Risk modelling of pharmaceuticals in the environment

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Risk modelling of pharmaceuticals

in the environment

Rik Oldenkamp (r.oldenkamp@science.ru.nl)

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

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

Pharmaceuticals in the environment

Aus der Beek et al., 2016

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

Gilbert, 2012

What pharmaceuticals? Where? Ecosystem? Human?

How to prioritise?

Pharmaceuticals in the environment

Boxall et al., 2012

Risk = Exposure / Effect

Environmental risk modelling of pharmaceuticals

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

Environmental risk modelling of pharmaceuticals

6/12

Environmental risk modelling of pharmaceuticals

6/12

Environmental risk modelling of pharmaceuticals

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 risk modelling of pharmaceuticals

Environmental fate modelling approaches

Multimedia fate models Single media flow models Large scale hydrological

models

Environmental fate modelling approaches

Multimedia fate models

SimpleBox (Hollander et al., 2009)

Environmental fate modelling approaches

Single media flow models

Environmental fate modelling approaches

Large scale hydrological models

Aquatic Risk Quotient:

RQ = PECPNEC

Environmental risk modelling of pharmaceuticals

Human health

Risk Quotient:

RQ = I ADI

Cfish

Cfruits/veg

Cmeat

Cmilk

Cdrinking water

Age & country specificConsumption patternsSoil ingestionSwimming behaviour

Environmental risk modelling of pharmaceuticals

Antibiotics

Ceftriaxone

Cefuroxime

Chlortetracycline

Ciprofloxacin

Doxycycline

Erythromycin

Levofloxacin

Ofloxacin

Oxytetracycline

Tetracycline

Trimethoprim

Antibiotics

Human health

Oldenkamp et al., 2013

Environmental risk modelling of pharmaceuticals

Boxall et al., 2012

Risk = Exposure / Effect

Environmental risk modelling of pharmaceuticals

?Ciprofloxacin Levofloxacin

Oldenkamp et al., 2014

Environmental risk modelling of pharmaceuticals

Boxall et al., 2012

Risk = Exposure / Effect

?

?

??

?

?

Uncertainty in risk modelling of pharmaceuticals

Realistic (most likely) estimateUnacceptable risk

?

Uncertainty in risk modelling of pharmaceuticals

Uncertainty in risk modelling of pharmaceuticals

“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)

Uncertainty in risk modelling of pharmaceuticals

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.

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

Model uncertainty

Multimedia fate models Single media flow models Large scale hydrological

models

Uncertainty in risk modelling of pharmaceuticals

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.

??

??

??

?

?

?

?

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

Parameter uncertainty

Straub, 2009

Conservative estimate

Unacceptable risk

?

Uncertainty in risk modelling of pharmaceuticals

How to adequately deal with it?

Realistic (most likely) estimate

Uncertainty in risk modelling of pharmaceuticals

Tiers in chemical risk assessment

Tier 1

Tier 2

Tier 3

Data Availability

(per substance)

Complexity RealismCost

Number of Substances

Conservative estimate

Unacceptable risk

Uncertainty in risk modelling of pharmaceuticals

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.

Monte Carlo simulation of uncertainty

RQ =PEC

PNEC

Consumption Excretion fraction Removal fraction Dilution

x x x

Deterministic

calculation

Aquatic risks in Dutch surface waters

Monte Carlo simulation of uncertainty

RQ =PEC

PNEC

Consumption Excretion fraction Removal fraction Dilution

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

Monte Carlo simulation of uncertainty

66% 34%

Oldenkamp et al., 2016

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 and variability

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%

Uncertainty and variability

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

Consumption Excretion fraction Removal fraction Dilution

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

Two-dimensional MC calculations

Variability

Uncertainty

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

Variability ratio

(VR)

Uncertainty ratio

(UR)

Two-dimensional MC calculations

Oldenkamp et al., 2016

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

Example case – Modelling effluent and sludge concentrations

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)

Example case – Modelling effluent and sludge concentrations

Example case – Modelling effluent and sludge concentrations

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

Example case – Modelling effluent and sludge concentrations

Does uncertainty or variability drive variation of trimethoprim concentrations in

effluents and sludges of WWTPs? And for triclosan?

Example case – Modelling effluent and sludge concentrations

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

mg/kg trimethoprim in their surplus sludge?

Trimethoprim

~75%

Example case – Modelling effluent and sludge concentrations

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%

Example case – Modelling effluent and sludge concentrations

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