WO1994029242A1 - The bioremediation of soils containing hydrocarbon using calcium peroxide - Google Patents

Abstract

A composition and method for bioremediating a soil contaminated with polynuclear aromatic hydrocarbon or hydrocarbon derived from petroleum. The composition contains a substoichiometric amount of calcium peroxide, a nutrient source for sustaining microbial growth and a buffer as needed. According to the method the composition or the components of the composition are administered to contaminated soil to shift the microbial population and/or enzyme production in a beneficial manner for either aerobic or mixed aerobic/anaerobic bioremediation. Transport of the actives is improved through water utilization.

Description

THE BIOREMEDIATION OF SOILS CONTAINING HYDROCARBON USING CALCIUM PEROXIDE

The invention is in the field of bioremediation of hydrocarbon containing soils. More particularly, it is in the field of remediating sous containing polynuclear aromatic hydrocarbons and petroleum derived hydrocarbons. The rate of biological degradation of many environmental contaminants has frequently been considered to be limited by the availability of the oxygen required for respiration by the degrading organisms. Diffusion, advection, and dispersion, the physical processes that limit oxygen transport to bacteria, have been looked to for improving the degradation rate. Biological treatment of wastewater using aeration and mixing of the contaminated water has been looked to for overcoming oxygen limitations. These techniques have also been employed in soils to increase oxygen availability, but with less efficiency than experienced in the water based systems due to the physical properties of soils.

Soil mixing is energy and labor intensive. Aeration by forcing air through soil generally adds large volumes of oxygen with low uptake efficiency because much of it is lost to the atmosphere with the additional liability of contaminant volatilization. Volatilization associated with soil aeration can be remedied using activated carbon air strippers, which have high associated costs.

We have discovered that the degradeability of hydrocarbon can be greatly enhanced using calcium peroxide on a substoichiometric basis relative to the hydrocarbon. The rate of biodegradation can be increased by 2 to 10 times over what would be experienced by the addition of oxygen alone. Such results require the use of sufficient nutrients to sustain microbial activity for bioremediation. These nutrients are generally made available as fertilizer added to the soil. The rate of degradation can be improved by maintaining a system pH within the range of 6 to 9 generally, and preferably within the range 7 to 8.5.

Water addition to such systems provides calcium forms which are water transportable to afford a wider range of contact with the hydrocarbon.

An increase in species diversity or enzyme diversity or both is achievable, and enzymes particularly suited for degrading fermentation products can be produced. The compositions and methods described and claimed herein can remediate compounds as diverse as gasoline and creosote.

Calcium peroxide administered on a substoichiometric basis relative to hydrocarbon in a hydrocarbon contaminated soil effectively remediates the soil in substantially less time than would be expected from the addition of an equivalent amount of oxygen, if calcium peroxide were considered solely as an oxygen source. To effect such remediation, the soil must have a pH within the range of 6 to 9, preferably 7 to 8.5. Buffers can be added as required to adjust the pH within these ranges. Moreover, appropriate nutrients must be present to promote microbial growth. These nutrients include nitrogen, potassium, and phosphorous, which preferably are present in the soil based on the weight of hydrocarbon to be degraded.

One aspect of the invention is a composition for remediating hydrocarbon contaminated soil through enhancement of microbial growth having 30 to 90 weight percent calcium peroxide, 0 to 30 weight percent buffer; from 1 to 10 percent nitrogen, from 0.1 to 10 percent potassium, and from 1 to 10 percent phosphorous.

The compositions for remediating hydrocarbon contaminated soil through the enhancement of microbial growth generally contain 30 to 80% calcium peroxide, 0 to 30% buffer, 1 to 10% nitrogen, oil to 10% potassium, and 1 to 10% phosphorus with the phosphorus percentage being expressed on the basis of P2O5.

Any calcium peroxide can be used, although typically the calcium peroxide used has less than 18% active oxygen. Preferably, the buffer is a potassium dihydrogen phosphate, a calcium monohydrogen phosphate, or a mixture of these two phosphates. Depending on the soil 0.01 to 1 parts by weight buffer per part of calcium peroxide is used.

Preferably, the formulation contains a calcium peroxide having less than 18% active oxygen. More preferably, the composition contains from 30 to 60 weight percent calcium peroxide, with the calcium peroxide having from 10 to less than 15 weight percent active oxygen.

Buffers which can be used include any buffers capable of providing a pH within the range of 6 to 9 which would not be detrimental to the biological activity of the microbial population. Preferred buffers include those which may also serve as a nutrient source for the microbial population, particularly those which can provide phosphorous. Particularly, preferred buffers are potassium dihydrogen phosphate and calcium monohydrogen phosphate in an amount of from 0.01 to 1 part by weight of buffer per part of calcium peroxide.

In another aspect of the invention, calcium peroxide is administered to hydrocarbon containing soil which is be remediated. It has been found that an effective amount of calcium peroxide is substoichiometric to the amount of hydrocarbon being remediated, considering the amount of oxygen available from the peroxide for degrading the hydrocarbon. Because this substoichiometric use of calcium peroxide provides an increase of from 2 to 10 times or more in the rate of degradation of the hydrocarbon than would be expected from the mere addition of the available oxygen equivalent to that afforded by the calcium peroxide, it is theorized that the calcium peroxide does more than just provide oxygen. Since calcium peroxide contains only calcium and oxygen, it is believed that the calcium unexpectedly activates biological processes which improves the ability of the microbial population to degrade hydrocarbon in the soil.

In the bioremediation process of this invention, the calcium peroxide is administered to the soil in an effective amount on a substoichiometric basis relative to the hydrocarbon to be remediated. Microbes in the soil are the hydrocarbons as a food source. Those microbes are stimulated by the substoichiometric amount of calcium peroxides, particularly if the pH of the soil is within the range of between 6 and 9, and if there is an effective amount of fertilizer in the soil. Sufficient time should be permitted for the calcium peroxide to shift microbial population and enzyme production in a manner favorable to utilization of the hydrocarbon. Although the calcium peroxide is an oxygen source, the shift is different than would occur with the addition of an equivalent amount of oxygen alone. Typically 0.01 to 1% calcium peroxide having 12 to 18% active oxygen is administered based on soil weight. In addition, 0 to 2 parts by weight of buffer per part of calcium peroxide is added. Given these condition if a time period of at least one week is provided, then bioremediation will occur at a. greater rate than if oxygen alone had been added instead of the calcium peroxide. Water can be used with calcium peroxide to form a water transportable form of calcium, which can be transported by the water through the system and intercellularly.

Moreover, the calcium peroxide can be replenished as needed. It is desirable to maintain a calcium peroxide concentration of 0.08% or higher based on soil weight.

The process also fosters the growth of aerobic microbes that are able to degrade the fermentation products produced by anaerobic microbes. These fermentation products include, but are not limited to, amino compounds, carboxylic acids, saccharides, and polymers of glucose and sucrose.

Hydrocarbons that can be bioremediated using the method of this invention are many. Those of particular interest include petroleum hydrocarbon, creosote, halogenated hydrocarbon, and polyaromatic hydrocarbon. For effective bioremediation the calcium peroxide, should be used in a soil having a pH within the range of from 6 to 9, and preferably within the range of 7 to 8.5, in the presence of nutrients required by the microbial population for degrading the hydrocarbon. These nutrients include nitrogen, potassium, and phosphorous. Sources of these nutrients known in the art can be used.

An effective amount of nutrient(s) for the microbial population to promote efficient degradation of the hydrocarbon should be used. This amount of nutrient(s) is expressible in relation to the hydrocarbon being degraded. Generally, a molar weight ratio of carbon to nitrogen to potassium to phosphorous of from 100:10:10:10 to 100:10:1 :1 is employable, with a preferred ratio of from 100:10:5:5 to 100:10:1 :1 , and a most preferred ratio of from 100:10:4:4 to 100:10:1 :1.

The molar weight ratio of nitrogen to potassium to phosphorus is variable to the need of the microbial population. Although the ratio of 10:2:2 has been found to be particularly effective, the relative amounts can vary.

The same buffers discussed above can be used. Generally, sufficient buffer is employed to provide the required pH. Because calcium peroxide is a source of alkalinity, the amount of buffer to be added can be expressed in relation to the amount of calcium peroxide employed. The amount of buffer varies with the soil and with the amount of calcium peroxide used. Buffer should be added as required.

Since soil composition varies widely according to location and previous use, buffer and nutrients requirements can vary. Those requirements can best be determined by analyzing the soil first, and then determining the amount of buffer or nutrient required using standard methods and the information provided herein. For some soil there may not be a need to add additional buffer or any or all the recommended nutrients because the recommended amounts are already present. For other soil, buffer and nutrients must be added. Hydrocarbons as a class can be remediated using the compositions or procedures disclosed herein. These include but are not limited to simple linear and branched hydrocarbons, polynuclear aromatic hydrocarbon such as creosote, and halogenated hydrocarbons, and halogenated polynuclear aromatic hydrocarbons. Suitable sources of nutrients include potassium nitrate, potassium phosphate, potassium sulfate, ammonia, ammonium nitrate, ammonium chloride, ammonium phosphate, ammonium sulfate, muriate of potash, and the like.

The most beneficial remediation occurs under aerobic conditions. Air is introduced to provide aerobic conditions by tilling, injection or other methods commonly used for that purpose. The same application methods can be used for the permeox composition and applications of this invention.

Oxygen requirements are also provided by the calcium peroxide, albeit on a substoichiometric basis. Calcium peroxide provides oxygen, on a sustained basis over a protracted time period. Generally, this time period exceeds three weeks, with time periods of 4 to 6 weeks being achievable.

It is desirable to replenish the calcium peroxide, buffer, and other nutrients as needed to maintain levels sufficient to sustain a reasonable remediation rate. Replenishment can occur at the end of 3 weeks, 4 weeks or at less frequent intervals. In order to provide a sustained, high remediation rate, additional calcium peroxide application of at least 0.03 weight percent preferably, or more preferably 0.06 weight percent are made at regular intervals.

We have determined that calcium peroxide can also improve remediation by enhancing and perhaps fostering an interaction between aerobic and anaerobic regimes in the soil. In particular, it fosters the growth of microbes capable of degrading fermentation products and promotes the production of enzyme needed to degrade those fermentation products. Calcium peroxide shifts microbial population and enzyme production in a manner favorable to degradation of the hydrocarbon. Moreover, this shift appears to differ from that which would be expected to occur from the addition of an equivalent amount of oxygen alone. This shift depends on the degree of contact and the contact time. Thus the more thoroughly the calcium peroxide, fertilizer, and buffer are mixed with the soil, the better. The addition of water to the system can provide transport for calcium peroxide or calcium peroxide products to other parts of the soil. These products can include soluble or partially soluble products such as calcium hydroxide, and other known products that meet these requirements. The transport of the calcium itself provides a benefit. In the experiments presented herein Permeox™ peroxygen was used as the source of calcium peroxide. Permeox™ is a registered trademark of the FMC Corporation for a peroxygen containing 60% calcium peroxide having approximately 13% active oxygen, and 40% inerts consisting of water and other calcium compounds, such as calcium hydroxide, and calcium carbonate. Examples

A series of plots were prepared. Nothing was done to the control plot. The remaining plots were plowed, and lime was added to provide a neutral pH. Fertilizer was added to maintain a target ratio of carbon:N:K:P of 100:10:2:2. Ammonium nitrate and monosodium phosphate were applied as necessary to maintain nutrient target ratios. Muriate of potash was added to provide potassium. For plots using calcium peroxide 0.1 weight percent Permeox™ peroxygen was added.

The plowed plots were plowed daily, soil conditions permitting: that is no plowing was done if the soil was too wet.

The Permeox plots were only plowed when amendments were made to maintain the established target nutrient levels.

The research study on the composition of the soil microbial community, included the following tasks: Microbiologic plates of soil bacteria were used for the isolation of representative bacterial communities from untreated control, mechanically- aerated, and calcium peroxide treated soils.

The isolates were tested for their biochemical potential (phenotype) using Biolog assay plates and then identified using the Biolog data base. The bacterial communities were examined to determine the relative number of bacterial species and microbial diversity within each of the treatment soils.

The biochemical responses of the bacterial isolates to the 95 Biolog biochemicals were then statistically evaluated and the data reduced into groups of functionally similar biochemicals based on the ability of bacteria to use certain compounds as sole sources of carbon.

The biochemical potential of the microbial communities from the three treatments were then statistically evaluated to determine whether the communities were phenotypically different. Soil Samples

The soil samples used in this study were collected as part of a pilot- scale study to determine the effectiveness of bioremediation in soils contaminated with creosote. That study evaluated the effect of two types of oxygenation treatments on the rate of biodegradation: mechanical aeration (daily plowing) and chemical aeration (addition of calcium peroxide). The experimental design included 4 plots per treatment with 5 subsamples per plot taken for bacterial analysis (Figure 1 ). Both treatments included nutrient additions and pH control. An untreated Control plot was sampled at the beginning and end of the 16 week study period. Both bacterial abundance (heterotrophs and phenanthrene-degrading bacteria) and contaminant concentrations were monitored over time. Results of the pilot- scale landfarm study showed that degradation rates were higher in the calcium peroxide treated plots (122 ppm TPNA/day for the calcium peroxide treatment versus 23 ppm TPNA/day for the plowed plots). (See figures 2(a) - 2(L) which describe the results for the indicated plots in Figure 1 ).

Spread plates were made from soil samples using Standard Methods (17th ed.) and half strength nutrient agar plates (DIFCO). Colonies were randomly selected from the agar plates using a numbered grid and a random number table. At least sixty colonies were selected from each plot. Colonies were picked and streaked for isolation on half strength nutrient agar.

Each colony was gram stained using the Huckle modification and identified using the appropriate gram negative or gram positive Biolog identification system (Biolog, Hayward, CA). Each Biolog plate contains 95 different biochemicals (Table 1 and Table 2), with 62 biochemicals in common for both gram positive and gram negative panels. The carbon oxidation response to the 95 biochemicals provides a "biochemical fingerprint" of each isolate, which was entered into the Biolog database for taxonomic identification. These two types of information (the "biochemical fingerprint" and the taxonomic identification) were both used to address the critical questions concerning the effect of calcium peroxide on soil microbial communities. Statistical Analysis Principal components analysis (PCA) along with Analysis of Variance

(ANOVA) were used to analyze the data set. The combination of PCA plus ANOVA is often used in the interpretation of data sets where a large number of original variables have been measured. All analyses were performed using the statistical packages StatView II™ and SPSS™. PCA was used to determine if the biochemical fingerprints of the calcium peroxide community was altered when compared to conventional soil aeration techniques and to the Control. This statistical method recognizes and quantifies the intercorrelations among original variables in a data set. It is the type of Factor Analysis of choice when the goal is to reduce the number of variables to a smaller set of new variables (principal components or Factors) that explain most of the variance in a data set. The Factors can be visualized as set of mutually perpendicular axes in multidimensional space. Rotation of these axes (orthogonal rotation) is commonly used to both maximize the variance explained along each axis, and to improve the interpretability of the results, without changing the underlying mathematical properties

In orthogonal rotation, the components remain uncorrelated with each other, and a loading matrix is produced. A loading matrix is a set of correlation coefficients between the original variables and the Factors. Varimax rotation, a type of orthogonal rotation, is the most common rotation technique used, was used for the present analysis. Varimax rotated loadings, like correlation coefficients, can assume values between -1.0 and +1.0.

Because PCA requires that there be no missing data, only the 62 biochemicals that were common to both the gram negative and gram positive Biolog plates (Table 1 and Table 2) were used. The response of the bacterial isolates to each of the ninety-five test chemicals in the Biolog™ plates was recorded in the data matrix as no oxidation (0), marginal oxidation (1 ), or oxidation (2). Factor scores (one for each Factor) are generated during PCA for each of the isolates. These are robust estimates of the score each isolate would have received had they been scored for new variables (the Factors) directly. These factor scores can than be used in analysis of variance, also known as ANOVA, to test for differences among the treatments. The procedure is advantageous because only 13 ANOVAs, one for each Factor, are required to examine treatment effects, rather than 62 ANOVAs, one for each of the original variables. Analysis of Variance was then used to evaluate the factor scores for all of the bacterial isolates and to test whether the carbon utilization phenotype of the microbial communities were different in the three soil treatments.

The horseshoe effect is a statistical artifact of PCA where scattergrams of Factor scores are shaped like a horseshoe. If this effect is seen in the data, corrections must be performed. Having three categories for response instead of only two (oxidation and no oxidation) improved the analysis and reduced the possibility of encountering the horseshoe effect. Additionally, the magnitude of the matrix (217 bacterial isolates by 62 biochemical tests), and having many more bacterial isolates than biochemical tests, also reduced the likelihood of this statistical artifact. The absence of this artifact was confirmed by examination of scattergrams, which showed random distributions.

Discussion of Results

Classic bacterial identification employs a dichotomous identification scheme that gives varying weight to certain physical or biochemical characteristics of the bacterium. The cell wall (characterized by the gram stain) is generally the most heavily weighted characteristic used for bacterial identification; it forms the apex of the taxonomic decision tree. Approved names and identification schemes for bacteria have been in flux for many years. For example, the 1957 edition of Bergy's Manual of Determinative Bacteriology contains 160 species of Pseudomonas, while the 1974 edition contains only 29 species.

Historically, bacterial identification schemes were developed to identify organisms having clinical (health-related) importance. Isolation and identification of environmental isolates is a fairly new endeavor and is still incomplete. It has recently been conservatively estimated that there are over 35,000 different species of bacteria in Costa Rica alone; while the total number of identified bacterial species in the world is only about 4,000. Clinically important bacteria tend to be fast growing fermenters and form the majority of all standard taxonomic databases. Environmental bacteria tend to have many more non-fermenters and were considered to be inert for many years due to the use of inappropriate culture methods. More recently, the abundant easily identifiable environmental isolates have been described, with the bulk of the literature describing those organisms thought to be of clinical or monetary importance (i.e., plant pathogens and symbionts). We used several identification techniques to assess whether calcium peroxide altered the bacterial communities in the soils. These techniques included evaluation of basic colony characteristics (color, gram reaction); changes in species diversity; and evaluation of the biochemical potential of the bacterial communities. Because classical bacterial taxonomy is imprecise and gives no information as to the role of an isolate in its environment, evaluation of the biochemical potential provides a more insightful assessment of soil microbial activity. Evaluation of biochemical potential would be a valuable assessment of the status of the soil bacterial communities exposed to environmental pollutants. General Characteristic of the Bacterial Communities

Basic cultural characteristics (gram stain, colony color, numbers of organisms examined, etc.) of the isolates from the landfarm study are given in Table 3. Several colonies in each sample remained mixed (i.e.: there were two different species of bacteria together that could not be readily separated in culture). Biochemical profiles were not performed on these organisms. Several colonies in each sample died during subculture. Many bacteria die quickly on solid media and some species are able to survive only three to five days on plated medium. Some organisms exhibited no reactions in any of the wells, while others exhibited positive reactions in all wells including the no carbon source control.

Table 3. Cultural history and basic characteristics of bacterial isolates.

CONTROL PERMEOX PLOW

Total # isolated 60 121 132 number mixed 4 8 8 number died 9 11 14 number unreactive 5 11 15 number 0 1 1 overreactive

Gram positive (#) 29 52 52

Gram negative (#) 18 51 57

Percent gram positive 61.70% 50.49% 47.71%

Percent gram 38.30% 49.51% 52.29% negative

Percent Colony Color Distribution

Beige 3.33% 2.48% 0.00%

Cream 61.67% 39.67% 37.40%

Orange 8.33% 4.96% 3.82%

White 5.00% 1 1.57% 12.21 %

Pink 0.00% 1.65% 3.05%

Yellow 21.67% 39.67% 43.51% bright yellow 76.92% 41.67% 14.04% pale yellow 0.00% 16.67% 29.82%

Total Pi mented 30.00% 46.28% 50.38%

The Control plot contained a high proportion of gram positive cells (61.7%) as opposed to the treatment plots (Permeox 50.49%, Plow 47.71 %). Gram positive cells have a more sturdy cell wall than do gram negative cells. They tend to survive environmental stress and resist desiccation better than gram negative cells. Soil mixing and nutrient additions had a stimulative effect of the gram negative community.

The Control plot also had a higher proportion of cream colored colonies (61.67%) than did the treatments (Permeox 39.67%, Plow 37.40%). The percent of yellow pigmented colonies that were bright yellow was different for the Control (76.92%) as compared to the two treatments (Permeox 41.67%, Plow 14.04%). The total percentage of pigmented colonies increased in the two treatments when compared to the Control (Figure 3). Pigmentation is an easy way to assess relative diversity. Pigments are abundant in soil bacteria and are thought to confer resistance to DNA damage caused by ultraviolet light. Pigments are aromatic compounds that are chelated to a metal ligand and include cartenoids, nonisoprenoids, flexirubins, and xanthomonadins. Because of their relatively complex nature, pigments are energy intensive to produce; nutritionally stressed cells could probably not afford to produce these pigments.

By initial evaluation of gram reaction and colony pigmentation, it is clear that the two treatments had an effect on the microbial population when compared to the Control. More pigments were produced and the proportion of gram negative cells increased. This indicates that conditions became more favorable for microbial growth.

Bacterial Taxonomy

The Biolog database contains an identification matrix of over 400 species of bacteria. Like most classical bacterial identification schemes, this identification system initially partitions bacteria by gram reaction. The organisms are then placed into wells of the appropriate panel and tested for oxidative catabolic metabolism of various compounds as a sole source of carbon. The reactions are then entered into a database that provides an identification with a similarity index for that identification. A similarity of less that 0.500 in 24 hours is defined as "no identification". If no organism is found in the database that provides a similarity of 0.500 or greater, the program will search it's database and give the identification of the next closest matches along with their similarity indices. Because of the uncertainty surrounding binomial identification, we grouped individuals in a species if there was any level of match on the Biolog output (Table 4a, 4b, and 4c). This assumes that, if anything, this treatment of the data would give conservative estimates of species diversity in the treatments groups.

There are several indices of community diversity or species richness used to describe the number and diversity of organisms found within an ecological niche. Most of these indices use abundance as well as the proportion of species in a sample. The Shannon-Wiener index scales the diversity index to the proportion of species within a sampling zone. For example, if the same number of individuals in four species are found, then the diversity would be four. If another species with a smaller number of individuals was found, then the diversity index would be less than five indicating that the fifth species was a relatively unimportant member of the community (with respect to abundance). The index is reasonably independent of sample size and is normally distributed. Shannon-Wiener diversity values were calculated for species identified using Biolog (Table 5). Both treatments contained greater species diversity than the Control plot, with diversity in the Permeox™ treatment higher than the Plowed treatment.

Table 5. Shannon-Wiener Diversity Index Scores for treatments. H is the Shannon-Wiener index and diversity is calculated by: diversity = e

TABLE 5

Shannon-Wiener Diversity Index Scores

H diversity

Control K 2.938 18.8705

Combined Permeox 3.777 43.6848

Combined Plow 3.498 33.0493

Principle Components Analysis

In order to evaluate whether the microbial communities were different in the treatments, the results of the Biolog biochemical tests were first statistically reduced using principal components analysis (PCA). Because the analysis requires that there be no empty cells, and to eliminate the use of the gram stain as the apex of the taxonomic decision tree, only the 62 biochemicals common to both the gram negative and gram positive Biolog plates were used for the PCA. The primary goal of the PCA is to focus attention on the underlying processes that are responsible for most of the variance in the data set this is achieved by grouping intercorrelated variables together into a fewer number of super-variables or Factors. The original 62 biochemicals were considered as 62 variables which, naturally, explained 100 percent of the variance in the data set. Application of Principal Components Analysis to the data indicated that the variables could be represented by thirteen Factors (Table 6). These thirteen Factors had Eigenvalues greater than 1.0 and explain 77% of the variance in the original data set. Before varimax rotation, the first four Factors explain 55% of the variance. With the first four Factors explaining 55% of the variance in the original data set, there is still substantial variation unexplained by the analysis. However, given the large number of chemicals bacteria can use as carbon and energy sources, this analysis provides significant insight into some major differences in biochemical capacity of the soil microbial communities. After Varimax rotation, the first four Factors explained 65% of the variance in the transformed data (Table 7). The values in the loading matrix are correlation coefficients between the original variables and the new Factors. The greater the loading (correlation), the stronger the association between a variable and Factor. Variables are grouped by strongly positive or negative correlation coefficients, those having an absolute value greater than 0.50 (Table 7). Variables that have large loadings of the same sign for the same component are positively intercorrelated; variables that have large loadings of opposite sign for the same component are negatively intercorrelated. This data set did not have any large negative correlation coefficients. By definition, variables with large loadings on one Factor are uncorrelated with variables having large loadings on a different Factor. Graphs of the factor scores for each combination of Factors were examined and found not to exhibit the horseshoe effect. In the following discussion, each Factor is given a name intended to describe the biochemical characteristics of the biochemicals that fall within that Factor (Table 7). The percentage of the variance in the data set associated with each Factor is given in parentheses after the name. Factor 1. Amino Acids and Carboxylic Acids (25%).

Factor 1 is the most important factor because it accounts for the highest percent of the variance in the data (25%). Most of the biochemicals in this Factor have amino-groups (R-NH2) and/or carboxylic acid groups (R- COOH). Factor 1 contains important compounds to "jump start" cellular metabolism. These amino acids are important building blocks and are important to nitrogen metabolism. Fermentation products, TCA cycle intermediates, and components of peptidoglycan and teichoic acids are also represented here. Amino acids are: alanine, asparagine, glycine, glutamic acid, and serine. It is interesting to note that alanine dehydrogenase is found in the genus Bacillus (a common soil genus) and is responsible for oxidative de- amination of glutamic acid. Both alanine and glutamate are important sources of ammonia for soil microorganisms. Pathways of ammonia assimilation are limited in bacteria. Ammonia is often a limiting nutrient and can be directly assimilated into only a few amino acids (glutamate, alanine, or aspartate), which serve as donors of their amino nitrogen via transamination to keto acid precursors to form all of the other amino acids. Glutamate formation seems to be the most widely utilized route of ammonia assimilation. It is interesting to see that two of the three key amino acids are grouped in Factor 1.

Hydroxybutyric acids are important fatty acid polymers that bacteria tend to store and can use immediately under stressful conditions. They are found as a storage product in granules in many bacteria (poly-β- hydroxybutyrate). Degradation of fatty acids is by inducible enzymes. Soil organisms would be able to use this important storage polymer. Factor 2. Saccharides with β linkages (8%).

This group of biochemicals is comprised of sugars that are joined by a β-1 ,4-linkage. Cleavage of the linkage requires a special enzyme that the bacteria showing this pattern of growth must have. It is likely that a beta- glucosidase, which highly specific for lactose degradation (also contained within this Factor) is present in these organisms that this enzyme is also able to catalyze the oxidation of the other compounds contained within Factor 2. Factor 3. External structural components (18%).

The biochemicals contained in this factor are generally monosaccharides and alcohols, common building blocks for cellular production of cell wall and cell membrane structures. These polymers include lipopolysaccharides, teichoic acids, teichuronic acids, and peptidoglycan components. These are more important building blocks that form the basic structure of cell walls, as opposed to Factor one compounds that are fairly biochemically labile. A community that catabolizes these compounds readily might be nutritionally stressed, and obtaining nutrients from the external structures of other cells.

Factor 4. Polymers of Glucose and Fructose (14%).

These compounds are associated with exopolymers produced by gram positive cells and by yeast cells. Enzymes able to catabolize these are usually inducible, that is, if the compound is not present in the environment, then the cell does not expend the energy required to produce the enzyme. Bacteria that are capable of oxidizing these compounds might similarly respond to their presence by rapidly reproducing. Factor 5. Sugar Phosphates (6%).

These compounds are sugar phosphates and are universally important in energy generation. All cells must have the ability to utilize these biochemicals. Factor 6. Detergents (4%).

Detergents are long chain hydrocarbons with hydrophobic and hydrophilic ends. This Factor differentiates between Control and Plowed treatments.

Factor 7. Fatty Acids (4%).

This Factor differentiates Permeox™ treatments from both Plowed and Control. These fatty acids are important fermentation end products. Fermentation end products (propionate, acetate, lactate) are also found in Factor 1 , which differentiates Permeox treatments from Plowed. Factor 8. Polyamide (3%)

The polyamide, putrescine does not contribute to the ability to differentiate among the treatments. Factor 10. Methyl Pyruvate (3%)

Methyl pyruvate is the only compound contained in Factor 10. It does not contribute to the ability to differentiate among the treatments.

Factor 11. α-cyclodextrin (3%) This chemical is a polymer, similar to those found in Factor 4. Factor

11 provides additional information in the differentiation between the Control treatment and both aeration treatments. This is similar to the previously discussed relationship between Factors 7 and 1 ,

Factor 12. Sucrose (3%). Sucrose is a sugar that has been grouped by itself. All other sugars in the data set are partitioned amongst the other Factors.

Factor 13. Pyrimidines and Amides. (6%).

Thymidine and uridine are pyrimidines. Thymidine is found only in

DNA, while uridine is found only in RNA. This Factor does not contribute to the ability to differentiate among the treatments.

Tests of Community Differences in Biochemical Potential

Using Analysis of Variance, the variance in the factor scores for each bacterial isolate was evaluated and compared to the variation within and between treatment groups. Recall that the relative response to each of the groups of biochemicals is represented by each of the Factors. The response of each soil microbial community to each of the biochemical groups (Factors) could be examined separately.

The results demonstrate that the bacterial communities in the different treatments were significantly different with respect to nine out of thirteen Factors including the first four factors. In fact, the Permeox™ treatment could be differentiated from the Plowed treatment by Factor 1 , with

Permeox™ having positive factor scores while the Plowed treatment had negative scores (Figure 3 and 3a). This means that bacteria in Permeox™ treated soils contained enzymes able to oxidize compounds in Factor one, while the Plowed community did not contain these enzymes. Permeox™ also had positive differentiation from the Control in Factors 2 and 4 (Figure

3c - 3d), while in Factor 3 the Control had positive differences from the two treatments.

The Permeox™ bacterial community had strong positive responses to most of the compounds tested and reflects the highest enzymatic and biochemical diversity of the treatments tested. This suggests that the Permeox™ treatment has stimulated biochemical (enzymatic) diversity in the microbial community. Conclusions Five lines of evidence demonstrated that Permeox™ altered the bacterial community in creosote contaminated soils when compared to an untreated Control and mechanically aerated (Plowed) soils. Gram Reaction. There were proportionally greater numbers of gram negative bacteria in the treatments than in the Control. The Control plot contained a higher percent of gram positive bacteria (61 %), while the two treatments had approximately 50% gram positive bacteria. Bacterial species diversity. Results from the Shannon-Wiener index indicated that species diversity was highest in the Permeox™ treatment, intermediate in the Plowed treatment, and lowest in the Control. Oxidation of selected compounds. The bacterial community in the Permeox ™ treated soil was differentiated from both Plowed and Control bacterial communities in their ability to oxidize functionally similar amino acids and carboxylic acids, saccharides with beta-linkages, and polymers of glucose and fructose. Enzymatic diversity. The Permeox™ treatment had positive mean factor scores (oxidation occurring and therefore production of enzymes for the compounds found within a Factor) for the four important Factors generated using PCA. Plowed treatments had no positive mean factor scores for the four important Factors generated using PCA, and the Control bacterial community had positive factor scores to two out of four important Factors. There are several possible explanations for the change in the Permeox ™ microbial community: (1) Availability of a slow release constant and consistent oxygen source at the micro-scale may provide a more stable habitat. Presence of enzymes that can oxidize fatty acids, which are fermentation products, in the (Factors 1 and 7) Permeox™ treated soils suggests that there are active aerobic and anaerobic microzones. Increased environmental diversity provides more niches for more bacterial species. (2) Permeox™ chemically oxidizes or "activates" certain chemical groups on substrate molecules, in essence pre-treats the contaminant, making it more available. This "activation" of the carbon or energy sources would stimulate bacterial growth and reproduction, and increase microbial diversity (3) Permeox™ provides an energy subsidy. This would decrease competition for energy sources among bacteria and allow an increased diversity in the community. (4) Permeox™ stimulates production of exoenzymes thereby increasing the rate of complex molecule breakdown. The stimulation of the microbial community in the soils of the treated plots was reflected in the results of the creosote degradation study. Permeox™ treated soils exhibited the fastest degradation rates for total polynuclear aromatic hydrocarbons and the fastest rate constants for the most abundant polynuclear aromatic hydrocarbon (PNA) constituents (fluoranthene and pyrene). Although the isolates in this study were not tested for their ability to degrade PNA constituents directly, it can be inferred that the higher PNA degradation rates in the Permeox™-treated plots is related to the altered microbial community documented here.

Table 1. List of all compounds in the gram positive and gram negative Biolog plates. Those biochemicals common to both plates are also listed

GRAM POSITIVE ONLY GRAM NEGATIVE ONLY COMMON ^" ro BOTH adenosine aconitic acid (cis-) acetic acid inositol (m-) adenosinβ-5'-monophosphatβ adonitol alaninamide lactic acid (DL-) amygdalin aspartic acid (glycyl-L-) alanine (D-) lactose (alpha-D-) arbutin aspartic acid (L-) alanine (L-) lactulose cyclodextrin 9beta) butyric acid (alpha-keto-) arabinose (L-) maltose deoxyadenosine (2'-) butyric acid (gamma-amino-) arabitol (D-) mannitol (D-) fructose-6-phosphate carnitine (D-.L-) asparagine (L-) mannose (D-) galactoside (alpha-methyl-D-) citric acid butanedibol (2,3-) melibiose (D-) galactoside (beta-methyl-D-) erythritol (i-) cellobiose methyl pyruvate glucose (3-methyl-) ethanol (2-amiπo-) cyclodextrin (alpha) propionic acid glucoside (alpha-methyl-D-) ethylamine (phenyl-) dextrin psicose (D-) glutamic acid (N-acetyl-L-) formic acid fructose (D-) putrescine hydroxyphenyl acetic acid (para-) galactonic acid lactone (D-) fucose (L-) pyroglutamic acid inulin galactosamine (N-acetyl-D-) galactose (D-) raffinose (D-) lactamide glucoasaminic acid (D-) galacturonic acid (D-) rhamnose (L-) O lactic acid methyl ester glucuronamide gentibiose serine (L-) malic acid (D-) glucuronic acid (D-) gluconic acid (D-) sorbitol (D-) malic acid (L-) histidine (L-) glucosaminβ (N-acetyl-D-) succinamic acid maltotriose itaconic acid glucose (alpha-D-) succinate (mono-methyl-) mannan leucine (L-) glucose-1 -phosphate succinic acid mannosamine (N-acetyl-D-) malonic acid glucose-6-phosphate sucrose mannoside (alpha-methyl-D-) omithine (L-) glucoside (beta-methyl-D-) thymidine melezitose phenylacetic acid (para-hydroxy-) glutamic acid (glycyl-L-) trehalose (D-) palantinose phenylalanine (L-) glutamic acid (L-) turanose pyruvic acid prolime (L-) glutaric acid (alpha-keto-) tween 40 ribose proline (hydroxy-L-) glycerol tween 80 salicin quinic acid glycerol phosphate (D,L-alpha) uridine sedoheptulosan saccharic acid (D-) glycine (L-alanyl-) valeric acid (alpha-keto-) stachyose sebacic acid glycogen xylitol tagatose serine (D-) hydroxybutyric acid (alpha-) thymidine-5'-monophosphate succinic acid (bromo-) hydroxybutyric acid (beta-) uridine-5'-monophosphate threoninβ (L-) hydroxybutyric acid (gamma-) xylose urocanic acid Inosinβ

Table 2. Sixty-two biochemicals common to both gram negative and gram positive Biolog plates. Compounds are given with three letter code names, used in the PCA mal rix, and the name of the type of compound

Compound Code Type Compound Code Type acetic acid ACA carboxylic acid hydroxybutyric acid (gamma-) HBG carboxylic acid alaninamide ALD amide inosine INO ribonucleoside alanine (D-) DAL amino acid inositol (m-) INM alcohol alanine (L-) LAL amino acid lactic acid (DL-) LTA carboxylic acid arabinose (L-) ARA sugar lactose (alpha-D-) LAD sugar arabitol (D-) ABL alcohol lactulose LUL sugar asparagine (L-) ASG amino acid maltose MLT sugar butanedibol (2,3-) BOL alcohol mannitol (D-) MAN alcohol cellobiose CEL carbohydrate mannose (D-) MNE sugar cyclodextrin (alpha) ACY carbohydrate melibiose (D-) MBO sugar dextrin DEX carbohydrate methyl pyruvate MPY carboxylic acid fructose (D-) FRD sugar propionic acid PPA carboxylic acid fucose (L-) FRL sugar psicose (D-) PSC sugar galactose (D-) GOE sugar putrescine PUT polyamine M galacturonic acid (D-) GTA carboxylic acid pyroglutamic acid PGA carboxylic acid gentibiose GEN carbohydrate raffinose (D-) RAF sugar gluconic acid (D-) GLA sugar acid rhamnose (L-) RHS sugar glucosamine (N-acetyl-D-) GLD amino sugar serine (L-) SEL carboxylic acid glucose (alpha-D-) GLU sugar sorbitol (D-) SOR alcohol glucose-1 -phosphate GOP sugar phosphate succinamic acid SUM carboxylic acid glucose-6-phosphate GSP sugar phosphate succinate (mono-methyl-) SMM carboxylic acid glucoside (beta-methyl-D-) GBM sugar succinic acid SCA carboxylic acid glutamic acid (glycl-L-) GTG carboxylic acid sucrose sue carboxylic acid glutamic acid (L-) GTL carboxylic acid thymidine THY pyrimidine glutaric acid (alpha-keto-) GAK carboxylic acid trehalose (D-) THO sugar glycerol GLY alcohol turanose TRE sugar glycerol phosphate (D.L-alpha) GLP alcohol phosphate tween 40 TWF long chain hydrocarbon glycine (L-alaπyl-) GLN amino acid tween 80 TWE long chain hydrocarbon glycogen GLG carbohydrate uridine URD pyrimidine hydroxybutyric acid (alpha-) HBA carboxylic acid valeric acid (alpha-keto-) VAL carboxylic acid hydroxybutyric acid (beta-) HBB carboxylic acid xylitol XYO alcohol

Tables 4a, 4b, and 4c. Taxonomic identification of bacteria isolated from creosote contaminated soils amended with nothing (4a), Permeox (4b), and plowing (4c). Numbers are the rounded off similarity indices for the Biolog identification. Values less than 0.5 are considered unreliable identification. A number or "x" is given for each individual isolate.

* CONTROL pic

Bacillus insolitus 0.8 0.8

Bacillus megaterium 0.5

Brucella abortus biovar 2 0.3 0.5

CDC Group A-5 Subgroup B 0.6

CDC Group B-1/B-3 0.2

CDC Group D-2^* 0.2 0.6 0.1 0.3 0.2 0.2 0.3 0.5

Corynebacterium bovis 0.4

Corynebacterium jeikeium 0.4

Corynebacterium nitrilophilus 0.2

Curtobacterium citreum 0.4

Curtobacterium pusillum 0.6

Lactococcus garvieae 0.4

Pseudomonas cepacia 0.2 0.1 0.4

Pseudomonas corrugatta 0.2 0.4 0.2

Pseudomonas glathei 0.5 0.3 0.4 0.3

Pseudomonas mendocina 0.6

Pseudomonas paucimobilis A 0.6

Rhodococcus equi 0.1

Rhodococcus erythropolis 0.2 0 0

Rhodococcus luteus 0.3

Rhodococcus rhodochrous 0

Staphlococcus aureus 0.2

Weeksella zoohelcum 0

Xanthomonas maltophilia 0.8

No Rxn / No ID X X X X X

Table 4a Table 4b. Species names for Permeox plots.

COMBINED rø EOX PLOTS

Acidovorax facillis 0.0

Acinetobacter calcoacaceticus/qenospecies 1 0.2

Agrobacteriuim tumefaciens 0.3

Alcaligenes faecalis type II 0.5 0.8

Alcaligenes xylosoxydans ss xylosoxydans 0.2

Bacillus coagulans 0.6

Bacillus gordonae 0.4 0.4

Bacillus insolitus 0.6 0.6 0.3

Bacillus megaterium 0.7

Brucella abortus biovar 2 0.7

Capnocytophage gingivalis* 0.2

CDC Group A-5 Subgroup B 0.4 0.8 0.2 0.4

CDC Group D-2 0.3 0.4 0.4 0.3 0.3

CDC Group ll-l 0.8

Cellulomonas cartae 0.1

Clavibacter michiganensis insidious 0.3

Corynebacterium bovis 0.1 0.4

Corynebacterium pseudodiphtheriticum 0.7 0.5

Deleya aesta 0.6 0.4 0.5 0.2 0.6

Enterobacter cloacae B 0.2

Erysipelothrix tonsillarum 1 0.5

Gemella morbillorum 0.2

Gluconobacter cerinus 0.5

Haemophilus ducreyi 0.4

Haemophilus somnus 0.7

Hydrogenophaga flava 0.3

Klebsiella pneumoniae B 0.6

Lactococcus hordinae 0.4

Methylobacterium mesophilicum 0.6

Micrococcus diversus 0.5 0.4 0.6 0.3

Micrococcus luteus 0.2

Moraxella bovis 0.2

Moraxella osloensis 0.6

Neisseria polysaccharea 0.3

Pasteurella anatipestifer 0.7

Pasteurella pheumotropica heyl 0.7

Pseudomonas andropogonis 0.1

Pseudomonas cepacia 0.2

Pseudomonas corrugata 0.6

Pseudomonas glathei 0.3

Pseudomonas mendocina 0.6 0.3 0.5

Pseudomonas resinovorans 0.3

Pseudomonas syringae pv lachrymans 0.5

Pseudomonas vesicularis 0.2

Psychrobacter immobilis 0.4 Table 4b (continued) COMBINED PERMEOX PLOTS

Rhizobium meliloti 0.5

Rhodococcus erythropolis 0.4 0.4

Rhodococcus luteus 0.2 0.3 0.3 0.3

Staphlococcus caprae 0.1

Staphylococcus kloosii 0.4

Staphylococcus lentus 0.1 0.0 0.1 0.2

Streptococcus equi ss equi 0.7

Streptococcus porcinus 0.2

Vibrio harveyi 0.4

Xanthomonas campestris pv xanthosoma^* 0.3 0.5

Xanthomonas oryzae PV oryzae B 0.4

NoRxn/NolD X X X X X X X X X X X

All Positive / No ID X

Table 4c. Species names for Permeox plots.

⁵ COMBINED PLOW PLOTS

Actinobacillus seminis^* 0.4

Alcaligenes dentrificans / Piechaudii 0.8 1 0.9 1 1 0.9

Alcaligenes faecalis type II 0.5 0.3

Arcanobacterium haemolyticum 0.1

Bacillus brevis 0.5 0.6

Bacillus coagulans 0.4 0.5

Bacillus insolitus 0.8 0.8 0.8 0.5 0.7 0.7 0.9 0.7 0.8

Bacillus megaterium 0.7 0.3 0.2

Brucella abortus biovar 2 0.7 0.6

CDC Group A-4 Subgroup B 0.6

CDC Group A-5 Subgroup B 0.7 0.7 0.9 0.7 0.8

CDC Group B-1 / B-3 0.4 0.3

CDC Group DF-3 0.7

CDC Group D-2 0.5 0.4 0.5

Corynebacterium jeikeium 0.7 0.8

Corynebacterium pseudodiphtheriticum 0.5 0.4 0.4

Deinococcus radiophilus 0.3

Deleya aesta 0.6

Gilardi pink gram neg rod 0

Haemophilus ducreyi 0.7 0.5

Haemophilus somnus 0.5 0.6

Kingella kingae 0.4 0.5 0.5 0.4 0.2 0.3 0.5

Leuconostoc lactis 0.8

Micrococcus diversus 0.7 0.7 0.7 0.5 0.5

Micrococcus luteus 0.5

Moraxella atlantae 0.3

Moraxella bovis 0.6 0.6 0.3 0.8 0.3

Moraxella phenylpyruvica^* 0.3 0.6

Pasteurella anatipestifer 0.6

Pasteurella multocida 0.8

Pasteurella pheumotropica heyl 0.2 0.3

Pseudomonas avenae 0.5

Pseudomonas cepacia 0.7

Pseudomonas glathei 0.3

Pseudomonas nautica 0.3

Pseudomonas syringae pv citrulli^* 0.7

Rhizobium meliloti 0.7

Staphylococcus cohnii 0.6

Staphylococcus lentus 0.1 0.2

Staphylococcus sciuri 0.2 0.3

Streptococcus bovis (GP-D) 0.6

Xanthomonas campestris pv hederae D^* 0.6

Xanthomonas oryzae PV oryzae B 0.3 0.4

No Rxn / No lD X X X X X X X X X X

All Positive / No ID X 2

- 26-

Table 6. Correlation coefficients after Varimax rotation for the 62 biochemicals on each Factor. The percent variance in the original data accounted for by each Factor is given. Three letter biochemical codes are given in Table 3. Boxes outline those correlation coefficients greater than 0.50 and indicate which Factor groups those biochemicals.

Factor #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 Variance 25% 8% 18% 14% 6% 4% 4% 3% 3% 3% 3% 3% Biochemical

GTL 0.84 0.04 0.17 -0.1 1 0.01 0.1 1 -0.04 0.01 -0.04 0.15 -0.03 -0.13 -0.13

ASG 0.82 0.04 0.24 -0.03 0.00 0.21 -0.07 -0.09 0.06 0.10 -0.08 -0.05 -0.10

GLN 0.08 0.06 0.14 0.08 0.03 0.04 -0.10 -0.32 -0.12 -0.06 0.10 -0.03 0.06

ACA 0.79 0.08 -0.07 0.07 0.09 0.04 0.20 0.04 0.13 0.10 -0.05 0.08 -0.08

SCA 0.78 0.05 0.17 -0.18 -0.03 0.17 -0.05 0.32 0.07 0.16 -0.08 0.01 0.08

LTA 0.78 -0.07 0.24 -0.01 -0.01 0.09 0.06 0.15 -0.09 0.24 0.00 0.10 0.00

LAL 0.78 0.06 0.20 0.09 -0.04 0.18 -0.08 -0.32 -0.01 0.05 -0.01 -0.09 0.14

DAL 0.78 0.04 -0.07 0.02 0.13 0.08 0.09 -0.33 0.11 -0.03 0.07 0.09 0.01

PPA 0.77 0.00 -0.13 0.01 0.15 -0.11 0.24 -0.10 0.05 -0.13 0.00 0.11 -0.06

GTG 0.77 0.13 0.20 -0.03 0.08 -0.04 -0.10 -0.05 0.05 -0.10 0.09 -0.15 -0.08

SUM 0.74 0.01 0.21 -0.19 0.06 -0.11 0.10 0.23 -0.20 -0.09 -0.02 -0.18 -0.10

GAK 0.74 0.04 -0.18 -0.01 0.18 -0.02 0.15 0.19 0.26 0.04 -0.00 0.10 -0.04

SMM 0.73 0.07 0.24 -0.10 -0.02 0.15 0.01 0.39 0.18 0.13 -0.08 -0.08 0.04

PGA 0.68 -0.07 0.33 -0.15 -0.00 0.09 -0.10 0.08 0.07 -0.21 0.18 0.05 -0.08

HBA 0.66 -0.07 -0.05 0.08 -0.05 -0.07 0.37 -0.02 -0.10 -0.28 0.01 0.01 0.15

SEL 0.65 0.10 0.17 0.07 0.03 0.15 0.12 -0.36 0.20 0.02 -0.05 -0.08 0.20

HBB 0.65 -0.11 0.06 -0.08 -0.05 0.30 -0.10 0.34 -0.05 0.16 0.04 0.10 0.14

GLA 0.57 -0.13 0.18 -0.19 0.07 -0.19 0.11 0.07 0.12 -0.11 -0.08 -0.00 0.38

MPY 0.51 -0.16 0.13 0.10 -0.05 -0.02 -0.00 0.08 -0.04 0.54 0.11 0.03 0.13

LAD 0.01 0.83 0.17 0.22 0.10 -0.00 0.02 -0.05 -0.08 -0.06 0.14 0.07 0.03

MBA 0.13 0.78 0.13 0.31 0.21 -0.03 0.04 -0.01 0.04 0.09 0.10 0.01 -0.00

RAF 0.06 0.65 0.20 0.29 0.31 -0.15 0.17 -0.01 0.08 0.15 -0.04 -0.15 0.03

LUL -0.12 0.65 0.1 1 0.20 0.07 0.08 -0.03 0.09 -0.05 -0.30 0.05 0.09 -0.05

GBM 0.12 0.60 0.05 0.42 0.30 -0.03 -0.06 -0.07 -0.03 0.08 -0.04 0.07 0.05

ABL 0.20 0.03 0.84 0.02 -0.04 0.05 0.02 -0.07 0.02 0.13 0.00 -0.03 -0.06

GOE 0.10 0.24 0.77 0.24 -0.01 0.08 -0.00 0.08 0.03 0.01 0.11 0.12 -0.05

RHS 0.20 0.17 0.76 0.06 0.17 0.07 0.13 0.05 -0.18 -0.00 -0.03 -0.29 -0.06 Table 6 (continued)

Factor #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 Variance 25% 8% 18% 14% 6% 4% 4% 3% 3% 3% 3% 3% 6% Biochemical

MAN -0.04 -0.11 0.75 0.27 -0.00 -0.08 -0.05 -0.03 0.02 -0.10 -0.24 0.08 0.30 SOR 0.02 0.01 0.74 0.13 0.04 0.09 -0.1 1 -0.07 0.02 -0.10 -0.16 0.20 0.41 INM 0.31 0.18 0.71 -0.09 0.12 -0.01 0.05 -0.30 0.13 -0.06 -0.04 0.00 -0.01 ARA 0.22 0.09 0.70 0.10 0.03 0.04 -0.07 0.12 -0.05 0.07 0.33 0.16 -0.03 FRL 0.13 0.22 0.68 -0.02 -0.02 0.22 0.27 0.21 -0.18 0.04 0.03 -0.17 0.07 GLD 0.05 0.08 0.66 0.28 0.25 -0.1 1 0.05 0.09 -0.21 -0.06 0.04 -0.20 -0.00 GLY 0.29 0.09 0.65 -0.04 0.06 0.20 0.06 -0.10 0.09 0.23 -0.22 -0.02 0.23 INO 0.27 -0.05 0.63 0.18 0.09 0.11 -0.04 -0.14 0.21 0.18 0.09 0.07 0.30 MNE 0.00 0.10 0.61 0.53 0.05 -0.04 0.01 0.00 0.04 -0.14 0.04 0.10 0.08 FRD -0.04 0.04 0.56 0.49 -0.03 -0.15 -0.10 0.02 0.08 -0.10 0.00 0.07 0.30 GTA 0.33 -0.09 0.53 0.07 0.22 0.13 -0.31 0.01 0.34 -0.15 0.22 0.06 -0.02 GLU -0.02 -0.01 0.51 0.60 0.03 0.24 0.00 0.08 0.12 -0.03 0.1 1 0.11 -0.14 DEX -0.11 0.16 -0.01 0.86 0.05 0.06 -0.06 -0.02 0.11 0.13 0.04 -0.01 0.25 GLG -0.1 1 0.12 -0.00 0.83 0.10 0.04 0.08 0.00 0.15 0.08 0.03 -0.13 0.23 MLT -0.06 0.10 0.28 0.79 0.03 0.18 0.03 0.00 -0.04 -0.05 0.05 -0.01 0.16 CEL 0.03 0.26 0.13 0.79 0.06 -0.15 0.05 -0.05 0.04 0.09 0.03 -0.09 0.02 THO -0.05 0.20 0.20 0.72 0.14 -0.00 0.08 -0.01 -0.17 -0.09 0.10 0.32 -0.06 TRE -0.08 0.29 0.13 0.71 0.03 -0.14 0.14 0.05 -0.08 -0.02 -0.12 0.31 0.11 GEN 0.09 0.44 0.06 0.62 0.07 0.08 -0.02 -0.15 0.07 -0.21 0.17 -0.17 -0.08 sue -0.04 0.27 0.25 0.52 0.07 -0.12 -0.00 -0.01 0.05 0.04 -0.06 0.58 0.20

GSP 0.08 0.21 0.15 0.08 0.83 0.06 -0.06 -0.01 0.03 0.05 -0.01 -0.05 0.14 GOP 0.06 0.32 0.09 0.22 0.82 0.02 0.05 -0.02 0.02 -0.04 0.01 -0.03 0.03 GLP 0.18 0.13 0.06 0.03 0.73 0.06 0.28 0.02 -0.06 -0.08 0.12 0.14 0.16 TWF 0.42 -0.07 0.23 0.05 0.10 0.68 0.05 -0.01 0.16 -0.05 -0.08 -0.04 0.07 TWE 0.43 -0.03 0.27 0.03 0.10 0.65 -0.03 0.00 0.08 0.02 0.11 -0.06 -0.25 BOL 0.07 -0.02 0.16 0.10 0.15 -0.08 0.67 -0.04 0.18 0.10 0.18 -0.01 -0.03 VAL 0.21 0.17 -0.06 0.05 0.05 0.17 0.59 0.05 0.01 -0.23 -0.13 -0.00 0.40 PUT 0.19 -0.02 0.01 0.14 -0.02 0.13 0.13 -0.01 0.76 0.01 0.07 0.02 0.03 Table 6 (continued)

Factor #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13

Variance 25% 8% 18% 14% 6% 4% 4% 3% 3% 3% 3% 3% 2%.

Biochemical

ACY -0.02 0.29 -0.01 0.24 0.10 -0.00 0.14 -0.03 0.11 0.05 0.75 -0.05 0.17 URD -0.04 0.03 0.24 0.21 0.14 -0.02 0.09 -0.02 -0.05 0.07 0.14 0.06 0.75 THY -0.12 -0.06 0.13 0.36 0.22 -0.09 0.02 0.02 0.06 0.09 0.05 0.06 0.75 ALD 0.42 0.08 0.05 0.36 -0.00 0.16 0.22 -0.19 0.02 -0.15 0.10 -0.23 0.43 PSC 0.03 0.23 0.47 0.32 -0.00 -0.11 0.12 0.17 0.15 0.02 -0.29 -0.14 0.39

HBG 0.43 -0.04 -0.10 0.07 0.14 -0.03 0.29 0.33 0.35 -0.25 -0.08 -0.12 0.31

XYO 0.03 0.07 0.49 0.09 0.10 0.19 0.37 0.02 -0.33 0.17 -0.01 0.16 -0.01

Table 7. Grouping of biochemicals under each Factor Treatments which were shown to be significantly different ("differentiate") in the ANOVA are shown. Treatment 1 =Permeox, 2=Plowed, and 3=Control. Refer to Table 3 for the biochemical classes for the compounds. Percent of the variance explained by the Factors is given parenthetically.

Claims

Claims:

1. A composition for remediating hydrocarbon contaminated soil through enhancement of microbial growth, characterized by 30 to 80%

5 calcium peroxide;

0 to 30% buffer;

1 to 10% nitrogen;

0.1 to 10% potassium; and

1 to 10% phosphorous, wherein the phosphorous percentage is 10 expressed on the weight of P2O5.

2. The composition of Claim 1 , characterized in that: the amount of calcium peroxide used is 30 to 60% and the calcium peroxide has less than 18% active oxygen; and the buffer is selected from the group consisting of potassium 15 dihydrogen phosphate and calcium monohydrogen phosphate in an amount of from 0.01 to 1 parts by weight of buffer per part of calcium peroxide

3. The composition of Claim 2, characterized in that: the calcium peroxide has 10 to less than 15% active oxygen.

4. A method for bioremediating soil comprising a microbial system, a ,20 hydrocarbon food source and oxygen, characterized by: administering to the soil an effective amount of calcium peroxide on a substoichiometric basis relative to the food source, for remediating the soil; determining the system pH; 25 adding buffer as needed to establish a pH within the range of between 6 and 9; introducing an effective amount of fertilizer to the soil based on the weight of hydrocarbon in the soil, as needed, in a molar weight ratio of carbon to nitrogen to potassium to phosphorous within the range of from 30 100:10:10:10 to 100:10:1 :1 , for degrading the hydrocarbon; and permitting sufficient time in the soil for microbial interaction with the calcium peroxide to shift microbial population and enzyme production in a manner favorable to utilization of the carbon, wherein the shift differs from that which would occur from the addition of an equivalent amount of oxygen 35 alone.

5. The method of Claim 4, characterized in that from 0.01 to 1 % calcium peroxide based on soil weight having from 12 to 18 % active oxygen is administered;

0 to 2 parts by weight of buffer per part of calcium peroxide, selected from the group consisting of potassium dihydrogen phosphate, and calcium monohydrogen phosphate, is added; and the microbial interaction with the calcium peroxide is permitted over a time period of at least one week; to provide a greater rate of biodegradation than would occur from the addition of oxygen alone.

6. The method of Claim 5, characterized in that the molar weight ratio of carbon to nitrogen to potassium to phosphorous is 100:10:5:5 to 100:10:1 :1.

7. A method for bioremediating soil, comprising treating a microbial system having a hydrocarbon food source and oxygen, characterized by: administering to the system an effective amount of calcium peroxide on a substoichiometric basis relative to the food source for remediating the soil; determining the system pH; adding buffer as needed to establish a pH within the range of between 6 and 9; and permitting sufficient time for the microbial population in the system to interact with the calcium peroxide to shift microbial population and enzyme production in a manner favorable to utilization of the food source, wherein the shift differs from that which would occur from the addition of an equivalent amount of oxygen alone, introducing an effective amount of a nitrogen, potassium, phosphorous fertilizer for the microbial population to degrade the hydrocarbon, as needed; contacting the calcium peroxide with water to form a water transportable form of calcium which can be transported both through the system and intercellularly, and replenishing the calcium peroxide as needed to maintain a calcium peroxide concentration of at least 0.08% based on the soil weight..

8. The method of Claim 7, characterized in that: from 0.01 to 1 % calcium peroxide, based on the weight of soil, having from 12 to 18 % active oxygen is administered; and

0 to 2 parts by weight of buffer per part of calcium peroxide, selected from the group consisting of potassium dihydrogen phosphate, and calcium monohydrogen phosphate, is added; and the bacteria is permitted to react with the calcium peroxide for at least one week; to provide greater species diversity and enzyme diversity than would occur from the addition of an equivalent amount of oxygen alone; and the replenishing occurs at intervals of at least one month.

9. The method of Claim 8, characterized in that the molar weight ratio of nitrogen to potassium to phosphorous is 10:2:2 based on the weight of hydrocarbon.

10. A method for treating a microbial system comprising a hydrocarbon food source and oxygen, characterized by: administering to the system an effective amount of calcium peroxide having an effective active oxygen content of less than 18% active oxygen, on a substoichiometric basis relative to the food source, for remediating the soil; determining the system pH; adding buffer as needed to establish a pH within the range of between 7 and 8.5; and permitting sufficient time in the system for microbial interaction with the calcium peroxide to shift microbial population and enzyme production in a manner favorable to utilization of the hydrogen food source, wherein the shift differs from that which would occur from the addition of an equivalent amount of oxygen alone.

11. A method for biodegradation of a hydrocarbon in contact with a diverse microbial population comprising a mixture of aerobic and anaerobic regimes and at least one species capable of degrading the hydrocarbon, characterized by: administering a mixture comprising from 0.01 to 0.15 weight percent calcium peroxide having from 10 to 15 weight percent active oxygen; sufficient buffer to maintain a pH within the range of from 6 to 9; and sufficient fertilizer to permit microbiological growth of aerobic microbes capable of degrading fermentation products produced by the anaerobic microbes.

12. The method of Claim 11 , characterized in that the fermentation products are selected from the group consisting of one or more amino compounds, carboxylic acids; saccharides, and polymers of glucose and sucrose. 3. The method of Claim 13 characterized in that the hydrocarbon is selected from the group consisting of one or more of petroleum hydrocarbon, creosote, halogenated hydrocarbon, and polyaromatic hydrocarbon.