Master’s thesisPhysical Geography and Quaternary Geology, 30 Credits

Department of Physical Geography

Transport and retention of heavy metals in contaminated

soil and groundwater

A case study from Pukeberg glassworks in Småland, Sweden

Gitte Laallam

NKA 1772017

Preface This Master’s thesis is Gitte Laallam’s degree project in Physical Geography and Quaternary Geology at the Department of Physical Geography, Stockholm University. The Master’s thesis comprises 30 credits (one term of full-time studies). Supervisor has been Jerker Jarsjö at the Department of Physical Geography, Stockholm University. Examiner has been Stefano Manzoni at the Department of Physical Geography, Stockholm University. The author is responsible for the contents of this thesis. Stockholm, 10 April 2017

Steffen Holzkämper Director of studies

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Abstract As much as 80 000 sites, including landfills from glass production industries, are estimated to be contaminated in Sweden. One of these landfills is located near the glassworks factory of Pukeberg, in the municipality of Nybro, Southeast Sweden. It has been found to be severely contaminated with heavy metals, which have toxic effects to humans and biota. The aim of this study is to investigate hydrologic properties at Pukeberg, including groundwater velocities and their potential (spatial) heterogeneity that would affect the transport of metals. Metal concentrations in soil, pore water and groundwater were measured to investigate such metal transport and retention at the site. Slug tests were conducted at the site to determine local discharges and water velocities. For comparison, total groundwater flows through the landfill were estimated by considering water balances of the upstream catchment. Results showed a much higher groundwater flow through the landfill at Pukeberg when estimated from water balance constraints of the upstream catchment, compared to when up-scaled from local measurements of hydraulic conductivities and gradients within the landfill. This points to the existence of aquifer heterogeneity, including preferential flow paths and low-flow zones. Depending on flow path, transport velocities of heavy metals could therefore differ considerably. With a pH of 7 in pore water, and relatively low DOC (dissolved organic carbon) concentrations, environmental conditions were found likely to favour a strong retention for most of the metals investigated. This conclusion is supported by our estimates of Kd (the soil-water partitioning coefficient), derived from the in-situ ratio between soil concentrations and pore water concentrations at the landfill. These estimates were consistently higher than previously reported laboratory results from leaching tests, as well as guideline values from the Swedish EPA. For instance, Kd for Pb and As was found to be 50600 l/kg and 16400 l/kg, respectively. Thus, for most metals, a limited transport downstream could be expected. Indications of high aquifer heterogeneity within the limited area investigated in this study however give rise to uncertainties, for instance regarding the representativeness of the taken samples. Possible future analyses of extended sampling schemes could decrease these uncertainties.

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Acknowledgements

First and foremost, I would like to thank my supervisor Jerker Jarsjö for his valuable feedback, patience and encouraging approach throughout this thesis. I also wish to thank him for giving me the opportunity of this project, from which I have gained invaluable knowledge within an interesting and important subject.

I am also grateful to Terese Uddh Söderberg, for never hesitating to offer a helping hand, and for sharing invaluable insight and expertize within the area. A special thanks for her warm welcome and delicious food, during my field visit in Kalmar. I also thank Anna Augustsson and Mats Åström for taking interest in the project, and of course for inviting me to Linnaeus University in Kalmar.

Last but not least I thank my family and friends, for always supporting and encouraging me. Finally, I thank my son for (occasionally) allowing me enough sleep to finish this project, as well as for offering plenty of smiles and laughter along the way.

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Table of contents Abstract .................................................................................................................................................................... 1

Acknowledgements .................................................................................................................................................. 3

1 Introduction ........................................................................................................................................................... 7

2 Study Site .............................................................................................................................................................. 9

2.1 Location and Climate ............................................................................................................... 9

2.2 Geology .................................................................................................................................... 9

2.3 Hydrology and hydrogeology ................................................................................................. 10

2.4 Contaminants and chemical parameters ................................................................................. 11

3 Methods ............................................................................................................................................................... 12

3.1 Heavy metals and chemical parameters in focus .................................................................... 12

3.2 Sampling and data collection ................................................................................................. 12

3.3 Transport in the saturated zone .............................................................................................. 14

3.4 Retention in the unsaturated zone .......................................................................................... 16

4 Results ................................................................................................................................................................. 17

4.1 Hydraulic conductivity and advective groundwater transport ................................................ 17

4.2 Environmental parameters ...................................................................................................... 19

4.3 Concentrations in soil ............................................................................................................. 19

4.4 Concentrations in pore water .................................................................................................. 21

4.5 Concentrations in groundwater .............................................................................................. 21

4.6 Kd values and concentration ratios between compartments.................................................... 22

5 Discussion ........................................................................................................................................................... 23

5.1 Slug test – Evaluation ............................................................................................................. 23

5.2 Aquifer Heterogeneity ............................................................................................................ 23

5.3 Flow directions and Advective Plume Transport ................................................................... 24

5.4 Kd values and Retention of Plume .......................................................................................... 25

5.5 Uncertainties due to values below detection limits or laboratory report limits ..................... 27

6 Conclusions ......................................................................................................................................................... 28

References .............................................................................................................................................................. 29

Appendix A – Site images...................................................................................................................................... 32

Appendix B – Data analysis and slug test .............................................................................................................. 33

Appendix C – Reported values............................................................................................................................... 35

Appendix D – Raw data ......................................................................................................................................... 36

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1 Introduction Already in 1969, the first environmental law in Sweden was introduced, and since then the legislation has been revised a number of times. The latest came into force in 1999 and provides a comprehensive framework on how to regulate and decrease negative environmental impact (Ebbesson 2015). Despite this, an estimated number of 80 000 sites are suspected to be contaminated, out of which many stems from industrial production that took place long before any environmental law existed (Swedish EPA 2006). In Småland, in southeast Sweden, glass production started as early as the 1700th century and have since come to include close to a hundred different glassworks in the area (County administrative board of Kronoberg 2001). Landfills of glass waste and other by-products from the glassworks have resulted in areas severely contaminated with heavy metals (Höglund et al. 2007). As part of a larger project involving several official organisations as well as concerned municipalities and counties, these sites have been investigated to determine the extent of contamination and where appropriate remediation strategies should be prioritized (Höglund et al. 2007). Pukeberg is one of the glass production sites and is located in the municipality of Nybro in southeast Sweden. The site has been classified to be severely contaminated, or risk class 1, according to the Swedish EPA’s method for investigating contaminated areas. Heavy metals such as arsenic, lead and cadmium have been found in highly elevated concentrations in the soil at Pukeberg (Elert and Höglund 2012). These metals are not known to be essential for any biological functions in humans but are, on the contrary, toxic to humans and some biota. Exposure could cause life threatening diseases such as cancer, heart disease and damage to the nervous system (Wuana and Okieimen 2011). Glass is typically considered to be an inert material (FSA 2002) and studies that have been investigating long term leaching processes from glass are lacking (Sterpenich and Libourel 2000). However, the high soil content of heavy metals in Pukeberg and other similar glassworks sites is accompanied with high concentrations also in the surrounding pore water. This indicates that leaching may contrarily to expectations to be considerable. A few kilometres downstream from the site, an important groundwater reservoir is located, and a nearby stream have been identified as a potential future drinking water reservoir (Elert and Höglund 2012). Considering the toxic effects of the contaminants, infiltration of the pore water to the underlying groundwater could be detrimental. Measurements of the groundwater located just a few metres beneath the landfill, however, shows only slightly elevated concentrations of the metals (Elert and Höglund 2012), which is consistent with observed low concentrations in nearby drinking water wells (Augustsson et al. 2016). Due to mixing with groundwater, pore water that infiltrates into the aquifer will be subject to dilution (Christensen et al. 2001) and thus result in a decrease in leachate concentration. In a study by Törnqvist (2016), dilution at the landfill was estimated based on the size of the catchment contributing with groundwater flow in relation to the landfill area. However, the estimated dilution effect could only partly explain the preliminary results from a first sampling campaign in August 2015, showing decreased metal concentrations in groundwater compared to pore water. Furthermore, in Törnqvist (2016) the dilution factor was calculated assuming a homogenous aquifer, which in theory would distribute the incoming water evenly across the landfill area. Properties regarding aquifer heterogeneity have not yet been investigated, however. If such heterogeneity exists, the calculated dilution would represent an average value. This implies that both less and more dilution can take place locally than the calculated average one. Christensen (2001) points out that local heterogeneity in aquifers in terms of different hydraulic conductivities is an important factor contributing to the dilution of leachates. Since the soils of Pukeberg are comprised of moraine, they are likely to exhibit heterogeneous characteristics. This would not only have impact on dilution but would also result in preferential flow paths of the groundwater (Espeby and Gustafsson 1998). Potential fractures in the underlying bedrock could further contribute flow channelling, which could impact downstream transport of contaminants.

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Bedrock, however, is often considered as impermeable and are therefore usually excluded from risk assessments regarding contamination transport (Sundqvist et al. 2009). This assumption is probably also due to a general lack of information regarding fractures and their connectivity (Saripalli et al. 2001). Despite their importance for understanding risks related to contamination, heterogeneities in aquifer properties are usually difficult to determine (Sundqvist et al. 2009; Espeby and Gustafsson 1998). For instance, contaminant travel times risk to be underestimated if variations in travel times and pathways are not considered (Persson et al., 2011). The transport behaviour of metals, however, are not only a result of physical processes, but also of chemical processes. It is common that parts of a contaminant plume are retained in the soil and thus inaccessible to the groundwater (Domenico and Schwartz 1998). There are many studies covering the subject of immobilization of metals due to retention (Christensen and Christensen 2000, Christensen and Jensen 1996, Kalbitz & Wennrich 1998). These studies cover issues such as how different environmental factors, such as pH and organic material, affect the mobility of heavy metals. Furthermore, the type of minerals in the soil (Garcia-Sánchez et al. 1999) as well as competition from other ions present affects retention further (Echeverria et al. 1998). Although these studies vary in approach and research focus, they all conclude that individual metals behave differently to environmental parameters and the overall composition of the soil. Fundamentally, the behaviour of metals are due to their inherent properties as chemical elements. Thus, environmental conditions that facilitates mobility for one species may have the opposite effect on another (Berggren Kleja et al. 2006). To increase the understanding of field-scale metal leaching under (potentially) heterogeneous conditions, it is of great value to make site-specific and spatially distributed observations of soil and water properties, including metal concentrations in the source zone and downstream of it. This study aims at measuring and interpreting the transport behaviour of the heavy metals found at Pukeberg, in the unsaturated zone as well as the saturated zone. It will investigate the hydrogeological properties of the landfill that could influence the transport of heavy metals. Factors such as water velocities and possible flow paths through the aquifer will be examined, as well as metal concentrations in soil, pore water and groundwater. Increasing the knowledge on how contaminants are transported through the landfill of Pukeberg, both vertically and horizontally, should prove helpful when considering associated risks and proper remediation strategies. Furthermore, such knowledge could be of help when assessing similar areas of contamination in southern Sweden. More specifically, the following questions will be investigated.

• Which hydrogeological properties does the landfill exhibit and how would they impact the transport of contaminants?

• To what extent can there be contaminant retention in the unsaturated zone of the landfill?

• Is it likely that contaminant plumes of dissolved metals are developing in the groundwater downstream of the landfill, and in that case, at what pace?

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2 Study Site 2.1 Location and Climate

Pukeberg glassworks is located in the municipality of Nybro, southeast Sweden, west of the city Kalmar (figure 1). Pukeberg glassworks is part of the so-called Kingdom of Crystal (in Swedish: Glasriket), an area in southern Sweden known for its many glassworks. The main industrial area including the glassworks factory can be observed in figure 1, along with most of the glass waste landfill, which covers an area of ca 0.03 km2. However, glass disposal has occurred at different locations over time. Therefore, the exact extent of the landfill is unknown. Glass residuals have been found both south and north of the main landfill area. The main landfill is comprised of a 1 to 2 m deep layer of glass mixed with soil. All glass detected in the main body of the landfill lies above the groundwater table. Waste has been observed to be located below the groundwater table, however, near the stream Lindåsaån north of the landfill (Elert and Höglund 2012).

The highest elevation in the vicinity of the Pukeberg factory is found at the eastern side of the landfill with 83 m above sea level (m.a.s.l.). The area dips slightly towards north-east, where the elevation is approximately 80 m.a.s.l. Until recently the landfill was covered with vegetation such as pines, mosses and shrubs. However, as part of the site preparation for a planned remediation, the area was cleared in 2015 and is today almost completely free of vegetation. A major road, road 25 (in Swedish: riksväg 25), passes south of the landfill in a west to northeast direction. The total annual average precipitation is 500 mm and the average monthly temperature ranges between -4 °C to 17 °C from January to July (SMHI 2014).

Figure 1. Industrial area of Pukeberg glassworks (yellow), landfill (orange) and catchment (dashed). Inset: Sweden (grey area) with the location of Pukeberg glassworks (black star) west of Kalmar. Original map from Lantmäteriet (2013).

2.2 Geology

Pukeberg is located in the border area for the estimated location of the highest historical coastline (Municipality of Nybro 2007). It is thus likely that the area was submerged below saline or brackish

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sea water when the latest ice cover retreated. Besides being located on top of an esker in a northwest-southeast direction, the landfill of Pukeberg also exhibits typical glacial characteristics with moraine as the dominant soil type.

The moraine itself mainly consists of different compositions of gravel, sand and silt. Larger blocks of granite in the moraine have been found in nearby places (County of Kalmar 2016a), but have not been identified at Pukeberg. However, difficulties encountered during the drilling of groundwater wells at the site might suggest that the moraine at the landfill also contains larger blocks. The depth of the moraine at the landfill is not known in detail, but drilled wells located north and south of the landfill have measured 8 to 9 m down to the bedrock (SGU 2016a). Other studies have reported depths of 6 m (Elert and Höglund 2012).

The porphyritic bedrock that underlies the moraine is closely related to granite, with similar characteristics such as the hardness and red colour (County of Kalmar 2016b). A larger fault located in a northwest-southeast direction coincides with the stream S:t Sigfridsån, east of the landfill (SGU 2016b).

2.3 Hydrology and hydrogeology

Pukeberg is located in the catchment of Ljungbyån, which covers 23 km2 and has a main flow direction towards the east. The outlet is located at the Baltic Sea coast (SMHI 2016). The subcatchment area upstream of Pukeberg landfill is estimated to cover 0.3 km2, according to a delineation made by Törnqvist (2016) (figure 1). Just a few kilometres downstream of Pukeberg, the same esker that underlies the landfill constitutes an important drinking water reservoir to the municipality of Nybro. The esker, which, is in a northwest to southeast direction, originates 4.5 km upstream from Pukeberg and ends 25 km further downstream. Although the hydraulic characteristics of the esker at Pukeberg have not been investigated to any extent, investigations of the esker further downstream are available (Knutsson 2004). The investigation shows that several groundwater divides across the esker are present and that the groundwater generally flows in a transverse direction. Assuming that similar properties applies at the esker close to Pukeberg, the groundwater flow to the landfill via the esker upstream would be limited. Furthermore, a major stream, S:t Sigfridsån, runs in the middle of the esker in a parallel direction, which suggests that the groundwater flow follows the topography in the area.

Since the groundwater direction follows the topography, it has a gradient that slightly dips towards the east (figure 2). The highest groundwater level of 71.1 m.a.s.l. is located upstream of the landfill, at the bottom left corner of the blue inset map of figure 2. The lowest groundwater levels of 69.3 m.a.s.l. is located at the top right corner of the inset map (figure 2).

A major stream, Lindåsaån, is located north of the landfill. It later joins the stream S:t Sigfridsån from the west. The smaller contributory stream Svartån joins S:t Sigfridsån from the west and is located south of the landfill (SMHI 2016).

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Figure 2. Groundwater gradient (blue inset). Highest groundwater level in the southwest (in m.a.s.l.), lowest in northeast. Orginal map from Lantmäteriet (2013).

2.4 Contaminants and chemical parameters

Metals used in glass production and soil contamination

Glass production for commercial purposes took place in Pukeberg between 1871 and ca 1990. Since then, glass production at the site has been limited to artistic purposes. Arsenic (As), lead (Pb) and cadmium (Cd) are known to be main components within older methods in glass production, and are included in processes such as refinement, stabilisation and colouring (County administrative board of Kronoberg 2001). Production methods changed during the twentieth century, when As was replaced by antimony (Sb) (Sternbeck et al. 2002). Other elements that have been used in different glass production methods include fluorine (F), barium (Ba), iron (Fe), copper (Cu), manganese (Mn), and cobalt (Co).

The soil concentrations used in this study were measured in 2015 by researchers at Linnaeus University, as part of a project funded by the Geological Survey of Sweden (SGU). The samples were collected from different depth intervals from locations 1 to 9 (figure 3) (appendix D: table D4), before they were sieved to 2 mm soil particles and then dried. The concentrations (mg/kg) of heavy metals were analysed using XRF (x-ray fluorescence). The XRF gives rise to uncertainties regarding reported soil concentrations, which for some metals fail to report specific values. Instead they could only be reported to be below a certain value. This is a consequence of disturbances caused by the specific composition of the sample. Thus, disturbances and consequently also “below values”, vary from sample to sample and for different metals. How these values are processed in this study is further explained in section 3.2. Since “below values” constitutes 80-100 % of the total sample size for V (vanadium), Cd and Co, these metals will be excluded from any analysis related to soil concentrations.

Environmental parameters in pore and groundwater

Environmental parameters had previously been measured with an YSI 659 MDS meter by researchers from Linnaeus University during the dates 9th of July, 11th of August, 1st of September, 2nd of November, 30th of November 2015 and 15th of February 2016. The following data is unpublished but summarized for the current study. The groundwater is slightly acidic with an average pH value of 6, whereas the pore water is neutral. The environment in the groundwater is oxidizing according to the

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high Eh (434 mV), with a dissolved oxygen level of 64 %. Conductivity and alkalinity are 331 µS/cm and 133 mg HCO3/l, respectively, for groundwater, and 849 and 432 for pore water. The average temperature in groundwater is 9.9 °C, with higher values detected during summer and lower during winter months.

3 Methods 3.1 Heavy metals and chemical parameters in focus

We consider twelve metals (As, Pb, Cd, Sb, Zn, Mn, Sr, Ni, Co, Cu, Cr and V) for which concentration measurements were done in both pore and groundwater, as well as in soil samples. This is necessary for the upcoming comparisons between water and soil systems. This selection includes metals that were main constituents in glass production at Pukeberg, such as As, Pb and Sb. An in depth analysis of these metals is of high concern due to their toxicity and elevated concentrations in either soil or water. In an extended analysis of the groundwater composition (including differences in composition between different wells), we additionally consider Fe, Ba, Al, Mo, K and Mg, as well as SO4, Na, F, Cl and Ca. DOC (dissolved organic carbon) and pH, being important environmental parameters affecting metal mobility, are also included in the analysis and later discussed in more detail than other parameters.

3.2 Sampling and data collection

Assumption regarding values below the detection limit

Due to low concentrations, some of the water samples were reported as “below reporting values” by the analysing laboratory ALS Scandinavia AB. These threshold values are different for different parameters (see appendix D, table D1). In this study, values below the reporting limit for water samples, as well as corresponding “below values” for soil samples, have been assigned half of the limit value. For example, if < 30 mg/l is reported, the value used for the data analysis will be 15 mg/l. An alternative approach would have been to set the value as zero, or their maximum value. The impact of the decision to use half of the limit value instead of the limit value, or zero, will be explored in the discussion section.

Pore water and groundwater samples

Water samples were collected during the dates 9th of July, 11th of August, 1st of September, 2nd of November, 30th of November 2015 and 15th of February 2016. Pore water samples were collected using tension lysimeters located at depths of 30 and 60 cm at locations 1, 2, 5 (figure 3, table 1) and groundwater from wells at locations 2, 4, 5, 7, 8, 10 and 11 (figure 3, table 2). Due to practical constraints, such as low water table, not all sample types were collected at all locations during each field campaign.

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Figure 3. Overview of the landfill at Pukeberg and sample locations 1 to 11 (map from Google Earth 2016). Groundwater samples were collected at locations marked with circles, pore water was collected from locations marked with a square and both pore and groundwater were collected at locations marked with triangles. Soil samples were collected at locations marked with red numbers.

Table 1. Pore water samples collected at depths of 30 and 60 cm at locations 1, 2 and 5 during the different field campaigns.

Date 2015-07-09 2015-08-11 2015-09-01 2015-11-02 2015-11-30 2016-02-15 Depth 30 60 30 60 30 60 30 60 30 60 30 60 Location 1 x x x x x x x x x x x x 2 x x x x

x x x x x x

5

x

x

x Table 2. Groundwater samples collected at each location (2, 4, 5, 7, 8, 10 and 11) during the different field campaigns.

Date Location

2015-07-09 2015-08-11 2015-09-01 2015-11-02 2015-11-30 2016-02-15

2 x x x x x x 4 x x x x x x 5 x x x x x x 7 x x x x x x 8 x x x x x x 10 x x

x

11

x

Unfiltered and filtered samples from both pore water and groundwater were analysed. Filtered samples, which were filtered using a 0.45 µm filter, are considered to represent dissolved concentrations but can in theory contain a certain amount of finer particles (<0.45 µm) (see e.g. Younger and Wolkersdorfer 2004, Jensen et al. 1999). However, since no difference could be

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observed in the present case when comparing the two different type of samples (i.e., filtered and unfiltered; appendix B: figure B1), this should not have any impact on the results. In this study, we focus the analyses and presentation on the filtered sample data since the associated “dissolved phase” labelling then agrees well with the actual characteristics of the samples (containing dissolved constituents and only very fine particles <0.45 µm).

3.3 Transport in the saturated zone

Estimates of hydraulic conductivity have been seen to increase with the measurement scale, both in bedrock and in unconsolidated material (Schulze-Makuch et al. 1999). In this study we compare estimates of discharge (Q) that were made in different ways. Local scale Q-values (Qlocal), reflecting local hydraulic conductivities and hydraulic gradients are here estimated from slug tests conducted at the landfill and observed differences in groundwater levels at the landfill. We compare these estimates with a Q-value of 1.7E-03 m3/s derived from the catchment-scale water balance, accounting for runoff from the entire catchment upstream of the landfill, which was calculated in a previous work by Törnqvist (2016).

The Q-value derived from the water balance was calculated using discharge data measured for the stream Ljungbyån, which is located upstream from where it is joined with S:t Sigfridsån. The data was collected from the Swedish Meteorological and Hydrological Institute (SMHI). Ljungbyån and the point at which discharge was measured does not belong to the same catchment as Pukeberg, but to a catchment nearby. However, due to the vicinity between the two catchments and their similar soil properties, it was assumed in the study by Törnqvist (2016) that hydrological properties such as discharge and evapotranspiration were similar as well. The stream discharge was first divided by the area of the catchment for Ljungbyån, to obtain the runoff (Q per unit area). This was used to calculate a corresponding Q (Qcatchment used in this study) for the catchment area belonging to Pukeberg (dashed grey area in Figure 1). In the latter case, since there are no streams passing the Pukeberg landfill, Qcatchment represents a groundwater flow through the landfill (orange area in Figure 1).

Since the width of the lower part of the catchment equals the width of the landfill (figure 1), we can estimate a corresponding flux through the landfill by dividing Qcatchment by the cross sectional area of the landfill. The following sections describe the detailed procedure to obtain Qlocal as well as the advective transport velocities derived from the two estimates of discharge. Performing Slug Tests

Slug tests were conducted on 21st of Mars 2016 to determine the hydraulic conductivity (K, m/s) at the site. Slug tests can be performed either by adding or subtracting a known volume of water to the well, which would result in an increasing or decreasing water level, respectively. Since we did not have availability to pumps, the tests in this study were conducted by adding a certain volume of water. Using a water level indicator, the water levels for each well at the site were first measured.

Groundwater wells have been installed at two separate occasions (2011 and 2015). Wells installed in 2011 have a diameter of 45 mm (locations 5, 10 and 11) and wells installed 2015 are 30 mm (locations 2, 4, 8 and 7) in diameter. To increase the water level 1 m, which should be sufficient to record the recovery, 1.5 l (45 mm wells) and 0.7 l (30 mm wells) of water were first added to the wells. The slug tests were conducted one at a time at each well, by adding the appropriate water volume. This was done for wells at locations 2, 4, 5, 8, and 11.

The added volumes of 1.5 and 0.7 l did however not result in increased water level. In additional slug tests at locations 2, 4, 5 and 8, larger water volumes of 5-10 l were therefore added. Later it was discovered that the well screen extended above the location of the groundwater table at all wells except well 8, which can explain the lack of water level rise. In summary, the success of the slug tests

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differed between locations and test runs. Values for K reported in this study are based on the two successful slug tests (for which the water level initially increased as expected). They were performed in wells at locations 4 and 5.

Interpretation of Slug Tests

When performing a slug test, the recovery of the water level is measured until the water level reaches the original level. Different methods have been developed to interpret the curve resulting from the time and water level response into values of K (see for example Hvorslev 1951, Cooper and Jacob 1953). For the slug tests conducted in this study, the Bouwer and Rice (1976) method was used, which was developed using results from an electrical analog model. The method is suitable for unconfined aquifers with wells that are not completely penetrating the aquifer, as in the present case. To calculate K according to Bouwer and Rice, first the time and water level response is plotted on a logarithmic scale for h (water level in m). A straight line is then drawn for the steeper part of the curve (figure 4 in Bouwer and Rice 1976), from which readings of h and t (time, s) are made and included in the subsequent equations. Following the Bouwer and Rice method, equation (1) was used to calculate hydraulic conductivity from the slug test results:

𝐾 =𝑟𝑐2ln(

𝑅𝑒𝑟𝑤

)

2𝐿𝑒

1

𝑡ln(

ℎ0

ℎ) Equation 1

where K is the hydraulic conductivity (m/s), rc is the radius of the well casing (m), rw is the radius of the well including screen (m), Le is the length of the screen (m), h0 and h (m) are the original and raised water level, respectively and t (s) is the time at h. Re is the radial distance over which head is dissipated (m). Values for ln(Re/rw) in a partially penetrating well, are further estimated using a method developed by Bouwer and Rice (equation 2):

ln (𝑅𝑒

𝑟𝑤) = [

1.1

ln(𝐿𝑤𝑟𝑤

)+

𝐴+𝐵𝑙𝑛(𝑏−𝐿𝑤𝑟𝑤

)

𝐿𝑒𝑟𝑤

]

−1

Equation 2

where Lw is the length of the well in the aquifer and b is the thickness of the saturated material. A and B are dimensionless values determined from figure 3 in Bouwer and Rice (1976). b is estimated based on reported depths of the soil (between 7 and 9 m) from observations in wells nearby (SGU 2016a), and the measured depths to the water table at locations 4 and 5 (5.03 and 4.08 m, respectively). Specifically, the resulting K used in this study when calculating Q is the average of results from three different assumed values of b for each location (4 and 5) based on reported soil depths in the area (7 m, 8 m and 9 m) and the depth to the water level at each location.

Discharge and water velocity

The groundwater discharge at local scale (Qlocal), in m3/s, through the landfill was calculated using Darcy’s law (equation 3):

𝑄 = 𝐾𝐴𝑑ℎ

𝑑𝑙 Equation 3

where K is the hydraulic conductivity (m/s) quantified as explained in the previous section. The cross sectional area of the landfill, A (315 m2), is calculated from the width of the landfill which is estimated to be 90 m across in a northwest-south east direction (see the width of the dashed area where the landfill is located in figure 1) and the depth of the aquifer. This is estimated to be 3.45 m based on the average thickness of the soil in the area and the depth to the groundwater table measured at locations 4 and 5. The hydraulic gradient, dh/dl, is based on groundwater level measurements at locations 4 and 8.

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This choice is motivated by the relative location of the two wells: one being located at the upstream and the other at the downstream end of the landfill, respectively. The distance between them (dl) is 34 m and the difference in hydraulic head (dh) was 0.17 m during the time of measurement.

The specific discharge (q, m/s) at local and catchment scale through the landfill was estimated by dividing Qlocal and Qcatchment, respectively, with the cross sectional area A (Equation 4) explained earlier. Dividing both Qlocal and Qcatchment by the same area provides comparable estimates of the specific discharge, with the assumption that each Q applies to the entire landfill.

In order to calculate an advective transport velocity (v, m/s), the specific discharge (q) from both local and catchment estimates needs to be divided by the total porosity (n) of the soil (equation 5), accounting for the fact that water only flows in the porous space of the soil (see, e.g., Espeby and Gustafsson 1998).

𝑞 = 𝑄

𝐴 Equation 4

𝑣 =𝑞

𝑛 Equation 5

where a reported value of porosity (0.39) for a similar material was used (Domenico and Schwartz 1998).

Downstream Transport of Plume

The resulting advective transport velocities representing local and catchment scale, calculated from equation 5, were used to estimate how far the plume could have reached, considering the time elapsed since the production at Pukeberg started. An assumption of no retention in the aquifer provides a maximum estimation of the extent of the plume. We furthermore estimate the possible retention of the metals in the groundwater system, by using measured soil and pore water concentrations to estimate site-specific Kd values (soil-water partitioning coefficients) as further described in section 3.4 below.

3.4 Retention in the unsaturated zone

Partitioning coefficients (Kd)

Partitioning coefficients (Kd) of metals between the solid phase in the soil and the liquid phase of water are reported from systems at equilibrium (US EPA 1999), and is commonly determined using batch experiments in the laboratory. However, not least due to possible scale-effects, it is of interest to investigate the actual (in-situ) ratios between the solid and aqueous phase concentrations of metals at the landfill. Even though a complete equilibrium between the two phases is probably not met in a strict sense in the natural environment, such in-situ ratios may still provide useful and complementary information to traditional laboratory results. In the present case, we will compare our in-situ results with leaching tests that have been performed in the Pukeberg area in previous laboratory studies (Elert and Höglund 2012), and general guideline values, based on leaching tests from contaminated sites in Sweden (Swedish EPA 2009).

More specifically, in-situ Kd values (l/kg) were calculated as the ratios between the average metal concentrations in soil (mg/kg) and pore water (mg/l):

𝐾𝑑 =𝐶𝑠𝑜𝑖𝑙

𝐶𝑝𝑜𝑟𝑒𝑤𝑎𝑡𝑒𝑟 Equation 6

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The upper first meter of soil concentrations from locations 1 to 5 were used since the lysimeters collecting pore water are located within this depth. For the calculation of average pore water concentrations, the measurements at locations 1, 2 and 5 were used.

Data analysis

The environmental parameters pH and DOC (mg/l) and their distribution in pore water (at depths of 30 and 60 cm, at locations 1, 2 and 5) and groundwater (locations 2, 4, 5, 7, 8 and 10) are included in the data analysis. This will provide important information when considering basic environmental conditions that might affect metal mobilization.

We here relate soil concentrations (mg/kg) of heavy metals at different soil depths from the landfill (location 1 -5) as means to illustrate the vertical variation of contaminants in soil. Soil concentrations from the first metre at each site individually will also be investigated to establish the spatial variation of contaminants. Using the first metre is necessary for the comparison between soil content and pore water concentrations, which were measured at 30 and 60 cm depth.

Average groundwater (µg/l) concentrations at the landfill (locations 2, 4 and 5) are compared to groundwater wells downstream (locations 7, 8 and 10). These are considered as downstream from the landfill since their soil does not contain glass material. The results will help establish how groundwater is distributed downstream from the landfill in terms of flow direction, as well as to what extent the water downstream is contaminated.

Retention of heavy metals in the upper unsaturated zone will be investigated in terms of concentrations at different depths (30 and 60 cm) in pore water. Average concentrations (µg/l) for each depth and location (1, 2 and 5) will be compared. Any observed trends of decreasing or increasing concentrations with depths, could indicate if retention occurs and to what extent it is occurring in the upper soil column. Ratios are also calculated between average concentrations in pore water at locations 1, 2 and 5 and average concentrations in groundwater at locations 2, 4 and 5. This will determine if measured groundwater concentrations could be the result of dilution alone or if other processes, such as retention, is likely to decrease the concentrations further.

4 Results

4.1 Hydraulic conductivity and advective groundwater transport

Slug test

The first slug tests at the site (Test no 1 of Table 3) did not result in expected water level rises of the wells in response to added water volumes. Instead water levels decreased. The largest decrease is observed for location 8 (1.88 m), which occurred during the first test. The repeated slug tests (Tests no 2 and 3 of Table 1) conducted in the wells at locations 2, 4, 5 and 8, did still not result in any increase at locations 2 and 8. For well 4 and 5 these volumes resulted in a water level rise of 5-10 cm, which was sufficient to record the recovery and use for further calculations of hydraulic conductivities (table 3, marked with asterisks). The water level responses at locations 4 and 5 as a function of time can be seen in figure B2 in appendix B.

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Table 3. Summary of water level response (m) at each location (with numbering as in Figure 3) after addition of water (l). A negative value of the response corresponds to a decline in water level whereas a positive value corresponds to a rise in water level. Hyphens mark occasions where no tests were conducted. Responses marked with asterisks were used for hydraulic conductivities.

Test no: 1 2 3 Location Added volume (l) Response (m) Added volume Response Added volume Response

2 5 -1.085 5 +0.025 - - 4 5 -0.895 5 +0.05* 10 +0.105* 5 1.5 -0.29 5 -0.44 5 +0.08* 8 5 -1.88 5 0 9 +0.005

11 1.5 0 - - - -

The hydraulic conductivity (K) for the three successful slug tests performed (table 4) was calculated to be approximately 7.5E-08 m/s (6.5 mm/day), which is the average value of a set of calculations where the uncertain soil depth was systematically varied between 7 to 9 m according to Table 4. The lowest estimated K (3.4E-08 m/s) was obtained when an estimated soil depth of 9 m was used for location 4 (test 1). The highest estimated K (1.7E-07 m/s) was obtained when an estimated soil depth of 7 m was used for location 5. In general, higher hydraulic conductivity values were estimated for location 5 (average 1.1E-07) than for location 4 (average 5.5E-08).

Table 4. Hydraulic conductivity, K (m/s), calculated from slug tests at locations 4 and 5 with different assumptions regarding the soil depths (7, 8, 9 m).

K (m/s)

Location : Test no Assumed depth 7 m

Assumed depth 8 m

Assumed depth 9 m

Average

4: 1 3.6E-08 3.5E-08 3.4E-08 3.5E-08 4: 2 1.1E-07 6.8E-08 5.0E-08 7.4E-08 5 1.7E-07 1.0E-07 7.0E-08 1.1E-07 Average total (m/s) 7.5E-08 Average total (mm/day) 6.5

Water discharge, advective transport velocity and plume transport

The estimated discharge (Q) and advective transport velocity (v) exhibit great differences between the two methods compared (table 5). The average hydraulic conductivity from the slug test (7.5E-08 m/s) combined with hydraulic gradients observed on-site resulted in an estimated flow through the region (Qlocal) of 1.2E-07 m3/s and an advective transport velocity as low as 0.03 m/year. Derived from the water balance equation, representing the catchment scale (dashed in Figure 1), total discharge through the landfill is on the order of 1.7E-03 m3/s (Qcatchment). This corresponds to a transport velocity of 443 m/year. The corresponding travel distances for the plume, over the 145 years of landfill existence is 4.4 m according to slug test results and 64235 m according to the water balance approach.

Table 5. Discharge (Q, m3/s) and advective transport velocity (v, m/s and m/year) calculated from slug test results (local scale) and the water balance approach (catchment scale).

Method Q (m3/s) v (m/s) v (m/year) Plume – travel distance (m) Slug test (local) 1.2E-07 9.6E-10 0.03 4.4

Water balance (catchment) 1.7E-03 1.4E-05 443 64235

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4.2 Environmental parameters

The lowest DOC-concentration in groundwater is observed at location 11 (1.88 mg/l) and the highest at location 2 (4.5). The corresponding values in pore water are observed for location 5 at 60 cm depth (11.0 mg/l) and location 1 at 30 cm depth (27.7). pH is lowest at location 8 (pH 5.6) in groundwater and at location 1 at 60 cm depth in pore water (6.7). The highest pH is observed at location 5 (pH 6.4) in groundwater and at location 2 at depth 30 cm (7.1) in pore water.

Figure 4. Average pH and DOC-concentrations (mg/l) for each location in groundwater (2, 4, 5, 7, 8, 10 and 11) and pore water at 30 and 60 cm depth (1:30, 1:60, 2:30, 2:60 and 5:60). Correlations between DOC concentrations and metal concentrations in pore water are weak for all of the investigated metals. The lowest and highest r2 values are observed for Ni (0.00) and Sr (0.37), respectively. Only four metals have r2 values above 0.1 (Sb, Zn, Cu and Cr). Table 6. Correlations between DOC and metal concentrations in pore water, for each sample occasion, expressed as r2 values.

As Pb Sb Cd Sr Zn Ni Co Mn Cu Cr V

r2 0.04 0.08 0.18 0.02 0.37 0.10 0.00 0.07 0.05 0.34 0.14 0.07

4.3 Concentrations in soil

Metal concentrations measured in soil profiles differ depending on the specific metal (figure 5). The main metals used in glass production (Sb, As and Pb), as well as other metals (Zn, Cu and Cr) are higher in the upper soil zone (0-1 and 1-2 m) compared to lower depths. Concentrations for Sr and Ni are found to be more evenly distributed throughout the entire soil column, whereas Mn is found to be high in both shallower and deeper depths with lower concentrations in the middle.

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Figure 5. Average metal concentrations (mg/kg) at different depths (m) of soil samples collected from locations 1-5. Note the scale difference between the figure axes.

At depth interval 0 – 1 m in soil, metal concentrations exhibit a large variation between different metals and locations (figure 6). A general pattern of low concentrations at locations downstream from the landfill (6, 7, 8, and 9) are observed, whereas elevated concentrations are found at the landfill (1, 2, 3, 4 and 5). Ni, Mn and Sr are however more evenly distributed between the different locations. At location 5, concentrations of the most common glass production metals (As, Pb and Sb) investigated in soil, are amongst the highest observed. At location 4, high concentrations are mainly observed for As and Cu, compared to other locations.

Figure 6. Average metal concentrations (mg/kg) from depth interval 0 – 1 m, at each location (1 – 9).

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4.4 Concentrations in pore water

Site observations reveal that metal concentrations of the pore water vary depending on location and depth (figure 7). Any trends for specific locations are not observed, however location 1 and 2 often exhibits reversed patterns. When the highest concentrations are observed at 30 cm (for As, Pb, Cd and Sb), they are much higher compared to their respective concentrations at 60 cm depth. Differences in concentrations for these metals between the two depths are generally less when higher concentrations are found at 60 cm.

Figure 7. Average pore water concentrations (µg/l) for locations 1, 2 and 5 measured at depths of 30 and 60 cm.

4.5 Concentrations in groundwater

A comparison between average substance concentrations in groundwater at the landfill (locations 2, 4, 5) and downstream of it (location 7, 8 and 10) are illustrated in figure 8, having main focus on metals. There are only five out of 24 substances that are observed to be the highest at the landfill (Pb, As, Zn, DOC and SO4). The average concentration of Cr, Co, Ni, Cu, Fe, Mn, Al, Na, and Cl were the highest at location 7, whereas Cd, V, Mo, Sb, Ba, Sr, F, K, Mg and Ca were the highest at location 10. In total, 19 of the 24 substances had highest concentrations downstream of the landfill, however none of them were peaking at location 8.

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Figure 8. Average concentrations (in µg/l or mg/l) at landfill (blue, average from locations 2, 4 and 5), downstream locations 7 (dark grey), 8 (medium grey) and 10 (light grey).

4.6 Kd values and concentration ratios between compartments

Kd values estimated from the in-situ ratio between soil concentrations and pore water concentrations at the landfill (table 7) are consistently higher than previously reported laboratory results from leaching tests, as well as guideline values from the Swedish EPA. The highest value measured at the site, 235100 l/kg, is observed for Cr, which can be compared with a corresponding guideline value of 1500. The lowest value of 340 is observed for Sr, for which neither leach test results nor guideline values were available. Of the most common glass production metals (As, Pb, Sb), Pb exhibits the highest Kd of 50600 l/kg, compared to values of 9700 and 1800 from leaching tests and guideline values, respectively. The here estimated Kd value for As is also high at 16400 l/kg, whereas the value for Sb was at the low end (1000).

Table 7. Kd-values for soil and pore water (l/kg). “Site ratios” refer to ratios from measured concentrations in soil and pore water at the landfill. “Leach test” refers to laboratory test results from 2012, and “Guideline” are guideline values determined by the Swedish EPA. Missing values are marked with a hyphen.

Kd (l/kg) As Pb Sb Sr Zn Ni Mn Cu Cr Site ratios 16400 50600 1000 340 1000 11100 1500 7400 235100 Leach test* 460 9700 250 - 630 - - 2100 - Guideline** 300 1800 80 - 600 300 - 600 1500

*Reference: Elert and Höglund 2012. **Reference: Swedish EPA 2009.

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Table 8 shows the ratio between the observed average pore water concentrations and groundwater concentrations of the investigated metals. For instance, As concentrations are four times higher in pore water compared to groundwater. Corresponding rations for Pb, Cd and Sb are 998, 206 and 15, respectively. Processes like dilution and retention may contribute to these results. However, as stated earlier in this thesis, water balance considerations show that the pore water is unlikely to be diluted more than by a factor 10 as it enters the groundwater system below the landfill. Only six (As, Sb, Sr, Cu, Cr, V) of the investigated metals have pore water – groundwater ratios that are lower than that, which indicates that the other metals may be subject to various degrees of retention.

Table 8. Ratios (PW/GW) between pore water (PW) and groundwater (GW) shows blue colour when dilution (of a factor of 10) is sufficient (approximately) to explain the lower concentrations in GW, and grey colour when dilution is not enough.

As Pb Cd Sb Sr Zn Ni Co Mn Cu Cr V

PW/GW 4 988 206 15 2 646 22 23 26 9 3 7

5 Discussion 5.1 Slug test – Evaluation

According to Bouwer (1989), when the well screen extends above the groundwater level as in the present case, this could result in K being overestimated due to water leaking horizontally into the unsaturated soil. Although uncertain, the here estimated K of 7.5E-08 m/s is within a reasonable range from a hydrogeological viewpoint, considering the material at the landfill. According to Espeby and Gustafsson (1998), the hydraulic conductivity for moraine with sandy/mo characteristics may vary from 10-6 to 10-9 m/s. In a study by Assmuth and Strandberg (1992), laboratory-based tests for hydraulic conductivity for glacial deposits such as sandy moraine in Finland, showed a large spatial variation. Values varied between 10-4 to 10-8 m/s, both within and between different test sites. The resulting value in the current study is thus within the possible range of conductivities for the investigated material. However, one should keep in mind that the variation observed in moraine is large (Assmuth and Strandberg, 1992). Although present results indicate that the two considered locations have similar hydraulic conductivities, one cannot rule out that other areas of the landfill might exhibit other hydraulic properties. In summary, the K-values used in this study should be considered reasonable but preliminary. It is recommended to perform repeated slug tests, after the problems related to the well screen position have been addressed. We note that the K-values derived from the slug tests influence some of our transport distance estimations, but not those derived from water balance considerations. The results and conclusions about plume retention and Kd values of different metals are independent of the slug test results.

5.2 Aquifer Heterogeneity

Important observations regarding aquifer properties could be obtained from the attempt of slug tests at location 8, even though it did not contribute with any values of K (table 3). Since the original groundwater level at the location was above the screen (as it should), it is likely that the response reflects actual properties of the aquifer. The lack of recorded response would then suggest that the potential recovery was too quick to record without more advanced equipment, indicating that conductivity was high. If the resulting K-values from location 4 and 5 are assumed to be correct, differences in hydraulic properties between the locations indicates that at least locally at the landfill, the aquifer possesses heterogeneous properties.

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Furthermore, the relatively high (through)flow rates of the landfill area that were calculated from water balance considerations suggest that there must be zones in, or beneath, the landfill that can support a much higher groundwater flow than what is possible in the estimated low-K regions near the test wells. This is consistent with the hypothesis that hydraulic properties are highly heterogeneous in the landfill region. In a study by Carrera (1993), it was concluded that spatial variability in hydraulic conductivities is directly or indirectly due to heterogeneities in the catchment.

As mentioned in the previous section, moraine is frequently heterogeneous, which could explain the observed differences in hydraulic properties. In addition, the characteristics of the underlying bedrock could further contribute to the observed heterogeneity. Previous experience from field investigations in other areas has shown a tendency of fractures in the upper ten metres of similar bedrock, with potential of much higher hydraulic conductivities (Sundqvist et al. 2009). Although the bedrock at Pukeberg has not been investigated to a larger extent, the general geology in the area surrounding the site has shown brittle faults (SGU 2016b). Thus, it is possible that shallow faults or fractures could provide a subsurface network of preferential flow paths for groundwater.

5.3 Flow directions and Advective Plume Transport

In order to understand contaminant spreading patterns, it is necessary to establish whether or not the different downstream wells may receive water from the landfill. According to the southwest-northeast groundwater flow direction (figure 2), it is likely that the groundwater at location 7 originates from south of the landfill. This is also supported by the chemical composition of the groundwater in that well. The composition differs from that of the landfill, by exhibiting comparatively low concentrations of e.g. As and Sb, and comparatively high concentrations of e.g. Mn, Al and Cl (figure 8). Generally, one can expect that downstream wells would have similar chemical composition as that of the source, however with lower concentrations due to dilution and possible also retention for some metals. Absence of typical glass production metals such as As and Sb and presence of metals commonly associated with road pollution, such as Cr and Fe (Klint 2001), rather suggests that location 7 does receive water from the south, including storm water from road 25. High levels of Cl and Na, which are the constituents of common road salt (Klint 2001), further supports this conclusion.

Based on the derived local flow direction, Location 10 is more likely to receive groundwater from the landfill. Compared to the other locations, concentrations of typical glass production metals are high at location 10 (e.g. Cd, As and Sb). However, concentrations of Sb and Cd were much higher than at the landfill, which indicates that there are contaminated areas downstream of the delineated landfill area that contributes to groundwater pollution. Location 8 is situated only 3 m east of the delineated landfill area. It also exhibits a chemical composition similar to that of the landfill, although metal concentrations are generally lower. Therefore it most likely to receives water directly from the landfill (unaffected by other sources).

Since the water chemistry in the wells located downstream are what to be expected considering the direction of the groundwater in the area and upstream sources, it is unlikely that potential preferential flow paths divert the water in other, unknown, directions. However, the heterogeneous properties of the catchment may still enhance the dilution of contaminants (Christensen et al. 2001). A larger spreading within areas of higher conductivities can also be expected (Assmuth and Strandberg 1992). As observed in table 5, the different groundwater velocities obtained showed a great variation in transport rates and distances of a contaminant plume.

If the area occupied by the present-day landfill started to become contaminated when production first started at Pukeberg, the front of the contaminant plume would then have had 145 years to travel via groundwater. Considering the total flow through the cross section at the downstream end of the landfill, as given by annual average water balances, and corresponding advective transport velocity as

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given by the effective cross sectional area (derived from geometrical constraints including soil porosity), the latter velocity corresponds to a travel distance of 64 kilometres over the 145-year time period. However, uncertainties in these results arise due to the identified heterogeneity in the catchment, with the potential of previously mentioned fractures in the bedrock beneath the landfill. The cross sectional area is based on the depth of the aquifer, which in this case is assumed to be the saturated zone of the soil at the landfill. Potential fractures beneath this zone, however, would enable a larger flow of water to pass through the landfill, resulting in lower velocities and subsequently also shorter travel distances regarding results derived from the water balance.

We also acknowledge that travel velocities may vary considerably downstream of the quantified cross section which would influence the actual travel distance, however, the present example indicates that it may be possible for contaminants (if transported by advection) to reach the stream S:t Sigfridsån 200 m east of the landfill, which discharges into the Baltic Sea, as well as the drinking water reservoir 3 km downstream of the landfill. Previous measurements of the stream have shown that heavy metal concentrations differ only slightly between locations upstream and downstream of Pukeberg (Elert and Höglund 2012). Observed elevated concentrations of As in the stream however indicate leakage from the glassworks, according to Elert and Höglund (2012). Knowing that contaminated soil has been found below the groundwater table north of the landfill, close to the stream Lindåsaån, makes it difficult to determine the exact origin of the elevated As concentrations in S:t Sigfridsån. The stream is also a recipient for the storm water of Nybro town, contributing with heavy metals to the stream, which further complicates the situation.

Local hydraulic conductivities and hydraulic gradients obtained from the observation wells at and near the Pukeberg landfill show that the 145-year advective transport distance can be much lower, with values down to 4 m, than the above-quantified estimate derived from the throughflow obtained from water balance constraints. This implies that, due to local heterogeneities, the contaminants from the landfill may only just have passed the downstream location 8, during the time of glass production at Pukeberg. Adding potential retention would slow down contaminant plumes further, perhaps resulting in plumes that do not reach downstream areas at all. Since metals behave differently, the extent of retention and advection will vary and so will potential downstream impacts. Elevated metal concentrations in groundwater measured at location 8 (figure 8) do indicate that it is affected by several metals that are associated with the landfill. The possible retention of individual metals will be further discussed in the next section.

5.4 Kd values and Retention of Plume

Partitioning of contaminants between the water phase and the solid (soil) phase, as for instance quantified through Kd values, contributes to contaminant retention, which makes travel times longer than the above-quantified advective travel times. It is clear from Table 7 that the Kd values calculated from measured concentrations in soil and pore water at the site are consistently higher than the guideline values based on leaching tests using samples from contaminated sites in Sweden (Swedish EPA 2009). They are also higher than Kd values derived from leaching tests using samples from the Pukeberg landfill area (Elert and Höglund 2012). Janssen et al. (1996) points out that equilibrium conditions achieved in laboratory may differ from field conditions when estimating Kd values, which could explain the different values obtained in the current study. Kd values are also known to be highly site specific due to their dependence on environmental parameters such as pH and DOC (Sauvé et al. 2000).

According to this comparison, Pb and Cr exhibits the largest difference between the two laboratory test methods, indicating larger retention than expected especially for these metals. This is further supported by results shown in figure 7, from which it appears as if most of the retention for especially Pb, but also for other metals (e.g. Mn and Cr), is occurring in the first 30 cm of the soil column. In

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contrast, Sb exhibits a much lower Kd value, indicating that it is leaching to a greater extent, which is consistent with the elevated concentrations observed in groundwater (figure 8).

The low ratios observed between pore and groundwater for Sb (table 8), further suggests that retention remains low in the deeper soil column. The opposite applies to Pb, where a high Kd value is accompanied by a high ratio between concentrations in pore and groundwater, indicating that retention is strong throughout the soil column in the unsaturated zone. For As and Cr, the high Kd values show that retention is likely to be strong in the upper soil column, but potentially weaker in (or near) the saturated zone according to the low ratios between pore and groundwater. However, Cr concentrations in soil (figure 5) are relatively high throughout the soil column which also could explain the lower ratios between pore and groundwater. Soil concentrations at lower depths are low for As, and is therefore unlikely to affect the low ratios observed between pore and groundwater.

With a pH close to 7 in pore water at all locations (figure 4), pH should be high enough to facilitate retention for most metals that are cations, such as Pb and Cu (Espeby and Gustafsson 1998). Although As is an anion, it generally adsorbs strongly to the soil when pH < 8, which is consistent with the high Kd value for As observed at Pukeberg. Also Sb shows consistency with known behaviour for the metal, occurring preferentially in solution when pH > 7 (Berggren Kleja et al. 2006), which could also explain the low Kd value observed.

The observed variation in local Kd values at Pukeberg cannot be fully explained by pH variation, since it is relatively small. Variations could also be subject to random behaviour due to the limited area investigated, especially when considering the large spatial variation of contaminants in the soil. However, variations in DOC concentrations between locations could also influence the observed Kd values. DOC has been found to be important for the mobility of metals when pH in soil is above 4.5 (Kalbitz and Wennrich,1998). DOC increases the solubility of metals (cations) by forming complexes (Espeby and Gustafsson 1998). Thus, with the relatively high pH observed in the present case, together with potentially high concentrations of DOC, conditions could favour an increased solubility of metals. For comparison, Löfgren and Zetterberg (2011), investigated data of DOC concentrations from 68 sites in Sweden. Concentrations from the sites ranged from 2.6 – 23.5 mg DOC/l with a median concentration above 8.2 mg /l at 50 % of the sites, which, according to the authors, should be considered high. The DOC concentrations observed at Pukeberg were slightly higher than that; 11.0 – 27.7 mg/l. However, a key question is to which extent differences in DOC concentrations may impact metal mobility.

Pore water concentrations for many of the metals (Pb, Cu, Ni, Co, Mn, Cr) at location 1 at depth 30 cm (figure 7), are high compared to other locations and depths, despite the fact that soil concentrations are more or less equal between locations or even low at site 1 (figure 6, see e.g. Pb). Modelling results of Thorslund et al. (2016) indicated that solubility of metals could increase by 80 % with a 50 % increase in DOC. Concentrations of DOC at location 1 (depth 30 cm) in the current study, are twice as high as those at other locations (figure 4). Furthermore, metals that are not commonly known to form complexes with DOC, such as As and Sb (Berggren Kleja et al. 2006), are found to be low at location 1 (depth 30 cm).

Thus, the results indicate that DOC concentrations are high enough in pore water at location 1 (depth 30 cm), to increase the mobility of metals. The opposite applies for the remaining locations, where relatively low metal concentrations in pore water could partly be due to low DOC concentrations, which at least would not promote metal transport. However statistically, correlations between DOC and metal concentrations in pore water are weak (table 6). This could partly be explained by the limited set of data. With more data, correlations could potentially be found when performing a multiple regression analysis, as have been achieved in previous work (Kalbitz and Wennrich 1998, Tipping at al. 2003). For instance, in the study by Tipping et al. (2003), correlations increased when considering chemical speciation of the pore water and soil, as well. The weak correlations that resulted

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from basic analyses were explained by the authors to be caused by the fact that metal binding to DOC could not be expressed in linear terms. The results from these studies further indicate that the effect of DOC, and thus also the mobility of metals and corresponding Kd values, are due to complex processes governed by highly site specific conditions. This may also result in local differences in metal behaviour, as illustrated in the present study.

The findings from this study support that DOC, in addition to pH, is a main factor when it comes to transport and retention of heavy metals. The present conditions in Pukeberg regarding these parameters seem to favour a generally strong retention at the landfill, such that only a limited part of the contaminant plume in the unsaturated zone reaches groundwater. Thus, regardless of groundwater velocities, areas downstream from the landfill are unlikely to be contaminated by most of the metals investigated in this study. Important to keep in mind, however, is that these results are based on a limited part of the landfill. The contaminant plume exhibits heterogeneous properties, which varies in concentration and composition both vertically and horizontally throughout the landfill. This makes it possible that other parts of the landfill, not included in this study, may contribute to (unmonitored) elevated concentrations of metals in groundwater. For instance, high As concentrations in groundwater can be observed at location 10 (figure 8).

Potential environmental changes, which could alter the present conditions at Pukeberg regarding pH and DOC concentrations, may have undesirable effects on the contaminant plume. For instance, measures such as the clear-cut that took place at Pukeberg might have influenced these parameters and subsequently also metal mobility. Although pH in the present case is relatively high, deforestation is generally followed by an acidification of the soil, since valuable base cations stored in the vegetation are removed (Brandtberg and Simonsson 2005). As for DOC, it was found in a study by Ivarsson and Jansson (1994) that concentrations in organic soil from a clear-cut area were half of that of a nearby mature forest. Furthermore, some vegetation types are known to remediate contaminated sites by the uptake of heavy metals (Wuana and Okieimen 2011, Pulford and Watson 2003).

The sensitivity to the environmental parameters should make the landfill at Pukeberg a top priority for monitoring (and potential actions). Any alteration in e.g. pH or DOC concentrations at the site could affect the quality of the drinking water reservoir downstream. Fortunately, the landfill at Pukeberg have recently been decided to be remediated, and the toxic waste will be removed. Many similar glass landfills remain in southern Sweden, however. The findings from this study could thus prove helpful when investigating and monitoring sites with characteristics similar to those at Pukeberg.

5.5 Uncertainties due to values below detection limits or laboratory report limits

Some of the considered metal concentrations in soil, pore water and groundwater were found to be below detection limits or report limits of the laboratory. We will in the following discuss uncertainties introduced in the here reported average values because of this, noting that for such below-limit-cases, we filled the corresponding data gap by assigning a numerical value corresponding to half of the limit value.

In the XRF measurements used for determining concentrations in soil samples, the highest proportion of uncertain values for metals included in the analysis are found for Sb and Cr, where corresponding values are 13 %. For the rest of the metals no such values are present. For Sb, a metal of greater importance in this study, Kd would only be affected by an increase or decrease of 2 %, depending if maximum or minimum values in soil concentrations would replace the half value assigned.

However, pore water concentrations for Sb are accompanied with the highest proportion of uncertain values. Kd values could thus also be affected depending on assigned values for pore water concentrations. Kd would be 23 % lower or higher, if the maximum, respectively minimum value would be used instead. Either way, Kd would still be considered to be relatively low for Sb and thus

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the overall results would be unaffected by these uncertainties. For the remaining metals, changing uncertain values in pore water would only have an impact of a few percentages increase or decrease.

For groundwater, uncertain values are only present for three of the metals, Pb, As and Fe. Pb is accompanied with the highest uncertainty. However, this would only have a limited effect of 3 % increase or decrease, should the assigned values be replaced by their maximum or minimum instead.

6 Conclusions Much higher values were obtained regarding the groundwater flow through the landfill at Pukeberg when estimated from water balance constraints of the upstream catchment, compared to when up-scaled from local measurements of hydraulic conductivities and gradients within the landfill. This points to the existence of aquifer heterogeneity, including preferential flow paths and low-flow zones. Depending on flow path, transport velocities of heavy metals could therefore differ considerably. The possible downstream contamination may hence be patchy, which increases the uncertainty in risk assessments. However, many of the metals investigated, such as Pb, exhibits high potential of retention in the unsaturated zone, decreasing the risk of further spreading via groundwater. An exception is Sb, which showed elevated concentrations in groundwater downstream from the landfill reflecting a low Kd value (soil-water partitioning coefficient).

More generally, the Kd values, estimated from the in-situ ratio between soil concentrations and pore water concentrations at the landfill, were consistently higher than previously reported laboratory results from leaching tests, as well as guideline values from the Swedish EPA. This indicates that the actual retention was higher than expected from the equilibrium case, using state-of-the art reference values.

The heterogeneous properties also applied to the soil system of the landfill, which exhibited a spatial variation in metal composition and concentration, both horizontally and vertically. The limited area investigated in this study however gives rise to uncertainties, for instance regarding the representativeness of the taken samples. Possible future analyses of extended sampling schemes could decrease these uncertainties.

Environmental properties such as pH and DOC concentrations seem to support our conclusion that retention at Pukeberg is high. However, the sensitivity of metal mobility to some environmental parameters were relatively high, which suggests that contaminants in Pukeberg may be sensitive to future environmental changes, which could alter the present conditions. Considering the short distance to an important drinking reservoir downstream, it would be highly appropriate to consider the precautionary principle in future management decisions, to mitigate possible effects of further spreading of harmful contaminants.

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References

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32

Appendix A – Site images

Figure A1. Images from the landfill and during groundwater sampling.

33

Appendix B – Data analysis and Slug test

Figure B1. Filtered vs Unfiltered samples for each metal (µg/l).

34

Table B1. Average concentrations for each location in mg/l or µg/l.

(µg/l) location

As

Pb

Cd

Sb

Sr

Zn

Ni

Co

Mn

Cu

Cr

V

Gro

undw

ater

2 12.4 0.1 0.07 21.8 248 2.9 0.4 0.1 6.8 2.7 0.1 0.5

4 2.4 0.1 0.05 9.0 201 2.6 2.0 0.6 35.0 2.1 0.1 0.2

5 0.3 0.2 0.09 6.5 250 1.8 0.1 0.0 0.8 2.4 0.2 0.2

7 0.1 0.1 0.05 0.1 67 2.0 1.3 0.3 76.9 2.9 0.2 0.1

8 0.8 0.0 0.09 13.7 130 1.9 0.2 0.1 0.8 2.3 0.1 0.2

10 2.1 0.0 0.21 43.4 293 1.4 0.2 0.1 3.2 2.7 0.1 0.4

11 2.2 0.0 0.02 0.4 69 1.2 0.2 0.1 9.8 1.3 0.3 0.2

Table B2. Average concentrations for each location in mg/l or µg/l, at 30 and 60 cm depth.

(µg/l) Location:depth

As

Pb

Cd

Sb

Sr

Zn

Ni

Co

Mn

Cu

Cr

V

Pore

wat

er

1:30 4.7 330.5 16.4 18.1 309.0 419.4 33.7 21.2 1405.0 37.4 0.7 0.7

1:60 17.3 37.0 24.3 308.5 422.4 3778.3 12.9 2.7 111.3 12.1 0.3 1.5

2:30 55.2 19.7 17.6 723.6 539.5 2532.0 5.1 0.6 4.8 13.8 0.5 2.2

2:60 5.5 28.9 3.5 175.2 529.0 306.5 26.0 0.9 53.6 30.5 0.3 4.0

5:60 27.3 18.3 5.5 118.3 449.3 161.3 2.0 0.4 1.1 12.6 0.2 0.6

Figure B2. Resulting plots from slug tests for location 4 (5 and 10 l water added) and 5 (5 l water added), showing time (x-axis, seconds) versus response in water level (y-axis, metres). Logarithmic y-axis.

35

Appendix C – Reported values Table C1. Values for bulk density (ρ) and total porosity (n) of soil used for calculation of retardation factors (R f) (Domenico and Schwartz 1998).

Bulk density (g/cm3) Total Porosity

Min Max Min Max

Fine sand 1.37 1.81 0.26 0.53

Medium sand 1.37 1.81 - -

Coarse sand 1.37 1.81 0.31 0.46

Gravely sand 1.37 1.81 - -

Table C2. Background values and health-based guideline values for groundwater (µg/l). Soil concentrations (mg/kg dry matter) derived for two types of land use: sensitive land use (KM) and less sensitive land use (MKM).

Groundwater (µg/l) Soil (mg/kg dry matter)****

Background value Health-based guideline value KM MKM

As 0.12* 10** 10 25

Cd 0.012* 3** 0.5 15

Co - - 15 35

Cr 0.19 50** 80 150

Cu 0.9* 2000** 80 200

Mn < 50* 400** - -

Ni 0.38 70** 40 120

Pb 0.03* 10** 50 400

Sb < 0.12*** 20** 12 30

Sr - - - -

V - - 100 200

Zn 4.3* - 250 500

* SGU 2013, ** WHO 2011, *** Sternbeck et al. 2002, **** Swedish EPA 2009

36

Appendix D

Table D1. Threshold values for pore water and groundwater. Values below the limits are not accredited according to the laboratory responsible for the chemical analyses.

F SO4 Cl DOC Ca Fe K Mg Na Al As Ba

mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l µg/l µg/l

<0.2 <1.0 <1.0 <0.5 <0.1 <0.0004 <0.4 <0.090 <0.1 <0.2 <0.05 <0.01

Cd Co Cr Cu Mn Mo Ni Pb Sb Sr V Zn

µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l µg/l

<0.002 <0.005 <0.01 <0.1 <0.03 <0.05 <0.05 <0.01 <0.01 <2.0 <0.005 <0.2

Table D2. Groundwater levels (meters above sea level) at different dates, for each groundwater well (location 2, 4, 5, 7, 8, 10, 11).

Date/location 2 4 5 7 8 10 11

2015-06-29 69.85 70.23 70.44 70.17 70.07 69.45 -

2015-07-09 69.74 70.17 70.37 70.09 69.69 69.30 -

2015-08-03 69.65 70.07 70.30 69.97 69.89 69.21 -

2015-08-11 69.61 70.02 70.26 69.93 69.85 69.14 -

2016-02-12 - - - - - - 71.00

2016-03-21 69.60 69.95 70.18 69.84 69.78 69.28 71.11

Table D3. Raw data for pH and DOC (mg/l) in pore water at locations 1, 2 and 5 at depths of 30 and 60 cm.

Location:Depth (cm) Date pH DOC mg/l

1:30 2015-07-09 6.92 28

1:30 2015-08-11 7.02 24.2

1:30 2015-09-01 6.97 12.8

1:30 2015-11-02 7.06 22.5

1:30 2015-11-30 6.47 52.3

1:30 2016-02-15 6.53 26.4

1:60 2015-07-09 6.77 10

1:60 2015-08-11 6.97 10.3

1:60 2015-09-01 6.39 10.1

1:60 2015-11-02 7.23 9.3

1:60 2015-11-30 6.84 11.7

1:60 2016-02-15 6.07 15.3

2:30 2015-07-09 7.32 12

2:30 2015-08-11 7.47 12.8

2:30 2015-11-02 7.57 11.9

2:30 2015-11-30 7.22 20.4

2:30 2016-02-15 6.11 10.5

2:60 2015-07-09 7.24 9.3

2:60 2015-08-11 7.21 10.4

2:60 2015-09-01 6.98 14

37

2:60 2015-11-02 7.62 11.9

2:60 2015-11-30 7.44 13.7

2:60 2016-02-15 6.15 10.4

5:60 2015-11-02 7.45 10.5

5:60 2015-11-30 7.53 13.8

5:60 2016-02-15 6.32 8.72

Table D4. Raw data for metal concentrations in soil (mg/kg) at different intervals of soil depth (m).

Location:soil depth (m)

Sr Pb Zn Ni Co Fe Mn Ca K Sb Cd As Cu Cr V

1:0-0.5 218 1216 687 199 <215 9837 417 7023 10700 190 <33 123 70 111 <59 175 1442 1040 220 <193 10300 430 8763 13000 204 <30 127 103 144 <69 1:0.5-1.0 204 3880 3583 214 <224 8765 614 20300 8497 498 <36 334 151 <69 <79 206 3839 4185 216 <209 7993 497 20900 8761 438 <36 254 96 80 <88 1:1.0-1.5 132 1553 706 171 318 13000 589 15100 6555 <72 <28 109 80 <71 <62 120 2264 963 223 293 12200 543 19400 5735 <73 <28 135 130 <71 <65 1:1.5-2.0 148 28 72 153 267 12000 354 4666 21900 <80 <31 <13 <36 <64 <57 149 27 71 216 <249 13200 383 4719 22300 96 <33 <13 <38 <65 <57 1:2.0-2.5 142 33 34 210 <215 10000 350 2604 19300 <82 <32 <13 <38 <61 <49 130 34 48 200 <226 10600 303 3335 21000 <84 <32 <13 <38 76 <56 1:2.5-3.0 159 37 52 256 <242 12000 289 4325 26000 <87 <34 <13 <40 <67 <59 174 29 46 221 <232 12000 300 4404 24100 <84 <32 <13 <38 <62 <56 1:3.0-3.5 151 29 49 235 <228 11300 360 3483 21500 <84 <33 <13 <39 <61 <56 156 34 64 177 <226 11600 437 3869 23500 <84 <34 <12 <39 <63 <53 1:3.5-4.0 162 23 54 190 <235 11800 400 4833 26800 <84 <33 <13 <39 <63 <56 170 27 54 243 <243 12200 342 4132 24300 104 <34 <12 <40 67 <54 2:0-0.5 163 2953 3267 238 <216 9737 670 19300 8972 330 39 212 83 131 <74 161 2697 1408 194 223 9789 434 15300 7002 238 <32 106 69 151 <65 2:0.5-1.0 153 2046 1131 143 <218 10800 429 23300 7863 138 <32 108 55 <65 <63 159 2121 1055 192 317 13000 604 27900 8712 125 <31 112 70 70 <63 2:1.0-1.5 169 6263 874 196 <285 15800 1144 21900 9096 473 <34 <142 64 <64 <63 152 2581 236 245 <238 13400 527 10600 11700 306 <31 <83 63 <63 <58 2:1.5-2.0 152 31 59 189 <240 13100 507 3818 22900 <81 <31 <12 <35 <62 <57 155 54 63 165 248 12300 319 3964 21100 <82 <32 <15 56 <63 <53 2:2.0-2.5 153 34 <28 180 <216 10100 325 3437 23300 <84 <33 <13 <36 64 <53 146 79 53 260 <232 11900 437 3860 23000 <82 <32 <17 <36 87 <57 2:2.5-3.0 139 41 48 153 <229 11000 433 3363 19400 <85 <32 <14 <37 84 <54 171 31 <28 143 <231 11100 346 3953 22100 <85 <33 <12 <37 <62 <56 2:3.0-3.5 186 29 52 284 <237 12400 257 4691 24900 <84 <32 <12 <37 75 <58 189 37 42 181 <244 13500 360 5213 29000 <84 <32 <12 41 <69 <61 2:3.5-4.0 215 56 41 231 <259 14600 498 5589 17400 <82 <32 <15 51 <61 <53 193 46 37 264 254 12800 415 4719 20400 <82 <32 <15 <37 <64 <54 2:4.0-4.5 411 41 <48 176 <427 19500 633 6722 13200 <130 <50 <22 <67 <68 <68 315 32 74 175 <251 14600 562 7860 15500 <77 <31 <13 46 <74 <75 2:4.5-4.8 331 <18 100 153 <370 22800 718 9378 20500 <100 <39 <12 <46 <75 <90 369 <18 99 237 <363 23100 904 8499 19100 <100 <39 <12 <42 <71 <84 3:0-0.9 154 2428 1815 192 219 8107 597 23600 7156 <79 35 175 85 111 <74 147 2165 1561 177 <191 8414 663 22500 6854 154 51 231 55 110 <67 3:0.9-1.8 130 3024 1254 193 <215 10600 425 24700 7007 223 <31 205 57 <69 <72 146 4006 1352 240 <224 10600 466 17500 7121 307 <34 275 163 121 <66 3:1.8-2.3 154 27 53 250 <186 13200 319 4941 18500 <64 <25 <9.8 <29 <63 <56 166 38 58 122 264 13000 280 4696 19400 <78 <31 <12 37 <66 <58 4:0-0.9 110 3286 1763 132 <191 8813 738 34200 5511 <77 <29 595 810 104 <80 117 3533 1651 154 <197 9365 575 32300 5085 150 <32 636 535 88 <82 4:0.9-1.8 178 6404 1030 228 <220 8212 627 22000 5662 343 <36 189 161 132 <73 174 5070 1379 207 <174 5675 749 19700 4821 342 <33 392 179 69 <76 4:1.8-2.3 184 52 59 191 <239 12600 431 4844 14800 154 <33 20 42 <64 <53 156 51 69 187 <237 12400 293 4971 13500 <86 <33 23 <38 <63 <52 4:2.3-2.8 141 18 62 191 <216 10300 415 3127 20700 <85 <32 <12 <38 <61 <54 156 27 29 207 255 10200 339 3083 18400 <85 <33 <11 <37 80 <48 4:2.8-3.3 142 22 45 232 <141 8865 307 3257 20000 <58 <23 <7.9 <26 <59 <49 147 <16 35 200 <201 8499 311 2601 20800 <84 <32 <12 <38 <59 <51 4:3.3-3.8 142 19 63 211 247 9438 389 3193 19900 <84 <33 <13 <37 <61 <49 164 28 39 194 354 9527 322 3566 20100 <83 <32 <12 <34 <60 <49 4:4.0-4.5 176 35 <28 246 <233 12200 380 4541 25000 <83 <32 <13 61 <67 <58 149 29 40 232 <230 11800 425 4500 23300 <83 <32 <13 <38 75 <55 4:4.5-5.0 182 31 64 194 276 10900 408 3642 17400 107 <34 <13 69 <58 <50 160 26 50 165 <229 11300 334 4392 19300 <84 <33 <13 61 <59 <49 4:5.0-5.3 192 31 68 156 259 13600 413 4788 20100 122 <33 <13 47 <64 <54

38

169 30 64 264 286 13400 448 4401 19900 <83 <32 <13 55 <63 <52 5:0-0.5 145 3560 377 181 <208 9156 383 7620 8949 199 <32 133 76 103 <55 116 2442 369 169 <168 6642 490 7199 7330 105 <30 212 65 93 <56 5:0.5-1.0 93 30900 941 399 <217 4773 417 13800 7993 967 <41 1439 201 104 <47 113 10300 602 180 <182 5256 526 13900 6136 505 <33 490 165 66 <46 5:1.0-1.3 161 7745 15500 391 <298 15500 3989 27200 8591 322 86 1330 126 125 <65 182 5049 11200 186 389 14100 2837 31800 8437 240 40 738 120 103 <67 5:1.3-2.0 160 146 110 257 <237 12700 371 4738 14300 <85 <33 54 <38 <61 <52 152 147 125 191 <230 12000 419 4783 15200 <83 <32 61 <39 64 <50 5:2.0-2.5 134 24 <26 186 <197 8220 233 2482 19000 <83 <32 <13 <35 <60 <49 133 24 32 182 <194 8142 284 2409 19300 <82 <32 <13 45 <57 <46 5:2.5-3.0 135 25 47 205 <193 7649 344 2856 18200 <85 <32 <12 42 121 <46 130 16 36 194 <206 9639 287 2448 19400 <87 <34 <12 <38 <61 <49 5:3.0-3.3 256 63 59 230 <285 16200 615 7067 23200 <89 <35 <17 <40 <63 <62 259 56 61 182 282 16000 518 7861 24800 <86 <33 <16 <41 <69 <69 6:0-0.5 144 18 35 163 242 13900 304 4575 16000 <77 <30 <11 <36 <66 <58 155 28 53 174 341 12600 368 4237 15400 <77 <30 <12 <36 92 <56 6:0.5-1.0 267 21 59 206 <285 17200 535 7161 25500 <87 <34 <12 <41 <71 70 223 26 47 192 <262 15400 498 6396 21800 <85 <32 <12 <39 <65 <61 6:1.0-1.4 211 29 33 197 <235 12300 369 5905 25800 <82 <32 <12 <36 <66 <66 189 19 31 190 320 11200 379 4347 23400 <75 <29 <11 52 <65 <60 7:0-0.7 126 37 86 197 <212 12400 290 3586 12400 <74 <29 <13 <32 <65 <57 119 52 65 130 <214 12600 307 4172 14100 <72 <28 <13 <29 <68 <57 7:0.7-1.3 126 28 58 153 225 11000 307 2726 14900 <80 <31 <12 <36 <62 <50 137 35 65 189 <216 10800 266 2873 15600 <81 <31 <13 43 <62 <49 7:1.3-2.0 149 24 35 142 <177 7435 150 3010 17600 <78 <31 <11 39 84 <53 126 31 37 164 <179 8186 230 2382 18600 <76 <30 <11 37 <59 <49 7:2.0-2.5 123 22 34 149 <182 8986 187 3014 11000 <72 <28 <10 33 160 <61 130 17 <23 142 196 9676 356 5863 14900 <72 <28 <9.8 <32 170 <65 7:2.5-3.0 243 19 49 141 310 14400 521 8334 13700 <76 <29 <11 40 <74 <70 231 18 58 154 <212 12000 387 5035 11200 <75 <29 <10 <35 71 <58

7:3.0-3.5 354 <18 49 246 <308 16000 417 6769 12800 112 <39 <13 <46 <57 <51 406 25 66 204 321 16800 582 7811 15300 <89 <35 <12 55 <61 <57 8:0-0.5 122 33 63 137 <189 9760 309 3915 10500 <73 <28 <11 <32 <62 <51 141 28 39 183 <180 9345 342 4424 11200 <73 <29 <11 <31 <63 <52 8:0.5-1.0 163 22 38 245 <215 11900 395 3361 19100 <80 <32 <11 <38 <62 <51 166 32 43 248 <233 12400 420 3483 17900 <84 <33 <12 <38 <61 <53 8:1.0-1.5 151 19 33 207 <213 10300 377 3391 19900 <81 <31 <11 <36 <61 <48 156 23 <26 214 <212 10400 351 2684 18600 <82 <31 <12 <34 103 <49 8:1.5-1.8 141 24 42 141 300 11900 364 3860 20400 <85 <33 <12 <39 78 <53 144 28 41 215 236 11300 473 3723 22300 <82 <32 <11 <34 82 <58 9:0-0.5 186 28 77 153 <211 12500 745 5683 11400 <72 <29 <11 <34 <68 <61 196 20 71 218 324 13000 707 4627 7314 <80 <32 <11 <36 <63 <54 9:0.5-1.0 142 23 40 171 <199 9258 339 1978 14200 <82 <32 <12 <37 <60 <48 121 36 62 185 <193 8833 435 2169 14300 <81 <31 <12 45 <60 <49 9:1.0-1.5 137 16 <27 219 <205 9625 364 2927 14600 <83 <33 <12 <39 <54 <46 122 19 48 172 <196 8592 290 1803 16800 <84 <33 <12 <38 <57 <48 9:1.5-5.0 158 36 <26 198 <208 10100 243 2874 15900 <81 <31 <12 60 <61 <50 130 30 46 142 <205 9525 334 2710 16200 <82 <32 <12 43 <61 <50

Table D5. Pore water concentrations (µg/l) at locations 1, 5 and 5 at 30 and 60 cm depth.

Location: depth

µg/l

(cm) Date As Cd Co Cr Cu Mn Ni Pb Sb Sr V Zn

1:30 2015-07-09 14 12 12 0.83 30 820 12 160 36

1.1 280

1:30 2015-08-11 3.67 21.3 27.2 0.63 35 1570 31.6 315 31.2 426 0.676 458

1:30 2015-09-01 1.65 31.8 40.4 0.714 40.7 2830 78.4 856 10.8 467 0.453 797

1:30 2015-11-02 < 1.536 25.6 39 0.733 33.5 2800 64.6 314 13.1 513 0.671 747

1:30 2015-11-30 5.1 5.89 6.55 0.726 45.6 320 10.3 202 10.7 109 0.886 165

1:30 2016-02-12 3.22 2.08 2.02 0.524 39.8 89.8 5.49 136 6.64 30.2 0.638 69.2

1:60 2015-07-09 15 26 3.4 0.21 12 350 7.2 40 210

1.2 3500

1:60 2015-08-11 20.1 26.8 2.8 0.199 8.56 83.7 13.4 26.3 261 529 1.54 3660

1:60 2015-09-01 19.4 23.3 1.16 0.427 10.9 23.1 11.8 70.3 296 453 2.48 3030

1:60 2015-11-02 14.7 26.5 2.18 0.338 11.1 37.7 17.4 56.8 < 436 505 1.99 4920

39

1:60 2015-11-30 1.95 22 6.21 0.172 7.55 155 20.1 4.59 187 424 0.685 4810

1:60 2016-02-12 32.5 20.9 0.647 0.27 22.3 18.1 7.28 24.3 105 201 1.16 2750

2:30 2015-07-09 47 15 0.6 0.33 23 16 2.8 13 410

2.1 2200

2:30 2015-08-11 96.7 25.9 1.34 1.62 10.2 5.04 8.17 27.4 515 533 3.49 2250

2:30 2015-11-02 40.2 13.5 0.546 0.341 10.5 2.17 7.49 5.77 < 1062 601 2.91 2020

2:30 2015-11-30 48.3 17.9 0.373 < 0.2 11.4 < 1.098 3.94 26.8 342 576 1.4 3240

2:30 2016-02-12 43.6 15.9 0.0947 < 0.236 14.1 < 0.009 3.08 25.5 235 448 1.12 2950

2:60 2015-07-09 5.8 3.2 1.6 0.15 22 240 23 2.1 130

2.3 290

2:60 2015-08-11 4.32 4.45 0.941 0.206 24 50.9 23.3 19.4 190 577 3.95 341

2:60 2015-09-01 3.63 3.88 0.935 0.396 34.9 23.9 26.4 44.2 220 494 5.96 273

2:60 2015-11-02 < 2 4.31 0.875 0.467 39.4 4.02 31.9 49.9 < 372 684 5.82 319

2:60 2015-11-30 6.68 2.93 0.509 0.247 33.3 1.68 27.9 39.9 189 523 3.99 277

2:60 2016-02-12 11.5 2.32 0.337 0.124 29.1 1.23 23.4 18 136 367 2.04 339

5:60 2015-11-02 18.9 4.61 0.698 0.398 15.4 2.57 2.64 2.15 < 364 454 0.939 130

5:60 2015-11-30 25.1 4.75 0.212 0.191 9.6 0.325 1.56 24.8 123 443 0.459 138

5:60 2016-02-12 37.8 7.06 0.192 0.134 12.8 0.325 1.73 27.9 50 451 0.357 216

Table D6. Groundwater concentrations (µg/l) at locations 2, 4, 5, 7, 8, 10 and 11.

µg/l

Location Date As Cd Co Cr Cu Mn Ni Pb Sb Sr V Zn

2 2015-07-09 14 0.12 0.23 0.093 6.1 22 < 0.20 0.23 28 0.38 9.8

2 2015-08-11 10.1 0.0626 0.111 0.172 1.72 12 0.52 0.0137 24.1 284 0.509 1.74

2 2015-09-01 10.4 0.0594 0.0802 0.129 1.89 3.5 0.368 0.0187 21.2 234 0.52 1.69

2 2015-11-02 12.7 0.0617 0.0729 0.124 3.33 1.87 0.396 0.0753 21.4 243 0.567 2.02

2 2015-11-30 12.6 0.0427 0.0469 0.0866 1.21 0.926 0.22 0.0124 16.5 209 0.426 0.961

2 2016-02-15 14.6 0.0484 0.0622 0.159 2.15 0.409 0.558 0.0151 19.7 269 0.499 0.949

4 2015-07-09 1.1 0.087 1 < 0.05 3.5 140 3.7 0.31 12

0.14 7.2

4 2015-08-11 1.5 0.0644 0.59 0.163 1.39 41.2 3.24 0.00875 12.3 228 0.212 1.22

4 2015-09-01 2.06 0.0559 0.701 0.118 1.42 22 2.31 0.0121 9.75 200 0.206 1.57

4 2015-11-02 2.78 0.0514 0.513 0.191 3.82 3.69 1.52 0.0767 7.42 199 0.245 3.15

4 2015-11-30 2.7 0.0316 0.226 0.12 1.19 2.14 0.629 < 0.01094 5.84 187 0.19 1.28

4 2016-02-15 4.17 0.0367 0.665 0.142 1.43 1.1 0.722 < 0.01536 6.55 192 0.202 0.924

5 2015-07-09 0.74 0.28 0.093 0.1 8.1 1.7 < 0.20 0.87 4.8

0.14 3.8

5 2015-08-11 0.0763 0.0339 0.0425 0.196 0.994 0.46 0.129 0.0123 5.73 260 0.171 1.98

5 2015-09-01 < 0.07 0.0266 0.0336 0.169 1.42 0.372 0.159 0.0363 5.89 236 0.195 2.36

5 2015-11-02 < 0.09 0.139 0.0386 0.248 2 0.833 0.191 0.0273 6.09 234 0.203 1.34

5 2015-11-30 < 0.1626 0.0124 0.0271 0.155 0.649 0.146 0.0926 < 0.01116 5.61 238 0.159 0.344

5 2016-02-15 < 1.524 0.0287 0.0364 0.185 0.998 1.01 0.0971 < 0.0734 11 283 0.225 0.741

7 2015-07-09 0.054 0.043 0.55 0.089 5.7 52 1.9 0.01 < 0.20

0.077 2.9

7 2015-08-11 0.105 0.0427 0.296 0.216 1.92 64 1.24 0.0317 0.155 72.3 0.135 1.74

7 2015-09-01 < 0.2 0.0579 0.301 0.11 2.43 80.2 1.29 0.0331 0.149 70.1 0.158 2.49

7 2015-11-02 0.163 0.0578 0.305 0.237 2.83 100 1.71 0.0496 0.166 74.2 0.174 2.26

7 2015-11-30 0.13 0.0429 0.277 0.169 1.97 102 1.01 0.0457 0.123 72.9 0.13 1.6

7 2016-02-15 0.112 0.0345 0.202 0.3 2.3 63.2 0.722 0.172 0.18 47.5 0.179 1.28

8 2015-07-09 2 0.066 0.085 0.11 3.7 2.6 < 0.20 0.11 17

0.17 2

8 2015-08-11 0.403 0.0826 0.059 0.19 1.37 0.471 0.16 0.00812 16 148 0.199 1.75

8 2015-09-01 0.277 0.0951 0.046 0.111 1.39 0.366 0.169 0.0029 15.6 128 0.236 1.81

40

8 2015-11-02 0.214 0.13 0.0625 0.212 5.06 0.878 0.28 0.167 13.1 122 0.222 4.06

8 2015-11-30 0.19 0.0918 0.0244 0.138 0.95 0.139 0.125 < 0.00748 10.8 115 0.171 0.866

8 2016-02-15 1.61 0.0973 0.0289 0.127 1.07 0.105 0.109 < 0.0101 9.9 137 0.177 1.04

10 2015-07-09 3.1 0.17 0.11 0.13 4 2.9 < 0.20 0.03 46

0.34 1.8

10 2015-08-11 0.311 0.216 0.241 0.18 2.61 6.37 0.351 0.00851 47.4 279 0.412 1.52

10 2016-02-15 2.83 0.232 0.0304 0.114 1.48 0.276 0.24 0.0138 36.9 307 0.396 0.8

11 2016-02-15 2.23 0.0223 0.0521 0.264 1.25 9.82 0.238 0.0383 0.387 68.7 0.154 1.21

Table D7. Groundwater concentrations (µg/l) at locations 2, 4, 5, 7, 8, 10 and 11.

mg/l µg/l

Location Date F SO4 Cl DOC Ca Na K Mg Fe Al Ba Mo

2 2015-07-09 2 43 19 5.3 47 28 16 9.4 2.4 39 49 1.4

2 2015-08-11 2.28 47.1 18.3 5.53 57.8 27.1 17.2 10.5 2.56 42.3 54.4 1.13

2 2015-09-01 2.18 50.6 19.8 4.36 56.8 25.7 17.1 10.3 4.6 45.2 45 1.04

2 2015-11-02 1.97 43.1 18.6 4.09 53.3 23.8 16.3 9.69 2.3 33.6 51.4 0.903

2 2015-11-30 1.82 42.6 18.9 3 45.3 21.4 14.9 8.57 1.45 28.6 42.7 0.872

2 2016-02-15 2.18 65.1 19.6 4.44 62.1 57.1 17 10.6 2.14 17.1 57.7 2.53

4 2015-07-09 1.5 33 21 3.9 34 18 12 7.7 11 40 34 0.93

4 2015-08-11 1.68 33.6 20.3 3.61 43.8 20 13.5 8.46 4.29 48 38.5 0.684

4 2015-09-01 1.47 33.8 21 3 42.9 19.5 13.6 8.23 3.32 50.8 34.4 0.576

4 2015-11-02 1.36 32.7 20 3.56 40.6 19.4 13.5 7.71 2.48 36.3 27.6 0.469

4 2015-11-30 1.24 35.9 20.1 2.84 38.1 18.3 13.4 7.46 1.28 28.5 32.9 0.52

4 2016-02-15 1.5 34.8 17.3 3.03 41.5 20.5 13.7 7.61 1.64 29.7 34.4 0.656

5 2015-07-09 1.8 51 19 4.7 60 23 15 8.7 1 22 39 2.6

5 2015-08-11 2.36 48.2 19.3 3.33 53.5 23.1 16.3 8.68 0.73 31.7 43.1 2.24

5 2015-09-01 2.38 50.4 20.8 2.88 53 22.4 16.8 8.58 1.13 40.6 40.6 2.18

5 2015-11-02 2.5 40.5 18.9 3.31 52 22 16.5 8.43 0.8 45.2 42.6 2.12

5 2015-11-30 2.62 28.4 21.6 1.86 53.5 20.2 17 9.01 < 0.4 39.3 41 2.8

5 2016-02-15 3.63 21.2 14.6 2.34 72.4 18.4 16.7 11 < 0.34 57 45.8 5.57

7 2015-07-09 0.78 13 59 4.4 8.3 38 1.8 2.3 7.5 130 21 0.25

7 2015-08-11 0.965 13.5 62 3.88 10.3 42.5 1.94 2.55 11.1 167 24.4 0.235

7 2015-09-01 0.915 13.6 65.7 3.25 10.7 42.7 2.01 2.59 12.6 153 23.1 0.237

7 2015-11-02 0.812 12.1 61.1 3.71 11.9 46.2 1.98 2.79 14.3 163 21.2 0.203

7 2015-11-30 0.724 15.2 63.5 2.88 11 42 1.92 2.84 7.43 178 25.5 0.212

7 2016-02-15 0.76 10.2 28.1 3.11 7.5 25.6 1.72 1.85 45.4 181 15.7 0.33

8 2015-07-09 1 20 19 3.6 22 14 7.1 5.9 5 10 28 1

8 2015-08-11 0.902 17.2 15.6 2.75 28.7 16 7.91 6.63 1.02 13.2 33.8 0.923

8 2015-09-01 1.03 21 18.9 2.16 26.1 14.7 7.37 6.04 1.05 14.5 27.4 0.816

8 2015-11-02 0.63 12.4 13.1 2.62 25.4 14.8 6.84 5.8 0.92 12.5 25.7 0.704

8 2015-11-30 0.822 20.4 19.4 1.85 24.1 13.9 6.62 5.58 0.58 10.2 28.3 0.708

8 2016-02-15 0.969 23.8 18.7 2.28 30.7 17.3 7.97 6.19 0.49 10.3 34.2 1.11

10 2015-07-09 4.3 49 18 4.7 67 23 14 9.4 1 61 55 3.1

10 2015-08-11 5.19 46.5 17.7 3.39 62.7 24.6 15.6 9.81 19.4 65.9 63.8 2.97

10 2016-02-15 4.59 23.8 29.3 1.95 78.8 23 16.7 11.5 1.23 98.3 63.6 2.79

11 2016-02-15 0.437 14.7 10.9 1.88 12.4 7.35 3.1 2.76 1.57 37.5 14.8 0.187