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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
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