Abstract-It is recognized that the pH of exposure solutions can influence the toxicity and bioaccumulation of ionizing compounds. The present study investigates whether it can be considered a general rule that an ionizable compound is more toxic and more bioaccumulative when in the neutral state. Three processes were identified to explain the behavior of ionizing compounds with changing pH: the change in lipophilicity when a neutral compound becomes ionized, electrical attraction, and the ion trap. The literature was screened for bioaccumulation and toxicity tests of ionizing organic compounds performed at multiple pH levels. Toxicity and bioconcentration factors (BCFs) were higher for acids at lower pH values, whereas the opposite was true for bases. The effect of pH was most pronounced when pH À pK a was in the range of À1 to 3 for acids, and À3 to 1 for bases. The factor by which toxicity and BCF changed with pH was correlated with the lipophilicity of the compound (log K OW of the neutral compound). For both acids and bases, the correlation was positive, but it was significant only for acids. Because experimental data in the literature were limited, results were supplemented with model simulations using a dynamic flux model based on the Fick-Nernst-Planck diffusion equation known as the cell model. The cell model predicts that bases with delocalized charges may in some cases show declining bioaccumulation with increasing pH. Little information is available for amphoteric and zwitterionic compounds; however, based on simulations with the cell model, it is expected that the highest toxicity and bioaccumulation of these compounds will be found where the compounds are most neutral, at the isoelectric point. Environ. Toxicol. Chem. 2011;30:239530: -240630: . # 2011

The uptake and accumulation of most electrolytes will change with pH because of the different speciation states of these compounds at various pH. Non-ionized compounds will partition into fatty and organic phases (such as cell membranes) more readily than the corresponding charged compounds, and therefore a higher toxicity can be expected. The current study examines the pH-dependent toxicity and bioaccumulation of the bivalent weak base chloroquine (pK(a): 10.47 and 6.33, log K(OW) 4.67) tested on Salix viminalis (basket willow) and Daphnia magna (water flea). The transpiration rates of hydroponically grown willow cuttings were used to determine the toxicity of chloroquine at pH levels of 6, 7, 8, and 9. Root concentration factors were calculated from solution measurements. Results showed more than 10-fold higher toxicity and four to seven times higher root concentration factor at pH 9 than at pH 6. The toxicity of chloroquine was tested on Daphnia magna using the standard Organisation for Economic Co-operation and Development acute toxicity test modified to accommodate testing at pH levels of 7, 8, and 9. Increasing toxicity was seen at higher pH. The results of the current study confirm that the toxicity of weak bases with intermediate pK(a) values is higher at high pH levels.

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

SESAMe v3.3, a spatially explicit multimedia fate model for China, is a tool suggested to support quantitative risk assessment for national scale chemical management. The key advantage over the previous version SESAMe v3.0 is consideration of spatially varied environmental pH. We evaluate the model performance using estimates of emission from total industry usage of three UV filters (benzophenone-3, octocrylene, and octyl methoxycinnamate) and three antimicrobials (triclosan, triclocarban, and climbazole). The model generally performs well for the six case study chemicals as shown by the comparison between predictions and measurements. The importance of accounting for chemical ionization is demonstrated with the fate and partitioning of both triclosan and climbazole sensitivity to environmental pH. The model predicts ionizable chemicals (triclosan, climbazole, benzophenone-3) to primarily partition into soils at steady state, despite hypothetically only being released to freshwaters, as a result of agricultural irrigation by freshwater. However, further model calibration is needed when more field data becomes available for soils and sediments and for larger areas of water. As an example, accounting for the effect of pH in the environmental risk assessment of triclosan, limited freshwater areas (0.03% or ca. 55 km(2)) in mainland China are modeled to exceed its conservative environmental no-effect threshold. SESAMe v3.3 can be used to support the development of chemical risk assessment methodologies with the spatial aspects of the model providing a guide to the identification regions of interest in which to focus monitoring campaigns or develop a refined risk assessment.

In this study, a trait-based macroinvertebrate sensitivity modeling tool is presented that provides two main outcomes: (1) it constructs a macroinvertebrate sensitivity ranking and, subsequently, a predictive trait model for each one of a diverse set of predefined Modes of Action (MOAs) and (2) it reveals data gaps and restrictions, helping with the direction of future research. Besides revealing taxonomic patterns of species sensitivity, we find that there was not one genus, family, or class which was most sensitive to all MOAs and that common test taxa were often not the most sensitive at all. Traits like life cycle duration and feeding mode were identified as important in explaining species sensitivity. For 71% of the species, no or incomplete trait data were available, making the lack of trait data the main obstacle in model construction. Research focus should therefore be on completing trait databases and enhancing them with finer morphological traits, focusing on the toxicodynamics of the chemical (e.g., target site distribution). Further improved sensitivity models can help with the creation of ecological scenarios by predicting the sensitivity of untested species. Through this development, our approach can help reduce animal testing and contribute toward a new predictive ecotoxicology framework.