Risk modelling of pharmaceuticals in the environment

Transcription

1 Risk modelling of pharmaceuticals in the environment How to deal with uncertainty and variability in the environmental risk assessment of pharmaceuticals? ANSWER workshop KWR; of June Rik Oldenkamp Environment Department University of York Department of Environmental Science Radboud University

2 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

3 Pharmaceuticals in the environment Jobling et al., 1998

4 Pharmaceuticals in the environment Kinch et al., 2014 Klein et al., 2018 Aus der Beek et al., 2016 Gilbert, 2012

5 Pharmaceuticals in the environment How to prioritise? What pharmaceuticals? Where? Ecosystem? Human?

6 Environmental risk modelling of pharmaceuticals Boxall et al., 2012 Risk = Exposure / Effect

7 Environmental risk modelling of pharmaceuticals Boxall et al., 2012 Risk = Exposure / Effect

8 Consumption information National databases LMMs incorporating socioeconomic and demographic predictors 5/1 2 ECDC (antibiotics)

9 Environmental risk modelling of pharmaceuticals 5/1 2

10 Environmental risk modelling of pharmaceuticals 6/1 2

11 Environmental risk modelling of pharmaceuticals 6/1 2 Primary and/or secondary treatment: estimation of removal from wastewater with SimpleTreat 4.0 model Struijs, 2014 Chemical properties Sorption to sludge (primary/activated) Biodegradation rates

12 Environmental risk modelling of pharmaceuticals

13 Environmental fate modelling approaches Multimedia fate models Single media flow models Large scale hydrological models

14 Environmental fate modelling approaches Multimedia fate models SimpleBox (Hollander et al., 2009)

15 Environmental fate modelling approaches Single media flow models

16 Environmental fate modelling approaches Large scale hydrological models

17 Environmental risk modelling of pharmaceuticals Aquatic Risk Quotient: RQ = PEC PNEC

18 Environmental risk modelling of pharmaceuticals C fish C fruits/veg C meat C milk C drinking water Human health Risk Quotient: RQ = I ADI Age & country specific Consumption patterns Soil ingestion Swimming behaviour

19 Environmental risk modelling of pharmaceuticals Human health Antibiotics Oldenkamp et al., 2013 Antibiotics Ceftriaxone Cefuroxime Chlortetracycline Ciprofloxacin Doxycycline Erythromycin Levofloxacin Ofloxacin Oxytetracycline Tetracycline Trimethoprim

20 Environmental risk modelling of pharmaceuticals Boxall et al., 2012 Risk = Exposure / Effect

21 Environmental risk modelling of pharmaceuticals? Ciprofloxacin Levofloxacin Oldenkamp et al., 2014

22 Uncertainty in risk modelling of pharmaceuticals?? Boxall et al., 2012??? Risk = Exposure / Effect?

23 Uncertainty in risk modelling of pharmaceuticals Realistic (most likely) estimate Unacceptable risk?

24 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).

25 Context uncertainty (ignorance) Shultz et al., 2004 Prakash et al., 2012

26 Uncertainty in risk modelling of pharmaceuticals Any departure from the unachievable ideal of complete determinism (Walker et al. 2003) 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.

27 Model uncertainty Multimedia fate models Single media flow models Large scale hydrological models

28 Uncertainty in risk modelling of pharmaceuticals Any departure from the unachievable ideal of complete determinism (Walker et al. 2003) 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.??????????

29 Parameter uncertainty Straub, 2009

30 Uncertainty in risk modelling of pharmaceuticals How to adequately deal with it? Conservative estimate Realistic (most likely) estimate Unacceptable risk?

31 Uncertainty in risk modelling of pharmaceuticals Tiers in chemical risk assessment Number of Substances Tier 1 Tier 2 Tier 3 Data Availability (per substance) Complexity Cost Realism

32 Uncertainty in risk modelling of pharmaceuticals How to adequately deal with it? Realistic and conservative estimate Conservative estimate Unacceptable risk

33 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.

34 Monte Carlo simulation of uncertainty Aquatic risks in Dutch surface waters x x x Consumption Excretion fraction Removal fraction Dilution RQ = PEC PNEC Deterministic calculation

35 Monte Carlo simulation of uncertainty Aquatic risks in Dutch surface waters x x x Consumption Excretion fraction Removal fraction Dilution RQ = PEC PNEC Probabilistic calculation

36 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 nonexistent or unknown Excretion fraction, removal efficiency Minimum, maximum

37 Monte Carlo simulation of uncertainty 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) Oldenkamp et al., 2016

38 Uncertainty and variability Uncertainty - variation due to incomplete knowledge??? Variability - variation due to intrinsic differences

39 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.

40 Uncertainty and variability 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?

41 Two-dimensional MC calculations Uncertainty Uncertainty Variability x x x Variability Consumption Excretion fraction Removal fraction Dilution RQ = PEC PNEC Uncertainty 1. Distinguish uncertain & variable parameters 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

42 Two-dimensional MC calculations Repeat this procedure several times (e.g. 1000) Variability ratio (VR) Uncertainty ratio (UR)

43 Two-dimensional MC calculations Ciprofloxacin Oldenkamp et al., 2016

44 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

45 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

46 Example case Modelling effluent and sludge concentrations 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(K OC ) (L/kg) N(2.61; 0.30) k bio (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) k bio (1/hr) LN(14.87; 2.42)

47 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)

48 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?

49 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%

50 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%

51 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? Trimethoprim Triclosan

52 Example case SimpleTreat 4.0 WWTP parameters Distribution Sewage inflow (L*PE -1 *d -1 ) LN(192.09; 1.33) Sludge loading rate (kg BOD *kg -1 dwt *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) O 2 in aerator (mg*l -1 ) T(1; 2; 2.5) Franco et al., 2013