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1
ROCK MAGNETIC CHARACTERIZATION OF THE MINE TAILINGS IN 1
PORTMAN BAY (MURCIA, SPAIN) AND ITS CONTRIBUTION TO THE 2
UNDERSTANDING OF THE BAY INFILLING PROCESS 3
4
Clara Gómez García1*, Fatima Martin-Hernandez1,2,, José Ángel López García3, Pedro 5
Martínez-Pagán4, Jose I. Manteca4, Carlos Carmona5 6
1 Department of Geophysics, Fac. Physics, Complutense University, 28040 Madrid 7
2 Instituto de Geociencias IGEO (UCM, CSIC), Fac. Physics, Complutense University, 28040 8
Madrid 9
3 Department of Crystallography and Mineralogy, Fac. Geology, Complutense University, 10
28040 Madrid 11
4 Department of Mining, Geological, and Cartographic Engineering, Universidad Politécnica de 12
Cartagena (UPCT), Paseo Alfonso XIII, 52, 30203, Cartagena (Murcia), Spain 13
5 Exploraciones Mineras SA, Avenida Apoquindo 4775, Oficina 602, Las Condes, Santiago, 14
Chile 15
* Now at: Instituto de Ciencias de la Tierra Jaume Almera, CSIC, Solé i Sabarís s/n, 08028 16
Barcelona, Spain 17
corresponding author: fatima@ucm.es 18
Abstract 19
The Portman Bay located in the SW Spain has suffered great changes since the 1950s associated 20
with the mining activity. The direct discharge into the sea of mining waste has caused the silting 21
of the bay in a few decades. In this study the magnetic properties of recent sands from a set of 22
52 samples from the bay and 16 samples from an opencast mine are magnetically analyzed. In 23
particular, we have measured magnetic susceptibility, hysteresis loops, IRM-acquisition, SIRM-24
2
IRM and back-field-coercivity spectra, thermomagnetic curves and FORC diagram and took 25
SEM micropictures. Data are correlated with classical magnetite content estimations derived 26
from X-ray diffraction (XRD) in three separated grain sized (coarse, medium and fine grain) 27
and heavy metal derived from X Ray Fluorescence (XRF) in samples from a parallel study at 28
the same sampling sites and the same sampling campaign. The results indicate high 29
concentrations of magnetite (up to 28%) compared to magnetically hard minerals that have not 30
been quantified. Higher concentrations of magnetite are recorded in the central part of the 31
beach, as indicated by Ms, SIRM and Kinit. Coercivity, however, indicates smaller values in the 32
center of the Bay, suggesting a larger size of the magnetic particles. The S-ratio or high 33
coercivity fraction is higher in older samples. Correlation of the magnetic properties with 34
magnetite grain size indicates a positive correlation between Ms and medium and fine grain size 35
fraction, determined by X-ray diffraction. In addition, magnetic properties show a direct 36
relationship between magnetite content and the heavy metals Pb, Cu and As. Our results 37
indicate that the deposition of magnetite was fast and properties has not been modified in depth. 38
However, spatial location in the Bay is significant when we examine distribution of grain sizes. 39
Also, results suggest that magnetite acts as captor of heavy metals. 40
Keywords: Iron ore, beach placer deposit, magnetite, Portman Bay, magnetic properties, 41
hysteresis loops, environmental magnetism 42
1 Introduction43
The Portman Bay is located in southeast Spain and belongs to the Cartagena - La Unión Mining 44
District. The Bay is at the foot of the Cartagena range mountain, named “Sierra Minera”, which 45
crosses the Mining District from west to east, parallel to the Mediterranean coast (Conesa et al., 46
2008). The Cartagena – La Unión district covers an area of about 10×5 km in the northeast-47
southwest direction and includes one of the largest Pb - Zn ore deposits in Spain (López-García 48
et al., 2011) (Figure 1a). 49
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The mining industry has been the most important economic activity in this region for more than 50
2000 years. The ore mining extraction around the Bay started before the Roman Empire, first by 51
the Iberians, followed by the Phoenicians and Carthaginians, and ended definitely in 1991. The 52
wastes generated by the first miners was then used as a source of minerals in later centuries 53
when the extraction activity decreased. This continued until the mid-nineteenth century when 54
the large-scale mining development of the region took place (Conesa et al., 2008; Martínez-55
Sánchez et al., 2008; Oyarzun et al., 2013). 56
Mining activity throughout the centuries in the region has produced large geomorphological 57
changes, particularly at the Portman Bay. In the 1950’s the multinational Peñarroya España S. 58
A. started opencast mining and built a mineral processing plant “Lavadero Roberto”. This 59
mineral processing plant dumped over 57 million tons of wastes into the Mediterranean Sea, 60
through the Portman Bay, from 1957 to 1990 (Martos-Miralles et al., 2001; Manteca Martínez et 61
al., 2005). As a result, the Portman Bay was completely filled in a few decades affecting 62
significantly the shoreline, which receded about one kilometre. The mineralogical composition 63
of the beach iron concentrates has been determined to be 17% of quartz, up to 35% of 64
magnetite, 12% of hematite and 36% of siderite (Manteca et al., 2014). Furthermore, the Bay, 65
presently, shows high concentrations of metals such as lead, copper, zinc, cadmium and 66
mercury as well as sulfur and iron oxides (Martínez Orozco et al., 1993; García et al., 2003). In 67
particular, Zn has been shown to be the most mobile cation with respect to Pb and As 68
(Martínez-Sanchez et al., 2008). Heavy metal concentration has already been found to be 69
anomalously high in several biotic elements of the area (e.g., Auernheimer, C. and S. Chinchon, 70
1997; Cesar et al, 2009; Benedicto et al., 2008). High concentration of pollutants seems to be 71
related to accumulation of metals in multitude of life elements suboptimal health status of fish 72
in the area (e.g., Martinez-Gomez et al., 2012). 73
Because of significant exposed changes occurred in the Bay within few decades as a result of 74
filling processes, several remediation actions have been proposed. All of these require a 75
complete quantification of the material deposited, reliable physical control on the sand 76
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composition and associated geophysical properties (Peña et al., 2013). In this particular aspect, 77
the tailing has been estimated to contain up to 444 � 103 t of magnetic iron concentrate in the 78
form of magnetite, hematite and siderite. Finally, if the present beach black sands represent 79
approximately 1/6 of the total bay infilling, the actual amount of iron in Portman Bay’s could be 80
much larger, at least on the order of 1 � 106 t (Manteca et al., 2014). 81
The use of magnetic properties the evaluate iron resources has been gaining importance in 82
exploration and mineral prospecting in the last decades (e.g., Clark, 1999; Clark, 2014 and 83
references therein). The work presented here aims to study the magnetic properties of recent 84
sands from the Portman Bay area. The concentration of magnetic minerals deposited is also 85
analyzed and we will discuss their spatial distribution along the Bay and the temporal 86
distribution as a function of the time of deposition. Also, the magnetic parameters will be 87
compared with estimations of heavy metals derived from a parallel spectrometric analysis in the 88
same sampling sites (Carmona Manzano, 2012). 89
It is particularly important to highlight that recently there is a rising interest in the regional 90
community to environmentaly regenerate the Portman Bay and recover the iron-bearing waste 91
as economic resource (Murciatoday, 2014). Thererefore the findings from this study are 92
significant not only in relation to the Portman Bay itself, and future remediation and sustainable 93
development of the area but also for coastal areas with similar concerns. 94
2 Geological‐Enviromentalcontext95
Sierra Minera is located at the eastern end of the Betic Ranges. It is characterized by an overlaid 96
layer structure of tectonostratigraphic complexes. The basement is formed by the Nevado - 97
Filábride (Paleozoic to Triassic) and Alpujárride (Permian to Triassic) complexes (Figure 2), 98
which emerge as superimposed thrust sheets of Alpine age and are affected by metamorphism 99
decreasing from bottom to top. In the Late Miocene, these basal units suffered a gravitational 100
collapse accompanied by a significant high-K calc-alkaline volcanism and marine sedimentation 101
in extensional basins of Miocene to Pliocene age. After the Neogene, there was an important 102
5
phase of fracturing followed by volcanic events and uplift of the Sierra, and the currently 103
erosive dismantling (Manteca Martínez and Ovejero Zapino, 1992; Vera, 2004; López-García et 104
al., 2011). 105
The following types of mineralization can be described in the Sierra Minera the following types 106
of mineralization (Oen et al., 1975; Manteca Martínez and Ovejero Zapino, 1992): (i) mantos: 107
masses and stratabound bodies found in Nevado - Filábride (second manto) and Alpujárride 108
(first manto) complexes. Both manto units have two sets of different minerals: (a) greenalite - 109
magnetite - sulphide - carbonate - silica (silicate manto) and (b) chlorite- sulphide-carbonate-110
silica (pyrite manto). Most of the lead and zinc produced in the district were extracted from 111
these manto-type deposits. (ii) Disseminations in the Miocene: irregular bodies elongated in a 112
NW – SE direction, with respect to the fracturing. They were the most important mineral 113
resource after the majority of mantos was used up. This is sphalerite-rich mineralization with 114
pyrite, marcasite and galena as minor phases. (iii) Veins and stockworks associated with 115
vulcanites and (iv) Monteras or Gossans type deposits are also developed. However the 116
stockworks (iii) and Gossan (iv) type were not subject to mining by the Peñarroya Company. 117
The main minerals that have been described are goethite, hematite, silica and, to a lesser 118
proportion, mineral of the jarosite group and alunite along with carbonates and clayey minerals 119
(López García, 1985; Carmona Manzano, 2012). 120
121
3 Materialsandmethods122
A total of 52 samples of recent sands of the Portman Bay (labeled as “PP samples”) have been 123
analyzed. Nine sampling sites have been selected, distributed along the Portman bay coastline 124
placer and one site at the interior of the bay (Figure 1b). Samples from sites PP1, PP1 - B, PP2, 125
PP2 – B y PP3 – B have been taken at several depths. The PP3 - A samples and a fraction of 126
PP5 samples (the PP5 - D) are hardpan-type or duricrust. Hardpan term refers to a dense layer of 127
that is normally found in the topsoil layer, using here the duricrust term meaning no soil 128
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development is necessary happening. Moreover, the PP4 samples are sulphated facies. In total, 129
26 samples were collected from the Bay. Furthermore, for each sampled point, two specimens 130
were taken to check their homogeneity. 131
A small specimen of rock material was sampled from an opencast mine, the Emilia open pit in 132
the vicinity of the town El Llano del Beal, 4 km north of Portman (Figure 2). This was divided 133
into 16 small fragments, due to it being highly heterogeneous in composition (M samples) and 134
represents the original mined material. This material is made to have a statistical sampling of 135
the magnetic properties of the original magnetic signal with respect to the mining processed 136
tailing. 137
Measurements include magnetic and non-magnetic techniques. Among the first, the magnetic 138
susceptibility of 25 PP samples was determined in a constant low-field (300 A/m) using a 139
Kappabridge KLY-4 manufactured by AGICO (Advanced Geoscience Instruments Co.) 140
(Hrouda et al., 2006). Also, low-field susceptibility has been measured at two frequencies for 52 141
PP samples using a Bartington Instruments MS2 Magnetic Susceptibility System. The 142
frequency dependence indicates the presence of ferromagnetic grains that are possessing 143
superparamagnetism (Tauxe, 2009). 144
Hysteresis loops have been measured up to 0.5 T with a spectrometer of coercivities developed 145
by the University of Kazan (Coercivity Spectrometer J-meter) (Jasonov et al., 1998) for 52 PP 146
samples and 16 M samples. Data have been corrected for paramagnetic/diamagnetic 147
contribution, to characterize the magnetic parameters in the studied area: paramagnetic 148
susceptibility (κpara), saturation magnetization (Ms), remanent magnetization saturation (Mrs), 149
coercive force (Bc) and initial susceptibility (κini). 150
The magnetic characterization also includes acquisition of Saturation of Isothermal Remanent 151
Magnetization (SIRM) up to 0.5 T and further demagnetization by static fields in the opposite 152
direction (back - field IRM) in a Coercivity Spectrometer J-meter. The coercivity of remanence 153
(Bcr) and total value of SIRM have been computed for all PP and M samples. Also, the amount 154
7
of high coercivity fraction has been estimated computing the S-ratio as the difference is 155
remanence acquired between 100mT and 500mT (Evans and Heller, 2003). Also, the IRM - 156
acquisition curves were modeled by a series of log-normal distributions of the IRM gradient in 157
order to infer the coercivity distribution associated to magnetic fractions. The mathematical 158
fitting has been modeled by the method outlined by Kruiver et al., (2001) and the associated 159
software. 160
Thermomagnetic curves were generated through a Petersen Instruments Variable Field 161
Translation Balance (VFTB) in order to determine the Curie/Neél temperature of the 162
ferromagnetic phases. Curie/Neél temperatures were determined for a selection of 26 PP 163
samples and 5 M samples by the maximum in second derivative method (Moskowitz, 1981; 164
Tauxe, 1998). 165
Magnetic parameters have been summarized in a Day plots attempting to discriminate domain 166
state of the magnetic particles (Day et al., 1977; Dunlop, 2002). First-Order Reversal Curve 167
(FORC) diagrams were measured in material from site PP1 in order to achieve knowledge about 168
particles interaction and/or domain state (Pike et al., 1999; Roberts et al., 2000). Sand material 169
was consolidated using non-magnetic glue into a gel cap. FORC diagrams have been processed 170
with the software and methodology described by Harrison and Feinberg (2008) using a 171
smoothing factor, SF-6. 172
Physical characterization and pseudo-compositional estimations have been achieved also by 173
scanning electron microscopy (SEM), helping in the identification of size and morphology of 174
the magnetic grains. The SEM was carried out in 6 PP samples (PP1B-A (30-38), PP2-B (25-175
30), PP3B–A SUP, PP4-A, PP5P-B y PP6C-B) for the study of the physical structure, particle 176
size, morphology and location within the sandy material of the ferromagnetic particles. 177
Magnetic properties have been compared with magnetite content estimated in grain size 178
fractions run in a parallel study, specific details can be found elsewhere (Carmona Manzano, 179
2012). Recent sand samples have been physically clasified into three fractions, Coase grain size 180
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(C > 0.40 mm), medium (0.40<M<0.20 mm) and fine (F<0.20 mm). Magnetite content in the 181
different particle sizes has been evaluated by X-ray diffraction. The most relevant heavy metal 182
and meteloids concentration has been measured by X-ray Fluorescense (XRF), finding 183
significant concentrations of Pb, Zn, Cu, As, Cd and Hg. 184
4 Results185
4.1 Low‐fieldsusceptibility186
Values obtained for the magnetic susceptibility defined in low-field (300 A/m) are summarized 187
in Figure 3a for sample PP and Figure 3b for M samples . They both show a large variability of 188
values between 0.74×10-5 and 14.70×10-5 m3/kg in the case of PP samples and from -0.07×10-5 189
to 6.35×10-5 m3/kg in M samples. Magnetic susceptibility of the sand samples are characterized 190
by two relatively equal maxima centered at 1.7 10-5 m3/kg and 12 10-5 m3/kg, which suggests a 191
bimodal distribution of the low field susceptibility (Figure 3a). The low field susceptibility of 192
the mine sample, however, is characterized by one maximum value centered at 1.12 10-5 m3/kg 193
(Figure 3b). 194
Measurements of the magnetic susceptibility in low-field with two frequencies indicate that 195
there is almost no frequency dependence. Values vary between 0.01% and 1.66%, therefore the 196
presence of superparamagnetic material (SP) is considered not to be significan (Hrouda, 2011). 197
4.2 Hysteresisloops198
The hysteresis loops all have very similar shapes for the PP samples (Figure 4a) and M samples 199
(Figure 5a). Magnetization reaches the reversible state at a saturating field of about 250 mT, 200
with no indications of a high coercivity fraction in the induced magnetization. Hysteresis 201
derived parameters, however, show slight differences depending on the source (PP and M 202
samples), as well as sampling site (PP1 to PP6) (Table 1). Remanence parameters (Ms and Mr), 203
display large variability, even recorded between twin samples form the same level and site, 204
indicating a highly heterogeneous concentration of magnetic minerals (Evans and Heller, 2003). 205
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Magnetic coercivity is less variable. The largest differences can be found associated to the 206
sample source, with values between 10.28 and 12.72 mT from the sand samples and values 207
between 11.52 and 15.49 mT, from the Emilia open pit samples (Table 1). 208
4.3 IRM‐acquisitionandback‐fieldSIRM209
SIRM acquisition up to 0.5 T and subsequent back-field SIRM curves of PP (Figure 4b) and M 210
samples (Figure 5b) present a very similar shape. The beach sand samples do not have an initial 211
remanence (Figure 4b) while samples from the mine have an original remanence magnetization, 212
which is observed by an initial value of the SIRM at low fields (Figure 5b). Samples approach 213
to saturation at about about 200 mT and asymptotically reach an almost constant value at 500 214
mT . Values of the S-ratio range between 11.72 and 4.15 % in sand beach samples and 7.49 and 215
2.19 in M samples (Table 1). Subsequent back-field SIRM shows Bcr values between 24.8 and 216
30.4 mT for samples PP and 22.5 and 32.4 mT for samples M (Table 1). 217
Both SIRM and back-field SIRM show one inflection point (Figure 4b, Figure 5b) which 218
suggests the presence of a single significant magnetic mineral responsible for the remanence 219
within the soft coercivity fraction. 220
4.4 IRM‐coercivityspectra221
IRM acquisition curves and first derivative have been mathematically modeled following the 222
procedure proposed by Kruiver et al.(2001). Figure 4c and Figure 5c show the example of the 223
derivative of the IRM acquisition curve (Gradient Acquisition Plot, GAP) and its corresponding 224
fitting using a series of log-normal distribution curves (Kruiver et al., 2001). All the analyzed 225
samples have a similar behaviour, the gradient of the IRM acquisition curve is characterized by 226
a main distribution that carries most of the magnetization (about 90% of the total SIRM) which 227
has been named as Component 1. It has been necessary to include two additional components of 228
low and high coercivity labeled Component 2 and Component 3, respectively, in order to reduce 229
the residual of the GAP curve. The low coercivity component (Component 2) provides an 230
average of 7% to magnetization and, at such low fields, its presence has been attributed to 231
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thermal activation of magnetic particles (Heslop et al., 2004). Component 3 contributes only 3% 232
to the total SIRM, but suggests the presence of a higher coercivity phase. 233
The field at which the SIRM acquisition curve reaches its inflexion point (B1/2), or maximum 234
value of the GAP, is used as an estimation of the average coercivity of the corresponding 235
magnetic population carrying the remanence. Table 1 summarizes the values of the B1/2 together 236
with the associated dispersion parameter (DP) of the coercivity distribution. 237
Both samples PP and M have a very similar coercivity, with a mean B1/2=38.42.9 mT for the 238
samples collected from the Bay and B1/2=36.28.4 for the samples belonging to the Emilia open 239
pit. The results confirm the well-constrained coercivity found in the samples derived from the 240
infilling process with the detachment having a larger variability of the magnetic properties 241
(Table 1) 242
243
4.5 Thermomagneticcurves244
Thermomagnetic curves for PP samples have very similar characteristics (Figure 4d) during 245
heating and subsequent cooling magnetization processes. The Curie/Neél temperature ranged 246
between 550 °C and 580 °C. This Curie/Neél temperature indicates the presence of either 247
magnetite (Curie temperature is 580 °C), or titanomagnetite with low Ti content (Dunlop and 248
Özdemir, 1997). The decrease found above 580 ºC suggests the presence of a magnetic mineral 249
that loses magnetization at 680 ºC, i.e., hematite (Figure 4d). Since the susceptibility of hematite 250
is lower than that of magnetite, the observation of the magnetization tail attributed to hematite 251
suggests it contains large concentrations of it (Evans and Heller, 2003). 252
A closer analysis to the heating curve reveals two minor changes in the curve slope, one 253
between 250 °C and 350 °C, approximately, and the second minor change around 450 °C and 254
550 °C, which might indicate the very mild transformation in the samples (Henry et al., 2005). 255
These two features might be indicative of the presence of iron hydroxides and/or jarosite, that 256
display a breackdown at about 250-300ºC and often transforms into maghemite-hematite 257
(Hanesch et al., 2006). The presence of goethite and jarosite has been confirmed by XRD 258
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measurements (Carmona, 2012) and it is also supported by the lower intensity of the cooling 259
with respect to the warming curve. 260
The Curie/Neél temperature in M samples (Figure 5d) is also around 580 °C. However, the 261
relationship between heating and cooling tracks has the opposite behavior. The heating curves 262
show lower magnetization than the cooling track of the curve. Also, there is no clear evidence of 263
a change in the slope of the curve for the 250-350 ºC range (Figure 5d). The high temperature 264
range does not suggest the presence of hematite (Figure 5d). 265
4.6 Domainstatesummary‐plots:DayplotandFORCdiagram266
The also called Day plot is a good estimation of the domain state for magnetite and magnetite-267
like particles (Day et al., 1977). This representation discriminates the domain state separating in 268
different areas of the diagram the coercivity ratios (Hcr/Hc) as a function of magnetization ratios 269
(Mr/Ms). Results from this study for samples PP and M are presented in Figure 6a. Both sets are 270
clustered in the pseudosingle domain state (PSD) of the classical interpretation of the plot (Day 271
et al., 1977) or a mixture of about 50% singledomain (SD) and 50% multidomain (MD) mixing 272
curve 2 in the model proposed by Dunlop (2002). 273
The sand samples deposited on the Bay are magnetically softer with data getting closer to the 274
MD state than samples from the mine (Figure 6a). Moreover, there is no significant shift toward 275
the superparamagnetic mineral fields (Dunlop, 2002). Considering only the PP samples in the 276
Day plot values are grouped according to the sampling site and spatial distribution within the 277
Bay (Figure 6a). 278
The FORC diagram for PP sample suggests a distributed particle size in the PSD to SD range, 279
as can be seen by the spread of the contour lines along the y-axes of the diagram (Pike et al, 280
1999; Roberts et al., 2000). 281
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4.7 SEM282
SEM micropictures using the secondary electrons method have been taken for every sampling 283
site. This technique allows a quick identification of the large metallic fraction, visible as bright 284
areas in the image (Reed, 2010). Figure 7 shows the images obtained for the six sampling sites. 285
Big sand grains appear partially covered by smaller bright zones in their corners and edges in all 286
samples (Figure 7a-f). A closer look into the morphological information of the grains shows 287
hexagonal structures and planar crystals suggested to be jarosite grains (Figure 7b and 7c). 288
Pseudocompositional analysis based on spectral identification of scattered electrons reveals 289
these bright zones are related to the presence of magnetite intergrowths with quartz grain 290
(Figure 7e-f). No heavy metals have been identified as single grains at this resolution level, 291
indicating their sizes are smaller than the detection limit of the technique. 292
5 Discussion293
The magnetic results provide information needed to answer three key questions in the study of 294
mining debris: (i) are there a distinct difference in the physical-magnetic properties of samples 295
from the Bay sands (PP samples) and original samples from the mine (M samples), (ii) is there a 296
correlation between PP and M samples; and (iii) is there a correlation of the magnetic 297
parameters with heavy metals content from the same sampling sites. 298
In order to study the spatial distribution along the Portman Bay, mean values and their 299
corresponding uncertainties from hysteresis and remanence parameters (Ms, Mrs, Hc, Hcr, Kint, 300
Kpara, SIRM, B1/2 and S-ratio) of the PP samples have been computed. An arithmetic mean and 301
standard deviations are graphically displayed in Figure 8. Sampling sites located at the edges of 302
the Bay (PP1, PP1 – B or PP6) are significantly and statistically different from those sites 303
located at the central part of the Bay. With these two spatial distributions (ends or central parts 304
of the Bay) we have a uniform trend for every parameter that is broken by the values of the PP3 305
– B sample: this sample has widely scattered values with a large error bar that includes values 306
within the general trend. 307
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The results are consistent because there is a lower content of paramagnetic material (Figure 8a) 308
in the central part of the Bay where the ferromagnetic contribution is greater (Figure 8b). 309
Besides, κini, Ms, Mr and SIRM show the same spatial distribution (Figure 8b, c, d and g). Bcr 310
has the same distribution that these parameters but with a very small variation range of its 311
values (Figure 8 f). Bc seems to show higher values at the ends of the Bay and lower in the 312
central parts though, the PP3 - B and PP4 areas have higher values than their adjacent (Figure 313
8e). However, differences between sampling sites are small and their statistical significance is 314
limited. B1/2 has its maximum for the PP4 area and the minimum for the PP1 area (Figure 8h). 315
The S-ratio seems to display a similar trend as that observed for Bcr and B1/2 (Figure 8i). Indeed, 316
S-ration statistically correlates to Bcr and B1/2 (Figure 9). The correlation between S and Bcr 317
indicates the higher coercivity might be related to the presence of a high coercivity mineral 318
(Figure 9a) while a correlation with the B1/2 might be related to variations in the magnetite grain 319
size (Figure 9b). Results are not conclusive, but statistically, the correlation seems to be stronger 320
with respect to B1/2, therefore a grain size dependence is expected between different sampling 321
sites. 322
We can establish three different classifications according to the variability of the parameters in 323
the Bay: (a) variation with depth, (b) spatial variation along the Bay and (c) temporal variation 324
based on the age of the sediments that have been deposited in the Bay by considering its 325
distribution. 326
(a) Taking into account the samples that have been collected at different depths in the Bay (PP1, 327
PP1 - B, PP2, PP2 - B and PP3 - B), the parameters obtained do not vary with respect to depth 328
(Table 1), at least for the studied depths, which did not exceed 80 cm. This suggests that 329
alterations, diagenetic processes or transformations due to the sedimentary aging are not 330
important (Evans and Heller, 2003). One should note, however, that the infilling was very rapid, 331
so that the upper most layers have not yet started to undergo alteration; this does not preclude 332
alteration of deeper strata, considering the total thickness of the mineral deposits which is 333
estimated to be 10 m (Martinez-Sanchez et al., 2008; Manteca et al., 2014). 334
14
(b) Figure 8 illustrates the spatial distribution of the hysteresis and remanence parameters but 335
these may also be related to temporal effects, since the filling of the bay was earlier in some 336
areas than others. Consequently, minerals have accumulated in some places for longer than in 337
others. 338
(c) The filling of the Bay began at the western edge of the bay (next to the discharge point 339
where the wastes were poured into the sea) (Martínez Orozco et al., 1993), then the next stage 340
was the waste infilling the bottom of the Bay from the south towards the north. Marine currents 341
affected the way in which sediments have been deposited and distributed since they play a key 342
role in differential particles sedimentation processes depending on their grain size. The coarsest 343
grains have remained on the beach while the finest ones have been deposited at sea bottom. In 344
addition, there is a dynamic mobility: in good weather conditions, the material has accumulated 345
in deposits that are partially or totally destroyed during storms, and they are reformed in another 346
place under more favorable weather conditions. Therefore, the coastline is constantly being 347
modified and where the emerged beach is considered as unstable (Martínez Orozco et al., 1993). 348
In view of bathymetric maps (Martínez Orozco et al., 1993) we assume that the minerals in PP4 349
and PP3 samples are the oldest ones because that sampled area is the farthest from the coastline 350
and this is observed in the similarities between parameters in PP4 and PP3A. Some of the 351
magnetic parameters for the PP3 – B samples are significantly different from those measured in 352
the adjacent PP3 –A area. Although we do not know the reason for this difference and the error 353
bars associated with PP3 – B samples cover the general trend, perhaps the different geological 354
properties that may present both samples have some influence: PP3 – A samples are duricrust 355
while PP3 - B samples are not. It should be noted that the PP3 – B samples also contain 356
sphalerite and abundance of jarosite (Carmona Manzano, 2012). Although these phases are 357
paramagnetic at room temperature (Hunt et al, 2013; Majzlan et al., 2000), they may be 358
associated with ferromagnetic intergrowths, which have not been observed under SEM. 359
15
The magnetite content determined by classical XRD has been correlated to the estimation 360
derived from magnetic hysteresis. Because magnetization curves are mainly controlled by 361
magnetite, Ms values from hysteresis can be transformed into percentage of magnetite dividing 362
by the tabulated value of 90 Am2/kg (Dunlop and Özdemir, 1997). Values from each level depth 363
have been averaged and standard deviation computed from Table 1. Estimations of magnetite by 364
both methods are recorded in Table 2. The concentration of magnetite in PP samples varies 365
between 21.83% and 0.97% by magnetic methods and 34.04 and 7.78% by X-ray diffraction 366
(Table 2). M samples were only studied by magnetic methods, giving a variation range of 367
magnetite from 5.8% to 0.02% (Table 1). 368
Magnetite content derived from hysteresis is correlated to κini and Mr parameters (Figure 10a 369
and b). The Pearson p-parameter for these two magnitudes gives a “strong correlation” with 370
respect to the magnetite content according to the classification given by Evans (1996) with 371
values higher than 0.8. Anthropogenic action constrains the range of values of the coercive field 372
since what we find in Portman Bay (such as the PP samples) are the products of the mine 373
extraction (such as M samples) (Figure 10c). Correlations are not conclusive, because the 374
magnetite concentration of M samples is much lower than in PP samples. 375
Hysteresis and remanence results can be correlated with the magnetic granulometry of the PP 376
samples and with the heavy metals content that was obtained by X-ray diffraction analysis 377
(Table 2) (Carmona Manzano, 2012). 378
Magnetite has been fractioned into three grain-sizes and quantified by XRD: coarse (C), (> 0.40 379
mm), medium (M), (0.40 to 0.20 mm) and fine (F) (<20 mm) grains. Bulk Ms derived from 380
magnetic hysteresis has been compared with the grain-size content in Figure 11a-c. The coarse-381
grain fraction is directly correlated with Ms while the opposite happens with the medium-grain 382
fraction (Figure 11 b). Fine particles do not exhibited a good agreement with Ms (Figure 11c). 383
Because magnetite is known to be an effective heavy metals captor (Biswal et al., 2013; Mayo 384
et al, 2007), Ms was compared with heavy metals content, specifically lead, zinc, iron, copper, 385
16
arsenic, cadmium and mercury determined by XRF (Carmona Manzano, 2012) (Table2). We 386
obtained the best correlations for lead, copper and arsenic (Figure 11d,e and f). Copper and 387
arsenic (Figure 11e-f) can not be correlated linearly, but we have few samples to try another fit. 388
Table 2 depicts the correlation found between the magnetite content by X-ray diffraction and 389
magnetic methods. We observe that the slope of the correlation is nearly 1 and the determination 390
parameter is high. In view of the results we can say that magnetic methods are faster and very 391
effective for studies of Portman Bay (Figure 12). Particularly, magnetic methods are more 392
suitable for low concentrations of magnetite, when XRD is known to have poor resolution. 393
6 Conclusions394
We can conclude related to the study of the rock magnetic characterization of the Portman Bay 395
in four main points: 396
397
a) The study of the magnetic properties of the PP samples has shown high magnetite 398
content. Predominantly high concentration of magnetite in all samples with 399
concentrations up to 21.8%. 400
b) Magnetically hard mineral phases such as hematite are detected by magnetic methods 401
but quantification is not possible. The magnetite is relatively hard with saturating fields 402
higher than 0.2 T. Despite having strong indications of the presence of iron hydroxides 403
in the system, they do not contribute significantly to the remanence. 404
c) We have also observed that the magnetic properties of the PP samples correspond to a 405
characteristic spatial distribution in the bay, and this distribution may be closely related 406
to how sediments were deposited in the Bay depending on marine currents. 407
d) We do not observe significant variations in the magnetic properties with depth, up to 408
values of about 80 cm, therefore diagenetic alterations, superficial transformations in 409
maghemite and / or aging alterations can be excluded. 410
411
17
From the results, we observed agreement between the PP samples and M samples , despite the 412
differences attributed to the transformation in the processing plant. 413
414
In view of future research, a much more comprehensive bay sampling would be necessary, with 415
more samples of the coastline and especially from inside the Bay. Having a dense spatial 416
distribution of samples from the bay would allow for a better understanding of the interaction 417
between deposition and latter reworking from currents, including depth effects. It would also 418
allow for a better statistical evaluation. This would further develop the theory of spatial 419
distribution along the Bay of the parameters of the magnetic properties, test to a greater extent 420
the use of magnetite as captor heavy metal, clarify the formation of magnetic minerals, 421
determine the oxidized phases found in the thermomagnetic curves, etc. 422
423
Acknowledgments 424
Sara Guerrero Suárez is specifically thanked for assisting part of the magnetic measurements in 425
the UCM-paleomagnetic lab. Also Prof. Eric C. Ferré for running FORC curves and hysteresis 426
at the USI-Carbondaile rock magnetic laboratory. The manuscript has been improved thanks to 427
two anonymous reviewers. 428
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23
549
Figure Caption 550
Figure 1. Location of the Portman Bay in the Iberian Peninsula (white dot in the upper and right panel) and the 551
collected samples along the coastline of the Bay (lower and left panel. Source: Google Earth). 552
Figure 2. Geology and mineral deposits of the Cartagena-La Unión Mining District (simplified after Manteca 553
Martínez and Ovejero Zapino (1992)) 554
Figure 3. Histograms of the low field magnetic susceptibility, κ at (300 A / m) for PP (a) and M (b) samples. 555
Figure 4. Summary of analyzes with PP1B – A (0.20 – 0.30) sample. (a) Measured hysteresis loops (black) and after 556
correction of the paramagnetic/diamagnetic contribution (red). (b) SIRM acquisition (up to 0.5 T) and SIRM back-557
field curves. (c) Gradient Acquisition Plot (GAP) for the raw data, the computed 1, 2 and 3 components and the sum 558
of components. (d) Thermomagnetic curve with the warming-up (red) and cooling-down (blue) values. Arrows 559
indicate the direction in which the process occurs. The inset shows a zoom into the higher temperature range of the 560
warming up curve. 561
Figure 5. Summary of analyzes with M11 sample. (a) Hysteresis loops without paramagnetic/diamagnetic correction 562
(black) and with such contribution (blue). (b) SIRM acquisition (up to 0.5 T) and SIRM back-field curves. (c) 563
Gradient Acquisition Plot (GAP) for the raw data, the computed 1, 2 and 3 components and the sum of components. 564
(d) Thermomagnetic curve with the warming-up (red) and cooling-down (blue) values. Arrows indicate the direction 565
in which the process occurs. The inset shows a zoom into the higher temperature range of the warming up curve. 566
Figure 6. (a) Day plot (Day et al., 1977) of PP and M samples and the corresponding theoretical mixing curves 567
proposed by Dunlop (2002). The inset shows zoom of PP samples (b). FORC diagram of material from site PP1. 568
Figure 7. Selection of images from PP samples obtained by SEM. Detection method of secondary electrons for a, b, c 569
and f panels and backscattered electrons method for d and e panels. The samples were in each case: PP1B-A (30- 38), 570
PP2-B (25-30), PP3B-A SUP, PP4-A-B and PP6C PP5P-B. The scales are at the lower and rigth part of each panel. 571
Figure 8. Spatial distribution along the Bay of the hysteresis parameters κpara (a), κini (b), Ms (c), Mr (d) and Bc (e) and 572
the parameters related with IRM acquisition Bcr (f), SIRM (g), B1/2 (h) and S-ratio (i). Red dots represent mean values 573
for clustered samples in the Bay. Black bars represent the standard deviation. 574
Figure 9. Correlation between the S-ratio (%) and Bcr (a) and B1/2 (b). Red squares represent mean values for PP 575
samples and blue diamond represents mean value of the M samples, all with their corresponding standard deviation. 576
24
Correlation has been computed only for PP samples and the figures display the R2 is the coefficient of determination 577
and p is the Pearson correlation coefficient. 578
Figure 10. Comparison and correlation between hysteresis parameters κini (a), Mr (b) and Bc (c) of PP (red) and M 579
(blue) samples. Black lines indicate the linear regression, R2 is the coefficient of determination and p is the Pearson 580
correlation coefficient. PP samples represent mean values for clustered samples in the Bay. Black bars represent the 581
standard deviation. Red dots represent mean values for clustered samples in the Bay. Black bars represent the 582
standard deviation. 583
Figure 11. Comparisons and correlations for Ms of PP samples. Comparisons with coarse (a), médium (b) and fine 584
(c) fractions, in percentage, and with Pb (d), Cu (e) and As (f) contents in parts per million. Black lines: linear 585
regression, R2: coefficient of determination and p: Pearson's correlation coefficient. 586
Figure 12. Correlation between the two methods used to calculate the concentration of magnetite in PP samples. 587
Black line: linear fit. R2: coefficient of determination and p: Pearson's correlation coefficient.. 588
589
Table Caption 590
TABLE 1: Summary of the rock magnetic properties and magnetic characterization of samples from the Portman 591
Bay Area (PP samples) and Mina Emilia (M samples). This includes saturation magnetization (Ms), saturation 592
magnetization of the remanence (Mr), coercivity (Bc), coercivity of remanence (Bcr), initial low field susceptibility 593
(Kinit), saturation of isothermal remanent magnetization (SIRM), median destructive field of the main modelled 594
coercivity spectra (B1/2) and its corresponding dispersion parameter (DP) and S-ratio (%). 595
TABLE 2: Percentage of magnetite (Mt) derived from magnetic measurements and comparison with the estimation 596
derived from XRD in the total fraction and the grain sized fraction (coarse, medium and fine). Heavy metal content 597
when measured 598
Portman Bay
SPAIN
POR
TUG
AL
FRANCE44°0
42°0
40°0
38°0
36°0
44°0
42°0
40°0
38°0
36°0
-10°0 -8°0 -6°0 -4°0 -2°0 -0°0 2°0 4°0
-10°0 -8°0 -6°0 -4°0 -2°0 -0°0 2°0
El GorguelCreek Mediterranean Sea
Portman Bay
La Unión
Portman
Sierra de Cartagena
Emilia Open Pit
Cabezo RajaoMine
Mediterranean Sea
Portman Bay
La Unión
Portman
Sierra de Cartagena
Emilia Open Pit
La CrisolejaMine
: Fault: Road: Town
El GorguelCreek
Stratigraphic column
: Mines, open pit or underground
: Lower Nevado Filábrides (Paleozoic): Upper Nevado Filábrides (Permian-Triassic): Alpujárrides (Permian-Triassic): Dykes and felsic domes (Upper Miocene): Marine Sediments (Tortonian and Messinian)
: Coastline
1 km : Quaternary Sediments
(a) (b)
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-500 0 500
m A( M
2 /kg)
B (mT)
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
-500 0 500M
(A m
2 /kg)
B (mT)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.1 1.0 10.0 100.0 1000.0
tneidarG
B (mT)
Raw data
Sum ofcomponents
Component 1
Component 2
Component 3
0
2
4
6
8
10
12
14
0 200 400 600 800
M (A
m2 /k
g)
T (°C)
Warm-up
Cool-down
(b) (a)
(c) (d)
0
1
2
400 600 800
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
-500 0 500
M (A
m2 /k
g)
B (mT)
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
-500 0 500M
(A m
2 /kg)
B (mT)
0.0
0.1
0.1
0.2
0.2
0.3
0.1 1.0 10.0 100.0 1000.0
tneidarG
B (mT)
Raw data
Sum ofcomponents
Component 1
Component 2
Component 3
0
1
2
3
4
5
6
0 200 400 600 800
M (A
m2 /k
g)
T (°C)
Warm-up
Cool-down
(a) (b)
(c) (d)
0
1
2
400 600 800
2.2 2.4 2.6 2.82.0 3.00.08
0.10
0.12
0.14
Hc (T)
0.00 0.04 0.08 0.12
x10-28
4
0
-4
-8
Hu (
T)
SD
PSD
MD
SF=6
(a) (b)
-4
-2
0
2
4
6
8
PP1
PP1
BP
P2P
P2B
PP3
AP
P3B
PP4
PP5
PP6
kpa
ra(1
0-7 m
3/k
g )
02468
10121416
PP1
PP1
BP
P2P
P2B
PP3
AP
P3B
PP4
PP5
PP6
� ini
(10-
5 m
3/k
g)
0
4
8
12
16
20
PP1
PP1
BP
P2P
P2B
PP3
AP
P3B
PP4
PP5
PP6
Ms
(Am
2 /kg)
0.0
0.4
0.8
1.2
1.6
2.0
PP1
PP1
BP
P2P
P2B
PP3
AP
P3B
PP4
PP5
PP6
Mr(A
m2 /k
g)
10
11
12
13
PP1
PP1
BP
P2P
P2B
PP3
AP
P3B
PP4
PP5
PP6
Bc
(mT)
22
24
26
28
30
32
PP1
PP1
BP
P2P
P2B
PP3
AP
P3B
PP4
PP5
PP6
Bcr
(mT)
0.0
0.4
0.8
1.2
1.6
2.0
PP1
PP1
BP
P2A
PP2
BP
P3A
PP3
BP
P4P
P5P
P6
SIR
M (A
m2 /k
g)
30
34
38
42
46
PP1
PP1
BP
P2A
PP2
BP
P3A
PP3
BP
P4P
P5P
P6
B1/
2(m
T)
(a) (b) (c)
(d) (e) (f)
(g) (h)
0 2 4 6 8
10 12 14
PP1
PP1
BP
P2A
PP2
BP
P3A
PP3
BP
P4P
P5P
P6
S-ra
tio(%
)(i )
y = 0.666x + 22.18 R = 0.8376
p=0.8373
0
5
10
15
20
25
30
0 2 4 6 8 10 12 14
Bcr
(mT)
S-ratio (%)
y = 1.7427x + 23.861 R = 0.8647
p=0.9299
0 5
10 15 20 25 30 35 40 45 50
0 2 4 6 8 10 12 14 B
1/2
(mT)
S-ratio (%)
(a) (b)
R² = 0.9549
0
4
8
12
16
0 10 20 30
� ini
( 10-
5 m
3/k
g)
Magnetite %
PP M
R² = 0.9873
0.0
0.4
0.8
1.2
1.6
2.0
0 10 20 30
Mr (
Am2 /k
g)
Magnetite %
PP M
10
12
14
16
0 10 20 30
Bc
(mT)
Magnetite %
PP M
(a) (b) (c)
p = 0.9911 p = 0.9640
R² = 0.7154
01020304050607080
0 10 20 30
C %
Ms (A m2/kg)
R² = 0.5016
0
10
20
30
40
50
60
70
0 10 20 30M
%Ms (A m2/kg)
0
5
10
15
20
25
30
35
0 10 20 30
F %
Ms (A m2/kg)
R² = 0.6287
0
1000
2000
3000
4000
5000
0 10 20
)mpp( bP
Ms (A m2/kg)
0
50
100
150
200
250
0 5 10 15 20
Cu
(ppm
)
Ms (A m2/kg)
0
500
1000
1500
2000
2500
0 5 10 15 20
As (p
pm)
Ms (A m2/kg)
(a) (b) (c)
(d) (e) (f)
p = -0.8458
p = 0.7082
p = 0.7929
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