MiBiPreT example: Electron Balance with Amersfoort data¶
Diagnostic plots for data analysis on microbial biodegredation at the contaminant Amersfoort site. Author: Alraune Zech
Data based on the PhD thesis of Johan van Leeuwen, 2021 'Biodegredation of mono- and polyaromatic hydrocarbons in a contaminated aquifer originating from a former Pintsch gas factory site' which is equivalent to the manuscript of van Leeuwen et al., 2022 'Anaerobic degradation of benzene and other aromatic hydrocarbons in a tar-derived plume: Nitrate versus iron reducing conditions', J. of Cont. Hydrol. The data was provided by Johan van Leeuwen.
Background: Amersfoort contaminant site¶
Close to the train station in Amersfoort, the Netherlands, the subsurface is contaminated with organic hydrocarbons forming a NAPL. Contamination originates from decades of operating manufactured gas plant, which dumped tar by-products in waste lagoons. The tar is a DNAPL and has spread into the underlying shallow unconfined aquifer. Sample wells were installed to measure various characteristics of the subsurface. The raw data contains measurements on
- environmental conditions, such as pH, redox potential, concentrations of oxygen, nitrate, etc
- contaminant concentration such as BTEX, indene, indane, naphtalene and multiple other (typically cyclic) petroleum hydrocarbons
- metabolite concentration, i.e. byproducts of degredation processes of contaminant
- isotope measurments for specific contaminants and samples
- counts of genes (RNA/DNA) of mibrobiota that is know to perform biodegredation as well as functional enzymes know to be responsible for biodegredation
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import mibiscreen as mbs
import mibiscreen as mbs
Data loading¶
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file_path = './amersfoort.xlsx'
file_path = './amersfoort.xlsx'
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contaminants_raw,_ = mbs.load_excel(file_path,
sheet_name = 'contaminants')
contaminants,units = mbs.standardize(contaminants_raw)
contaminants_raw,_ = mbs.load_excel(file_path,
sheet_name = 'contaminants')
contaminants,units = mbs.standardize(contaminants_raw)
================================================================ Running function 'standardize()' on data ================================================================ Function performing check of data including: * check of column names and standardizing them. * check of units and outlining which to adapt. * check and cleaning of values ============================================================== Running function 'check_columns()' on data ============================================================== 29 quantities identified in provided data. List of names with standard names: ---------------------------------- sample_name --> sample_nr well --> obs_well depth --> depth aquifer --> aquifer benzene --> benzene toluene --> toluene ethylbenzene --> ethylbenzene pm-xylene --> pm_xylene styrene --> styrene o-xylene --> o_xylene iso-propylbenzene --> isopropylbenzene n-propylbenzene --> n_propylbenzene 3-ethyltoluene --> 3_ethyltoluene 4-ethyltoluene --> 4_ethyltoluene 1,3,5-trimethylbenzene --> 135_trimethylbenzene 2-ethyltoluene --> 2_ethyltoluene 1,2,4-trimethylbenzene --> 124_trimethylbenzene 4-isopropyltouene --> 4_isopropyltouene 1,2,3-trimethylbenzene --> 123_trimethylbenzene 1,3-diethylbenzene --> 13_diethylbenzene indene --> indene 1,2,4,5-tetramethylbenzene --> 1245_tetramethylbenzene 2-methylindene --> 2_methylindene naphtalene --> naphthalene 2-methylnaphtalene --> 2_methylnaphthalene 1-methylnaphtalene --> 1_methylnaphthalene 2-ethylnaphtalene --> 2_ethylnaphthalene 2,6-dimethylnaphtalene --> 26_dimethylnaphthalene 1,6-dimethylnaphtalene --> 16_dimethylnaphthalene --------------------------------------------------------- Identified column names have been standardized ________________________________________________________________ 0 quantities have not been identified in provided data: --------------------------------------------------------- --------------------------------------------------------- Not identified quantities have been removed from data frame ================================================================ ================================================================ Running function 'check_units()' on data ================================================================ ________________________________________________________________ All identified quantities given in requested units. ================================================================ Running function 'check_values()' on data ================================================================ Quantities where not all values could be transformed to numerical (int/float): ----------------------------------------------------------- sample_nr obs_well depth ================================================================ ================================================================
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environment_raw,_ = mbs.load_excel(file_path,
sheet_name = 'environment')
environment,units = mbs.standardize(environment_raw)
environment_raw,_ = mbs.load_excel(file_path,
sheet_name = 'environment')
environment,units = mbs.standardize(environment_raw)
================================================================ Running function 'standardize()' on data ================================================================ Function performing check of data including: * check of column names and standardizing them. * check of units and outlining which to adapt. * check and cleaning of values ============================================================== Running function 'check_columns()' on data ============================================================== 24 quantities identified in provided data. List of names with standard names: ---------------------------------- sample_name --> sample_nr well --> obs_well depth --> depth aquifer --> aquifer pH --> pH EC --> EC Redox --> redoxpot DOC --> DOC chloride --> chloride oxygen --> oxygen nitrate --> nitrate nitrite --> nitrite sulfate --> sulfate sulfide --> sulfide Mn2 --> manganese2 Fe2 --> iron2 Na --> sodium Mg --> magnesium K --> potassium Ca --> calcium phosphate --> phosphate acetate --> acetate fluoride --> fluoride bromide --> bromide --------------------------------------------------------- Identified column names have been standardized ________________________________________________________________ 0 quantities have not been identified in provided data: --------------------------------------------------------- --------------------------------------------------------- Not identified quantities have been removed from data frame ================================================================ ================================================================ Running function 'check_units()' on data ================================================================ ________________________________________________________________ All identified quantities given in requested units. ================================================================ Running function 'check_values()' on data ================================================================ Quantities where not all values could be transformed to numerical (int/float): ----------------------------------------------------------- sample_nr obs_well depth ================================================================ ================================================================
Electron balance: Simple NA screening analysis¶
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mbs.reductors(environment,
include = True,
ea_group = 'ONS')
mbs.reductors(environment,
include = True,
ea_group = 'ONS')
Out[5]:
1 12.789841 2 2.596891 3 1.669628 4 15.327849 5 1.069199 6 0.166493 7 6.497425 8 2.928226 9 11.929774 10 10.956322 11 8.762786 12 5.930307 13 5.174298 14 2.661290 15 2.177419 16 3.233611 17 5.428226 18 9.453187 19 0.178993 20 0.012500 21 0.012500 22 0.095747 23 0.178993 24 0.108247 25 0.108247 26 0.501574 27 4.950065 28 14.541129 29 6.799194 30 15.347581 31 6.507830 32 3.282661 33 17.951639 Name: total_reductors, dtype: float64
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mbs.oxidators(contaminants,include = True,
contaminant_group='BTEX')
mbs.oxidators(contaminants,include = True,
contaminant_group='BTEXIIN')
mbs.oxidators(contaminants,include = True,
contaminant_group='BTEX')
mbs.oxidators(contaminants,include = True,
contaminant_group='BTEXIIN')
WARNING: There are quantities from name list not in data Maybe data not in standardized format. Run 'standardize()' first. _________________________________________________________________
Out[6]:
1 0.003532 2 4.593173 3 1.330056 4 1.519835 5 23.970065 6 44.082629 7 19.740769 8 20.404649 9 0.767852 10 0.230424 11 0.003532 12 0.006973 13 28.781054 14 18.420932 15 14.621742 16 8.211821 17 2.709059 18 3.604108 19 6.596482 20 10.111647 21 9.638039 22 3.379366 23 0.692402 24 0.395568 25 0.038864 26 0.031641 27 0.010302 28 5.430876 29 3.708611 30 0.003532 31 0.003532 32 0.003532 33 0.005074 Name: total_oxidators_BTEXIIN, dtype: float64
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data_NA = mbs.merge_data([environment,contaminants])
mbs.electron_balance(data_NA,include = True)
data_NA = mbs.merge_data([environment,contaminants])
mbs.electron_balance(data_NA,include = True)
WARNING: There are quantities from name list not in data Maybe data not in standardized format. Run 'standardize()' first. _________________________________________________________________
Out[7]:
0 2481.262794 1 929.515473 2 480.489287 3 2.677492 4 1.833353 5 4345.807388 6 3538.259747 7 1842.751507 8 3621.560176 9 15.536562 10 47.548481 11 0.179781 12 0.144471 13 0.148917 14 0.329137 15 850.478973 16 0.044606 17 0.393775 18 2.003731 19 2.622892 20 10.085204 21 0.003777 22 1.255306 23 0.143508 24 0.565381 25 0.027135 26 0.001236 27 0.001297 28 0.028333 29 0.258511 30 0.273649 31 2.785262 32 15.852007 Name: e_balance, dtype: float64
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na_traffic = mbs.sample_NA_traffic(data_NA,include = True)
na_traffic = mbs.sample_NA_traffic(data_NA,include = True)
Electron balance as bar plot¶
Preprocess data selecting all relevant quantities and save in dictionary (electron_balance_bar_data_prep()) as input for visualization rountine electron_balance_bar(). Applied here to all samples for electron acceptors oxygen, nitrate, and sulfate and electron reducing contaminant groups BTEXIIN and BTEX. Samples are sorted according to available electrons.
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#data_NA.total_reductors.iloc[[21,11,20,3,17,18]]
#data_NA.total_oxidators_BTEX.iloc[[21,11,20,3,17,18]]
data_NA.total_oxidators_BTEXIIN.iloc[[21,11,20,3,17,18]]
data_NA.e_balance.iloc[[21,11,20,3,17,18]]
#data_NA.total_reductors.iloc[[21,11,20,3,17,18]]
#data_NA.total_oxidators_BTEX.iloc[[21,11,20,3,17,18]]
data_NA.total_oxidators_BTEXIIN.iloc[[21,11,20,3,17,18]]
data_NA.e_balance.iloc[[21,11,20,3,17,18]]
Out[9]:
21 0.003777 11 0.179781 20 10.085204 3 2.677492 17 0.393775 18 2.003731 Name: e_balance, dtype: float64
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electron_balance_bar_dict = mbs.electron_balance_bar_data_prep(data_NA)
mbs.electron_balance_bar(electron_balance_bar_dict,
sample_nr = True,
figsize = [12,3],
xtick_autorotate = True,
)
print(electron_balance_bar_dict)
electron_balance_bar_dict = mbs.electron_balance_bar_data_prep(data_NA)
mbs.electron_balance_bar(electron_balance_bar_dict,
sample_nr = True,
figsize = [12,3],
xtick_autorotate = True,
)
print(electron_balance_bar_dict)
{'EA capacity': {'height': array([8.76278616e+00, 3.28266129e+00, 4.95006504e+00, 1.45411290e+01,
6.79919355e+00, 1.53475806e+01, 1.79516389e+01, 6.50783039e+00,
1.27898413e+01, 1.19297737e+01, 1.09563215e+01, 5.17429761e+00,
2.66129032e+00, 2.17741935e+00, 6.49742456e+00, 5.93030697e+00,
1.06919875e+00, 3.23361082e+00, 5.42822581e+00, 9.45318678e+00,
1.53278486e+01, 1.66493236e-01, 1.66962799e+00, 2.92822581e+00,
2.59689126e+00, 1.78993236e-01, 1.25000000e-02, 1.25000000e-02,
9.57466181e-02, 1.78993236e-01, 1.08246618e-01, 1.08246618e-01,
5.01573881e-01]), 'color': 'C1', 'sample_nr': 0 1024(16)
1 1033(22)
2 241(16)
3 241(21)
4 241(30)
5 241(36)
6 320(21)
7 323(21)
8 323(9)
9 352(9)
10 4016(16)
11 4031(17)
12 4031(21)
13 4031(30)
14 A003(10.8)
15 A003(21)
16 A005(12)
17 A005(22)
18 A005(31)
19 A005(39)
20 A005(8.5)
21 A010(12)
22 A012(8.5)
23 A026(10.7)
24 A036(7.8)
25 A039(21)
26 A044(18)
27 A044(22)
28 A044(24)
29 A044(30)
30 A044(40)
31 A044(42)
32 A044(54)
Name: sample_nr, dtype: str}, 'BTEXIIN': {'height': array([3.53158326e-03, 3.53158326e-03, 1.03021340e-02, 5.43087632e+00,
3.70861052e+00, 3.53158326e-03, 5.07357860e-03, 3.53158326e-03,
3.53158326e-03, 7.67851571e-01, 2.30424217e-01, 2.87810543e+01,
1.84209321e+01, 1.46217418e+01, 1.97407691e+01, 6.97290252e-03,
2.39700653e+01, 8.21182068e+00, 2.70905931e+00, 3.60410843e+00,
1.51983520e+00, 4.40826289e+01, 1.33005644e+00, 2.04046491e+01,
4.59317342e+00, 6.59648224e+00, 1.01116468e+01, 9.63803936e+00,
3.37936646e+00, 6.92401638e-01, 3.95567942e-01, 3.88640739e-02,
3.16410329e-02]), 'color': 'C0'}, 'BTEX': {'height': array([3.53158326e-03, 3.53158326e-03, 4.28920300e-03, 4.16760907e+00,
2.78858465e+00, 3.53158326e-03, 5.07357860e-03, 3.53158326e-03,
3.53158326e-03, 1.41670537e-01, 1.45881113e-01, 1.58015156e+01,
9.65300536e+00, 1.02849443e+01, 1.58151311e+01, 4.69704045e-03,
1.26420567e+01, 4.91760516e+00, 1.54552483e+00, 2.60458257e+00,
3.47378305e-01, 2.95967151e+01, 2.44586609e-01, 1.61503861e+01,
1.62814324e+00, 4.04665034e+00, 8.00020711e+00, 7.33705660e+00,
1.46777163e+00, 4.41362845e-01, 2.36227425e-01, 2.68080394e-02,
2.86065501e-02]), 'color': 'C2'}, 'EA capacity minor': {'height': array([0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 5.17429761, 2.66129032, 2.17741935, 6.49742456,
0. , 1.06919875, 3.23361082, 0. , 0. ,
0. , 0.16649324, 0. , 2.92822581, 2.59689126,
0.17899324, 0.0125 , 0.0125 , 0.09574662, 0.17899324,
0.10824662, 0. , 0. ]), 'color': 'C1'}}
Electron balance analysis for selected samples¶
Preprocess data selecting quantities for specified sample selection, save in dictionary and visualize.
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electron_balance_bar_dict = mbs.electron_balance_bar_data_prep(data_NA,
list_samples = [21,11,20,3,17,18])
mbs.electron_balance_bar(electron_balance_bar_dict,
loc = 'upper right')
electron_balance_bar_dict = mbs.electron_balance_bar_data_prep(data_NA,
list_samples = [21,11,20,3,17,18])
mbs.electron_balance_bar(electron_balance_bar_dict,
loc = 'upper right')
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(<Figure size 375x280 with 1 Axes>,
<Axes: title={'center': 'Electron balance per sample'}, xlabel='Samples', ylabel='Electron capacity/needed [mmol e-/l]'>)
