Kari Tuppurainen

Effect of catalysts and chlorine source on the formation of organic chlorinated compounds

Abstract Two series of catalyzed incineration tests were performed in a 32 kW laboratory pilot plant to study the effect of metal catalysts on the formation of chlorobenzenes, chlorophenols and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). In the first series, iron(III), tin(II) and copper(II) chlorides were added to the flame as catalysts and the combustion parameters were adjusted for optimum am[ incomplete combustion. The aim was to study the distribution of organic chlorinated compounds between particles and the gas phase in the flue gases when the combustion parameters were varied. Concentrations of particle-bound PCDD/Fs were high in the incomplete combustion tests, and those in the gas phase were still high even in the optimum combustion tests. In the second test series, copper(II), iron(III) and manganese (II) nitrates were added to the flame. Sodium chloride, representing inorganic chlorine, and tetrachloroethylene representing organic chlorine, were used as chlorine sources in the basic fuel with the different catalysts. Metals and chlorine accounted for 0.5 weight-% of the total fuel flow. The basic fuel used in these investigations was an aliphatic liquid free of aromatic hydrocarbons. Organic chlorine with the catalyst promoted the formation of particle-bound PCDD/Fs, whereas inorganic chlorine was observed to promote the formation of PCDD/Fs more effectively in the gas phase than in the particle phase. Consequently, different mechanisms of formation may be involved in the gas phase and on particle surfaces.

Binding of some dioxins and dibenzofurans to the Ah receptor. A QSAR model based on comparative molecular field analysis (CoMFA)

Abstract Based on comparative molecular field analysis (CoMFA), a quantitative structure-activity relationship, model for the cytosolic aryl hydrocarbon (Ah) receptor binding affinity of 50 chlorinated dibenzo-para-dioxin and dibenzofuran analogues is presented. The electronic structures of these compounds were calculated using the semiempirical AM1 method. Their extreme stabilities were rationalized using the frontier molecular orbital theory. The results indicate that their binding affinity was mainly influenced by steric and electrostatic interactions, whereas charge-transfer and hydrogen bonding mechanisms are probably of minor importance.

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure–activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure–activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.

Effect of urea on fly ash PCDD/F concentrations in different particle sizes

The effect of urea as an inhibitor for reducing polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/F) concentrations in flue gases was studied in a pilot scale plant, together with the effect on the particle size distribution of these compounds. Total PCDD/F concentrations decreased by a maximum of 74%, the decrease being greatest for the most highly (octa-) chlorinated isomers. The PCDD/F reduction affected all the particle size classes when an adequate amount of urea was used (1% of the fuel input), which indicates that inhibition, unlike formation, is independent of the fly ash particle size distribution.

On the electronic structure of MX compounds

Abstract The electronic properties of chlorofuranones including 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), one of the strongest mutagens, were studied using principal-component analysis of the AM1 data to elucidate the key features related to the high mutagenic activity of MX. The electronic structure of MX and its analogues is rationalized using the frontier electron and perturbational molecular orbital theories. Some general aspects of molecular mutagenicity and carcinogenicity are qualitatively discussed using the hard-soft acid-base principle.

Alternative QSAR models for selected estradiol and cytochrome P450 ligands: comparison between classical, spectroscopic, CoMFA and GRID/GOLPE methods

The performance of the spectroscopic EVA (eigenvalue) and EEVA (electronic eigenvalue) methods was tested with data sets applying coumarin 7-hydroxylation inhibitors (28 compounds) for cytochrome P450 mouse CYP2A5 and human CYP2A6 enzymes and 11β-, 16α-, and 17α-substituted estradiol derivatives (30 compounds) for the lamb uterine estrogen receptor, and compared with the performance of the classical Hansch-type, CoMFA and GRID/GOLPE methods. Besides the internal predictability, the external predictability of the models was tested with several randomized training and test sets to ensure the validity and reliability of the models. Partial least squares (PLS) regression was employed as a general statistical tool with the EVA and EEVA methods. Some supplementary models were also built using only one PLS component with McGowans volumes (MgVol and MgVol2) as additional descriptors and employing multiple linear regression (MLR) as the modelling tool. In general, both the internal and external performance of the EVA model, and more especially the EEVA model, with one PLS component and MgVol parameters was satisfactory, being either as good as or clearly better than that of the Hansch-type, CoMFA and GRID/GOLPE models.

Characterisation of the chemical and biological properties of molecules with QSAR/QSPR and chemical grouping, and its application to a group of alkyl ethers

This study presents a QSAR/QSPR modelling and chemical grouping (read-across) approach to provide information on the biological properties of a group of aliphatic ethers, with accurate biological predictions restricted to those physico-chemical and (eco)toxicological properties where the performance of QSAR/QSPR has been shown to be acceptable. The mathematical methods used ranged from multivariate regression models to PLS (partial least-squares), SVM (support vector machines) and Sammons mapping. A novel grouping approach, based on a set of key descriptors, has been proposed to give a compact picture of the structural and biological properties of the compounds, and to provide a more mechanistic basis for the interpretations of chemical groups. Besides being a straightforward case study, the paper also exemplifies the capabilities and limitations of the methods in predictive toxicology on a more general level.