sutra2.Transport_Removal module

class sutra2.Transport_Removal.MicrobialOrganism(organism_name, alpha0_suboxic: Optional[int] = None, alpha0_anoxic: Optional[int] = None, alpha0_deeply_anoxic: Optional[int] = None, pH0_suboxic: Optional[int] = None, pH0_anoxic: Optional[int] = None, pH0_deeply_anoxic: Optional[int] = None, mu1_suboxic: Optional[int] = None, mu1_anoxic: Optional[int] = None, mu1_deeply_anoxic: Optional[int] = None, organism_diam: Optional[int] = None)

Bases: object

Placeholder class which will later be replaced by the QSAR functionality of AquaPriori ?!

For removal of microbial organisms (‘mbo’) / pathogen

Returns

• microbial_species_dict (dict)

• removal_function = ‘mbo’

class sutra2.Transport_Removal.Substance(substance_name, molar_mass: Optional[int] = None, partition_coefficient_water_organic_carbon: Optional[int] = None, dissociation_constant: Optional[int] = None, halflife_suboxic: Optional[int] = None, halflife_anoxic: Optional[int] = None, halflife_deeply_anoxic: Optional[int] = None)

Bases: object

Placeholder class which will later be replaced by the QSAR functionality of AquaPriori.

For removal of organic micro pollutants (omp)

Returns

• microbial_species_dict (dict)

• removal_function = ‘omp’

class sutra2.Transport_Removal.Transport(well: AnalyticalWell, pollutant: Substance)

Bases: object

# @Steven –> verander in ‘Transport’ Substance + Organism –> Pollutant Returns concentration in a groundwater well for a given Organic Micro Pollutant or microbial species.

well

The AnalyticalWell object for the schematisation of the aquifer type.

Type

object

omp_inialized

Boolian indicating whether the Substance object has been initialized

Type

bool

df_flowline

Column ‘flowline_id’: int Column ‘flowline_type’: string Column ‘flowline_discharge’: float Column ‘particle_release_day’: float Column ‘input_concentration’: float Column ‘endpoint_id’: int Column ‘well_discharge’: float Column ‘name’: string Column ‘removal_function’: string Column ‘total_breakthrough_travel_time’: float Column ‘breakthrough_concentration’: float

Type

pandas.DataFrame

df_particle

Column ‘flowline_id’: int Column ‘zone’: string Column ‘travel_time’: float Column ‘xcoord’: float Column ‘ycoord’: float Column ‘zcoord’: float Column ‘redox’: float Column ‘temp_water’: float Column ‘travel_distance’: float Column ‘porosity’: float Column ‘dissolved_organic_carbon’: float Column ‘pH’: float Column ‘fraction_organic_carbon’: float Column ‘solid_density’: float Column ‘input_concentration’: float Column ‘steady_state_concentration’: float Column ‘omp_half_life’: float Column ‘log_Koc’: float Column ‘pKa’: float Column ‘Koc_temperature_correction’: float Column ‘omp_half_life_temperature_corrected’: float Column ‘retardation’: float Column ‘breakthrough_travel_time’: float

Type

pandas.DataFrame

substance: object

The Substance object with the OMP of interest.

substance_dict: dictionary

Nested dictionary with the following per substance.

substance_name: String,

substance_name of the substance (for now limited dictionary to ‘benzene’, ‘AMPA’, ‘benzo(a)pyrene’

log Koc: float

distribution coefficient of organic carbon and water [-]

molar_mass: float

molar mass of substance [g/mol]

pKa: float

disassociation constant for acic H-OMP [-]

omp_half_life: float

per redox zone (‘suboxic’, ‘anoxic’, deeply_anoxic’), [days]

organism: object

The microbial organism object with the microbial organism (mbo) of interest

organism_dict: dictionary

‘alpha0’: float

reference_collision_efficiency [-] per redox zone (‘suboxic’, ‘anoxic’, deeply_anoxic’)

‘pH0’: float

reference pH for calculating collision efficiency [-] per redox zone (‘suboxic’, ‘anoxic’, deeply_anoxic’)

‘organism_diam’: float

diameter of pathogen/species [m]

‘mu1’: float

inactivation coefficient [1/day] per redox zone (‘suboxic’, ‘anoxic’, deeply_anoxic’)

calc_advective_microbial_removal(df_particle: Optional[DataFrame] = None, df_flowline: Optional[DataFrame] = None, endpoint_id: str = 'well1', trackingdirection='forward', grainsize=0.00025, temp_water=11.0, rho_water=999.703, redox='anoxic', por_eff=0.33, pH=7.5, organism_name=None, organism_diam=2.33e-08, mu1=0.023, alpha0=1e-05, pH0=6.8, conc_start=1.0, conc_gw=0.0)

Calculate the steady state concentration along traveled distance per node for each pathline from startpoint to endpoint_id’ after advective microbial removal.

For more information about the advective microbial removal calculation:

BTO2012.015: Ch 6.7 (page 71-74)

#SR_todo: remove df_flowline ‘substance’, ‘removal_function’, ‘input_concentration’ [species independent vars] #SR todo: remove df_particle ‘lamda’ # –> not input, but output (replace ‘substance’ by ‘name’ of OMP or MBO)

df_particle

Column ‘flowline_id’: int Column ‘xcoord’: float Column ‘ycoord’: float Column ‘zcoord’: float Column ‘relative_distance’: float Column ‘travel_time’: float Column ‘total_travel_time’: float Column ‘porosity’: float Column ‘porewater_velocity’: float

Type

pandas.DataFrame

df_flowline

Column ‘flowline_id’: int or str

Identifier of pathline

Column ‘flowline_type’: str

Source of contamination [‘diffuse_source’,’point_source’,]

Column ‘flowline_discharge’: float

Flux per flowline (=’pathline’) [m^3 d-1]

Column ‘particle_release_day’: float or int

Delay in release of contaminants [days]

Column ‘endpoint_id’: int or str

Endpoint id of the flowline

Column ‘well_discharge’: float

Cumulative flux of flowlines to well [m^3 d-1]

Type

pandas.DataFrame

endpoint_id

ID for which to calculate the final concentration.

Type

str or int

tracking_direction

tracking direction of particles [‘forward’,’backward’]

Type

str

grainsize

grain diameter of sediment [m]

Type

float

temp_water

Water temperature [degrees celcius]

Type

float

rho_water

Water density [kg m-3]

Type

float

redox

redox condition [‘suboxic’,’anoxic’,’deeply_anoxic’]

Type

str

por_eff

effective porosity [-]

Type

float

pH

pH of the water [-]

Type

float

mu1

inactivation coefficient [day-1]

Type

float

pH0

reference pH for which alpha0 was determined

Type

float

alpha

‘sticky coefficient’ [-], pH corrected

Type

float

alpha0

‘reference sticky coefficient’, for a reference pH [pH0]

Type

float

organism_diam

organism/species diameter [m]

Type

float

v_por

porewater velocity [m/d]

Type

float

conc_start

starting concentration

Type

float

conc_gw

initial groundwater concentration

Type

float

distance_traveled

distance between points [m]

Type

float

traveltime

time between start and endpoint [days]

Type

float

Returns

• df_flowline (pandas.DataFrame) –

  Column “name”: str

  organism name of organism to calculate removal for

  Column “organism_diam”: str

  organism diam of organism to calculate removal for

  Column “removal_function”: str

  filled with ‘mbo’ -> removal function for microbial organisms other option -> ‘omp’ -> for removal of organic micropollutants

  Column “input_concentration”: float

  Starting concentration of OMP or mbo of interest [N/L]

  Column “input_concentration_gw”: float

  Starting concentration in ambient groundwater [N/L]

  Column “breakthrough_concentration”: float

  Breakthrough_concentration over flowline (after reaching endpoint_id) [N/L]

  Column “breakthrough_travel_time”: float

  Cumulative traveltime per flowline from start to endpoint [days]

  Column “concentration_in_well”: float

  Steady-state concentration in well for endpoint_id (assuming all flowlines were contaminated a traveltime duration ago)

• df_particle (pandas.DataFrame) –

  Column “relative_distance”: float

  distance between previous and current node

  Column “porewater_velocity”: float

  groundwater velocity between previous and current node

  Column “k_att”: float

  ’attachment coefficient’ [day-1]

  Column “lamda”: float

  ’removal rate’ [day-1] (redox dependent) –> calculated

  Column “alpha”: float

  ’sticky coefficient’ [-], pH corrected

  Column “steady_state_concentration”: float

  Steady-state concentration at each node, calculated [N/L]

calc_lambda(df_particle, df_flowline, redox='anoxic', mu1=0.149, por_eff=0.33, grainsize=0.00025, alpha0=0.001, pH=7.5, pH0=7.5, temp_water=10.0, rho_water=999.703, organism_diam=2.33e-08, v_por=0.01)

For more information about the advective microbial removal calculation: BTO2012.015: Ch 6.7 (page 71-74)

Calculate removal coefficient lambda [/day].

Parameters

• redox (str) – redox condition [‘suboxic’,’anoxic’,’deeply_anoxic’]

• mu1 (float) – inactivation coefficient [day-1]

• por_eff (float) – effective porosity [-]

• grainsize (float) – grain diameter of sediment [m]

• alpha0 (float) – ‘reference sticky coefficient’, for a reference pH [pH0]

• pH (float) – pH of the water [-]

• pH0 (float) – reference pH for which alpha0 was determined

• temp_water (float) – Water temperature [degrees celcius]

• rho_water (float) – Water density [kg m-3]

• alpha (float) – ‘sticky coefficient’ [-], pH corrected

• organism_diam (float) – Organism/species diameter [m]

• v_por (float) – porewater velocity [m/d]

• const_BM (float) – Boltzmann constant [1,38 × 10-23 J K-1]

Returns

• df_flowline (pandas.DataFrame) – Column “organism_diam” for each node

• df_particle (pandas.DataFrame) – Column “relative_distance” for each node Column “porewater_velocity” for each node Column “k_att” for each node Column “lamda” for each node Column “alpha” for each node

compute_concentration_in_well_at_date()

Calculates the concentration in the well up to a specific date, taking into account the start and end date of the contamiantion and start date of the well.

Returns

df_well_concentration –

Column ‘time’: float

Array of time starting at minimum of the ‘start_date_well’ or ‘start_date_contamination’ as time = 0

and the other value is set as negative.

Column ‘date’: datetime.date

Array of dates starting at the minimum of the ‘start_date_well’ or ‘start_date_contamination’

Column total_concentration_in_well: float

Summed concentration of the OMP in the well.

Return type

pandas.dataframe

compute_omp_removal()

Calculates the concentration in the well of each flowline. Returns the values in ‘df_flowline’ and ‘df_particle’ as attributes of the object.

Returns

• df_flowline (pandas.DataFrame) – Column ‘flowline_id’: Integer Column ‘flowline_type’: string Column ‘flowline_discharge’: Float Column ‘particle_release_day’: Float Column ‘input_concentration’: float Column ‘endpoint_id’: Integer Column ‘well_discharge’: float Column ‘name’: string Column ‘removal_function’: string Column ‘total_breakthrough_travel_time’: float The breakthrough concentration in the well for the OMP taking into account retardation. Column ‘breakthrough_concentration’: float The breakthrough concentration in the well for the OMP taking into account sorption and biodegradation.

• df_particle (pandas.DataFrame) – Column ‘flowline_id’: int Column ‘zone’: string Column ‘travel_time’: float Column ‘xcoord’: float Column ‘ycoord’: float Column ‘zcoord’: float Column ‘redox’: float Column ‘temp_water’: float Column ‘travel_distance’: float Column ‘porosity’: float Column ‘dissolved_organic_carbon’: float Column ‘pH’: float Column ‘fraction_organic_carbon’: float Column ‘solid_density’: float Column ‘input_concentration’: float Column ‘steady_state_concentration’: float The steady state concentration at the well of the OMP for the flowline, [mass/L] Column ‘omp_half_life’: float Column ‘log_Koc’: float Column ‘pKa’: float Column ‘Koc_temperature_correction’: float The temperature corrected Koc value, only if ‘temp_correction_Koc’ is ‘True’ in the HydroChemicalSchematisation. Column ‘omp_half_life_temperature_corrected’: float The temperature corrected OMP half-life value, if ‘temp_correction_halflife’ is ‘True’ in the HydroChemicalSchematisation. Column ‘retardation’: float Column ‘breakthrough_travel_time’: float

compute_pathogen_removal()

plot_age_distribution(df_particle: DataFrame, vmin=0.0, vmax=1.0, orientation={'row': 0}, fpathfig=None, figtext=None, x_text=0, y_text=0, lognorm=True, xmin=0.0, xmax=None, ymin=None, ymax=None, line_dist=1, dpi=192, trackingdirection='forward', cmap='viridis_r', show_vadose=True)

Create pathline plots with residence times using colours as indicator. with: - df_particle: dataframe containing xyzt-points of the particle paths. - fpathfig = output location of plots figtext: figure text to show within plot starting at x-location ‘x_text’ and y-location ‘y_text’. lognorm: True/False (if vmin <= 0. –> vmin = 1.e-5) xmin: left boundary figure xmax: right boundary figure ymin: lower boundary figure ymax: upper boundary figure line_dist: min. distance between well pathlines at source (m) line_freq: show every ‘X’ pathlines dpi: pixel density trackingdirection: direction of calculating flow along pathlines” cmap: Uses colormap ‘viridis_r’ (viridis reversed as default) show_vadose: T/F (if True, include vadose zone flow lines; default = True)

plot_concentration(xlim=None, ylim=None, as_fraction_input=None, x_axis='Date')

Plot the concentration of the given OMP as a function of time since the start of the contamination

Parameters

• x_axis (string) – Choice of the x-axis as ‘Time’ in years starting at 0, or as the ‘Date’ since the minimum of ‘start_date_well’ or ‘start_date_contamination’

• as_fraction_input (bool) – If ‘True’ plots concentration on y-axis as a fraction of the sum of the input concentration (diffuse and point source), [C/C0]

• xlim (array) – The x-axis limits

• ylim (array) – The y-axis limits

• here? (ReturnVage @MartinK what to put) –

plot_logremoval(df_particle: DataFrame, df_flowline: DataFrame, vmin=0.0, vmax=1.0, orientation={'row': 0}, fpathfig=None, figtext=None, x_text=0, y_text=0, lognorm=True, xmin=0.0, xmax=None, line_dist=1, dpi=192, trackingdirection='forward', cmap='viridis_r')

Create pathline plots with log removal using colours as indicator. with: - df_particle: dataframe containing xyzt-points of the particle paths. - fpathfig = output location of plots figtext: figure text to show within plot starting at x-location ‘x_text’ and y-location ‘y_text’. lognorm: True/False (if vmin <= 0. –> vmin = 1.e-5) line_dist: min. distance between well pathlines at source (m) line_freq: show every ‘X’ pathlines dpi: pixel density trackingdirection: direction of calculating flow along pathlines” cmap: Uses colormap ‘viridis_r’ (viridis reversed as default)

plot_travel_time_distribution(df_particle, times_col='total_travel_time', distance_col='xcoord', index_col='flowline_id', fpath_fig=None)

Plot cumululative travel times using column ‘times_col’ relative to distance ‘distance_col’ per pathline with index column ‘index_col’.