1
B. Curtis and L. Mutch
EOS 240 – B02
March 28, 2013
Water and Sediment Chemistry of Dease Lake, BC – NTS 104J 16
By analysing water and stream sediment data gathered by Jackaman et al. (2002)1, with the British
Columbia Ministry of Energy and Mines, for the Dease Lake, BC area (NTS 104J 16, see Figure 1), we
attempt to define relationships amongst rock type, catchment area, associated elements, water and
sediment chemistry, and Stallard-Edmond classifications. In identifying these relationships, we hope to
gain a better understanding of water and sediment ion and element concentrations and of how these
relationships can be informative when applied to data from other regions.
Figure 1. NTS 104J Dease Lake, BC area rock types and sampling locations after Jackaman et al. (2002)1
Distinguishing rock type based on water and sediment chemistry
19 water and sediment samples were divided according to rock type – 7 volcanic, 9 sedimentary
(excluding two duplicates from sites 1150 and 1200), and 3 intrusive – and for each rock type the
minimum, maximum, median, and 3rd quartile statistics were calculated for each of the ions and
elements listed in Table 1, for water samples, and Table 2, for sediment samples. Due to the small
sample size, these relationships were difficult to determine – in particular, the 3 samples representing
intrusive rock do not constitute a large enough sample size to draw meaningful statistics from. More
data is certainly needed to demonstrate these relationships more clearly. Also, the concentrations are
likely subject to some variations due to variations in rock type in the various catchment areas: some
catchment areas include multiple rock types. Nonetheless, keeping these factors in mind, there are a
few trends that may be present in the data.
Looking at the water sample data in Table 1, we see that Ca, SO42-, Si, and Mg have the highest
concentrations – with Ca having by far the highest concentration. It appears as though the sulphate ions
are present in lower concentrations in intrusive rocks than in volcanic and sedimentary; however, aside
from this, the concentrations of the four are rather consistent over all three rock types. We see that
there are also relatively elevated concentrations of K in volcanic rocks and Mo in intrusive rocks
compared with the other rock types. Again, these are slight differences based on a very small sample
size, so more data is needed to tell whether they are significant or not. It is possible that lower
concentrations of sulphate may be indicative of intrusive rocks.
2
Looking at the sediment samples data in Table 2, we see that Fe, Ca, Mg, and K are present in the
highest concentrations. These concentrations are also rather consistent across the three rock types, so
the concentrations of these four elements would not be informative of rock type based on this data set.
We do see elevated levels of Pb, Mo, Ni, and Zn in the sedimentary samples, so with more data, these
elements may be indicative of sedimentary rock. The likely reason why Fe is present in much higher
concentrations in the sediment samples is due to it being largely oxidized out of the water samples.
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Relationship between water and sediment chemistry
In Figures 2-5 element concentrations for water and sediment samples are plotted against one another
to determine possible relationships. Looking at Figure 2, we can see that Ca concentrations in sediment
and water samples appear to have no significant relationship. With an R2 value of 0.0017, we cannot
predict that high concentrations of Ca in sediments will lead to high concentrations in water or vice
versa.
Mg shows a slight trend in that locations with low concentrations in sediment have low concentrations
in water (see Figure 3). However, the R2 value is quite low (0.4921) and the data points are more closely
focused on the left hand side of the graph, which suggests that the relationship is not strong.
For Al, there is no relationship between concentration in sediment and water samples (see Figure 4).
With a low R2 value of 0.1018, we cannot make predictions about the concentration in one knowing the
other.
Cu has little to no relationship between sediment and water concentrations. The R2 value is low and
deviation from the trend line is high, therefore, there is no correlation between concentrations of Cu in
water and sediments (see Figure 5). Mg shows the strongest relationship of all four elements.
Figure 2. Relationships of [Ca] in water
and sediment
y = 0.0012x + 0.9003
R² = 0.0017
1.5
[Mg] in Sediment (%)
[Ca] in Sedimnet (%)
2.0
Figure 3. Relationships of [Mg] in water
and sediment
1.0
0.5
0.0
0
20
40
2.5
2.0
1.5
1.0
y = 0.0654x + 0.7318
R² = 0.4921
0.5
0.0
0
60
Figure 4. Relationships of [Al] in water
and sediment
1.0
y = 0.0091x + 1.3182
R² = 0.1018
0.0
20
30
[Cu] in Sediment (ppm)
[Al] in Sediment (%)
1.5
10
30
Figure 5. Relationships of [Cu] in water
and sediment
2.0
0
20
[Mg] in Water (ppm)
[Ca] in water (ppm)
0.5
10
70
60
50
40
30
20
10
0
y = 8.8669x + 36.721
R² = 0.0293
0.0
[Al] in water (ppb)
0.5
1.0
1.5
[Cu] in water (ppb)
Relationship between catchment area and sediment chemistry
For NTS 104J 16, catchment areas were plotted as a function of [Ca], [Fe] (see Figure 6), [Cr], and [Mn]
(see Figure 7) to determine if catchment area had any effect on the concentrations of those particular
elements. The main data clusters of each of the four elemental concentrations were oriented in vertical
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bands which suggest that no meaningful relationship can be found between concentration and
catchment area. If such a relationship was present in the data, we would expect the clusters to lie along
a positively sloping band, if the axes were positively correlated, or a negatively sloping band, if the axes
were negatively correlated. One interesting observation, that may become clearer with a larger data set,
is that the high outliers of [Fe] and particularly [Mn] are associated with smaller catchment areas. This
may be due to sediment samples for the smaller catchments being collected more proximally to any
potentially weathered Fe or Mn deposits, allowing less time for their dilution than those located more
distally in the larger catchments.
Figure 7. Relationship between sediment [Cr]
and [Mn] and catchment area
12
10
8
6
Ca
4
Fe
2
0
0
2
4
Catchment area (km2)
Catchment area (km2)
Figure 6. Relationship between sediment
[Ca] and [Fe] and catchment area
12
10
8
6
Cr
4
Mn
2
0
0
6
5
10
15
Log2 of concentration (ppm)
Concentration (%)
Catchment area (km2)
When that data was subdivided by rock type and
Figure 8. Relationship between sediment
again plotted against the catchment area in an
[Ca] and catchment area for sedimentary
attempt to reveal any relationships present, the
rock samples only
results were rather inconclusive. Realistically, the
5
only relationship revealed in the plots was Ca in
4
sedimentary rocks as shown in Figure 8. There is a
3
possible trend of increasing concentrations with
2
increasing catchment area as is shown in the
Ca
1
2
R² = 0.6791
relatively high R value of 0.6791; however, as the
0
differences in concentrations are small, this may be
0.50
0.75
1.00
1.25
1.50
reaching for an interpretation that isn’t there at all.
Concentration (%)
Again, more data is needed to confirm or refute
this relationship. If it does exist, it may be
explained by the increased weathering area leading to increased accumulation of Ca in stream
sediments. NTS 104 16 is rather diverse in rock types, and perhaps for other sections of NTS 104J the
relationships are more pronounced.
Element associations
By looking at the data from each sample site in NTS 104J 16 some correlations in elements and relative
concentrations can be found (see Table 3). Site 1245 has maximum values for fluoride, Mg, Fe (in both
sediment and water), Ba, and Cd. The weathering of rocks containing minerals such as biotite and
pyroxenes may contribute to the high levels of Mg and Fe. High levels of Mg and Fe are associated with
alterations zones2. 1245 is in the sedimentary portion of the map and is located downstream from a
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small lake. Therefore, a wide range of ions from source rocks further upstream are likely contributing to
these values. There appears to be an anomaly in Cd concentrations of 1.21 ppm at this site (see Table 3).
Again, the creeks running through this site are exposed to ultramafic rocks further upstream which may
lead to this anomaly.
Site 1190 has maximum values of Al and Cu. These elements are both ore minerals. Cu can be found in
many rocks such as chalcopyrite, and Al results from the weathering of rocks such as feldspars.
Sites 1180 and 1195 both have maximum concentrations of Cu and Zn. Site 1180 is not shown on the
map, however, 1195 is in an ultramafic outcrop, with sources likely originating from volcanic dominated
outcrop.
Element associations compared with those found by Lefebure et al. (2001)2
According to Lefebure et al. (2001)2, the NTS 104J 16 map area is relatively rich in Cu, Zn, Ba, and Se,
which are associated with volcanic massive sulphides; Au and Cu, which are associated with phorphyry
copper-gold deposits; Au, Bi, As, Mo, and W, which are associated with intrusive-related gold; and Au,
Ag, As, Sb, and Hg, which indicate epithermal veins. Some of these associations are visible in our data
and others are not, see Table 3.
Sites 1295 and 1245 certainly show an association between anomalously high levels of Ba, Zn, and
possibly Se. According to Lefebure et al.2, this could indicate volcanic massive sulphide deposits,
however, our data shows no association between these elements and Cu. Actually our data shows no
associations between Cu and any of the other elements, but the highest concentrations of Cu are not
particularly anomalous in our data. Perhaps this fact is a contributing factor to our data showing no
association between Au and Cu which could indicate copper-gold porphyry potentials2.
At sites 1185, 1200, and 1295, Au, Mo, Bi, show some association with one another, which could
indicate intrusive-related gold deposits2, but As and W show no signs of association in our data.
The association between Ni and Cr, that Lefebure et al.2 writes about, as indicating ultramafic-related
mineral deposits, is also clearly seen at sites 1200, 1235, and 1290.
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Stallard-Edmond classification
According to the Stallard-Edmond classification, areas with a milliequivalent sum between 0.2 and 0.45
meq/L indicate weathering limited silicate terrain. Milliequivalent sums between 0.45 and 3 meq/L
indicate carbonate or shale terrain and milliequivalent sums close to 3 meq/L indicate evaporite river
systems. By this classification, Table 4 shows that 6 sites of carbonate terrain are in areas of volcanic
source rock, 7 sites of carbonate terrain are in areas of sedimentary source rock and 3 are in ultramafic
source rock. The majority of sites that have carbonate or shale terrain systems, according to river
chemistry, are in areas dominated by volcanic rock or sedimentary source rock.
Site 1185 indicates silicate terrain weathering and it is located in an area dominated by sedimentary
rock. The milliequivalent sum of the sample site 1235 suggests evaporitic weathering. This site is also
located in a sedimentary source area.
There is no clear relationship between carbonate terrain and the source geology, however, the majority
of sites with carbonate terrain are in sedimentary rock areas. With only one evaporitic terrain, there is
not enough evidence to suggest a relationship between source geology and river system. And the same
is true for the weathering limited silicate terrain at site 1185.
Table 4. Milliequivalent sums, weathering type, and source geology of
Ca2+ Mg2+, Na+, and K+ from NTS 104J 16 sample sites.
Sample
Milliequivalent sum of
River
Source Geology
2+
2+
+
+
site
Ca Mg , Na and K
classification
(Meq/L)
1010
2.55
Carbonate
Volcanic
1055
1.96
Carbonate
Volcanic
1150
1.58
Carbonate
Sedimentary
1155
2.13
Carbonate
Sedimentary
1160
1.66
Carbonate
Volcanic
1175
1.51
Carbonate
Sedimentary
1180
1.03
Carbonate
1185
0.34
Weathering
Sedimentary
limited silicate
terrain
1190
1.09
Carbonate
Volcanic
1195
0.98
Carbonate
Ultramafic
1200
2.92
Carbonate
Sedimentary
1225
0.92
Carbonate
Volcanic
1230
1.17
Carbonate
Volcanic
1235
3.89
Evaporite
Sedimentary
1245
0.85
Carbonate
Sedimentary
1275
0.77
Carbonate
Ultramafic
1280
1.60
Carbonate
Ultramafic
1290
1.48
Carbonate
Sedimentary
1295
1.13
Carbonate
Sedimentary
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Sample calculations for ppm to meq/L for site 1010, Table 4
Ca2+ : (29.29 ppm Ca2+)x(1 mg/L )x(1g/ 1000mg)x(1 mol/40.078 g) = 0.00073 mol/L
Ca2+ = 2N or 2 moles of charge per 1 mole of Ca2+
0.00073 mol/L x 2 = 0.00146 eq/L = 1.46 meq/L
Mg2+ : (12.157 ppm Mg2+)x(1 mg/L )x(1g/ 1000mg)x(1 mol/24.305 g) = 0.00050 mol/L
Mg2+ = 2N or 2 moles of charge per 1 mole of Mg2+
0.00050 mol/L x 2 = 0.0010 eq/L = 1.00 meq/L
Na+ : (1.03 ppm Na+)x(1 mg/L )x(1g/ 1000mg)x(1 mol/22.99 g) = 0.000045 mol/L
Na+ = 1N or 1 moles of charge per 1 mole of Na+ = 0.000045 eq/L = 0.045 meq/L
K+ : (1.53 ppm K+)x(1 mg/L )x(1g/ 1000mg)x(1 mol/39.098 g) = 0.00004 mol/L
K+ = 1N or 1 moles of charge per 1 mole of K+ = 0.00004 eq/L = 0.04 meq/L
Meq Sum = Ca2+ + Mg2+ + Na+ + K+ = 1.46 meq/L + 1.00 meq/L + 0.045 meq/L + 0.04 meq/L = 2.55 meq/L
References
1. Jackaman, W., Friske P W B., 2001, Regional stream sediment and water data, Dease Lake,
British Columbia (NTS 104J): Natural Resources Canada and the B.C. Ministry of Energy and
Mines RGS 55, GSC Open File 4011.
2. Lefebure, D., Jackaman, W., Mihalynuk, M., Nelson, J., 2001, Anomalous RGS Survey Results
West of Dease Lake – New Massive Sulphide Targets: BCSG Survey Geological Fieldwork 2001,
Paper 2002-1, p. 383-388.