TKTD models as well as other mechanistic effect models can be applied for a variety of questions in environmental risk assessment of chemicals. For example there is a lot of interest in the prediction of effects from fluctuating or pulsed exposure to pollutants (also referred to as intermittent, episodic or time-variable exposure).
In the study "A method to predict and understand fish survival under dynamic chemical stress using standard ecotoxicity data" we apply TKTD modelling to a problem in pesticide risk assessment. The abstract is provided below and the study is available at the journal's website (open access).
Abstract: The authors present a method to predict fish survival under exposure to fluctuating concentrations and repeated pulses of a chemical stressor. The method is based on toxicokinetic-toxicodynamic modeling using the general unified threshold model of survival (GUTS) and calibrated using raw data from standard fish acute toxicity tests. The model was validated by predicting fry survival in a fish early life stage test. Application of the model was demonstrated by using Forum for Co-ordination of Pesticide Fate Models and Their Use surface water (FOCUS-SW) exposure patterns as model input and predicting the survival of fish over 485 d. Exposure patterns were also multiplied by factors of five and 10 to achieve higher exposure concentrations for fish survival predictions. Furthermore, the authors quantified how far the exposure profiles were below the onset of mortality by finding the corresponding exposure multiplication factor for each scenario. The authors calculated organism recovery times as additional characteristic of toxicity as well as number of peaks, interval length between peaks, and mean duration as additional characteristics of the exposure pattern. The authors also calculated which of the exposure patterns had the smallest and largest inherent potential toxicity. Sensitivity of the model to parameter changes depends on the exposure pattern and differs between GUTS individual tolerance and GUTS stochastic death. Possible uses of the additional information gained from modeling to inform risk assessment are discussed. (link at journal, open access)
Our paper on risk assessment of fluctuating exposures is an example of applied TKTD modelling: Ashauer R, Wittmer I, Stamm C and Escher BI (2011): Environmental Risk Assessment of Fluctuating Diazinon Concentrations in an Urban and Agricultural Catchment Using Toxicokinetic–Toxicodynamic Modeling. Environmental Science & Technology, 45 (22), 9783-9792. (link at journal here).
Temporally resolved environmental risk assessment of fluctuating concentrations of micropollutants is presented. We separated the prediction of toxicity over time from the extrapolation from one to many species and from acute to sublethal effects. A toxicokinetic–toxicodynamic (TKTD) model predicted toxicity caused by fluctuating concentrations of diazinon, measured by time-resolved sampling over 108 days from three locations in a stream network, representing urban, agricultural and mixed land use. We calculated extrapolation factors to quantify variation in toxicity among species and effect types based on available toxicity data, while correcting for different test durations with the TKTD model. Sampling from the distribution of extrapolation factors and prediction of time-resolved toxicity with the TKTD model facilitated subsequent calculation of the risk of undesired toxic events. Approximately one-fifth of aquatic organisms were at risk and fluctuating concentrations were more toxic than their averages. Contribution of urban and agricultural sources of diazinon to the overall risk varied. Thus using fixed concentrations as water quality criteria appears overly simplistic because it ignores the temporal dimension of toxicity. However, the improved prediction of toxicity for fluctuating concentrations may be small compared to uncertainty due to limited diversity of toxicity data to base the extrapolation factors on.
Calculation of the effect and risk curves (middle and upper panels in Figure 2). Toxic effects from time variable exposure patterns are predicted with a mechanistic effect model (TKTD model). Extrapolation between species and from lethal to sublethal effects is achieved by multiplying the exposure concentration (C(t)) with extrapolation factors. The extrapolation factors account for the variation in sensitivity of different species and effect types, based on the available ecotoxicological data (see Table 1 in the paper and see Scheme 1 in the paper for explanation of the calculation steps).
Measured concentrations of diazinon (lower panels) in different parts of the catchment, effect curves simulated for different percentiles (solid lines from left to right: 95th, 90th, 87.5th, 85th, and 80th percentile) of the extrapolation factor distribution (middle panels) and the risk (top panels). Risk is the fraction of probabilistic simulations that show toxic effects, i.e. the fraction of affected combinations of species and effect type (see Figure 1 and Scheme 1 in paper for calculation steps). In the lower panels, a peak originating from the agricultural part of the catchment is indicated by (a) and elevated concentrations originating from the urban part by (b). Note the different scale of the y-axis in the lower panel (station AGR). Day 0 corresponds to the March 10, 2007. The effect curve of the example AA-EQS value is plotted in the middle panels (dashed line).
These results are discussed in the paper, I will not repeat the discussion here. Please read the paper and let me know what you think.
I have received really helpful and interesting feedback (many thanks to Stefan Reichenberger at footways and Tjalling Jager) and would like to share some of these aspects here. The method as described in the paper is as good as we can currently get (in my opinion), however in the future some of the following aspects could be improved: