UNIVERSITY OF GOTHENBURG
Department of Earth Sciences
Geovetarcentrum/Earth Science Centre
Evaluating the mobility of metals
in polluted ground in Uddevalla,
Sweden, using the BCR three-step
sequential extraction procedure
Linn Carlström Ödegaard
ISSN 1400-3821
Mailing address
Geovetarcentrum
S 405 30 Göteborg
B689
Master of Science (120 credits) thesis
Göteborg 2012
Address
Geovetarcentrum
Guldhedsgatan 5A
Telephone
031-786 19 56
Telefax
031-786 19 86
Geovetarcentrum
Göteborg University
S-405 30 Göteborg
SWEDEN
Evaluating the mobility of metals in polluted ground in Uddevalla,
Sweden, using the BCR three-step sequential extraction procedure
Linn Carlström Ödegaard
Department of Earth Sciences, Geology, University of Gothenburg,
Box 460, 40530 Gothenburg, Sweden.
Abstract
The optimized BCR sequential extraction procedure (proposed by the Standards, Measurements and
Testing Programme (S, M & T) of the European Union) was applied to soil samples from an area
(Uddevalla harbor, Sweden) with a long history of pollution. Samples were analyzed to investigate
the contamination risk of the areas surrounding the fill material in the harbor area. The result of this
sequential extraction method is used to assess the long term mobility and bioavailability of the
metals released to the environment due to changes in redox potential and pH. Following fractions
were extracted: exchangeable (including metals bond to carbonates), reducible (bond to Fe and Mn
oxides), oxidizable (bond to soil organic matter) and residual (bond to mineral structures). The result
indicated easily mobilized forms of Cd and Zn in the area. Further a mobilization of As, Cd, Cr, Cu,
Mo, Pb and Zn due to changes in redox potential is estimated. The mobilized metals have an inherent
toxicity, which together with their classification according to the Swedish Environmental Protection
Agency, indicates a contamination risk of the environment surrounding this area.
Key worlds: contaminated soil, BCR sequential extraction, metal fractionation, mobility.
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Evaluating the mobility of metals in polluted ground in Uddevalla,
Sweden, using the BCR three-step sequential extraction procedure
Linn Carlström Ödegaard
Department of Earth Sciences, Geology, University of Gothenburg,
Box 460, 40530 Gothenburg, Sweden.
Sammanfattning
Den optimerade sekventiella laknings metoden (framtagen av den Europeiska Unionens program
Standard, Mätningar och Tester (S, M & T) tillämpades på jordprover tagna i ett område (Uddevalla
hamn, Sverige) med en lång föroreningshistorik. Proverna analyserades för att undersöka
föroreningsrisken på omgivningarna kring hamnområdets fyllnadsmassor. Sekventiell lakning
användes för att fastställa den långsiktiga mobiliteten och biotillgängligheten av de metaller som
mobiliseras vid förändringar av pH och redox potentialen i marken. Följande fraktioner extraherades:
utbytbara (inkluderar metaller bundna till karbonater), reducerande (metaller bundna till Fe- och
Mn- oxider), oxiderande (metaller bundna till jordens organiska material) och residual fraktionen
(metaller bundna till mineralernas struktur). Resultatet indikerar lätt mobiliserade former av Cd och
Zn i området. Vidare visar resultatet mobilisering av As, Cd, Cr, Cu, Mo, Pb och Zn vid förändringar av
redox potentialen. De mobiliserade metallernas inneboende toxicitet tillsammans med deras
klassifikation enligt Naturvårdsverket indikerar en föroreningsrisk på den omgivande miljön.
Nyckelord: förorenade områden, BCR sekventiell lakning, metall fraktionering, mobilitet.
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1. Introduction ......................................................................................................................................... 7
2. Background .......................................................................................................................................... 8
2.1 Pollution history ............................................................................................................................ 8
2.2 Previous research ........................................................................................................................ 10
2.3 Geology, groundwater, surface water......................................................................................... 10
2.4 Processes affecting mobility of metals in the ground ................................................................. 13
2.5 Properties of the metals found ................................................................................................... 15
2.6 Sequential extraction .................................................................................................................. 17
3. Method .............................................................................................................................................. 19
3.1 Fieldwork ..................................................................................................................................... 19
3.2 Lab work ...................................................................................................................................... 21
4. Results ............................................................................................................................................... 24
4.1 Sequential extraction .................................................................................................................. 24
4.2 Comparison between extraction and digestion .......................................................................... 26
4.3 Grain-size analysis ....................................................................................................................... 28
4.4 Loss on ignition ............................................................................................................................ 30
4.5 Correlations ................................................................................................................................. 30
5. Discussion .......................................................................................................................................... 39
5.1 Sample composition .................................................................................................................... 39
5.2 Heterogeneity of samples ........................................................................................................... 40
5.3 Mobility of the metals ................................................................................................................. 40
5.4 Correlations ................................................................................................................................. 42
5.5 Controlling processes in the ground ........................................................................................... 43
5.6 Estimation of the mobilized metal´s groundwater concentrations ............................................ 44
5.7 Impact of environmental changes and soil treatment ................................................................ 46
6. Conclusions ........................................................................................................................................ 48
7. Acknowledgment............................................................................................................................... 49
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8. References ......................................................................................................................................... 50
9. Appendix............................................................................................................................................ 53
9.1 Appendix 1 ................................................................................................................................... 53
9.2 Appendix 2 ................................................................................................................................... 55
9.3 Appendix 3 ................................................................................................................................... 57
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1. Introduction
When undertaking an environmental investigation of a polluted area the contamination risk is a
central question (Naturvårdsverket, 2006). However, measurements of the total metal content will
not demonstrate the actual environmental impact of the polluted area, because some metals occur
within the minerals themselves and others tend to adsorb on to particles in the ground. These
adsorbed metals will not affect the surrounding environment unless later desorbed. Thus
parameters, such as the bioavailability and mobility of the metals in the ground, give more
information about the environmental contamination risks.
A sequential extraction procedure can be used to investigate these parameters. This method
demonstrates, among other things, the mobile fraction of the metals in soil and sediment (Pueyo, et
al., 2001). In this study the optimized BCR three-step sequential extraction method was used to
determine the geochemical properties of the study area (Pueyo, et al., 2001). By stepwise adding
stronger and stronger extraction mediums to one sample, four separate fractions of the metals are
achieved: operationally defined as the exchangeable fraction, the reducible fraction, the oxidizable
fraction and the residual fraction of the measured metals.
The study area is situated in the inner harbor of Uddevalla in Sweden, in an area called Riverside
(Figures 1 and 2). The Riverside area has been a part of a small bay, which later has been filled up
with waste material in order to expand the harbor (Tyréns, 2011b). Thus these waste materials
together with emissions and spill from industries have made the ground polluted. Since the
municipality of Uddevalla might develop buildings for public indoor activities in the area, the degree
of pollution must be investigated. This study was done together with the company Tyréns AB.
The aim of this study is to determine the contamination risk of the study area and to investigate the
mobility and bioavailability of the metals in the ground. Another objective is to interpret the
processes controlling metal mobility in the ground. In connection with this, it is appropriate to ask
what will happen if conditions changes in the study area. Will these changes increase the mobility of
the metals?
Figure 1. Map showing location of
study area. Map adapted from
Google, 2011.
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Figure 2. The orientation and extension of the study area Riverside.
2. Background
2.1 Pollution history
Industries have been active for approximately 150 years in the harbor area of Uddevalla (Tyréns,
2011b). Railways with different kind of wagons were situated in the Riversideängen area in the
1950s. Lubricant was likely leaching down to the ground from these wagons (Wennström, 2006). In
the 1970s trucks and tank wagons were present in the area according to photographs (Tyréns,
2011b). The area seems to have been a storing place for wood railroad ties during the 1980s. The
upper one to four meters of the ground in this area consist of fill material, underlain by clay with a
thickness of 20-30 meters (Tyréns, 2010b). Dredge material, building waste, garbage from
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households and decay products from the industry are examples of material which were dumped in
this area.
Coal, coke, oil and grain were imported to the adjacent docks of Badö and Bäve in the past (Figure 2)
(Tyréns, 2010b). These materials were transferred to oil storage tanks, coal enclosures and grain silos
in the area (Figure 3). Other sources of pollution in this area may have been welding sites, foundry,
oil depot, plate works, varnish and gas stations. Activities here may have released pollutions, such as
metals, aliphatic chemicals, aromatic chemicals, PAH, PCB, TBT and dissolving agents.
Figure 3. Industries in the area. Some of them are still active today. As shown, some parts of
the previous Bäve dock lie below water level, due to a landslide in 2008.
The pollution has also been extensive in the nearby Uddevalla yard, situated at Byfjorden (Figure 2)
(Wennström, 2006). This yard has been filled up with sand from islands, dredge material (most of it
clay) and blasted stones. In this area extensive burning, welding, blasting, production of bricks, use of
mordant, use of asbestos, possible use of PCB in transformer stations and painting took place. The
chemicals used in these industries were, among others: aluminum, bottom paint, chromium, cupper,
degreasing agent, mercury, oils, PCB, petroleum, rarefactions, sulphuric acid, turpentine and zinc.
Many of the different techniques used on the yard were performed on bare ground, which suggests
that the metals probably have sunk directly down to the ground. The fill material consisting of
permeable sand and stones permit extensive leaching. This fill material also consists of unidentified
material of unknown permeability. It is possible that waste material from these industries have been
dumped at the Riverside area, since brick was found in the investigation by Tyréns AB in 2011
(Tyréns, 2011b).
On-going pollution may also occur from harbors and active industries both along Byfjorden and
upstream Bäveån (Wennström, 2006). At the Badö dock loading of grain takes place (Tyréns, 2010b).
Acid and leaching solution is also pumped from the dock through pipelines and transferred to tanks.
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In the Badö/Bäve area existing railways are transporting timber. According to Tyréns AB (2010b) ongoing activities in the Badö/Bäve area do not significantly impact on the area.
2.2 Previous research
A pre-study of the Riversideängen area was performed by Tyréns AB (Tyréns, 2011b). In this study a
simplified risk classification was done according to MIFO (Metodik för Inventering och riskklassning
av Förorenade Områden) phase 2 (Naturvårdsverket, 1999). The area was classified as less sensitive
ground, due to the limited visits to the area and the absence of groundwater extraction (Tyréns,
2011b). The metals detected in this study, which exceeded the guideline values for less sensitive
ground, were the following: arsenic, barium, copper, lead and zinc. The source of these abnormal
high values of metals is probably the fill material and the material involved in the various industries
in the area (Tyréns AB, 2011b).
High values of metals were found both above and at groundwater level, which allow spreading of
pollutants in several directions (Tyréns, 2011b). Spreading can also occur from the fill material down
to the underlying sediment. The fill material was considered as leachy, which increases the risk for
spreading to the surrounding environment. The area is also exposed for floods during times of high
water stand in Byfjorden, which further increases the infiltration flux. The pollutants were found at
shallow depth, which gives increased risk for negative effects on the people visiting the area. Taken
together the high values of pollutants and the increased risk for spreading made Tyréns AB classify
the Riversideängen area as risk class 1. Risk class 1 means that a very large threat exists for people
and the environment if no intervention takes place (Tyréns, 2011b).
Previous risk classification of the adjacent area Badö/Bäve has been done by Tyréns AB, which
classified the area according to MIFO phase 1 and MIFO phase 2 (Tyréns, 2010a). The area was also
classified as risk class 1 (Naturvårdsverket, 1999). The study also indicated that the fill material was
very heterogeneous (Tyréns, 2010a).
2.3 Geology, groundwater, surface water
2.3.1 Geology
The bedrock in the study area consists of acidic to intermediate intrusive bedrock, e.g. granite and
granodiorite (Figure 4). At some places the bedrock is visible at surface according to the soil map
viewer of the Geological Survey of Sweden (Figure 5). Some till is also present in the area according
to Figure 5. The bedrock is overlain by a layer of clay, with a thickness of about 20-30 meters (Tyréns,
2010b, and references within). Above the clay the study area is covered by fill material with a
thickness of one to four meters.
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Figure 4. The bedrock map of the study area. Map redrawn from the Geological Survey of Sweden.
The study area is flat, with a difference in altitude of 0.3 meter (Tyréns, 2011b). The whole area was
previously a part of a bay. This was later filled up with waste material in order to extend the dock of
Badö/Bäve (Tyréns, 2011b). This has resulted in ground of low stability according to geotechnical
investigations done by the company Bohusgeo in 2009 (Tyréns, 2010B, and references within). A
survey investigation of the stability in the area classified the inner part of the Riversideängen as
stable, but a more detailed inspection of the area was deemed urgent (Flygfältsbyrån, 2001). From
the border of Bäveån to 50 meter in at the harbor the area is classified as having an unsatisfactory
stability. In 2008 a landslide occurred at the Bäve dock, which made this part of the dock unusable
(Figure 3) (Tyréns, 2010B, and references within). In November 2011 the landslide risk along the
river Bäveån was interpreted as acute according to the municipally of Uddevalla (TT, 2011). The
municipally of Uddevalla is planning to build pressure embankments in Bäveån in order to avoid a
landslide. The building of a new stone wall along Bäveån will also help avoiding another landslide.
The area around Bäveån was previously a part of the Uddevalla inlet, which covered the whole area
with water 10 000 years ago (Uddevalla, 2011). The mixing of fresh and saltwater gave favorable
environments for the marine animal life. Thus shells and their fragments were deposited in the area.
As the depressed land started to rise again after the ice had left marine currents and waves
concentrated the shells. One of the world´s greatest embankments of shells occurs in Uddevalla.
When Tyréns AB did a pre-investigation of the Riversideängen area gravel consisting of shells was
found in some parts of the area (Tyréns, 2011a).
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Figure 5. Redrawn soil map from the Soil map viewer of the Geological Survey
of Sweden (© Sveriges Geologiska Undersökning). The study area is marked
with a circle.
2.3.2 Groundwater
The potential groundwater excavation in the study area is 1-5 liters/ second in the soil according to
the groundwater map of the Geological Survey of Sweden (SGU, 2012). Groundwater magazine is
present about 0.5 kilometers from this area. According to the map viewer of the Geological Survey of
Sweden no groundwater wells are present in the study area (SGU, 2012).
In a previous investigation done by Tyréns AB in the adjacent area Badö/Bäve values of hydraulic
conductivity was measured with a slug test to be between 1*10-4 and 1*10-6 m/s (Tyréns, 2010a).
Thus the ground was considered as permeable according to the classification by Naturvårdsverket
(1999). The groundwater level was detected at 0.68-1.10 meter below surface in a previous research
of the study area by Tyréns AB, 2011b.
No dominant groundwater flow direction was detected in the investigation of the adjacent area
Badö/Bäve (Tyréns, 2010a). This was probably due to the many anthropogenic made pathways for
the groundwater in the area. In addition the irregularity of the water pressure from Byfjorden
contributes to varying groundwater flow directions.
Floods occur in the lower parts of Uddevalla on a yearly basis (Wennström, 2006, and references
within). Thus risk for flood occurs in the harbor of Uddevalla. When the area is exposed for floods the
groundwater level rises and risk for spreading of the pollutants increases.
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2.3.3 Surface water
According to an investigation of Bäveån done by VISS (VattenInformationsSystem Sweden) in 2009
the river suffers from eutrophication, heavy metals and industrial pollutions (VISS, 2011). The
investigation was done on the part of Bäveån that runs through the study area Riversideängen. No
previously measurements on the surface water in the study area have been done.
2.4 Processes affecting mobility of metals in the ground
Knowledge about processes affecting the metals mobility is necessary when determining their
leaching capacity (Naturvårdsverket, 2006). Thus these following important processes and features
will be described: adsorption, advection, diffusion, hydrodynamic dispersion, preferential flow
pathways, biological transport and transport bond to colloids and dissolved organic carbon (DOC),
soil texture and cation exchange capacity (CEC).
2.4.1 Adsorption
Most of the metals present in groundwater are dissolved (Naturvårdsverket, 2006). However, some
of them are bond to small suspended particles, such as organic material, clay particles and Fe and Mn
oxides. Metals cations often attach to these various particles in a process called adsorption on their
way down to the groundwater. Later these metals can desorb from the particles and give increased
metal content in the groundwater.
Adsorption is defined as when a dissolved element sticks onto the surface of a particle
(Naturvårdsverket, 2006). This can happen due to two different processes; ion exchange and surface
complex bonding. Ion exchange happens when a dissolved cation bonds electrostatic to a negative
charged surface. However, for the metals surface complex bonding is a more important mechanism.
Through this process metal ions bond to surfaces of, for instance, oxides and organic material. All
ions want to form surface complex bonds in various degrees; this process is also pH dependent.
Some cations, e.g. lead and copper, are adsorbed stronger at higher pH levels. While anions, e.g.
arsenate and sulfate, are adsorbed stronger at lower pH levels.
Redox (reduction-oxidation) processes is also an important factor to take into consideration in the
leaching process (Naturvårdsverket, 2006). If the redox condition changes the element´s mobility
affects. One example is arsenic which tends to adsorb onto iron oxide. If the conditions change to
more reducing environment arsenic is released from the iron oxide and the leaching capacity of
arsenic increases.
2.4.2 Advection
Advection is a process when metals are transported as a part of the water movement
(Naturvårdsverket, 2006). Advection is one of the most important transport mechanisms for metals
in the ground. Adsorption is a process which contributes to less advection, since metals are adsorbed
to particles instead of being dissolved and transported together with water.
2.4.3 Diffusion
The transport of an element from areas with high concentration to areas with low concentration is
called diffusion (Naturvårdsverket, 2006). Thus diffusion is only present when an uneven distribution
of an element exists. Diffusion can be an important transport process for metals if a low flow velocity
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exists in the ground. One example of when diffusion exists as an important parameter is the
transport of metals through compact layers of clay in, e.g., sealed landfills.
2.4.4 Hydrodynamic dispersion
Hydrodynamic dispersion is a similar transport process to diffusion (Naturvårdsverket, 2006). This
process develops due to velocity differences of the water within the soil pores. As water runs faster
closer to the center of pores and also faster in bigger pores than in small this gives a velocity variance
within the pores. The velocity variance contributes to a mixing between new incoming pore water
and already existing. In the presence of a high advection speed the hydrodynamic dispersion is that
extensive that one can disregard the diffusion process. However, when the water speed is trivial no
hydrodynamic dispersion occurs.
2.4.5 Preferential flow pathways
If “macro pores” for instance pipelines, root systems and cracks, exist this might be the preferred
flow pathway for the water (Naturvårdsverket, 2006). Thus this might be the major transport
pathway for the existing metals. These “macro pores” contribute to a faster transport of the metals
in these areas, while the transport in the smaller surrounding pores becomes less important.
2.4.6 Transport bond to colloids and DOC
Colloids are very small particles existing evenly distributed within the soil and groundwater
(Naturvårdsverket, 2006). They are naturally occurring clay particles and organic material. Due to the
particles large specific surfaces metals often adsorb to their surfaces (Mulla, et al., 1985). As colloids
are easily transported through the pore spaces they are also an important transport mechanism for
metals.
As mentioned above metals often adsorb to organic material (Naturvårdsverket, 2006). Organic
material might be stationary, but can also be an important transport mechanism for metals. Some
organic material is very water soluble and is therefore present in a form called Dissolved Organic
Carbon (DOC). Metals bond to DOC increase their leaching capacity. If the adsorption rate to DOC of
the metal increase the leaching capacity will increase.
Vegetation and animal life will also contribute to an increased spreading of metals
(Naturvårdsverket, 2006). Plants transfer metals from the deeper ground through their root system
up to the surface. As animals later might feed from this vegetation the metal get included in the food
web. Digging animals and root systems might also destroy sealed landfills.
2.4.7 Soil texture and CEC capacity
Soil texture, soil type and Cation Exchange Capacity (CEC) are important factors affecting the mobility
of metals in ground conditions (USEPA, 1999). Fine-grained textures, like in clay deposits, immobilize
metals to a larger extent than large textures due to their lower permeability. In addition smaller grain
sizes have a high CEC and thus metals bind harder to these soils. CEC is the maximum amount of
cations that can adsorb to the negatively charged surface of a soil. As small particles have greater
specific surfaces, than large particles, for the cations to adsorb onto, giving a higher CEC.
The CEC capacity varies depending on different types of clay mineral as well (USEPA, 1999). Kaolinite
has a quite low CEC, while smectite has a high CEC and thus adsorb metals to a greater extent
14
(Parfitt, et al., 1995). Organic material present in soil has a high CEC capacity due to its negatively
charged surface (USEPA, 1999).
Another important aspect is the pH level of the soil, as greater pH gives a higher CEC capacity
(USEPA, 1999). Thus a soil with a high pH, high clay content and a high humus content hold metals
much harder than sandy soil with low humus content and low pH. The sandy soil will most likely have
a greater leaching capacity than the first soil.
2.5 Properties of the metals found
The partition coefficient (kd value) of an metal indicates its fixation to the sediment (Nyhlén, 2004). A
large kd value implies a strong fixation of the metal to the sediment. With a small kd value a large risk
occurs for leaching of the metal out into the environment. The Swedish Environmental Protection
Agency has determined general kd values for metals in order to estimate their leaching capacity
(Naturvårdsverket, 2009). The general kd values vary between 15- 1800 l/kg for the metals in this
study. These general kd values are given in the lower region in order to avoid underestimation of the
leaching capacity (Nyhlén, 2004).
Antimony (Sb) is a metalloid often used in alloys of colors and weapons (Naturvårdsverket, 2006).
Antimony is also used in flame retardants. Exposure to high concentrations of antimony to humans
and animal life is toxic. The estimated kd level of antimony is 80 l/kg, which implies a high leaching
capacity (Naturvårdsverket, 2009). Antimony is to some extent bond to Fe and Al oxides at pH <7
(Naturvårdsverket, 2006). With conditions of oxygenated soil and low pH levels (<7) antimony will
bond to the ground to some extent.
Arsenic (As) is used, e.g., in the industry of pressure impregnation of wood (Uppsala University,
2002). Arsenic risks spreading out into the environment when burning of this wood takes place. Air
pollution of arsenic may also occur around metal industries and smelting plants. Exposure of
airborne arsenic to humans can give injuries on lungs and skin, which in addition may develop to
cancer. Inorganic forms of arsenic can give acute poisoning. High levels of arsenic are also highly
toxic to animals (Naturvårdsverket, 2006). The estimated kd value of arsenic is 300 l/kg
(Naturvårdsverket, 2009). Arsenic is strongly bond to the sediment during oxygenated conditions
when pH is lower than 8, especially to Fe and Al oxides (Naturvårdsverket, 2006). Consequently, the
risk for leaching of arsenic increases as soon as these conditions changes.
Barium (Ba) is used in various industries, such as the production of ceramics, color, glass, rubber and
brick (Naturvårdsverket, 2006). Exposure to high levels of barium may lead to respiratory distress and
injuries on heart and kidney. The kd value of barium is estimated to 1200 l/kg (Naturvårdsverket,
2009). Thus barium should have a solid fixation to the sediment. Barium is to a large extent adsorbed
to clay particles in the ground (Naturvårdsverket, 2006). Ba can be adsorbed to organic matter. In
environments with high pH barium is adsorbed to Fe oxides.
Levels of Cadmium (Cd) in the environment have increased the last century due to its presence in
phosphate fertilization and its increased industrial use (Uppsala University, 2002). The spreading of
cadmium in Sweden has decreased since the 1970s. However, coal firing in surrounding countries
contributes to increased levels of cadmium in our soils. Exposure of cadmium to humans occurs
through intake of grains and smoking, as plants such as the tobacco plant enrich cadmium. Exposure
15
to high levels of cadmium can give injuries on kidney and lungs. Normally our kidneys in Sweden
contain 30-40 µm cadmium/ g kidney (Sterner, 2010). This is not far from the 200 µm cadmium which
can give morphological changes. In addition high levels of cadmium are toxic to animals and fish
(Naturvårdsverket, 2006). Cadmium has an estimated kd value of 200 l/kg (Naturvårdsverket, 2009).
Adsorption of cadmium in ground occurs to organic matter and to a smaller extent to Fe, Mn and Al
oxides (Naturvårdsverket, 2006). The adsorption capacity increases at high pH levels. Cadmium
precipitates as sulphides in anaerobic environments. This implies an increased leaching capacity of
cadmium at low pH levels and during anaerobic conditions.
Chromium (Cr) is one of the most important metals in Sweden (Sterner, 2010). Various forms of
chromium exist and some forms can be very toxic, cause cancer and allergy. During anaerobic
conditions and at a low pH level chromium (III) is present, which is bond hard to the ground to
organic material (Naturvårdsverket, 2006). The form chromate is present in environments with dry
soils and with high pH level and is relatively leachable. The kd value of chromium (VI) is estimated to
15 l/kg (Naturvårdsverket, 2009). The kd value of chrome total is estimated to 1500 l/kg. Taken these
factors together, the highest risk for chromium leaching out into the environment is with conditions
of high pH.
Cobalt (Co) belongs to one of our essential trace metals (Sterner, 2010). In addition cobalt is included
in the vitamin B12. Cobalt spreads to the environment in many various ways; e.g. through cobalt
producing industries, through its use as alloy in hard metal and through burning of fossil fuel
(Uppsala University, 2002). Exposure to cobalt can give allergic eczema and lung problems. Direct
intake of cobalt can give various negative effects, such as sickness, vomiting, tinnitus and an
increased quantity of red corpuscle. Cobalt has an estimated kd value of 300 l /kg (Naturvårdsverket,
2009). Adsorption of cobalt in ground occurs to Mn oxides and organic matter (Naturvårdsverket,
2006). The leaching capacity of cobalt increases at low pH levels.
Copper (Cu) spreads to the environment through its production and use (Uppsala University, 2002).
Humans are mainly exposed to copper through drinking water, since copper is dissolved from water
pipes and water heaters. Copper is an essential trace metal for humans, but exposure to high levels
can be toxic. In addition it may lead to irritated gastric mucosa and diarrhea among small children.
The kd value of copper is estimated to be 600 l/kg (Naturvårdsverket, 2009). Copper is to a large
extent adsorbed to organic matter in the ground (Naturvårdsverket, 2006). Thus the level of organic
matter in ground is fundamental for the leaching process of copper. Copper is also adsorbed to Fe, Al
and Mn oxides.
Lead (Pb) has been in use in our society for more than a 1000 years (Uppsala University, 2002).
Organic lead compounds were previously used in motor fuel and contributed to more than 50% of
the air pollution of lead. Inorganic lead is to a lesser extent absorbed into our bodies than organic
lead. However, children have a much greater uptake of inorganic lead than adults. In addition
children can suffer from failure of the liver both from organic and inorganic lead, while adults only
can be afflicted by organic lead. Acute lead toxicity can also occur with symptoms, such as headache,
stomach pain and nerve symptoms. Lead is assumed to be one of the most toxic heavy metals for
the vegetation, only mercury and cadmium is seen as more toxic (Sterner, 2010). Lead has a quite
high estimated kd value of 1800 l/kg, which Implies a strong bonding to the sediment
16
(Naturvårdsverket, 2009). Adsorption of lead is strong both to organic material and Fe, Al and Mn
oxides, even during conditions of low pH (Naturvårdsverket, 2006). Lead is to a major part
transported together with dissolved humus complex in ground.
Molybdenum (Mo) has an origin in alloys, medicine used by veterinarians and burning of coal and
domestic waste (Naturvårdsverket, 2006). The access of molybdenum for plants is essential, but
excessive intake can obstruct the plants uptake of copper. This can lead to copper deficiency for
grass-eating animals. The toxic effects of molybdenum to humans are not entirely investigated.
However, molybdenum is not considered as cancer causing. The kd value of molybdenum is
estimated to 80 l/kg (Naturvårdsverket, 2009. Mo is strongly bond in the ground in environments
with low pH (Naturvårdsverket, 2006). When pH is high molybdenum is relatively mobile. In reducing
environments Mo is strongly bond to the ground, due to its reaction with sulphides and organic
material.
Nickel (Ni) is used as alloy when producing stainless steel and is also used as nickel plating (Uppsala
University, 2002). Exposure of nickel to humans can give contact allergy and may also lead to an
increased risk of cancer. High levels of nickel are toxic to animals (Naturvårdsverket, 2006). The kd
value of nickel is estimated to 300 l/kg (Naturvårdsverket, 2009). Nickel is to a large extent adsorbed
to organic material in the ground (Naturvårdsverket, 2006). At high pH levels adsorption to Fe, Al and
Mn oxides may also occur. Consequently the mobility of nickel increases in environments with low
pH.
Vanadium (V) is spread out into the environment by, e.g., emissions and ashes from oil power plants
(Naturvårdsverket, 2006). The activity of microorganisms in the ground can be negatively affected by
vanadium. As a result the uptake of phosphorus (P) by plants gets harder, since the mineralization of
P by microorganism is reduced. Vanadium can also be toxic to humans, but is not cancer causing.
Vanadium is strongly bond in the ground when pH <10 and during reducing and oxygenated
conditions. The kd value of vanadium is estimated to 1000 l/kg (Naturvårdsverket, 2009).
Zinc (Zn) is a metal used in many various ways; e.g. as zincification, galvanization and as color
pigment (Uppsala University, 2002). Zinc is one of our essential trace metals and is incorporated into
various enzymes. When heating zinc oxide forms, which can give short-lived flu-like symptoms to
humans. If exposure to zinc chloride occurs edema of lungs may develop, which can be life
threatening. Zinc has an estimated kd value of 600 l/kg (Naturvårdsverket, 2009). Zinc is to a large
extent adsorbed to organic material in ground (Naturvårdsverket, 2006). At high pH levels zinc is
adsorbed to Fe and Mn oxides. Thus when pH level decreases the mobility of zinc largely increases.
2.6 Sequential extraction
By measuring the total content of metals in a sample one will get an unrepresentative result of their
impact on the environment as mentioned above (Naturvårdsverket, 2006). This is due to the fact that
some parts of the metals tend to be strongly bond to the soil and will not likely impact the
surrounding environment. In addition this part of the metals will not be bioavailable. Further the
total metal content includes metals which are included in the earth’s natural minerals, which do not
contribute to pollution. A better approach is to measure the mobility of the metals; how strong the
17
metals are bond to the solid ground particles and their form of bonding. With knowledge of the
mobility one can determine how the metals will affect the surrounding environment including
groundwater, streams, animal life and vegetation.
Sequential extraction can be used to measure the mobility and bioavailability of metals
(Naturvårdsverket, 2006). In sequential extraction one uses different extraction mediums in order to
interpret how hard and how the metals are bond to the ground. The metal´s likely effect on our
environment can also be interpreted. In Figure 6 a conceptual model of sequential extraction in
general is shown. By adding different extraction mediums step by step to the same soil sample the
mobility is evaluated. After each extraction step the residue is analyzed for metals.
Figure 6. By using different extraction mediums on a soil sample one can receive
information about the mobility during various processes. Figure redrawn from
Nordbäck, et al. (2004).
The sequential extraction procedure that was influential in directing environmental research on
element fractionation was first established by Tessier, et al. (1979). They suggested that by adding
stronger and stronger extraction mediums in five steps one could receive information about how the
metals are bond in the ground. This method has received criticism throughout the years
(Naturvårdsverket, 2006). One of the criticisms has been the difficulty to avoid similarity between the
residues received from the various extraction steps. One example is the dissolution of carbonate
bond metals with sodium acetate, which also dissolute metals bond to oxides and organic material.
Other concerns with sequential extraction have been the lack of comparability between studies as
the procedure has varied, increased risk for deviations as the steps increases, and the long time
required for the performance (Sutherland, 2010). The European commission funded a program
during the 1980s in order to develop the sequential extraction procedure. This program was named
Community Bureau of Reference (BCR, now called the Standards, Measurements and testing (SM&T)
Programme). This program developed a three step sequential extraction procedure in 1993. Due to
inconsistence in the in the extraction procedure a certification was done of an optimized three step
18
sequential extraction procedure in 2001. This sequential extraction was named the BCR optimized
three step sequential extraction procedure and aims to define three steps of available metals in the
ground: acid extractable, reducible and oxidizable (Pueyo, et al., 2001). In the method once
established by Tessier, et al. (1979) the exchangeable and the carbonate bond fractions was
evaluated separately. In the BCR method these two fractions are combined (Pueyo, et al., 2001). The
procedure of sequential extraction described by Pueyo, et al. (2001) is followed in this study in order
to interpret the mobility of the metals available, their bioavailability and their various ways of
binding to particles in the ground. The following fractions are achieved:
Fraction 1 is the exchangeable fraction of the total metal content (Kazi, et al., 2005). This fraction is
also called the acid-soluble fraction. Fraction 1 represents metals which are exchangeable and
includes metals bond with weak electrostatic charges to mainly organic particles and clay minerals
(Rao, et al., 2007, and references within). Fraction 1 represents metals bond to carbonates as well.
Changes of pH in the soil can cause mobilization of this fraction. Usually only a smaller fraction of the
total metal content belongs to this step.
Fraction 2 is the reducible fraction of the metals (Kazi, et al., 2005). This fraction represents metals
which are largely bond to manganese (Mn) and iron (Fe) oxides in the soil. These oxides are known as
“sinks” for metals in polluted environments (Rao, et al., 2007). Metals which belong to this fraction
get released during reducing conditions (Kazi, et al., 2005).
Fraction 3 is termed the oxidizable fraction of the metals (Kazi, et al., 2005). This fraction is strongly
bond to organic material (e.g. detritus or organic coatings on mineral particles) and to sulphides in
the soil. Metals which belong to this fraction are often adsorbed in the soil for longer periods (Rao, et
al., 2007, and references within). Due to the high molecular weight of humic matter it will slowly
release small amounts of metals (Bakircioglu, et al., 2010, and references within). During oxidizing
conditions decomposition of organic material may occur, which may lead to mobilization of the
soluble metals adsorbed onto these organic particles.
The Residual fraction represents the part of the metals which is bond inside the crystal lattices of
the minerals (Kazi, et al., 2005). Due to their hard bonding to the soil, this fraction is not extractable.
Thus the metals bond to this fraction is not considered as mobile.
3. Method
3.1 Fieldwork
Soil sampling was done from the 29th of November to the 6th of December, 2011, during six days. The
area was subjected to flooding due to stormy weather one day prior to sampling. Areas below the
flood water level were not sampled until water had left the area in order to avoid cross
contamination. Sampling was done with a tracked vehicle and a 100 mm screw auger (Figure 7). The
auger sampling was performed together with the company Bohusgeo AB and Tyréns AB. Sampling
was done on both asphalt and grassland. So called disturbed soil samples were collected from the
soil surface down to 1.1 meters in the ground in this study. The samples consisted of filling material
19
with various compositions. Sampling was performed at every half meter if the sampling material was
homogeneous.
Figure 7. The tracked vehicle and the 100
mm. screw auger used for sampling.
If a change in grain size or soil type occurred sampling was divided prior to that, in order to keep
these soil types separated. Samples was collected with a spatula from the screw auger and
transferred to diffusion secured plastic bags to avoid loss of organic metals. The samples were held in
a cooler box to avoid changes in their metal concentration. The sampling stations where samples
were collected in this study are shown in Figure 8.
20
Figure 8. The study area Riverside with sampling stations of this study
marked.
3.2 Lab work
XRF and PID measurements were performed together with Tyréns AB in their lab. The sequential
extraction, total digestion, LOI and grain-size analysis were performed in the laboratories of the
Department of Earth Sciences, Gothenburg University.
3.2.1 XRF and PID measurements
In lab X-Ray Fluorescence (XRF) measurements were performed on the samples to get an estimation
of the metal concentration. Four samples with the highest metal concentration were chosen for
further analyzes in this study. An estimation of the contamination of organic metals was performed
with a Photo Ionization Detector (PID) measurer. These results were not evaluated in this study. All
samples were stored in a cooler (4 °C) prior to analyses to avoid changes in soil nature.
3.2.2 Sequential extraction
Samples were dried in beakers for 105 °C over night. Sequential extraction was performed on
material <2 mm, due to the decreased pollution rate on larger sized particles according to the
Swedish Environmental Protection Agency (Naturvårdsverket, 1999). Sieving was performed to
exclude particles larger than 2 mm. Subsamples of 1 gram were collected for further analysis. The
21
sequential extraction was performed according to the BCR optimized three-step sequential
extraction procedure (Table 1) (Pueyo, et al., 2001). Some modifications were made in the procedure
due to lack of recommended equipment. Instead of using a mechanical end-over-end shaker at a
speed of 30 ± 10 rpm a shaking table was used with a speed of 124 / min to suspend the samples.
Another modification from the procedure was the digestion in concentrated 1:4 HF: HNO3 instead of
aqua regia on the residue from Fraction 3. This change can give higher concentration in this study
compared to others. The supernatant from each sequential extraction step was analyzed with
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) to receive the metal concentration. The
ICP-MS used in this analyze was unable to measure the concentration of mercury. Therefore,
mercury is not analyzed in this study. Certified reference materials for lake sediments (BCR-601 and
BCR-701) and for sewage sludge amended soil (BCR-483) are available for method validation and
quality control (Pueyo, et al., 2007). Since no certified reference material is available for
contaminated soil and due to the time limit in this study this validation has not been performed.
Table 1. The BCR optimized three-step sequential extraction procedure according to Pueyo, et al,
(2001).
BCR sequential extraction scheme
Fraction 1. 0.11 M acetic acid (CH3COOH). 40 ml is added, shaking for 16 h at room temperature.
Fraction 2. 0.5 M hydroxyl ammonium chloride (NH2OH.HCl) + 2 M nitric acid (HNO3). 40 ml is
added, shaking for 16 h at room temperature.
Fraction 3. 8.8 M hydrogen peroxide (H2O2). 10 ml is added, occasionally manual shaking for 1 hour,
placed in water bath (85±2 °C) in 1 h with occasional manual shaking for first ½ h and then reduce
volume to less than 3 ml. The procedure is repeated, but the volume is reduced to about 1 ml. After
cooling 50 ml of 1 M ammonium acetate (NH4OAc) is added with pH 2.0 ±0.1 (adjusted with
concentrated HNO3) and shaking for 16 h at room temperature.
Residual. Aqua regia (conc. 1:1 HNO3: HCl). To the residue from Fraction 3 Aqua regia is added
(modified to conc. 1:4 HF: HNO3 in this study).
3.2.3 Total digestion
The total amount of metal extracted was measured on a separate 1 gram sample from the same soil
sample through digestion with concentrated 1:4 HF: HNO3. Prior to analyze 1 gram/ sample on
material < 2 mm was finely crushed with an agate mortel and pestle to receive complete dissolution.
The samples were analyzed through ICP-MS. As an internal check of the procedure the summary of
Fraction 1, Fraction 2, Fraction 3 and the residual is compared with the result from the this total
digestion.
3.2.4 LOI
As an estimation of the humus content of the samples, called Loss On Ignition (LOI), was performed.
By taking approximately 5 gram of a previously dried sample and heating it 550 °C for 2 hours in a
porcelain crucible a value is received, that can be used as an rough estimation of the organic matter.
22
The value is received by weighing the sample before and after heating. A correction factor of 1 was
subtracted from the result achieved based on the grain-size. The samples were crushed coarsely in a
pestle prior to analyze. Two LOI measurements were performed on each sample to minimize the
measurement error. After heating the crucibles were transferred to a desiccator to cool before
weighing.
3.2.5 Grain-size analyze
Grain-size analyze were performed by dry sieving, wet sieving and pipette analysis. Due to the large
sample sizes the samples were divided in two with a sample splitter prior to analysis. To make the
wet sieving easier the samples were dry sieved with sieves of 2 mm and 4 mm prior to wet sieving.
Material on the sieves was weighted and recorded. Material <2 mm was analyzed through wet
sieving.
About 20 ml of 0.05 M Na4P2O7 dispersant was added to the samples before wet sieving. The samples
were disaggregated by manual agitation. Dispersion through ultrasound at amplitude 70% of the
samples was also performed to avoid aggregates. On a holder stand sieves were placed in funnels
with following net sizes: 500 µm, 250 µm, 180 µm, 125 µm, 90 µm and 63 µm. The wet sediment
samples were added in the topmost sieve. Flushing with distilled water was performed on each sieve
with beginning of the topmost until the water passing the sieve was clear. Beneath the last sieve a
2000 ml beaker was placed to gather sediment <63 microns. Sediment <63 µm was analyzed by the
pipette method. When finished the sieves were placed on paper and dried in oven at 60 °C over
night. After drying the sieves were weighted. Sieves were emptied by hitting them twice upside
down on a paper and then weighting the empty sieve. The weight of the soil in each sieve was
recorded.
The sediment smaller than 63 µm was centrifuged at a speed of 3000 rpm for 10 minutes to
concentrate the sediment. To remove unwanted organic matter the sediment was transferred to a
2000 ml beaker and 200 ml 15% hydrogen peroxide (H2O2) was added to each sample. The beakers
were placed on a water bath for several hours at 80°C. When the addition of new hydrogen peroxide
did not cause new foaming the removal of the organic matter was considered complete. Distilled
water was added up to 500 ml in the beakers and all water was boiled away at 100°C to complete
dryness.
The grain-size analysis for sediment <63 µm were performed by pipette analysis. Before pipette
analysis started, the dry samples were weighted. Prior to analysis 125 ml of dispersant (0.05 M
Na4P2O7) was added and the samples were run through ultrasound with amplitude 70 % to
disaggregate the samples. Samples were transferred to a 1000 ml cylinder and distilled water was
added to obtain exactly 1000 ml. The temperature of the cylinder water was measured overnight in
order to calculate the sampling times and to stabilize the temperature. The sampling times was
calculated according to a version of Stoke´s law:
Tmin= (Depthcm)/(1500*A*dmm2) (d=grain diameter in mm.)
A= 0.0907T + 1.764 (T=temperature in C°)
In this study whole phi intervals were sampled from 4φ - 10φ.The first sample was taken at 20 cm
and the rest were taken at 10 cm depth. Samples were collected with a 20-ml pipette were the exact
23
depth of sampling was marked. With a cylinder stirring rod the samples were stirred vigorously
without breaking the water surface in the cylinder prior to sampling. The stirring stopped a few
seconds before first sampling. Samples were taken up to the 20-ml line in the pipette and transferred
to a previously weighted beaker. Between each sampling the pipette were rinsed with distilled water.
After sampling beakers and samples were dried at 105 °C over night and weight of samples in
beakers was recorded.
4. Results
4.1 Sequential extraction
The following Figures 9-20 show the distribution in percentage (%) of each sequential extraction step
of the total extractable content of each metal measured. For each figure the sampling stations RS24,
RS56, RS58 and RS60 are presented as single bars.
For arsenic (Figure 10) the major part belongs to Fraction 2 at the sampling station RS24. At the other
sampling stations the residual fraction is the dominating one. A relatively large part belongs to
Fraction 2 at the sampling station RS58 as well. For cadmium Fraction 2 is dominating at almost all
sampling stations (Figure 12). A significant part belongs to Fraction 1 and the Residual fractions as
well. Sampling station RS56 is not following the same trend as the other stations; at this station
Fraction 3 is dominating. For chromium in Figure 13 two trends are visible; at sampling station RS24
and RS58 Fraction 3 is the dominating and in sampling stations RS56 and RS60 the Residual fraction is
the dominating fraction. The Residual fraction is the major fraction for copper (Figure 15). Fraction 2
and Fraction 3 have a significant distribution as well. For lead Fraction 2 is dominating (Figure 16).
The Residual fraction is the second largest fraction. The Residual fraction is the dominating fraction
of molybdenum (Figure 17). Although a part of the total content belongs to Fraction 3 as well. The
residual fraction is the dominating fraction for zinc according to Figure 20. A significant part of the
mobile fraction also belongs to Fraction 1 and Fraction 2 as well. At sampling station RS56 the
distribution of Fraction 3 is quite considerable. The major part of the metals antimony, barium,
cobalt, nickel and vanadium belongs to the Residual fraction for all sampling stations (Figures 9, 11,
14, 18 and 19).
Figure 9. The results from the sequential extraction
of antimony.
Figure 10. The results from the sequential
extraction of arsenic.
24
.
Figure 12. The results from the sequential extraction
of cadmium.
Figure 11. The results from the sequential
extraction of barium.
.
Figure 13. The results from the sequential
extraction of chromium.
Figure 14.The results from the sequential
extraction of cobalt.
Figure 15. The results from the sequential extraction
of copper.
Figure 16. The results from the sequential
extraction of lead.
25
Figure 17. The results from the sequential
extraction of molybdenum.
Figure 19.The results from the sequential
extraction of vanadium.
Figure 18.The results from the sequential
extraction of nickel.
Figure 20. The results from the sequential
extraction of zinc.
4.2 Comparison between extraction and digestion
As an internal check of the sequential extraction method Pueyo, et al. (2001) suggests, in the BCR
sequential extraction protocol, a comparison between the summary of all sequential steps with the
total amount of metal extracted from a separate 1 gram sample from the same sample. Pueyo, et al.
(2001) suggests a digestion of the separate sample in aqua regia. Since the residue in this study was
digested in concentrated 1:4 HF: HNO3 the digestion of the separate sample in this study was
performed with concentrated 1:4 HF: HNO3 instead of aqua regia in order to get comparable results.
The deviation or similarity between the sequential step summary and the total dissolution is shown
in Table 2. A deviation exists for all elements between the summary of all extraction steps and the
total digestion as shown in the Recovery column (Table 2). The deviation is largest for cadmium (Cd)
with a recovery between 16-38 % (Table 2). Cobalt (Co) has the best agreement (recovery 46-96 %)
between the extraction steps and the digestion procedure (Table 2). However, the agreement
between the sum of all extraction steps and the total digestion is generally weak for all elements.
26
Table 2. The comparison between the sum of all sequential extraction steps and the total digestion
of a separate sample.
Total metal
digestion (ppm,
mg/kg)
Recovery (%)*
(extraction/digestion)
Element
Sample
∑1+2+3+residual
(ppm, mg/kg)
As
RS24
RS56
RS58
RS60
6.3
81.8
41.7
78.8
44.9
69.0
117.8
66.6
14
119
35
118
Ba
RS24
RS56
RS58
RS60
2923.4
3671.5
5201.1
2404.4
2534.8
2378.6
8157.5
3797.7
115
154
64
63
Cd
RS24
RS56
RS58
RS60
1.5
1.1
1.5
0.2
4.0
3.4
8.1
1.5
38
33
18
16
RS24
RS56
RS58
RS60
43.1
37.7
27.0
26.8
88.0
39.3
44.3
47.0
49
96
61
57
RS24
RS56
RS58
RS60
27.5
291.5
14.9
64.4
107.6
255.8
0.0
6.2
26
114
0
10
RS24
RS56
RS58
RS60
585.2
172.5
171.9
167.7
379.8
347.8
545.6
451.8
154
50
31
37
RS24
RS56
RS58
RS60
11.4
9.4
3.6
3.9
13.0
7.7
6.7
8.4
88
122
54
46
RS24
RS56
RS58
RS60
121.0
142.7
73.6
87.7
209.6
115.3
112.2
135.2
58
124
68
65
Co
Cr
Cu
Mo
Ni
*Recovery= [step1+step2+step3+residual/total metal digestion]*100
27
…Table 2 continued.
∑1+2+3+residual
Total metal
digestion
(ppm, mg/kg)
Recovery (%)*
Element
Sample
Pb
RS24
RS56
RS58
RS60
237.0
407.2
848.4
160.0
442.8
1150.9
4504.7
407.2
54
35
19
39
RS24
RS56
RS58
RS60
19.0
29.8
10.5
9.7
13.4
17.6
18.1
12.2
135
169
58
79
V
RS24
RS56
RS58
RS60
391.5
189.0
113.4
124.2
920.3
209.2
164.4
204.2
43
90
69
61
Zn
RS24
RS56
RS58
RS60
1183.2
702.2
1424.9
360.6
3160.9
1801.9
8326.3
850.6
37
39
17
42
Sb
(ppm, mg/kg)
*Recovery= [step1+step2+step3+residual/total metal digestion]*100
4.3 Grain-size analysis
The results from the dry sieving, wet sieving and pipette analysis are combined for each sample in
following figures. The sediment type is sandy gravel for the samples RS24, RS56 and RS60 according
to Figures 21, 22 and 24. Sample RS58 has a composition of gravelly sand (Figure 23).
Figure 21. The grain-size composition of the sample taken at the sample station RS24.
28
Figure 22. The grain-size composition of the sample taken at the sample station RS56.
Figure 23. The grain-size composition of the sample taken at the sample station RS58.
Figure 24. The grain-size composition of the sample taken at the sample station RS60.
29
4.4 Loss on ignition
The humus content estimations made by measuring Loss on Ignition (LOI) for all samples are shown
in the following table (Table 3). Two LOI measurements were performed on every sample. Thus the
value presented in Table 3 is a mean value of these two measurements. The highest humus content
estimation is found in sample station RS58 (9.4 %) and the lowest in sample station RS24 (6.3 %).
Table 3. Shows the humus content estimation for the samples measured by LOI.
Sample
LOI (%)
RS24
6.3
RS56
8.1
RS58
9.4
RS60
6.4
4.5 Correlations
4.5.1 Correlation with LOI
Metals tend to adsorb onto organic material due to their large specific surfaces (Mulla, et al., 1985).
Thus soils with high humus content tend to have higher metal content than soils with low humus
content. By plotting the measured LOI values (an estimation of the humus content) against the
concentration of metals in the supernatants from the various sequential extraction steps and total
extractable amount (sum of all steps) their relationship can be evaluated.
One should keep in mind that the soil analyzed in this study is fill material, which probably implies a
relatively weak relationship between these two variables, due to the unnatural deposition. However,
if the soil dumped in the study area is naturally deposited prior to dumping a relationship may be
visible. In each figure the metal´s trend line for each plot is visible together with the R2 value (Figures
25-29 and 46-52 (Appendix 2)). The R2 value is a correlation coefficient, which describes the
relationship between the plotted variables. The R2 value can be between 0-1. Values closer to 1 infer
a good correlation.
The probability for each correlation coefficient (R2 value) is also calculated. A hypothesis test called
the t test (student t test) was performed to get a probability value (p value) for the null hypothesis
related each correlation coefficient. The null hypothesis is the probability that the given R2 value
could be obtained by a random population. The software R for statistical computing and graphics was
used to measure the p value (Institute for Statistics and Mathematics of the WU Wien, 2012). A p
value above 0.05 implies that the correlation coefficient not is statistically significant, because the
null hypothesis cannot be rejected with 95% confidence. The p value for each correlation coefficient
is presented in the parenthesis after each R2 value.
No correlation with LOI exists for the metals cadmium, copper, nickel, vanadium and zinc (Figures 47,
49, 50, 51 and 52 in Appendix 2). The correlation between the metal antimony and LOI is poor for all
fractions, except for Fraction 3, which has a R2 value of 0.9 (Figure 46, Appendix 2). Chromium has a
30
poor correlation with LOI, except a positive correlation with LOI for Fraction 1 (R2 value of 0.76)
(Figure 48 (Appendix 2)).
Arsenic shows a positive correlation with LOI for Fractions 1, 2 and 3 with R2 values of 0.57, 0.95 (not
statistically significant) and 0.84, respectively (Figure 25). The supernatants from Fraction 1-Fraction
3 of barium show a positive correlation with LOI (Figure 26). However, their correlations coefficients
are not statistically significant. Nevertheless, the residual fraction and the sum of all steps of barium
show a positive correlation with R2 values of 0.94 and 0.93, respectively. Cobalt has a positive
correlation with LOI for Fraction 2, but the value is not statistically significant (Figure 27). Lead has
positive correlation with LOI for all sequential steps with R2 values ranging from 0.82-0.92 (Figure 28).
However, Fraction 1-Fraction 3 has not statistically significant values. The correlation for the sum of
all steps of lead with LOI is also positive, with a R2 value of 0.91. Molybdenum has a positive
correlation with LOI for Fraction 2, with a R2 value of 0.92 (not statistically significant) (Figure 29).
Figure 25. The correlation between LOI and the
concentrations of arsenic. The R2 value for each
correlation: Fraction 1: 0.57 (p value 0.001),
Fraction 2: 0.95 (p value 0.72), Fraction 3: 0.84 (p
value 0.004, Residual: 0 (p value 0.15) and Sum of
all steps: 0.02 (p value 0.09).
Figure 26. The correlation between LOI and the
concentrations of barium. The R2 value for each
correlation: Fraction 1: 0.47 (p value 0.13),
Fraction 2: 0.36 (p value 0.08), Fraction 3: 0.62 (p
value 0.21), Residual: 0.94 (p value 0.01) and Sum
of all steps: 0.93 (p value 0.01).
31
Figure 27. The correlation between LOI and the
concentrations of cobalt. The R2 value for each
correlation: Fraction 1: 0.42 (p value 0.42), Fraction
2: 0.68 (p value 0.68), Fraction 3: 0.03 (p value
0.025), Residual: 0.11 (p value 0.11) and Sum of all
steps: 0.13 (p value 0.13).
Figure 28. The correlation between LOI and the
concentrations of lead. The R2 value for each
correlation: Fraction 1: 0.82 (p value 0.58), Fraction
2: 0.87 (p value 0.14), Fraction 3: 0.87 (p value
0.11), Residual: 0.92 (p value 0.004) and Sum of all
steps: 0.91 (p value 0.08).
Figure 29. The correlation between LOI and the
concentrations of molybdenum. The R2 value for
each correlation: Fraction 1: 0 (p value 0), Fraction
2: 0.93 (p value 0.29), Fraction 3: 0.06 (p value
0.001), Residual: 0.15 (p value 0.49) and Sum of all
steps: 0.14 (p value 0.83).
4.5.2 Correlation with clay content
Metals tend to adsorb onto the surface of clay particles, due to their large specific surfaces (USEPA,
1999). Clay particles also have a high cation exchange capacity (CEC), which further increases the
amount of metals adsorbed to their surfaces. The correlation between clay particles and metals in
this study might be affected by the unnatural deposition of the fill material. In addition the clay
content in the samples taken is quite low; about 2-3 %.
32
In following figures the correlation between each metal and the clay content (%) of each sampling
station is presented (Figures 30-33 and 53-61 (Appendix 3)). Trend lines for each sequential
extraction step and the sum of all steps is presented in the following figures to estimate their
relationship with the clay content. The R2 value of each trend line is also presented as a
measurement of the degree of correlation between the variables. The probability (p value) of each
correlation coefficient (R2 value) is also calculated to measure the statistical significance.
No correlation is visible between the clay content (%) and the metals cadmium, copper, nickel,
vanadium and zinc (Figures 54, 57, 59, 60 and 61 (Appendix 3)). No correlation exists between
antimony and the clay content, except for a slight correlation in Fraction 3 with a R2 value of 0.75
(Figure 53, Appendix 3). No correlation exists between chromium and the clay content, except for
Fraction 1 with a R2 value of 0.72 (Figure 55, Appendix 3). The correlation between the metals cobalt
and molybdenum and the clay content is poor, except for Fraction 2, where both metals show
positive correlation (R2 values of 0.98 and 0.81 respectively) (Figures 56 and 58, Appendix 3).
The correlation between arsenic and the clay content is positive for Fraction 1 and 2 with R2 values of
0.98 and 0.91 (not statistically significant) respectively (Figure 30). The correlation between barium
and the clay content is positive for all fractions (Fraction 2 and Fraction 3 not statistically significant)
(Figure 31). The correlation between lead and the clay fraction is positive for all fractions, except for
Fraction 3 and Residual fraction (Figure 32). However, the positive correlations show no statistical
significance (Figure 45).
Figure 30. The correlation between arsenic and the
clay content for all sampling stations. The R2 value
for each correlation: Fraction 1: 0.98 (p value
0.006), Fraction 2: 0.91 (p value 0.11), Fraction 3:
0.39 (p value 0.93), Residual: 0.06 (p value 0.12)
and Sum of all steps: 0.01 (p value 0.07).
Figure 31. The correlation between barium and the
clay content for all sampling stations. The R2 value
for each correlation: Fraction 1: 0.84 (p value 0.04),
Fraction 2: 0.82 (p value 0.07), Fraction 3: 0.98 (p
value 0.16), Residual: 0.69 (p value 0.01) and Sum
of all steps: 0.72 (p value 0.01).
33
Figure 32. The correlation between lead and the
clay content for all sampling stations. The R2 value
for each correlation: Fraction 1: 0.94 (p value 0.36),
Fraction 2: 0.86 (p value 0.13), Fraction 3: 0.29 (p
value 0.07), Residual: 0.39 (p value 0.004) and Sum
of all steps: 0.81 (p value 0.08).
4.5.3 Correlation with <63 µm
The correlation between the concentrations of the metals and the abundance of particles <63 µm
was also plotted. The low clay content in the samples taken was too low to be reliably considered
separately. Particles smaller than <63 µm consists of silt and clay. Some metals might be correlated
to silt particles to a larger extent than to clay particles. The following figures show metals which have
a positive correlation with particles <63 µm. In addition the correlation is better with particles <63
µm than with the clay content for the presented metals.
The R2 value of each trend line is presented as a measurement of the degree of correlation between
the variables. The probability (p value) of each correlation coefficient (R2 value) is also calculated to
measure the statistical significance.
The correlation between the metal antimony and the content of particles <63 µm is positive for
Fraction 2, the Residual fraction and Sum of all steps (Figure 33). However, the correlation
coefficients of the Residual fraction and the Sum of all steps are not statistically significant. Figure 34
shows a positive correlation between chromium and the content of particles <63 µm for the Residual
fraction and the Sum of all steps. However, their correlation coefficients are not statistically
significant. Molybdenum shows a positive correlation for the Fraction 3 (Figure 35). Nickel has a
positive correlation with the content of particles <63 µm in the Fraction 3 fraction, the Residual
fraction and in the Sum of all steps (Figure 36).
34
Chemical concentration (ppm)
Antimony (Sb)
35
30
25
20
15
10
5
0
Fract.1
Fract.2
Fract.3
Residual
7
9
11
<63 µm (%)
Figure 33. The correlation between antimony and
the particles <63 µm for all sampling stations. The
R2 value for each correlation: Fraction 1: 0.025 (p
value 0.0004), Fraction 2: 0.69 (p value 0.0004),
Fraction 3: 0.06 (p value 0.00006), Residual: 0.99
(p value 0.25) and Sum of all steps: 0.97 (p value
0.20).
Figure 34. The correlation between chromium and
the particles <63 µm for all sampling stations. The
R2 value for each correlation: Fraction 1: 0.14 (p
value 0.0004), Fraction 2: 0 (p value 0.005),
Fraction 3: 0.52 (p value 0.51), Residual: 0.76 (p
value 0.33) and Sum of all steps: 0.78 (p value
0.26).
Figure 35. The correlation between molybdenum
and the particles <63 µm for all sampling stations.
The R2 value for each correlation: Fraction 1: 0 (p
value 0), Fraction 2: 0.08 (p value 0.00002),
Fraction 3: 0.76 (p value 0.00001), Residual: 0.51
(p value 0.18) and Sum of all steps: 0.56 (p value
0.33).
Figure 36. The correlation between nickel and the
particles <63 µm for all sampling stations. The R2
value for each correlation: Fraction 1: 0.34 (p value
0.0005), Fraction 2: 0.29 (p value 0.0007), Fraction
3: 0.77 (p value 0.0003), Residual: 0.93 (p value
0.01) and Sum of all steps: 0.95 (p value 0.009).
35
4.5.4 Correlations of each sequential extraction step
Three different fractions are achieved when following the BCR sequential extraction steps. By
plotting each sequential extraction fraction against the variable which it should be related with, their
relationships can be evaluated. The correlations of Fraction 1 and Fraction 3 have been plotted
before, but for each metal. Now each sequential extraction step is assembled in a single figure, to
make the interpretations easier. The trend lines of each plot are not shown in the following figures
due to the plotted values centration in the figures. Instead the R2 values of each trend line are
presented. The probability (p value) of each correlation coefficient (R2 value) is also calculated to
measure the statistical significance.
Fraction 1 is the exchangeable fraction, which is loosely bond in the sediment (Kazi, et al., 2005). Due
to the metals tendency to adsorb onto clay particles a correlation between Fraction 1 and the clay
content in the samples may be visible.
No correlation is visible between Fraction 1 and the clay content for the metals antimony, cadmium,
cobalt, copper, molybdenum, nickel and vanadium (Figure 37). The correlation between the plotted
variables is positive for the metals arsenic, barium, chromium, lead and zinc. However, the
correlation coefficient (R2 value) of lead and zinc are not statistically significant.
Fraction 2 is the reducible fraction of the sequential extraction steps. It is metals bond to manganese
and iron oxides in the soil (Rao, et al., 2007). Since the metals are bond to iron oxides a correlation
may be visible between the metals in Fraction 2 and the concentration of iron in the samples.
Manganese was not measured and therefore cannot be correlated with the metal concentrations.
The correlation between Fraction 2 and the concentration of iron in the samples are shown in Figure
38. No correlation exists between the metals cadmium, chromium, copper, molybdenum, nickel,
vanadium, zinc and the concentration of iron. A positive correlation is visible between the metals
arsenic, antimony, barium, cobalt, lead and the concentration of iron.
Fraction 3 is the oxidizable fraction, which is metals bond to organic material in the ground (Kazi, et
al., 2005). The LOI concentration was measured for each sample in this study. Since LOI is an
estimation of the organic content in the samples a correlation might exists between LOI and Fraction
3.
The correlation between the concentrations of metals in Fraction 3 and the LOI measurements are
shown in Figure 39. No correlation exists between the metals cadmium, chromium, cobalt, copper,
molybdenum, nickel, vanadium, zinc and the content of LOI. A correlation is visible between the
metals arsenic, antimony, barium, lead and the content of LOI. However, the correlation coefficient
(R2 value) of the metals barium and lead are not statistically significant.
36
Figure 37. The correlation between Fraction 1 and
the clay content (%). The R2 values for each
correlation are the following: As: 0.98 (p value
0.006), Sb: 0 (p value 0.70), Ba: 0.84 (p value 0.04),
Cd: 0.53 (0.0008), Cr: 0.72 (p value 0.001), Co: 0.01
(p value 0.002), Cu: 0 (p value 0.89), Pb: 0.94 (p
value 0.36), Mo: 0 (p value 0), Ni: 0.04 (p value
0.25), V: 0.07 (p value 0.04), Zn: 0.61 (p value 0.14).
Figure 39. The correlation between Fraction 3 and
LOI (%). The R2 values for each correlation are the
following: As: 0.95 (p value 0.004), Sb: 0.89 (p
value 0.001), Ba: 0.62 (p value 0.21), Cd: 0.32 (p
value 0.002), Cr: 0.28 (p value 0.08), Co: 0.03 (p
value 0.03), Cu: 0.16 (p value 0.04), Pb: 0.87 (p
value 0.11), Mo: 0.06 (p value 0.001), Ni: 0.04 (p
value 0.005), V: 0.32 (p value 0.78), Zn: 0.25 (p
value 0.06).
Figure 38.The correlation between Fraction 2 and
the concentration of iron (Fe). The R2 values for
each correlation are the following: As: 0.62 (p
value 0.003), Sb: 0.78 (p value 0.003), Ba: 0.66 (p
value 0.003), Cd: 0.03 (p value 0.0003), Cr: 0.17 (p
value 0.003), Co: 0.85 (p value 0.003), Cu: 0.17 (p
value 0.06), Pb: 0.54 (p value 0.13), Mo: 0.48
(0.007), Ni: 0.01 (p value 0.0003), V: 0.23 (p value
0.27), Zn: 0 (p value 0.04).
0,01
37
0,23
4.5.5 Correlation between metals
A correlation between the different concentrations of the metals measured can give information
about the controlling processes. The correlation between the metals arsenic, cadmium, lead and zinc
shows their relationships. These metals are plotted due to their toxicity even in small concentrations
in groundwater (Swedish Environmental Protection Agency, 2000).
In the following figures the metals are plotted against each other in each sequential extraction step
and in the sum of all steps (Figures 40-44). The trend lines of each plot are not shown due to the
concentration of many plots in the following graphs. Instead the R2 value of each plot is presented,
which shows the correlation between the metals plotted. The probability (p value) of each
correlation coefficient (R2 value) is also calculated to measure the statistical significance.
No correlation is visible between arsenic and cadmium (Figures 40-44). Arsenic and zinc shows no
correlation (Figures 40-44). No correlation is visible between cadmium and lead (Figures 40-44). Lead
and zinc shows no correlation (Figures 40-44).
Cadmium and zinc show a positive correlation for all fractions, except the Residual fraction with an R2
value of 0.47 (Figure 43). Note that the correlation coefficients of Fraction 1 and Fraction 3 are not
statistically significant (Figure 40 and Figure 42). The correlation between arsenic and lead is positive
for Fraction 1-Fraction 3 (Figures 40-42). However, the correlation coefficients of these plots are not
statistically significant.
Figure 40. The relationship between the plotted
metals in the first sequential extraction step. These
are the following R2 values of each plot: As-Cd:
0.66 (p value 0.48), As-Pb: 0.92 (p value 0.29), AsZn: 0.74 (p value 0.13), Cd-Pb: 0.47 (p value 0.28),
Cd-Zn: 0.96 (p value 0.13), Pb-Zn: 0.60 (p value
0.15).
Figure 41. The relationship between the plotted
metals in Fraction 2. These are the following R2
values of each plot: As-Cd: 0.20 (p value 0.04), AsPb: 0.99 (p value 0.14), As-Zn: 0.14 (p value 0.04),
Cd-Pb: 0.22 (p value 0.13), Cd-Zn: 0.92 (p value
0.04), Pb-Zn: 0.17 (p value 0.76).
38
Figure 42. The relationship between the plotted
metals in Fraction 3. These are the following R2
values of each plot: As-Cd: 0.51 (p value 0.05), AsPb: 0.98 (p value 0.07), As-Zn: 0.47 (p value 0.05),
Cd-Pb: 0.57 (p value 0.06), Cd-Zn: 0.90 (p value
0.9), Pb-Zn: 0.58 (p value 0.11).
Figure 43. The relationship between the plotted
metals in the residual fraction. These are the
following R2 values of each plot: As-Cd: 0.75 (p
value 0.11), As-Pb: 0.01 (p value 0.02), As-Zn: 0.76
(p value 0.02), Cd-Pb: 0.07 (p value 0.003), Cd-Zn:
0.47 (p value 0.02), Pb-Zn: 0.24 (p value 0.04).
Figure 44. The relationship between the plotted
metals of the sum of all extraction steps. These are
the following R2 values of each plot: As-Cd: 0.49 (p
value 0.06), As-Pb: 0.01 (p value 0.1), As-Zn: 0.57 (p
value 0.04), Cd-Pb: 0.32 (p value 0.08), Cd-Zn: 0.82
(p value 0.03), Pb-Zn: 0.51 (p value 0.13).
5. Discussion
5.1 Sample composition
The grain-size analyses show similarities between all four sample stations in composition, except the
slightly larger sand distribution in sample station RS58 (Figure 24). The style of deposition prior to
39
dumping of the soil in the study area cannot be determined, due to difficulties of interpreting
disturbed soil samples taken with a screw auger. The sample collected in the sample station RS24
was collected on a grassland surface. From this appearance this sample is judged to be more like
naturally deposited sediment compared to the other samples, which were collected below an asphalt
surface. However, the whole area consists of fill material, which implies a rather unnatural
deposition.
5.2 Heterogeneity of samples
In the comparison between the sum of all extraction steps and the total metal content of a separate
(1 gram) sample differences was achieved (Table 2). The dissimilarity between the two variables
might be explained by laboratory error. However, by just looking at the samples in the laboratory
their internal heterogeneity was visible. The samples consist of fill material, with a variety of different
pollution sources, which will contribute to a great variability in the contamination level. Therefore,
dissimilarities in the pollution content between two measurements of subsamples from the same
sample are not unexpected, since the BCR method does not allow grinding and homogenizing the
sample. In the BCR optimized three steps sequential extraction method the samples consisted of
freshwater sediment, which often have a greater homogeneity than soil consisting of fill material
(Pueyo, et al., 2001). Thus this comparison used as an internal check of the procedure might be
better performed on freshwater sediment samples.
With this dissimilarity in mind it can be concluded that in order to achieve a representative
investigation of the study area, more samples should be collected and measured. Due to the costs of
analyzing and the limited time in this study only four samples could be analyzed. As seen in Table 2
the concentrations of the total metal digestion is sometimes higher and sometimes lower than the
sum of all sequential extraction steps, which further supports the interpreted heterogeneity of the
samples. If the concentration in the total metal digestion had been only lower or higher than the sum
of all steps, laboratory errors might have been a more likely explanation.
5.3 Mobility of the metals
5.3.1 Mobile metals
Cadmium and zinc are the most mobile metals in this study area (Figure 12 and 20). These metals
have the greatest distribution in the exchangeable fraction (Fraction 1), of all metals measured. The
mobility of cadmium corresponds well with the estimated kd value of cadmium (200 l/kg) by the
Swedish EPA, which implies a rather high mobility (Naturvårdsverket, 2009). Easily mobilized forms of
cadmium have also been found in other studies of polluted soils (Davidson, 1998). Moreover,
cadmium had the greatest mobilization in agricultural soil in Finland (Kaasalainen and Yli-halla, 2003)
and in Turkey (Bakircioglu, et al., 2010). Cadmium had also the highest mobility and bioavailability in
urban soils in China (Yongfeng, et al., 2007).
The estimated kd value is higher for zinc than for cadmium, with a value of 600 l/ kg
(Naturvårdsverket, 2006). However, for both zinc and cadmium their mobility increases with
decreased pH level, which might explain their high mobility. Zinc is also found in this soluble fraction
to a large extent in topsoil samples from refuse dumpsites in another study (Umoren, et al., 2007).
Zinc is an essential trace element to humans (Naturvårdsverket, 2006). However, intake of high
concentrations can be toxic.
40
5.3.2 Metals mobile during anaerobic conditions
Arsenic, cadmium, chromium, copper, lead and zinc all belong, to some extent, to the reducible
fraction (Fraction 2) (Figures 10, 12, 13, 15, 16 and 20). In this study area reducing conditions can
occur due to the close presence of the river Bäveån (Figure 2).
Arsenic is strongly bond in the soil during oxygenated conditions, which implies higher mobility
during reducing conditions (Naturvårdsverket, 2006). The mobilization of arsenic may negatively
affect the environment, since high levels of arsenic is toxic to both humans and animals
(Naturvårdsverket, 2006).
About 50-80 % of lead belongs to the reducible fraction in all sampling stations (Figure 16). This result
corresponds well with another study of metals in topsoil samples from refuse dumpsites, where lead
also was distributed mainly in the reducible fraction (Umoren, et al., 2007). Since lead is toxic both to
humans and to vegetation mobilization might negatively impact the environment (Sterner, 2010).
About 40 % of the cadmium concentration belongs to the reducible fraction (Figure 12). According to
the Swedish EPA the mobility of cadmium increases during anaerobic conditions (Naturvårdsverket,
2006). This further increases the risks of mobilization of cadmium in this fraction. High
concentrations of cadmium are toxic to animals if mobilization occurs.
About 20-30% of the metals chromium, copper and zinc belong to the reducible fraction (Figures 13,
15 and 20). These metals are toxic to humans if mobilization occurs.
The flooding of the study area one day prior to sampling might have affected the mobility of the
metals in the reducible fraction. The rising groundwater level might have contributed to a reducing
environment, which may have mobilized the metals bond to this fraction. Thus these measurements
may show lower concentrations of metals, than if sampling has occurred one day prior to flooding.
However, since these flooding events happen on a yearly basis this situation is significant for the
study area (Wennström, 2006).
5.3.3 Metals mobile during aerobic conditions
The metals cadmium, chromium, copper, molybdenum and zinc are all distributed in the oxidizable
fraction to some extent (Figures 12, 13, 15, 17 and 20). Metals distributed in the oxidizable fraction
are adsorbed to organic material in the soil (Rao, et al., 2007). If these deposits get exposed to an
oxidizing environment degradation of the organic material may occur. Thus metals adsorbed to this
organic material may become mobilized.
In the sampling stations RS24 and RS58 about 70 % of chromium belongs to the oxidizable fraction
(Figure 13). Since chromium is toxic and cancer causing, a negative impact on the environment
occurs in connection with mobilization (Naturvårdsverket, 2006). Cadmium, copper, molybdenum
and zinc are distributed in about 10-50 % in the oxidizable fraction (Figures 12, 15, 17 and 20). These
metals are absorbed to organic material to various extents according to the Swedish EPA, which
corresponds well with this study. If mobilization of these metals occurs the environment might be
negatively affected due to the toxicity of these metals.
41
5.3.4 Immobile metals
The metals antimony, barium, cobalt, nickel and vanadium are all largely associated with the residual
fraction (80-95 %) (Figures 9, 11, 14, 18 and 19). The low mobility corresponds well with the
estimated kd values of barium and vanadium of 1200 l/kg and 1000 l/kg, respectively
(Naturvårdsverket, 2009). However, these results correspond poorly with the estimated kd values of
antimony, cobalt and nickel of 80 l/kg, 300 l/kg and 300 l/kg, respectively. However, nickel and
vanadium are often found in the residual fraction of soils (Davidson, et al., 1998). In addition the long
exposure time of the fill material in this area (about 100 years) might have contributed to
mobilization of the metals (Tyréns AB, 2010b). Thus the only part left might be the metal which is
largely bond inside the crystal lattice.
In the optimized three-step sequential extraction procedure the residual fraction was digested in
aqua regia (1:3 nitric acid: hydrochloride acid) (Pueyo, et al., 2001). In this study the residual fraction
is digested in 1:4 hydrogen fluoride: nitric acid. The solution used in this study is stronger than aqua
regia and thus should dissolve more metals. This might give a larger distribution of the residual
fraction in this study compared to studies with digestion in aqua regia. Further, this might give a
lower distribution of the metals in the mobile fractions (Fractions 1-3) compared to other studies
where aqua regia has been used. Therefore, even larger care should be taken when handling this soil,
due to the possible underestimation of the mobility of the metals in this study.
5.4 Correlations
5.4.1 Correlation of each metal
All metals are correlated to various variables in order to investigate the controlling processes in the
ground. The aim is to investigate how the metals are bond in the soil. However, due to the unnatural
origin of the soil and the small number of samples analyzed, clear conclusions are not possible to
make.
Some correlations are visible between the metals and the LOI (%), the clay content and the content
of particles <63 µm. Since the clay content is quite small in all samples (2-3 %), the correlation with
this factor was not expected to be that good. In general the correlation is better to particles <63 µm
than to the clay content for the metals. This might be due to the higher content of particles <63 µm
in the soil (7-10 %) compared to the clay content in the samples.
In general it is hard to find correlations between the concentration of the metals and the plotted
variables from the fill material, due to the mixing of various materials and the unnatural deposition.
If the fill material was naturally deposited prior to being used as a fill material, correlations might
have been easier to find. In this study the original mode of deposition cannot be determined and,
therefore, conclusions are hard to make.
5.4.2 Correlation of each sequential extraction step
The different sequential extraction steps are correlated with related components to investigate their
relationships. The exchangeable fraction is correlated with the clay content in the ground, due to
metals tendency to adsorb onto clay particles (Figure 37) (Kazi, et al., 2005). This correlation is
positive for five metals (As, Ba, Cr, Pb and Zn) and poor for seven metals (Sb, Cd, Co, Cu, Mo, Ni and
V). Cadmium and zinc are the only metals which are presented in the exchangeable fraction to a large
42
extent. One reason for the poor correlation of some of the metals might be the small distribution of
the metals in this fraction. The small clay content in the samples (2-3 %) may be another reason. The
metals in the exchangeable fraction might also be adsorbed to all particles <63 µm, carbonates and
organic material instead of or in addition to the clay particles.
The correlation between the reducible fraction and the concentration of iron in the samples is shown
in Figure 38. Since the metals in this fraction are bond to Fe and Mn oxides a correlation to the Fe
concentration might be visible. A correlation is observed for four metals (Sb, Ba, Co and Pb), but not
for eight metals (Cd, Cr, Cu, Mo, Ni, V and Zn). The reason for the poor correlation with iron for some
metals in this fraction might be because these metals are bond to Mn oxides instead, which are not
measured in this study.
The correlation between the oxidizable fraction and the LOI content (%) shows positive correlation
for four metals (As, Sb, Ba and Pb) and no correlation for eight metals (Cd, Cr, Co, Cu, Mo, Ni, V and
Zn) (Figure 39). Since the oxidizable fraction is metals largely bond to organic material, a correlation
might be visible. A reason to the lack of correlation for some metals might be that the LOI
measurements (an estimation of the humus content) do not give sufficient information about the
organic material available in the soil. Another reason might be the heterogeneity of the samples,
which makes the LOI measurements of five grams on each sample unrepresentative for the total
sample.
5.4.3 Correlation between metals
The correlations between the measured metals in Figures 40-44 show a correlation between
cadmium and zinc. Cadmium and zinc are chemically very similar and are in minerals often found
together (Swedish Environmental Protection Agency, 2000). Both zinc and cadmium are adsorbed to
organic material in the soil. The correlation between cadmium and zinc might infer the importance of
the organic content in the ground for the mobility of these metals.
Correlation is also found between the metals arsenic and lead in this study (Figures 40-44). However,
some of these correlations are not statistically significant. The relationship between these metals is
harder to find. Arsenic is strongly bond in the ground to Fe and Al oxides during oxidizing conditions
(Naturvårdsverket, 2006). While lead is bond hard in the ground to Fe and Al oxides and organic
material during reducing conditions. The relationship between arsenic and lead might be explained
by their shared adsorption to Fe and Al oxides.
5.5 Controlling processes in the ground
The correlations made might infer the importance of organic material for the adsorption of metals in
the soil, as the best correlation is found between LOI and lead (Figure 28). Further, the explanation
for the correlation between cadmium and zinc is interpreted as a result of their combined adsorption
to organic material (Figures 40 – 44). Another important variable of the adsorption of metals in the
soil seems to be the content of particles <63 µm available in the soil (Figures 33-36).
However, the mixing of different compositions and different sources of soil in this study area might
contribute to the absence of a general adsorption pattern of the metals. One part of the soil might
have originated from a completely different environment than another, which might give different
controlling processes in the ground. In general it appears as the metals are randomly distributed in
43
this study area. A higher content of silt and clay (particles <63 µm) and organic material in the soil
will, nevertheless, favor a higher content of metals if other factors do not predominate.
5.6 Estimation of the mobilized metal´s groundwater concentrations
The relative abundance of the elements in the different sequential extraction steps represents the
mobility and bioavailability of the different metals (Figures 9- 20). However, the concentration of the
metals, which risks leaching out into the surrounding environment, is also of great importance. High
concentrations of metals often give a negative impact on the environment.
Since Fractions 1-3 (the exchangeable, reducible and oxidizable fractions) are the fractions which
might mobilize during different conditions, their concentrations in groundwater are estimated. The
residual fraction is not considered as mobile or bioavailable, due to its bonding inside the crystal
lattice (Kazi, et al., 2005). The estimated metal concentrations presented are evaluated considering
the estimated groundwater volume in the area. The metals presented in each fraction are the metals
which are supposed being mobilized in the various fractions according to this study (Figures 9- 20).
To estimate the concentration of the chemicals which risk mobilization during various conditions
following equation is used:
Mc*Vs*ρs*L
(µg/l)
P*I*A
Mc = Metal concentration in each sequential
P = Yearly precipitation (100 cm (SMHI, 2011))
extraction fraction (µg/l)
I = Infiltration (10%)
Vs = Volume soil down to one meter in the
A = Area (13 500 m2)
study area (13500 m3)
ρs = Soil density (2 g/cm3 )
L = Leaching of metals (1% of oxidizable and reducible fraction, 10 % of extractable fraction)
The high concentrations of the metals in the ground despite the long exposure time of the metals in
the study area (about 100 years) imply a rather slow leaching process of metals in this area. The
leaching of the metals in the area is here estimated to 1 % on a yearly basis of the reducible and
oxidizable parts of the metals. The exchangeable fraction of the metals is more loosely bound in the
ground. In addition this study shows lower metal concentrations in the exchangeable fraction, which
implies a higher leaching rate. Thus the leaching rate of the exchangeable fraction is estimated to 10
% on a yearly basis. Usually the infiltration rate of an area with deposits of this type, but without
asphalt, is estimated to 30 %. However, since some parts of the study area are asphalted, the
infiltration rate is estimated to 10 % in this equation.
The results from this equation can be used as a very rough indication of the amount of metals
released from the study area from the various fractions identified by sequential extraction. These
values can also be used in order to decide which conditions release the largest amount of metals. The
values presented are mean values of the four sampling stations RS24, RS56, RS58 and RS60. The
results from the equation are shown in the following table and are also presented in Figure 45:
44
Table 4. The estimated concentration of the metals possible released to the groundwater from the
three most mobile fractions. Values in µg/l.
Fractions
As
Cd
Cr
Cu
Mo
Pb
Zn
Exchangeable
0.3
0.4
0.1
1.5
0
7
105
Reducible
0.3
0
0.3
2
0
13
11
Oxidisable
0.1
0
0.3
2
0
0.9
3
As seen in Table 4 the highest estimated metal concentration in groundwater exists during reducing
conditions, except for cadmium and zinc. In order to get these metal concentrations anoxic
conditions must develop for a longer time period.
To get an idea of the pollution impact of these metal concentrations one can compare with the
classification of arsenic, cadmium, lead and zinc in groundwater by the Swedish Environmental
Protection Agency (SEPA) in Table 5 (Swedish Environmental Protection Agency, 2000). The reason
why the classification by SEPA only deals with these metals is probably due to their high toxicity,
even in small concentrations. Moderate concentrations in this classification system are considered as
the level at which the metal´s concentrations begin to negatively affect aquatic biota. Cadmium
shows moderate concentrations in groundwater in the exchangeable fraction (Table 4). The
exchangeable fraction of lead shows high concentrations in groundwater, while the reducible
fraction shows very high concentration (Table 4). The exchangeable fraction of zinc shows high
concentrations in groundwater (Table 4). These concentrations together with the metals inherited
toxicity might contribute to negative effects on the surrounding environment if mobilization occurs.
Table 5. The classification for metal and arsenic in groundwater by the Swedish EPA. Table
redrawn from the Swedish Environmental Protection Agency (2000).
The results from all analyzes in this study are presented in Appendix 1 (Tables 6-9). These tables show
the classification of metals in polluted ground according to the SEPA (Naturvårdsverket, 2011). The
study area is classified as less sensitive ground according to a MIFO phase 2 investigation (Tyréns,
2011b). The concentrations of metals that exceed the limiting values for these metals in the polluted
45
ground classification would most reasonably be unacceptable for good groundwater quality, due to
the more sensitive nature of groundwater.
The high concentrations of barium measured in all sampling stations may be due to the previously
use of barite in the surrounding industries or due to dumping of material containing high values of
barite (Tables 6-9, Appendix 1). Barium is used in the production of ceramics, color, glass, rubber and
brick (Naturvårdsverket, 2006). In the field, pieces of brick were found at many sampling stations,
which might be one reason for the high concentrations of barium.
Copper exceeds the concentration in the classification of sensitive ground by SEPA in Fraction 2 and
Fraction 3 for sampling stations RS24 (Table 6, Appendix 1). These measurements indicate negative
impact of copper on the environment if these fractions mobilize, due to the toxicity of copper to both
humans and animals (Naturvårdsverket, 2006).
To summarize the environment around the study area seems to be subjected to a contamination risk
if mobilization occurs, due to the estimated concentrations of the metals cadmium, lead and zinc in
the groundwater. Due to lack of classification of the other metals analyzed their impact on the
environment cannot be evaluated. However, due to the toxicity of these metals for both humans and
animals their mobilization might contribute to a negative effect on the environment
(Naturvårdsverket, 2006). The highest metal concentrations are estimated for periods of long lasting
anoxic conditions (the reducible fraction) (Table 4).
In addition, both the adjacent river Bäveån and the Byfjorden marine area can be negatively affected
by these metals due to their effects on aquatic biota (Swedish Environmental Protection Agency,
2000). The river Bäveån is already suffering from eutrophication and contamination from
surrounding industries (VISS, 2011). This means that the river is sensitive to further contamination. In
addition, a project of testing the effects of mixing and oxygenating the upper water in Byfjorden is
ongoing at the University of Gothenburg (Källberg, 2012). One of the goals of this project is to create
an environment, which can reintroduce fish and vegetation to these waters. Ongoing pollution from
the study area will not contribute to a good environment for fish and vegetation to live in. The acute
landslide risk in the area is also a reason for removing this polluted soil from this area, before the
polluted soil ends up in the river (TT, 2011).
5.7 Impact of environmental changes and soil treatment
The BCR sequential extraction method gives information about the distribution of the metals in the
different binding forms: exchangeable, reducible, oxidizable and residual fractions. From this
information risk assessment of mobilization during different conditions can be evaluated.
Environmental changes can be one reason for an increased mobilization of the metals.
Anthropogenic disturbances of the soil can be another reason for changed mobilization of the
metals.
Cadmium and zinc are the most mobile metals due to their presence in the exchangeable fraction
(Figures 12 and 20). If changes occur in the pH level these metals risk leaching out into the
surrounding environment (Figure 45). Changes of pH in the ground might occur due to more acidic
rain, which could result from climate changes.
46
The metals arsenic, cadmium, chromium, copper, lead and zinc all belong to the reducible fraction to
some extent (Figures 10, 12, 13, 15, 16 and 20). This fraction largely represents metals bond to Mn
and Fe oxides in the ground (Rao, et al., 2007). If reducing conditions occur these metals might leach
out in to the surrounding environment. Reducing conditions can occur due to a raised groundwater
table (Figure 45). Due to the presence of the river Bäveån the area is subjected to flooding on a
yearly basis (Wennström, 2006, and references within). When flooding occurs the groundwater level
rises and anaerobic conditions occur in the ground. During these conditions mobilization of the
metals bond to the reducible fraction might take place and allow spreading into the surrounding
environment. Rising of the groundwater table can also occur after periods of high precipitation. If the
soil is removed to a sealed landfill, anaerobic conditions might develop in the landfill (Figure 45).
During these conditions the metals bond to the reducible fraction might be mobilized.
Cadmium, chromium, copper, molybdenum and zinc are all bond in the oxidizable fraction to some
extent (Figures 12, 13, 15, 17 and 20. These metals are adsorbed to organic material in the ground
(Rao, et al., 2007). As mentioned above the organic material in the soil might be subjected to
degradation during oxidizing conditions. When organic material is degraded the metals bond to it
may become mobilized. Oxidizing conditions can occur when the groundwater level is lowered
(Figure 45). In this study area this occurs when the water is retreating after the yearly flood event
(Wennström, 2006). Lowering can also occur during periods of low precipitation or if the
groundwater is drained from the area. If the soil is removed from the area and dumped on land or on
a landfill the soil gets exposed to an oxidizing environment. During these conditions oxidizable metals
bond to this fraction might be mobilized and spread out in to the surrounding environment (Figure
45).
Figure 45. A conceptual model over the mobilization of the metals
and their estimated concentration in groundwater (µg/l) in this study
47
area during various conditions.
With these different scenarios in mind it can be concluded that the metals in this study area can be
mobilized during various conditions. Due to the fluctuating groundwater level metals in the soil risks
mobilization at least on a yearly basis. The samples analyzed in this study are taken from the surface
down to a depth of 1.1 meter in the ground. The groundwater level is found at a depth between 13.5 meters in this study. This means that the samples are exposed to a fluctuating groundwater level.
The metals in the area might be mobilized out into the groundwater during the conditions mentioned
above.
In this area many pipelines and cables lie in the ground, since the area has been industrially used and
is located adjacent to households. These pipelines and cables can act as preferred flow pathways for
the groundwater in the area and thus further increase the mobility of the metals in the ground
(Naturvårdsverket, 2006). Previous investigations have classified the ground as leachy, using a slug
tests (Tyréns, 2010a). The high leaching susceptibility of the ground together with the preferential
flow path ways contribute to a high mobilization of metals if the right (wrong) conditions occur.
However, the fill material is underlain by clay with a thickness of 20-30 meter, which prevents
spreading of the metals downwards. Instead the spreading of the metals increases laterally.
6. Conclusions

Easily mobilized forms of Cd and Zn are present in the study area. These metals are bond in
the soil with weak electrostatic charges and risk mobilization due to changes in pH level. As,
Cd, Cr, Cu, Mo, Pb and Zn risk mobilization due to changes in redox potential. Ba, Co, Ni, Sb
and V are not considered as mobile and bioavailable metals in this study.

The fluctuating groundwater level in the area might cause mobilization of the metals. This
due to the changing redox potential, which mobilizes metals bond to the oxidizable and
reducible fractions. Removal of the soil to a dumping site might also cause mobilization, due
to changing oxygen conditions in the soil.

The estimated metal concentrations in groundwater in this area, if mobilization of the metals
occurs, indicate a contamination risk on the surrounding areas.

The contamination risk of the adjacent river Bäveån and Byfjorden due to the mobilization of
the metals in the study area and the landslide risks in the area are two reasons for removing
this soil to a dumping site with controlled conditions.

In general, it appears as if the metals are randomly distributed in this study area.
Nevertheless, it is concluded that a higher content of silt and clay (particles <63 µm) and
organic material contents in the soil contribute to a higher content of metals in some areas.

More samples are needed to be analyzed in order to get a better representation of the study
area, due to the heterogeneity of the samples taken.
48
7. Acknowledgment
Thanks to my supervisor professor Rodney Stevens for support and help throughout the work with
my thesis. Thanks to the company Tyréns AB for welcoming me to their company and for their
support and founding. A special thank to my supervisor at Tyréns AB, Jenny Rönnegård. I would also
like to thank student Ardo Robijn for being a helpful opponent at my thesis. Thanks also to Monika
Lindhe-Frederiksen and student Olof Johansson-Ström for help in laboratory.
49
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Chimica Acta, 680, 10-20.
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52
9. Appendix
9.1 Appendix 1
>Less sensitive ground
>Sensitive ground
Table 6. Measured metal concentration in sample station RS24 and the classification of the metals
according to Swedish EPA classification of metals in polluted ground (values in mg/kg)
(Naturvårdsverket, 2011).
FRACTION FRACTION
RESIDUAL
2
3
V
2,48
68,44
17,49
303,05
Cr
0,00
7,37
10,43
9,69
Fe
121,70
7 143,67 1 919,01 157 450,17
Co
0,87
1,66
0,91
39,67
Ni
1,91
3,33
2,50
113,27
Cu
0,00
91,49
106,27
387,46
Zn
277,35
329,37
88,27
488,20
As
0,17
4,52
1,63
0,00
Mo
0,00
0,02
0,95
10,42
Cd
0,26
0,58
0,05
0,61
Sb
0,17
0,27
0,46
17,18
Ba
14,96
71,13
19,90
2 817,44
Pb
0,00
119,43
19,81
97,72
*Measured on a separate 1 gram sample.
RS24
FRACTION 1
TOTAL
CONTENT *
920,32
107,58
185 458,48
87,95
209,57
379,82
3 160,89
44,92
12,97
3,95
13,37
2 534,83
442,82
SUM
1+2+3+RESIDUAL
391,47
27,50
166 634,54
43,11
121,01
585,21
1 183,19
6,32
11,40
1,50
18,09
2 923,44
236,96
Table 7. Measured metal concentration in sample station RS56 and the classification of the metals
according to Swedish EPA classification of metals in polluted ground (values in mg/kg)
(Naturvårdsverket, 2011).
RS56
FRACTION 1
V
Cr
Fe
Co
Ni
Cu
Zn
As
Mo
Cd
Sb
Ba
Pb
0,00
0,15
419,15
0,69
2,05
0,00
59,13
0,00
0,00
0,04
0,06
10,12
8,88
FRACTION FRACTION
RESIDUAL
2
3
9,49
7,02
172,46
4,05
12,48
274,77
13 320,94 4 105,21 155 748,52
1,51
2,14
33,32
4,58
5,05
131,06
28,72
68,03
75,76
189,84
177,31
275,94
5,97
3,67
72,18
0,02
1,36
7,99
0,22
0,56
0,32
0,25
0,75
28,71
28,41
16,41
3 616,55
225,06
45,63
127,61
*Measured on a separate 1 gram sample.
53
TOTAL
CONTENT*
209,23
255,78
184 553,12
39,30
115,31
347,83
1 801,91
68,95
7,65
3,42
17,63
2 378,59
1 150,91
SUM
1+2+3+RESIDUAL
188,97
291,46
173 593,82
37,65
142,74
172,51
702,22
81,82
9,37
1,14
29,78
3 671,49
407,18
.
Table 8. Measured metal concentration in sample station RS58 and the classification of the metals
according to Swedish EPA classification of metals in polluted ground (values in mg/kg)
(Naturvårdsverket, 2011).
FRACTION FRACTION
RESIDUAL
2
3
V
0,41
6,12
4,36
102,54
Cr
0,28
4,16
10,49
0,00
Fe
411,40
3 167,35 1 247,32 97 187,73
Co
0,80
0,77
0,88
24,58
Ni
2,26
3,94
2,46
64,93
Cu
1,38
30,91
44,02
95,54
Zn
470,83
276,61
95,41
582,04
As
1,66
12,28
4,25
23,53
Mo
0,00
0,00
0,96
2,65
Cd
0,37
0,52
0,20
0,38
Sb
0,09
0,10
1,57
8,76
Ba
25,83
137,88
93,33
4 944,02
Pb
46,06
613,68
49,66
138,96
*Measured on a separate 1 gram sample.
RS58
FRACTION 1
TOTAL
CONTENT*
164,36
0,00
137 849,60
44,29
112,21
545,63
8 326,27
117,75
6,65
8,07
18,05
8 157,53
4 504,69
SUM
1+2+3+RESIDUAL
113,43
14,93
102 013,79
27,03
73,60
171,85
1 424,89
41,72
3,61
1,47
10,52
5 201,05
848,36
Table 9. Measured metal concentration in sample station RS60 and the classification of the metals
according to Swedish EPA classification of metals in polluted ground (values in mg/kg)
(Naturvårdsverket, 2011).
FRACTION FRACTION
2
3
V
0,05
5,30
5,09
Cr
0,13
4,47
7,48
Fe
498,26
8 090,67 2 111,71
Co
1,12
1,43
1,13
Ni
2,72
3,81
2,83
Cu
10,54
35,88
37,74
Zn
31,70
52,71
30,63
As
0,18
4,28
1,14
Mo
0,00
0,03
1,00
Cd
0,07
0,10
0,00
Sb
0,00
0,10
0,17
Ba
10,12
41,71
20,58
Pb
2,26
67,21
7,58
*Measured on a separate 1 gram sample.
RS60
FRACTION 1
RESIDUAL
113,77
52,29
126 703,47
23,14
78,36
83,54
245,57
73,16
2,87
0,07
9,41
2 331,98
82,93
54
TOTAL
CONTENT*
204,24
6,19
224 541,57
47,03
135,22
451,84
850,60
66,65
8,37
1,49
12,22
3 797,67
407,22
SUM
1+2+3+RESIDUAL
124,21
64,37
137 404,11
26,82
87,72
167,71
360,62
78,75
3,89
0,24
9,68
2 404,39
159,98
9.2 Appendix 2
Figure 46. The correlation between LOI and the
concentrations of antimony. The R2 value for each
correlation: Fraction 1: 0 (p value 0.002), Fraction
2: 0.08 (p value 0.002), Fraction 3: 0.9 (p value
0.001), Residual: 0 (p value 0.17) and Sum of all
steps: 0.01 (p value 0.13).
Figure 47. The correlation between LOI and the
concentrations of cadmium. The R2 value for each
correlation: Fraction 1: 0.2 (p value 0.002), Fraction
2: 0.06 (p value 0.002), Fraction 3: 0.32 (p value
0.002), Residual: 0 (p value 0.002) and Sum of all
steps: 0.22 (p value 0.001).
Figure 48. The correlation between LOI and the
concentrations of chromium. The R2 value for
each correlation: Fraction 1: 0.76 (p value
0.002), Fraction 2: 0.39 (p value 0.057),
Fraction 3: 0.28 (p value 0.09), Residual: 0.02 (p
value 0.32) and Sum of all steps: 0.03 (p value
0.25).
Figure 49. The correlation between LOI and the
concentrations of copper. The R2 value for each
correlation: Fraction 1: 0.2 (p value 0.17), Fraction
2: 0.37 (p value 0.08), Fraction 3: 0.16 (p value 0.04),
Residual: 0.28 (p value 0.14) and Sum of all steps:
0.3 (p value 0.08).
55
Figure 50. The correlation between LOI and the
concentrations of nickel. The R2 value for each
correlation: Fraction 1: 0.02 (p value 0.004),
Fraction 2: 0.32 (p value 0.01), Fraction 3: 0.37 (p
value 0.004), Residual: 0.07 (p value 0.01) and
Sum of all steps: 0.06 (p value 0.008).
Figure 51. The correlation between LOI and the
concentrations of vanadium. The R2 value for each
correlation: Fraction 1: 0.2 (p value 0.0004), Fraction
2: 0.29 (p value 0.41), Fraction 3: 0.32 (p value 0.78),
Residual: 0.28 (p value 0.04) and Sum of all steps:
0.29 (p value 0.05).
Figure 52. The correlation between LOI and the
concentrations of zinc. The R2 value for each
correlation: Fraction 1: 0.32 (p value 0.14), Fraction 2:
0.08 (p value 0.04), Fraction 3: 0.25 (p value 0.06),
Residual: 0.21 (p value 0.02) and Sum of all steps: 0.28
(p value 0.03).
56
9.3 Appendix 3
Figure 53. The correlation between antimony and
the clay content for all sampling stations. The R2
value for each correlation: Fraction 1: 0 (p value
0.70), Fraction 2: 0.48 (p value 0.001), Fraction 3:
0.75 (p value 0.003), Residual: 0.37 (p value 0.06)
and Sum of all steps: 0.31 (p value 0.05).
Figure 55. The correlation between chromium and
the clay content for all sampling stations. The R2
value for each correlation: Fraction 1: 0.72 (p
value 0.001), Fraction 2: 0.18 (p value 0.05),
Fraction 3: 0 (p value 0.003), Residual: 0.21 (p
value 0.30) and Sum of all steps: 0.22 (p value
0.23).
57
Figure 54. The correlation between cadmium and the
clay content for all sampling stations. The R2 value for
each correlation: Fraction 1: 0.53 (p value 0.001),
Fraction 2: 0.14 (p value 0.001), Fraction 3: 0 (p value
0.0003), Residual: 0 (p value 0.001) and Sum of all
steps: 0.09 (p value 0.007).
Figure 56. The correlation between cobalt and the
clay content for all sampling stations. The R2 value
for each correlation: Fraction 1: 0.01 (p value
0.002), Fraction 2: 0.98 (p value 0.006), Fraction 3:
0.21 (p value 0.01), Residual: 0.36 (p value 0.006)
and Sum of all steps: 0.44 (p value 0.005).
Figure 57. The correlation between copper and
the clay content for all sampling stations. The R2
value for each correlation: Fraction 1: 0 (p value
0.89), Fraction 2: 0.18 (p value 0.06), Fraction 3:
0.3 (p value 0.03), Residual: 0.13 (p value 0.13)
and Sum of all steps: 0.17 (p value 0.08).
Figure 58. The correlation between molybdenum
and the clay content for all sampling stations. The
R2 value for each correlation: Fraction 1: 0 (p value
0), Fraction 2: 0.81 (p value 0.003), Fraction 3: 0.16
(p value 0.003), Residual: 0.47 (p value 0.18) and
Sum of all steps: 0.49 (p value 0.11).
Figure 59. The correlation between nickel and the
clay content for all sampling stations. The R2 value
for each correlation: Fraction 1: 0.04 (p value 0.25),
Fraction 2: 0 (p value 0.01), Fraction 3: 0.19 (p
value 0.41), Residual: fraction 0.61 (p value 0.01)
and Sum of all steps: 0.6 (p value 0.01).
58
Figure 60. The correlation between vanadium and
the clay content for all sampling stations. The R2
value for each correlation: Fraction 1: 0.07 (p value
0.04), Fraction 2: 0.19 (p value 0.29), Fraction 3:
0.29 (p value 0.15) Residual: 0.37 (p value 0.03) and
Sum of all steps: 0.32 (p value 0.05).
Figure 61. The correlation between zinc and the clay
content for all sampling stations. The R2 value for each
correlation: Fraction 1: 10.61 (p value 0.14), Fraction 2:
0.05 (p value 0.04), Fraction 3: 0 (p value 0.05),
Residual: 0.46 (p value 0.02) and Sum of all steps: 0.37
(p value 0.03).
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