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QSPR-based prediction of adsorption of halogenatedaromatics on yellow-brown soilD.B. Wei b , C.D. Wu c , L.S. Wang b & H.-Y. Hu aa Department of Environmental Science and Engineering, ESPC State Key Joint Laboratory , TsinghuaUniversity , 100084, Beijing, People's Republic of Chinab School of Environment , Nanjing University , 210093, Nanjing, People's Republic of Chinac School of Paper and Environmental Engineering , South China University of Technology , 510641,Guangzhou, People's Republic of ChinaPublished online: 13 May 2010.

To cite this article: D.B. Wei , C.D. Wu , L.S. Wang & H.-Y. Hu (2003) QSPR-based prediction of adsorption of halogenated aromatics onyellow-brown soil , SAR and QSAR in Environmental Research, 14:3, 191-198, DOI: 10.1080/10629360310000101773

To link to this article: http://dx.doi.org/10.1080/10629360310000101773

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QSPR-BASED PREDICTION OF ADSORPTION OFHALOGENATED AROMATICS ON YELLOW-BROWN

SOIL

D.B. WEIa,b,*, C.D. WUc, L.S. WANGb and H.-Y. HUa

aDepartment of Environmental Science and Engineering, ESPC State Key Joint Laboratory, TsinghuaUniversity, Beijing 100084, People’s Republic of China; bSchool of Environment, Nanjing University,

Nanjing 210093, People’s Republic of China; cSchool of Paper and Environmental Engineering,South China University of Technology, Guangzhou 510641, People’s Republic of China

(Received 23 November 2002; In final form 5 February 2003)

Halogenated aromatic compounds exist widely in soil and aqueous environment. The study of their transport anddistribution is quite important for pollution control and risk assessment. In the present work, the adsorptioncoefficients of 28 halogenated benzenes, anilines and phenols on yellow-brown soil were measured with batchequilibrium method, and a prediction model was developed through the quantitative structure–property relationship(QSPR) technique. Then the obtained model was tested with Monte Carlo simulation and Jacknife methods. Theresults indicated that it was robust enough to estimate soil adsorption behaviors for the tested compounds. Based onthe obtained model, it could be deduced that the adsorption of halogenated aromatics on yellow-brown soil was not asimple partitioning process but involved complicated interactions.

Keywords: Soil adsorption; Halogenated aromatic compounds; QSPR; Robustness; Monte Carlo simulation test;Jacknife test

INTRODUCTION

Halogenated compounds have been extensively used as intermediates in the synthesis of

pesticides, herbicides, plastic products and drugs. Millions of tons of these chemicals have

been released into the environment, and especially, polyhalogenated monoaromatics have

been accumulated in nearly all the compartments [1].

The environmental fate of chemicals depends on a variety of physical, chemical and

biological processes [2]. Mathematical models that attempt to integrate these phenomena are

widely used to predict the environmental transport and distribution of the organic pollutants

among the different compartments of the biosphere [3]. Use of these models requires a

variety of abiotic and biotic parameters as inputs. The soil adsorption coefficient (KOC) is one

of the key input parameters in models used to estimate the environmental mobility and fate of

ISSN 1062-936X print/ISSN 1029-046X online q 2003 Taylor & Francis Ltd

DOI: 10.1080/10629360310000101773

*Corresponding author. Tel.: þ86-10-62772838. Fax: þ86-10-62771472. E-mail: weidongbin@tsinghua.org.cn

SAR and QSAR in Environmental Research, 2003 Vol. 14 (3), pp. 191–198

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pollutants [4]. However, the experimental determination of KOC is time-consuming and

expensive, so, the estimated values based on quantitative structure–property relationship

(QSPR) equations are commonly used in recent years [5–8]. Thus, the development of

estimation methods is important.

In the present paper, the KOC values of 28 halogenated aromatic compounds were

measured. Considering the studied water–soil system, some molecular descriptors with

possible effects on soil sorption process and describing comprehensive structural information

were selected and calculated. These descriptors included molecular connectivity indices

(MCIs), linear solvation energy parameters (LSERs) and semi-empirical quantum chemical

parameters (QCs). MCIs allow the description of molecules based on bonding and branching

patterns. The LSER concept is a general approach to describe solvation and partitioning or

related properties in diverse media. QC parameters, derived from quantum chemical

calculation of molecular orbital, have obvious advantages. They are not restricted to closely

related compounds, they can be easily obtained; and they describe clearly defined molecular

properties. It was expected that significant descriptors with clear physicochemical meaning

could be selected through chemometrics methods, and used to develop a robust model for

estimating the soil adsorption, with, in addition, the possibility to better elucidate a possible

adsorption mechanism.

MATERIALS AND METHODS

Twenty-eight substituted aromatic compounds were purchased from Aldrich Inc.; all of them

were more than 99% purity. Yellow-brown soil sample, air-dried, grinded and pass through

80-mesh sieve was provided by the Institute of Soil Sciences, Chinese Academy of Science.

The soil properties were the following [9]: pH ¼ 7:42; organic matter content 0.866%, sand

10.3%, silt 68.8% and clay 20.9%.

The octanol/water partition coefficients (KOW) of 28 tested compounds were determined at

25 ^ 0.58C with shake-flask method in our laboratory, and the results were published

elsewhere [10]. All of the 28 log KOW values are listed in Table I.

The adsorption experiment was conducted with the batch equilibrium method

according to the OECD guidelines for testing of chemicals [11]. Tested chemicals were

prepared in 0.01 mol/l CaCl2 solution. A measured quantity of 1.0000 g soil sample was

weighted into 10-ml glass centrifuge tubes, 10.00 ml chemical solution was added, and

the tubes were sealed. The suspension was then agitated (200 rpm) in an isothermal

shaker bath (25 ^ 0.58C) for 24 h to reach sorption equilibrium, and subsequently

centrifuged for 20 min (2000 rpm) to separate phases. Supernatant was introduced into

another 5 ml glass centrifuge tube, and then centrifuged at 4000 rpm for 20 min. Soil-free

tubes containing only the initial solutions were used as control groups to check

adsorption of chemical onto glass or caps. Chemical-free tubes containing only soil

suspension were used as blank references. The equilibrium concentration for chemical in

the supernatant was determined by UV/Vis spectrophotometer (Shimadzu UV-2201), and

the amount of solute adsorbed by the soil was then calculated by the difference. All the

experiments were conducted in triplicate, and 7–10 points in each isotherm were

selected.

Forty-two molecular connectivity indices (MCIs) [12] were calculated by CONNECT, a

computer program designed in our laboratory. Four linear solvation energy parameters

(LSERs) were obtained from an estimation method [13]. Ten semi-empirical quantum

chemical parameters (QCs) were calculated based on the semi-empirical method CS MOPAC

(Cambridge software Inc, 1997) using AM1 Hamiltonian.

D.B. WEI et al.192

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QSPR-BASED PREDICTION OF ADSORPTION 193

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The statistical analyses were conducted using STATGRAPHICS (version 4.0) (STSC Inc

and Statistical Graphics Cooperation, 1985).

RESULTS

Based on the hypothesis that soil adsorption of non-ionic organics is a partitioning process,

n-octanol/water partition coefficient (KOW) plays an important role in KOC prediction [14].

The main advantage of the KOW-estimation method is that a large number of

experimentally validated KOW values are available from KOW databases, and the missing

KOW values can be easily estimated from QSPR [15,16]. However, the application of KOW

to estimate KOC remains a controversial subject and the discussion on its usefulness is still

going on. For instance, Oepen and coworkers [17] demonstrated that for compounds with

carboxylic or amino groups, simple log KOC versus log KOW correlation was insufficient for

a reliable prediction of the soil sorption coefficient. Pussemier and coworkers [18],

observed a decreasing reliability of the KOW approach with an increasing polarity of the

studied compounds. Their results showed that adsorption coefficients of acids were under-

estimated while those of bases were over-estimated by the KOW-method. As for different

chemical classes, their regressions showed significantly different intercepts, slopes and

regression coefficients [19]. This implies that the solute’s hydrophobicity is an insufficient

descriptor for soil adsorption.

Feng et al. [20] pointed out the fact that organic carbon phase of soil was more

cohesive and a stronger hydrogen bond donator solvent than n-octanol. This could explain

the difficulties in finding suitable KOW –KOC correlations for polar substances.

Additionally, adsorption processes of compounds with polar or ionizable groups may

depend greatly on non-hydrophobic or non-dispersive interactions [2]. The hypothesis that

soil sorption of non-ionic organics is a partition process [14] remains questionable. There

is some evidence that at least for soil with low organic carbon content the partitioning

approach is insufficient [21].

In the present study, it was found that all of their adsorption isotherms fitted the Freundlich

adsorption equation, and their correlation coefficient values were greater than 0.960.

The experimental log KOC values are shown in Table I. The relationship between log KOC and

log KOW was first examined.

log KOC ¼ 2:607 þ 0:071 log KOW n ¼ 28; r ¼ 0:195; r 2 ¼ 0:039;

SE ¼ 0:369; F ¼ 1:025; p ¼ 0:321ð1Þ

It was obvious that there was a poor correlation, the r value is very low, and the standard

error SE is high. Consequently, Eq. (1) cannot be used to estimate soil sorption coefficients

for tested compounds. Considering the complexity of tested water–soil system, some of

descriptors reflecting the electrostatic, steric and solvation effects, shape and size of

molecules, were selected as candidate variables to develop a prediction equation by using

multiple stepwise regression analysis.log KOC ¼ 18:372ð^1:871Þ þ 1:551ð^0:104Þb2 2:998ð^0:139Þa

þ 0:024ð^0:002ÞVCSE 2 15:436ð^1:617ÞOV þ 1:818ð^0:123Þ3xC

n ¼ 28; r2adj ¼ 0:960; SE ¼ 0:074; F ¼ 128:9; p , 0:0001

ð2Þ

In Eq. (2) b, a are hydrogen bond terms. They represent the exoergic effects of hydrogen

bond involving the solvent as hydrogen bond donor acid (HDB) and the solute as hydrogen

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bond acceptor base b (HBA), and the solute as hydrogen bond donor acid a and the solvent

as hydrogen bond acceptor base. VCSE is the Connolly Solvent-Excluded volume (A3). OV is

ovality, which represents the shape of chemical molecule. 3xC is the third cluster index that

denotes the number and type of substituents on benzene ring.

DISCUSSION

Considering the structures of the studied compounds and variables involved in Eq. (2),

the soil adsorption behavior is mainly related to the shape, size and electrostatic

characteristics of chemical molecules. As mentioned above, b, a are hydrogen bond terms,

which reflect the bonding characteristics of the compounds in water–soil system, VCSE

represents the size of solute molecule, OV and 3xC reflect the shape of compound. Equation

(2) shows that the increase of solute basicity, or the decrease of solute acidity, would cause

the increase of KOC. Both of the higher VCSE and 3xC, or lower OV are benefit to soil

adsorption of compounds. Furthermore, the Student t-test values are 14.960, 221.633,

14.797, 11.876 and 29.545 for b, a, 3xC, VCSE and OV, respectively. It can be concluded

that the descriptors b and a are the most significant parameters underlining the importance

of hydrogen bonding in the adsorption process. Moreover, the studied compounds are

polar, and the organic carbon content of yellow-brown soil is low. In order to test the

significance of the parameters involved in Eq. (2), one of them was deleted and the

regression was rerun with the rest of the parameters. The results showed that the r2adj was

the lowest one (0.137) when a was deleted, while it was the highest one (0.801) when OV

was deleted. The order of significance of these parameters agreed with the order of

absolute t-values. This result was similar to previous conclusions [2,20]. Additionally, it

was also proved that there was no serious multicollinearity among the parameters included

in Eq. (2). Thus, Eq. (2) is successful in estimating log KOC for the 28 halogenated

aromatics. The results are plotted in Fig. 1.

FIGURE 1 Comparison between observed and predicted log KOC based on Eq. (2).

QSPR-BASED PREDICTION OF ADSORPTION 195

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Because only 28 observations were available in the present work, a Monte Carlo

simulation method was used to test whether the prediction Eq. (2) was reliable [22]. The null

hypothesis was that the observed log KOC values were independent of structure descriptors

(b, a, VCSE, OV and 3xC) when using Monte Carlo simulation to test Eq. (2). Twenty-eight

bogus values of predicted log KOC were generated with a random number generation

stochastically sampling from a Weibull distribution (shape 10.243, scale 2.958) of

experimental observations. A regression equation was developed between these 28 bogus

values of log KOC and the structure descriptors, and the correlation coefficient of this spurious

equation was recorded as r 2*. The parameters were not changed in any manner when the

spurious equations were derived; only the predicted values were varied. Repeating such a

Monte Carlo simulation 200 times resulted in 200 sets of 28 random numbers of log KOC, as

well as 200 spurious equations and their corresponding r 2* with the range from 0.016 to

0.476.

On the basis of results of Chi-square goodness-of-fit test applied to the fitted

empirical distribution of r 2*, obtained from the Monte Carlo simulation, the Chi-square

value was 7.805, and the significance level 0.800 greater than 0.05 suggested significant

sufficiency of fit and no distinct difference from a Weibull distribution (shape 1.952,

scale 0.205) (Fig. 2). From the Weibull probability distribution of r 2*, it is known that

at the probability of 0.975 exceeding a critical value of 0.400 is required, in this case, to

determine the significant difference between the values of r 2 and r 2*. Since r 2 (0.960)

of Eq. (2) is greater than r 2* in our study, it can be regarded that the prediction

by Eq. (2) considerably differs from the random prediction by spurious equations.

Therefore, the predictive reliability of Eq. (2) is verified by the Monte Carlo simulation

method.

In addition, a modified form of “Jacknife” [23] method was also applied to test the data set

where a random number of observations were deleted at a time, and the regression was rerun

for the rest of observations. Figure 3 vividly shows the effects of each compound on

robustness of Eq. (2) using a modified leave-one-out method by comparing the values of r 2.

It can be seen obviously that the obtained model was quite “robust”. Moreover, the K–S

FIGURE 2 The empirical distribution of r 2*.

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(Kolmogorov–Smirnov) goodness-of-fit test [24] was used to test the frequency distribution

of residuals for prediction Eq. (2). The results are listed in Table II, which indicated that the

frequency of distribution of residuals agreed with the Normal function Nð9:691 £

10215; 0:0062Þ: The Chi-square value is 0.779, and the significance level is 0.378. Based on

above analyses, the log KOC value can be predicted very well, to some extent, by combined

structural descriptors.

CONCLUSION

Soil adsorption is an important and complicated process. Especially, for halogenated

aromatics such as benzenes, anilines and phenols. Their soil adsorption behaviors cannot be

considered as simple partitioning process due to the low correlation with lipophilicity

(log KOW). Conversely, there is a good correlation between log KOC and combined structural

descriptors. It can be concluded that the adsorption process should include hydrogen bond

action for yellow-brown soil with low organic matter content. This result is in agreement

with previous works showing that the adsorption is more correlated to a certain adsorption

site of soil than soil organic matter partition. However, addition of studies on adsorption

characteristics are necessary. The tests of robustness with two different methods also indicate

that the obtained model is so robust that it could be used to predict the adsorption behavior as

well as investigate adsorption mechanism to a certain extent.

TABLE II Results of K–S test

log KOC

Estimated KOLMOGOROV Statistic DPLUS 0.0919Estimated KOLMOGOROV Statistic DMINUS 0.0860Estimated overall statistic DN 0.0919Approximate significance level 0.9721

FIGURE 3 The plot of r 2 for Eq. (2) by leave-one-out method.

QSPR-BASED PREDICTION OF ADSORPTION 197

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Acknowledgements

This project was supported by National Natural Science Fund of P.R. China and China

Postdoctoral Science Foundation.

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