P ROBABILISTIC R ISK A SSESSMENT (T IER 2)

12.3.3 P ROBABILISTIC R ISK A SSESSMENT (T IER 2)

The use of probabilistic approaches allows the quantification of likelihood of effects which by definition is risk. In probabilistic approaches, the risk is expressed as the degree of overlap between the exposure and the effects that is acceptable for

a certain level of protection that would be attained [120]. PRA approaches use SSD combined with distributions of exposure concentrations to better describe the likeli- hood of exceedances of effect thresholds and thus the risk of adverse effects. The frequency of occurrence of levels of exposure (return frequencies) could be classified as follows: typical case (50th percentile), reasonable worst case (90th percentile), and extreme worst case (99th percentile). From the resulted SSDs, expos- ure levels that would protect 90%, 95%, or indeed any percentage of the species can be determined. Of course, there are a number of concerns such as what level, if any, of species affected might be acceptable; which species might be affected; how they might

be affected; and are they economically, ecologically, or otherwise important. Hart [121] in his summary of an EU-funded workshop on pesticide PRA identified several strengths and weaknesses of PRA within the context of EC Directive 91=414=EEC [17]. Strengths of PRA include (1) the ability to quantify the type, magnitude, and frequency of toxic effects and communicate more ‘‘mean- ingful’’ outputs to decision-makers and the public; (2) the ability to quantify vari- ability, uncertainty, and model sensitivity; (3) the better use of available information by taking into account all available toxicity data to quantify variation between species and not just the more sensitive or representative organism for the ecosystem only; and (4) finally, probabilistic methods are also more prone to be coupled with new approaches such as GIS and population modeling. Potential weaknesses include the greater complexity that could lead to misleading results, the requirement of more

Monitoring of Pesticides in the Environment 345 toxicity data and thus the increased animal testing, the lack of available expertise and

guidance, and the lack of established criteria for decision-makers [121]. PRAs could be applied for all organisms as well as for human health and has been recommended for regulatory assessment of pesticides [91]. The general con- cepts have been reviewed and discussed [120 –123]. The different PRA methods are developed similarly, but they may be used for different purposes. Some uses include the setting of environmental quality objectives and criteria, whereas others are used for assessing risks of known exposure. As a first example of a PRA method of risk assessment, the inverse method of Van Straalen and Denneman [111] is presented. The method is based on the assumption that the frequency distribution of effect end points for different species is log-logistic [119]. The parameters describing the distribution could be estimated for the mean and the standard deviation of the ln-transformed data set of a number of toxicity end points of a given pesticide reported in the literature. From this distribution, a concentration is calculated that

is hazardous for 5% of the species in an ecosystem (HC 5 , Equation 12.8), which is an acceptable level for protecting aquatic ecosystems [56,124].

( 12:8) where

HC 5 ¼ exp(x m k L s m ) ,

m¼ the number of the test species x m ¼ the mean of the ln-transformed toxicity end points (LC 50 or EC 50 or NOEC) s m ¼ the standard deviation of the ln-transformed effect levels k L ¼ the extrapolation constant as reported in Ref. [125]

The 95% confidence level provides a strict or safe HC 5 , whereas the 50% confidence level provides the most probable or mean. In most studies, the 50% confidence level was used.

The hazard or ecological risk is estimated by defining it as the probability, F, that a random species will be affected by the measured field concentrations (C).

k L = ln (95=5)s m

This method has been followed by several researchers in pesticide risk assessment in aquatic systems [3,56]. For estimating the combined risk (SF) from pesticide mixtures, the equation for the addition of probabilities can be used as follows:

F[A 1 þ A 2 A n ]

¼ X F[Ai] F[Ai 1 Ai 2

i¼ 1 i 1 <i 2

þ ( 1) rþ 1 X F[Ai (nþ 1 Ai 2 Ai 1) 1) r

F [A 1 A 2 A n ] : ( 12:10)

i 1 <i 2 <...<i r

The summation P

i 1 <i 2 r F[Ai 1 Ai 2 Ai r ] is taken over all of r possible subsets of the ecological risk r of the compounds {1, 2, . . . , n}. The equation does not account for synergistic or antagonistic interactions.

346 Analysis of Pesticides in Food and Environmental Samples

A second generic method of PRA is presented later. The method has been used by

a number of authors [120,126 –128] and is currently implemented by the USEPA [109,129]. Toxicity data for all species are combined to produce a distribution curve of effects concentration where appropriate data for all species fitted to log-normal distributions, while other models or bootstrapping models can be also used. The exposure data (measured values from monitoring programs or estimated by modeling) are plotted on the same axes as the effects data. The extent of overlapping between the two curves indicates the probability of exceeding an exposure concentration associ- ated with a particular probability of effects of the studied pesticide. For plotting cumulative percentage (or cumulative probability) of the total distribution, both the acute and chronic toxicity data and the environmental pesticide concentrations are separately sorted into ascending order and ranked. These data are then converted to a cumulative percentage of the total distribution using the following equation [126]:

Cumulative percent ( 12:11) where n is the total number of environmental concentration or toxicity data used to

perform the quantitative assessment. These percentiles were plotted against the log- transformed concentration, and a linear regression was performed to characterize each distribution (Figure 12.3A and B). Alternatively, straight-line transformations

Exposure Toxicity Method

detection limit

Nondetections are (probability scale)

assumed to follow

(probability scale)

Cumulative frequency a lower extension

Cumulative frequency

of the distribution Concentration (log scale)

(A) EC50 concentration (log scale) (B)

20 Toxicity distribution

10 Exposure distribution

Criterion of assessment 10 of species sensitivity 5 5

Cumulative frequency distribution of environmental concentrations 2 (5⬘ centile)

2 1 1 Cumulative frequency distribution

Concentration (ng/mL)

FIGURE 12.3 Graphical representation of combination of (A) exposure and (B) toxicity data expressed as linearized probability distributions (C) for the probabilistic risk estimation.

Monitoring of Pesticides in the Environment 347 of probability functions are obtained by probit transformation according to the

equation:

(x

e (x m) ,m,s) ¼ =2s ffiffiffiffiffiffiffiffiffi ,

2ps

where m is the distribution mean and s is the distribution standard deviation [120]. Approaches for handling data below the detection limits include the assigning of values as zero or one-half the detection limit. Alternatively, nondetected concentra- tions are assumed to be distributed along a lower extension of the distribution (Figure 12.3A). The use of distribution curves for exposure and toxicity data allows the application of a joint probability method (Figure 12.3C) to perform the environ- mental risk assessment. In this way, any level of effect is associated with an exposure concentration and inversely for any concentration level a probability of exceedance of this level can be determined [120]. In the example provided (Figure 12.3C), the con- centration at which 5% of species toxicity values will be exceeded is ~50 mg=L.

in other words this concentration would be exceeded 10% of the times. The final step in the probabilistic approach is to generate a joint probability plot of the exceedance of data (exceedance profile). This can be performed by solving the functions describing the probability of exceeding both an exposure and an effect concentration with appropriate fitted regression models or from Monte Carlo modeled data [91]. The graphical representation of a joint probability curve (JPC) which describes the prob- ability of exceeding the concentration associated with a particular degree of effect is shown in Figure 12.4. In such type of representation, the closer the JPC to the axes, the lesser the probability of adverse effect (Figure 12.5) [91].

There is a debate over which value from a range of species sensitivities is most appropriate to protect the various environmental compartments. The 5th percentile value is a generically applicable level of species protection used by USEPA [129,130], European [115,131], and Australian [132] quality criteria. While the 5th percentile is therefore the accepted norm, previous studies with pyrethroids and atrazine have proposed that the 10th percentile effect concentration is adequate [124].

40 30 30 40 40 20 20 toxicity value 20

5 5 Percent exceedence of the 0 0 20 40 60 80 100 Cumulative frequency distribution

10 10 of species sensitivity

1 Percent of unprotected species Concentration (ng/mL)

of environmental concentrations 2 1 2 Cumulative frequency distribution

FIGURE 12.4 Graphical representation of the derivation of a joint probability curve (excee- dance profile from exposure and toxicity probability functions). (Modified from Solomon, K., Giesy, J., and Jones, P., Crop Prot., 19, 649, 2000.)

348 Analysis of Pesticides in Food and Environmental Samples

Less acceptable

Decreasing risk

Percent exceedence

More acceptable Percent of species

FIGURE 12.5 Illustration of the use of the joint probability curve in decision making. (From ECOFRAM, Ecological Committee on FIFRA Risk Assessment Methods, Aquatic and Terrestrial Final Draft Reports, USEPA, 1999, www.epa.gov=oppefed1=ecorisk=index.htm)

This level of species protection is not universally accepted, especially if the unpro- tected 10% are keystone species and have commercial or recreational significance. However, protection of 90% of the species in 90% of the time (10th percentile) has been recommended by the SETAC [133].

For PRA, however, a harmonization of the methods used through calibration and validation should be established for their appropriate use in environmental risk assessment. In addition, methods for dealing with spatial and temporal variation and regional scenarios are recommended to be developed and validated with the help of GIS approaches.

As a generic conclusion, the methods that can be followed for pesticide risk assessment in the environmental compartments could include comparisons between point estimates and=or distributions of exposure and toxicity data, depending on the data available and the questions that are addressed in the assessment (Table 12.3). An example of tiered approach for risk characterization is showed in Table 12.4. Once risk has been characterized, it is necessary to follow basic guidelines for risk management and communication strategies [134].