Risk modelling of pharmaceuticals in the environment

Risk modelling of pharmaceuticals

in the environment

Rik Oldenkamp ([email protected])

Environment Department – University of York

Department of Environmental Science – Radboud University

How to deal with uncertainty and variability in the

environmental risk assessment of pharmaceuticals?

ANSWER workshop KWR; 18-21 of June

mailto:[email protected]

Who am I

• MSc Human and Ecological Risk Assessment 2011;

• PhD at Radboud University 2015;

• Currently postdoctoral researcher at Radboud University and University

of York

Pharmaceuticals in the environment

Jobling et al., 1998


Aus der Beek et al., 2016

Kinch et al., 2014 Klein et al., 2018

Gilbert, 2012

What pharmaceuticals? Where? Ecosystem? Human?

How to prioritise?


Boxall et al., 2012

Risk = Exposure / Effect

Environmental risk modelling of pharmaceuticals

5/12

Consumption information

National databases

ECDC (antibiotics)

LMMs incorporating socio-

economic and demographic

predictors

5/12


6/12


6/12


Primary and/or secondary treatment: estimation of removal from

wastewater with SimpleTreat 4.0 model

Struijs, 2014

• Sorption to sludge (primary/activated)

• Biodegradation rates

Chemical properties

Environmental fate modelling approaches

Multimedia fate models Single media flow models Large scale hydrological

models


Multimedia fate models

SimpleBox (Hollander et al., 2009)


Single media flow models


Large scale hydrological models

Aquatic Risk Quotient:

RQ = PECPNEC


Human health

Risk Quotient:

RQ = I ADI

Cfish

Cfruits/veg

Cmeat

Cmilk

Cdrinking water

Age & country specificConsumption patternsSoil ingestionSwimming behaviour


Antibiotics

Ceftriaxone

Cefuroxime

Chlortetracycline

Ciprofloxacin

Doxycycline

Erythromycin

Levofloxacin

Ofloxacin

Oxytetracycline

Tetracycline

Trimethoprim

Antibiotics

Human health

Oldenkamp et al., 2013


Boxall et al., 2012



?Ciprofloxacin Levofloxacin



Boxall et al., 2012


?

?

??

?

?

Uncertainty in risk modelling of pharmaceuticals

Realistic (most likely) estimateUnacceptable risk

?



“Any departure from the unachievable ideal of complete determinism” (Walker et al. 2003)

Context uncertainty

Uncertainty relating to the framing of the problem

(the problem definition), and to the boundaries of

the assessment (what part of the real world is

captured).

Shultz et al., 2004 Prakash et al., 2012

Context uncertainty (ignorance)


Context uncertainty

Related to the framing of the problem (the problem

definition), and to the boundaries of the assessment

(what part of the real world is captured).

Model uncertainty

Uncertainty due to a lack of sufficient understanding

of the system within the model’s boundaries.


Model uncertainty

Multimedia fate models Single media flow models Large scale hydrological

models


Context uncertainty

Related to the framing of the problem (the problem

definition), and to the boundaries of the assessment

(what part of the real world is captured).

Model uncertainty

Uncertainty due to a lack of sufficient understanding

of the system within the model’s boundaries.

Parameter uncertainty

Uncertainty due to a lack of knowledge on the

model’s true parameter values.

??

??

??

?

?

?

?


Parameter uncertainty

Straub, 2009

Conservative estimate

Unacceptable risk

?


How to adequately deal with it?

Realistic (most likely) estimate


Tiers in chemical risk assessment

Tier 1

Tier 2

Tier 3

Data Availability

(per substance)

Complexity RealismCost

Number of Substances

Conservative estimate

Unacceptable risk


How to adequately deal with it?

Realistic and conservative estimate

Monte Carlo simulation of uncertainty

Monte Carlo simulation is a methodology in which a process is

simulated not once, but many times, each time with different starting

conditions. The result of this assemblage of simulations forms a

distribution of possible outcomes.


RQ =PEC

PNEC

Consumption Excretion fraction Removal fraction Dilution

x x x

Deterministic

calculation

Aquatic risks in Dutch surface waters


RQ =PEC

PNEC


x x x

Probabilistic

calculation

Aquatic risks in Dutch surface waters

Monte Carlo simulation – commonly used distributions

Type Characteristics Examples Parameters

Normal Symmetric, continuous,

unbound

Sampling

uncertainty,

measurement error

Arithmetic mean (AM),

standard deviation

(SD)

Lognormal Continuous and

symmetric on log-scale,

bound at zero

Concentrations, rate

constants, most

natural phenomena

Geometric mean

(GM), geometric

standard deviation

(GSD)

Triangular Bound at min and max,

most likely value known

Excretion fraction,

removal efficiency

Mode, minimum,

maximum value

Uniform Bound at min and max,

most likely value non-

existent or unknown

Excretion fraction,

removal efficiency

Minimum, maximum


66% 34%


To obtain a high importance

score, a parameter must have:

1. considerable influence on

the result

2. large variance (broad range

of input values)

Uncertainty and variability

Uncertainty - variation due to incomplete knowledge

???

Variability - variation due to intrinsic differences


Uncertainty: The exact value of a parameter is not known.

Uncertainty can be reduced by additional research.

Variability: The value of a parameter differs between individuals

(interindividual), places (spatial) or in time (temporal).

Variability is inherent to the system and cannot be

reduced by additional research.

66% 34%


How to interpret the variance in the risk indicator?

Variation in RQ is completely driven by uncertainty:

There is a 34% probability that all surface waters are at risk.

Variation in RW is completely driven by variability:

34% of all surface waters are at risk.

What if the variance in some input parameters is caused by

uncertainty and in others by interindividual variability?

Two-dimensional MC calculations

RQ =PEC

PNEC


x x x

2. Pick a value for the uncertain

parameters

3. Perform a MC simulation for

the variable parameters

4. A possible distribution for the

variability is obtained

1. Distinguish uncertain &

variable parameters

Variability

Uncertainty

Variability

Uncertainty

Uncertainty


Variability

Uncertainty

Repeat this procedure several times (e.g. 1000)

Variability ratio

(VR)

Uncertainty ratio

(UR)



Ciprofloxacin

Example case – Modelling effluent and sludge concentrations

• Spreadsheet version of mass-balance model SimpleTreat 4.0 (orange sheets)

• Adapted for Monte Carlo simulations (blue sheets)

• Pre-filled with distributions for parameters of European WWTPs (Franco et al.,

2013)

Struijs, 2014


1. Input distributions for properties of trimethoprim (required fields turn green)

2. Select dropdown 2D simulation

3. Which distributions to classify as uncertain and which as variable?

4. Run 2D simulation (101x101 iterations) and check results

5. Save results in new spreadsheet and run the model for triclosan

Physico-chemical parameters Distribution

Trimethoprim (base)

Solubility (mg/L) LN(400; 1.56)

Vapour pressure (Pa) LN(4.93*10-9; 8.08)

pKa (-) N(6.92; 0.27)

Log(KOC) (L/kg) N(2.61; 0.30)

kbio (1/hr) 0

Triclosan (acid)

Solubility (mg/L) LN(6.05; 1.56)

Vapour pressure (Pa) LN(6.13*10-4; 8.08)

pKa (-) N(8.00; 0.10)

Koc (L/kg) N(4.67; 0.2)

kbio (1/hr) LN(14.87; 2.42)



Does uncertainty or variability drive variation of trimethoprim concentrations in

effluents and sludges of WWTPs?

UR (5p-95p) VR (5p-95p)1.19 2.39


Does uncertainty or variability drive variation of trimethoprim concentrations in

effluents and sludges of WWTPs? And for triclosan?


What is the probability that 30% of WWTPs exceed a concentration of 100

mg/kg trimethoprim in their surplus sludge?

Trimethoprim

~75%


What is the probability that 30% of WWTPs exceed a concentration of 100

mg/kg trimethoprim in their surplus sludge? And for triclosan?

Triclosan

~50%


Based on the relative importance of WWTP characteristics and chemical

properties, what would you recommend for

1. Prioritisation of WWTPs for further monitoring?

2. Further research on trimethoprim and triclosan?

TrimethoprimTriclosan

Example case – SimpleTreat 4.0

WWTP parameters Distribution

Sewage inflow (L*PE-1*d-1) LN(192.09; 1.33)

Sludge loading rate (kgBOD*kgdwt-1*d-1) T(0.04; 0.15; 0.6)

Water temperature (°C) N(15; 6)

Solids inflow (g*PE-1*d-1) LN(60.76; 1.50)

OC raw sewage (g*g-1) N(0.4; 0.03)

BOD (gBOD*PE-1*d-1) LN(53.10; 1.20)

pH (-) N(7.5; 0.35)

Depth primary settler (m) T(3; 4; 4.9)

Depth aeration tank (m) T(2; 3; 6)

Depth secondary clarifier (m) T(2.5; 3; 4.5)

OC activated sludge (g*g-1) N(0.37; 0.03)

TSS effluent (mg*L-1) LN(3.76; 3.42)

TSS removed primary (%) N(0.55; 0.07)

O2 in aerator (mg*L-1) T(1; 2; 2.5)

Franco et al., 2013

Risk modelling of pharmaceuticals in the environment

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