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Converting S isotope systematics, and S concentrations

  • This notebook shows simple functions for common conversion workflows, e.g., converting from d34 to 34/32S ratios, converting SO3 to S in ppm etc.

[1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import PySulfSat as ss

Converting between different forms of S isotope notation

  • S isotopes are sometimes quoted in terms of

    \[\delta^{32}S\]

    , where the

    \[\frac{32S}{34S}\]

    ratio of the sample has been referenced to a community standard.

  • In the literature, the most commmonly used standard is the Canyon Diablo Troilite (CDT). A lot of papers seem to use the ratio for the Vienna-CDT (VCDT) quoted in Ding et al. (2001, https://doi.org/10.1016/S0016-7037(01)00611-1), of 1/22.6436, which is what these functions use by default (although any ratio can be entered).

[2]:

# Lets load in some S data from Lerner et al. (2021)
df=pd.read_excel('Sulfur_Conversions.xlsx')
df.head()

[2]:
δ34S δ34S SEE 34S/32S ratio raw measured 34S/32S ratio (1SE) raw measured 34S/32S ratio IMF SEE_scaled S_ppm
0 -0.246318 0.467748 0.044336 0.000016 0.044153 0.000021 1000
1 -2.816959 0.921352 0.044222 0.000039 0.044039 0.000041 1100
2 -2.546365 0.767581 0.044234 0.000031 0.044051 0.000034 900
3 -0.584560 0.723941 0.044321 0.000029 0.044138 0.000032 500
4 0.317419 0.450185 0.044361 0.000015 0.044178 0.000020 400

Lets first convert from 34/32S to d34S using the default standard value

[3]:

d34_std=ss.convert_3432S_to_d34(S3432=df['34S/32S ratio IMF'])
d34_std

[3]:

0   -0.220035
1   -2.790743
2   -2.520142
3   -0.558286
4    0.343717
5   -0.490636
6   -1.144588
7   -0.084735
Name: 34S/32S ratio IMF, dtype: float64

And lets conver this back

[4]:

d3432S_calc=ss.convert_d34_to_3432S(d34S=d34_std)
d3432S_calc

[4]:

0    0.044153
1    0.044039
2    0.044051
3    0.044138
4    0.044178
5    0.044141
6    0.044112
7    0.044159
Name: 34S/32S ratio IMF, dtype: float64

[5]:

## Lets check
plt.plot(df['34S/32S ratio IMF'], d3432S_calc, '.r')

[5]:

[<matplotlib.lines.Line2D at 0x1ac5e5769d0>]
../../_images/Examples_Other_Useful_Functions_Converting_S_values_8_1.png

Lets convert S concentrations around

  • You have to enter S concentration of the form you have, and it returns a dataframe of all the other possible forms.

[6]:

df=ss.convert_S_types(S_ppm=df['S_ppm'])
df

[6]:
S_wt S_ppm SO2_wt SO2_ppm SO3_wt SO3_ppm SO4_wt SO4_ppm
0 0.100 1000.0 0.199791 1997.910494 0.249687 2496.865741 0.299582 2995.820989
1 0.110 1100.0 0.219770 2197.701544 0.274655 2746.552316 0.329540 3295.403087
2 0.090 900.0 0.179812 1798.119445 0.224718 2247.179167 0.269624 2696.238890
3 0.050 500.0 0.099896 998.955247 0.124843 1248.432871 0.149791 1497.910494
4 0.040 400.0 0.079916 799.164198 0.099875 998.746297 0.119833 1198.328395
5 0.035 350.0 0.069927 699.268673 0.087390 873.903010 0.104854 1048.537346
6 0.020 200.0 0.039958 399.582099 0.049937 499.373148 0.059916 599.164198
7 0.100 1000.0 0.199791 1997.910494 0.249687 2496.865741 0.299582 2995.820989 
[7]:

df=ss.convert_S_types(S_ppm=33)
df

[7]:
S_wt S_ppm SO2_wt SO2_ppm SO3_wt SO3_ppm SO4_wt SO4_ppm
0 0.0033 33.0 0.006593 65.931046 0.00824 82.396569 0.009886 98.862093 
[8]:

df=ss.convert_S_types(S_ppm=np.linspace(0, 1, 100))
df

[8]:
S_wt S_ppm SO2_wt SO2_ppm SO3_wt SO3_ppm SO4_wt SO4_ppm
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1 0.000001 0.010101 0.000002 0.020181 0.000003 0.025221 0.000003 0.030261
2 0.000002 0.020202 0.000004 0.040362 0.000005 0.050442 0.000006 0.060522
3 0.000003 0.030303 0.000006 0.060543 0.000008 0.075663 0.000009 0.090782
4 0.000004 0.040404 0.000008 0.080724 0.000010 0.100883 0.000012 0.121043
... ... ... ... ... ... ... ... ...
95 0.000096 0.959596 0.000192 1.917187 0.000240 2.395982 0.000287 2.874778
96 0.000097 0.969697 0.000194 1.937368 0.000242 2.421203 0.000291 2.905039
97 0.000098 0.979798 0.000196 1.957549 0.000245 2.446424 0.000294 2.935299
98 0.000099 0.989899 0.000198 1.977730 0.000247 2.471645 0.000297 2.965560
99 0.000100 1.000000 0.000200 1.997910 0.000250 2.496866 0.000300 2.995821

100 rows × 8 columns