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  • 1. Golsteijn, Laura
    et al.
    Iqbal, Muhammad Sarfraz
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Cassani, Stefano
    Hendriks, Harrie W. M.
    Kovarich, Simona
    Papa, Ester
    Rorije, Emiel
    Sahlin, Ullrika
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Huijbregts, Mark A. J.
    Assessing predictive uncertainty in comparative toxicity potentials of triazoles2014In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 33, no 2, p. 293-301Article in journal (Refereed)
    Abstract [en]

    Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species. (c) 2013 SETAC

  • 2.
    Iqbal, Muhammad Sarfraz
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Sahlin, Ullrika
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Treatment of Epistemic Uncertainty in Environmental Fate Models –Consequences on Chemical Safety Regulatory Strategies2012Conference paper (Other academic)
    Abstract [en]

    The practical impact of treatment of epistemic uncertainty on decision making wasillustrated on two kinds of decisions from chemical regulation. First, regulatory strategies derivedfrom a simplified decision model based on toxicity and persistence showed that regulated level ofexposure is more conservative (safe) when uncertainty has been given a non-probabilistictreatment. Persistence and its uncertainty had been assessed by a Level II fugacity model forwhich input parameters had been quantified either by Bayesian probabilities, fuzzy numbers(non-probabilistic), or combinations of these (probability boxes). These findings are restricted tohow we let decision makers respond to uncertainty in model predictions by the chosen set ofdecision rules. Further, the use of either treatment depends on the quality and quantity ofbackground knowledge and the required level of detail on the assessment. In the absence ofexperimentally tested physicochemical endpoints, European chemical regulation REACH allowsthe use of non-testing strategies such as Quantitative Structure-Property Relationships (QSPR) topredict the required information. The second decision problem was to select which chemicalsubstances to prioritize for experimental testing in order to strengthen the background knowledgefor chemical regulation with respect to the uncertainty in QSPR predictions. We found that thevalue of reducing uncertainty, given by the expected gain in net benefit for society, was affectedby its treatment and there were no consistent order of testing of the three compounds. However,value of information is a Bayesian probabilistic approach that, unless developed further, loose itsinterpretability under other treatments of uncertainty. The framework of a predictive model, riskmodel, decision model and value of information analysis provides a computational template forfurther evaluation of the effect of treatment of uncertainty on decision making.

    Download full text (pdf)
    Treatment of Epistemic Uncertainty in Environmental Fate Models
  • 3.
    Iqbal, Muhammad Sarfraz
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Environmental Modeling and Uncertainty2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Environmental fate models are used to evaluate the fate and effects of chemicals for risk assessment. Fate models may be effective and low-cost substitutes for field measurements and are helpful to project future scenarios. Environmental models describe processes for chemical fate largely determined by environmental and chemical-specific parameters. There is uncertainty in such input parameters arising from lack of knowledge and inherent variability in environmental processes.

         The objectives of this thesis are to demonstrate and evaluate ways to quantify, implement, and reduce uncertainty in chemical-specific input parameters and  in the process to improve the overall treatment of uncertainty in environmental modeling. The methods to treat uncertainty were combinations of multimedia environmental modeling, the use of testing and non-testing information in risk assessment, and probabilistic and non-probabilistic measures of uncertainty.

        This thesis contains six case studies related to chemical safety assessment which illustrate different aspects of treatment of uncertainty in a regulatory context. Dependent on nature of uncertainty and the available information, the approaches to treat uncertainty were probabilistic, non-probabilistic or combinations of these. Some case studies were put into the perspective to support chemical regulation under REACH. In three studies, the contribution of uncertainty in input parameters was evaluated on characteristics of uncertainty in assessed persistence, long-range transport potential and comparative toxicity potentials of chemicals in the environment. In other studies, the focus was on decision making such as prioritization of chemicals for risk assessment and the need for further testing to reduce input uncertainty.

         The main contributions are useful applications of a broader treatment of uncertainty in environmental modeling that address gaps and quality in available background knowledge.  Epistemic uncertainty is treated by filling knowledge gaps using non-testing information from QSARs. Uncertainty in non-testing information is given a probabilistic treatment based on statistical principles of inference. Poor quality of background knowledge, such as sparse data or low confidence in individual QSAR predictions, is treated by non-probabilistic measures. Finally, the suggested treatments of uncertainty are implemented and evaluated in the context of chemical risk assessment. 

  • 4.
    Iqbal, Muhammad Sarfraz
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Golsteijn, Laura
    Öberg, Tomas
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Sahlin, Ullrika
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Papa, Ester
    Kovarich, Simona
    Huijbregts, Mark A. J.
    The influence of uncertainty in quantitative structure-property relationships on persistence and long-range transport potential: the case of polybrominated diphenyl ethers (PBDEs)2012Conference paper (Other academic)
  • 5.
    Iqbal, Muhammad Sarfraz
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Golsteijn, Laura
    Radboud University.
    Öberg, Tomas
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Sahlin, Ullrika
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Papa, Ester
    Insubria University.
    Kovarich, Simona
    Insubria University.
    Huijbregts, Mark A. J.
    Radboud University.
    Understanding quantitative structure-property relationships uncertainty in environmental fate modeling2013In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 32, no 5, p. 1069-1076Article in journal (Refereed)
    Abstract [en]

    In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure–property relationships (QSPRs). Such predictions are often given as point estimates; however, little is known about the extent to which uncertainties associated with QSPR predictions contribute to uncertainty in fate assessments. In the present study, QSPR-induced uncertainty in overall persistence (POV) and long-range transport potential (LRTP) was studied by integrating QSPRs into probabilistic assessments of five polybrominated diphenyl ethers (PBDEs), using the multimedia fate model Simplebox. The uncertainty analysis considered QSPR predictions of the fate input parameters' melting point, water solubility, vapor pressure, organic carbon–water partition coefficient, hydroxyl radical degradation, biodegradation, and photolytic degradation. Uncertainty in POV and LRTP was dominated by the uncertainty in direct photolysis and the biodegradation half-life in water. However, the QSPRs developed specifically for PBDEs had a relatively low contribution to uncertainty. These findings suggest that the reliability of the ranking of PBDEs on the basis of POV and LRTP can be substantially improved by developing better QSPRs to estimate degradation properties. The present study demonstrates the use of uncertainty and sensitivity analyses in nontesting strategies and highlights the need for guidance when compounds fall outside the applicability domain of a QSPR.

  • 6.
    Iqbal, Muhammad Sarfraz
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Öberg, Tomas
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Description and Propagation of Uncertainty in Input Parameters in Environmental Fate Models2013In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 33, no 7, p. 1353-66Article in journal (Refereed)
    Abstract [en]

    Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound-related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five-compartment Level I fugacity model and reported empirical data from the literature for three well-known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.

  • 7.
    Iqbal, Muhammad Sarfraz
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Öberg, Tomas
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Uncertainty in fugacity-based multimedia modeling: probabilistic and non-probabilistic methods2011In: Posterpresentation vid SETAC Europe 21st Annual Meeting i Milano, 17-19 maj, 2011, 2011Conference paper (Other academic)
  • 8.
    Sahlin, Ullrika
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Golsteijn, Laura
    Iqbal, Muhammad Sarfraz
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Peijnenburg, Willie
    Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments2013In: ATLA (Alternatives to Laboratory Animals), ISSN 0261-1929, Vol. 41, no 1, p. 91-110Article in journal (Refereed)
    Abstract [en]

    Chemical regulation allows non-in vivo testing (i.e. in silico-derived and in vitro-derived) information to replace experimental values from in vivo studies in hazard and risk assessments. Although non-in vitro testing information on chemical activities or properties is subject to added uncertainty as compared to in vivo testing information, this uncertainty is commonly not (fully) taken into account. Considering uncertainty in predictions from quantitative structure-activity relationships (QSARs), which are a form of non-in vivo testing information, may improve the way that QSARs support chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system. We argue that it is useful to consider uncertainty in QSAR predictions, as it: a) supports rational decision-making; b) facilitates cautious risk management; c) informs uncertainty analysis in probabilistic risk assessment; d) may aid the evaluation of QSAR predictions in weight-of-evidence approaches; and e) provides a probabilistic model to verify the experimental data used in risk assessment. The discussion is illustrated by using case studies of QSAR integrated hazard and risk assessment from the EU-financed CADASTER project.

  • 9.
    Öberg, Tomas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Iqbal, Muhammad Sarfraz
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    The chemical and environmental property space of REACH chemicals2012In: Chemosphere, ISSN 0045-6535, E-ISSN 1879-1298, Vol. 87, no 8, p. 975-981Article in journal (Refereed)
    Abstract [en]

    The European regulation on chemicals, REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals), came into force on 1 June 2007. With pre-registration complete in 2008, data for these substances may provide an overview of the expected chemical space and its characteristics. In this paper, using various in silico computation tools, we evaluate 48782 neutral organic compounds from the list to identify hazardous and safe compounds. Two different classification schemes (modified Verhaar and ECOSAR) identified between 17% and 25% of the compounds as expressing only baseline toxicity (narcosis). A smaller portion could be identified as reactive (19%) or specifically acting (2.7%), while the majority were non-assigned (61%). Overall environmental persistence, bioaccumulation and long-range transport potential were evaluated using structure-activity relationships and a multimedia fugacity-based model. A surprisingly high proportion of compounds (20%), mainly aromatic and halogenated, had a very high estimated persistence (> 195 d). The proportion of compounds with a very high estimated bioconcentration or bioaccumulation factor (> 5000) was substantially less (6.9%). Finally, a list was compiled of those compounds within the applicability domain of the models used, meeting both persistence and bioaccumulation criteria, and with a long-range transport potential comparable to PCB. This list of 68 potential persistent organic pollutants contained many well-known compounds (all halogenated), but notably also five fluorinated compounds that were not included in the EINECS inventory. This study demonstrates the usability of in silico tools for identification of potentially environmentally hazardous chemicals.

  • 10.
    Öberg, Tomas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    Iqbal, Muhammad Sarfraz
    Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
    The REACH space of organic chemistry and hazard properties.2011In: Presentation vid 6th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (CMTPI-2011) i Maribor, 3-7 september, 2011., 2011Conference paper (Other academic)
1 - 10 of 10
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