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October 2001
Vol. 10, No. 10,
pp 57–58, 60.
 
 
 
Regulations and You
Predicting environmental remediation rates

Correlation analysis offers a means for estimating the reaction rates of organic pollutants.

Delegates from 110 countries met in Geneva in 1999 in an ongoing attempt to develop an international treaty to control or phase out 12 persistent organic pollutants (POPs), including the agrochemicals Dieldrin, Endrin, and DDT (1). These POPs are suspected endocrine disrupters. It is claimed that they cause immune, reproductive, and child development disorders, and they have been implicated in the decline or extinction of many species of animals (2).

Although POPs have already been banned in many countries, they are still used in some parts of the world because of economics or the lack of locally available alternative herbicides and pesticides. In many such areas, it is argued that the consequences of not controlling the mosquito that carries malaria are far higher than the potential problems created by the presence of DDT in the local environment. Hence, POPs pollute the environment because of their continued use, persistence, and the apparent ease with which they are transported globally. According to one source, it was claimed in 1972 that already more than a million pounds of DDT had been deployed around the globe since World War II and that many U.S. citizens then carried as much as 10 parts per million of DDT in their body fat (2). DDT has even been found in the bodies of birds and fish in Antarctica, thousands of miles from the nearest dispersion site.

Of further concern, some of the newer and supposedly safer agrochemicals have been implicated in amphibian decline (3). For example, on the eastern slopes of the Sierra Nevada mountains in California, several species of amphibians are thriving, but on the western slopes, where significant concentrations of agrochemicals have been found in the water, the same species are in decline. The suspected agrochemicals, including atrazine, metolachlor, and chlorpyrifos, are being deployed to the west of the mountains. Apparently, the westerly winds then carry the agrochemicals and deposit them on the western mountain slopes where they impact the amphibian populations (see Figure 1).

As organochlorine agrochemicals and POPs are likely to continue polluting our environment for the foreseeable future, the ability to reliably predict dehalogenation rates would aid in the development of remediation methods and could provide a basis for legislative regulation of new organochlorine products.

Dehalogenation is among the most important processes involved in contaminant fate but, despite numerous kinetic studies, there are few reliable methods that can be used to explain or predict the rates of dehalogenation by environmental reductants. However, quantitative structure–property relationships (QSPRs) from correlation analysis offer a potential approach to predicting such reaction rates. Correlation analysis in the chemical sciences typically involves regression of substrate property data (in this case, the dechlorination rate) for a series of related compounds with one or several convenient descriptor variables, preferably calculated from just the molecular structure of the substrate (such as molecular weight, number of nitrogen atoms, energy of highest occupied molecular orbital, etc.). The resulting QSPRs can then be used to estimate property values for compounds that were not included in the original data set. Molecular structure descriptors calculated by computer-aided chemistry have an advantage in that large, consistent sets of calculations can be performed with commercially available software and include some quantum-chemistry descriptors that are not directly accessible by experimentation.

The predictive power of QSPRs makes them enormously important in regulatory decision making relevant to new chemicals in the environment. In addition, correlation analysis is an important tool for data validation and mechanistic investigations. In pursuit of these goals, Paul Tratnyek and co-workers at the Oregon Graduate Institute recently published a QSPR in Environmental Science & Technology to predict dechlorination rates of organochlorine pollutants (4).

The Methodology
The Tratnyek study was based on dechlorination of chlorinated alkanes and alkenes by metallic iron turnings (zero-valent iron). Correlations with three classes of descriptors were investigated. First, as the rate-determining step is considered by some researchers to be mass-transport controlled, various transport-based descriptors were tested, such as diffusion coefficients, solubilities, sediment–water partition coefficients, and vapor pressure. However, these descriptors yielded correlation coefficients (r2) of less than 0.5. (The closer r2 is to 1, the better the correlation.)

Second, published thermodynamic estimates of one- and two-electron reduction potentials were considered. Although the one-electron reduction potentials provided a good correlation with dehalogenation rates, estimates could be made only for compounds in which the dechlorination mechanisms and pathways had previously been characterized. This obviously limits the scope and usefulness to just those compounds that have been thoroughly investigated and so precludes the ability to predict the remediation rates of new or hypothetical compounds.

Finally, various theoretically derived structure-based descriptors, including quantum-chemistry calculations using the semiempirical method, MOPAC (5), and ab initio 6-31G*, were generated with a commercial computer-aided chemistry package, the CAChe WorkSystem (6). The correlation of each descriptor was tested by regression analysis against the experimental reaction rate constants. The CAChe WorkSystem provides a convenient method to automatically generate more than 100 different descriptors from a library of chemical structures that can be sketched in or loaded from a database. The two-dimensional descriptors include various Kier & Hall topological indices, functional group counts, and atom type counts, etc. CAChe also calculates quantum-chemistry descriptors and properties based on three-dimensional optimized structures such as molecular orbital energy levels, Fukui frontier orbital reactivity indices, partial charges on atoms, heats of formation, and solvent-accessible surface areas. Additionally, regression analysis tools are incorporated into the computerized system.

FIGURE 2: Graphic depicting the correlation of ELUMO with dechlorination rates.
FIGURE 2: Graphic depicting the correlation of ELUMO with dechlorination rates.
The Results
The energy of the lowest unoccupied molecular orbital (Elumo) provided the best correlation to the log of the dechlorination rate constant with an r2 of 0.85, as shown in Figure 2. Elumo was calculated both by MOPAC and through ab initio quantum mechanics calculations. While the ab initio results (r2 = 0.86) were marginally better than the MOPAC results (r2 = 0.85), they were considerably more expensive in computer time. Each ab initio calculation took hours or days, whereas each MOPAC calculation took just seconds or minutes. Of the quantum-chemical descriptors that have been used in QSPR analysis, the energy of the LUMO is the most easily justified for reduction of chlorinated aliphatics. This is because the LUMO is the frontier molecular orbital into which electron transfer takes place, and the energy of this orbital helps determine the driving force for the reaction.

Much of the remaining error in the correlation is probably attributable to the large experimental variation in reported dechlorination, which, for some compounds, ranged over 2 orders of magnitude. The variations were mainly among different research groups, and were systematically related to the methodologies used. However, for the purposes of this QSPR, the simple averages of the experimental results for each compound were used.

The Elumo regression equation was successful at predicting the dechlorination rates of several new compounds that were subsequently measured and verified as reported in the journal paper (4). As the Elumo is calculated from the molecular structure alone and no empirical data is needed, this QSPR can be applied to predict remediation rates of hypothetical compounds such as proposed new agrochemicals.

The correlation of Elumo with the dechlorination rate provides some insight into the possible mechanism. It is observed that organochlorines with a lower Elumo undergo faster dechlorination (see Figure 2). Clearly, the lower the Elumo of the organochlorine, the easier the organochlorine can accept electrons from iron, as there is a smaller gap to jump. This is consistent with electron transfer being the rate-limiting step.

Finally, this QSPR study represents an important step toward the goal of reliably predicting organochlorine remediation rates in the environment. QSPR methods have also been successfully applied to predicting environmental fate and biological activities such as toxicity and carcinogenicity.

References

  1. Hileman, B. Chem. Eng. News 1999, 77 (38), 9.
  2. Cartwright, F. F. Disease and History, 1972, Barnes and Noble, 1991, 223–224.
  3. Hileman, B. Chem. Eng. News 1999, 77 (13), 22.
  4. Scherer, M.; Balko, B.; Gallagher, D.; Tratnyek, P. Environ. Sci. Technol. 1998, 32, 3026–3033.
  5. Steward, J. J. P. J. Comput. Chem. 1989, 10, 209, 221–264.
  6. CAChe Worksystem; CAChe Group, Fujitsu: Beaverton, OR; www.cachesoftware.com.


David A. Gallagher works in the CAChe Group at Fujitsu America Inc. in Portland, OR. Send your comments or questions regarding this article to tcaw@acs.org or the Editorial Office 1155 16th St N.W., Washington, DC 20036.

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