trace metals in forest soils in southwestern china · 2 trace metals in forest soils in...

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


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,


77

Figures 4.13a,b V distributions in the A- and B horizons at the studied sites and


77

Figures 4.14a,b Zn distributions in the A- and B horizons at the studied sites,


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


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


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

)


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.


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]


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


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].


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


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].


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


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


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)


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)


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


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]


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


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.


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



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.


← 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.


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.


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.


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


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.


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.


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).


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”


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


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.


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.


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


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.


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


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


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.


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


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)


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.


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).


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


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.


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


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


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.


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).


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)


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.


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).


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.


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.


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.


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


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).


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.


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.


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


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


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


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


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.


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


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


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


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


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.


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]


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


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

References

Adriano, D.C., 1986. Trace elements in the terrestrial environment. Springer-Verlag, New York. Ahonen-Jonnarth, U., Finlay, R.D., 2001. Effects of elevated nickel and cadmium concentrations on growth and nutrient uptake of mycorrhizal and non-mycorrhizal Pinus sylvestris seedlings. Plant Soil 236(2): 129-138. Alloway, B.J., 1999. Land contamination and reclamation. In Harrison, R.M. (ed.), Understanding our environment: an introduction to environmental chemistry and pollution (3rd edition), Royal Society of Chemistry, Cambridge, UK: 199-236. Alloway, B.J. (ed.), 1995a. Heavy metals in soils (2nd editition). Blackie, London, UK. Alloway, B.J., 1995b. Introduction. In Alloway, B.J. (ed.), Heavy metals in soils (2nd editition), Blackie, London, UK: 3-10. Alloway B.J., 1995c. Soil processes and the behaviour of heavy metals. In Alloway, B.J. (ed.), Heavy metals in soils (2nd editition), Blackie, London, UK: 11-37. Alloway B.J., 1995d. The origin of heavy metals in soils. In Alloway, B.J. (ed.), Heavy metals in soils (2nd editition), Blackie, London, UK: 38-57. Appelo, C.A.J., Postma, D., 1993. Geochemistry, groundwater and pollution. Balkema, Rotterdam, the Netherlands. Aunan, K., Fang, J., Hu, T., Seip, H.M., Vennemo, H., 2006. Climate change and air quality - measures with co-benefits in China. Environ Sci Technol 40(16): 4822-4829. Balsberg Påhlsson, A., 1989. Toxicity of heavy metals (zinc, copper, cadmium, lead) to vascular plants. Water Air Soil Pollut 47(3-4): 287-319. Berthelsen, B.O., Steinnes, E., 1995. Accumulation patterns of heavy metals in soil profiles as affected by forest clear-cutting. Geoderma 66(1-2): 1-14. Boss, C.B., Fredeen, K.J., 1999. Concepts, Instrumentation and Techniques in Inductively Coupled Plasma Optical Emission Spectrometry (2nd ed.). Perkin-Elmer Corp., Norwalk, CT, USA: 3-1 - 3-36. Bowen, H.J.M., 1979. Environmental chemistry of the elements. Academic Press, London, UK.

100

BP, 2005: Statistical Review of World Energy 2005. Available from: www.bp.com/statisticalreview (Accessed: 15.05.2006) Bringmark, L., Lundin, L., 2004. Report on the assessment of heavy metal stores and fluxes at ICP IM sites. In: Kleemola, S., Forsius, M. (eds.), 13th Annual Report 2004. UNECE ICP Integrated Monitoring. The Finnish Environment 710. Finnish Environment Institute, Helsinki, Finland: 27-32. Available from: http://www.environment.fi/default.asp?node=6335&lan=en#a0 (Accessed: 06.01.2006) Bååth, E., 1989. Effects of heavy metals in soil on microbial processes and populations (a review). Water Air Soil Pollut 47(3-4): 335-379. Chen, H., Chunrong, Z., Cong, T., Yongguan, Z., 1999. Heavy metal pollution in soils in China: Status and Countermeasures. Ambio 28(2): 130-134. Chen, S., Zheng, Y., Zhao, Q., 1997. The metal element feature of TSP in atmosphere in the urban district of Chongqing. Chongqing Enviro Sci 19(4): 30-32 (In Chinese with English abstract) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Cheng, S, 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Crosby, D.G., 1998. Environmental Toxicology and Chemistry. Oxford University Press, New York, NY, USA: 224.

Dagang, T., 2006. Pers. Comm. Vehicle Emission Control Center of State Environmental Protection Agecy, No.8, Dayangfang, Beiyuan, Chaoyang District,Beijing 100012, China.

Davies, B.E., 1995. Lead. In Alloway, B. J. (ed.), Heavy metals in soils (2nd editition), Blackie, London, UK. de Vries, W., Forsius, M., Lundin, L., Haußmann, T., Augustin, S., Ferretti, M., et al., 2002. Cause-effect Relationships of Forest Ecosystems. Joint report by ICP Forests and ICP Integrated Monitoring. UN/ECE, Geneva, Switzerland. Available from: http://www.icp-forests.org/RepOth.htm (Accessed: 19.01.2006) Duxbury, T., 1986. Microbes and heavy metals : an ecological overview. Microbiol Sci 3(11): 330-333. EIA, 2006: World Energy Overview: 1994-2004. Energy Information Administration. US Department of Energy.

101

Available from: http://www.eia.doe.gov/iea/overview.html (Accessed: 30.10.2006) Esbensen, K., Schönkopf, S. and Midtgaard, T., 1994. Multivariate analysis in practice. Camo, Trondheim, Norway. Essington, M.E., 2004. Soil and water chemistry: an integrative approach. CRC Press, Boca Raton, Florida, USA. Fan, S, 1999. Measurement and analysis of heavy metals in the area of Helan Mountain. J Ningxia Uni (Natural Sci Edi) 20(4): 349-350 (in Chinese with English abstract) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Guo, D., 1994. Environmental sources of Pb and Cd and their toxicity to man and animals. Adv Environ Sci (Beijing) 2(3): 71-76 (In Chinese with English abstract) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Hansen, H., Larssen, T., Seip, H.M., Vogt R.D., 2001. Trace metals in forest soils at four sites in southern China. Water Air Soil pollut 130: 1721-1726 Haiyan, W., Stuanes, A.O., 2003. Heavy Metal Pollution in Air - Water - Soil - Plant System of Zhuzhou City, Hunan Province, China. Water Air Soil Pollut 147(1-4): 79-107. He S., Hu X., 1991. The residual of Cd, As, Hg in the soil environment at Guangzhou. Agro-environmental Protection 10(2): 71-72 (In Chinese) in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Ilyin, I., Travnikov, O., Aas, W., 2005. Heavy Metals: Transboundary Pollution of the Environment, EMEP Status Report 2/2005. MSC-E & CCC, Moscow, Russia and Kjeller, Norway. Available from: http://www.msceast.org/publications.html (Accessed: 04.05.2006) Ilyin, I., Travnikov, O., 2003. Heavy Metals: Transboundary Pollution of the Environment, MSC-E Technical Report 5/2003. MSC-E, Moscow, Russia. Available from: http://www.msceast.org/publications.html (Accessed: 04.05.2006) Johansson, K., Bergbaeck, B., Tyler, G., 2001. Impact of atmospheric long range transport of lead, mercury and cadmium on the Swedish forest environment. Water Air Soil Pollut Focus 1(3-4): 279-297.

102

Kingston, H.M. and Walter, P.J., 1998. The art and science of microwave sample preparation for trace and ultratrace elemental analysis. In: Montaser, A., Inductively Coupled Plasma Mass Spectrometry, Wiley-VCH, New York, USA. Lasat, M.M., 1999. Phytoextraction of metals from contaminated soil: a review of plant/soil/metal interaction and assessment of pertinent agronomic issues. J Hazard Subst Res 2: 1-5 - 5-25. Larssen, T., Lydersen, E., Tang, D., He, Y., Gao, J., Liu, H., et al., 2006. Acid rain in China. Environ Sci Technol 40(2): 418-425. Larssen, T., Tang, D., He, Y. (eds.), 2004. Integrated Monitoring Program on Acidification of Chinese Terrestrial Systems— IMPACTS. Annual report—results 2003, NIVA Report SNO 4905/2004. Norwegian Institute for Water Research: Oslo, Norway. Lee, C.S.L., Li, X., Zhang, G., Peng, X., Zhang, L., 2005. Biomonitoring of trace metals in the atmosphere using moss (Hypnum plumaeforme) in the Nanling Mountains and the Pearl River Delta, Southern China. Atmos Environ 39(3): 397-407. Li, Y., Zhang, Z., Cao, H., Yu, Y., 2000. Research on total suspended particles and metals pollution in Xi'an. Xi'an Yike Daxue Xuebao 21(2): 166-168 (in Chinese with English abstract) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Linsinger, T., 2005. Application note 1. Comparison of a measurement result with the certified value. European Commission- Joint Research Centre. Institute for Reference Materials and Measurements (IRMM), Geel, Belgium. Available from: http://www.erm-crm.org/pdf/application_note_1_english_en.pdf (Accessed: 08.08.2006) McGrath, S.P., 1995. Chromium and nickel. In Alloway, B.J. (ed.), Heavy metals in soils (2nd editition), Blackie, London, UK: 152-178. Milestone, 2003: Determination of Mercury in Soil SRM Using a Direct Mercury Analyzer. Application Report N. 4, Milestone, Italy. Milestone, 2002: DMA-80 Direct Mercury Analyzer User Manual Revision 2. Milestone, Sorisole, Italy. Milestone, 2000: Ethos plus application notes for milestone microwave system. Milestone, Sorisole, Italy.

103

MSC-E, 2006: http://www.msceast.org/hms/emission.html (Accessed: 24.04.2006) NEPA, 1994. The atlas of the soil environmental background value in the People’s Republic of China. China Environmental Science Press, Beijing, China. Nkoane, B., Wibetoe, G., Lund, W. and Torto, N., 2006. A multitelemental study on metallophytes from mineralized areas in Botswana using ICP-AES and ICP-MS. Geochemistry: Exploration, Environment, Analysis (In press). Pacyna, E.G., Barret, K.J., Pacyna, J.M., 2002. Comparison of Emissions with concentrations in the Air and precipitation of Mercury, Lead and Cadmium measured at selected EMEP stations. Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutions in Europe, (EMEP)EMEP/CCC-Note 1/2002. Norwegian Institute For Air Research (NILU). Kjeller, Norway. Available from: http://www.nilu.no/projects/ccc/reports/cccn1-2002.txt (Accessed: 31.10.2006) Pacyna J.M., Münch J. and Axenfeld F., 1991. European inventory of trace metal emissions to the atmosphere. In Vernet J.-P. (ed.), Heavy metals in the environment, Elsvier, Amsterdam, the Netherlands. Rademacher P., 2001. Atmospheric Heavy Metals and Forest Ecosystems. UN/ECE, Geneva, Switzerland. Available from: http://www.icp-forests.org/RepOth.htm (Accessed: 04.01.2006) Steinnes, E., 2001. Metal contamination of the natural environment in Norway from long range atmospheric transport. Water Air Soil Pollut: Focus 1(3-4): 449-460. Steinnes, E., Allen, R.O., Petersen, H.M., Rambaek, J.P., Varskog, P., 1997. Evidence of large scale heavy-metal contamination of natural surface soils in Norway from long-range atmospheric transport. Sci Total Environ 205(2,3): 255-266. Thomas, R., 2004. Practical Guide to ICP-MS. Marcel Dekker Inc., New York, USA. Travnikov, O. and I.Ilyin I., 2005. Regional Model MSCE-HM of Heavy Metal Transboundary Air Pollution in Europe. EMEP/MSC-E Technical Report 6/2005. MSC-E, Moscow, Russia. Available from: http://www.msceast.org/publications.html (Accessed: 02.05.2006) Tyler, G.; Balsberg Påhlsson, A.M.; Bengtsson, G.; Bååth, E.; Tranvik, L., 1989. Heavy metal ecology of terrestrial plants, microorganisms and invertebrates. Water Air Soil Pollut 47(3-4): 189-215.

104

US-EPA, 1998: Mercury in solids and solutions by thermal decomposition, amalgamation, and atomic absorption spectrophotometry. EPA method 7473. US-EPA, Washington DC, USA. Available from: www.epa.gov (Accessed: 19.04.2006) UNECE, 2006: http://www.unece.org/env/lrtap/hm_h1.htm (Accessed: 25.04.2006) UNECE, 2004: Protocol on Heavy Metals, done at Aarhus, Denmark, on 24 June 1998, 2004. In: Handbook for the 1979 Convention on Long-range Transboundary Air Pollution and its Protocols, United Nations publication, New York, USA and Geneva, Switzerland: 129-163. Available from: http://www.unece.org/env/lrtap/full%20text/1998.Heavy.Metals.e.pdf (Accessed: 11.01.2006)

Vogt, R.D., 2006. Pers. Comm. Department of Chemistry, University of Oslo. P. O. Box 1033, Blindern, 0315 Oslo.

Vogt, R.D., Guo, J., Zhang, X., Zhao, D., Xiang, R., Xiao, J., et al., 2006. Water chemistry in forested acid sensitive sites in sub-tropic Asia receiving acid rain and alkaline dust. Appl Geochem (In press). Wong, C.S.C., Li, X.D., Zhang, G., Qi, S.H., Peng, X.Z., 2003. Atmospheric deposition of heavy metals in the Pearl River Delta, China. Atmos Environ 37(6): 767-776. Xie, R.K., Seip, H.M., Wibetoe, G., Nori, S. and McLeod, C.W., 2006. Heavy coal combustion as the dominant source of particulate pollution in Taiyuan, China, corroborated by high concentrations of arsenic and selenium in PM10. Sci Total Environ 370: 409-415. Xu J., Yang J., 1995. Heavy metals in the terrestrial ecosystem. China Environmental Science Publisher, Beijing: 24-36 (In Chinese) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Yang, S., Yang, Y., Qian L., et al., 1987. The characteristics and sources of particles in atmosphere ai Tianjing. Acta Sci Circumstantiae 7(4): 411-422 (In Chinese with English abstract) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Zhang, K., Gao, H.-W., 2003. Research on source and sink of East Asia's sand - dust aerosols. Anquan Yu Huanjing Xuebao 3(3): 7-12 (in Chinese with English abstract).

105

Zhang, N., 2001. Effects of air settlement on heavy metal accumulation in soil. Soil Environ Sci 10(2): 62-69 (In Chinese with English abstract) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198. Zhang, H., Ma, D.S., Xie, Q.L., Chen, X.L., 1999. An approach to studying heavy metal pollution caused by modern city development in Nanjing , China. Environ Geol 38(3): 223-228. Zhou B., Xu J., Hu G., 1994a. The metal characteristic in the air suspended particles in Shanghai. Shanghi Environ Sci 13(9): 21-26 (In Chinese) as cited in Cheng, S., 2003. Heavy metal pollution in China: origin, pattern and control: a state-of-the-art report with special reference to literature published in Chinese journals. Environ Sci & Pollut Res 10(3): 192-198.

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



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)


∆m1

(mg kg-1)

t-factor at 95% CI


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)


∆m1

(mg kg-1)

t-factor at 95% CI


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)


∆m1

(mg kg-1)

t-factor at 95% CI


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

134

H.2 San Joaquin Soil

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


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


Sample mass (g)

Peak height


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

161

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

trace metals in forest soils in southwestern china · 2 trace metals in forest soils in...

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