for the B horizon. In
this cluster, Hg, BS% and pH are strongly correlated (Figure 4.21).
PC1
PC2
0.10.0-0.1-0.2-0.3
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
0
0
LOI pHBS
CEC
Zn
V
Sn
Pb
Ni
MnHg
Fe
Cu
Cr
Co
Cd
As
Figure 4.21. Loading plot of PC2 vs PC1 for the B horizon
4.3.6 Assessment of the trace metals behavior in the soil profile Significant differences between concentrations in A and B horizons were used as an
indicator to assess the trace metals accumulation and their movement towards the lower
soil horizons
The results of F-test and two-sample t-test (see Section 3-6) are given in appendices L
and M, respectively. The ratios (median (or mean) values for trace metal in A horizon) to
(median (or mean) value in B horizon) for the studied trace metals are also summarized in
Cluster E
Results and Discussion
93
Table 4.14. The cases with significant differences between the A and B horizons are
specified in Table 4.14.
At TSP, concentrations of As, Cd, Hg and Pb are significantly higher in the A horizon. In
the case of Co, Mn and Ni the concentrations are much higher in the B horizon. At LCG,
Hg is the only metal showing a significant ratio (The ratio is large also for Cd, but it was
not found to be significant at the 95% level). At LGS, the A horizon contains
significantly higher concentrations of As, Cd, Co, Cu, Ni, Pb, Sn and Zn (Table 4.14).
Table 4.14. Quantification of the difference between metal concentrations in the A and B horizons. Values in bold stand for the cases with significant differences (at the 95% level) between the A and B horizons Trace metal A/B ratio TSP LCG LGS Median1 Mean2 Median Mean Median Mean As 1.1 1.3 1.4 1.4 1.6 1.4 Cd 3.8 3.8 3.2 4.0 3.4 3.6 Co 0.4 0.4 0.8 0.9 1.3 1.4 Cr 0.8 0.8 0.6 0.7 1.0 1.0 Cu 1.1 1.1 0.9 1.0 1.1 1.2 Fe 1.0 1.1 1.0 1.1 0.8 0.7 Hg 3.5 3.4 2.6 2.4 1.2 1.0 Mn 0.4 0.5 2.1 2.0 1.2 1.1 Ni 0.7 0.7 0.8 0.8 1.2 1.3 Pb 2.4 2.3 1.4 1.5 1.2 1.2 Sn 1.5 1.5 1.3 1.5 1.1 1.2 V 1.0 1.0 1.0 1.1 0.7 0.7 Zn 0.9 0.9 1.1 1.1 1.3 1.5
1) (Median values for trace metal in A horizon) / (Median value in B horizon) 2) (Mean value for trace metal in A horizon) / (Mean value in B horizon)
As the PCA indicates (Figure 4.18), Co, Mn and Ni are correlated with pH. Therefore
significant lower concentrations of these metals in the A horizon at TSP can be related to
metal mobilization at low pH-values and leaching down to the B horizon.
Trace Metals in Forest Soils in Southwestern China
94
It has been shown that Hg and Pb are strongly complexed with organic compounds in soil
and are therefore not as susceptible to acidification as Cd [Johnsson et al., 2001]. PCA
(Figure 4.18) does not indicate a strong correlation between Hg and Pb concentrations
and soil organic matter (the correlation coefficients for Hg and Pb vs LOI are 0.3313 and
0.0134, respectively). This vague picture could be explained on the basis of different
depositions at the sites; a variable not considered in the PCA. As there is extremely high
sulfur deposition at TSP (Table 3.1), higher depositions of trace metals are also expected.
Therefore accumulation of Hg and Pb in the A horizon may be associated with the
atmospheric input as well as much higher organic content in this horizon (see Table 3.1).
Cd can be easily released from soil by acid deposition [Berthelsen and Steinnes, 1995].
Figure 4.18 also indicates considerable correlation between Cd concentration and pH.
Therefore accumulation of Cd in the A horizon at TSP, in spite of its sensitivity to
acidity, might be the result of high atmospheric deposition.
Accumulation of Hg in the A horizon at LCG (Table 4.14) could be related to Hg
atmospheric input and Hg complexation with the organic matter. As seen in Table 3.1,
LCG has the highest organic content in the A horizon.
At LGS, most of the metals tend to show higher concentrations in the A horizon. High
metal concentrations in the upper horizon do not necessarily reflect the atmospheric
input; natural metal content in the top soil depends on its concentration in the underlying
mineral soil [Steinnes, 2001]. Therefore high metal content in the A horizon could be
mainly related to higher content in shale [Alloway, 1995a and references therein]. Both
Zn and Cu are essential micronutrients3 and extensively circulated in the soil-plant
system [Berthelsen and Steinnes, 1995]. Therefore there might be some contribution of
plant cycling which leads to accumulation of Cu and Zn in the top soil. The internal
cycling of Cd in the soil-plant system is much higher than of Pb, mostly due to higher
mobility of Cd in surface soils [Berthelsen and Steinnes, 1995 and references therein]. Cd
is not a micronutrient, even if it may be essential at very low concentrations [Alloway,
3 Inadequate supply of ”micronutrients” or ”essential trace elements” cause adverse effects in the organisms [Alloway, 1995b]
Results and Discussion
95
1995c and reference therein]. Hence the Cd contribution from plant cycling to the A-
horizon should be less than for Cu and Zn. However, Cd shows much higher level in the
A horizon compared with the other metals. This might be an indication of atmospheric
deposition.
In addition uptake in microbial structures and accumulation in the under-ground biomass
may delay the removal of metals such as Zn, Cd, As and Pb from the top soil [Steinnes,
2001; Berthelsen and Steinnes, 1995].
97
5 Conclusion and Further work
In general, levels of the investigated trace metals are moderate. However Cd, Pb, Hg and
to some extent As are often found in elevated concentrations in the upper horizon
compared with background and standard values.
Average metal concentrations found in this study are mostly comparable in size with data
available from similar studies in China and Europe. Markedly higher Cd in a previous
study may be due to the poor accuracy of the semi-quantitative method used in that study.
Metal levels are fairly similar among the studied sites. High levels of Cd and Pb at TSP
and Pb and somewhat V at LCG may be associated with proximity to the big cities
Chongqing and Guiyang and large emission sources. Metal contents at the remote site
(LGS) are similar or even higher than at the other sites; this can be mainly explained on
the basis of higher metal content in the parent material. There may be a contribution from
long-range transportation of trace metals; the deposition being enhanced by high
precipitation in this area.
Evaluation of the variations within and among the macroplots, indicates the highest
variations for Fe, Mn and V in all the cases, while the lowest variations are found for As
and Cd.
Study of metals behavior in the soil profile and in combination with soil characteristics
indicates the leaching of Co, Mn and Ni down to the B horizon, while Hg, Pb and Cd
have accumulated in the upper horizon at TSP. At LCG, Hg is the only metal that has
significantly higher concentration in the upper horizon. Accumulation of the above
mentioned metals in the A horizon may be due to high atmospheric depositions. In the
case of Hg and Pb, it might in addition be associated with higher organic content of A
horizons. However, the PCA does not show strong correlation between Hg and Pb and
Trace Metals in Forest Soils in Southwestern China
98
soil organic content (LOI). A possible explanation is that the deposition varies
considerably among these sites and this is not a variable in the PCA. Higher contents of
most of the studied metals in the A horizon at the remote site (LGS) can be mainly
explained by the higher metal concentrations in the underlying mineral soil. However,
much higher levels of Cd might reflect the atmospheric deposition. In addition, plant
cycling processes in the case of micronutrients (Cu and Zn) and microbial uptake and
accumulation in biomass might affect the behaviors of metals in soil profiles.
Since the Cd-mobility increases with acidification, high levels of Cd in combination with
low pH-values may lead to ecotoxicological impacts in the studied area.
Further work should include evaluation of potential future problems caused by Cd, Pb
and Hg using a modeling approach, i. e. calculation of so-called “critical loads”, which
has been used in Europe. Through this approach an acceptable metal load to different
ecosystems may in principle be calculated. Further, conducting sequential extraction
experiments on separate soil horizons might help to understand the distribution of trace
metals in soil and their bioavailability. Measurements of deposition fluxes of trace metals
should also be carried out. In addition, the isotopic Pb ratios can be studied in order to get
information about the sources of Pb. This study is limited to only three sites and
extension of the study to other forested areas with similar soils could be valuable.
99
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107
List of Appendices
Appendix A. Microwave programs................................................................................. 109
Appendix B: Single and multi-element standards ......................................................... 111
Appendix C: Calibration ................................................................................................. 113
C.1 ICP-AES............................................................................................................... 113 C.2 ICP-MS................................................................................................................. 116 C.3 DMA..................................................................................................................... 119
Appendix D: Statistics .................................................................................................... 121
Appendix E: Limit of detection ...................................................................................... 123
E.1 Formulas and related inputs.................................................................................. 123 E.2 Results .................................................................................................................. 124
Appendix F: Analyses of reference materials ................................................................ 125
F.1 ICP-MS................................................................................................................. 125 F.2 DMA-80................................................................................................................ 126
Appendix G: Accuracy test based on uncertainties ................................................... 127
G.1 Formulas............................................................................................................... 127 G.2 Result ................................................................................................................... 128
Appendix H: CRM certificates .................................................................................... 131
H.1 Montana Soil ........................................................................................................ 131 H.2 San Joaquin Soil................................................................................................... 134
Appendix I: Sample references.................................................................................... 137
I.1 Sample reference 1 (LCG)..................................................................................... 137 I.2 Sample reference 2 (LGS) ..................................................................................... 138
Appendix J: Control solutions ..................................................................................... 139
Appendix K. Trace metal Concentrations .................................................................. 141
K.1 ICP-MS results ................................................................................................... 141 K.2 DMA results......................................................................................................... 145 K.3 Mass of the samples ............................................................................................. 147 K.4 Concentrations in soil......................................................................................... 148 K.5 Median and mean concentrations, background and standard values ................... 154
Appendix L. Principal Component Analysis (PCA) .................................................. 155
L.1 Inputs .................................................................................................................... 155 L.2 Outputs ................................................................................................................. 156
Appendix M: Two tailed F-test.................................................................................... 159
Appendix N: Two-Sample t-test .................................................................................. 161
Appendix O: Omni Range............................................................................................ 171
109
Appendices
Appendix A. Microwave programs
Figure A.1. Washing procedure
Figure A.2. Final selected digestion procedure
111
Appendix B: Single and multi-element standards Table B.1: Single and multi-element standards used to prepare the inter-calibration and calibration solutions: Product
No.
Element Concentration
(µg/mL)
Matrix Supplier
1013 As 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
1014 Cd 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
1008 Co 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
1023 Cr 1000 ± 0.5 2.5% HNO3
+0.04% HCl
Teknolab A/S, Drøbak, Norway
1001 Cu 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
1004
1032
Fe
Hg
1000 ± 0.5
1000±0.5
2.5% HNO3
2.5% HNO3
Teknolab A/S, Drøbak, Norway
Teknolab A/S, Drøbak, Norway
1005-7 Mn 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
SS-1203 Ni 1002 ± 2 1.4%(abs) HNO3 Teknolab A/S, Kolbotn,
Norway
1017 Pb 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
1058-1 Sn 1000 ± 0.5 4.9% HCl Teknolab A/S, Drøbak, Norway
1068-1 V 1000 ± 1 2.5% HNO3 Teknolab A/S, Drøbak, Norway
1002 Zn 1000 ± 0.5 2.5% HNO3 Teknolab A/S, Drøbak, Norway
SS-028311
Cd, Cr (III), Co, Cu, Fe, Pb, Mn, Ni, V, Zn
100 2.5% (abs) HNO3 Teknolab A/S, Kolbotn, Norway
113
Appendix C: Calibration
C.1 ICP-AES C.1.1 Calibration solutions for ICP-AES
All of the dilutions in this part were carried out according to the following equation:
C1 V1 = C2 V2
where C is the concentration and V is the volume.
1) 2 multi-element stock solutions were prepared by diluting several single element stock
solutions:
- multi-element stock solution 1: 10 ppm As, Cd, Ni
- multi-element stock solution 2: 100 ppm Pb, Zn, Cu, Fe
The amount of the single element standards used to prepare the multi-element stock
solutions are given in Tables C-1 and C-2. Both were diluted to 250ml.
Table C.1. The amount of single element standards to prepare multi-element stock solution 1
Standard solution Volume (ml)
As standard solution (1000 ppm) 2.5
Cd standard solution (1000 ppm) 2.5
Ni standard solution (1002 ppm) 2.495
114
Table C.2. The amount of single element standards to prepare multi-element stock solution 2
Standard solution Volume (ml)
Pb standard solution (1000 ppm) 25
Zn standard solution (1000 ppm) 25
Cu standard solution (1000 ppm) 25
Fe standard solution (1000 ppm) 25
2) 5 calibration solutions (blank and 4 standard solutions) were made using the multi-
element stock solutions 1 and 2. They were matrix matched and diluted to 50mL (Table
C-3):
Table C.3. The amount of acids and stock solutions used to prepare the calibration solution for ICP-AES Concentration of calibration solutions (mg L-1)
Multi-element stock solution1 (mL)
Multi-element stock solution2 (mL)
HNO3 (mL) HF (mL)
0 0 0 7.0 4.0 0.2 (As, Cd, Ni) 2 (Pb, Zn, Cu, Fe)
1.0 1.0 7.0 4.0
0.5 (As, Cd, Ni) 5 (Pb, Zn, Cu, Fe)
2.5 2.5 7.0 4.0
1(As, Cd, Ni) 10 (Pb, Zn, Cu, Fe)
5.0 5.0 7.0 4.0
2.5 (As, Cd, Ni) 20 (Pb, Zn, Cu, Fe)
12.5 10.0 7.0 4.0
115
C.1.2 Calibration curves for ICP-AES
116
C.2 ICP-MS C.2.1 Calibration solutions for ICP-MS
All of the dilutions in this part were carried out according to the following equation:
C1 V1 = C2 V2
where C is the concentration and V is the volume.
1) 1ml of 1000 mg L-1 single element standards of As, Cd, Co, Cr, Cu, Fe, Mn, Ni,
Pb, Sn, V and Zn were diluted to 100ml to make 10 mg L-1 multi-element stock
solution.
2) 5ml of 10 mg L-1 multi-element stock solution was diluted to 50mL to make 1 mg
L-1 multi-element stock solution.
3) 7 calibration solutions (blank and 6 standard solutions) were made using the 1 mg
L-1 multi-element stock solution and matrix matched. Details are summarized in
Table C-4.
Table C.4. The amount of acids and stock solutions used to prepare the calibration solution for ICP-MS Concentration of calibration solutions (µg L-1)
1 mg L-1 multi-element stock solution (mL)
HF (mL) HNO3 (mL) Final volume (mL)
0 0 4.0 7.0 50
4 0.2 4.0 7.0 50
10 0.5 4.0 7.0 50
30 1.5 4.0 7.0 50
501 2.5 4.0 7.0 50
80 4.0 4.0 7.0 50
100 5.0 4.0 7.0 50 1) Control solution.
117
C.2.2 Calibration curves for ICP-MS
Cu calibration curve
R 2 = 0.9996
0 10000 20000 30000 40000 50000 60000 70000 80000 90000
0 20 40 60 80 100
Cr calibration solution
R2 = 0.9993
02000400060008000
100001200014000160001800020000
0 20 40 60 80 100
Co calibration curve
R 2 = 0.9998
0 20000 40000 60000 80000
100000 120000 140000 160000 180000 200000
0 20 40 60 80 100
As calibration curve
R 2 = 0.9991
0 2000 4000 6000 8000
10000 12000 14000 16000 18000 20000
0 20 40 60 80 100
Inte
nsity
Concentration (µg L-1)
Fe calibration curve
R2 = 0.9998
050
100150200250300350
0 200 400 600 800 1000
Cd calibration curve
R2 = 0.9995
0
500010000
15000
20000
25000
3000035000
40000
0 20 40 60 80 100
118
Calibration curves for ICP-MS (Continued)
V calibration curve
R 2 = 0.997
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0 20 40 60 80 100
Pb calibration curve
R 2 = 0.9993
0 20000 40000 60000 80000
100000 120000 140000 160000 180000 200000
0 20 40 60 80 100
Mn calibration curve
R 2 = 1
0 2000 4000 6000 8000
10000 12000 14000 16000
0 200 400 600 800 1000
Sn calibration curve
R2 = 0.999
0
10000
20000
30000
40000
50000
60000
70000
80000
0 20 40 60 80 100
Zn calibration curve
R2 = 0.9996
02000400060008000
100001200014000160001800020000
0 20 40 60 80 100
Concentration (µg L-1)
Inte
nsity
Ni calibration curve
R2 = 0.9997
0
500010000
15000
20000
2500030000
35000
40000
0 20 40 60 80 100
119
C.3 DMA C.3.1 DMA calibration solutions and calibration curves Table C.5. DMA calibration solutions
Used Hg solution (µg/mL)
Mass (mg) Hg (ng)
0.0 100.0 0.0 20.0 51.8 1.0 20.0 202.4 4.0 20.0 405.3 8.1 200.0 67.5 13.5 200.0 98.0 19.6 130.0 195.4 25.4 130.0 233.8 30.4 200.0 196.1 39.2 130.0 387.0 50.3 1250.0 74.9 93.6 1250.0 162.1 202.6 1250.0 240.3 300.4 1250.0 321.1 401.4 1250.0 402.2 502.7
C.3.2 Control solution for DMA
100 µL of the 1000 mg L-1 Hg stock solution (single element standard) was diluted to
100ml in a glass volumetric flask.
121
Appendix D: Statistics Formulas used to calculate Mean (Xavr.), Standard Deviation (SD) and Relative Standard
Deviation% (RSD%) in this document are given below:
• Xavr. = ∑xi / n
• SD = √ [ ∑(xi –x avr.)2 / (n-1)] • RSD% = (SD / Xavr.)*100%
where:
xi = Individual data n = Sample size
123
Appendix E: Limit of detection
E.1 Formulas and related inputs
• Following formulas were used to calculate the limit of detection (LOD) and
method detection limit (MDL) in ICP-AES and ICP-MS analyses:
I. LOD = 3 * SD 7
where: LOD = Limit Of Detection (µg L-1) SD = Standard Deviation of ten measured blank (µg L-1) II. MDL = LOD * V / m
where: MDL = Method Detection Limit (µg g-1) LOD = Limit Of Detection (µg L-1) V = Final volume of the digested sample solution (L) = 0.05 L m = Mass of the digested material (g) = 0.3 g
• Following formula were used to obtain the limit of detection (LOD) and method
detection limit (MDL) in DMA-80 analysis:
I. LOD = 3 * SD 1
where: LOD = Limit Of Detection (ng) SD = Standard Deviation of ten measured empty sample boats as blank (ng) II. MDL = LOD / m
where: MDL = Method Detection Limit (ng g-1) LOD = Limit Of Detection (ng) m = Average mass of the samples (g) = 0.0760 g
7 Miller J. N. and Miller J. C., 2000. Statistics and chemometrics for analytical chemistry (4th ed.). Prentice Hall, Harlow, England
124
E.2 Results Table E.1. Limits of detection for ICP-MS analysis Trace metal Blank 1
(µg L-1) Blank 2 (µg L-1)
Blank 3 (µg L-1)
Blank 4 (µg L-1)
Blank 5 (µg L-1)
Blank 6 (µg L-1)
Blank 7 (µg L-1)
Blank 8 (µg L-1)
Blank 9 (µg L-1)
Blank 10 (µg L-1)
SD (µg L-1)
LOD (µg L-1)
MDL (µg g-1)
As 1.6044 1.7326 1.6105 1.6410 1.5251 1.5434 1.4092 1.4092 1.3482 1.3299 0.14 0.41 0.07
Cd 0.0419 0.0615 0.0475 0.0559 0.0503 0.0447 0.0391 0.0587 0.0391 0.0671 0.01 0.03 0.005
Co 0.2901 0.3059 0.3012 0.3012 0.2953 0.3023 0.2959 0.2907 0.2930 0.3041 0.01 0.02 0.003
Cr 3.2890 3.5362 3.3655 3.1831 3.1890 3.1478 3.1243 3.1419 3.2714 3.5303 0.15 0.46 0.08
Cu 1.0671 1.0749 1.0191 0.9931 1.0178 1.0463 1.0126 1.0723 1.0061 1.0528 0.03 0.09 0.01
Fe 48.8217 38.3599 45.3344 52.3089 52.3089 41.8471 41.8471 55.7962 45.3344 55.7962 6.16 18.49 3.08
Mn 1.0638 1.0638 1.0638 1.1303 1.0638 1.1303 1.1968 0.8643 1.1968 1.0638 0.09 0.28 0.05
Ni 2.5142 2.8938 2.9763 3.0561 3.1193 3.1386 3.2734 3.0918 3.1001 3.0863 0.21 0.62 0.10
Pb 0.3002 0.2797 0.2985 0.2991 0.3035 0.2985 0.2858 0.2791 0.2841 0.2863 0.01 0.03 0.005
Sn 2.7101 2.5417 2.6108 2.5206 2.6364 2.6575 2.5928 2.5447 2.6289 2.7056 0.07 0.20 0.03
V 0.1269 0.0923 0.0808 0.0923 0.1154 0.1039 0.1039 0.1154 0.0692 0.1385 0.02 0.06 0.01
Zn 449.2331 450.9254 445.0743 439.0973 438.5332 443.7241 436.3248 437.5070 434.1044 437.4290 5.72 17.15 2.86
Table E.2. Limit of detection for DMA-80 analysis Blank 1 Blank 2 Blank 3 Blank 4 Blank 5 Blank 6 Blank 7 Blank 8 Blank 9 Blank 10 SD (ng) LOD (ng) MDL (ng g-1)
Peak height 0.00142
0.00109
0.00142 0.00062
0.00109
0.00115
0.00515
0.0046
0.00237
0.00472
Hg (ng) 0.06 0.04 0.06 0.02 0.04 0.05 0.17 0.15 0.08 0.15 0.05 0.16 2.13
125
Appendix F: Analyses of reference materials
F.1 ICP-MS F.1.1 Formula
• Calculated concentration (µg g-1) = [Measured concentration (µg L-1)] * [V (L)] / [m (g)] where. V= Final volume of the digested reference material m = Mass of the reference material
• Corrected mass for the moisture (g) = Original mass (g) * (1- moisture content)
F.1.2 Individual results
F.1.2.1 Montana soil
Table F.1. Mass of the reference material (g)*
Replicate 1 Replicate 2 Replicate 3 Mass 0.3016 0.2986 0.2995 * No moisture content Table F.2. Measured and calculated concentrations for 3 replicates for Montana Soil Trace metal
Measured concentration (µg L-1)
Calculated concentration
(µg g-1) *
Rep. 1 Rep. 2 Rep. 3 Rep. 1 Rep. 2 Rep. 3 As 3847.777 3644.26 3665.371 637.9 610.2 611.9
Cd 117.2959 121.207 109.6236 19.4 20.3 18.3
Co 58.2088 60.3471 58.6421 9.7 10.1 9.8
Cr 182.1109 176.084 202.9604 30.2 29.5 33.9
Cu 18015.05 18077.2 17910.82 2986.6 3027.0 2990.1
Fe 194829.3 167256 180393.6 32299.3 28006.7 30115.8
Mn 66491.58 60088 65883.41 11023.1 10061.6 10998.9
Ni 71.85258 67.1241 70.58137 11.9 11.2 11.8
Pb 35521.3 35898.1 33408.39 5888.8 6011.1 5577.4
V 431.5558 428.636 417.0801 71.5 71.8 69.6
Zn 41389.32 38739.8 40997.02 6861.6 6486.9 6844.2
* V = 0.05 L
126
F.1.2.2 San Joaquin Soil Table F.3. Mass of the reference material (g)*
Replicate 1 Replicate 2 Replicate 3 Original mass 0.3036 0.2978 0.3007 Corrected mass 0.2936 0.2880 0.2908 * Moisture content = 0.033 Table F.4. Measured and calculated concentrations for 3 replicates of San Joaquin Soil Trace
metal
Measured
concentration
(µg L-1)
Calculated
concentration
(µg g-1) *
Rep. 1 Rep. 2 Rep. 3 Rep. 1 Rep. 2 Rep. 3
As 55.57975 51.49454 49.24244 18.9 17.9 16.9
Cd 1.069968 0.987173 0.910747 0.4 0.3 0.3
Co 41.23924 38.68502 37.63575 14.1 13.4 12.9
Cr 333.387 318.32 300.9693 113.6 110.5 103.5
Cu 93.26086 84.41702 81.04596 31.8 29.3 27.9
Fe57 89476.92 82406.35 84303.84 30475.8 28613.3 28990.3
Mn 1302.009 1193.845 1194.995 443.5 414.5 410.9
Ni 231.2523 207.2864 200.039 78.8 72.0 68.8
Pb 49.03609 49.61294 51.62807 16.7 17.2 17.8
V 329.2914 313.1222 305.5933 112.2 108.7 105.1
Zn 316.9955 275.9365 265.992 108.0 95.8 91.5
* V = 0.1 L
F.2 DMA-80 Table F.5. Concentrations (µg g-1) of 3 replicates of San Joaquin Soil in each time analysis Date Concentration Replicate 1 Replicate 2 Replicate 3 10/5-2006 1.53132 1.47535 1.46959
12/5-2006 1.49632 1.47402 1.48809
14/5-2006 1.53121 1.4871 1.38037
127
Appendix G: Accuracy test based on uncertainties
G.1 Formulas - Difference between the measured and certified values ∆m = | cm – cCRM| Where: ∆m = Absolute difference between mean measured value and certified value cm = Mean measured value cCRM = Certified value - Expanded uncertainty U∆ = 2 . u∆ Where: U∆ = Expanded uncertainty of difference between result and certified value u∆ = Combined uncertainty of result and certified value u∆ = (u 2
m + u 2CRM )1/2
where: um = Uncertainty of the measurement result which in these cases is the standard deviation of 3 measurements uCRM = Uncertainty of the certified value which in this case is calculated using the follwing equation: uCRM = (Stated uncertainty in the reference material certificate) / (t-factor at 95% confidence interval with n-1 degree of freedom) where: n being the number of laboratories stated in the certificate which in these cases is 9 Appendix H shows the certificates of the certified values.
128
G.2 Result Table G.1. ICP-AES. Accuracy test based on uncertainty for Montana Soil
Table G.2. DMA. Accuracy test based on uncertainty for San Joaquin Soil
1) ∆m = | Certified value-Mean measured value| 2) uCRM = (Stated uncertainty in the reference material certificate) / (t-factor at 95% confidence interval with 8degree of freedom) 3) Um = Standard deviation of the measured value (n=3) 4) u∆ = (u 2
m + u 2CRM )1/2
5) U∆ = 2. u∆
Trace metal
Certified value (mg kg-1)
Mean measured value (n=3) (mg kg-1)
∆m1
(mg kg-1)
t-factor at 95% CI
Stated uncertainty of certified value (mg kg-1)
UCRM2
mg kg-1
Um3
(mg kg-1)
u∆4
(mg kg-1)
U∆5
(mg kg-1)
Remarks
As 626 603 23 2.31 38 16.45 57 59.33 118.65 No significant difference Cd 21.8 24.4 2.6 2.31 0.2 0.09 2 2.00 4.00 No significant difference Cu 2950 2851 99 2.31 130 56.28 121 133.45 266.89 No significant difference Cu 2950 2718 232 2.31 130 56.28 94 109.56 219.12 Significant difference Fe 33800 21869 11931 2.31 1000 432.90 695 818.80 1637.59 Significant difference Ni 14.3 15.9 1.6 2.31 1 0.43 2.5 2.54 5.07 No significant difference Pb 5532 4953 579 2.31 80 34.63 211 213.82 427.65 Significant difference Zn 6952 5510 1442 2.31 91 39.39 385 387.01 774.02 Significant difference
Date Certified value (mg kg-1)
Mean measured value (n=3) (mg kg-1)
∆m1
(mg kg-1)
t-factor at 95% CI
Stated uncertainty of certified value (mg kg-1)
UCRM2
mg kg-1
Um3
(mg kg-1)
u∆4
(mg kg-1)
U∆5
(mg kg-1)
Remarks
10.mai 1.4 1.49 0.09 2.31 0.08 0.03 0.03 0.05 0.09 No significant difference 12.mai 1.4 1.49 0.09 2.31 0.08 0.03 0.01 0.04 0.07 Significant difference 14.mai 1.4 1.47 0.07 2.31 0.08 0.03 0.08 0.09 0.17 No significant difference
129
Table G.3. ICP-MS. Accuracy test based on uncertainty for Montana soil
1) ∆m = | Certified value-Mean measured value| 2) uCRM = (Stated uncertainty in the reference material certificate) / (t-factor at 95% confidence interval with 8degree of freedom) 3) um = Standard deviation of the measured value (n=3) 4) u∆ = (u 2
m + u 2CRM )1/2
5) U∆ = 2. u∆
Trace metal
Certified value (mg kg-1)
Mean measured value (n=3) (mg kg-1)
∆m1
(mg kg-1)
t-factor at 95% CI
Stated uncertainty of certified value (mg kg-1)
UCRM2
mg kg-1
Um3
(mg kg-1)
u∆4
(mg kg-1)
U∆5
(mg kg-1)
Remarks
As 626 620 6 2.31 38 16.45 15 22.26 44.52 No significant difference
Cd 21.8 19.4 2.4 2.31 0.2 0.09 1 1.00 2.01 Significant difference
Cu 2950 3001 51 2.31 130 56.28 22 60.42 120.85 No significant difference
Fe 33800 30140 3660 2.31 1000 432.90 2146 2189.23 4378.46 No significant difference
Mn 10100 10695 595 2.31 400 173.16 548 574.71 1149.42 No significant difference
Ni 14.3 11.6 2.7 2.31 1 0.43 0.4 0.59 1.18 Significant difference
Pb 5532 5826 294 2.31 80 34.63 224 226.66 453.32 No significant difference
V 76.6 71 5.6 2.31 2.3 1.00 1.2 1.56 3.12 Significant difference
Zn 6952 6731 221 2.31 91 39.39 211 214.65 429.29 No significant difference
130
Table G.4. ICP-MS. Accuracy test based on uncertainty for San Joaquin Soil
Trace metal Certified value (mg kg-1)
Mean measured value (n=3) (mg kg-1)
∆m1
(mg kg-1)
t-factor at 95% CI
Stated uncertainty of certified value (mg kg-1)
UCRM2
mg kg-1
Um3
(mg kg-1)
u∆4
(mg kg-1)
U∆5
(mg kg-1)
Remarks
As 17.7 17.9 0.2 2.31 0.8 0.35 1 1.06 2.12 No significant difference
Cd 0.38 0.34 0.04 2.31 0.01 0.00 0.03 0.03 0.06 No significant difference
Co 13.4 13.5 0.1 2.31 0.7 0.30 0.6 0.67 1.34 No significant difference
Cr 130 109 21 2.31 4 1.73 5 5.29 10.58 Significant difference
Cu 34.6 29.7 4.9 2.31 0.7 0.30 2 2.02 4.05 Significant difference
Fe 35000 29360 5640 2.31 1100 476.19 985 1094.07 2188.13 Significant difference
Mn 538 423 115 2.31 17 7.36 18 19.45 38.89 Significant difference
Ni 88 73 15 2.31 5 2.16 5 5.45 10.90 Significant difference
Pb 18.9 17.2 1.7 2.31 0.5 0.22 0.5 0.54 1.09 Significant difference
V 112 109 3 2.31 5 2.16 3 3.70 7.40 No significant difference
Zn 106 98 8 2.31 3 1.30 8 8.10 16.21 No significant difference 1) ∆m = | Certified value-Mean measured value| 2) uCRM = (Stated uncertainty in the reference material certificate) / (t-factor at 95% confidence interval with 8degree of freedom) 3) um = Standard deviation of the measured value (n=3) 4) u∆ = (u 2
m + u 2CRM )1/2
5) U∆ = 2. u∆
131
Appendix H: CRM certificates
H.1 Montana Soil
132
133
134
H.2 San Joaquin Soil
135
136
137
Appendix I: Sample references
I.1 Sample reference 1 (LCG) Table I.1. Concentration of trace metals in individual replicates of “sample reference 1 (LCG)”, mean, SD and RSD%
* Concentration in soil (µg g-1) = Concentration in solution (µg L-1) * V (L) / m (g)
Where: V= Final volume of the digested sample = 0.05 L m = Mass of the sample
Concentration ( mg kg-1 soil)* Trace metal
Rep. 1 Rep. 2 Rep. 3 Rep. 4 Rep. 5
Mean (expected) mg kg-1
SD mg kg-1
RSD%
As 14.5 14.6 14.4 14.5 14.1 14.4 0.2 1.5
Cd 0.4 0.38 0.37 0.39 0.39 0.39 0.01 3.4
Co 3.7 3.6 3.6 3.6 3.6 3.6 0.1 1.7
Cr 49.4 48.9 47.7 46.4 47 47.9 1.3 2.6
Cu 18.6 17.9 17.6 17.2 17.5 17.7 0.5 3.0
Fe 21262.1 20563.7 20460.5 20134.0 19884.7 20461.0 522.4 2.6
Mn 137.7 122.7 131.4 140.8 132.3 133.0 6.9 5.2
Ni 14.8 14.3 14.2 13.8 14.1 14.3 0.4 2.7
Sn 5.3 5.2 4.8 4.8 4.7 5.0 0.3 5.4
V 77.9 73.7 72.7 73.6 69.8 73.5 2.9 3.9
Zn 54.3 49.9 49.3 47.1 49.3 50.0 2.6 5.2
138
I.2 Sample reference 2 (LGS) Table I.2. Concentration of trace metals in individual replicates of “sample reference 2 (LGS)”, mean, SD and RSD%
Concentration (mg kg-1 soil)* Trace metal
Rep. 1 Rep. 2 Rep. 3 Rep. 4 Rep. 5 Mean (expected)
SD
mg kg-1
RSD%
As 18.2 16.5 17.1 16.7 16.4 17.0 0.7 4.3
Cd 0.39 0.37 0.43 0.38 0.38 0.39 0.02 6.15
Co 6.3 6.2 6.6 6.1 6.0 6.2 0.2 3.9
Cr 44.5 42.1 42.1 40.3 38.1 41.4 2.4 5.7
Cu 16.6 15.1 15.8 14.6 14.1 15.2 1.0 6.5
Fe 22630.8 22508.8 24471.5 24311.61 23266.6 23437.8 918.7 3.9
Mn 746.4 774.7 716.2 785.2 780.9 760.7 29.1 3.8
Ni 13.7 13.1 13.7 12.7 12.3 13.1 0.6 4.7
Pb 33.0 33.2 29.8 35.3 31.8 32.6 2.0 6.2
Sn 3.6 3.8 3.5 3.5 3.6 0.1 3.1
V 50.9 49.9 53.5 54.6 53.8 52.5 2.0 3.9
Zn 60.6 58.2 64.0 55.8 54.4 58.6 3.8 6.5
* Concentration in soil (µg g-1) = Concentration in solution (µg L-1) * V (L) / m (g)
where: V= Final volume of the digested sample = 0.05 L m = Mass of the sample
139
Appendix J: Control solutions Table J.1. Result of the control solution analyses and recalibration of ICP-MS (µg L-1) Trace metal
As Cd Co Cr Cu Fe Mn Ni Pb Sn V Zn
Control 1 47.3300 48.1533 43.9814 45.2380 45.8390 102.9267 50.2662 45.5423 50.0811 59.7727 47.2932 56.5342 Control 2 43.0416 44.0800 40.5362 41.4094 42.2754 102.9267 44.6179 41.5968 49.1586 54.8969 43.3627 50.9917 RECALIBRATION Control 3 58.7904 56.6152 56.3470 54.1022 58.3482 96.5737 50.0861 56.5423 50.3086 68.9634 52.5450 78.2724 Control 4 55.7813 55.2929 53.2327 50.7399 55.1839 122.9948 49.7893 53.5092 46.7346 67.3189 50.2743 75.6110 Control 5 50.8937 50.7671 46.8878 45.0954 50.1525 122.9948 50.3087 48.8331 47.3991 62.9793 48.4528 68.4912 Control 6 40.5619 42.7553 41.3286 39.8380 43.0741 105.6844 44.4468 41.2419 46.6447 53.7549 45.6430 58.2551 RECALIBRATION Control 7 57.3662 52.7225 54.3875 54.2651 58.4276 147.0164 53.1557 55.8227 48.4425 67.0916 53.2162 79.6791 Control 8 36.8913 38.8129 41.6396 40.0989 42.6759 124.6444 43.2885 41.2766 46.9969 51.4181 42.3274 55.6235 RECALIBRATION Control 9 55.2343 51.0655 49.9285 51.1947 51.1493 79.8399 50.3969 53.0371 48.8175 54.1199 48.8488 55.5454 Control 10 41.1482 36.6035 35.8284 32.8256 41.5143 274.2328 145.2069 40.4001 51.0798 38.9439 175.4426 44.5455 RECALIBRATION Control 11 54.4013 51.2625 50.8490 52.6144 52.9773 88.5240 53.6794 52.0039 45.2178 51.8597 61.9396 59.1955 Control 12 44.3418 41.8252 41.2123 40.8355 41.9842 70.2571 51.5020 42.2244 44.0437 42.2130 53.7128 48.1607 RECALIBRATION Control 13 37.3599 31.4258 34.9494 36.1017 35.1636 68.8520 32.9637 35.4708 47.1588 33.8596 29.8043 38.3789 RECALIBRATION Control 14 54.6403 55.1124 53.6228 51.8696 53.7387 90.6817 49.5713 52.1521 47.0170 57.8848 55.8949 61.5321 Control 15 50.3588 42.8628 46.9730 48.0940 47.6748 66.9318 40.6330 48.0706 55.9545 43.7362 39.6981 56.2250 Control 16 48.3721 41.8328 44.9360 46.7060 44.1585 79.8863 38.2656 47.4734 54.2276 42.2105 36.2799 57.1492
140
TableJ.2. Result of the control solution for DMA-80 analyses Mass (g) Height Hg
(ng) Measured value (ng/g)
Expected value (ng/g)
Recovery%*
Control 1 0.2 0.3002 209.6 1048.01 1000 104.8
Control 2 0.198 0.3028 211.63 1068.82 1000 106.9
Control 3 0.1963 0.2971 190.52 970.54 1000 97.0
Control 4 0.1972 0.3304 213.86 1084.5 1000 108.4
Control 5 0.0993 0.1660 102.83 1035.54 1000 103.5
* Recovery % = (Measured value/ Expected value)*100%
141
Appendix K. Trace metal Concentrations
K.1 ICP-MS results
Table K.1. Heavy metal concentrations in the individual samples of the A horizon (µg L-1 solution)
As Cd Co Cr Cu Fe Mn Ni Pb Sn V Zn LGS Macroplot 1 LGS-1-1 83.5415 2.4093 42.4538 262.4620 106.1200 135662.3000 2753.8440 122.1671 133.2259 22.2298 324.9902 348.4156 LGS-1-2 83.0023 1.8448 33.4619 246.7306 99.7165 132691.3000 2239.2930 91.5663 143.0976 -5.8910 323.3648 146.7863 LGS-1-3 115.5805 2.3100 35.9392 232.3033 108.8382 127670.4000 1937.4600 97.0681 175.5123 21.2059 310.0182 134.3210 LGS-1-4 78.8626 1.3327 30.9998 233.2203 98.2580 136178.9000 1852.6100 82.7535 134.8005 16.8574 333.7790 282.1309 LGS-1-5 91.5251 2.6340 43.2052 218.7319 106.3787 124598.2000 4806.1310 114.3484 170.7068 20.5232 299.8816 215.1865 Macroplot 2 LGS-2-7 94.9343 2.8692 81.9898 257.1639 140.4698 157755.8000 4230.6790 115.7639 198.8207 21.8530 333.6810 302.8989 LGS-2-8 116.0153 2.8639 96.9414 286.0999 161.5214 167365.8000 4898.7340 141.7307 207.4865 20.0445 368.2155 358.8110 LGS-2-9 97.3346 2.5347 145.4655 294.0675 144.7551 156509.4000 4491.3490 151.9843 173.3355 19.7697 338.1571 373.1416 LGS-2-10 97.4042 3.0469 89.8775 219.6693 132.5441 134026.0000 4510.8270 113.0846 199.8377 19.4771 279.2002 153.2920 Macroplot 4 LGS-4-16 65.4695 2.4668 67.4406 229.6339 113.4309 144866.8000 5354.1280 103.2438 163.7197 23.2227 322.3764 489.4697 LGS-4-17 54.9464 3.0573 81.6732 224.4784 129.7735 126154.3000 5441.2220 120.3051 151.4835 -5.3281 280.9971 615.3971 LGS-4-18 59.0165 2.7124 94.6700 265.6613 131.1232 147448.8000 4977.5730 138.1331 149.7538 16.1881 319.6565 556.0274 LGS-4-19 63.3997 3.2298 72.5058 218.6504 122.5076 129903.4000 5615.5220 117.5416 157.1877 16.7643 297.3087 544.4720 Macroplot 8 LGS-8-36 114.2412 1.8030 36.0517 238.2128 82.7815 142170.1000 2854.8330 75.8784 154.7416 19.8894 318.8805 101.5653 LGS-8-38 100.9068 2.3167 37.1432 246.5925 90.6525 139606..4 4528.2950 78.0062 194.1816 21.3467 312.9356 349.0933 LGS-8-39 112.2061 2.7228 39.9765 252.0695 85.3534 142784.1000 4980.9310 82.2058 181.5273 -5.8910 338.8187 204.9276 LGS-8-40 102.3439 2.2734 41.5746 235.8694 86.4257 146850.2000 5788.8010 82.7029 168.5016 21.1881 344.6589 423.2987
142
Table K.1 (Continued) As Cd Co Cr Cu Fe Mn Ni Pb Sn V Zn TSP Macroplot 1 TSP-4 75.4819 1.5691 15.0232 290.5674 55.9814 95965.0400 732.5298 50.7146 303.9188 17.3884 285.8652 193.0039 TSP-5-A 87.2804 1.5734 13.3296 282.9213 61.6217 188109.8000 1294.0310 47.0239 449.2489 18.4229 568.8643 176.3231 Macroplot 4 TSP-16 108.7929 1.2730 16.5635 298.8732 73.0176 93758.1900 683.2256 55.1819 343.7087 24.3924 363.9737 207.2095 Macroplot 5 TSP-21 78.4847 1.2484 16.0433 287.8910 63.4508 94069.8000 728.2965 53.3144 229.9263 18.9234 263.2695 182.5345 TSP-25 69.1298 1.7270 9.5549 219.2291 54.5699 59156.5700 537.7318 32.0949 230.3111 -5.1225 197.5676 137.7800 Macroplot 6 TSP-27 74.8219 0.9523 13.4789 246.6846 58.7448 86436.1300 682.4935 54.0559 262.3893 18.0411 286.3008 178.5586 TSP-29 88.2849 1.6530 15.5051 289.5983 68.3750 87997.3800 600.3409 60.1435 294.4966 21.2550 286.3404 226.0503 LCG Macroplot 1 75.0306 1.2799 25.0057 253.0612 75.7471 193288.4000 719.5857 81.5017 308.7217 21.7871 730.9205 185.8942 Macroplot 6 57.0162 0.7264 66.6911 361.8430 112.9997 131599.8000 1214.9030 123.1529 181.1471 18.3512 526.5531 256.1994 Macroplot 7 85.9116 2.3035 21.5110 285.2446 105.7259 121943.3000 793.0764 84.9304 29.5669 438.2085 297.7094 Macroplot 10 63.3301 0.8048 7.3995 146.6771 27.0240 67451.2200 181.7754 24.7957 121.5997 12.6065 243.9632 106.4787
143
Table K.2. Heavy metal concentrations in the individual samples of the B horizon (µg L-1 solution) As Cd Co Cr Cu Fe Mn Ni Pb Sn V Zn LGS Macroplot 1 LGS-1-1 50.4515 0.6651 38.2875 199.5306 91.5628 364178.2000 4012.0070 89.9493 128.3588 13.0047 877.4669 210.9445 LGS-1-2 56.4236 0.4517 33.1796 243.9213 94.8720 110563.7000 1298.0740 94.9010 104.1298 12.3905 269.7575 235.5640 LGS-1-3 51.3311 0.5689 28.7165 239.4106 94.9835 118686.9000 1309.5060 76.8067 104.3095 12.4611 295.1590 134.6239 LGS-1-4 66.2526 0.6958 44.9698 334.4485 116.4953 151691.2000 1843.5590 125.9107 113.5313 17.4503 350.7640 279.8483 LGS-1-5 50.7259 0.3862 37.0623 250.6714 91.0927 118814.7000 1790.5940 101.8349 107.3893 11.7454 320.3218 214.0818 Macroplot 2 LGS-2-7 63.4395 1.1514 50.3896 213.2810 130.0186 418293.5000 6108.5990 75.5904 168.8026 11.0537 870.4621 206.2610 LGS-2-8 61.6948 1.0088 95.3638 218.4646 144.7905 400874.6000 8491.8820 92.7440 180.0733 10.6761 865.4422 246.0127 LGS-2-9 71.6642 0.7517 102.7638 284.7728 124.4269 147572.4000 2591.3700 125.0829 140.9315 11.0651 323.0336 181.7865 LGS-2-10 62.3279 1.0068 84.6955 240.5901 130.2660 125334.6000 2638.3050 92.2356 166.4282 10.5869 262.7850 242.8405 Macroplot 4 LGS-4-16 48.5929 0.9896 57.0562 222.2045 91.9128 110854.6000 4578.2950 84.6194 164.4358 12.1594 241.5528 330.4328 LGS-4-17 49.7295 1.4585 55.4672 232.6365 114.0133 114828.3000 4990.8850 95.4000 154.3930 12.8366 284.2356 406.3671 LGS-4-18 50.5045 0.7069 57.9068 247.2207 111.7613 119470.9000 4097.6290 89.0037 161.1567 12.0824 269.7880 339.1108 LGS-4-19 45.9950 0.5034 45.8441 215.4544 91.0238 95756.3300 3724.3530 72.5028 140.4913 10.9785 232.0717 253.7846 Macroplot 8 LGS-8-36 56.5032 0.4583 32.2294 181.5968 63.3483 351181.1000 4390.6490 65.8665 126.6420 14.0575 791.3324 166.0679 LGS-8-38 68.1880 0.5276 36.9979 247.7307 71.7034 128833.4000 2273.9810 71.3697 124.2192 13.8956 287.2166 166.7424 LGS-8-39 78.3066 0.4758 39.9779 261.5019 71.1430 145617.8000 2867.5690 68.6947 144.7778 14.2165 340.4978 184.7534 LGS-8-40 69.4132 0.5310 34.0545 226.9304 65.3039 131840.4000 2332.9220 70.4265 113.3830 13.2762 269.2344 210.6892
144
Table K.2 (Continued) As Cd Co Cr Cu Fe Mn Ni Pb Sn V Zn TSP Macroplot 1 TSP-4 70.8741 0.3397 35.1898 356.0358 49.5398 96768.2700 896.4864 74.5956 119.6666 12.4071 308.0071 210.1316 TSP-5-A 86.2899 0.3287 44.1583 387.9393 57.9624 111093.8000 1013.5540 87.2775 130.5620 13.9377 362.0215 246.6621 Macroplot 4 TSP-16 76.9406 0.4547 27.9098 377.1123 58.3711 92100.3200 1449.9360 72.1646 147.7444 13.4962 395.1005 213.3616 Macroplot 5 TSP-21 58.5394 0.3506 37.5679 290.2521 51.4069 78279.9800 1717.0700 65.9671 131.9027 9.2525 257.7952 181.7380 TSP-25 45.3335 0.4328 27.6045 250.7753 48.7266 60724.4200 1522.0220 47.3464 124.1237 8.0064 201.3153 138.9570 Macroplot 6 TSP-27 66.1464 0.3671 47.0631 280.8132 63.3043 83580.5500 2234.0410 79.6693 133.6636 10.8862 291.9440 219.6977 TSP-29 70.2154 0.3506 40.8447 325.6647 70.2457 96532.9300 1842.4470 89.9483 131.0065 12.5150 347.9141 253.3412 LCG Macroplot 1 45.8541 0.3561 38.8958 428.2379 103.2743 118121.7000 454.2584 130.4624 132.6563 16.0276 533.6176 251.3880 Macroplot 6 47.7027 0.3890 61.2102 470.8352 117.0222 129169.7000 558.8123 139.7116 186.3982 16.6850 550.5081 217.2299 Macroplot 7 62.9166 0.2904 17.9761 407.3391 88.2443 126699.7000 247.1317 79.7210 121.6737 13.8249 451.0038 180.9281 Macroplot 10 50.1782 0.2410 12.5302 213.3972 28.5227 74637.6000 222.8292 51.5737 92.1096 8.1389 250.5409 141.7392
145
K.2 DMA results Table K.3. Hg concentration in the individual samples A horizon B horizon
Concentration Concentration Sample mass (g)
Peak height
Hg content (µg) (µg g-1)
Sample mass (g)
Peak height
Hg content (µg) (µg g-1)
LGS Macroplot 1 LGS-1-1 0.0775 0.6086 28.95 0.3736 0.0792 0.45955 17.73 0.22385 LGS-1-2 0.0736 0.60769 28.9 0.39269 0.0789 0.54378 21.94 0.27808 LGS-1-3 0.0758 0.56688 23.19 0.3059 0.0748 0.50296 19.84 0.26524 LGS-1-4 0.0761 0.52818 21.12 0.27757 0.0793 0.50661 20.02 0.25249 LGS-1-5 0.0757 0.60566 25.39 0.33542 0.0822 0.43394 16.54 0.20116 Macroplot 2 LGS-2-7 0.0703 0.7354 34.27 0.48753 0.0712 0.0608 36.71 0.51562 LGS-2-8 0.0679 0.7182 32.84 0.48373 0.0767 0.06355 38.4 0.5006 LGS-2-9 0.0774 0.76763 38.75 0.50069 0.0777 0.05826 35.15 0.45242 LGS-2-10 0.073 0.8016 40.08 0.549 0.0595 0.7483 32.58 0.54763 Macroplot 4 LGS-4-16 0.0729 0.33331 14.62 0.20061 0.0748 0.3025 11.51 0.15385 LGS-4-17 0.0767 0.35769 15.79 0.20592 0.0734 0.30627 11.05 0.1505 LGS-4-18 0.0821 0.36909 16.35 0.19911 0.0767 0.44089 16.86 0.21976 LGS-4-19 0.0745 0.59707 28.3 0.37981 0.0744 0.46757 18.11 0.24342 Macroplot 8 LGS-8-36 0.0769 0.66347 29 0.37709 0.0772 0.53938 21.71 0.28119 LGS-8-38 0.0757 0.64071 30.82 0.40713 0.07 0.50649 20.02 0.28595 LGS-8-39 0.0772 0.63181 30.3 0.39245 0.0743 0.5433 21.92 0.29496 LGS-8-40 0.0734 0.58834 27.8 0.37877 0.08 0.52948 21.19 0.26488
146
Table K.3: (Continued) A horizon B horizon
Concentration Concentration Sample mass (g)
Peak height
Hg content (µg) (µg g-1)
Sample mass (g)
Peak height
Hg content (µg) (µg g-1)
TSP Macroplot 1 TSP-4 0.0737 0.51612 23.84 0.32343 0.075 0.17642 6.07 0.08093 TSP-5 0.0764 0.50379 23.18 0.3034 0.0758 0.16197 5.55 0.07316 Macroplot 4 TSP-16 0.0739 0.52465 24.29 0.32874 0.0782 0.24488 8.63 0.11035 Macroplot 5 TSP-21 0.0795 0.58061 27.37 0.34423 0.071 0.1488 5.07 0.07144 TSP-25 0.0753 0.36827 16.31 0.21656 0.0804 0.12614 4.27 0.05309 Macroplot 6 TSP-27 0.073 0.366 16.2 0.22187 0.0774 0.22339 7.81 0.10093 TSP-29 0.0733 0.47049 21.44 0.29243 0.075 0.18227 6.28 0.08377 LCG Macroplot 1 0.0758 0.71844 35.56 0.46915 0.08 0.24394 8.59 0.10742 Macroplot 6 0.0752 0.4656 21.18 0.28168 0.0852 0.37636 13.97 0.164 Macroplot 7 0.0771 0.78433 39.88 0.5172 0.0787 0.2937 10.54 0.13395 Macroplot 10 0.0798 0.53218 24.7 0.30952 0.0783 0.49761 19.57 0.24998
147
K.3 Mass of the samples
Table K.4 Mass of the samples from the A horizon (g) Sample Mass Sample Mass Sample Mass Sample Mass LGS-1-1 0.2924 LGS-2-9 0.3003 LGS-8-38 0.299 TSP-25 0.2982 LGS-1-2 0.2995 LGS-2-10 0.2945 LGS-8-39 0.3026 TSP-27 0.2961 LGS-1-3 0.3026 LGS-4-16 0.3021 LGS-8-40 0.2937 TSP-29 0.2947 LGS-1-4 0.2975 LGS-4-17 0.2925 TSP-4 0.2935 LCG-1-5 0.3043 LGS-1-5 0.3004 LGS-4-18 0.3007 TSP-5-A 0.2909 LCG-26-30 0.2988 LGS-2-7 0.3072 LGS-4-19 0.3007 TSP-16 0.2987 LCG-31-35 0.2968 LGS-2-8 0.3168 LGS-8-36 0.3003 TSP-21 0.2989 LCG-46-50 0.3033 Table K.5 Mass of the samples from the B horizon (g) Sample Mass Sample Mass Sample Mass Sample Mass LGS-1-1 0.3014 LGS-2-9 0.2933 LGS-8-38 0.2961 TSP-25 0.3055 LGS-1-2 0.3051 LGS-2-10 0.3037 LGS-8-39 0.2985 TSP-27 0.2931 LGS-1-3 0.2915 LGS-4-16 0.2958 LGS-8-40 0.2969 TSP-29 0.3032 LGS-1-4 0.2916 LGS-4-17 0.3008 TSP-4 0.2981 LCG-1-5 0.2997 LGS-1-5 0.293 LGS-4-18 0.2958 TSP-5-A 0.298 LCG-26-30 0.3041 LGS-2-7 0.2928 LGS-4-19 0.2904 TSP-16 0.3036 LCG-31-35 0.2963 LGS-2-8 0.2943 LGS-8-36 0.2982 TSP-21 0.306 LCG-46-50 0.3024
148
K.4 Concentrations in soil K.4.1 A horizon Table K.6. Heavy metal concentrations the individual samples of the A horizon (µg g-1 soil) 1
As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn LGS Macroplot 1 LGS-1-1 14.29 0.41 7.26 44.88 18.15 23.20 0.37 470.90 20.89 22.78 3.80 55.57 59.58 LGS-1-2 13.86 0.31 5.59 41.19 16.65 22.15 0.39 373.84 15.29 23.89 53.98 24.51 LGS-1-3 19.62 0.39 6.10 39.44 18.48 21.68 0.31 328.94 16.48 29.80 3.60 52.63 22.80 LGS-1-4 13.25 0.22 5.21 39.20 16.51 22.89 0.28 311.36 13.91 22.66 2.83 56.10 47.42 LGS-1-5 15.23 0.44 7.19 36.41 17.71 20.74 0.34 799.96 19.03 28.41 3.42 49.91 35.82 avr. 15.3 0.35 6.3 40.2 17.5 22.1 0.34 457.0 17.1 25.5 3.4 53.6 38.0 SD 2.5 0.09 0.9 3.1 0.9 1.0 0.05 201.5 2.8 3.4 0.4 2.5 15.6 RSD% 16.7 24.79 14.8 7.7 5.0 4.4 14.03 44.1 16.5 13.2 12.2 4.6 41.0 Macroplot 2 LGS-2-7 15.45 0.47 13.34 41.86 22.86 25.68 0.49 688.59 18.84 32.36 3.56 54.31 49.30 LGS-2-8 18.31 0.45 15.30 45.15 25.49 26.42 0.48 773.16 22.37 32.75 3.16 58.11 56.63 LGS-2-9 16.21 0.42 24.22 48.96 24.10 26.06 0.50 747.81 25.31 28.86 3.29 56.30 62.13 LGS-2-10 16.09 0.46 14.85 36.30 21.90 22.15 0.55 745.34 18.69 33.02 3.22 46.13 25.33 avr. 16.5 0.5 16.9 43.1 23.6 25.1 0.5 738.7 21.3 31.7 3.3 53.7 48.3 SD 1.2 0.03 4.9 5.4 1.6 2.0 0.03 35.7 3.2 1.9 0.2 5.3 16.2 RSD% 7.5 7.3 29.1 12.5 6.6 7.9 6.0 4.8 14.9 6.1 5.3 9.8 33.5 Macroplot 4 LGS-4-16 10.84 0.41 11.16 38.01 18.77 23.98 0.20 886.15 17.09 27.10 3.84 53.36 81.01 LGS-4-17 9.39 0.52 13.96 38.37 22.18 21.56 0.21 930.12 20.56 25.89 48.03 105.20 LGS-4-18 9.81 0.45 15.74 44.17 21.80 24.52 0.20 827.66 22.97 24.90 2.69 53.15 92.46 LGS-4-19 10.54 0.54 12.06 36.36 20.37 21.60 0.38 933.74 19.54 26.14 2.79 49.44 90.53 avr. 10.1 0.48 13.2 39.2 20.8 22.9 0.25 894.4 20.0 26.0 3.1 51.0 92.3 SD 0.7 0.06 2.0 3.4 1.6 1.6 0.09 49.5 2.4 0.9 0.6 2.7 9.9 RSD% 6.5 12.65 15.4 8.7 7.5 6.8 36.13 5.5 12.2 3.5 20.6 5.2 10.8
149
Table K.6 (continued)
As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn
LGS Macroplot 8 LGS-8-36 19.01 0.30 6.00 39.65 13.78 23.66 0.38 475.17 12.63 25.76 3.31 53.08 16.91 LGS-8-38 16.95 0.39 6.24 41.42 15.23 23.44 0.41 760.67 13.10 32.63 2.88 52.54 58.61 LGS-8-39 18.54 0.45 6.61 41.65 14.10 23.59 0.39 823.02 13.58 29.99 55.98 33.86 LGS-8-40 17.42 0.39 7.08 40.15 14.71 25.00 0.38 985.50 14.08 28.69 3.61 58.68 72.06 avr. 18.0 0.38 6.5 40.7 14.5 23.9 0.39 761.1 13.3 29.3 3.3 55.1 45.4 SD 1.0 0.06 0.5 1.0 0.6 0.7 0.01 212.9 0.6 2.9 0.4 2.8 24.7 RSD% 5.3 16.15 7.3 2.4 4.5 3.0 3.60 28.0 4.7 9.8 11.1 5.2 54.5 TSP Macroplot 1 TSP-4 12.86 0.27 2.56 49.50 9.54 16.35 0.32 124.79 8.64 51.77 2.96 48.70 32.88 TSP-5 15.00 0.27 2.29 48.63 10.59 32.33 0.30 222.42 8.08 77.22 3.17 97.78 30.31 avr. 13.9 0.27 2.4 49.1 10.1 24.3 0.31 173.6 8.4 64.5 3.1 73.2 31.6 SD 1.5 0.00 0.2 0.6 0.7 11.3 0.01 69.0 0.4 18.0 0.1 34.7 1.8 RSD% 10.9 0.82 7.8 1.3 7.4 46.4 4.52 39.8 4.7 27.9 4.7 47.4 5.8 Macroplot 4 TSP-16 18.21 0.21 2.77 50.03 12.22 15.69 0.33 114.37 9.24 57.53 4.08 60.93 34.69 Macroplot 5 TSP-21 13.13 0.21 2.68 48.16 10.61 15.74 0.34 121.83 8.92 38.46 3.17 44.04 30.53 TSP-25 11.59 0.29 1.60 36.76 9.15 9.92 0.22 90.16 5.38 38.62 2.89 33.13 23.10 avr. 12.4 0.25 2.1 42.5 9.9 12.8 0.28 106.0 7.1 38.5 3.0 38.6 26.8 SD 1.1 0.06 0.8 8.1 1.0 4.1 0.09 22.4 2.5 0.1 0.2 7.7 5.3 RSD% 8.8 22.91 35.7 19.0 10.5 32.1 32.20 21.1 35.0 0.3 6.4 20.0 19.6 Macroplot 6 TSP-27 12.63 0.16 2.28 41.66 9.92 14.60 0.22 115.25 9.13 44.31 3.05 48.35 30.15 TSP-29 14.98 0.28 2.63 49.13 11.60 14.93 0.29 101.86 10.20 49.97 3.61 48.58 38.35 avr. 13.8 0.22 2.5 45.4 10.8 14.8 0.26 108.6 9.7 47.1 3.3 48.5 34.3 SD 1.7 0.08 0.3 5.3 1.2 0.2 0.05 9.5 0.8 4.0 0.4 0.2 5.8 RSD% 12.0 38.34 10.2 11.6 11.0 1.6 19.40 8.7 7.9 8.5 11.9 0.3 16.9
150
Table K.6 (continued) As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn LCG Macroplot 1 12.33 0.210 4.11 41.58 12.45 31.76 0.469 118.24 13.39 50.73 3.58 120.10 30.54 Macroplot 6 9.54 0.121 11.16 60.55 18.91 22.02 0.282 203.30 20.61 30.31 3.07 88.11 42.87 Macroplot 7 14.41 0.386 3.61 47.87 17.74 20.46 0.517 132.98 14.25 4.96 73.53 49.96 Macroplot 10 10.44 0.133 1.22 24.18 4.45 11.12 0.309 29.97 4.09 20.05 2.08 40.22 17.55 1) Heavy metal concentration in soil (µg g-1) = Heavy metal concentration in solution (µg L-1) * V / M [except Hg which was the instrument output] where: Trace metal concentrations in solution (µg L-1) are found in table K.1 V = final volume of the digested sample (L) = 0.05 L M = Mass of the sample (g) [table K.3] 2) mg g-1
151
K.4.2 B horizon Table K.7. Trace metal concentrations in the individual samples of the B horizon (µg g-1 soil) 1
As Cd Co Cr Cu Fe2 Hg Mn Ni Pb Sn V Zn LGS Macroplot 1 LGS-1-1 8.37 0.11 6.35 33.10 15.19 60.41 0.22 665.56 14.92 21.29 2.157 145.56 34.99 LGS-1-2 9.25 0.07 5.44 39.97 15.55 18.12 0.28 212.73 15.55 17.06 2.03 44.21 38.60 LGS-1-3 8.80 0.10 4.93 41.06 16.29 20.36 0.26 224.61 13.17 17.89 2.14 50.63 23.09 LGS-1-4 11.36 0.12 7.71 57.35 19.97 26.01 0.25 316.11 21.59 19.47 2.99 60.14 47.98 LGS-1-5 8.66 0.07 6.32 42.78 15.54 20.27 0.20 305.56 17.38 18.33 2.00 54.66 36.53 avr. 9.3 0.09 6.1 42.8 16.5 29.0 0.24 344.9 16.5 18.8 2.3 71.0 36.2 SD 1.2 0.02 1.1 8.9 2.0 17.8 0.03 185.2 3.2 1.6 0.4 42.1 8.9 RSD% 12.9 24.56 17.3 20.8 12.0 61.2 12.8 53.7 19.4 8.7 18.2 59.2 24.6 Macroplot 2 LGS-2-7 10.83 0.20 8.60 36.42 22.20 71.43 0.52 1043.13 12.91 28.82 1.89 148.64 35.22 LGS-2-8 10.48 0.17 16.20 37.12 24.60 68.11 0.50 1442.72 15.76 30.59 1.81 147.03 41.80 LGS-2-9 12.22 0.13 17.52 48.55 21.21 25.16 0.45 441.76 21.32 24.02 1.89 55.07 30.99 LGS-2-10 10.26 0.17 13.94 39.61 21.45 20.63 0.55 434.36 15.18 27.40 1.74 43.26 39.98 avr. 10.9 0.16 14.1 40.4 22.4 46.3 0.50 840.5 16.3 27.7 1.8 98.5 37.0 SD 0.9 0.03 3.9 5.6 1.5 27.2 0.04 492.5 3.6 2.8 0.1 57.2 4.9 RSD% 8.0 17.09 27.9 13.8 6.9 58.6 7.86 58.6 21.9 10.0 3.8 58.0 13.2 Macroplot 4 LGS-4-16 8.21 0.17 9.64 37.60 15.54 18.74 0.15 773.88 14.30 27.79 2.05 40.83 55.85 LGS-4-17 8.27 0.24 9.22 38.67 18.95 19.09 0.15 829.60 15.86 25.66 2.13 47.25 67.55 LGS-4-18 8.54 0.12 9.79 41.79 18.89 20.19 0.22 692.63 15.04 27.24 2.04 45.60 57.32 LGS-4-19 7.92 0.09 7.89 37.10 15.67 16.49 0.24 641.24 12.48 24.19 1.89 39.96 43.70 avr. 8.2 0.16 9.1 38.8 17.3 18.6 0.19 734.3 14.4 26.2 2.0 43.4 56.1 SD 0.2 0.07 0.9 2.1 1.9 1.5 0.05 83.7 1.4 1.6 0.1 3.6 9.8 RSD% 3.1 43.9 9.4 5.4 11.1 8.3 24.43 11.4 10.0 6.2 5.0 8.2 17.4
152
Table K.7 (Continued) As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn LGS Macroplot 8 LGS-8-36 9.47 0.077 5.40 30.45 10.62 58.88 0.281 736.19 11.04 21.23 2.36 132.68 27.84 LGS-8-38 11.51 0.089 6.25 41.83 12.11 21.76 0.286 383.99 12.05 20.98 2.35 48.50 28.16 LGS-8-39 13.12 0.080 6.69 43.80 11.92 24.39 0.295 480.33 11.51 24.25 2.38 57.03 30.95 LGS-8-40 11.69 0.089 5.73 38.22 11.00 22.20 0.265 392.88 11.86 19.09 2.24 45.34 35.48 avr. 11.4 0.084 6.0 38.6 11.4 31.8 0.28 498.3477 11.6 21.4 2.33 70.9 30.6 SD 1.5 0.006 0.6 5.9 0.7 18.1 0.01 164.41404 0.4 2.1 0.06 41.5 3.5 RSD% 13.1 7.70 9.4 15.3 6.3 56.9 4.47 32.991832 3.8 10.0 2.77 58.5 11.5 TSP Macroplot 1 TSP-4 11.89 0.057 5.90 59.72 8.31 16.23 0.081 150.37 12.51 20.07 2.08 51.66 35.24 TSP-5 14.48 0.055 7.41 65.09 9.72 18.64 0.073 170.06 14.64 21.91 2.34 60.74 41.39 avr. 13.2 0.056 6.7 62.4 9.0 17.4 0.077 160.2 13.6 21.0 2.2 56.2 38.3 SD 1.8 0.001 1.1 3.8 1.0 1.7 0.005 13.9 1.5 1.3 0.2 6.4 4.3 RSD% 13.9 2.29 16.0 6.1 11.1 9.8 7.131 8.7 11.1 6.2 8.2 11.4 11.3 Macroplot 4 TSP-16 12.67 0.075 4.60 62.11 9.61 15.17 0.110 238.79 11.88 24.33 2.22 65.07 35.14 Macroplot 5 TSP-21 9.56 0.057 6.14 47.43 8.40 12.79 0.071 280.57 10.78 21.55 1.51 42.12 29.70 TSP-25 7.42 0.071 4.52 41.04 7.97 9.94 0.053 249.10 7.75 20.31 1.31 32.95 22.74 avr. 8.5 0.064 5.3 44.2 8.2 11.4 0.062 264.8 9.3 20.9 1.4 37.5 26.2 SD 1.5 0.010 1.1 4.5 0.3 2.0 0.013 22.2 2.1 0.9 0.1 6.5 4.9 RSD% 17.9 14.95 21.5 10.2 3.7 17.7 20.84 8.4 23.1 4.2 10.1 17.3 18.7 Macroplot 6 TSP-27 11.28 0.063 8.03 47.90 10.80 14.26 0.101 381.11 13.59 22.80 1.86 49.80 37.48 TSP-29 11.58 0.058 6.74 53.70 11.58 15.92 0.084 303.83 14.83 21.60 2.06 57.37 41.78 avr. 11.4 0.060 7.4 50.8 11.2 15.1 0.092 342.5 14.2 22.2 2.0 53.6 39.6 SD 0.2 0.003 0.9 4.1 0.5 1.2 0.012 54.6 0.9 0.8 0.1 5.3 3.0 RSD% 1.8 5.632 12.4 8.1 5.0 7.8 13.139 15.9 6.2 3.8 7.5 10.0 7.7
153
Table K.7 (Continued) As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn LCG Macroplot 1 7.65 0.059 6.49 71.44 17.23 19.71 0.107 75.78 21.76 22.13 2.67 89.02 41.94 Macroplot 6 7.84 0.064 10.06 77.41 19.24 21.24 0.164 91.88 22.97 30.65 2.74 90.51 35.72 Macroplot 7 10.62 0.049 3.03 68.74 14.89 21.38 0.134 41.70 13.45 20.53 2.33 76.11 30.53 Macroplot 10 8.30 0.040 2.07 35.28 4.72 12.34 0.250 36.84 8.53 15.23 1.35 41.42 23.44 1) Trace metal concentration in soil (µg g-1) = Heavy metal concentration in solution (µg L-1) * V / M [except Hg which was the instrument output] where: Trace metal concentration in solution (µg L-1) is found in table K.2 V = final volume of the digested sample (L) = 0.05 L M = Mass of the sample (g) [Table K.5] 2) mg g-1
154
K.5 Median and mean concentrations, background and standard values Table K.8. Median and mean concentrations in the studied sites, background and standard values (mg kg-1) LGS TSP LCG Chinese European Background
A horizon
B horizon
A horizon
B horizon
A horizon
B horizon
standards standards values
Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean
As 15.9 15.0 10.1 9.9 13.8 14.1 11.4 11.3 11.4 11.7 8.1 8.6 15 d.n.a.1 9.6-13.7
Cd 0.421 0.413 0.124 0.123 0.249 0.241 0.060 0.060 0.171 0.212 0.054 0.05 0.2 0.5 0.080-0.120
Co 9.9 10.7 7.6 8.7 2.4 2.4 6.7 6.2 3.9 5.02 4.8 5.4 d.n.a. 20 11.6-20.0
Cr 40.5 40.8 39.6 40.3 45.4 46.9 50.8 53.9 44.7 43.5 70.1 63.2 90 50 ≤118
Cu 19.1 19.0 16.9 16.9 10.1 10.5 9.0 9.5 15.1 13.4 16.1 14 35 30 14.9-20.7
Fe2 23.4 23.4 17.1 31.3 15.7 17.1 15.2 14.7 21.2 21.3 20.5 18.7 d.n.a. d.n.a. d.n.a.
Hg 0.320 0.368 0.263 0.301 0.280 0.288 0.077 0.08 0.389 0.394 0.149 0.16 0.15 0.3 0.080-0.150
Mn 749.9 697.8 480.3 589.3 111.5 127.2 249.1 253 125.6 121.1 42.3 61.6 d.n.a. d.n.a. 510-712
Ni 18.6 17.9 15.4 14.8 8.4 8.51 13.6 12.3 13.8 13.1 17.6 16.7 40 35 >51.0
Pb 27.6 28.0 23.8 23.2 47.1 51.1 21.0 21.8 30.3 33.7 21.3 22.1 35 50 18.5-23.9
Sn 3.3 2.7 3.2 2.12 3.2 3.28 2.1 1.9 3.3 3.4 2.5 2.3 d.n.a. d.n.a. d.n.a.
V 53.7 53.4 38.5 71.0 54.7 54.5 0.3 51.4 80.8 80.5 82.6 74.3 d.n.a. d.n.a. 76.8-96.6
Zn 46.9 54.9 36.6 39.8 31.6 31.43 38.3 34.8 36.7 35.2 33.1 32.9 100 100 67.3-88.5 1) Data not available 2) values in mg g-1
155
Appendix L. Principal Component Analysis (PCA)
L.1 Inputs Table L.1. Data matrix for PCA in the A horizon Plot As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn pHH2O CEC BS% LOI
LGS 1 15.2 0.35 6.3 40.2 18 22.1 0.34 457 17.1 26 2.53 53.6 38 3.74 14 40.1 20.7 LGS 2 16.5 0.46 16.9 43.1 24 25.1 0.5 739 21.3 32 3.31 53.7 48.3 4.4 22 74.6 26 LGS 4 10.1 0.48 13.2 39.2 21 22.9 0.25 894 20 26 2.1 51 92.3 5.81 19 98 16.3 LGS 8 18 0.38 6.5 40.7 14 23.9 0.4 761 13.3 29 2.21 55.1 45.4 4.11 12 40.9 21.4 TSP 1 13.9 0.27 2.4 49.1 10 24.3 0.3 174 8.36 64 3.06 73.2 31.6 3.56 13 30.5 27.3 TSP 4 18.21 0.21 2.77 50 12 15.7 0.33 114 9.24 58 4.08 60.9 34.7 3.31 16 21.9 25.9 TSP 5 12.4 0.25 2.1 42.5 9.9 12.8 0.28 106 7.15 39 3.03 38.6 26.8 3.84 9.4 21 20.6 TSP 6 13.8 0.22 2.4 45.4 11 14.8 0.26 109 9.67 47 3.33 48.5 34.3 3.93 8.7 20.9 15.4 LCG 1 12.33 0.21 4.11 41.6 12 31.8 0.47 118 13.4 51 3.58 120 30.5 3.51 18 26.2 22.9 LCG 6 9.54 0.12 11.2 60.5 19 22.0 0.28 203 20.6 30 3.07 88.1 42.9 3.81 18 27.1 30.3 LCG 7 14.41 0.39 3.61 47.9 18 20.5 0.52 133 14.3 * 4.96 73.5 50 3.85 25 37.2 37.7 LCG 10 10.44 0.13 1.22 24.2 4.5 11.1 0.31 30 4.09 20 2.08 40.2 17.6 3.56 11 28.9 17.7
Table L.2. Data matrix for PCA in the B horizon Plot As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn CEC BS pHH2O LOI
LGS1 9.3 0.09 6.1 42.8 17 29 0.24 345 16.5 19 2.3 71 36.2 2.18 21 4.17 7.91 LGS2 10.9 0.16 14.1 40.4 22 46.3 0.5 841 16.3 28 1.8 98.5 37 2.52 29 4.14 13.8 LGS4 8.2 0.16 9.1 38.8 17 18.6 0.19 734 14.4 26 2 43.4 56.1 8.21 89 5.3 10.2 LGS8 11.4 0.08 6 38.6 11 31.8 0.28 498 11.6 21 2.33 70.9 30.6 2.08 32 4.43 9.64 TSP1 13.2 0.06 6.7 62.4 9 17.4 0.08 160 13.6 21 2.2 56.2 38.3 3.4 11 3.67 4.36 TSP4 12.67 0.08 4.6 62.1 9.6 15.2 0.11 239 11.9 24 2.22 65.1 35.1 4.07 9.5 3.5 4.74 TSP5 8.5 0.06 5.3 44.2 8.2 11.4 0.06 265 9.3 21 1.4 37.5 26.2 2.58 11 3.59 2.91 TSP6 11.4 0.06 7.4 50.8 11 15.1 0.09 343 14.2 22 2 53.6 39.6 3.38 14 3.69 3.44 LCG1 7.65 0.06 6.49 71.4 17 19.7 0.11 75.8 21.8 22 2.67 89 41.9 7 14 3.89 6.51 LCG6 7.84 0.06 10.1 77.4 19 21.2 0.16 91.9 23 31 2.74 90.5 35.7 7.13 15 3.99 7.03 LCG7 10.62 0.05 3.03 68.7 15 21.4 0.13 41.7 13.5 21 2.33 76.1 30.5 5.66 13 4 6.41 LCG10 8.3 0.04 2.07 35.3 4.7 12.3 0.25 36.8 8.53 15 1.35 41.4 23.4 3.22 14 4.1 7.15
156
L.2 Outputs
Table L.3. Loadings in the A horizon Variable PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 As 0.004 0.134 -0.622 0.360 -0.106 -0.190 -0.095 -0.454 Cd 0.301 -0.093 -0.310 0.180 0.177 -0.082 0.084 0.375 Co 0.339 0.075 0.081 -0.005 -0.264 0.153 0.014 0.234 Cr 0.024 0.347 0.320 0.415 -0.099 -0.295 -0.136 0.109 Cu 0.332 0.135 0.054 0.121 -0.195 -0.007 -0.253 0.122 Fe 0.199 0.291 -0.080 -0.311 0.355 -0.359 -0.020 0.157 Hg 0.103 0.243 -0.491 -0.379 -0.098 0.167 -0.088 0.201 Mn 0.336 -0.094 -0.183 0.073 0.013 -0.266 0.059 -0.135 Ni 0.322 0.146 0.152 -0.027 -0.188 -0.098 -0.352 0.020 Pb -0.158 0.289 -0.065 0.328 0.527 0.081 0.243 0.224 Sn -0.109 0.375 -0.051 0.284 0.072 0.529 -0.312 0.067 V 0.001 0.380 0.172 -0.386 0.329 -0.131 -0.152 -0.273 Zn 0.318 -0.087 0.176 0.193 0.274 -0.009 -0.002 -0.446 pH 0.300 -0.210 0.147 0.111 0.244 0.103 -0.002 0.113 CEC 0.274 0.240 0.046 -0.123 -0.042 0.461 0.240 -0.383 BS% 0.337 -0.126 -0.022 0.011 0.132 0.226 0.290 0.027 LOI 0.010 0.402 0.096 0.045 -0.353 -0.176 0.667 0.019 Variable PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 As 0.006 -0.217 -0.087 -0.187 0.056 -0.254 -0.087 0.007 Cd 0.294 0.472 0.251 0.159 0.039 -0.367 0.033 0.225 Co -0.276 -0.486 0.292 0.160 0.320 -0.322 -0.281 -0.060 Cr -0.263 0.021 -0.198 0.407 -0.115 -0.034 0.212 0.268 Cu 0.293 0.102 -0.132 0.017 -0.112 0.025 0.096 -0.770 Fe 0.170 -0.097 -0.243 0.035 0.308 0.284 -0.408 0.062 Hg -0.428 0.097 -0.130 0.137 -0.469 0.073 0.007 0.015 Mn -0.272 0.030 0.264 0.035 0.302 0.507 0.442 -0.058 Ni 0.299 -0.189 0.160 -0.433 -0.331 0.121 0.025 0.421 Pb 0.049 -0.381 0.285 -0.061 -0.308 0.119 0.068 -0.183 Sn -0.047 0.255 -0.145 -0.147 0.413 0.160 -0.054 0.097 V -0.097 0.081 0.117 -0.099 0.114 -0.501 0.348 -0.110 Zn -0.141 0.174 -0.080 0.223 -0.262 -0.003 -0.466 -0.053 pH -0.456 0.152 -0.022 -0.549 -0.041 -0.059 -0.043 -0.033 CEC 0.212 0.049 0.272 0.219 -0.038 0.154 0.068 0.131 BS% 0.137 -0.297 -0.644 0.003 0.041 -0.138 0.359 0.134 LOI -0.041 0.259 -0.067 -0.324 0.004 -0.022 -0.121 -0.051 Variable PC17 As 0.198 Cd -0.037 Co -0.116 Cr 0.267 Cu 0.091 Fe 0.219 Hg -0.073 Mn -0.232 Ni -0.204 Pb -0.080 Sn -0.241 V -0.133 Zn -0.393 pH 0.456 CEC 0.464 BS% -0.166 LOI -0.175
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Table L.4. Loadings in the B horizon Variable PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 As 0.062 0.068 -0.274 0.651 -0.447 0.216 0.163 0.170 Cd -0.330 0.177 0.071 0.165 0.082 0.008 0.174 -0.334 Co -0.316 -0.043 -0.106 0.226 0.412 -0.132 -0.073 0.418 Cr 0.055 -0.467 -0.028 0.090 -0.034 0.274 0.198 -0.106 Cu -0.328 -0.184 -0.068 -0.094 0.065 -0.153 0.171 -0.483 Fe -0.283 0.071 -0.367 -0.071 -0.170 -0.081 -0.038 -0.176 Hg -0.253 0.215 -0.290 -0.247 -0.012 0.188 0.167 0.352 Mn -0.289 0.258 -0.003 0.263 0.070 -0.119 -0.125 -0.249 Ni -0.195 -0.386 -0.024 -0.123 0.088 -0.386 -0.031 0.322 Pb -0.258 -0.209 0.014 0.281 0.370 0.476 -0.430 0.003 Sn -0.103 -0.398 -0.033 0.031 -0.488 -0.142 -0.492 -0.016 V -0.204 -0.273 -0.353 -0.145 -0.117 0.069 0.139 -0.121 Zn -0.236 -0.096 0.345 0.317 -0.151 -0.379 0.379 0.202 CEC -0.114 -0.288 0.410 -0.111 -0.008 0.398 0.344 0.031 BS -0.247 0.167 0.392 0.036 -0.174 0.086 -0.133 -0.055 pH -0.246 0.160 0.323 -0.227 -0.321 0.099 -0.277 0.132 LOI -0.321 0.143 -0.113 -0.253 -0.179 0.247 0.145 0.208 Variable PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 As 0.194 -0.118 -0.176 -0.212 -0.090 -0.218 0.028 0.082 Cd -0.301 -0.414 0.453 -0.109 -0.048 -0.063 0.024 0.283 Co 0.374 0.208 0.228 -0.016 -0.335 0.273 0.089 0.145 Cr 0.246 -0.051 0.357 0.293 0.352 0.102 0.444 -0.185 Cu 0.391 -0.304 -0.423 -0.041 -0.292 0.070 0.037 -0.162 Fe 0.289 0.199 0.243 0.294 0.286 -0.164 -0.513 0.199 Hg -0.183 -0.287 -0.336 0.377 0.141 0.198 0.230 0.270 Mn -0.173 0.477 -0.218 0.272 0.067 -0.289 0.414 -0.165 Ni -0.047 -0.178 -0.024 -0.213 0.213 -0.609 0.157 0.096 Pb -0.177 -0.169 -0.174 -0.042 0.199 -0.005 -0.297 -0.178 Sn -0.252 -0.063 0.064 0.284 -0.371 0.148 0.050 0.111 V -0.262 0.400 -0.091 -0.540 0.172 0.315 0.046 0.088 Zn -0.201 0.001 -0.080 0.143 0.225 0.303 -0.286 -0.264 CEC -0.008 0.305 -0.148 0.168 -0.265 -0.269 -0.178 0.338 BS 0.097 0.061 0.107 -0.241 0.094 0.131 0.271 0.287 pH 0.374 -0.066 -0.094 -0.145 0.267 0.040 0.000 -0.110 LOI -0.125 0.024 0.308 -0.076 -0.330 -0.184 -0.007 -0.594 Variable PC17 As -0.024 Cd -0.336 Co -0.134 Cr -0.020 Cu 0.108 Fe 0.165 Hg 0.046 Mn -0.133 Ni 0.074 Pb 0.108 Sn -0.040 V -0.137 Zn 0.051 CEC -0.115 BS 0.658 pH -0.540 LOI 0.176
159
Appendix M: Two tailed F-test
• Formula
F = s 21 /s 2
2 Where: 1 and 2 stand for A and B horizons and allocated in the equation so that F is always ≥ 1 s2 : variance DFA (Degree of freedom) = nA -1 DFB (Degree of freedom) = nB -1
160
Table M.1. F-tests data As Cd Co Cr Cu Fe Hg Mn Ni Pb Sn V Zn
TSP s 4.900 0.002 0.160 25.609 1.214 49828599.000 0.003 1903.802 2.323 181.153 2.669 431.159 22.279
s 5.078 0.000 1.764 78.265 1.841 7680485.000 0.000 6230.624 6.136 2.120 0.143 123.156 45.165
DFA 6 6 6 6 6 6 6 6 6 6 6 6 6
DFB 6 6 6 6 6 6 6 6 6 6 6 6 6
F-value 1.036 38.533 11.006 3.056 1.516 6.488 7.200 3.273 2.642 85.457 18.725 3.501 2.027 Critical F-value 4.995 4.995 4.995 4.995 4.995 4.995 4.995 4.995 4.995 4.995 4.995 4.995 4.995 Significant difference No Yes Yes No No Yes Yes No No Yes Yes No No LCG s 4.667 0.015 18.320 228.891 43.372 71420237.000 0.014 5070.323 46.334 243.904 1.443 1099.076 203.314
s 1.879 0.000 13.206 359.998 41.627 18357692.000 0.004 708.953 47.441 40.905 0.415 521.245 61.613
DFA 3 3 3 3 3 3 3 3 3 3 3 3 3
DFB 3 3 3 3 3 3 3 3 3 3 3 3 3
F-value 2.485 128.319 1.387 1.573 1.042 3.890 3.529 7.152 1.024 5.963 3.475 2.109 3.299 Critical F-value 9.605 9.605 9.605 9.605 9.605 9.605 9.605 9.605 9.605 9.605 9.605 9.605 9.605 Significant difference No Yes No No No No No No No No No No No LGS s 10.875 0.007 27.426 12.198 13.106 2854596.600 0.018 48545.866 14.470 12.050 0.143 11.830 715.205
s 2.623 0.002 14.453 36.348 17.290 377461808.000 0.015 102200.570 9.444 17.687 0.086 1759.890 138.081
DFA 16 16 16 16 16 16 16 16 16 16 16 16 16
DFB 16 16 16 16 16 16 16 16 16 16 16 16 16
F-value 4.146 2.621 1.898 2.979 1.319 132.229 1.156 2.105 1.532 1.468 1.655 148.769 5.180 Critical F-value 2.766 2.766 2.766 2.766 2.766 2.766 2.766 2.766 2.766 2.766 2.855 2.766 2.766 Significant difference Yes No No Yes No Yes No No No No No Yes Yes
2A
2B
2A
2B
2A
2B
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Appendix N: Two-Sample t-test “As” at TSP Sample N Mean StDev SE Mean 1 7 14.60 2.21 0.84 2 7 11.40 2.25 0.85 Difference = mu (1) - mu (2) Estimate for difference: 3.20000 95% CI for difference: (0.60278, 5.79722) T-Test of difference = 0 (vs not =): T-Value = 2.68 P-Value = 0.020 DF = 12 Both use Pooled StDev = 2.2301 (P-Value = 0.020) < (Critical P-value = 0.05) → Significant difference “As” at LCG Sample N Mean StDev SE Mean 1 4 11.70 2.16 1.1 2 4 8.60 1.37 0.69 Difference = mu (1) - mu (2) Estimate for difference: 3.10000 95% CI for difference: (-0.02939, 6.22939) T-Test of difference = 0 (vs not =): T-Value = 2.42 P-Value = 0.052 DF = 6 Both use Pooled StDev = 1.8087 (P-Value = 0.052) > (Critical P-value = 0.05) → No significant difference “As” at LGS Sample N Mean StDev SE Mean 1 17 14.00 3.30 0.80 2 17 10.00 1.62 0.39 Difference = mu (1) - mu (2) Estimate for difference: 4.00000 95% CI for difference: (2.15557, 5.84443) T-Test of difference = 0 (vs not =): T-Value = 4.49 P-Value = 0.000 DF = 23 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference “Cd” at TSP Sample N Mean StDev SE Mean 1 7 0.2400 0.0480 0.018 2 7 0.06400 0.00800 0.0030 Difference = mu (1) - mu (2) Estimate for difference: 0.176000 95% CI for difference: (0.130995, 0.221005) T-Test of difference = 0 (vs not =): T-Value = 9.57 P-Value = 0.000 DF = 6 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference
162
“Cd” at LCG Sample N Mean StDev SE Mean 1 4 0.210 0.120 0.060 2 4 0.0530 0.0100 0.0050 Difference = mu (1) - mu (2) Estimate for difference: 0.157000 95% CI for difference: (-0.034609, 0.348609) T-Test of difference = 0 (vs not =): T-Value = 2.61 P-Value = 0.080 DF = 3 (P-Value = 0.080) > (Critical P-value = 0.05) → No significant difference “Cd” at LGS Sample N Mean StDev SE Mean 1 17 0.4300 0.0800 0.019 2 17 0.1200 0.0500 0.012 Difference = mu (1) - mu (2) Estimate for difference: 0.310000 95% CI for difference: (0.263393, 0.356607) T-Test of difference = 0 (vs not =): T-Value = 13.55 P-Value = 0.000 DF = 32 Both use Pooled StDev = 0.0667 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference “Co” at TSP Sample N Mean StDev SE Mean 1 7 2.450 0.400 0.15 2 7 5.99 1.33 0.50 Difference = mu (1) - mu (2) Estimate for difference: -3.54000 95% CI for difference: (-4.78127, -2.29873) T-Test of difference = 0 (vs not =): T-Value = -6.74 P-Value = 0.000 DF = 7 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference “Co” at LCG Sample N Mean StDev SE Mean 1 4 5.02 4.28 2.1 2 4 5.41 3.63 1.8 Difference = mu (1) - mu (2) Estimate for difference: -0.390000 95% CI for difference: (-7.256117, 6.476117) T-Test of difference = 0 (vs not =): T-Value = -0.14 P-Value = 0.894 DF = 6 Both use Pooled StDev = 3.9683 (P-Value = 0.894) > (Critical P-value = 0.05) → No significant difference
163
“Co” at LGS Sample N Mean StDev SE Mean 1 17 12.10 5.24 1.3 2 17 8.80 3.80 0.92 Difference = mu (1) - mu (2) Estimate for difference: 3.30000 95% CI for difference: (0.10223, 6.49777) T-Test of difference = 0 (vs not =): T-Value = 2.10 P-Value = 0.044 DF = 32 Both use Pooled StDev = 4.5770 (P-Value = 0.044) < (Critical P-value = 0.05) → Significant difference “Cr” at TSP Sample N Mean StDev SE Mean 1 7 46.70 5.06 1.9 2 7 54.90 8.85 3.3 Difference = mu (1) - mu (2) Estimate for difference: -8.20000 95% CI for difference: (-16.59524, 0.19524) T-Test of difference = 0 (vs not =): T-Value = -2.13 P-Value = 0.055 DF = 12 Both use Pooled StDev = 7.2085 (P-Value = 0.055) > (Critical P-value = 0.05) → No significant difference “Cr” at LCG Sample N Mean StDev SE Mean 1 4 43.5 15.1 7.6 2 4 63.2 19.0 9.5 Difference = mu (1) - mu (2) Estimate for difference: -19.7000 95% CI for difference: (-49.3927, 9.9927) T-Test of difference = 0 (vs not =): T-Value = -1.62 P-Value = 0.156 DF = 6 Both use Pooled StDev = 17.1611 (P-Value = 0.156) > (Critical P-value = 0.05) → No significant difference “Cr” at LGS Sample N Mean StDev SE Mean 1 17 40.80 3.49 0.85 2 17 40.20 6.03 1.5 Difference = mu (1) - mu (2) Estimate for difference: 0.600000 95% CI for difference: (-2.880165, 4.080165) T-Test of difference = 0 (vs not =): T-Value = 0.36 P-Value = 0.726 DF = 25 (P-Value = 0.726) > (Critical P-value = 0.05) → No significant difference
164
“Cu” at TSP Sample N Mean StDev SE Mean 1 7 10.70 1.10 0.42 2 7 9.50 1.36 0.51 Difference = mu (1) - mu (2) Estimate for difference: 1.20000 95% CI for difference: (-0.24047, 2.64047) T-Test of difference = 0 (vs not =): T-Value = 1.82 P-Value = 0.095 DF = 12 Both use Pooled StDev = 1.2369 (P-Value = 0.095) > (Critical P-value = 0.05) → No significant difference “Cu” at LCG Sample N Mean StDev SE Mean 1 4 13.40 6.59 3.3 2 4 14.00 6.45 3.2 Difference = mu (1) - mu (2) Estimate for difference: -0.600000 95% CI for difference: (-11.881736, 10.681736) T-Test of difference = 0 (vs not =): T-Value = -0.13 P-Value = 0.901 DF = 6 Both use Pooled StDev = 6.5204 (P-Value = 0.901) > (Critical P-value = 0.05) → No significant difference “Cu” at LGS Sample N Mean StDev SE Mean 1 17 20.60 3.62 0.88 2 17 16.90 4.16 1.0 Difference = mu (1) - mu (2) Estimate for difference: 3.70000 95% CI for difference: (0.97566, 6.42434) T-Test of difference = 0 (vs not =): T-Value = 2.77 P-Value = 0.009 DF = 32 Both use Pooled StDev = 3.8994 (P-Value = 0.009) < (Critical P-value = 0.05) → Significant difference “Fe” at TSP Sample N Mean StDev SE Mean 1 7 16906 7059 2668 2 7 14764 2771 1047 Difference = mu (1) - mu (2) Estimate for difference: 2142.00 95% CI for difference: (-4635.62, 8919.62) T-Test of difference = 0 (vs not =): T-Value = 0.75 P-Value = 0.479 DF = 7 (P-Value = 0.479) > (Critical P-value = 0.05) → No significant difference
165
“Fe” at LCG Sample N Mean StDev SE Mean 1 4 21340 8451 4226 2 4 18666 4285 2143 Difference = mu (1) - mu (2) Estimate for difference: 2674.00 95% CI for difference: (-8918.57, 14266.57) T-Test of difference = 0 (vs not =): T-Value = 0.56 P-Value = 0.593 DF = 6 Both use Pooled StDev = 6700.0234 (P-Value = 0.593) > (Critical P-value = 0.05) → No significant difference “Fe” at LGS Sample N Mean StDev SE Mean 1 17 23373 1690 410 2 17 31451 19428 4712 Difference = mu (1) - mu (2) Estimate for difference: -8078.00 95% CI for difference: (-18104.68, 1948.68) T-Test of difference = 0 (vs not =): T-Value = -1.71 P-Value = 0.107 DF = 16 (P-Value = 0.107) > (Critical P-value = 0.05) → No significant difference “Hg” at TSP Sample N Mean StDev SE Mean 1 7 0.2900 0.0500 0.019 2 7 0.0850 0.0200 0.0076 Difference = mu (1) - mu (2) Estimate for difference: 0.205000 95% CI for difference: (0.156870, 0.253130) T-Test of difference = 0 (vs not =): T-Value = 10.07 P-Value = 0.000 DF = 7 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference “Hg” at LCG Sample N Mean StDev SE Mean 1 4 0.390 0.120 0.060 2 4 0.1600 0.0600 0.030 Difference = mu (1) - mu (2) Estimate for difference: 0.230000 95% CI for difference: (0.065856, 0.394144) T-Test of difference = 0 (vs not =): T-Value = 3.43 P-Value = 0.014 DF = 6 Both use Pooled StDev = 0.0949 (P-Value = 0.014) < (Critical P-value = 0.05) → Significant difference
166
“Hg” at LGS Sample N Mean StDev SE Mean 1 17 0.290 0.130 0.032 2 17 0.300 0.120 0.029 Difference = mu (1) - mu (2) Estimate for difference: -0.010000 95% CI for difference: (-0.097403, 0.077403) T-Test of difference = 0 (vs not =): T-Value = -0.23 P-Value = 0.817 DF = 32 Both use Pooled StDev = 0.1251 (P-Value = 0.817) > (Critical P-value = 0.05) → No significant difference “Mn” at TSP Sample N Mean StDev SE Mean 1 7 125.6 43.6 16 2 7 251.6 78.9 30 Difference = mu (1) - mu (2) Estimate for difference: -126.000 95% CI for difference: (-200.236, -51.764) T-Test of difference = 0 (vs not =): T-Value = -3.70 P-Value = 0.003 DF = 12 Both use Pooled StDev = 63.7423 (P-Value = 0.003) < (Critical P-value = 0.05) → Significant difference “Mn” at LCG Sample N Mean StDev SE Mean 1 4 121.1 71.2 36 2 4 61.5 26.6 13 Difference = mu (1) - mu (2) Estimate for difference: 59.6000 95% CI for difference: (-33.3907, 152.5907) T-Test of difference = 0 (vs not =): T-Value = 1.57 P-Value = 0.168 DF = 6 Both use Pooled StDev = 53.7448 (P-Value = 0.168) > (Critical P-value = 0.05) → No significant difference “Mn” at LGS Sample N Mean StDev SE Mean 1 17 697 220 53 2 17 605 320 78 Difference = mu (1) - mu (2) Estimate for difference: 92.2000 95% CI for difference: (-99.6461, 284.0461)
167
T-Test of difference = 0 (vs not =): T-Value = 0.98 P-Value = 0.335 DF = 32 Both use Pooled StDev = 274.5906 (P-Value = 0.335) > (Critical P-value = 0.05) → No significant difference ”Ni” at TSP Sample N Mean StDev SE Mean 1 7 8.60 1.52 0.57 2 7 12.20 2.48 0.94 Difference = mu (1) - mu (2) Estimate for difference: -3.60000 95% CI for difference: (-5.99539, -1.20461) T-Test of difference = 0 (vs not =): T-Value = -3.27 P-Value = 0.007 DF = 12 Both use Pooled StDev = 2.0568 (P-Value = 0.007) < (Critical P-value = 0.05) → Significant difference ”Ni” at LCG Sample N Mean StDev SE Mean 1 4 13.10 6.81 3.4 2 4 16.70 6.89 3.4 Difference = mu (1) - mu (2) Estimate for difference: -3.60000 95% CI for difference: (-15.45226, 8.25226) T-Test of difference = 0 (vs not =): T-Value = -0.74 P-Value = 0.485 DF = 6 Both use Pooled StDev = 6.8501 (P-Value = 0.485) > (Critical P-value = 0.05) → No significant difference ”Ni” at LGS Sample N Mean StDev SE Mean 1 17 19.50 3.80 0.92 2 17 14.70 3.07 0.74 Difference = mu (1) - mu (2) Estimate for difference: 4.80000 95% CI for difference: (2.38658, 7.21342) T-Test of difference = 0 (vs not =): T-Value = 4.05 P-Value = 0.000 DF = 32 Both use Pooled StDev = 3.4543 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference “Pb” at TSP Sample N Mean StDev SE Mean 1 7 51.9 13.5 5.1 2 7 22.10 1.46 0.55 Difference = mu (1) - mu (2) Estimate for difference: 29.8000 95% CI for difference: (17.2418, 42.3582) T-Test of difference = 0 (vs not =): T-Value = 5.81 P-Value = 0.001 DF = 6
168
(P-Value = 0.001) < (Critical P-value = 0.05) → Significant difference “Pb” at LCG Sample N Mean StDev SE Mean 1 4 33.7 15.6 7.8 2 4 22.10 6.40 3.2 Difference = mu (1) - mu (2) Estimate for difference: 11.6000 95% CI for difference: (-9.0297, 32.2297) T-Test of difference = 0 (vs not =): T-Value = 1.38 P-Value = 0.218 DF = 6 Both use Pooled StDev = 11.9231 (P-Value = 0.218) > (Critical P-value = 0.05) → No significant difference “Pb” at LGS Sample N Mean StDev SE Mean 1 17 27.70 3.47 0.84 2 17 23.50 4.21 1.0 Difference = mu (1) - mu (2) Estimate for difference: 4.20000 95% CI for difference: (1.50471, 6.89529) T-Test of difference = 0 (vs not =): T-Value = 3.17 P-Value = 0.003 DF = 32 Both use Pooled StDev = 3.8578 (P-Value = 0.003) < (Critical P-value = 0.05) → Significant difference “Sn” at TSP Sample N Mean StDev SE Mean 1 7 2.90 1.63 0.62 2 7 1.900 0.380 0.14 Difference = mu (1) - mu (2) Estimate for difference: 1.00000 95% CI for difference: (-0.54792, 2.54792) T-Test of difference = 0 (vs not =): T-Value = 1.58 P-Value = 0.165 DF = 6 (P-Value = 0.165) > (Critical P-value = 0.05) → No significant difference “Sn” at LCG Sample N Mean StDev SE Mean 1 4 3.40 1.20 0.60 2 4 2.300 0.640 0.32 Difference = mu (1) - mu (2) Estimate for difference: 1.10000 95% CI for difference: (-0.56390, 2.76390) T-Test of difference = 0 (vs not =): T-Value = 1.62 P-Value = 0.157 DF = 6 Both use Pooled StDev = 0.9617 (P-Value = 0.157) > (Critical P-value = 0.05) → No significant difference
169
“Sn” at LGS Sample N Mean StDev SE Mean 1 17 2.600 0.380 0.092 2 17 2.100 0.290 0.070 Difference = mu (1) - mu (2) Estimate for difference: 0.500000 95% CI for difference: (0.263846, 0.736154) T-Test of difference = 0 (vs not =): T-Value = 4.31 P-Value = 0.000 DF = 32 Both use Pooled StDev = 0.3380 (P-Value = 0.000) < (Critical P-value = 0.05) → Significant difference “V” at TSP Sample N Mean StDev SE Mean 1 7 55.3 20.8 7.9 2 7 53.1 11.1 4.2 Difference = mu (1) - mu (2) Estimate for difference: 2.20000 95% CI for difference: (-17.21555, 21.61555) T-Test of difference = 0 (vs not =): T-Value = 0.25 P-Value = 0.809 DF = 12 Both use Pooled StDev = 16.6711 (P-Value = 0.809) > (Critical P-value = 0.05) → No significant difference “V” at LCG Sample N Mean StDev SE Mean 1 4 80.5 33.2 17 2 4 74.3 22.8 11 Difference = mu (1) - mu (2) Estimate for difference: 6.20000 95% CI for difference: (-43.07475, 55.47475) T-Test of difference = 0 (vs not =): T-Value = 0.31 P-Value = 0.769 DF = 6 Both use Pooled StDev = 28.4788 (P-Value = 0.769) > (Critical P-value = 0.05) → No significant difference “V” at LGS Sample N Mean StDev SE Mean 1 17 52.80 3.44 0.83 2 17 71.0 42.0 10 Difference = mu (1) - mu (2) Estimate for difference: -18.2000 95% CI for difference: (-39.8667, 3.4667) T-Test of difference = 0 (vs not =): T-Value = -1.78 P-Value = 0.094 DF = 16 (P-Value = 0.094) > (Critical P-value = 0.05) → No significant difference
170
“Zn” at TSP Sample N Mean StDev SE Mean 1 7 31.80 4.72 1.8 2 7 34.80 6.72 2.5 Difference = mu (1) - mu (2) Estimate for difference: -3.00000 95% CI for difference: (-9.76269, 3.76269) T-Test of difference = 0 (vs not =): T-Value = -0.97 P-Value = 0.353 DF = 12 Both use Pooled StDev = 5.8068 (P-Value = 0.353) > (Critical P-value = 0.05) → No significant difference “Zn” at LCG Sample N Mean StDev SE Mean 1 4 35.2 14.3 7.2 2 4 32.90 7.85 3.9 Difference = mu (1) - mu (2) Estimate for difference: 2.30000 95% CI for difference: (-17.65818, 22.25818) T-Test of difference = 0 (vs not =): T-Value = 0.28 P-Value = 0.787 DF = 6 Both use Pooled StDev = 11.5350 (P-Value = 0.787) > (Critical P-value = 0.05) → No significant difference “Zn” at LGS Sample N Mean StDev SE Mean 1 17 59.6 26.7 6.5 2 17 40.0 11.8 2.9 Difference = mu (1) - mu (2) Estimate for difference: 19.6000 95% CI for difference: (4.9171, 34.2829) T-Test of difference = 0 (vs not =): T-Value = 2.77 P-Value = 0.011 DF = 22 (P-Value = 0.011) < (Critical P-value = 0.05) → Significant difference
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Appendix O: Omni Range
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