Reliable QSAR for estimating Koc for persistent organic pollutants: correlation with molecular connectivity indices

Prediction models on biomass and yield of rice affected by metal (oxide) nanoparticles using nano-specific descriptors

The use of in silico tools to investigate the interactions between metal (oxide) nanoparticles (NPs) and plant biological responses is preferred because it allows us to understand molecular mechanisms and improve prediction efficiency by saving time, labor, and cost. In this study, four models (C5.0 decision tree, discriminant function analysis, random forest, and stepwise multiple linear regression analysis) were applied to predict the effect of NPs on rice biomass and yield. Nano-specific descriptors (size-dependent molecular descriptors and image-based descriptors) were introduced to estimate the behavior of NPs in plants to appropriately represent the wide space of NPs. The results showed that size-dependent molecular descriptors (e.g., E-state and connectivity indices) and image-based descriptors (e.g., extension, area, and minimum ferret diameter) were associated with the behavior of NPs in rice. The performance of the constructed models was within acceptable ranges (correlation coefficient ranged from 0.752 to 0.847 for biomass and from 0.803 to 0.905 for yield, while the accuracy ranged from 64% to 77% for biomass and 81% to 89% for yield). The developed model can be used to quickly and efficiently evaluate the impact of NPs under a wide range of experimental conditions and sufficient training data.

QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices

Octanol-water partition coefficient (logK_ow) and soil organic carbon content normalized sorption coefficient (logK_oc) values are two important physicochemical properties in the context of bioaccumulation and environmental fate of organic compounds and their environmental risk assessment. Simple, interpretable and easy-to-derive extended topochemical atom (ETA) indices obtained from 2D structural representation of compounds were used for quantitative structure-property relationship (QSPR) modeling of these two endpoints. Linear regression based models developed using only ETA indices show encouraging statistical and validation results. Based on the information obtained from developed QSPR models, we may conclude that molecular volume, branching pattern, presence of hydrophobic Cl atoms, cyclicity/fusion, polar environment, electron density, unsaturation content, hydrogen bonding propensity or hydrogen bond donor atoms, local topology, presence of heteroatoms and aromaticity are crucial factors in controlling the logK_ow and logK_oc values of the compounds. The suggested explanatory features for different classes of chemicals or the whole diverse set can help in safer designing of chemicals, which is one of the primary agenda of the "Green Chemistry" program.

Pharmaceutical and Personal Care Products: From Wastewater Treatment into Agro-Food Systems

Irrigation with treated wastewater (TWW) and application of biosolids introduce numerous pharmaceutical and personal care products (PPCPs) into agro-food systems. While the use of TWW and biosolids has many societal benefits, introduction of PPCPs in production agriculture poses potential food safety and human health risks. A comprehensive risk assessment and management scheme of PPCPs in agro-food systems is limited by multiple factors, not least the sheer number of investigated compounds and their diverse structures. Here we follow the fate of PPCPs in the water-soil-produce continuum by considering processes and variables that influence PPCP transfer and accumulation. By analyzing the steps in the soil-plant-human diet nexus, we propose a tiered framework as a path forward to prioritize PPCPs that could have a high potential for plant accumulation and thus pose greatest risk. This article examines research progress to date and current research challenges, highlighting the potential value of leveraging existing knowledge from decades of research on other chemicals such as pesticides. A process-driven scheme is outlined to derive a short list that may be used to refocus our future research efforts on PPCPs and other analogous emerging contaminants in agro-food systems.

Distribution of toxicity values across different species and modes of action of pesticides from PESTIMEP and PPDB databases

The continuous use of compounds contained in commodities such as processed food, medicines, and pesticides, demands safety measures, in particular, for those in direct contact with humans and the environment. Safety measures have evolved and regulations are now in place around the globe. In the case of pesticides, attempts have been made to use toxicological data to inform of potentially harmful compounds either across species, on different routes of exposure, or entirely new chemicals. The generation of models, based on statistical and molecular modeling studies, allows for such predictions. However, the use of these models is framed by the available data, the experimental errors, the complexity of the measurement, and the available computational algorithms, among other factors. In this work, we present the methodologies used for extrapolation across different species and routes of administration and show the appropriateness of developing predictive models of pesticides based on their type and mode of action. The analyses include comparisons based on structural characteristics and physicochemical properties. Whenever possible, the scope and limitations of the methodologies are discussed. We expect that this work will serve as a useful introductory guide of the tools employed in the toxicity assessment of agrochemical compounds.

QSPR modeling of the logKow and logKoc of polymethoxylated, polyhydroxylated diphenyl ethers and methoxylated-, hydroxylated-polychlorinated diphenyl ethers

In the present study, the structural parameters of 209 types of polymethoxylated diphenyl ethers (PMeODEs), 209 types of polyhydroxylated diphenyl ethers (PHODEs), seven types of methoxylated-polychlorinated diphenyl ethers (MeO-PCDEs) and seven types of hydroxylated-polychlorinated diphenyl ethers (HO-PCDEs) were calculated using the Gaussian 09 program at the B3LYP/6-311G** level. Using structural and positional parameters as descriptors, quantitative structure-property relationships (QSPR) models for the prediction of n-octanol/water partition coefficient (logK_ow) and soil sorption coefficient normalized to organic carbon (logK_oc) were established and verified. The position parameters N₂₍₆₎, N₃₍₅₎ and N₄ were the main positional factors influencing logK_ow and logK_oc of PMeODEs and PHODEs. The molecular polarizability α was entered into the QSPR models of the logK_ow and logK_oc of PMeODEs, PHODEs and MeO/HO-PCDEs, indicating that the molecular volume could influence the two environment-related properties of DEs significantly. All of the established QSPR models showed good goodness-of-fit, robustness, and predictive ability. The two models for all of the tested DEs are slightly inferior compared with the models for only a class of compounds. In addition, application domain analysis indicated that the models reliably predicted the logK_ow and logK_oc of the mon- to hexa-DEs.

A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors

The soil sorption partition coefficient logK_oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK_oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK_oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls

Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

QSAR models for degradation of organic pollutants in ozonation process under acidic condition

Although some researches about the degradation of organic pollutants have been carried out during recent years, reaction rate constants are available only for homologue compounds with similar structures or components. Therefore, it is of great significance to find a universal relationship between reaction rate and certain parameters of several diverse organic pollutants. In this study, removal ratio and kinetics of 33 kinds of organic substances were investigated by ozonation process, including azo dyes, heterocyclic compounds, ionic compounds and so on. Most quantum chemical parameters were conducted by using Gaussian 09 at the DFT B3LYP/6-311G level, including μ, q H(+), q(C)minq(C)max, ELUMO and EHOMO. Other descriptors, bond order (BO) as well as Fukui indices (f(+), f(-) and f(0)), were calculated by Material Studio 6.1 at Dmol(3)/GGA-BLYP/DNP(3.5) basis for each organic compound. The recommended model for predicting rate constants was lnk'=1.978-95.484f(0)x-3.350q(C)min+38.221f(+)x, which had the squared regression coefficient R(2)=0.763 and standard deviation SD=0.716. The results of t test and the Fisher test suggested that the model exhibited optimum stability. Also, the model was validated by internal and external validations. Recommended QSAR model showed that the highest f(0) value of main-chain carbons (f(0)x) is more closely related to lnk' than other quantum descriptors.

Molecular properties affecting the adsorption coefficient of pesticides from various chemical families

Forty pesticides were selected in function of their chemical families and their physico-chemical properties to represent a wide range of pesticide properties. Adsorption of these pesticides was studied on two soils by batch experiments. The two soils differed largely in organic matter and calcite contents. Distribution coefficient Kd was determined for each pesticide on the two soils. Adsorption was higher for the soil having the highest organic matter content and the lowest calcite content. In order to identify pesticide properties governing retention, eight molecular descriptors were determined from three-dimensional (3D) structure of molecules. Class-specific quantitative structure properties relationship (QSPR) soil adsorption models using one and two parameters were developed from experimental Kd. Three properties seemed to influence most retention of pesticides: hydrophobicity, solubility, and polarisability. Models combining these properties were suggested and discussed.

Molecular properties affecting the adsorption coefficient of phenylurea herbicides

The adsorption of 12 pesticides of the phenylurea family was studied by batch experiments in order to determine the adsorption coefficient, K d. The study was conducted in two soils chosen for their differences in organic matter and calcite contents. K d pesticide adsorption coefficients were higher for soil S1 than for soil S2 due to the presence of a higher organic matter content and a lower calcite content in soil S1. To identify pesticide properties governing retention, 18 molecular descriptors were considered. Class-specific quantitative structure-property relationship (QSPR) soil sorption models using one, two, and three descriptors were developed from our experimental data using linear regressions. One of the aims of this work was to check whether QSPR models that did not include literature values of K ow were able to predict K d coefficients in satisfactory agreement with our experimental data. The influence of the level of theory in determining K ow and polarisability predictors on the predictive performance of the model was also examined by comparing quantum chemistry and empirical (QikProp) approaches. The one-descriptor model using "quantum" polarisability α was found to perform almost as well as or better than the other models.

Sorption behavior of 17 phthalic acid esters on three soils: effects of pH and dissolved organic matter, sorption coefficient measurement and QSPR study

This work studies the sorption behaviors of phthalic acid esters (PAEs) on three soils by batch equilibration experiments and quantitative structure property relationship (QSPR) methodology. Firstly, the effects of soil type, dissolved organic matter and pH on the sorption of four PAEs (DMP, DEP, DAP, DBP) are investigated. The results indicate that the soil organic carbon content has a crucial influence on sorption progress. In addition, a negative correlation between pH values and the sorption capacities was found for these four PAEs. However, the effect of DOM on PAEs sorption may be more complicated. The sorption of four PAEs was promoted by low concentrations of DOM, while, in the case of high concentrations, the influence of DOM on the sorption was complicated. Then the organic carbon content normalized sorption coefficient (logKoc) values of 17 PAEs on three soils were measured, and the mean values ranged from 1.50 to 7.57. The logKoc values showed good correlation with the corresponding logKow values. Finally, two QSPR models were developed with 13 theoretical parameters to get reliable logKoc predictions. The leave-one-out cross validation (CV-LOO) indicated that the internal predictive power of the two models was satisfactory.

Linear and non-linear relationships between soil sorption and hydrophobicity: model, validation and influencing factors

The hydrophobic parameter represented by the octanol/water partition coefficient (logP) is commonly used to predict the soil sorption coefficient (K(oc)). However, a simple non-linear relationship between logK(oc) and logP has not been reported in the literature. In the present paper, soil sorption data for 701 compounds was investigated. The results show that logK(oc) is linearly related to logP for compounds with logP in the range of 0.5-7.5 and non-linearly related to logP for the compounds in a wide range of logP. A non-linear model has been developed between logK(oc) and logP for a wide range of compounds in the training set. This model was validated in terms of average error (AE), average absolute error (AAE) and root-mean squared error (RMSE) by using an external test set with 107 compounds. Nearly the same predictive capacity was observed in comparison with existing models. However, this non-linear model is simple, and uses only one parameter. The best model developed in this paper is a non-linear model with six correction factors for six specific classes of compounds. This model can well predict logK(oc) for 701 diverse compounds with AAE = 0.37. The reasons for systemic deviations in these groups may be attributed to the difference of sorption mechanism for hydrophilic/polar compounds, low solubility for highly hydrophobic compounds, hydrolysis of esters in solution, volatilization for volatile compounds and highly experimental errors for compounds with extremely high or low sorption coefficients.

Determination of the soil-water partition coefficients (logK(OC)) of some mono- and poly-substituted phenols by reversed-phase thin-layer chromatography

In order to determine the soil-water partition coefficient for eleven mono- and poly-substituted phenolic compounds, for which there is still no literature data available, the possibility of using thin-layer chromatography (TLC) as a means for rapid and reliable logK(OC) estimation was examined. A series of chromatographically derived descriptors: R(M)(0), b, C(0) and PC1 (first principal component), calculated from retention data obtained under reversed-phase conditions, were used for the assessment of models as well as for a direct calibration procedure. The final calibration models are discussed with regard to the achieved accuracy and statistical quality, the type of descriptors used and the corresponding chromatographic conditions. The estimated logK(OC) values of the studied phenols were compared with those obtained by other means: (a) the present OECD guideline based on an HPLC technique; (b) the KOCWIN software package, available free of charge from the US Environmental Protection Agency web site and (c) general LSER models established by Nguyen and coworkers, and Poole and coworkers. The proposed method showed the best agreement with the results obtained by the OECD procedure, followed by the LSER models of Poole and Nguyen. Lower quality correlations were achieved with the KOCWIN calculated values, especially those predicted by molecular connectivity indices.

Phenanthrene and 2,2',5,5'-PCB sorption by several soils from methanol-water solutions: the effect of weathering and solute structure

The effect of the sorption of phenanthrene and 2,2',5,5'-polychlorinated biphenyl (PCB52) by five differently weathered soils were measured in water and low methanol volume fraction (f(c)0.5) as a function of the apparent solution pH (pH(app)). Two weathered oxisols (A2 and DRC), and moderately weathered alfisols (Toronto) and two young soils (K5 and Webster) were used. The K(m) (linear sorption coefficient) values, which log-linearly decreases with f(c), were interpreted using a cosolvency sorption model. For phenanthrene sorption at the natural pH, the empirical constant (alpha) ranged between 0.95 and 1.14, and was in the order of oxisols (A2 and DRC)<alfisols (Toronto)<young soils (K5 and Webster). Smaller alpha values for highly weathered soils are indicative of smaller solute sorption reduction than those predicted from the increment of the solute's activity coefficient in the solution phase. A similar trend was observed for PCB52 sorption. The K(m) values measured at the range of pH 3-7 also showed an inversely log-linear relationship. The regression slope (alphasigma) calculated from the cosolvency sorption model as a function of pH(app) only varied within <5%, with the exception for phenanthrene sorption by two highly weathered soils, which had 10% greater alphasigma values obtained at acidic pH(app). This phenomenon is a result of the greater acid enhancement effect on phenanthrene sorption by the oxisols, which is reduced with increasing f(c). These results revealed an unexplored relationship between the cosolvent effect on the sorption and the properties of the soil organic matter (a primary sorption domain) as a function of the degree of soil weathering.

Desorption of sediment-associated polychlorinated dibenzo-p-dioxins, dibenzofurans, diphenyl ethers and hydroxydiphenyl ethers from contaminated sediment

The transport and bioavailability of sediment-associated contaminants are often controlled by the contaminants' desorbing behaviour. This study examines the desorption kinetics of polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), diphenyl ethers (PCDEs) and hydroxydiphenyl ethers (HO-PCDEs) from the highly contaminated River Kymijoki sediment in Finland. The desorption kinetics data were generated using Tenax((R)) extraction, and a first-order three-compartment kinetic model was fitted to the data. The desorption data was compared to the previously published accumulation data from this same location to investigate the relationship between the rapidly desorbing fraction (F(r)) and biota-sediment accumulation factors (BSAFs) as well as semipermeable membrane device sediment accumulation factors (SSAFs). The PCDDs, PCDFs, PCDEs and HO-PCDEs were tightly attached to sediment particles and formed a large very slowly desorbing fraction (F(vs)). Rapidly desorbing fractions (F(r)) varied between 0.8% and 8% of total amount in sediment. The size of the desorbing fraction was congener-specific and F(r) decreased with the increasing lipophilicity of congeners. The size of the F(r) was unable to explain the small variation in the BSAFs of Lumbriculus variegatus but may help to explain the observed variation in the SSAFs. To our best knowledge, this study is the first effort to investigate the desorption of PCDDs, PCDFs, PCDEs and HO-PCDEs in field-contaminated sediments. The major finding that the very slow desorption of these chemicals will continue years, provides essential information for the modern risk assessment process.

Estimation of gas-phase reaction rate constants of alkylnaphthalenes with chlorine, hydroxyl and nitrate radicals

Quantitative structure-property relationship/quantitative structure-activity relationship (QSPR/QSAR) models were developed for rate constants (k) of alkylnaphthalene reactions with chlorine (Cl), hydroxyl (*OH) and nitrate (NO(3)) radicals using partial least squares (PLS) regression. Quantum chemical descriptors computed by Parametric Method 3 (PM3) Hamiltonian were used as predictor variables. The cross-validated Q(cum)(2)values for the optimal QSPR/QSAR models of alkylnaphthalenes are 0.896, 0.728 and 0.774 for Cl, *OH and NO(3) radicals, respectively. Results from this study showed that rate constants with Cl, *OH and NO(3) are governed by different molecular structural descriptors. In the developed optimal QSPR/QSAR models, frontier molecular orbital energies and atomic charges are major descriptors that affect log k values. When the highest occupied molecular orbital (E(HOMO)) energy, the lowest unoccupied molecular orbital (E(LUMO)) energy, E(LUMO)+E(HOMO), and the average of net atomic charges on carbon atoms (Q(Cave)) are higher, the corresponding alkylnaphthalene reaction rate constants would be higher. In contrast, higher values of the most positive net atomic charges on hydrogen atoms (Q(H)(+)) could lead to the decrease of log k values. H-atom abstraction may occur on the hydrogen atom with the highest atomic charge (Q(H)). Radical addition reaction may also occur on the carbon (e.g. the carbon bonded to the alkyl group) with higher atomic charge (Q(C)) values. Other descriptors such as molecular weight (M(w)), standard heat of formation (DeltaH(f)), total energy (TE), electronic energy (EE), core-core repulsion energy (CCR), average molecular polarizability (a) and dipole moment (mu) were also important descriptors in the QSPR/QSAR models.

Statistical external validation and consensus modeling: a QSPR case study for Koc prediction

The soil sorption partition coefficient (log K(oc)) of a heterogeneous set of 643 organic non-ionic compounds, with a range of more than 6 log units, is predicted by a statistically validated QSAR modeling approach. The applied multiple linear regression (ordinary least squares, OLS) is based on a variety of theoretical molecular descriptors selected by the genetic algorithms-variable subset selection (GA-VSS) procedure. The models were validated for predictivity by different internal and external validation approaches. For external validation we applied self organizing maps (SOM) to split the original data set: the best four-dimensional model, developed on a reduced training set of 93 chemicals, has a predictivity of 78% when applied on 550 validation chemicals (prediction set). The selected molecular descriptors, which could be interpreted through their mechanistic meaning, were compared with the more common physico-chemical descriptors log K(ow) and log S(w). The chemical applicability domain of each model was verified by the leverage approach in order to propose only reliable data. The best predicted data were obtained by consensus modeling from 10 different models in the genetic algorithm model population.

A topological substructural molecular design to predict soil sorption coefficients for pesticides

A TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach was used to predict the soil sorption coefficients for a set of pesticide compounds. The obtained model accounted for more than 85% of the data variance and demonstrated the importance of the dipole moment, the standard distance, the polarizability, and the hydrophobicity in describing the property under study. In addition, we compared this new model to a previous one using different descriptors such as WHIM and molecular connectivity indices. Finally, the TOPS-MODE was used to calculate the contribution of different fragments to the soil sorption coefficient of the compounds studied. The present approximation proved to be a good method for studying the soil sorption coefficient for pesticides, but it could also be extended to other series of chemicals.

QSPR study on soil sorption coefficient for persistent organic pollutants

Quantitative structure-property relationship (QSPR) models of soil sorption coefficients for 32 persistent organic pollutants were constructed using our recently introduced Lu index and novel distance-based atom-type DAI topological indices. Using multiple linear regression technique, a 6-variable model was obtained with the correlation coefficient of estimations (R) being 0.95, and the standard error of estimations (s) being 0.23, and the correlation coefficient (R(cv)) and the standard error (s(cv)) in the leave-4-out cross-validation procedure are 0.90 and 0.31, respectively. The results in this study indicate that soil sorption coefficients of POPs are dominated by molecular size while some DAI indices have smaller influence.

Sorption of naphthoic acids and quinoline compounds to estuarine sediment

The sorption of 16 ionizable organic compounds (IOCs) to an estuarine sediment was measured in synthetic estuarine water as a function of IOC concentration (1-100 microM) at fixed ionic strength (0.4 M), pH (7.6), and sediment concentration (0.018 g sediment kg(-1) suspension). Of the 16 IOCs, 11 were naphthoic acids and five were quinoline compounds. The linear sorption distribution coefficient (Kd) was used to correlate sorption to IOC physicochemical and molecular characteristics. With respect to naphthoic acid, sorption increased with the addition of ortho-substituent groups and with increasing chain length of the 1-acid group, and the greatest increase occurred with ortho-hydroxyl, carbonyl, and carboxyl groups. With respect to quinoline, sorption decreased with substituent group addition (except for nitro group) and with additional heterocyclic N atoms. For the naphthoic acids, log Kd exhibited a positive correlation with water solubility (log Sw) indicative of sorption primarily to mineral surfaces under the solution chemistry. For the quinoline compounds, log Kd exhibited a negative correlation with log Sw and a positive correlation with n-octanol/water partition coefficient (log K(OW)) indicative of sorption primarily to organic matter. For both compounds, poor or no correlations were established between log Kd and acid dissociation constant (pKa1), and between log Kd and a variety of molecular connectivity indexes. The results from this study demonstrate that the sorption of IOCs differ depending on their backbone structure and may differ between parent compound and ionizable degradation product.

QSAR analysis of soil sorption coefficients for polar organic chemicals: substituted anilines and phenols

Based on descriptors of n-octanol/water partition coefficients (logKow), molecular connectivity indices, and quantum chemical parameters, several QSAR models were built to estimate the soil sorption coefficients (logKoc) of substituted anilines and phenols. Results showed that descriptor logKow plus molecular quantum chemical parameters gave poor regression models. Further study was performed to improve the QSAR model by using artificial neural networks (ANNs). It showed that ANN model with suitable network architecture could make a better agreement between predicted and measured values of the soil sorption coefficients. The quality of the QSAR models confirmed the suitability of ANN to predict the soil sorption coefficients for polar organic chemicals of substituted anilines and phenols.

Fuzzy QSARs for predicting logKoc of persistent organic pollutants

Fuzzy regression methodology has been employed in this study to develop a relationship for logKoc for persistent organic pollutants (POPs) using other property and molecular descriptors. Fuzzy regression is distinct from statistical regression and is used to characterize the imprecision arising from limited data and/or incomplete model descriptions. The study is based on the premise that statistically based QSARs do not fully account for all the sorbate-sorbent interactions pertinent to the partitioning of POPs and as such these relationships have inherent fuzziness associated with them. A comparison between the statistical and fuzzy logKow-logKoc relationship indicated that the fuzzy regression model enveloped all scatter in the data and provided a tighter fit around the mid-point values (least-square estimates). In addition, fuzzy regression was also employed to characterize imprecision associated with a three parameter QSAR that employs molecular connectivity indicies. A comparison between fuzzy and statistical regression analysis indicated that the fuzziness in this model was primarily associated with characterization of local (atomic) scale interactions while statistical randomness manifested at both local and global (molecular) scales. Experimental and estimation artifacts appear to have a higher impact on statistical regression than fuzzy regression. However, the superiority of the fuzzy regression seems to diminish with increasing correlation between the inputs and the output variable.

Bioavailability of sediment-associated PCDD/Fs and PCDEs: relative importance of contaminant and sediment characteristics and biological factors

Factors that determine accumulation of sediment-associated polychlorinated dibenzo-p-dioxins and furans and polychlorinated diphenyl ethers into semipermeable membrane devices (SPMDs) and benthic oligochaete worms (Lumbriculus variegatus) were examined. These factors included both physical-chemical and structural characteristics of the contaminants (water solubility, lipophilicity, dipole moment, molecular size, and conformation) and sediment characteristics (organic carbon content, particle size, aromaticity, and polarity of organic carbon). The results of partial least squares regression analysis indicated that lipophilicity alone is not a sufficient predictor for contaminant bioaccumulation potential, even though it is a significant contributor. It was shown that contaminant molecular size and conformation (specifically planarity/nonplanarity) as well as sediment characteristics also have a significant role. The studied factors contributed up to 63-88% of the variation in accumulation data for SPMDs and 50-65% for oligochaetes. Comparison of (bio)accumulation factors (BAF28d for oligochaetes and AF28d for SPMDs) revealed that accumulation of contaminants in oligochaetes is largely influenced by biological factors (e.g., feeding habits), while the physical-chemical nature of the process is emphasized for SPMDs.

TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in herbicides using computer-aided molecular design. Two series of compounds, one containing herbicide and the other containing nonherbicide compounds, were processed by a k-Means Cluster Analysis in order to design the training and prediction sets. A linear classification function to discriminate the herbicides from the nonherbicide compounds was developed. The model correctly and clearly classified 88% of active and 94% of inactive compounds in the training set. More specifically, the model showed a good global classification of 91%, i.e., (168 cases out of 185). While in the prediction set, they showed an overall predictability of 91% and 92% for active and inactive compounds, being the global percentage of good classification of 92%. To assess the range of model applicability, a virtual screening of structurally heterogeneous series of herbicidal compounds was carried out. Two hundred eighty-four out of 332 were correctly classified (86%). Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments toward herbicidal property; also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general "in silico" technique to experimentation in herbicides discovery.

General and class specific models for prediction of soil sorption using various physicochemical descriptors

Diverse chemical descriptors were explored for use in QSAR models aimed to screen the soil sorption potential of organic compounds. The descriptors included logP, HyperChem QSARProperties descriptors, a combination of connectivity indices, geometrical, and quantum chemical measures, and two sets from the DRAGON and CODESSA program packages, respectively. Generally, the univariate logP models were capable of capturing most of the variation and give an indication of the sorption potential. The multivariate models required refined variable selection procedures but were shown to include crucial descriptors for modeling compound classes with specific chemical characteristics.

Estimation of soil organic partition coefficients: from retention factors measured by soil column chromatography with water as eluent

The retention factors (k) of 104 hydrophobic organic chemicals (HOCs) were measured in soil column chromatography (SCC) over columns filled with three naturally occurring reference soils and eluted with Milli-Q water. A novel method for the estimation of soil organic partition coefficient (Koc) was developed based on correlations with k in soil/water systems. Strong log Koc versus log k correlations (r>0.96) were found. The estimated Koc values were in accordance with the literature values with a maximum deviation of less than 0.4 log units. All estimated Koc values from three soils were consistent with each other. The SCC approach is promising for fast screening of a large number of chemicals in their environmental applications.

Linear solvation energy relationships regarding sorption and retention properties of hydrophobic organic compounds in soil leaching column chromatography

The capacity factors of a series of hydrophobic organic compounds (HOCs) were measured in soil leaching column chromatography (SLCC) on a soil column, and in reversed-phase liquid chromatography on a C18 column with different volumetric fractions (phi) of methanol in methanol-water mixtures. A general equation of linear solvation energy relationships, log(XYZ) XYZ0 + mV(I)/100 + spi + bbetam + aalpham, was applied to analyze capacity factors (k'), soil organic partition coefficients (Koc) and octanol-water partition coefficients (P). The analyses exhibited high accuracy. The chief solute factors that control logKoc, log P, and logk' (on soil and on C18) are the solute size (V(I)/100) and hydrogen-bond basicity (betam). Less important solute factors are the dipolarity/polarizability (pi*) and hydrogen-bond acidity (alpham). Log k' on soil and log Koc have similar signs in four fitting coefficients (m, s, b and a) and similar ratios (m:s:b:a), while log k' on C18 and logP have similar signs in coefficients (m, s, b and a) and similar ratios (m:s:b:a). Consequently, logk' values on C18 have good correlations with logP (r > 0.97), while logk' values on soil have good correlations with logKoc (r > 0.98). Two Koc estimation methods were developed, one through solute solvatochromic parameters, and the other through correlations with k' on soil. For HOCs, a linear relationship between logarithmic capacity factor and methanol composition in methanol-water mixtures could also be derived in SLCC.