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The European REACH legislation accepts the use of non-testing methods, such as QSARs, to inform chemical risk assessment. In this paper, we aim to initiate a discussion on the characterization of predictive uncertainty from QSAR regressions. For the purpose of decision making, we discuss applications from the perspective of applying QSARs to support probabilistic risk assessment. Predictive uncertainty is characterized by a wide variety of methods, ranging from pure expert judgement based on variability in experimental data, through data-driven statistical inference, to the use of probabilistic QSAR models. Model uncertainty is dealt with by assessing confidence in predictions and by building consensus models. The characterization of predictive uncertainty would benefit from a probabilistic formulation of QSAR models (e.g. generalized linear models, conditional density estimators or Bayesian models). This would allow predictive uncertainty to be quantified as probability distributions, such as Bayesian predictive posteriors, and likelihood-based methods to address model uncertainty. QSAR regression models with point estimates as output may be turned into a probabilistic framework without any loss of validity from a chemical point of view. A QSAR model for use in probabilistic risk assessment needs to be validated for its ability to make reliable predictions and to quantify associated uncertainty.

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A great deal of research has been devoted to the characterization of metal exposure due to the consumption of vegetables from urban or industrialized areas. It may seem comforting that concentrations in crops, as well as estimated exposure levels, are often found to be below permissible limits. However, we show that even a moderate increase in metal accumulation in crops may result in a significant increase in exposure. We also highlight the importance of assessing exposure levels in relation to a regional baseline. We have analyzed metal (Pb, Cd, As) concentrations in nearly 700 samples from 23 different vegetables, fruits, berries and mushrooms, collected near 21 highly contaminated industrial sites and from reference sites. Metal concentrations generally complied with permissible levels in commercial food and only Pb showed overall higher concentrations around the contaminated sites. Nevertheless, probabilistic exposure assessments revealed that the exposure to all three metals was significantly higher in the population residing around the contaminated sites, for both low-, median- and high consumers. The exposure was about twice as high for Pb and Cd, and four to six times as high for As. Since vegetable consumption alone did not result in exposure above tolerable intakes, it would have been easy to conclude that there is no risk associated with consuming vegetables grown near the contaminated sites. However, when the increase in exposure is quantified, its potential significance is harder to dismiss - especially when considering that exposure via other routes may be elevated in a similar way.