A model is presented for the acute toxicity of organophosphorus (OP) pesticides belonging to the class of phosphorothionates. The acute toxicity of these pesticides is governed by the irreversible inhibition of the enzyme acetylcholinesterase (AChE), after their metabolic activation to oxon analogues. The model is based on the idea that, for chemicals exhibiting an irreversible receptor interaction, mortality is associated with a critical amount of “covalently occupied” target sites, i.e., the “critical target occupation” (CTO). For a given compound and species, this CTO is associated with a critical time-integrated concentration of the oxon analogue in the target tissue, which can be modeled by the critical area under the curve (CAUC) that describes the time−concentration course of the phosphorothionate in the aqueous phase or in the entire aquatic organism. In contrast to the classical critical body residue (CBR) model, the CTO model successfully describes the 1−14-d LC50(t) data of several phosphorothionates in the pond snail and guppy. Furthermore, the time dependency of lethal body burdens (LBBs) of phosphorothionates is explained by the model. Although the CTO model is specifically derived for OP pesticides, it can be applied to analyze the acute toxicity and to estimate incipient LC50 values of organic chemicals that exert an irreversible receptor interaction in general.

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Assessment of aquatic effects requires the derivation of a predicted no‐effect concentration (PNEC). In the framework of the Dutch “Plan of Action Laundry and Cleaning Products,” PNECs were derived for linear alkyl benzene sulfonate (LAS), alcohol ethoxylates (AE), alcohol ethoxylated sulfates (AES), and soap. All stages in an aquatic effects assessment were used: initial (assessment factors based mainly on short‐term toxicity data), refined (statistical extrapolation based on long‐term toxicity data), and comprehensive (field studies). Where necessary (i.e., where other structures had been tested in toxicity tests), the toxicity data were normalized to these structures using quantitative structure‐activity relationships (QSARs) for short‐term toxicity. Results from statistical extrapolation were compared with field no observed effect concentrations (NOECs), and a final PNEC was derived. Final PNECs for LAS, AE, AES, and soap were 250, 110, 400, and 27 μg/L, respectively. These PNECs were compared with predicted environmental concentrations (PECs) in surface water that were derived from monitoring results of removal of these surfactants in seven representative wastewater treatment plants. It is concluded that for LAS, AE, and AES, the PECs in the environment are about 50 to 100 times lower than the PNECs. The PEC for soap is about equal to the PNEC that is based on acute toxicity data. However, because the available chronic toxicity data for soap demonstrate that this substance is not more toxic than the other three surfactants, there is no reason for concern. On the basis of the results of the risk characterization, it has been concluded in the Netherlands that in properly functioning wastewater treatment plants, the risks for the aquatic compartment from the use of LAS, AE, AES, and soap are low.

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No physicochemical parameter in environmental toxicology and chemistry is better known than K OW , the octanol-water partition coefficient. This parameter, also called log P or log P oct , originates from research into quantitative structureactivity relationships (QSARs) [1]. The first studies were from Corwin Hansch and were focused on optimizing the biological as well as the pharmacological activity of chemicals. The coefficient K OW is a measure of hydrophobicity, and several processes, including sorption and accumulation, are driven by hydrophobicity. It is the key parameter for environmental fate and exposure modeling programs [2]. An ecological risk assessment for organic compounds without consideration of its K OW value seems impossible. Also, as an input parameter in QSARs, K OW is dominant. Guidance documents for risk assessment of existing chemicals list numerous models for the prediction of bioconcentration factors, soil sorption, and sorption to dissolved organic carbon that are all based on K OW [3]. Classic examples are QSARs for prediction of soil sorption [4], bioaccumulation [5,6], and baseline toxicity [7]. A clear example of the strength of K OW as a parameter is in the development of models for the prediction of quality criteria for chemicals that act via narcosis. Here, predictions of effect concentrations, bioconcentration factors, and sediment sorption from K OW are combined into one overall model [8,9]. Several papers in the "Top 100" show examples of methods for estimating parameters from octanol-water partition coefficients, including the estimation of aqueous solubility [10] and bioconcentration factors [11].While K OW itself is a simple parameter, measurement is not so straightforward. A hydrophobic chemical is dissolved in octanol in a shake flask or a glass vial, and water is added. The system is shaken until equilibrium, and the concentrations are measured in both phases [12]. However, small octanol droplets remain suspended in the aqueous phase, and these small droplets are difficult to remove from the aqueous phase. In particular, for hydrophobic chemicals this will lead to an overestimation of the aqueous concentrations and an underestimation of the true K OW , as well as a large variation in experimental K OW values [13].For We now have 30 to 40 years' experience in applying K OW in research in environmental chemistry and toxicology. The QSAR models based on K OW now appear to be very useful for exposure, hazard, and risk assessment. The advantages of K OW are that experimental data are available for many chemicals and estimates may be obtained from software that is based on the molecular structure of the compound, including EPISUITE [18] and SPARC [19]. Other experimental approaches for predicting K OW are based on comparisons of K OW with retention indices on a C18 high-performance liquid chromatographic column [20].However, octanol does not predict very well the interactions of polar compounds with, for example, a cell membrane, a soil particle, or a humic acid molecule. As ...