Chlorophenols and alkylphenols: A review and correlation of environmentally relevant properties and fate in an evaluative environment

Shiu, Wan Ying; Ma, Kuo-Ching; Varhanickova, Dana; Mackay, Donald

doi:10.1016/0045-6535(94)90252-6

Cited by 148 publications

(80 citation statements)

References 177 publications

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“…All 20 values, including that for phenol, were collected from the literature [36,37] The second application for log K OC considers the situation when not all 20 experimental values of the chlorophenols are known. We found in the literature only 12 values for the soil sorption coefficient K OC for chlorophenols and phenol [38].…”

Section: Resultsmentioning

confidence: 99%

“…To apply this the experimental property values must be available for all diagram positions adjacent to B. For example, in Figure 2 we present the reaction poset diagram for chlorophenols, in which each vertex (compound) has attached the experimental value for log K ow [36,37]. The poset-average log K ow predicted value for 4-chlorophenol (4-ClP) is computed with the formula: …”

Section: Average-poset Modelmentioning

confidence: 99%

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Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Ivanciuc

Klein

2006

IJMS

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Due to their widespread use in bactericides, insecticides, herbicides, and fungicides, chlorophenols represent an important source of soil contaminants. The environmental fate of these chemicals depends on their physico-chemical properties. In the absence of experimental values for these physico-chemical properties, one can use predicted values computed with quantitative structure-property relationships (QSPR). As an alternative to correlations to molecular structure we have studied the super-structure of a reaction network, thereby developing three new QSSPR models (poset-average, clusterexpansion, and splinoid poset) that can be applied to chemical compounds which can be hierarchically ordered into a reaction network. In the present work we illustrate these poset QSSPR models for the correlation of the octanol/water partition coefficient (log K ow ) and the soil sorption coefficient (log K OC ) of chlorophenols. Excellent results are obtained for all QSSPR poset models to yield: log K ow , r = 0.991, s = 0.107, with the cluster-expansion QSSPR; and log K OC , r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPR models predict environmentally important properties of chlorophenols.

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Section: Resultsmentioning

confidence: 99%

Section: Average-poset Modelmentioning

confidence: 99%

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Ivanciuc

Klein

2006

IJMS

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“…First, chlorophenols generally have high water solubility and low volatility. Therefore, they would be prone to remain in areas close to sources (36). Halogenated phenols have not been reported in any Arctic biota (37).…”

Section: Evidence For Bioaccumulation and Metabolism Of Ocs As A Sourmentioning

confidence: 99%

Identification of 4-Hydroxyheptachlorostyrene in Polar Bear Plasma and Its Binding Affinity to Transthyretin: A Metabolite of Octachlorostyrene?

Sandau

Meerts

Letcher

et al. 2000

Environ. Sci. Technol.

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A new compound, 4-hydroxyheptachlorostyrene (4-OHHpCS), was identified as a major component in the chlorinated phenolic compound fraction of polar bear plasma. The structure was hypothesized to be 4-OH-HpCS based on mass spectral interpretation, the assumption that it was a metabolite of octachlorostyrene, and the similarity of the structure to hydroxylated polychlorinated biphenyls (OH-PCBs) identified in plasma. High-resolution, electron impact (EI) ionization mass spectrometry of the methylated compound indicated a molecular formula of C 9 H 3 OCl 7 and major fragment ions of [M -15] + , [M -35] + , and [M -43] + , which was a mass spectral pattern identical to a synthesized and methylated 4-OH-HpCS standard. The identity was further confirmed by matching gas chromatography (GC) retention times on three different GC columns of differing polarity. Levels of 4-OH-HpCS ranged from 2.89 to 22.9 ng/g wet weight in polar bear plasma (N ) 30) and constituted between 3.8 and 24.8% of the total quantified level of chlorinated phenolic compounds. The mean ratio of 4-OHHpCS to CB153 concentrations in polar bear plasma samples was 0.712 (( 0.580 SD), which suggests selective retention of the 4-OH-HpCS in plasma. The presumed mechanism of retention involves 4-OH-HpCS binding to transthyretin (TTR). The presence of TTR was confirmed for the first time in polar bear plasma by binding of 125 Ithyroxine (T 4 ), the natural ligand of TTR, to separated plasma proteins. The binding affinity of 4-OH-HpCS to human TTR was tested and found to be 1.1 relative to T 4 . This suggests that 4-OH-HpCS has the potential to disrupt T 4 and retinol transport, by analogy to OH-PCBs with similar structure. Metabolism of octachlorostyrene (OCS) is the most likely source of 4-OH-HpCS. OCS was shown to be present at low concentrations in polar bear tissues as well as in plasma of ringed seal, the principal prey species of polar bears. The ratio of 4-OH-HpCS to OCS and 4-OHHpCS to CB153 concentrations were 150-and 44-fold higher in polar bear plasma than in ringed seal plasma. This study indicates that the phenolic metabolites of relatively minor contaminants possess the capacity to bind to circulating proteins, and their significance as potential endocrine-disrupting agents may be underestimated.

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“…A third group of substances, which is difficult to assess in USES-LCA, are dissociating substances, such as chlorophenols. In a first approximation, the apparent solubility K ow and K oc were estimated following Bockting et al (1993) and Shiu et al (1994), and uptake of dissociating pesticides by plants was estimated on the basis of results of Briggs et al (1987). These approximations cause substantial uncertainty in the toxicity potentials of these substances.…”

Section: -     mentioning

confidence: 99%

Uncertainty and variability in environmental life-cycle assessment

Huijbregts

2002

Int J LCA

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General introductionIncreased rates of resource depletion, land use, solid waste generation and emissions of pollutants became issues of broad public concern in the beginning of the seventies (Meadows et al., 1972). Unfortunately, most of these problems are still with us today, and some of them have increased substantially (World Resources Institute, 1996). One way to decrease the environmental pressure of human activities is the environmentally improvement of production processes (Reijnders, 1996). In this respect, an important question to be answered is 'how exactly can we measure the environmentally improvement of production processes?' Environmental life-cycle assessment (LCA) aims to answer that question. The first section of this chapter provides an overview of the LCA framework. Although the framework itself is well-established, the application of the framework is in many aspects problematic. A brief overview of the problems associated with the actual implementation of the framework is described in second paragraph. Finally, the aim and the structure of the present thesis is outlined in the third paragraph. § 1.1 Environmental life cycle assessment Environmental life cycle assessment (LCA) is a tool for the assessment of the environmental impact of product systems (Heijungs et al. 1992). It considers the full life cycle of a product from resource extraction to waste disposal. LCA is generally divided into four phases: goal and scope definition, inventory analysis, impact assessment, and interpretation (Figure 1.1; ISO, 1997a).In the goal and scope definition, the aim and the subject of an LCA study are determined and a 'functional unit' is defined. An example of a functional unit is 'the consumption of 100 kg apples' with, for instance, the aim to compare the environmental impacts of different agricultural production processes of apples. In the inventory analysis, for each of the product systems considered data are gathered for all the relevant processes involved in the life cycle. A product system can be considered as a combination of processes needed for the functioning of a product or service. The outcome of the inventory analysis is a list of all extractions of resources and emissions of substances caused by the functional unit for every product system considered, generally disregarding place and time of the extractions and releases. The matrix method can be used to perform the inventory analysis (Heijungs, 1996(Heijungs, , 1997 where i is the vector of environmental interventions, H is the environmental intervention matrix, representing the extractions of resources and emissions of substances per unit process, G is the technology matrix representing the inter-process flows needed for the functioning of the product system, and u is the external supply vector, related to the functional unit. Life cycle impact assessment (LCIA) aims to improve the understanding of the inventory result. Firstly, it is determined which extractions and emissions contribute to which impact categories. An impact category can...

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Chlorophenols and alkylphenols: A review and correlation of environmentally relevant properties and fate in an evaluative environment

Cited by 148 publications

References 177 publications

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Identification of 4-Hydroxyheptachlorostyrene in Polar Bear Plasma and Its Binding Affinity to Transthyretin: A Metabolite of Octachlorostyrene?

Uncertainty and variability in environmental life-cycle assessment

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