. V. 5033...: . mum—37.33... ‘ L gflw‘wan 1T. 1. y . 1““; . 5?. 1,... . way. 3.va «mu. .... .9,” LEEM‘M‘fié .. L“: k I. .u. . . am? air a... _ (5%, ..4 ..., ~33prtel“ u. nut 12.... .. .A l ‘1. .334st 3.13% ’ZDDl This is to certify that the thesis entitled The Relationship of Chromium and Selected Heavy Metals on the Microbial Community Structure in Sediments presented by Hilary Thatcher has been accepted towards fulfillment of the requirements for ' 0 Master 5 degree in Envaronmental Geosciences Major profes or Date April 25, 2001 0.7639 MS U is an Affirmative Action/Equal Opportunity Institution LIBRARY Michigan State UnIversIty PLACE IN RETURN Box to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 6/01 cJCIFIC/DateDw.p65—p.15 THE RELATIONSHIP OF CHROMIUM AND SELECTED HEAVY METALS ON THE MICROBIAL COMMUNITY STRUCTURE IN SEDIMENTS By Hilary Thatcher A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Geological Sciences 2001 ABSTRACT THE RELATIONSHIP OF CHROMIUM AND SELECTED HEAVY METALS ON THE MICROBIAL COMMUNITY STRUCTURE IN SEDIMENTS By Hilary Thatcher The purpose of this research was to determine if chromium and selected heavy metals have a relationship the microbial community structure in sediments. Soils contaminated with chromium as a result of waste discharge from a former leather tannery afforded an opportunity to study these influences. Sediment samples were taken from diverse environmental settings and analyzed for total metal concentrations, chromium partitioning and microbial community structure. Terminal-Restriction Fragment Length Polymorphism (T-RFLP) analyses was conducted to determine the structure of the microbial community. Multivariate statistical techniques were used to examine the relationships among sediment geochemistry and microbial communities. The results show that the microbial community structure be related to 1) total chromium concentrations, 2) partitioning of chromium among soil phases and 3) total organic matter concentrations. Other elements did not seem to have a relationship with the microbial communities. For my mother, Susan R. Richards, Thank you for all your help, -H.J.T iii ACKNOWLEDGEMENTS I would like to thank several of my teachers and fellow students who have helped me over the past years. Foremost, among them was my advisor, David T. Long, who was very influential at several critical stages of my academic career. Others include: my thesis committee members, Terence L. Marsh and Graham Larson, and my fellow graduate student Gary Icopini, who provided technical guidance that helped me accomplish the goals of this research. Also, a special thank you to Robert L. Anstey who helped answer a number of statistical related questions. Several others assisted with laboratory work and sample analyses during this research. I thank them all. iv TABLE OF CONTENTS LIST OF FIGURES ................................................................................................ I. INTRODUCTION ................................................................................... 1.1 Past Work I]. BACKGROUND INFORMATION... 2.1 Study Site ............................................................................................... 2.2 Site Characteristics ................................................................................ 3.1 Site Location and Sample Collections ................................................... 3.2 Clean Procedures ................................................................................... 3.3 Total Chemical Extractions ................................................................... 3.4 Solid Phase Organic Carbon Content .................................................... 3.5 Sequential Chemical Extractions ........................................................... 3.6 Microbial Analysis ................................................................................. 3.7 Multivariate Statistical Modeling .......................................................... IV. RESULTSANDDISCUSSION... 4.1 Total Chemical Extraction Results ........................................................ 4.2 Sequential Chemical ExtractionResults.................. 4.3 Microbial Analysis Results .................................................................... 4.4 Multivariate Statistical Modeling .......................................................... 4.5 Cluster Analysis ..................................................................................... V. GEOCHEMICAL-MIROBIAL COMPARISONS... 5.1 Absolute Chromium Concentrations and Microbial Population ........... 5.2 Chromium Partitioning and Microbial Population ................................ 5.3 Multi-Elemental Concentrations and Microbial Population .................. 5.4 Chromium, Iron, Manganese and Organic Matter and Microbial Population .............................................................................................. 6.1 Summary .......................................................................... ..................... REFERENCES ........................................................................................................ APPENDICES ......................................................................................................... APPENDIX A ......................................................................................................... A-l: Absolute Concentrations for Multi-Elements .................................................. vi ix M 13 l3 l4 13 16 l6 19 21 24 24 29 38 41 53 57 57 59 71 78 92 92 95 99 99 99 A-2: Logarithmic Data for Multi-Elements ............................................................. A-3: Non-Parametric Data for Multi-Elements ....................................................... APPENDIX B: Result of T-RFLP Analysis ............................................................. APPENDIX C: Sample ID Conversion Table .......................................................... APPENDIX D ......................................................................................................... D-l: R-Mode Analysis of Logarithmic Multi-Elemental Data ................................ D-2: R—Mode Analysis of Logarithmic Chromium Partitioning Data ..................... D-3: R-Mode Analysis of Non-Parametric Multi-Elemental Data .......................... D-4: R-Mode Analysis of Non-Parametric Chromium Partitioning Data ................ APPENDIX E .......................................................................................................... E—l: Q—Mode Analysis of Logarithmic Multi-Elemental Data ................................ E-2: Q-Mode Analysis of Logarithmic Chromium Partitioning Data ...................... E—3: Q-Mode Analysis of Non-Parametric Multi-Elemental Data .......................... E-4: Q-Mode Analysis of Non-Parametric Chromium Partitioning Data ................ vi 105 111 115 137 138 138 141 143 146 148 148 149 150 151 W Table 1. Soil descriptions for each sample site. ....................................................... Table 2. Sequential Chemical Extractions Procedure .............................................. Table 3. Total concentrations of chromium, iron and manganese in sediment samples. Percent organic matter is given in column four. A dashed line indicates that there are no data for that sample .................. Table 4. Sequential chemical extraction data for chromium. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). The dashed line indicates that there is no data for that sample .................................................................. Table 5. Example of T-RFLP data .......................................................................... Table 6. Factor scores for Q-mode factor analysis for total chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor .............................................................. Table 7. Factor scores for Q—mode factor analysis for sequential chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor ................................................... Table 8. Results of R—mode factor analysis on soils from the total extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table ........................................................... Table 9. Results of R-mode factor analysis on soils from the sequential chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table ............................................................................................................ vii 18 26 32 39 43 44 46 50 Table 10. t-Test analysis results at a confidence level equal to 95%. Cluster A represents absolute chromium found in sample sites that group within cluster 4 from figure 5. Cluster B represents absolute chromium found in sample sites that group within clusters 1,2 and 3 from figure 5 .............................................................................................. Table 11. Comparison table showing chromium partitioning versus microbial populations. The results of the sequential chemical extraction cluster tree (Figure 7) are listed in column one along with the cluster number that the sample can be found in. The shaded area indicates what cluster the sample site is associated within the microbial cluster tree. . ............................................................................. Table 12a. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by exchangeable (EX) phase ........................................................................... Table 12b. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by weakly acid soluble (WAS) phase ........................................................ Table 12c. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by easily reducible (ER) phase ................................................................... Table 12d. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by moderately reducible (MR) phase .............................................................. Table 12e. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by basic oxidizible (OX1) phase. .................................................................... Table 121'. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by acid oxidizible (0x2) phase. ..................................................................... Table 13. Comparison of multi-elemental concentrations and microbial community structure. The results of the total chemical extraction cluster tree (Figure 6) are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. .......................................................................... viii 58 62 65 66 67 68 69 70 74 Table 14. Comparison of chromium, iron, manganese and organic matter cluster tree with the microbial cluster tree. The results of the chromium, iron, manganese and organic matter cluster tree are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree ............................................... Table 15. Comparison of chromium, iron, and manganese cluster tree with microbial cluster tree. The results of the chromium, iron and manganese cluster tree (Figure in the appendix) are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree ................................................................ Table 16. Comparison of iron, manganese and organic matter cluster tree with microbial cluster tree. The results of the iron, manganese and organic matter cluster are listed in the first colume. The shaded area indicates what cluster the sample is associated within the microbial cluster tree .................................................................................................. ' Table 17. Comparison of iron and manganese cluster tree with the microbial cluster tree. The results of the iron and manganese cluster tree are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree ............................... ix 81 84 87 90 LIST OF FIGURES Figure 1. Map of the study site, showing the different vegetation types (modified from Icopini, 2000). .............................................................. 5 Figure 2. A map of the study site, showing sample sites and locations. .................. 7 Figure 3. X-Y scatter plot comprised of total concentrations of chromium, iron and rmnganese. Scales are logarithmic. Organic matter is represent by OM in the legend. .. ............................................................ 28 Figure 4. A summary of the sequential extraction data represented as average percent of chromium extracted from each phase. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2) ................................... 37 Figure 5. Microbial cluster tree produced using data using data fiom Table B-1 , Appendix B. The cluster tree has been divided into four clusters as indicated by the numbers 1,2,3 and 4. The x-axis is the measurement of the sequence distance ..................................................... 40 Figure 6. Cluster tree of soils from total chemical extractions. The cluster tree has been divided into five clusters as indicated by the numbers 1,2,3, 4 and 5 .......................................................................................... 55 Figure 7. Cluster tree for chromium from sequential chemical extractions. The cluster tree has been divided into four clusters as indicated by the numbers 1,2,3 and 4. ............................................................................... 56 Figure 8. Pie diagrams representing percent chromium for each sequential chemical extraction phase. Cluster 1, 2 and 3 represent geochemical clusters from Table 11. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2) ........................................... 64 Figure 9a, b and c. Geochemical fingerprint of multi-elements. The average concentration for each elements was calculated and divided by the average world soil concentration and then converted to log 10, this number was then graphed for the five major clusters that were produced in Figure 6 ............................................................................... 76 Figure 9d and e. Geochemical fingerprint of multi-elements. The average concentration for each elements was calculated and divided by the average world soil concentration and then converted to log 10, this number was then graphed for the five major clusters that were produced in Figure 6 .................................................................................. Figure 10. Cluster tree of absolute chromium, iron, manganese concentrations and percent organic matter ............................................................................... Figure l 1. Cluster tree of absolute chromium, iron and manganese concentrations... Figure 12. Cluster tree of absolute iron, manganese concentrations and percent organic matter ............................................................................................ Figure 13. Cluster tree of iron and manganese concentrations ............................... xi 77 80 83 86 89 I. INTRODUCTION In recent years bioremediation has been used as a remediation technique for dealing with contaminated sites. Bioremediation is the use of microorganisms to biotransform or degrade hazardous organic contaminants in soils, subsurface material, water, sludge’s and residue to a form that is less toxic or less bioavailable. Although they cannot be destroyed or degraded, bioremediation techniques still can be used to remediate sites contaminated with metals. In such cases, the form of the metal in solutions (e. g., changes in redox state, complexing) can render the metal less toxic or the metal can be made immobile, biostabilized, in sediments and thus less bioavailable. Maintaining the integrity of the microbial population is essential for continued immobilization of metals in sediments. Thus, it is important to understand how the contaminant affects the microbial community in order for biostablization efforts to be successful. The purpose of this research is to examine how or if metal contaminant might influence the structure of a microbial community. The study site is a wetland that received chromium contaminated waste fiom a former leather tannery and offers a range of chromium concentrations that allows for the study of the relationship between metal contamination and microbial populations. 1.1 - Past Work There are two aspects in understanding the interaction among metals and microbes; one aspect is the affect that microbes have on metal form and cycling, the other is the influence that metals have on microbial populations. The most studied of these is the specific influence microbes have on a form of metal. Microbes use metals as terminal electron acceptors in anaerobic respiration, which greatly influences the geochemistry of these metals (Lovely, 1995). One example of this influence is the microbial reduction of manganese (IV), which can serve to oxidize organic matter in contaminated aquatic sediments, and has the potential to reduce manganese minerals and the release of trace metals bound to manganese (IV) oxides (Lovely, 1993). Microbial oxidation of organic contaminants coupled to iron (III) reduction removes significant amounts of pollutants from many contaminated aquifers (Lovely, 1997). Some iron (DD-reducing microorganisms also can reduce contaminated metals and metalloids such as uranium, technetium, cobalt, chromium and selenium (Lovely, 1997). It was found that microbes could be used to remove uranium from uranium-contaminated waters and soils. Uranium can potentially be immobilized in subsurface environments by stimulating the activity of U (VD-reducing bacteria (Lovely, 1997). Besides uranium, the microbial reduction of chromium (VI) to chromium (III) has been documented (Llovera et al., 1993; Shen and Wang, 1994). It is possible to bioremediate sites polluted with chromate or dichromate [chromium (VI)] by stimulating reduction of the chromium (VI) to chromium (ID) by bacteria (Wang and Shen, 1995). A variety of bacteria are known to be able to reduce chromium (VI) to chromium (HI) enzymatically (DeLeo and Ehrlich, 1994; Wang and Shen, 1995). Chromium (III) may hydrolyze and precipitate as a chromium-hydroxide or it may bind to the remaining soil organic matter (Palmer and Puls, 1994). Few researchers have investigated the influence of metals on microbial populations. Kolesnikov et al., (1999) studied the effect of copper, zinc, cadmium, mercury and lead on the microbial system in chemozem, a grassland soil with a dark humic horizon more than 25 cm thick. The effect of copper, zinc, cadmium, mercury and lead concentrations on the microbial system in chemozem was studied in vegetative pots 3, 15, 30 and 180 days after contamination (Kolesnikov et al., 1999). They found that contamination by these heavy metals had a significant efiect on the populations of the soil microorganisms and the structure of the microbial communities in chemozem, but the influence was not well quantified or understood (Kolesnikov et al., 1999). Ibekwe et al., (1998) tested if the toxicity of metals to plants and microbes depends on the chemical activities of metals in the soil. They found that there was a significant decrease of total cell counts of rhizobia bacteria when coupled with high levels on zinc (II) and cadmium (II). Meyer et al., (1998) studied the eflect of depleted uranium (DU) on soil fimction and fimctional diversity of bacterial communities. Depleted uranium, as defined by the Department of Defense, contains less that 0.3% of 235U. Bench topsoil microcosms were used and constructed from 2-quart Mason jars. Mixed-bed ion exchange resin bags were placed in the bottom of each jar. These bags allowed for the indirect estimation of soil nitrogen availability in the microcosms via a LECO CHN-IOOO Analyzer. Soil function and fimctional diversity were determined through the use of Biolog plates. Biolog plates are plates that consist of a number of wells containing tetrazolium dye, supemate from soil samples taken from the microcosms were combined with a potassium phosphate buffer (pH = 7) and placed in the wells. The Biolog plates were incubated in order for color development to occur. An intense color indicates that cell respiration took place. The results showed that DU reduced bacterial functional diversity when DU concentrations were greater then SOOppm From the above studies, there seems to be a growing body of evidence that metals can alter microbial community structure. Much of the work has been done in the laboratory. More fieldwork is needed to better understand metal-microbial community relationships. The previous studies also were based on total metal concentrations. The form of the metal and associated geochemical structure of soils and sediments are also likely to play a role in how metals influence microbial community structure. For example, questions tlmt need to be address might include: how do total abundances of other associated elements and organic matter affect the microbial population, how does the partitioning of metals among the various phases of sediments change how metals influence the microbial community structure. The purpose of this study is to gain insights into these questions by determining if and how the absolute abundances of chromium, the partitioning of chromium among soil phases, chromium's association with selected heavy metals and organic matter influence the microbial community structure in sediments. This is done by quantifying the amount of heavy metals in sediments, examining the microbial community structure and comparing the geochemical data with the microbial analysis through the use of multivariate data analysis (factor and cluster analyses). II. BACKGROUND INFORMATION 2.1 — Study Site The study site was located in Sault Ste. Marie, Michigan. This site was home to a leather tannery that operated from the 1890’s to the late 1950’s. Vegetation was abundant and the entire site was considered to be a wetland, see Figure 1 (Cannelton, 1999). The surface soil was abundant in organic matter and in a number of areas the soils have been described as peat (Cannelton 1992). The site was covered by waste produced from the tannery such as; scrap leather, hair, bricks, concrete, scrap wood, scrap metal, glass and cans (Cannelton, 1999). Wooded Wetland Running Water Grassy Wetland Pond I E] I Swarnpy/Cattails @To Be Removed Woodland Grass 123% Beach Figure 1. Map of the study site, showing the different vegetation types (modified from Icopini, 2000). The tanning process used harsh chemicals when processing leather. Chemicals such as hydrochloric acid, dichromate salts and sodium hydroxide were used and resulted in waste containing high concentrations of chromic salts. The vats used were made of galvanized metal, which would result in liquid waste containing heavy metals (Ellis, 1998). Previous investigations at this site have concluded that the disposal of waste generated from leather tanning operations resulted in significant heavy metal contamination especially chromium, of the near surface soils to a maximum depth of approxirmtely six feet below land surface (Ellis, 1998). These investigations have also found chromium in the form of chromium (III), which is typically very insoluble (Cannelton, 1999). Previous soil speciation studies showed that chromium was predominantly extracted by the moderately reducflile (MR) and basic oxidizable extractions (OX1) (Icopini, 2000). This was interpreted to indicate that the most likely form of chromium in the soils was either chromium hydroxide or chromium associated with organic matter (Icopini, 2000). The aqueous phase chromium concentrations in the surface and pore waters at the site were higher than would be predicted by inorganic thermodynamic calculations. These slightly elevated concentrations appear to be related to the formation of Cr-DOCmmplem and the dominant form in the soils were Cr (OH); (Icopini, 2000). 2.2 - Site Characteristics An engineered embankment of boulders was installed along the entire shoreline of the site property in the early 1990’s, to eliminate erosion and transport of heavy metals impacted soil fi'orn the site by the river flowing adjacent to the site. A lS-foot high ridge, extending east to west across the middle of the site, separates areas of higher elevation and lower elevation. As a result of the embankment and ridge, an extensive wetland, that was generally water-saturated throughout the year, had been created. Wooded and grassy wetlandareasmake up the bulkofthe sampling area(Figure 2). Figure 2. A map of the study site, showing sample sites and locations. Soil types and textures at the site are spatially heterogeneous (Ellis, 1998). Descriptions of soil at the sample points are given in Table 1. The term soil in this study is used to describe the near surface unconsolidated geologic and detrital organic material in both upland areas and low-lying wetland areas of the site (Ellis, 1998). Soil textures were described in the field during collection of soil samples during the initial site characterization (Ellis, 1998) (Table 2). Samples were collected at the surface and at different depths. Sampling intervals below land surface were taken at depths of 1.0, 1.5, 3.0, 3.5, 4.5 and 6.0 feet. An AMSTM hand auger was used to remove intervening material between the sampling depths (Icopini, 2000). Samples were labeled according to site location and depth. Samples that were collected at the surface are identified by site location only. Some sample sites were collected more than once, these sample sites end in a Roman numeral. Table 1. Soil descriptions for each sample site. : .' o, ’ "I:’\‘ "'4. ‘ ' :1 . r-‘g «‘03-. ‘. , 9%.; ~ gm . (‘H fw".‘:; u".:; if? {:g'i’ , ‘3‘; ' My 1' “i am ‘Ea "1’ (AW-kid V , IIark brown iltand C 'v ‘ying rganic w: erwith hips ofred B7 H17(V) 3.5 aturated ganic tter and ' clay. k brown M243.0 [:Ean coarse B9 H19 lack silty with lumps of ' gravely erial N21 Very dark brown silt iwith lots of roots and other organic matter C4 ark brown iltysand, tsand oodchips [121 kbrown iltysand estotan N21(II)1.5 Very dark brown silt with lots of toots and bther organic matter C8 lack silty 120m) Dark, brown organic rich N23 ark brown rganic er with mesilt C10 ark brown iltysand ark brown rganic rich ' sand hair 022m) Table l (cont’d). Soil descriptions for each sample site. mfg) '~, 5.33:; ' ’VW‘V'M “‘3! I ;~ xflr 1‘1 ’2‘ é“?:l‘~é"‘“i1..'7.‘3‘-a if” :2 ,, . . E ' . ... ~‘-_~ , l F ‘f , ’. ‘ i " "hi": ' a. r . -r g. . ””117“. .- WI»: -~.'-.- ., , ' . - . 4 " - mi - - ‘t§ 7' .~-~. “'4‘ "I' " ’v m". ‘ _":'y’ _\<.: .‘r,¢‘§‘ A; - --.-‘A~l 5'I »: . 5 3 7;? lack/dark Very dark Q24 ark Bro own silt brown silt rganic rich ' lots of with lots of ilty sand organic matter roots and prganic matter, wet H17 (I) Dark brown K20(II)1.5 Wood Q26 lack mud, ilt and 'ch detrital rganic lay er, very ist H17 (1]) k brown K22 Very dark T27 ery dark ilt and brown/black wn silt, rganic organic rich rganic rich er, very ' with lots ' roots and ist f roots, wet ' H17 (III) k brown K22(Il) ery dark U26 lBrown sand ilt and own/black rganic rganic rich er, very ilt with lots ist f roots, wet, ' wood H17(IV) k brown M20 k, brown M20 k, brown ilt and rganic rich rganic rich rganic ilty sand. ilty sand. tter, very ttom 2" ttom 2" ist usty tan silty usty tan silty H17(V) ark brown M24 k brown M24 brown ilt and ilt and ilt and rganic aying ecaying er, very rganic matter rganic rmtter ist l2 III. METHODS 3.1 - Site Locations and Sample Collection Soil sampling locations were located by designing a sampling grid that covered the entire site (Ellis, 1998). A GIS analysis was performed on a preexisting data set (Cannelton, 1992; Cannelton, 1995) to determine the optimum spacing between sampling locations and led to the development of the sampling grid used in this study (Icopini, 2000). An existing local datum was used as the starting point for the grid. The sampling locations were then established by measuring distances with surveying equipment and a ZOO-fl. steel tape from known positions (Icopini, 2000). Once located, each sampling location was marked with a labeled wooden stake. The locations of the 74 sampling sites that resulted are shown in Figure 2. Soil samples were placed in 125ml specimen sample cups and frozen until analyses. Soil samples were taken at varying depths at each sampling node. In order to accomplish this, soil cores were collected with an AMSTM stainless steel, split spoon, coring device, with two-inch diameter, plastic core liners to contain the sample (Icopini, 2000). Sampling intervals below land surface were taken at depths of 1.0, 1.5, 3.0, 3.5, 4.5 and 6.0 feet. 3.2 — Clean Procedures The water that was used for preparing solutions, reagents, and washing glassware was distilled deionized water (DDW). All the glassware that was used for making standards and reagents was rinsed three times with DDW, acid-washed in 10 % HCl for 13 24 hours then rinsed three times with DDW, and soaked in DDW for 24 hours. Items were then placed into containers, which were then placed into a class 100 hood until dry. Nucleopore polycarbonate membrane filters (0.4 micrometer (um)) were used for filtering extraction leachate from samples. These filters were cleaned in dilute HN03 for 24 hours, then rinsed with DDW and soaked in DDW until use. 3.3 - Total Chemical Extractions Total chromium, iron and manganese concentrations were determined using a Perkin-Elmer 5100 Atomic Absorption Spectrophotomer (AAS) Flame. Other elements were determined by inductively coupled plasma mass spectrometry (ICP-MS). The instrument was a Micromass Platform with hexapole technology. This technology allows for the determination of elements such as arsenic, not easily determined by more traditional ICP-MS technologies. The other elements included potassium (K), chromium (Cr), manganese (Mn), iron (Fe), barium (Ba), magnesium (Mg), vanadium (V), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), strontium (Sr), cadmium (Cd), lead (Pb), aluminum (Al), selenium (Se), scandium (Sc), titanium (Ti), mercury (Hg) and calcium (Ca). Analyses were done in the Geological Sciences Laboratory at Michigan State University. Cahbration standards for sample analyses were prepared using DDW and the extraction chemicals to form the background matrix and certified 1000 milligrams per liter (mg/L) stock solutions. Necessary dilutions of leachate samples were prepared to maintain similar matrices between samples and standards. All chemicals used were analytical metal grade. Analytical precision, in terms of the relative standard deviation, l4 was set at less than 15 % for all AAS. Analytical accuracy was assessed by comparison to National Institute of Standards and Testing (NTST) standard reference materials (SRM). Standard calibration curves were compared to SRM 1643c or l643d (Trace Elements in Water) values, which were required to be within 15 % of certified values (Ellis, 1998). The following was the method that was used for total metal armlysis, digestions were conducted by Slmne Snavley: l. Dried samples were placed into ceramic mortar and ground into a fine powder with a pestle. Approximately 0.500g of sample was then weighed into a Teflon digestion vessel. This was repeated for each sample. Mortar and pestle were rinsed with DDW in between each grinding of sample in order to prevent cross contamination. Ten milliliters of lSN HNO3 was added to each Teflon vessel. Caps were screwed onto each Teflon vessel and a pressure monitor was attached to any sample that was not a blank. The vessels were placed into a tray that was then rotated inside a CEM MDS-81D microwave. The microwave was then programmed to run for 15 minutes at 100% maximum power with pressure regulated to a 150 psig maximum followed by a 20 minute (0% power) cool down period to allow pressure in the vessels to fall below 10 psig. A CEM MDS-81D microwave with pressure regulation was used for digestions. 15 4. After digestion was completed 50ml of DDW was added to each vessel and mixed thoroughly. Samples were then filtered through a 0.4urn Nuclepore filter into 60ml acid washed bottle. 3.4 — Solid Phase Organic Carbon Content The organic carbon content of the soils was determined by a loss on ignition method. Organic matter content was determined on sub-splits of homogenized soil taken prior to the sequential chemical extractions (Icopini, 2000). The method was modified after a procedure developed by researchers from the Department of Soil Science at the University of Wisconsin, Madison, WI (Shulte et al., 1991). Analyses were done in the Plant and Soil Testing Lab at Michigan State University. 3.5 - Sguential Chemical Extractions Selective chemical extractions were used to remove metals associated with different phases of soils. These extractions are designed to target specific phases within the sediment such as carbonate minerals. Chemical extractants were applied to a soil sample in sequence starting with the least aggressive extractant (Icopini, 2000). The extraction phases are as follows (Table 2): exchangeable (EX) (metals bound to exchange sites on clays), weakly acid soluble (WAS) (metals associated with carbonates), easily reducible (ER) (metals associated with manganese-oxides), moderately reducible (MR) (metals associated with Fe-oxides), basic oxidizible (OX1) (metals associated with organic matter) and acid oxidizible (OX2) (metals associated with sulfides). The sequential chemical extraction procedure (Table 2) used to determine heavy metal 16 speciation in this study was similar to that developed by Tessier et al., (1979), with modifications by Belzile et al., (1989) and Matty (1992). Chromium concentrations in the sequential chemical extractions were determined using a Perkin Elmer 5100 Atomic Absorption Spectrophotometer. Calibration standards for sample fluid analyses were prepared using DDW and the extraction chemicals to form the background matrix and certified 1000 milligrams per liter (mg/L) stock solutions (J .T. Baker Analyzed). All chemicals, reagents and blanks used were analytical metal grade or better. 17 Table 2. Sequential Chemical Extractions Procedure. Exchangeable Exchange Sites (EX) On Clay 10 mL Weakly Acid Carbonates Minerals 1.0M NaOAc, pH 5 20° C, 5 hours Soluble 10 mL (WAS) Easily Reducible Manganese- Oxides 0.1M NHZOHHCI 25° C, 5 hours (ER) and Reactive Iron- in 0.1M HN03 Oxides 25 mL Moderately Crystalline Iron- 0.04M NHZOHHCL 96° C, 6 hours Reducible Oxides v/v 25%HOAc (MR) 20 mL Basic Oxidizable Organic Matter NaOCl, pH 9.5 96° C, 15 min. (OX1) 3 times, 6 mL then 3.2M NH40Ac 25° C, 1 hour 5 mL Acid Oxidizable Sulfides 0.02M HNO3 85° C, 5 hours (0x2) 3 mL 30% H202, pm 25° c, 1 hour 8mL 3.2M NH40AC SmL then add DDW to make 25mL l8 3.6 - Microbial A_n_alyiis_ DNA Extraction for T-RFLP: Community DNA was extracted fiom soil using Soil DNA Isolation kits from MoBioTM and procedures recommended by the vendor. O.25--5 g of soil was added to the lysis buffer and incubated at 70°C for 5 minutes. The sample was vortexed for 10 seconds followed by a second incubation at 70°C for 5 minutes. The remainder of the protocol was as described by the vendor. Generally, DNA extracted using this procedure was of suflicient quality for amplification with standard PCR protocols. Purification of DNA: If necessary, extracted DNA was further purified on 1% low melting point agarose (Boehringer Mannheim). This step efiectively separates humics from genomic DNA and yields DNA of uniform purity. The band containing genomic DNA was excised, the agarose was digested with agarase (Boehringer Mannheim) according to manufacturer’s protocol, and the DNA was separated, washed, and concentrated in a Microcon- 1 00 colurrm (Amicon, Inc. Beverly, Mass). PCR Amplification and Purification of PCR products for T-RFLP: The primers used for T-RFLP analysis were the 27-forward Hex-labeled “eubacterial” primer; 5’-AGA GTT TGA TCC TGG CTC AG (Operon Inc.), and the 1392 reverse, 5’-ACG GGC GGT GTG TRC (Operon Inc.). PCR amplifications were performed in a final volume of 100 pl under the following conditions; 1.25-2.5 units of Gibco-BRL Taq polymerase and 1x buffer supplied by the vendor, 3mM MgClz, 0.5 uM EU8F hex, 0.25 M 1392R, O.25mM dNTP’s, 4ng/ul BSA, and lOng to lOOng of community DNA per reaction. Amplification was performed in either a PE 9600 or PE 19 2400 thermocycler progranmled with the following parameters; a pre-cycle soak at 94°C for 4 minutes followed by 35 cycles of 94°C for 30 seconds, 60°C for 30 seconds, and 72°C for 1.5 minutes, terminated with a 10 minute extension at 72°C. PCR product was purified with Promega Wizard PCR Preps according to the vendor’s protocol. DNA was washed off the column with 60ul of water and quantitated spectrometrically at 260nm. Restriction digests: Restriction digests were performed using Rsa I, Hha I, and Msp I from Gibco- BRL according to manufacturer’s guidelines. 200-400 ng of PCR product was digested in a 20p] volume with 15 U of enzyme for not more than 3 hours. Sequencing gel parameters for T-RFLP: Restriction digests of PCR amplified community DNA were denatured at 94°C, chilled and loaded onto a 36-cm, 6% denaturing polyacrylamide gel. Electrophoresis was for up to 20 hours in an ABI automated sequencer (model 373A, Applied Biosystems Instruments, Foster City, Calif.) run in GeneScan mode with limits of 2,500V and 40 mA. Each lane included both sample and ABI Tamara 2500 size standards (Liu et. aL 1997). The determination of terminal restriction fragment sizes was made with reference to the internal size standards by the vendor provided software (GeneScan 2.1). T-RFLP data analysis: Initial analysis of the ABI gels was performed with GeneScan 2.1 sofiware fi'om ABI. In general, the differences in fragment sizing between gel lanes was usually less than 0.5 base although extended reads and gel anomalies could increase differences up to 1.0 base. Lanes were aligned one to another using the terminal fiagments common to all 20 lanes as conserved landmarks similar to a conserved sequence island in a gene. Dr. Terence L. Marsh at the Center for Microbial Ecology conducted the T-RFLP analysis. 3.7 — Multivariate Statistical Modeling Two types of multivariate statistical approaches were used in this study, factor analysis and cluster analysis. Factor analysis was used to establish relationships among the metal concentrations and the microbial communities. The analysis was done on three variations of the data 1) absolute concentrations, 2) logarithmic (data transformed logarithmically in attempt to account for log-normal distributions, and 3) ranked (a non- parametric study). It was considered that absolute differences in metal concentrations might be important in microbial processes; therefore, the results and interpretations of this study are presented in absolute concentrations. Factor analysis is a multivariate statistical technique that is designed to reduce the number of variables in the system and combines variables that behave similarly into “factors” (Rumrnel, 1968). The two types of factor analysis that were used in this study are Q-mode and R-mode. Q-mode factor analysis was used to analyze the data sets (total metal concentrations and chromium partitioning among the sediment phases) and to divide samples into groups that were similar in terms of their variables. R-mode factor analysis was used to analyze the data sets, which divides variables into similar groups rather than samples. These analyses were done using SASTM for personal computer and followed techniques outlined by Long et al., (1992). Cluster analysis was performed on both total and sequential chemical extraction data sets to identify homogenous subgroups within the data set. Hierarchical cluster 21 analysis joins the two “closest” samples as a cluster and continues joining a sample with another sample, a sample with a cluster or a cluster with another cluster until all samples are combined into one cluster. A Ward-Euclidean joining algorithm was used to define how distances between clusters were measured. Ward’s method averages all distances between pairs of samples. The Euclidean distance is the "total geochemical difference" between any two samples, the distance matrix is then clustered using an algorithm that searches the rmtrix for pairs of samples that are most closely related, then iteratively adds samples to these closely related samples by collapsing the matrix as samples are added to clusters. The final result was a tree that groups samples into clusters. The analysis was done using SYSTATT”, The microbial data were clustered using a program called PaupTM for MacInto sh by Sinauer Publishing. The data entered into PaupTM were in binary form. Maximum likelihood was the statistical approach used for the microbial cluster analysis. This technique searches for and finds the tree that maximizes the probability of observing the tree obtained from the data (Burlage, 1998). This approach was the most analogous to that used by SYSTATTM for the geochemical cluster analysis. The geochemical cluster trees were then compared with the microbial cluster trees to determine if clusters could be matched between the two types of data. This posed a slight problem because the two data types were not clustered using the same clustering program due to the difleremes in the nature of the data sets, the geochemical data was non-binary data, where as, the microbial data was binary. Attempts made to cluster both data sets in one cluster program were unsuccessful. NEXUSTM (Page, 1998) is a program that has the potential to not only cluster both binary and non-binary datasets, but 22 also compare the results. However, formatting the rather complicated binary data from the microbial analysis, to import into the program was unsuccessful. Thus, comparisons of the results from the clustering of the geochemical and microbial datasets were done visually, but informed by study of geochemical trends. 23 V1. RESULTS AND DISCUSSION 4.1 - Total Extraction Results Table 3 shows total concentrations of chromium, iron, and manganese and percent organic matter (OM) in the soils. Chemical concentrations for other elements are shown in Table A-1 , Appendix . Iron, manganese and organic matter are singled out in Table 3 because they are known to influence the cycling of chromium. Iron oxides and particulate organic matter can sequester chromium (Takacs, 1988). Some iron (III)- reducing microorganisms can reduce chromium (Lovely, 1997). The oxidation of Cr(III) to Cr(VT) by Mn-oxides has also been demonstrated in a number of studies (Schroeder and Lee, 1975; Bartlett and James, 1979; Takacs, 1988; Eary and Kai, 1987). Dissolved organic matter can complex chromiurn(III), which increases the solubility of chromiumflll) in soil environments (James and Bartlett, l983a,b; Davis et al., 1994; Walsh and O’Halloran, 1994 a and b). The results of the total concentrations fi‘om this study are similar to those fi‘om the earlier study at the site (Ellis, 1999). Concentrations for chromium show great variability from sample to sample, ranging from approximately 9 ppm (sample BS) to 282,000ppm (sample H17 (V) top). The average concentration for chromium is 25,000ppm. Sample J19 (III) 3.5 has the lowest concentration of manganese equaling 16.4 ppm and 120 (11) 1-1.5 contains the highest amount at 1,680 ppm. The average concentration for manganese is 307ppm. Sample J19 (III) 6 has the least amount of iron (2,930 ppm) found in the sample set and sample E18 has the highest concentration at 35,800 ppm and an average concentration of 10,600ppm. Organic matter was not 24 obtained for all the samples sites. The sample with the highest percent organic matter is N21(III)1.5 containing 71% and the lowest is G18(II)3.5 with 0.20%. The average for organic matter is 28%. The relationships among iron, manganese and organic matter with chromium were illustrated in a x—y scatter plot (Figure 3), constructed using the data in Table 3. The figure shows that as organic matter concentrations in the soils increase, chromium concentrations also increase. Chromium concentrations do not appear to be directly related to iron or manganese concentrations in the sediments. The scatter plot show that there is a direct relationship with organic matter and chromium concentrations. It is possible that not only chromium may have an association with the microbial population but a combination of organic matter and chromium. 25 Table 3. Total concentrations of chromium, iron and manganese in sediment samples. Percent organic matter is given in column four. A dashed line indicates that there are no data for that sample. Samples Chromium Manganese Iron Organic , ppm ppm pnm Mattert‘fi) BS 8.88 134.13 4730.00 4.90 B7 22.22 267.87 8909.62 6.10 B9 12.73 76.26 9333.891 1.60 C4 47.27 130.38 7202.92 4.70 C8 47.64 198.15 10642.80i 7.40 C10 18.19 200.32 7639.34 6.30 C16 14.95 165.44 9818.63 10.00 D9 49.12 157.89 15622.50I 6.60 D11 90.14 188.98 8369.87 ---- Dl7 5465.07 372.71 25032.81 29.20 E8 170.08 141.00! 8710.76 1.80 E16 1192.81 412.46 23576.56 34.00 E18 27830.48 508.90! 35832.04 13.80 G14 22134.39' 956.78 27936.38 37.10 G18 765.05 278.44 6613.491 ---- G18(II) 493.61 258.91 6888.98 6.90 G18(II) 1.5 20.98 77.74 6082.38 1.10 G18(II) 3.5 12.00I 79.76 7652.80 0.20 H15 24715.12 283.87 8463.06 30.50 H17 (1) 137620.87 414.75 7169.78 40.60 H17 (11) 135355.32 410.64 7295.87 ---- H17 (111) 141518.58 379.41 7624.28 ---- H17(IV) 120383.58 909.59 6120.02 --- Hl7(V) 281551.80! 590.96 3060.67 34.20 H17(V)l.5 237170.11 364.51 4579.17 30.70 H17(V) 3.5 9881.40l 542.44 10523.26 25.90 H19 451.87 574.50l 9238.98 19.90 H21 29.54 1 15.94 7276.12 2.00 120(11) 308.00I 532.32 23785.89I 34.50 120(11) 1.5 506.65 1680.50I 30630.32 44.00 120(11) 3.5 1971.33 1555.38 26300.08 50.10 120 451.60 685.66 30742.01 44.80 122 20556.33 74.201 81 14.47 --- 127 31660.56 120.32 8423.48 ----- J19 (I) 29137.90I 354.33 21484.28 47.40 .1 1901) 31543.52 128.82 1 1568.28 27.20 J19(III)1.5 80186.17 19.03 3421.73 46.00 J19(IH)3.5 35000.00] 16.43 4063.38 70.60 26 Table 3 (cont’d). Total concentrations of chromium, iron and manganese in sediment samples. Percent organic matter is given in column four. A dashed line indicates that there are no data for that sample. ' Samples Chromium Manganese Iron Organic ppm ppm ppm Matter (%) J19(III)6 1 1001.40] 50.29 2926.01 59.20 J19(IV) 23924.00l 140.21 13412.54 19.5 J19(IV) 1.5 30479.41 36.88 4031.96 ----- J19(IV)3.5 24917.23 28.82 5081.55 ---- J19(IV) 6.0 31083.28 27.05 4430.81 ---- J21 3795.37 305.54 7748.67 43.50 J23 38640.74 47.78 13184.03 53.40 K20 23589.15 771.491 10031.16 63.80 K20(II)1.5 50494.50 582.76 10488.52 60.40 K22 7149.65 311.57 10635.76 66.40 K22(II) 4027.41 131.14 8370.44 62.80 M20 439.00 127.99I 10186.10I 1.70 M24 0.5 90.55 141.96 7302.38 3.20 M24 1.0 18.76 116.84 9315.37 1.10 M24 3.0 33.14 652.09' 8907.11 11.00 N21 21121.16 607.35 10792.04 55.50 N21(II)1.5 23659.54 284.70I 7551.81 72.00 N23 1 1252.17 268.55 17806.85 58.30 i022(II) 13398.74 339.21 7516.48 37.80 022111)].5 32387.79! 161.70 9922.20! 30.10 022(II)3.5 5421.46 94.98 5980.26 5.10 O22(II)4.5 697.77 61.64 4698.39 3.00 022 850.03 47.97 3436.46 1.60 024 12984.80] 700.43 14482.67 46.90 P23 6842.60I 177.23 5013.90] 22.60 P25 5768.45 161.07 10542.39] 51.40 P25(II) 10565.25 182.04 1 1592.30I 55.00 P25(II) 1.5 6127.77 159.83 10777.45 ---- P25(II) 3.5 1646.16 69.12 6263.66 17.90 QZ4(II)1.5 16393.91 93.63 6562.50 23.80 QZ4(II)3.5 3853.83 60.01 6570.73 5.30 Q24 17536.61 236.63 9912.94 27.80 026 638.40] 583.86 13907.87 18.30 T27 648.07 1 17.27 5584.60 0.60 U26 2435.13 180.47 9702.03 9.60 Average 24963.13 307.12 10618.50] 27.55 27 3% E a. 5 E 11110 oMn 0 d. 100 I Fe ‘L (M E E 10 1 10 100 1000 10000 100000100000 0 Cr (ppm) Figure 3. X-Y scatter plot comprised of total concentrations of chromium, iron and manganese. Scales are logarithmic. Organic matter is represent by OM in the legend. 28 4.2 — Sguential Chemical Extraction Results The results for the partitioning of chromium among soil phases for samples at each sample site are shown in Table 4. A pie diagram (Figure 4) was constructed using the average percent of chromium in each phase to characterize the general partitioning in the study area. In terms of any other fiaction, chromium associates more with the moderately reducible (MR) phase (70%) than with the weakly acid soluble (WAS) phase (14%). Chromium percent in the other phases include basic oxidizable (OX1) phase (7%), easily reducible (ER) phase (5%) and acid oxidizable (OX2) phase (4%). The exchangeable (EX) phase lms significantly less chromium associated with it (0.4%). The results (Table 4) indicate that there is great variation of range for chromium concentrations within each phase. Sample B5 has the lowest amount of concentration of chromium associated with the MR phase that of 2.640 ppm and sample H17 (1) has the highest with 55,500 ppm. The sample containing the least amount of chromium associated with the WAS phase is again B5 (0.182 ppm) and the highest amount is sample J19(IV)3.5 with 27,800 ppm. For the OX1 phase concentrations range for 0.007 ppm (sample C4) to 9,530 ppm [sample H 17 (1)] and the ER phase ranges from 0.098 ppm (B9) to 3,000 ppm [111701) 1.5]. The EX phase has the least amount of chromium which ranges from 0.025 ppm (sample E8) to 765 ppm [Hl7 (V) 1.5]. Chromium partitioning results for samples taken two years prior (Ellis, 1999) show similaritiesiwhen compared with the current study. Results fi'om the previous study also found that very little chromium was associated with the EX and OX2 phases (Icopini, 2000). The earlier study also found that chromium associated more with the MR phase (49%) tlmn the OX1 phase (41%) (Icopini, 2000). Chromium is associated 29 less with the ER and WAS phases, 6% and 3%, respectively (Icopini, 2000). However, there are differences between the data presented here and that of the past study. For example, the MR phase was found to contain a higher percent of chromium then previously found. Another, difference concerns the percent chromium for the MR and OX1 phases. The current study found chromium to associate more to the MR phase (70%), than the WAS phase (14%) followed by the OX2 phase (7%) where as the previous study found chromium associating more with the MR phase (49%) than the OX1 phase (41%) followed by the ER phase (<6%). The WAS phase had twice the amount of chromium (14%) compared to 7% found in the previous study. These differences could be a result of the dataset being a subset of samples from the original dataset, because the original dataset contained additional samples, this could compromise the overall results when compare with the dataset in the current study. Although the distrlbution of chromium among the different phases of the sediment can be observed from the results of the extractions, making interactions as to what these fi'actions are is not necessarily a straightforward procedure. Sequential chemical extractions were intended for systems exposed to oxygen (oxic systems) (Tessier, 1979). For interpreting metal associations with the EX, WAS, and OX1, which theoretically attack metals bound to exchange sites on clays, metals associated with carbonates and organic matter respectively; the knowledge of the oxidation state of the environment is not a factor. However, this is not the case for the MR, ER, and OX2 phases. In oxic systems, redox sensitive metals (iron and manganese) exist in oxidized forms and precipitate out from solution as oxy-hydroxides (Baes and Mesmer, 1976). Many samples in this study were taken fi'om a system that lacks oxygen (anoxic system) 30 in which iron and manganese oxy—hydroxides (ER and MR phases) are thermodynamically unstable. Therefore, it is assumed that iron and manganese oxy- hydroxides do not exist under anoxic conditions. Thus, it is unclear what phase of the sediment is being attacked by the ER and MR leaches. The geochemical belmvior of chromium affords some insight into this problem. In a soil system, such as the study site, the abundance of organic matter and lack of manganese oxy—hydroxides create an environment in which the most common form of chromium will be chromium (III) (Palmer and Puls 1994). Chromium (IH) is highly insoluble, precipitating out of solution as Cr(OH)3 (Bartlett and Kimble, 1976; Palmer and Puls, 1994; Kai et a1. 1989). This solid however is rmde soluble with the solutions used in the MR extraction. Considering the chemistry of the solutions used in the selective chemical attacks, and the knowledge of the geochemical behavior of chromium; it can be concluded that a dominant form of chromium in the soils at the study site is a Cr(OH)3 mineral/amorphous solid (Cannelton, 1999). The conclusion that can be drawn from these results is that chromium is associated with the MR phase of the soil. The ER phase contributes only minor amounts of chromium. Previous work could not be found which demonstrates that chromium forms sulfide minerals (the OX2 phase) so little chromium should be found to associate with this phase (Icopini, 2000). In summary, the major phases controlling the partitioning of chromium in the soils at the study are the MR and OX1, which are most likely chromium hydroxide and chromium associated with organic matter respectively. This is expected and is a pattern that has been shown for other environments (Gephart, 1982; Rezabek, 1988). The role of iron oxides in sequestering chromium in the oxic soils at the site is unclear. 31 Table 4. Sequential chemical extraction data for chromium. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). The dashed line indicates that there is no data for that sample. Sample” ' EX ‘ WAS ERw V MR ' OX1 ' OX2 Total - Cr, Cr Cr Cr Cr Cr , Cr , , PPm PP“! PP!“ PP“! PPm PPm B 5 0.031924 0.18218 0.142656 2.638068 0! -- 2.994828 1.07% 6.08% 4.76% 88.09% 0.00% -- B 7 0.073304 0.622206 0.455132 6.298874 0.010601 0.473787 7.933904 0.92% 7.84% 5.74% 79.39% 0.13% 5.97% B 9 0.05159 0.21597 0.098373 3.762425 0| -- 4.128358 1.25% 5.23% 2.38% 91.14% 0.00% - C 4 0.061367 0.844724 0.456575 5.646527 0.007328 0.274974 7.291495 0.84% 1 1.59% 6.26% 77.44% 0.10% 3.77% C 8 0.061128 0.784006 0.320269 4.869343 0.013122 0.32053 6.368398 0.96% 12.31% 5.03% 76.46% 0.21% 5.03% C 10 0.111688 0.923846 0.585682 9.130438 0.054877 -- 10.80653 1.03% 8.55% 5.42% 84.49% 0.51% -- C 16 0.13591 2.89284 4.520062 11.95762 0.034071 0.529566 20.07007 0.68% 14.41% 22.52% 59.58% 0.17% 2.64% D 9 0.054493 1.270878 0.862647 11.93827 0.072296 0.525897 14.72448 0.37% 8.63% 5.86% 81.08% 0.49% 3.57% D11 0.100259 1.126425 1.495744 13.94724 0.0754 0.841288 17.58636 0.57% 6.41% 8.51% 79.31% 0.43% 4.78% D 17 1.415684 21.729 50.71538 1233.836 3.144351 120.5969 1431.437 0.10% 1.52% 3.54% 86.20% 0.22% 8.42% E8 0.025441 0.935145 1.890918 39.02167 0.128912 0.359704 42.36179 0.06% 2.21% 4.46% 92.12% 0.30% 0.85% E 16 0.277588 3.312904 2.603314 466.8115 0.92945 27.03728 500.972 0.06% 0.66% 0.52% 93.18% 0.19% 5.40% E 18 2.277007 34.80456 94.28317 5333.369 22.27881 504.0278 5991.04 0.04% 0.58% 1.57% 89.02% 0.37% 8.41% G 14 4.281975 274.6354 2448.637 10817.13 4.559789 20.24121 13569.49 0.03% 2.02% 18.05% 79.72% 0.03% 0.15% G 18 0.218475 5.2501 6.248742 303.6426 0.445801 -- 315.8057 0.07% 1.66% 1.98% 96.15% 0.14% -- G 18 (II) 0.314323 8.546558 6.903985 271.9403 0.306556 0.779096 288.7908 0.11% 2.96% 2.39% 94.17% 0.11% 0.27% GlS(Il)1.5 0.058 5.599909 1.678689 17.39124 0 0.5634 25.29124 0.23% 22.14% 6.64% 68.76% 0.00% 2.23% 32 Table 4 (cont’d). Sequential chemical extraction data for chromium. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). The dashed line indicates tint there is no data for that sample. “Sample X EX WAS ER ' IMR ' OX1 0X2 Total Cr ”Cr _ , Cr Cr _ Cr_ Cr 1 Cr_ g PI)“l PM“ PM“ PPm PPIll PPm G18(HI)3.5 0.050797 0.425849 0.409199 4.289532 0| 0.800525 5.975902 0.85% 7.13% 6.85% 71.78% 0.00% 13.40% H15 4.985394 140.3759 2406.469' 9587.161 4.214198 9.08475 12152.29 0.04% 1.16% 19.80% 78.89% 0.03% 0.07% H17(I) 31.17031 277.8103 306.1469 55467.6 9526.948 1213.336 66823.01 0.05% 0.42% 0.46% 83.01% 14.26% 1.82% H17(H) 26.61341 296.3961 283.5029 53623.98 8950.866 956.6184 64137.98 0.04% 0.46% 0.44% 83.61% 13.96% 1.49% H17(III) 23.96097 283.9987 336.6095 34380.88 8171.495 667.113 43864.06 0.05% 0.65% 0.77% 78.38% 18.63% 1.52% H17(IV) 8.923598 326.9915 273.7736 27519.6 9107.694 119.8424 37356.83 0.02% 0.88% 0.73% 73.67% 24.38% 0.32% H17(V) 21.29106 227.5172 1019.277 40170.44 5646.294 14.3279 47099.15 0.05% 0.48% 2.16% 85.29% 1 1.99% 0.03% H17(VL1.5 764.601 7939.782 3000.245 51608.58 4971.693 240.1288 68525.03 1.12% 11.59% 4.38% 75.31% 7.26% 0.35% H17(V) 3.5 0.436777 120.6121 3.382427 6007.706 1668.244 336.1524 8136.534 0.01% 1.48% 0.04% 73.84% 20.50% 4.13% H19 1.456782 2.696951 5.177289 92.92937 0.394462 4.052568 106.7074 1.37% 2.53% 4.85% 87.09% 0.37% 3.80% H21 0.745492 0.288453 0.70091 11.06748 0 0.327474 13.12981 5.68% 2.20% 5.34% 84.29% 0.00% 2.49% [20 0.247894 5.194509 8.838078 57.93217 0.273078 13.60636 86.09209 0.29% 6.03% 10.27% 67.29% 0.32% 15.80% 120(11) 0.240406 5.40855 9.549654 61.14115 0.255322 28.50294 105.098 0.23% 5.15% 9.09% 58.18% 0.24% 27.12% 12001) 1.5 1.577992 18.64033 23.30041 80.4489910422149] 69.12654 193.5164 0.82% 9.63% 12.04% 41.57% 0.22% 35.72% 120 (II) 3.5 1.45491615750028 72.59037 427.2728 0.796679l 178.1651 737.7801 0.20% 7.79% 9.84% 57.91% 0.1 1% 24.15% 122 3.666737 138.6424 117.9445 9721.487 5.486339' 18.2675 10005.49 0.04%. 1.39% 1.18% 97.16% 0.05% 0.18% I27 0.135518 1.888 0.801194 14.7336910016332 0.560392 18.13513 0.75% 10.41% 4.42% 81.24% 0.09% 3.09% 33 Table 4 (cont’d). Sequential chemical extraction data for chromium. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). The dashed line indicates that there is no data for that sample. Sample EX I WAS ER MR OX1 6X2 Total .. Cr Cr Cr Cr Cr Cr Cr PPm ppm 1’1"“ 1’1"“ 1’1"“ 1’1"“ J 19(1) 10.10228 220.5058 137.9256 12988.65 8.386927 352.1262 13717.7 0.07% 1.61% 1.01% 94.69% 0.06% 2.57% J 19(II) 2.158399l 188.7063 88.23529 14097.17 4.392996 32.15072 14412.81 0.01% 1.31% 0.61% 97.81% 0.03% 0.22% J19(III)1.5 24.53769 10659.86 220.011 11222.71 3831.293 14.01967 25972.43 0.09% 41.04% 0.85% 43.21% 14.75% 0.05% J19(IH)3.5 9.373277 271.9257 287.2895 8439.485 3098.375 670.4327 12776.88 0.07% 2.13% 2.25% 66.05% 24.25% 5.25% J19(III)6 2.143282 4459.828 229.8016 4261.791 1278.413 310.9614 10542.94 0.02% 42.30% 2.18% 40.42% 12.13% 2.95% J19(IV) 3.358605 14514.28 208.6065 10023.56 3835.69' 151.1663 28736.66 0.01% 50.51% 0.73% 34.88% 13.35% 0.53% J19(IV)3.5 5.588125 27815.45 264.1077 8626.129'3111.l31 1012.483 40834.89 0.01% 68.12% 0.65% 21.12% 7.62% 2.48% J 21 33.79065 8.94563 31.25 942.0732 1.516972 68.77541 1086.352 3.11% 0.82% 2.88% 86.72% 0.14% 6.33% J23 3.979102 558.3922 180.9191 14524.9 2548.055 3.336393 17819.58 0.02% 3.13% 1.02% 81.51% 14.30% 0.02% K20 12.51263 345.7912 381.4184 8063.973 11.67424 239.37291 9054.742 0.14% 3.82% 4.21% 89.06% 0.13% 2.64% K20(IDl.5 10.2124614354096 818.8082 28125.67 11.43326 279.7214 29289.39 0.03% 0.15% 2.80% 96.03% 0.04% 0.96% K22 3.198718 626.5854 72.37061 1741.907 1042.669 4.299942 3491.031 0.09% 17.95% 2.07% 49.90% 29.87% 0.12% K2201) 1.295763 458.1907 34.3643 545.5218 278.3188 2.772053 1320.463 0.10% 34.70% 2.60% 41.31% 21.08% 0.21% M20 2.354567 0.55993 10.8185 366.7596 0.053682 0.448166 380.9944 0.62% 0.15% 2.84% 96.26% 0.01% 0.12% M24 0.185603 101.1459 5.423951 23.46586 23.28683 0.261209 153.7694 0.12% 65.78% 3.53% 15.26% 15.14% 0.17% M24 1.0 0.059993 95.07589 1.185834 16.31502 10.56198 0.653884 123.8526 0.05% 76.77% 0.96% 13.17% 8.53% 0.53% M24 3.0 0.049094 138.2941 1.103933 51.52838 11.75929 5.016618 207.7514 0.02% 66.57% 0.53% 24.80% 5.66% 2.41% 34 Table 4 (cont’d). Sequential chemical extraction data for chromium. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). Thedashedlineindicatesthatthereisnodataforthatsample. Sample EX WAS ER MR 0X1 0X2 Total Cr Cr Cr Cr Cr Cr Cr JP“! PP“! PP“! PP“1 ppm PPm N21 6.014716 13.41832 171.9713 6193.247 8.140258 120.5653 6513.357 0.09% 0.21% 2.64% 95.09% 0.12% 1.85% N21(II)1.5 5.277671 229.0693 415.5496 7972.424 7.846955 715.9134 9346.081 0.06% 2.45% 4.45% 85.30% 0.08% 7.66% N23 1.747455 3690.62 122.3248 2444.894 2266.492 165.0754 8691.154 0.02% 42.46% 1.41% 28.13% 26.08% 1.90% 022 0.321462 3631.314 90.73745 222.9627 34.91145 0.27371 3980.521 0.01% 91.23% 2.28% 5.60% 0.88% 0.01% 022(II) 4.088538 5843.708 75.66992 5800.78 2821.722 24.990091 14570.96 0.03% 40.1 1% 0.52% 39.81% 19.37% 0.17% 022(II) 1.5 13.34926 243.2002 2697.09 6576.132 2577.533 2.509218 12109.81 0.1 1% 2.01% 22.27% 54.30% 21.28% 0.02% 022(II) 3.5 1.363407 120.9767 857.271 1066.772 100.0296 1.466237 2147.879 0.06% 5.63% 39.91% 49.67% 4.66% 0.07% 022(II) 4.5 0.33734916486235 88.63441 232.4667 16.03722 0.149183 402.4872 0.08% 16.12% 22.02% 57.76% 3.98% 0.04% 024 6.122982 108.7327 112.4725 4975.545 6.457506 54.44725 5263.778 0.12% 2.07% 2.14% 94.52% 0.12% 1.03% P23 1.912573 25.44574 26.59968 2935.137 2.19945912994313 3290.726 0.06% 0.77% 0.81% 89.19% 0.07% 9.10% P25 1.917853 379.9985 35.62486 2279.991 1990.615 33.74387 4721.891 0.04% 8.05% 0.75% 48.29% 42.16% 0.71% P25(II) 2.18787 1492.618 50.41677 1726.42 3049.185 112.2666 6433.094 0.03% 23.20% 0.78% 26.84% 47.40% 1.75% P25(II)1 2.259164 3920.118 48.51146 1563.343 1443.686 10.95673 6988.874 0.03% 56.09% 0.69% 22.37% 20.66% 0.16% P25(II)3 0.47109 1167.798 44.63908 906.6783 323.0643 18.68477 2461.336 0.02% 47.45% 1.81% 36.84% 13.13% 0.76% 024 6.868371 217.3131 603.0296 11500.53 6.42585911022572 12436.42 0.06% 1.75% 4.85% 92.47% 0.05% 0.82% QZ4(III)1 6.647517 464.9763 2093.749 9992.268 3.672648 33.54372 12594.86 0.05% 3.69% 16.62% 79.34% 0.03% 0.27% QZ4(III)3 3.48257 154.937 457.2687 2975.376 1.53772 2.084428 3594.686 0.10% 4.31% 12.72% 82.77% 0.04% 0.06% 35 Table 4 (cont’d). Sequential chemical extraction data for chromium. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizab1e(OX1) and acid oxidizable (OX2). The dashed line indicates that there is no data for that sample. Sample EX WAS ER MR 0X1 OX2 Total Cr Cr Cr Cr Cr Cr Cr PPm PPm PPm 1’1"“ 1’1"“ PPm Q26 0.48156 8.116715 8.424054 194.5494 0.484596 2.358735 214.4151 0.22% 3.79% 3.93% 90.73% 0.23% 1.10% T27 0.207966 6.295186 15.76654 409.9302 0.025899 0.410121 432.6359 0.05% 1.46% 3.64% 94.75% 0.01% 0.09% U26 0.322843 21.7749 36.2078 2468.924 0.597608 2.219728 2530.047 0.01% 0.86% 1.43% 97.58% 0.02% 0.09% 36 Figure 4. A summary of the sequential extraction data represented as average percent of chromium extracted from each phase. The sequential extraction phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). ER 5% 70% 37 4.3 — Microbial Analysis Results T-RFLP is a culture-independent technique used to assess the structure of microbial communities. Because 90-99% of species in a typical habitat cannot be cultured with current techniques, it is imperative to use culture independent approaches in comparative community analyses. T-RFLP is based on the extensive phylogenetics of bacteria derived from comparative 16S rRNA sequence analysis developed by Woese and colleagues (Woese, 1987). T-RFLP takes advantage of the extensive rRNA sequence database for the design of PCR primers and nnpping restriction sites within the sequences. This procedure determines and uses difl‘erences in the sizes of terminal restriction fi‘agments ("length polymorphism") to measure the structure of microbial communities. Because all cells require the target molecule, each population (species) can be measured with this metric. This result is a collection of terminal restriction fi‘agments from one community that can be compared to a similar collection derived from a second community. Table 5 is an example of the results of the Terminal-Restriction Fragment Length Polymorphism (T-RFLP). For the complete table of the results for T-RFLP see Appendix B. Samples are listed in the first column and sample sizes in base pairs (bp) are listed in the columns that rennin. For example, sample B5 has a peak height of 65bp for a sample size of 32bp. The number, i.e. 65bp, indicates the presences of a fiagment at a peak height for a given sample. The results of the clustering analysis using the T-RFLP data are shown in Figure 5. The complete dataset of Table 5 was transformed to binary form prior to being entered into the clustering program. Any cell in the spreadsheet containing a number (e.g., 38 containing the number that indicates the presences of a fiagment at a peak height for a given sample) was replaced with the letter “A”, blank cells were then replaced with the letter “C”. The cluster tree was broken up into four major clusters. The first branch of the cluster tree is divided into two clusters, the first cluster containing one sample, B9, and the second cluster containing the rest of the dataset. This larger cluster was then sub- divided into three clusters as determined by the second branch of the cluster tree. Cluster 2 contains sample site; C4, C16, 120, H19, E16, D17, 121 and 022 (H) 1.5. Cluster 3 contains sample sites; B5, C8, B7, C10, D9 and P23 and the remainder of the 44 sample sites fall into cluster 4. Table 5. Example of T-RFLP data. Sam Sam Size in base 323648656769727375777879808283 85 65 93 6 1 75 262 54 11 51 82 39 H7351“ Figure 5. Microbial Cluster Tree produced using data from Table B-1, Appendix B. The cluster tree has been divided into four clusters as indicated by the numbers 1,2,3 and 4. The x-axis is the measurement of the sequence distance. 40 4.4 — Multivariate Statistical Modeling Q-MODE Factor Analysis Q-mode factor analysis, as discussed earlier, was used to determine if individual geochemical populations existed that could then be related to the microbial populations. Data used for this analysis were from Table 3. Twenty-one chemicals were considered in the analysis; potassium (K), chromitun (Cr), manganese (Mn), iron (Fe), barium (Ba), magnesium (Mg), vanadium (V), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), strontium (Sr), cadmium (Cd), lead (Pb), aluminum (Al), selenium (Se), scandium (Sc), titanium (Ti), mercury (Hg) and calcium (Ca). The data matrix used for the Q-mode factor analysis had the samples as columns and variables as rows. Factor scores, which are used to estimate the relative importance of the variables in defining the populations, were calculated via a FORTRAN program (Davis 1986; Long et al., 1992). The numbers of factors that can be used to define the data set are interpreted fiom the relative importance of the eigenvalues describing the data set. The choice of the cut—off used was the default setting of an eigenvalue greater tlmn 1. The results of the Q-mode factor scores are shown in Table 6. Factor 1 accounts for 77%, factor 2 for 22% and factor 3 less then 0.1% of the variability. The factor scores (Table 6) allow for insight into the variables influencing the individual factors. Examining the values of a variable along a row and choosing its highest value(s) subjectively determined the relative importance of a variable on a factor. Variables that are interpreted to be important in controlling the factors are shown in bold print, and a negative sign denotes there is an inverse relationship between the variable and the factor. Thus, the three factors can be described by the variables listed after the factor at the 41 bottom of Table 6. These three factors can be thought of as sub-populations or end members of the entire data set. The most important population was essentially the one dominated by a majority of the elements (Factor 3). Q-rnode factor analysis was also used to analysis absolute sequential chemical extraction data for chromium from Table 4. Factor 1 and Factor 2 account for 86% and 13% of the variability. Table 7 shows that Factor 1 was dominated by CI'Ex, CrER, CrMR, Cram and Croxz and Factor 2 contains CrEx . This shows that one population dominates the system with respect to chromium partitioning. In conclusion, both the results of the Q-mode factor analysis for total and sequential chemical extractions show that there was one dominant population. Since, the data set was essentially comprised of only one population, R-mode factor analysis was able to be performed to filrther explore the data set. 42 Table 6. Factor scores for Q-mode factor analysis for total chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. Eigenvalue Difference Proportion Cumulative Factorl 56.074472 40.182501 0.7681 0.7681 Factor2 15.891910 14.875606 0.2177 0.9858 Factor3 1.0163643 1.0015764 0.0139 0.9998 FACTOR SCORES Variable Factor] Factor2 Factor3 K 2,887.20 200.87 12,185.66 Cr 82,134.83 5,788.85 77,910.43 Mn 2,115.99 241.19 3,610.07 Fe 92,073 .32 13,044.29 515,357 .44 Ba 1,891.63 142.96 2,455.34 Mg 1 1,425.15 1,712.97 20,918.98 V 12.39 18.35 426.18 Co 13.1 1 1 .00 67.66 Ni 82.15 0.89 99.77 Cu 400.18 7.03 1 14.59 Zn 4,149.96 1 15.06 10,230.3 As 28.87 4.65 66.28 Sr 296.53 67.51 -l,097.54 Cd 2.2 0.27 6.33 Pb 871.77 53.74 398.07 Al 25,669.55 3,1 19.69 165,367.49 Se 2.58 0.92 8.06 Sc 3.6 0.64 6.77 Ti 525.04 142.43 5,157.43 Hg 1 .3 0.12 1.80 Ca 106,640.18 25,239.31 429,512.31 Factor]: Cr, Cu and Pb Factor2: None Factor3: K, Mn, Fe, Ba, Mg, V, Co, Ni, Zn, As, Sr, Cd, Al, Se, Sc, Ti, Hg and Ca 43 Table 7. Factor scores for Q-mode factor analysis for sequential chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. , Eigenvalue Difference Proportion Cumulative Factor 1 61.0782121 51.1572089 0.8603 0.8603 Factor 2 9.9210033 9.9202197 0.1397 1.0000 Factor Scores Variable Factor] ’ Factor2 “7 Crgx 6.84 4.77 CrWAs 58.22 195.53 Cm; -1,278.8 -148.46 CrML 5,236.57 4,092.57 Croxr 4,213.16 1,827.54 Croxz -393.19 39.15 Factor]: Cl‘Ex, Crag, CrMR, Crox1 and Croxz Facotr2: CrWAs R—MODE Factor Analysis The purpose of R-mode factor analysis was to analyze the dataset, and divide variables into similar groups rather than samples as in Q-mode. The results of the R- mode factor analysis for the entire soil database are shown in Table 8. The same chemicals considered in the Q-mode factor analysis were used in the R-mode analysis. The data matrix used for the R-mode factor analysis had the samples as rows and variables as columns. The eigenvalue measures the amount of variation in the total sample accounted for by each factor (Garson, 1998). R-mode factor analysis was used to analysis the entire soil database data (Table A-1, Appendix A), results are shown in Table 8. Using the same method as for the Q- mode analysis, the dominant element for each factor are shown in bold print. The resulting factors are shown at the bottom of the table. Manganese, iron, copper, zinc and lead load on Factor 1. Factor 2 had loadings of cadmium, selenium and mercury. Magnesium, strontium, scandium and calcium load on Factor 3. Chromium, vanadium 44 and arsenic load on Factor 4. Factor 5 had loadings of potassium, cobalt, nickel and 8 titanium and Factor 6 has loadings of barium and aluminum. R-mode factor analysis was also used to analysis chromium partitioning data from Table 4 and the results can be found in Table 9. The relative amounts of data variance that are explained by the factors and the relative importance of the variables in characterizing a factor can also be found in Table 9. The dominant element for each factor are shown in bold print. The resulting factors are shown at the bottom of the table. Factor 1 had loadings of CrWAs, CrMR, Cr,ox1 and Croxz and Factor 2 had loadings of Cl'Ex and C PER. 45 Table 8. Results of R—mode factor analysis on soils from the total extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table. Eigenvalue Difference Proportion Cumulative » Factor 1 5.167975 1.652159 0.2461 0.2461 Factor 2 3.515816 0.917822 0.1674 0.4135 Factor 3 2.597994 0.76206 0.1237 0.5372 Factor 4 1.835934 0.279468 0.0874 0.6247 Factor 5 1.556467 0.428074 0.0741 0.6988 Factor 6 1.128393 0.12879 0.0537 0.7525 Factor Loadings " ' Factor] Factor2 Factor3 Factor4 Factors Factor6 K 0.48586 0.02388 0.24067 -0.1517 0.6263 -0.60392 Cr 0.12986 0.14226 0.12891 0.93716 -0.23917 0.19486 M 0.73479 -0. 17121 0.48172 0.16968 0.18712 -0.28076 Fe 0.76281 0.02046 0.42231 -0.20237 0.59254 -0.22743 Ba 0.132 0.1 1577 0.01632 -0.03226 -0.06669 0.68382 flg 0.3148 -0.02891 0.79986 ~0.11286 0.54935 -0.26124 V 0.29433 0.46562 0.19627 0.72495 0.28995 -0.05892 Co 0.65752 -0.07636 0.36016 -0.09606 0.80048 -0. 1632 Ni 0.02597 0.13739 ~0.07822 0.03817 0.34772 -0.07717 Cu 0.8482 0.20495 0.14191 0.34758 0.03968 0.06189 Zn 0.59166 0.28964 0.28715 0.10756 -0.01905 0.01405 As 0.07603 -0.00155 0.26415 0.94578 -0.11293 0.04431 Sr 0.36298 0.21566 0.79468 0.49226 -0.02329 0.06946 Cd 0.16868 0.95626 0.02387 0.10752 0.00672 0.03292 Pb 0.738 0.44372 -0.00696 0.08417 0.00147 0.27003 Al 0.10475 -0.06731 0.03054 -0.13409 0.17379 -0.57272 Se 0.18744 0.96042 0.05472 0.14765 -0.01843 0.09145 Sc 0.35618 0.01822 0.69166 0.32046 0.61509 0.02759 Ti -0.16806 -0.19467 0.12822 -0.269 0.83876 -0.24636 fig 0.24319 0.80487 -0.08469 0.22301 -0. 10307 0.48493 Ca 0.18188 -0.03649 0.91745 0.19089 0.04956 -0.00329 46 Table 8 (cont’d). Results of R-mode factor analysis on soils from the total extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. Variance Explained by Each Factor Eliminating Other Factors Factor] Factor2 Factor3 Factor4 Factors Factor6 2.7358352 2.6190525 2.1700363 2.4279608 2.0054061 1.2795492 Factor 1: Mn, Fe, Cu, Zn and Pb Factor 2: Cd, Se and Hg Factor 3: Mg, Sr, Sc and Ca Factor 4: Cr, V, and As Factor 5: K, Co, Ni and Ti Factor 6: Ba and A1 ‘ Factor Scores Sample Factor] Factor2 Factor3 Factor4 Factors Factor6 B5 -0.83477 -0.38303 -1.01741 -0.42406 -0.77848 -0.08843 87* -0.54796 -0.42076 -0.85648 -0.41 163 -0.27403 -0.33863 B9 -0.79801 -0.39801 -1 .05233 -0.48177 -0.76747 -0.0872 C4 -0.7479 -0.37062 -0.8573 -0.35933 -0.09883 -0.22727 C8 -0.64081 -0.40883 -1 .0275 -0.47204 -0.90629 -0.25107 C10 -0.6383 -0.45425 -0.74673 -0.54703 -0.34439 -0.38273 C16 -0.49235 -0.37767 0.19943 -0.5915 0.35843 -0.57764 D9 -0.47115 -0.25766 -0.79672 -0.23274 0.26451 -0.51079 D11 -0.59729 -0.43781 -0.43839 -0.54967 0.4486 -0.53273 [)17 0.93389 -0.36126 0.43818 -0.48259 1.43826 -0.53522 E8 -0.47876 -0.49341 -0.29403 -0.678 0.71803 -0.41429 E16 0.33505 -0.53157 -0. 13894 -0.5774 0.79014 -0.35106 E18 2.34793 -0.38456 1.89234 -0.62443 3.62279 -1.46332 Gl4 1.00778 -0.41305 3.64946 -0.33988 0.14068 -0.76559 G18 -0.18848 -0.3514 -0.56325 -0.86195 -0.153 -4.35083 G]8(II)0-0.5 -0.53465 -0.27059 -0.58927 -0.37308 -0.12792 -0.21555 Gl8(II)l-1.5 -0.99907 0.08771 -1.31533 -0.07086 1.81271 -0.35719 G18(II)3-3.5 -0.61953 -0.40004 -0. 14602 -0.35544 1.48609 -0.29819 H15 -0.43662 -0.07814 3.49059 0.43046 -0.3438 0.35895 H17 (I) 0.13376 0.0479 -0.20431 3.07802 -0.12832 -0.35886 H17 (II) 0.16719 0.11237 -0.00394 3.02842 0.10235 -0.12379 H17 (11]) 0.11457 0.15872 -0.05789 3.09399 0.12288 -0.11825 H17(IV) 0.56225 -0.19944 0.42963 1.91299 -0.96597 -0.45549 H17(V) 0.51667 0.00577 0.11681 4.36763 -0.86694 0.67827 H 17(V)l.5 -0.15668 0.01587 1.55436 3.00836 -1.0859 0.66362 H17(V) 3.5 -0.5158 -0.62399 4.09426 0.8828 0.62745 -0.24905 H 19 -0.26933 -0.55436 -0.46349 -0.51414 -0.64568 -0.60754 47 Table 8 (cont’d). Results of R-mode factor analysis on soils from the total extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table. Factor Scores , Sample Factor] Factor2 Factor3 Factor4 Factors Factor6 H21 -0.68086 -0.46258 -0.24285 -0.61621 1.13734 -0.41447 120(II) 0-0.5 2.31666 -0.07162 0.25689 -0.01153 1.93824 -0.27376 120(II) l-].5 3.48815 -0.66466 0.63042 -0.2537 0.7359 -0.78849 120(II) 3-3.5 3.45429 -0.50238 0.97982 —0.16564 0.42572 -0.65742 [20 3.4288 -0.03015 0.14489 -0.42678 0.87488 -0.01031 I22 0.01281 3.1856 0.12805 0.441 -0.17508 -1.17746 [27 -0.58617 -0.39972 -0.40288 -0.29533 0.73806 -0.46319 J19 (I) 0.67344 3.0273 0.22612 0.21929 1.39184 -0.05259 J 19(1I) -0.16365 0.77707 -0.50432 -0.0998 -0.47606 1.14574 J19(III)1.5 0.59508 1.11354 -1.49572 0.61488 -1.32165 3.13398 J19(IH)3.5 -0.12196 -0.07512 -0.92425 -0.08792 -1.78667 0.49215 J19(III)6 -0.7541 -0.1901 -0.81879 -0.33165 -l.52298 0.22955 J19(IV)0.5 0.22831 0.58507 -0.18132 -0. 16893 0.48097 2.29478 JlgIV) 1.5 0.59513 0.73042 -l.24193 0.23006 -1.00979 1.79154 J19(IV)3.5 -0.091 56 -0.07533 -0.93397 -0.08667 -1 .595 86 0.58369 J19(IV) 6.0 -0.26091 -0.08763 -0.91798 -0.04526 -1.55599 0.42638 J21 -0.30829 0.01209 -0.55707 -0.512 0.27542 -l.11304 J23 1.23089 6.5241] 0.01612 0.49521 -0.11961 0.84344 K20 0.38244 -0.02825 0.49505 -0.19995 -0.44054 -0.45537 K20(II)1.5 1.70145 0.43368 0.56317 -0. 14984 -1.51724 0.13957 K22 -0.22729 0.34522 0.42437 -0.25864 -0.90202 -0.34661 K22(II)top -0.54234 0.10127 0.10854 -0.36252 -1.2494 -0.03864 M20 -0.5162 -0.46988 -0.01623 -0.68326 1.51824 -0.35689 M24 0.5 -0.65677 -0.40007 -0.21815 -0.49217 0.76042 -0.35838 M24 1.0 -0.71974 -0.38524 -0.47839 -0.60921 -0.07841 -0.25664 M24 3.0 -0.02187 -0.44762 1.22984 -0.36426 0.96892 -0.70248 N21 0.30384 -0.19135 -0.03466 -0.27155 -0.5814 -0.32749 N21(II)1.5 -0.01694 0.03759 0.28721 -0.17841 -0.96711 0.7218 N23 0.03985 -0. 10094 0.0286 -0.3633 -0. 14012 -0.05683 022(II) 0.5 -0.38446 -0.20711 -0.15911 -0.2862 -0.57732 -0.16358 022(II)].5 -0.066 -0.1 596 0.65188 -0.1 1 162 0.37604 0.8102 022(II)3.5 -0.69716 -0.30599 0.12897 -0.47222 -0.43937 0.25446 022(II)4.5 -1.0432 -0.37666 -0.26126 -0.52644 -0.1 1 121 -0.0105 022 -0.99142 -0.35408 -0.57392 -0.49788 -0.60537 0.06162 024 0.34427 -0.16514 0.16742 -0.25939 0.11324 -0.42243 48 Table 8 (cont’d). Results of R-mode factor analysis on soils from the total extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table. Factor Scores Sample Factor] Factor2 Factor3 Factor4 Factors Factor6 P23 -0.70055 -0.16234 -0.80899 -0.36293 -1.40529 0.17462 P25 -0.43685 -0.08504 -0.03427 -0.18651 0.55181 0.35158 P25(II) 0-0.5 -0.39057 0.14592 0.12476 -0.01859 1.00519 0.54347 P25(II) 1-1.5 -0.70765 -0. 14874 0.0933 -0.00966 1.45634 0.43378 P25(II) 3-3.5 -0.98711 -0.40428 -0.4082 -0.38917 0.5125 0.17862 QZ4(II)1.5 -O.34449 -0.25232 -0.071 1 -0.34448 -0.67979 1.58139 QZ4(II)3.5 ~0.79855 -0.38483 -0.06988 -0.45224 -0.98274 0.32493 024* 0.1845 -0.2483 0.21483 -0.48656 -0.06867 3.88672 026 -0.37029 -0.50531 0.16754 -0.34565 1.13775 -0.08472 T27 -0.89515 -0.4635 -0.79108 -0.5721 7 -l.04807 0.0141 U26 -0.64944 -0.47091 -0.21718 -0.49931 0.51307 0.07302 N23 Factor 1: B9, D11,120(II) 1-1.5,120(II) 3-3.5, 120, K20 (II) 1.5, M24 1.0 022 (II)3.5, 022(II) 4.5, 022, P25 (11) 3-3.5, Q26 and U26 Factor 2: 122, J23 Factor 3: B5, B7, C4, C10, D9, G14, G18 (11), H15, H17 (V) 3.5, J19(IV) 3.5, M24 3.0, 022(II)1.5 and P23 Factor 4: C16, G18, H17(I), H17(II), H17(III), H17(IV), H17(V), H17(V) 1.5 and Factor 5: ca, 017, E8, E16, E18, (31801) 1.15, Gl8(II) 3-3.5,H19, H21, 12001), 127, 119(1), 11901035, Jl9(III)6, J19(IV)6, K22, K22(II), M20, M24, N21, N21(II)1.5, 02201), P25, P25(II), P25(II)1.5, Q24(II)1.5, Q240035 and T27 Factor 6: G18,, 11901), 11901015, 1190V), 1190V)15,121, 024 and Q24 49 Table 9 . Results of R-mode factor analysis on soils fi'orn the sequential chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table. Eigenvalue , Difference Prgiortion Cumulative Factor 1 2.731714 1.382521 0.4553 0.4553 Factor 2 1.349193 0.399842 0.2249 0.6802 Factor 3 0.949352 0.470473 0.1582 0.8384 Factor 4 0.478878 0.090789 0.0798 0.9182 Factor 5 0.388088 0.285312 0.0647 0.9829 Factor 6 0.102777 0.0171 1.0000 Factor 1 - Factor 2 Variance Explained by Each EX 0.28713 0.84886 Factor Eliminating Other Factors WAS 0.43760 0.08449 ER 0.12854 0.86225 Factorl Factor2 MR 0.33739 0.60268 2.1409684 1.5741753 0X1 0.88700 0.32138 OX2 0.85289 0.04743 Factor 1: CrWAs, CrMR, Cram and Croxz Factor 2: CrEx and CrER ' Factor Scores Sample Factor] Factor2 Factor3 Factor4, Factors Factor6 B7 -0.64024 04173 -].01741 -0.42406 -0.77848 -0.08843 C4 -0.64054 -0.41727 -0.85648 -0.41163 -0.27403 -0.33863 C8 -0.64048 -0.41741 -1.05233 -0.48177 -0.76747 -0.0872 C16 -0.64029 -0.41385 -0.8573 -0.35933 -0.09883 -0.22727 D9 -0.64004 -0.41702 -1.0275 -0.47204 -0.90629 -0.25107 E8 -0.63976 -0.41585 -0.74673 -0.54703 - -O.38273 E16 —0.59087 -0.42026 0.19943 -0.5915 0.35843 -0.57764 E18 0.21215 -0.49815 -0.79672 -0.23274 0.26451 -0.51079 G14 -0.57718 1.62979 -0.43839 -0.54967 0.4486 -0.53273 6181105 -0.63385 -0.40697 0.43818 -0.48259 1.43826 -0.53522 H15 -0.62383 1.58735 -0.29403 -0.678 0.71803 -0.41429 H171 3.83175 0.41475 -0.13894 -0.5774 0.79014 -0.35106 H1711 3.33073 0.46456 1.89234 -0.62443 3.62279 -1.46332 H17IH 2.34224 0.30503 3.64946 -0.33988 0.14068 -0.76559 50 Table 9 (cont’d). Results of R-mode factor analysis on soils fi'om the sequential chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table. Factor Scores Sample Factor] Factor2 Factor3 Factor4 Factors Factor6 H1 71V 1.53478 0.35356 -0.56325 -0.86195 -0.153 4.35083 H17V 1.07667 1.1994 -0.58927 -0.37308 -0. 12792 -0.21555 H17V1 1.82054 6.44611 -1.31533 -0.07086 1.81271 -0.35719 H17V3 0.24423 -0.45963 -0.14602 -0.35544 1.48609 -0.29819 H19 -0.63315 -0.40713 3.49059 0.43046 -0.3438 0.35895 I20 -0.62044 -0.4158 -0.20431 3.07802 -0. 12832 -0.35886 [20110 -0.59874 -0.42271 -0.00394 3.02842 0.10235 -0. 12379 12011] -0.53963 -0.42566 -0.05789 3.09399 0.12288 -0.1 1825 120113 -0.3 7567 -0.43 795 0.42963 1.91299 -0.96597 -0.45549 122 -0.39402 -0. 15787 0.1 1681 4.36763 -0.86694 0.67 827 127 -0.63989 -0.41664 1.55436 3.00836 -1.0859 0.66362 J 191 0.17044 -0.22355 4.09426 0.8828 0.62745 -0.24905 J 19H ~O.26835 -0. 1223 -0.46349 -0.51414 -0.64568 -0.60754 J191111 0.67926 0.02676 -0.24285 -0.61621 1.13734 -0.41447 J 191113 0.98469 -0.30802 0.25689 -0.01 153 1.93824 -0.273 76 J 191116 0.2827 -0.33239 0.63042 -0.2537 0.7359 -0.78849 J19IVO 1.02388 -0.20463 0.97982 -0. 16564 0.42572 -0.65742 J19IV3 2.72987 -0.71 158 0.14489 -0.42678 0.87488 -0.01031 J2] -0.51735 -0.24332 0.12805 0.441 -0. 17508 -1.17746 J23 0.09239 0.01015 -0.40288 -0.29533 0.73806 -0.46319 K20 -0.12346 -0.05331 0.22612 0.21929 1.39184 -0.05259 K2011] 0.34269 0.58368 -0.50432 -0.0998 -0.47606 1 . 14574 K22 -0.41519 -0.31055 -1.49572 0.61488 -1.32165 3.13398 K2211top -0.56481 -0.37717 -0.92425 -0.08792 -1.78667 0.49215 M20 -0.63266 -0.39196 -0.81879 -0.33165 -1.52298 0.22955 M240 -0.63256 -0.41299 -0.18132 -0.16893 0.48097 2.29478 M24] -0.63397 -0.41728 -1.24193 0.23006 -1.00979 1.79154 M243 -0.62468 -0.41928 -0.93397 -0.08667 -1.59586 0.58369 N2] -0.33631 -0.21422 -0.91798 -0.04526 -1.55599 0.42638 N23 0.15319 -0.3544 -0.55707 -0.512 0.27542 -1.1 1304 N21111 0.55845 -0.30296 0.01612 0.49521 -0.1 1961 0.84344 022 -0.47531 -0.36961 0.49505 «0 19995 ~0.44054 -0.45537 022110 0.21 196 -0.26006 0.56317 -0.14984 -1.51724 0.13957 02211] -0.33509 1.83665 0.42437 -0.25 864 -0.90202 -0.34661 022H3 -0.67356 0.2569 0.10854 -0.36252 -1.2494 -0.03 864 51 Table 9 (cont’). Results of R-mode factor analysis on soils from the sequential chemical extractions. At the top of the table are the eigenvalues and proportion of the variance explained by each factor. The center of the table shows the loadings of the variables on the factors. The variables that comprise each factor are listed at the bottom of the table. Factor Scores Sample Factor] Factor2 Factor3 Factor4 Factors Factor6 022114 -0.63838 -0.34539 —0.01623 -0.68326 1.51824 -0.35689 024 -0.45105 -0.24675 —0.21815 -0.49217 0.76042 -0.35838 P23 -0.13821 -0.48914 -0.47839 -0.60921 -0.07841 -0.25664 P25 -0.22441 -0.33612 1.22984 -0.36426 0.96892 -0.70248 P25110 0.08596 -0.36592 -0.03466 -0.27 1 55 -0.5814 -0.32749 P2511] -0.19869 -0.3581 0.28721 -0.17841 -0.96711 0.7218 P25113 -0.49569 -0.37998 0.0286 -0.3633 -0.14012 -0.05683 023 -0.2719 0.21332 -0.15911 -0.2862 -0.57732 -0.16358 0241111 -0.53499 1.3506 0.65188 -0.1 1 162 0.37604 0.8102 0241113 -0.60451 -0.00597 0.12897 -0.47222 -0.43937 0.25446 Q26 -0.63344 -0.40705 -0.26126 -0.52644 -0.1 1 121 -0.0105 T27 -0.63219 -0.39828 -0.57392 -0.49788 -0.60537 0.06162 U26 -0.58324 -0.34885 0.16742 -0.25939 0.11324 -0.42243 Factor 1: E16, H17(I), H17(II), J19(IV), J 19(IV)3.5, M24, P25(II)3.5, QZ4(III)3.5, Q26, T27 and U26 Factor 2: 614, H15, H17(V), H17(V) 1.5, K20 (II) 1.5 and 022(II) 1.5 Factor 3: B7, C4, C16, E8, H17(III), H19, J 19(1) and P25 Factor 4: 120, 120(II), 120(H)1 .5, 120(H)3.5, 122, 127, P23 and Q23 Factor 5: C8, D9, 018(11), H17(V)3.5, .1 19(11), J 19(III)1.5, J 19(III)3.5, J19(IH)6, J21, 123, K22, K220), M24 3, N21, 022(II)3.5, 022(II) 4, P25(II) and P25(II)1.5 Factor 6: E18, H17(IV), 121, K20(II)1.5, M24 1.0, N23, N21(II)1.5 and Q24(III)1.5 52 4.5 - Cluster Anaysis Absolute multi-elemental concentrations (Table A-1, Appendix A) were used for the cluster analysis, these results can be found in Figure 6. The first branch in the cluster tree produced two clusters, in order to explore a more detailed relationships among geochemistry/microbial clusters the second set of branches were used in the analysis. The second set of branches breaks off into five major clusters. These five major clusters were labeled numerically 1 thru 5. The result of the R-mode factor analysis (Table 8) suggests that one population dominates the system. One factor dominates the sample sites, Factor 5, as described by the factor scores at the bottom of Table 8.. Factor 5, from the R-mode factor analysis results, contains a number of the same sample sites that group within cluster 1 of Figure 6 produced by the entire soil database. Another similarity was that almost all the sample sites within clusters 4 and 5 produced by the entire soil database cluster tree group together in Factor 4 from the R-mode factor analysis results (Table 8). Absolute chromium concentrations for sequential chemical extraction was also clustered, Figure 7. The largest cluster was again identified as cluster 1, followed by cluster 2, 3, 4 and finally cluster 5 being the smallest cluster containing only samples J19(III)1.5, J19 (IV) 0.5 and Jl9(IV) 3.5. When comparing the cluster tree produced using the entire soil database (Figure 6) and the cluster tree for chromium partitioning (Figure 7) the only similarity between the two cluster tree diagrams were that samples H171, II, III, IV and V cluster near and/or with each other, as do samples 1191, H, J19 (IV) 0.5, J19 (IV) 3.5 and J19III 3.5. Differences among the two figures are that sample site J 19 (III) 1.5 does not cluster with 53 the H17’s in Figure 7, as in Figure 6. Sample sites M24 surface, 1.0 and 3.0 cluster together in Figure 7 but not in Figure 6. In Figure 6, sample site K20(II) 1.5 clusters in the second largest cluster, cluster 2, but in Figure 7 this sample site clusters in one of the smaller clusters, cluster 4. In conclusion, sample sites H171, H, 111, IV,V .1191, II, .119 (IV) 0.5, J 19 (IV) 3.5 and J 19111 3.5 cluster near and/or with each other in both the chromium partitioning and the entire soil database cluster tree diagrams. These samples sites (11171, 11, III, IV,V J19I, 11, J19 (IV) 0.5, .119 (IV) 3.5 and J19III 3.5), as indicated by both absolute and sequential databases, contain high concentrations of chromium. 54 Cluster Tree Sggm 8836; 5 33:2: 3 g éaeiiéiéaaaéaa‘ 53 5 : s§§§§§S§§9§§§§2§§§ ‘*$*“fi"%¢Vfi:£;EEEF=‘T l l 1 I 0 100000 200000 300000 IDBunmes Figure 6. Cluster tree of soils from total chemical extraction data. The cluster tree has been divided into five clusters as indicated by the numbers 1,2,3, 4 and 5. 55 Cluster Tree 4 7v 38 as. 1 l l 1 0 50000 100000 150000 Distances Figure 7. Cluster tree for chromium from sequential chemical extractions. The cluster tree has been divided into four clusters as indicated by the numbers 1,2,3 and 4. 56 V. GEOCHEMICAL—MICROBIAL COMPARISONS 5.1 - Absolute Chromium Concentrations and Microbial Populations. This section explores how absolute chromium concentrations, chromium partitioning among sediment phases, selected heavy metal concentrations and organic matter concentrations relate to microbial populations. The first step was to determine if absolute total chromium concentrations could be related to difierent microbial populations. A t-test was used for this analysis, which is a parametric test that assumes that the data samples are fi'om one population with an asymptotically normal distribution. The microbial clusters were organized into two groups, A and B, in order to determine if absolute chromium concentrations for samples in the lager cluster (A) are different for those samples sites in the smaller clusters (B), (Figure 5). The first group (A) contains sample sites that are within cluster 4. The second group (B) contains sample sites that are within clusters 1, 2 and 3. The t-test then compared chromium concentrations for the sample sites between groups A and B at a 95% confidence level. The test determines if absolute chromium concentrations (Table 3) for sample sites within clusters A and B are the Same. If the calculated t-value was greater then the critical t-value, then groups A and B Would be different in terms of their chromium concentrations. If the t-value was less then the critical t-value then the chromium concentrations from groups A and B would be conSidered to be the same. At the 95% confidence level the calculated t-value was slightly larger then the critical t-value (Table 10). 57 Samples sites that contain high concentrations of chromium tend to group together in absolute geochemical clusters 4 and absolute geochemical cluster 5, these samples sites also group within microbial cluster 4. Microbial cluster 4 also has lower concentrations of chromium that group within absolute geochemical cluster 1. Microbial cluster 1, 2 and 3 tend to have absolute chromium concentrations that are intermdiate. This could suggest that neither extremely high nor low absolute chromium concentrations influence the microbial community structure, but that a median range of absolute chromium concentrations may have an effect on microbial populations. Table 10. t—Test analysis results at a confidence level equal to 95%. Cluster A represents absolute chromium found in sample sites that group within cluster 4 from figure 5. Cluster B represents absolute chromium found in sample sites that group within clusters 1,2 and 3 from figure 5. Confidence Level = 95% Cluster A Cluster B Sample Size 41 15 Mean 39411.9922 4251.8633 ifference = 5 160.1289 Variance 4.07E+09 7.42E+071Ratio = 54.8889 lt-Value robability DF ICritical t-Value Feneral 2.1 141 0.0391 54 2.0049 aired 2.89641 0.0117 14 2.1448 o-Variance = - 1.8020E+008 td Deviation = 4763.0352 [UnPaired 3.4422 0.0013 44 2.0154] 58 5.2 - Chromium Partitioning and Microbial Population. In section 4.3 the microbial cluster tree was divided into four distinct clusters and in section 4.6 the total and sequential chemical extraction cluster trees were divided into five clusters, these results show that all three types of cluster trees have one dominate cluster. Also, the t-test was used to determine that absolute chromium concentrations are different throughout the microbial population. To firrther assess the influence chromium has on the microbial population, chromium partitioning and its effects on the microbial community is examined in the following section. To determine if there is a relationship between chromium partitioning and the microbial population, a table comparing the results from the cluster analysis of the sequential chemical extractions (SCE) and of the microbial populations was prepared (Table 11). The sample sites are listed in colunm one, the SCE cluster number (Figure 7) in column two. The shaded area indicates the sample sites association within the microbial cluster tree. Microbial clusters], 2 and 3 group together within SCE cluster 1. Microbial cluster 4, contains over half of the microbial sample sites, groups within SCE clusters 2, 3, and 4. Only a few microbial sample sites in microbial cluster 4 group within SCE cluster 1. Microbial clusters 1, 2 and 3 group exclusively within SCE cluster 1 and that the majority of microbial sample site (microbial cluster 4) do not group within SCE cluster. This could indicate that there is a relationship between chromium partitioning and the microbial community. 59 The previous section suggests that there is a potential relationship between SCE clusters and microbial clusters, but further analysis is needed. In order to determine if there is a SCE/microbial relationship a SCE geochemical fingerprint was constructed. Table 11 summarizes the average absolute chromium fiom the sequential chemical extractions for each of the five clusters in Figure 7 was used to construct the pie diagrams (Figure 8). This approach could then be used to infer why certain microbes’ associate with the diflerent SCE clusters was used to examine sample association in each phase. The pic diagrams (Figure 8) show tlmt except for SCE cluster 5, the MR phase is dominant in sequestering chromium. The most dominant phase sequestering chromium in SCE cluster 5 is WAS phase. The percentage of the absolute chromium concentrations associated with the OX 1 phase decreases from SCE cluster 1 to SCE cluster 5. The pie diagrams also shows that SCE cluster 1 has the highest percent of OX 2. Microbial clusters 1,2 and 3 tend to be confined to SCE cluster 1, while microbial cluster 4 occurs in all SCE cluster, this could indicate that something is unique about microbial clusters 1, 2 and 3. The amount of Cr associated with the OX2 phase appears to be the main diflerence in Cr partitioning among all SCE clusters. The pic diagrams are averages and there is considerable range of percent for the contribution of each phase in each pie diagram. Therefore, it is not clear how unique the OX2 phase is, so further analysis to determine if high percentage of Cr in the OX2 phase are only associated with SCE cluster 1. Six tables (Table 12a-f) were constructed to determine if the OX2 phase associates only with SCE cluster 1. The data used to construct the Tables 12a-f was the percent chromium for each SCE phase. Samples associated with microbial cluster 2 are 60 shaded light gray and samples associated with microbial clusters 1 and 3 are dark gray. The data on each table was then sorted by a phase (e.g., EX, WAS, ER, MR, OX1 and OX2). When the samples sites are sorted by the OX2 phase, microbial clusters 1, 2 and 3 group together (e.g., light and dark gray shades line up next to each other). When the sample sites were sorted by the remaining SCE phases the light and dark shades do not line up next to each other as well. It is unclear why the sample sites associated with microbial clusters 1, 2 and 3 group together when sorted by the OX2 phase. It is possible that the OX2 phase is being altered by the microbes or that the OX2 phase may have a more reactive form or Cr. 61 Table 11. Comparison table showing chromium partitioning versus microbial populations. The results of the sequential chemical extraction cluster tree (Figure 7) are listed in column one along with the cluster number that the sample can be found in. The shaded area indicates what cluster the sample site is associated within the microbial cluster tree. MICROBE CLUSTER Geochemistry Cluster 2 3 62 Table 1 1 (cont’d). Comparison table showing chromium partitioning versus microbial populations. The results of the sequential chemical extraction cluster tree (Figure 7) are listed in column one along with the cluster number that the sample can be found in. The shaded area indicates what cluster the sample site is associated within the microbial cluster tree. MICROBE CLUSTER Geochemistry Cluster 2 3 P25 0.5 1...; 1.5 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 63 Cline! l Cluster 2 OX2 EX 0X1, 2% 1 0% WAS 6% \/ [10% MR 76% Cliner 3 Clister 4 OX2 EX OX2“ EX% WAS% 1% ‘ 0% ER 1% \ 0% 4% OX1% ' / 3- 2% OX1\ 1% 12% ’7‘“ 14% MR% 82% 81% Cluster 5 1% Figure 8. Pic diagrams representing percent chromium for each sequential chemical extraction phase. Cluster 1, 2 and 3 represent geochemical clusters from Table 11. The sequential emotion phases are; exchangeable (EX), weakly acid soluble (WAS), easily reducible (ER), moderately reducible (MR), basic oxidizable (OX1) and acid oxidizable (OX2). Table 1211. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by exchangeable (EX) phase. Sorted by EX W 91 .23% 47.45% 1.81 13.13% 42.46% 1 .41 2.18% 40.42% 2.95% 56.09% 0.69% 22.37% 20.66% 0.1 47. 0.10% 65 Table 12b. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by weakly acid soluble (WAS) phase. Sorted by WAS 0.82% 9.63% 12.04% 0. 0.02% 42.30% 2.18% 40.42% 2.95% 1. 1 47. 1.81% 0.03% 0.69% Table 12c. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by easily reducible (ER) phase. Sorted by ER U26 1. P25(II)3- 0.02% 47.45% 1.81% 36.84% 13.13% 0.76% 1 l 40.42% 12.13% 9.63% 12.04% 41.57% 1% 67 Table 12d. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by moderately reducible (MR) phase. Sorted by MR ER MR M24 1.0 13.17% P2501) 0.03% 56.09% P2501) 0. 1.41% P25(II)3- 2.18% 40.42% 12. 41.57% 68 Table 12e. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by basic oxidizible (OX1) phase. EX 5.68% 0.10% 0.02% 0.02% 0. 1 2% Sorted by OX1 2.20% 5.34% 1. 4.31% 12.72% 82.77% 0.04% 0.06% 12.04% 4 0.96% 1 42.30% 2.18% 40.42% 2.95% 76% 0. 1 47.45% 1.81% 0.69% 17 23.20% 0.78% 26.84% 47.40% 1.75% 69 Table 121'. Tables representing the percent chromium associated with each sequential chemical extraction phase. Data below is sorted by acid oxidizible (OX2) phase. by 0.01% .23% % 0.10% 4.31% 12.72% 77% 0.06% . 97 0.09% 17 49.90% 0. 0.03% 56.09% 22.37% 20.66% 0.16% 14V 65.78% 15. 0 l 76.77% 13. 0.02% 47.45% 1.81% 36.84% 13.13% 0.76% 2.21% 3. 90. l. 23.20% 0.78% 26.84% 47.40% 1.75% 42. 1.41 28. 0.53% 4 0.82% 9.63% 12.04% 41 .57% 0.22% 35.72% 70 5.3 - Multi-Elgmental Concentrations and Microbial Population It has been established that there is a relationship between the level of total chromium concentrations in the sediment and microbial populations and between the type of chromium partitioning (particularly the OX2 phase) and microbial populations. It is not necessarily clear however, how important just the chromium concentration is in the associations with the microbial communities. For example, could the concentration of some other chemical work with chromium to cause these relationships. To explore this idea the possible influence of other chemicals on the observed associations of chromium with microbial populations was considered. In this section the possible influences of the 26 chemicals listed in the methods section are examined. In the next section, the specific roles the concentrations of manganese, iron, and organic matters play the associations are studied. These three chemicals were considered because they are ones that have important influences on chromium behavior in the environment [e.g., oxidation of Cr(III) to Cr(VI)] by manganese oxides, adsorption or co-precipitation with iron oxides, and adsorption to organic matter. A table was prepared to compare the relationships of all of the chemicals (Table 13) using the results from the cluster analyses of the absolute chemical extraction data (Table A-1, Appendix) and of the microbial cluster data (Figure 6). The results of the total chemical extraction clustering (Figure 6) are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. As with the chromium partitioning data versus microbial population comparison (Table 11) there appears to be a relationship between microbial clusters 1, 2 and 3 and geochemical cluster 1, since microbial clusters 1, 2 and 3 group within geochemical cluster 1. A 71 geochemical fingerprint, which shows general patterns among heavy metal concentrations, was constructed to gain a better understanding of the groupings (Figure 9a-e). The geochemical fingerprint will indicate what chemicals might be characterizing the diflerent groupings so tint associations to microbial populations could be determined. In order to construct the geochemical fingerprint the average concentrations for each element was calculated and divided by the average world soil concentration (Reirnann et a1. 1998) and then converted to log 10. This number was graphed (F igure9a-e). Log10 was used in order to display the data easier and to see how heavy metal concentrations from the current study compared or differed from the average world soil concentration. Five geochemical fingerprints were constructed for each of the five clusters produced in the total chemical extraction cluster tree (Figure 6). The geochemical fingerprinti (Figure 9a) shows that geochemical cluster 1, involving absolute concentrations of elements. It was found to be enriched in such elements as; chromium, arsenic, zinc, cadmium, lead, selenium and mercury. Absolute geochemical cluster 2 (Figure 9b) was enriched in barium, copper, zinc, cadmium, lead, selenium and mercury. Absolute cluster 3 (Figure 90) was emiched in zinc, mercury, lead and selenium. Absolute geochemical clusters 4 and 5 (Figure 9d and 9e) were enriched in copper, zinc, arsenic, cadmium, lead, selenium and mercury. Chromium concentrations increase fiom absolute geochemical cluster 1 thru 5, absolute geochemical cluster 5 having the highest concentration. All five of the clusters were depleted in elements such as; potassium, aluminum and titanium. Concentrations for nickel, manganese and copper remain the same throughout clusters 1- 5. Arsenic concentrations also increase fiom absolute geochemical cluster 1 thru 5 similar to chromium. Arsenic, like chromium, can be used by microbes as electron donors or 72 acceptors to metabolism energy (Ehrlich, 1997). Future work would need to be done to determine if arsenic, the form of arsenic, and if arsenic more so then chromium, influences the microbial community. 73 Table 13. Comparison of multi—elemental concentrations and microbial community structure. The results of the total chemical extraction cluster tree (Figure 6) are listed in thefirstcohnnn. Theshadedareaindicateswhatclusterthesampleisassociatedwithin the microbial cluster tree MICROBE CLUSTER Geochemical Cluster 1 2 3 7 17 1.5 17 III 17 I 17 II 7 l8 1 5 5 4 4 4 4 4 1 l 1 1 1 1 1 1 1 1 1 1 l 1 1 l 1 1 1 1 l 1 1 1 1 74 Table 13 (cont’d). Comparison of multi-elemental concentrations and microbial community structure. The results of the total chemical extraction cluster tree (Figure 6) arelistedinthe firstcolurnn. Theshadedarea indicates whatclusterthesarnpleis associated within the microbial cluster tree MICROBE CLUSTER Geochemical Cluster 1 2 3 1 1 1 1 1 l 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 75 cumin fatigue-nan CLUSTER! 8.8.;oaa . A $th CUISTER 3 2.8.3.8. '5' Nb» Figure 9a-c. Geochemical fingerprint of multi-elements. The average concentration for each elements was calculated and divided by the average world soil concentration and thenconvertedto log 10,thisnumberwasthengrapmd forthe five majorclusterstlmt were produced in Figure 6. 76 CLUSTER 4 D CHIS]! I S 4 3 2 1 O -1 -2 -3 E Figure 9d,e. Geochemical fingerprint of multi~elements. The average concentration for each elements was calculated and divided by the average world soil concentration and then converted to log 10, this number was then graphed for the five mjor clusters that wereproducedinFigureé. 77 5.4 Chromium, Iron, Manganese and Organic Matter and Microbial Population To determine how the associations of chromium, iron, manganese and organic matter influence the microbial structure, cluster trees were constructed for chromium, iron, manganese and organic matter and then compared with the microbial cluster tree. Chromium, iron, manganese and organic matter concentrations where clustered (Figure 10). The results were then used to construct a comparison table with the microbial cluster (Figure 5) tree (Table 14). The results of all four variables are combined in the clustering show microbial clusters 1, 2 and 3 still group together in geochemical cluster 1 (Table 14). To determine the extent to which organic matter may be influencing this pattern; chromium, iron and mnganese were clustered together (Figure 12). Figure 12, as with Figure 11, has a cluster (cluster 1) containing the largest number of sample sites. A comparison table (Table 15) was then constructed using the results in Figure 12 and Figure 5. The results of Table 15 shows microbial clusters 1, 2 and 3 no longer group close together within geochemical cluster 1. These results would indicate that since microbial clusters 1, 2 and 3 no longer group close together once organic matter is eliminated from the system, organic matter has an influence on the microbial community structure. To determine if chromium plays a role influences microbial structure, iron, manganese and organic matter were clustered together (Figure 13). The sample sites that grouped close together within cluster’s 1 of Figure’s 11 and 12 no longer assemble near each other. When a comparison table (Table 16) was constructed using Figure 5 and 78 Figure 13, microbial clusters 1, 2 and 3 were no longer found exclusively in geochemical cluster 1 but group within geochemical clusters 2-5. The results of Table 16 indicate tlmt microbial clusters 1, 2 and 3 no longer group close together because chromium is no longer a factor, which means that chromium has an influence on the microbial population. To determine if chromium and organic matter influence the microbial population, iron and manganese were clustered (Figure 14). The results of Figure 14 show that geochemical cluster 1 is no longer domimted by a majority of the sample sites. A comparison table (Table 17) was constructed using the results of Figure 5 and 14. The results of the comparison table (Table 17) shows that microbial clusters 1, 2, and 3 no longer group close together within the same geochemical cluster, as a result of both chromium and organic matter being removed fi'om the system. In summary, when chromium, iron, manganese and organic matter are clustered together microbial clusters 1, 2 and 3 group together within chromium, iron, manganese and organic matter cluster 1. When chromium, iron and manganese are clustered and compared with the microbial cluster, microbial clusters 1, 2 and 3 no longer group only in chromium, iron, manganese cluster 1. When iron and manganese are clusters, the iron, manganese geochemical cluster 1 no longer contains a majority of the sample sites and microbial clusters 1, 2 and 3 no longer group together. These results show that chromium and organic matter may play a role in the structure of the microbial cormnunity. How these two factors exactly influence the microbial population are unknown and requires firrther study. 79 Cluster Tree 5 H1‘NTOP t H17V15 r 4 .— H171 3 J19|||15 ‘ ma l l l l 1 I Q Q 0% 06 0% 9% KOQQQ (2990 $000 “90° 6°09 Distances Figure 10. Cluster tree of absolute chromium, iron, manganese concentrations and percent organic matter. 80 Table 14. Comparison of chromium, iron, manganese and organic matter cluster tree with the microbial cluster tree. The results of the chromium, iron, manganese and organic matter cluster tree are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. MICROBE CLUSTER 2 3 5 5 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 1 l 1 1 1 1 1 1 1 81 Table 14 (cont’d). Comparison of chromium, iron, manganese and organic matter cluster tree with the microbial cluster tree. The results of the chromium, iron, manganese and organic matter cluster tree are listed in the first colunm. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. MICROBE CLUSTER Geochemical Cluster 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 l 1 l 1 82 Cluster Tree 1 l 1 T l l l l l QQOQQQQQQQQQQQOQQ ,o9°,o9°.,o9° ,o9°.,o9°.,o9°1o9°,,o9° Distances Figure 11. Cluster tree of absolute chromium, iron and manganese concentrations. 83 Table 15. Comparison of chromium, iron, and manganese cluster tree with microbial cluster tree. The results of the chromium, iron and manganese cluster tree are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. CLUSTER Geochemistry 2 3 84 Table 15. Comparison of chromium, iron, and manganese cluster tree with microbial cluster tree. The results of the chromium, iron and manganese cluster tree are listed in the first column. The slmded area indicates what cluster the sample is associated within the microbial cluster tree. MICROBE CLUSTER Geochemical Cluster 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 l 1 1 l 1 1 1 1 1 1 1 1 85 Cluster Tree Distances Figure 12. Cluster tree of absolute iron, manganese concentrations and percent organic matter. 86 Table 16. Comparison of iron, manganese and organic matter cluster tree with microbial cluster tree. The results of the iron, manganese and organic matter cluster are listed in the first colurrm. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. MICROBE CLUSTER Geochemical Cluster 1 2 5 5 5 5 5 5 5 4 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 87 Table 16 (cont’d). Comparison of iron, manganese and organic matter cluster tree with the microbial cluster tree. The results of the iron, manganese and organic matter cluster treearelistedinthefirstcolurnn. Theshadedareaindicateswhat clusterthesampleis associated within the microbial cluster tree. MICROBE CLUSTER Geochemical Cluster 1 2 3 15 2 1 10 1 1.5 4 0.5 1 17 1.5 .5 t—__—--H~wwwwwwwww 88 Cluster Tree smog 933a4 3-". ‘38 1:5 (5 88 5 b 8 I I l 0 50000 100000 150000 Distances '00) —1 Figure 13. Cluster tree of absolute iron and manganese concentrations. 89 Table 17. Comparison of iron and manganese cluster tree with the microbial cluster tree. The results of the iron and manganese cluster tree are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. MICROBE CLUSTER Geochemical Cluster 1 2 5 5 5 5 5 5 5 4 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 90 Table 17 (cont’d). Comparison of iron and manganese cluster tree with the microbial cluster tree. The results of the iron and manganese cluster tree are listed in the first column. The shaded area indicates what cluster the sample is associated within the microbial cluster tree. MICROBE CLUSTER Geochemical Cluster 1 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 1 l 1 1 l 1 1 1 1 l 91 VI. SUMMARY 6.] - Summary Understanding the relationship among heavy metals and the microbial community structure can aid in bioremediation/biostablization efforts in contaminated soils. The purpose of this study was to determine if the concentrations of chromium and selected heavy metals could be related to the microbial community structure in sediments. The study site was a wetland that received chromium contaminated waste from a former leather tannery and provided an ideal environment to study the relationship between contaminants and microbes. To quantify the-amount of heavy metals in the sediments, samples were collected at the surface and at difl‘erent depths. Total chemical extractions (chromium plus 19 additional metals) and sequential chemical extractions (chromium) were performed on soil samples taken fiom the site. Soil samples were also analyzed for organic carbon content. Microbial T-RFLP analysis was used to examine microbial populations and community structure. Factor and cluster analysis was used to establish relationships among the metal concentrations and the microbial population. Chromium concentrations were found to vary with the concentrations of organic matter, but not with iron or manganese concentrations. Chromium partitioning results indicate that chromium associates more with the moderately reducible (MR) phase than the other sediment phases. The chemicals used to define the MR phase were designed to dissolve iron oxides. However, in this wetland environment, significant portions of the 92 soils are anoxic and iron oxides would not be expected to be stable. In addition, considering the lack of a relationship between chromium concentrations and iron concentrations and knowledge of the low solubility of chromium hydroxides at near neutral pHs, the form of chromium on the MR phase is interpreted to be as chromium hydroxide rather than sorbed to iron oxides. The results also show that four major microbial populations or clusters could be identified. Five clusters were identified in the sequential chemical extraction geochemical data for chromium. Five clusters were also identified in the data set of the total sediment concentrations for twenty-one elements including chromium. Two of the microbial clusters were found to be associated with chromium partitioned to the OX 2 phase. When chromium, iron, manganese and organic matter were clustered and compared, microbial clusters 2 and 3 grouped together within the geochemical cluster 1. As chromium, iron, manganese and organic matter were removed and re-clustered, microbial clusters 2 and 3 no longer group within microbial cluster 1. Not as many sample sites group in geochemical cluster 1 when chromium and organic matter are removed. These results show that chromitun and organic matter play a role in the structure of the microbial community. It has also been found absolute chromium concentrations may also have an influence on the microbial populations. What influence chromium and organic matter have on the microbial community structure is unknown. This research demonstrated a possible association between chromium concentrations and form and microbial populations. However, various issues arose that still need to be addressed. 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Bacterial reduction of hexavalent chromium. J. Ind. MicrobioL 14: 159-163. Woese, CR (1987). Bacterial Evolution. Microbiol. Rev., 51: 221-271. 98 Appendix A Concentrations are in mg/L (ppm). Table A-1: Absolute Concentrations for Mum-Elements. Samples K Cr Mn Fe Ba ML V Co BS 210.57 8.88 134.13 4730.00 33.06 477.20 7.59 0.86 87* 306.87 22.22 267.87 8909.62 34.79 739.46 13.30 1.47 B9 143.44 12.73 76.26 9333.89 18.54 516.24 9.86 0.81 C4 259.62 47.27 130.38 7202.92 30.75 874.14 15.88 1.43 C8 245.48 47.64 198.15 10642.80 38.78 390.22 10.16 0.56 C10 371.94 18.19 200.32 7639.34 43.47 1029.30 8.43 1.25 C16 502.46 14.95 165.44 9818.63 72.48 2468.32 11.83 1.69 D9 360.20 49.12 157.89 15622.50 64.75 949.44 29.01 1.35 D] 1 574.78 90.14 188.98 8369.87 73.83 1415.40 11.67 1.63 017 738.20 5465.07 372.71 25032.81 359.39 2443.54 17.69 3.22 E8 477.01 170.08 141.00 8710.76 226.06 2405.24 13.37 2.48 E16 480.16 1192.81 412.46 23576.56 63.03 891.92 10.95 3.22 E18 1247.38 27830.48 508.90 35832.04 677.68 4801.35 31.12 6.04 614 525.48 22134.39 956.78 27936.38 496.75 4305.18 17.63 2.21 (:18 961.82 765.05 278.44 6613.49 170.75 1282.83 9.33 0.78 (Hm 359.70 493.61 258.91 6888.98 141.50 915.70 9.58 1.47 GlSflDl- 400.16 20.98 77.74 6082.38 19.82 1300.68 13.60 1.63 1.5 (21801) 3- 623.50 12.00 79.76 7652.80 33.90 1914.94 17.36 2.52 3.5 H15 237.56 24715.12 283.87 8463.06 177.16 2989.32 12.57 1.63 H17 (1) 388.26 137620.87 414.75 7169.78 204.64 1126.13 61.96 1.08 H17 ([1) 331.00 135355.32 410.64 7295.87 224.40 1279.84 55.77 1.70 1117 (111) 321.98 141518.58 379.41 7624.28 218.02 1194.72 57.40 1.56 H17(IV) 399.32 120383.58 909.59 6120.02 249.83 1607.82 26.61 1.07 H17(V)” 104.42 281551.80 590.96 3060.67 366.96 805.81 39.75 1.67 H17(V)].5 51.97 237170.11 364.51 4579.17 178.82 1108.44 24.06 1.64 H17(V) 398.07 9881.40 542.44 10523.26 123.50 4492.34 18.28 1.18 3.5 H19 479.56 451.87 574.50 9238.98 255.67 853.45 7.90 0.67 H21 503.72 29.54 115.94 7276.12 23.43 2333.22 13.39 2.51 120(II) 0- 1105.74 308.00 532.32 23785.89 387.76 1081.56 24.53 5.48 0.5 120(II) 1- 701.82 506.65 1680.50 30630.32 280.63 1894.55 15.13 4.34 1.5 120(II) 3- 591.94 1971.33 1555.38 26300.08 271.05 3664.73 21.65 3.49 3.5 120 900.09 451.60 685.66 30742.01 369.36 1463.51 18.56 4.20 [22 656.69 20556.33 74.20 8114.47 292.71 1937.83 45.10 0.76 127 587.40 31660.56 120.32 8423.48 32.37 1662.50 14.11 1.95 99 Table A-1: Absolute (cont’d) Samples K Cr Mn Fe Ba “1L V Co .119 (1) 854.30 29137.90 354.33 21484.28 1086.60 1762.51 36.63 2.31 .1 19(11) 209.87 31543.52 128.82 11568.28 2476.60 1310.12 22.41 1.28 J19(III)1.5 38.20 80186.17 19.03 3421.73 2415.27 411.22 10.58 0.46 J19(IH)3.5 42.25 35000.00 16.43 4063.38 347.99 658.09 2.73 0.43 J19(III)6 57.75 1 1001.40 50.29 2926.01 109.20 745.68 2.74 0.43 J19(IV)0.S 302.98 23924.00 140.21 13412.54 3613.89 1425.39 19.57 2.02 Jl9(IV) 104.33 30479.41 36.88 4031.96 860.48 745.35 12.00 0.66 1.5 J 19(1V)3.5 56.13 24917.23 28.82 5081.55 338.57 799.26 3.74 0.33 Jl9(IV) 105.21 31083.28 27.05 4430.81 258.11 780.20 3.52 0.37 6.0 J21 1133.61 3795.37 305.54 7748.67 260.66 1048.54 11.68 0.98 J23 286.12 38640.74 47.78 13184.03 350.23 1983.87 46.99 1.93 K20 414.52 23589.15 771.49 10031.16 384.30 2180.53 13.39 1.22 K20(II)1.5 157.63 50494.50 582.76 10488.52 714.31 1817.51 9.19 1.77 K22 268.45 7149.65 311.57 10635.76 268.61 1971.70 16.15 0.79 K22(Il)top 96.84 4027.41 131.14 8370.44 157.95 1729.47 11.19 0.57 M20 451.17 439.00 127.99 10186.10 25.66 2888.12 14.87 3.21 M24 0.5 474.18 90.55 141.96 7302.38 32.54 2051.22 13.95 2.07 M24 1.0 320.31 18.76 116.84 9315.37 15.31 1686.42 8.70 1.24 M24 3.0 754.55 33.14 652.09 8907.11 346.43 2652.79 14.49 2.31 N21 612.17 21121.16 607.35 10792.04 913.80 1284.79 12.89 1.16 N21(ll)l.5 177.52 23659.54 284.70 7551.81 1401.32 1458.38 9.85 1.09 N23 261.17 11252.17 268.55 17806.85 291.51 1591.40 13.73 1.54 022(II) 0.5 312.55 13398.74 339.21 7516.48 368.05 1261.82 11.23 1.05 022(II)1.5 367.26 32387.79 161.70 9922.20 1766.61 1484.66 14.87 1.97 022(II)3.5 252.00 5421.46 94.98 5980.26 584.84 1280.00 7.99 1.13 022(Il)4.5 244.31 697.77 61.64 4698.39 48.33 1381.87 7.69 0.97 022 160.20 850.03 47.97 3436.46 84.76 1226.73 6.78 0.97 024 447.09 12984.80 700.43 14482.67 332.64 1570.11 16.65 2.19 P23 17.38 6842.60 177.23 5013.90 129.64 744.03 5.54 0.56 P25 43.05 5768.45 161.07 10542.39 92.66 1814.56 17.90 2.33 P25(Il) 0- 67.96 10565.25 182.04 11592.30 150.10 1900.28 20.02 1.90 0.5 P2501) 1- 60.84 6127.77 159.83 10777.45 101.37 1615.72 21.67 2.05 1.5 P2501) 3- 61.30 1646.16 69.12 6263.66 28.09 1601.91 12.36 1.64 3.5 QZ4(I])1.5 1 1.44 16393.91 93.63 6562.50 2713.82 1 159.84 9.08 1.48 Q24(II)3.5 10.03 3853.83 60.01 6570.73 101.93 1234.36 5.56 1.12 5224* 34.19 17536.61 236.63 9912.94 7522.57 1547.60 14.07 1.96 Q26 80.21 638.40 583.86 13907.87 80.84 1938.82 18.75 2.08 T27 15.24 648.07 117.27 5584.60 17.34 1325.04 5.04 0.77 U26 56.71 2435.13 180.47 9702.03 54.77 2034.15 12.36 2.33 100 Table A-1: Absolute (cont’d) Samples Ni Cu Zn As Sr Cd Pb B5 5.96 5.00 43.48 2.33 6.49 0.19 41.04 37* 2.47 4.84 38.28 5.88 5.73 0.24 24.90 B9 1.31 2.72 25.90 1.42 2.73 0.15 9.42 C4 3.48 5.85 46.76 2.85 6.03 0.23 27.70 C8 0.03 2.27 39.06 1.90 5.65 0.19 16.90 C10 1.84 4.12 41.48 2.59 8.27 0.25 22.47 C16 3.91 18.10 86.98 3.99 38.53 0.23 35.00 D9 3.35 6.04 48.02 3.21 8.30 0.24 24.49 Dll 4.93 7.84 67.09 2.81 12.54 0.24 31.10 017 17.87 115.98 364.99 8.87 41.32 0.85 266.72 E8 3.17 13.75 113.24 1.19 10.64 0.23 57.68 E16 11.14 23.17 86.19 4.56 40.60 0.59 50.40 E18 34.46 156.66 1573.05 5.29 80.25 0.90 321.82 G14 6.26 40.38 556.16 17.84 207.93 0.69 205.15 G18 2.14 8.59 165.71 3.38 18.36 0.20 42.57 (1180]) 2.96 12.88 95.49 10.88 11.73 0.37 34.80 Gl8(II)1- 252.98 2.87 32.43 4.44 5.85 0.13 2.46 1.5 (2180]) 3- 6.88 7.31 27.44 1.04 6.65 0.12 2.56 3.5 H15 5.35 22.09 157.02 71.38 215.30 0.57 128.62 H17 (I) 7.05 118.89 341.33 305.07 67.93 1.42 266.69 H17 (ID 9.56 112.69 349.14 323.18 68.82 2.36 259.64 H17 (Ill) 10.22 111.67 340.71 320.01 69.82 2.02 262.72 H17(IV) 9.76 90.82 675.87 240.24 82.1 1 2.73 366.03 H17(V)top 12.96 169.19 390.03 408.73 117.90 4.91 464.69 H17LV)1.5 8.63 46.47 340.43 214.61 262.96 6.38 283.12 H17(V) 2.85 31.96 91.23 265.75 125.05 0.38 58.32 3.5 H19 1.02 14.27 158.08 2.31 28.20 0.20 38.60 H21 4.83 9.16 28.21 0.97 8.05 0.13 9.43 120(II) 0- 15.57 164.82 594.41 15.62 68.86 2.01 1639.05 0.5 120(II) l- 19.61 284.46 517.71 10.35 73.15 1.07 1833.65 1.5 120(II) 3- 17.53 338.23 512.75 10.09 71.37 0.85 2227.02 3.5 120 9.33 192.73 763.66 17.02 68.99 2.33 4713.85 122 26.84 132.89 288.74 3.17 86.49 103.36 660.40 127 6.48 3.62 123.79 1.86 9.51 0.28 20.16 J 19 (I) 70.02 51.57 479.59 12.47 89.11 86.47 653.92 119(11) 7.65 29.37 247.11 6.40 38.28 28.08 321.94 J19(III)1.5 2.46 138.46 45.19 41.29 26.78 3.39 2602.20 JI9QII)3.5 3.16 118.62 587.02 13.53 23.04 2.36 441.08 101 Table A-1: Absolute (cont’d) Samples Ni Cu Zn As Sr Cd Pb J 19(III)6 0.60 48.12 60.06 5.50 16.50 0.60 125.70 J19(IV)0.5 6.35 25.32 139.38 6.55 83.74 5.10 1212.06 Jl90V) 4.39 157.46 50.44 26.40 33.34 5.11 2806.39 1.5 J190V)3.5 4.82 149.07 327.44 13.02 25.39 1.38 409.93 J19(IV) 2.12 125.57 256.63 22.51 22.14 1.41 280.62 6.0 .121 4.38 12.33 61.00 3.89 17.59 6.19 119.21 J23 9.07 114.70 1202.06 7.32 94.43 143.65 3038.69 K20 6.21 51.79 396.97 6.53 68.65 8.43 643.82 K2001)1.5 6.14 107.86 3000.66 13.82 76.50 13.69 985.26 [(22 12.19 25.39 411.94 4.69 89.33 14.45 152.10 K2201)top 1.24 17.52 345.44 3.25 70.26 5.12 123.58 M20 5.89 11.02 39.60 1.87 9.65 0.19 43.92 M24 0.5 4.51 4.71 29.98 1.50 9.40 0.23 14.46 M24 1.0 2.17 2.70 13.93 0.59 4.99 0.11 4.34 M24 3.0 5.88 31.88 100.53 2.25 107.38 0.48 36.84 N21 4.36 35.15 512.35 11.37 39.74 1.35 287.11 N2101)1.5 3.92 39.21 566.60 8.45 84.44 0.92 554.65 N23 6.43 37.59 324.80 11.14 40.14 1.21 236.62 022(II) 0.5 3.35 25.44 201.30 5.48 51.43 4.30 154.64 022(II)1.5 6.87 44.00 628.62 9.20 74.53 1.48 323.89 022(II)3.5 2.70 9.48 284.37 1.73 38.06 0.28 197.03 02201)4.5 1.50 3.05 30.70 1.17 17.88 0.11 48.68 022 1.37 3.49 38.39 1.82 10.63 0.14 37.89 024 7.14 34.55 322.11 11.36 36.26 2.88 181.35 P23 1.25 15.47 186.73 8.03 10.63 0.81 74.87 P25 6.43 28.70 119.75 9.87 26.53 1.08 21 1.42 P2501) 0- 8.71 63.87 168.95 10.60 34.98 1.36 119.90 0.5 P2501) 1- 15.51 33.62 86.53 11.92 29.89 0.73 55.55 1.5 P2501) 3- 5.41 9.43 26.45 4.38 10.88 0.22 9.72 3.5 (84001.5 3.73 29.56 455.70 5.63 45.84 0.56 215.50 Q24(ll)3.5 1.63 7.89 81.62 2.41 24.38 0.25 80.92 Q24* 5.72 35.07 472.14 8.09 43.33 1.60 282.11 Q26 4.88 11.53 84.79 2.70 32.45 0.49 127.42 T27 2.92 2.16 8.00 0.93 4.90 0.08 7.71 U26 4.39 10.13 49.28 4.40 15.16 0.37 25.90 102 Table A-1: Absolute (cont’d) Samples Al Se Sc Ti Hg Ca B5 2167.54 1.18 0.42 129.96 0.35 1009.35 B7* 3533.30 1.26 0.49 165.20 0.30 1131.29 B9 2421.05 0.93 0.28 129.99 0.24 514.12 C4 3805.65 1.09 0.56 200.60 0.22 996.53 C8 1738.96 0.99 0.25 106.11 0.17 888.65 C10 3491.13 1.01 0.49 162.83 0.16 1672.45 C16 5365.63 1.35 0.87 217.41 0.25 12253.56 D9 4670.30 1.10 0.52 210.38 0.19 1490.23 D11 3811.39 1.10 0.79 260.58 0.17 6173.72 017 4829.41 2.09 1.48 236.13 0.64 13624.84 E8 4093.70 0.59 0.60 266.79 0.13 2005.15 E16 3712.03 1.74 1.11 196.42 0.66 9263.41 E18 13959.56 0.83 1.79 436.62 0.27 26370.63 G14 4725.53 2.06 1.40 130.72 0.42 78910.09 G18 74996.20 0.41 0.53 184.15 0.12 4670.25 01801) 4125.20 2.74 1.00 165.61 0.84 2158.24 G18(11)1- 4717.85 1.45 0.89 250.01 0.44 1198.88 1.5 (21801) 3- 4333.65 1.38 1.86 304.64 0.36 1628.06 3.5 1115 3324.45 3.03 2.42 98.12 2.81 91769.68 H17 (1) 2636.88 1.92 1.12 129.09 1.05 12777.97 H17 01) 2702.68 3.10 1.47 130.52 1.87 13276.37 H17 ([11) 2858.62 3.30 1.46 141.65 1.84 12626.39 [1 170V) 1847.10 2.85 0.97 62.97 1.42 18888.74 H17(V)top 2815.94 2.15 1.54 72.53 5.73 17346.18 H17(VL1.5 3206.21 1.86 1.70 79.30 3.51 31331.64 H17(V) 3.5 4621.74 1.29 2.79 235.59 0.89 111813.95 H19 3371.12 0.71 0.42 154.59 0.21 7640.70 1121 4030.21 1.14 0.96 331.49 0.23 2157.30 120(II) 0- 7443.40 2.90 2.56 141.71 2.72 10894.01 0.5 12001) 1- 8309.78 1.87 1.43 121.45 1.99 20401.27 1.5 [2001) 3- 4909.05 1.72 1.18 91.84 1.52 22581.96 3.5 120 8461.29 1.78 1.66 114.45 0.59 21483.70 [22 3248.93 14.08 0.79 98.08 1.66 16038.43 127 4172.30 1.19 1.08 266.63 0.24 2275.37 J19 (I) 3910.82 10.28 1.45 198.17 14.95 12879.33 J19(II) 1707.44 4.23 0.54 88.63 5.05 4723.04 J19(III)1.5 1044.65 2.14 0.79 47.97 20.45 2289.76 J19(IH)3.5 549.58 2.14 0.31 16.12 1.52 5200.57 103 Table A-1: Absolute (cont’d) Samples , A1 Se Sc Ti Hg Ca J19(III)6 672.14 2.64 0.37 35.47 0.80 6975.52 J190V)0.5 2829.36 3.75 1.18 211.21 9.24 3255.72 J190V) 1.5 1595.76 2.17 0.81 98.68 11.89 3624.02 .1190V)3.5 595.08 1.65 0.36 37.37 3.35 5399.77 J190V) 6.0 707.81 2.10 0.36 39.78 2.82 5459.65 .121 2942.18 3.49 0.79 204.05 1.13 3709.25 .123 3224.74 24.90 1.09 120.45 26.62 13482.49 K20 4370.18 3.40 0.95 125.47 1.33 16241.59 K20(n)1.5 3443.24 2.87 0.67 53.23 2.47 15575.89 K22 1886.24 4.35 0.59 60.15 0.77 17241.14 K22([I)top 1283.83 3.54 0.46 44.98 0.77 14260.96 M20 4074.35 1.37 1.02 350.95 0.29 2545.13 M24 0.5 4078.88 1.44 1.19 254.22 0.29 3186.49 M24 1.0 2582.87 1.55 0.73 176.95 0.30 2263.34 M24 3.0 4785.48 1.84 1.51 291.33 0.63 26079.96 N21 2872.67 2.61 0.72 91.18 1.33 14506.85 N21(II)1.5 2342.21 3.31 0.85 77.61 2.11 19006.69 N23 3264.65 3.24 0.98 127.41 1.41 1 1922.79 0220]) 0.5 3058.42 2.02 0.63 145.42 0.90 11995.1 1 022(II)1.5 4945.58 1.91 1.47 247.97 1.81 31694.82 022(II)3.5 2509.04 1.45 0.80 183.78 0.84 29290.69 0220945 2077.71 1.33 0.74 251.17 0.63 16523.30 022 1630.37 1.45 0.55 157.82 0.44 8626.71 024 3094.79 3.90 1.06 143.60 1.20 14344.26 P23 1063.65 2.81 0.39 40.79 1.20 3918.19 P25 4074.72 3.23 1.48 218.19 2.33 8042.78 P2501) 0- 5636.16 3.49 1.87 312.37 6.01 9461.47 0.5 P2501) 1- 4891.63 2.94 2.07 383.67 2.07 9140.78 1.5 P2501) 3. 4253.05 1.30 1.12 302.25 0.68 2995.75 3.5 Q24([I)1.5 1576.421 1.118855 0.734602 72.64645 0.949331 25650.2 Q24(II)3.5 3259.576 1.554276 0.851 151 114.0666 1.578947 18503.67 Q24* 3686.179 1.552658 1.107322 134.8285 2.972919 21692.59 Q26 5346.005 1.822258 1.418671 375.8744 0.464737 6460.603 127 2103.197 0.676779 0.233372 89.49825 0.268378 1017.748 U26 4155.448 1.359535 0.950471 257.9988 0.397032 3801.476 104 Appgndix A Table A-2: Logarithmic Data for Multi-Elements. Samples K Cr Mn Fe Ba Mg V Co 85 2.32 0.95 2.13 3.67 1.52 2.68 0.88 -0.06 B7* 2.49 1.35 2.43 3.95 1.54 2.87 1.12 0.17 B9 2.16 1.10 1.88 3.97 1.27 2.71 0.99 -0.09 C4 2.41 1.67 2.12 3.86 1.49 2.94 1.20 0.15 C8 2.39 1.68 2.30 4.03 1.59 2.59 1.01 -0.26 C10 2.57 1.26 2.30 3.88 1.64 3.01 0.93 0.10 616 2.70 1.17 2.22 3.99 1.86 3.39 1.07 0.23 D9 2.56 1.69 2.20 4.19 1.81 2.98 1.46 0.13 D11 2.76 1.95 2.28 3.92 1.87 3.15 1.07 0.21 D17 2.87 3.74 2.57 4.40 2.56 3.39 1.25 0.51 E8 2.68 2.23 2.15 3.94 2.35 3.38 1.13 0.39 E16 2.68 3.08 2.62 4.37 1.80 2.95 1.04 0.51 E18 3.10 4.44 2.71 4.55 2.83 3.68 1.49 0.78 614 2.72 4.35 2.98 4.45 2.70 3.63 1.25 0.35 618 2.98 2.88 2.44 3.82 2.23 3.11 0.97 -0.11 61801) 2.56 2.69 2.41 3.84 2.15 2.96 0.98 0.17 618(11)1-1.5 2.60 1.32 1.89 3.78 1.30 3.11 1.13 0.21 61801) 3-3.5 2.79 1.08 1.90 3.88 1.53 3.28 1.24 0.40 1115 2.38 4.39 2.45 3.93 2.25 3.48 1.10 0.21 1117 (I) 2.59 5.14 2.62 3.86 2.31 3.05 1.79 0.04 H1701) 2.52 5.13 2.61 3.86 2.35 3.11 1.75 0.23 1117 (111) 2.51 5.15 2.58 3.88 2.34 3.08 1.76 0.19 H17(IV) 2.60 5.08 2.96 3.79 2.40 3.21 1.42 0.03 H17(V)top 2.02 5.45 2.77 3.49 2.56 2.91 1.60 0.22 H17(V)].5 1.72 5.38 2.56 3.66 2.25 3.04 1.38 0.22 H17(V) 3.5 2.60 3.99 2.73 4.02 2.09 3.65 1.26 0.07 1119 2.68 2.66 2.76 3.97 2.41 2.93 0.90 -0.17 1121 2.70 1.47 2.06 3.86 1.37 3.37 1.13 0.40 12001) 0-0.5 3.04 2.49 2.73 4.38 2.59 3.03 1.39 0.74 12001) 1-1.5 2.85 2.70 3.23 4.49 2.45 3.28 1.18 0.64 12001)3-3.5 2.77 3.29 3.19 4.42 2.43 3.56 1.34 0.54 120 2.95 2.65 2.84 4.49 2.57 3.17 1.27 0.62 122 2.82 4.31 1.87 3.91 2.47 3.29 1.65 -O. 12 127 2.77 4.50 2.08 3.93 1.51 3.22 1.15 0.29 J19 0) 2.93 4.46 2.55 4.33 3.04 3.25 1.56 0.36 J19(II) 2.32 4.50 2.11 4.06 3.39 3.12 1.35 0.11 J19(III)1.5 1.58 4.90 1.28 3.53 3.38 2.61 1.02 -0.33 J19(IH)3.5 1.63 4.54 1.22 3.61 2.54 2.82 0.44 -0.36 105 Table A-2: Logarithmic (cont’d). Samples K Cr Mn Fe Ba M V Co 1190106 1.76 4.04 1.70 3.47 2.04 2.87 0.44 -0.37 J19(IV)0.5 2.48 4.38 2.15 4.13 3.56 3.15 1.29 0.31 Jl90V) 2.02 4.48 1.57 3.61 2.93 2.87 1.08 -0. 18 1.5 Jl90V)3.5 1.75 4.40 1.46 3.71 2.53 2.90 0.57 -0.48 Jl9(IV) 2.02 4.49 1.43 3.65 2.41 2.89 0.55 -0.43 6.0 .121 3.05 3.58 2.49 3.89 2.42 3.02 1.07 -0.01 .123 2.46 4.59 1.68 4.12 2.54 3.30 1.67 0.29 K20 2.62 4.37 2.89 4.00 2.58 3.34 1.13 0.09 K200Q1.5 2.20 4.70 2.77 4.02 2.85 3.26 0.96 0.25 K22 2.43 3.85 2.49 4.03 2.43 3.29 1.21 -0.11 K22(ll)top 1.99 3.61 2.12 3.92 2.20 3.24 1.05 -0.24 M20 2.65 2.64 2.11 4.01 1.41 3.46 1.17 0.51 M24 0.5 2.68 1.96 2.15 3.86 1.51 3.31 1.14 0.32 M24 1.0 2.51 1.27 2.07 3.97 1.19 3.23 0.94 0.09 M24 3.0 2.88 1.52 2.81 3.95 2.54 3.42 1.16 0.36 N21 2.79 4.32 2.78 4.03 2.96 3.11 1.11 0.06 N2101)l.5 2.25 4.37 2.45 3.88 3.15 3.16 0.99 0.04 N23 2.42 4.05 2.43 4.25 2.46 3.20 1 . 14 0.19 02201) 0.5 2.49 4.13 2.53 3.88 2.57 3.10 1.05 0.02 022(II)1.5 2.56 4.51 2.21 4.00 3.25 3.17 1.17 0.29 022(II)3.5 2.40 3.73 1.98 3.78 2.77 3.11 0.90 0.05 022(II)4.5 2.39 2.84 1.79 3.67 1.68 3.14 0.89 -0.01 022 2.20 2.93 1.68 3.54 1.93 3.09 0.83 -0.01 024 2.65 4.11 2.85 4.16 2.52 3.20 1.22 0.34 P23 1.24 3.84 2.25 3.70 2.11 2.87 0.74 -0.25 P25 1.63 3.76 2.21 4.02 1.97 3.26 1.25 0.37 P2501) 0- 1.83 4.02 2.26 4.06 2.18 3.28 1.30 0.28 0.5 P2501) 1- 1.78 3.79 2.20 4.03 2.01 3.21 1.34 0.31 1.5 P2501) 3- 1.79 3.22 1.84 3.80 1.45 3.20 1.09 0.21 3.5 Q24(IDl.5 1.06 4.21 1.97 3.82 3.43 3.06 0.96 0.17 Q2401)3.5 1.00 3.59 1.78 3.82 2.01 3.09 0.75 0.05 Q24* 1.53 4.24 2.37 4.00 3.88 3.19 1.15 0.29 Q26 1.90 2.81 2.77 4.14 1.91 3.29 1.27 0.32 T27 1.18 2.81 2.07 3.75 1.24 3.12 0.70 -0.11 U26 1.75 3.39 2.26 3.99 1.74 3.31 1.09 0.37 106 Table A-2: Logarithmic (cont’d). Samples Ni Cu Zn Cd Pb A1 Se Sc as 0.78 0.70 1.64 -0.73 1.61 3.34 0.07 -0.38 137* 0.39 0.68 1.58 -0.62 1.40 3.55 0.10 .031 B9 0.12 0.43 1.41 -0.82 0.97 3.38 .003 .055 C4 0.54 0.77 1.67 -0.64 1.44 3.58 0.04 .025 C8 -1.48 0.36 1.59 -0.73 1.23 3.24 -0.01 -0.61 C10 0.27 0.62 1.62 -0.60 1.35 3.54 0.00 .031 C16 0.59 1.26 1.94 —0.63 1.54 3.73 0.13 -0.06 119 0.53 0.78 1.68 -0.61 1.39 3.67 0.04 -0.28 1111 0.69 0.89 1.83 -0.62 1.49 3.58 0.04 .010 1117 1.25 2.06 2.56 .007 2.43 3.68 0.32 0.17 E8 0.50 1.14 2.05 -0.63 1.76 3.61 .023 .022 E16 1.05 1.36 1.94 .023 1.70 3.57 0.24 0.05 E18 1.54 2.19 3.20 .004 2.51 4.14 -0.08 0.25 (:14 0.80 1.61 2.75 —0.16 2.31 3.67 0.31 0.15 618 0.33 0.93 2.22 .070 1.63 4.88 -0.38 -0.28 61801) 0.47 1.11 1.98 -0.43 1.54 3.62 0.44 0.00 618(II)l- 2.40 0.46 1.51 -0.88 0.39 3.67 0.16 .005 1.5 61801) 3- 0.84 0.86 1.44 -0.93 0.41 3.64 0.14 0.27 3.5 1115 0.73 1.34 2.20 .024 2.11 3.52 0.48 0.38 H17 (IL 0.85 2.08 2.53 o. 15 2.43 3.42 0.28 0.05 H17 (11) 0.98 2.05 2.54 0.37 2.41 3.43 0.49 0.17 1117 (111) 1.01 2.05 2.53 0.30 2.42 3.46 0.52 0.16 H17(IV) 0.99 1.96 2.83 0.44 2.56 3.27 0.45 .001 11174me 1.1 1 2.23 2.59 0.69 2.67 3.45 0.33 0.19 H17(V)l.5 0.94 1.67 2.53 0.80 2.45 3.51 0.27 0.23 H17(V) 3.5 0.46 1.50 1.96 .042 1.77 3.66 0.11 0.45 H19 0.01 1.15 2.20 -0.69 1.59 3.53 -0.15 -0.38 1121 0.68 0.96 1.45 -0.89 0.97 3.61 0.06 -0.02 [2001) 00.5 1.19 2.22 2.77 0.30 3.21 3.87 0.46 0.41 12001) 1-1.5 1.29 2.45 2.71 0.03 3.26 3.92 0.27 0.16 120(II) 33.5 1.24 2.53 2.71 .007 3.35 3.69 0.24 0.07 120 0.97 2.28 2.88 0.37 3.67 3.93 0.25 0.22 122 1.43 2.12 2.46 2.01 2.82 3.51 1.15 -0.10 127 0.81 0.56 2.09 .055 1.30 3.62 0.07 0.03 .119 (1) 1.85 1.71 2.68 1.94 2.82 3.59 1.01 0.16 11901) 0.88 1.47 2.39 1.45 2.51 3.23 0.63 -0.26 J19(III)1.5 0.39 2.14 1.66 0.53 3.42 3.02 0.33 -0. 10 119011135 0.50 2.07 2.77 0.37 2.64 2.74 0.33 -0.51 J19(III)6 0.22 1.68 1.78 -0.22 2.10 2.83 0.42 -0.44 J19(IV)0.5 0.80 1.40 2.14 0.71 3.08 3.45 0.57 0.07 Jl9(IV)3.5 0.68 2.17 2.52 0.14 2.61 2.77 0.22 -0.45 107 Table A-2: Logarithmic (cont’d). Samples Ni Cu Zn Cd Pb Al Se Sc J19(IV) 6.0 0.33 2.10 2.41 0.15 2.45 2.85 0.32 -0.44 .121 0.64 1.09 1.79 0.79 2.08 3.47 0.54 -0. 10 .123 0.96 2.06 3.08 2.16 3.48 3.51 1.40 0.04 K20 0.79 1.71 2.60 0.93 2.81 3.64 0.53 -0.02 K2001)1.5 0.79 2.03 3.48 1.14 2.99 3.54 0.46 017 K22 1.09 1.40 2.61 1.16 2.18 3.28 0.64 -0.23 K220I)top 0.09 1.24 2.54 0.71 2.09 3.11 0.55 -0.34 M20 0.77 1.04 1.60 -O.73 1.64 3.61 0.14 0.01 M24 0.5 0.65 0.67 1.48 -0.64 1.16 3.61 0.16 0.08 M24 1.0 0.34 0.43 1.14 -0.96 0.64 3.41 0.19 —0.14 M24 3.0 0.77 1.50 2.00 —0.32 1.57 3.68 0.27 0.18 N21 0.64 1.55 2.71 0.13 2.46 3.46 0.42 -0. 14 N21(II)1.5 0.59 1.59 2.75 -0.03 2.74 3.37 0.52 -0.07 N23 0.81 1.58 2.51 0.08 2.37 3.51 0.51 -0.01 022(II) 0.5 0.52 1.41 2.30 0.63 2.19 3.49 0.30 -0.20 022(II)1.5 0.84 1.64 2.80 0.17 2.51 3.69 0.28 0.17 022(II)3.5 0.43 0.98 2.45 -0.56 2.29 3.40 0.16 -0. 10 022(n)4.s 0.18 0.48 1.49 -0.97 1.69 3.32 0.13 .013 022 0.14 0.54 1.58 -0.84 1.58 3.21 0.16 -0.26 024 0.85 1.54 2.51 0.46 2.26 3.49 0.59 0.02 P23 0.10 1.19 2.27 -0.09 1.87 3.03 0.45 -0.41 P25 0.81 1.46 2.08 0.03 2.33 3.61 0.51 0.17 P2501) 0- 0.94 1.81 2.23 0.13 2.08 3.75 0.54 0.27 0.5 925m) 1- 1.19 1.53 1.94 -0.14 1.74 3.69 0.47 0.32 1.5 P2501) 3- 0.73 0.97 1.42 -0.66 0.99 3.63 0.1 1 0.05 3.5 Q2401)1.5 0.57 1.47 2.66 -0.26 2.33 3.51 0.19 -0.07 Q24([1)3.5 0.21 0.90 1.91 -0.60 1.91 3.20 0.05 -0. 13 924* 0.76 1.54 2.67 0.20 2.45 3.57 0.19 0.04 Q26 0.69 1.06 1.93 -0.31 2.11 3.73 0.26 0.15 T27 0.46 0.33 0.90 -1.09 0.89 3.32 -0.17 -0.63 U26 0.64 1.01 1.69 -0.43 1.41 3.62 0.13 -0.02 108 Table A-2: Logarithmic (cont’d). Samples Ti Hg Ca As Sr B5 2.11 -0.45 3.00 0.37 0.81 B7* 2.22 -0.52 3.05 0.77 0.76 B9 2.11 -0.62 2.71 0.15 0.44 C4 2.30 -0.66 3.00 0.45 0.78 C8 2.03 -0.76 2.95 0.28 0.75 C10 2.21 -0.79 3.22 0.41 0.92 C16 2.34 -0.60 4.09 0.60 1.59 D9 2.32 —0.72 3.17 0.51 0.92 D11 2.42 -0.78 3.79 0.45 1.10 D17 2.37 -0.19 4.13 0.95 1.62 E8 2.43 -0.88 3.30 0.07 1.03 E16 2.29 -0. 18 3.97 0.66 1.61 E18 2.64 -0.57 4.42 0.72 1.90 614 2.12 -0.37 4.90 1.25 2.32 618 2.27 -0.93 3.67 0.53 1.26 61801) 2.22 -0.08 3.33 1.04 1.07 Gl8(I])1-1.5 2.40 -0.35 3.08 0.65 0.77 61801) 3-3.5 2.48 -0.45 3.21 0.02 0.82 H15 1.99 0.45 4.96 1.85 2.33 H17 (1) 2.11 0.02 4.11 2.48 1.83 H1701) 2.12 0.27 4.12 2.51 1.84 H17 ([11) 2.15 0.26 4.10 2.51 1.84 H17(IV) 1.80 0.15 4.28 2.38 1.91 H17(V)top 1.86 0.76 4.24 2.61 2.07 [117(V)1.5 1.90 0.55 4.50 2.33 2.42 H17(V) 3.5 2.37 -0.05 5.05 2.42 2.10 H19 2.19 -0.67 3.88 0.36 1.45 H21 2.52 -0.64 3.33 -0.01 0.91 120(II) 0-0.5 2.15 0.43 4.04 1.19 1.84 [200]) 1-1.5 2.08 0.30 4.31 1.01 1.86 [2001) $3.5 1.96 0.18 4.35 1.00 1.85 120 2.06 -0.23 4.33 1.23 1.84 122 1.99 0.22 4.21 0.50 1.94 127 2.43 -0.61 3.36 0.27 0.98 .1190) 2.30 1.17 4.11 1.10 1.95 11901) 1.95 0.70 3.67 0.81 1.58 .119011)1.5 1.68 1.31 3.36 1.62 1.43 J19(IH)3.5 1.21 0.18 3.72 1.13 1.36 J190Il)6 1.55 -0.09 3.84 0.74 1.22 J19(IV)0.5 2.32 0.97 3.51 0.82 1.92 J19(IV) 1.5 1.99 1.08 3.56 1.42 1.52 Jl9(IV)3.5 1.57 0.52 3.73 1.11 1.40 J19(IV) 6.0 1.60 0.45 3.74 1.35 1.35 109 Table A-2: Marithmic (cont’d). Samples Ti [IL Ca As Sr 121 2.31 0.05 3.57 0.59 1.25 J23 2.08 1.43 4.13 0.86 1.98 K20 2.10 0.12 4.21 0.81 1.84 K20([1)1.5 1.73 0.39 4.19 1.14 1.88 K22 1.78 -0.11 4.24 0.67 1.95 K2201)top 1.65 -0.11 4.15 0.51 1.85 M20 2.55 -0.54 3.41 0.27 0.98 M24 0.5 2.41 -0.53 3.50 0.18 0.97 M24 1.0 2.25 -0.52 3.35 -0.23 0.70 M24 3.0 2.46 -0.20 4.42 0.35 2.03 N21 1.96 0.12 4.16 1.06 1.60 N2101)1.5 1.89 0.33 4.28 0.93 1.93 N23 2.11 0.15 4.08 1.05 1.60 02201) 0.5 2.16 -0.04 4.08 0.74 1.71 022(II)1.5 2.39 0.26 4.50 0.96 1.87 022(II)3.5 2.26 -0.08 4.47 0.24 1.58 022(II)4.5 2.40 -0.20 4.22 0.07 1.25 022 2.20 -0.36 3.94 0.26 1.03 024 2.16 0.08 4.16 1.06 1.56 P23 1.61 0.08 3.59 0.90 1.03 P25 2.34 0.37 3.91 0.99 1.42 P2501) 0—0.5 2.49 0.78 3.98 1.03 1.54 P2501) 1-1.5 2.58 0.32 3.96 1.08 1.48 P2501) 3-3.5 2.48 -0.17 3.48 0.64 1.04 QZ4(II)1.5 2.06 0.20 4.27 0.75 1.66 Q2401)3.5 1.86 -0.02 4.41 0.38 1.39 Q24* 2.13 0.47 4.34 0.91 1.64 Q26 2.58 -0.33 3.81 0.43 1.51 T27 1.95 -0.57 3.01 -0.03 0.69 U26 2.41 -0.40 3.58 0.64 1.18 110 Table A-3: Non-Parametric Data for Multi-Elements. Apmndix A Samples K Cr Mn Fe Ba 1V_1g V Co Ni Cu Zn As B5 48 73 48 64 63 71 65 58 32 62 59 58 87* 38 66 31 34 61 68 40 39 59 63 64 37 B9 53 71 61 31 71 70 54 59 68 70 71 67 C4 43 63 50 51 66 60 27 41 50 61 57 53 C8 45 62 35 20 60 73 53 68 73 72 62 61 C10 30 69 34 44 59 56 61 44 64 65 60 56 C16 18 70 40 29 54 8 46 29 48 43 46 47 D9 32 61 44 11 55 57 9 42 51 60 56 51 Dll 15 60 36 40 53 38 48 33 38 58 51 54 D17 8 38 21 6 20 9 22 7 6 14 20 29 E8 21 58 46 36 35 10 39 11 53 47 42 68 E16 19 46 18 8 56 59 51 6 12 41 48 43 E18 1 17 16 1 12 1 8 1 3 7 2 41 614 16 23 3 4 14 3 23 16 29 26 10 12 618 4 48 29 54 41 44 57 61 62 56 36 49 61801) 33 53 32 53 44 58 56 4O 55 48 44 23 61801)]- 26 67 60 59 70 42 36 35 1 69 65 44 1.5 618(11) 3- 11 72 59 43 62 19 24 9 23 59 69 70 3.5 H15 47 19 28 37 40 5 42 34 37 42 38 8 H1741) 29 4 17 52 38 52 1 52 22 12 23 4 H17 01) 34 5 19 49 36 46 3 28 15 16 21 2 11171111) 35 3 20 45 37 50 2 36 13 17 24 3 H17(1VL 27 6 4 58 34 29 10 53 14 19 5 6 H17(V)top 55 1 10 72 19 62 6 30 10 4 19 1 H17(V)1.5 65 2 22 66 39 53 12 31 19 24 25 7 HflV) 3.5 28 33 14 23 46 2 20 47 57 33 45 5 H19 20 54 13 33 33 61 63 64 71 46 37 59 H21 17 65 56 50 69 11 37 10 40 55 68 71 12001) 0-0.5 3 57 15 7 15 54 11 2 8 5 7 14 120(II) 1-1.5 9 52 1 3 28 21 28 3 5 2 11 25 12001) 3-3.5 13 44 2 5 29 4 15 5 7 1 12 26 120 5 55 7 2 17 35 19 4 16 3 4 13 I22 10 25 62 41 26 18 5 63 4 10 29 52 127 14 12 53 38 65 27 32 24 25 66 40 63 119(1) 6 16 23 9 8 24 7 15 2 22 14 18 J19(II) 49 13 51 17 4 41 13 43 20 36 32 36 lll Table A-3: Non-Parametric (cont’d) Samples K Cr Mn Fe Ba Mg V Co Ni Cu Zn As .1 19011)].5 68 7 72 71 5 72 52 69 60 9 58 9 J 19([[[)3.5 67 10 73 68 22 69 73 70 54 13 8 16 J19(III)6 62 31 66 73 47 65 72 71 72 23 53 39 .1 190V)0.5 39 20 47 14 2 37 17 21 28 40 39 34 J ”(“115 56 15 69 69 10 66 45 65 44 6 54 10 .1190V)3.5 64 18 70 62 24 63 70 73 41 8 26 17 Jl9(IV) 6.0 54 14 71 67 32 64 71 72 63 11 31 11 .121 2 42 26 42 31 55 47 55 45 49 52 48 .123 40 9 68 15 21 15 4 25 17 15 3 33 K20 25 22 5 26 16 12 38 46 30 21 18 35 K20([1)1.5 52 8 12 24 11 22 58 27 31 18 1 15 K22 41 34 25 21 30 16 26 60 11 39 17 42 K2201)top 57 40 49 39 42 25 50 66 70 44 22 50 M20 23 56 52 25 68 6 30 8 33 51 61 62 M24 0.5 22 59 45 48 64 13 34 19 42 64 67 66 M24 1.0 36 68 55 32 73 26 60 45 61 71 72 73 M24 3.0 7 64 8 35 23 7 31 14 34 34 43 60 N21 12 24 9 18 9 43 41 48 46 29 13 20 N2101)1.5 50 21 27 46 7 36 55 51 47 27 9 30 N23 42 30 30 10 27 31 35 37 26 28 27 22 02201) 0.5 37 28 24 47 18 47 49 54 52 38 33 40 022(II)1.5 31 1 1 41 27 6 34 29 22 24 25 6 28 022(II)3.5 44 39 57 60 13 45 62 49 58 53 30 65 022(II)4.5 46 49 64 65 58 39 64 57 66 68 66 69 022 51 47 67 70 5 1 49 66 56 67 67 63 64 024 24 29 6 12 25 32 25 17 21 31 28 21 P23 70 35 39 63 45 67 68 67 69 45 34 32 P25 66 37 42 22 50 23 21 13 27 37 41 27 P2501) 0- 59 32 37 16 43 20 16 26 18 20 35 24 0.5 P2501) 1- 61 36 43 19 49 28 14 20 9 32 47 19 1.5 P2501) 3- 60 45 63 57 67 30 43 32 36 54 70 46 3.5 Q2401)1.5 72 27 58 56 3 51 59 38 49 35 16 38 24(11)3.5 73 41 65 55 48 48 67 50 65 57 50 57 Q24* 69 26 33 28 1 33 33 23 35 30 15 31 926 58 51 11 13 52 17 18 18 39 50 49 55 T27 71 50 54 61 72 40 69 62 56 73 73 72 U26 63 43 38 30 57 14 44 12 43 52 55 45 112 Table A-3: N on-Parametric (cont’d) Samples Sr Cd Pb A1 Se Sc Ti Hg Ca 135 66 65 52 57 60 65 44 55 70 117* 69 54 61 34 58 62 32 57 68 a9 73 66 69 55 68 71 43 64 73 C4 67 59 59 31 65 56 25 66 71 C8 70 64 65 62 67 72 52 69 72 C10 63 51 63 35 66 61 33 71 64 C16 33 56 56 7 54 39 21 62 32 D9 62 53 62 16 64 60 23 68 66 011 53 55 58 30 63 44 12 70 44 D17 29 35 25 12 31 13 18 45 26 E8 56 57 46 24 72 54 10 72 63 E16 30 41 48 32 41 26 27 44 37 E18 14 34 20 2 69 7 1 61 7 (:14 3 39 32 14 32 20 41 52 3 618 48 62 51 1 73 59 28 73 49 61801) 54 48 57 23 23 32 31 38 61 61801)1-1.5 68 68 73 15 49 38 16 50 67 618(11) 3.3.5 65 70 72 19 51 6 7 54 65 H15 2 42 37 38 17 3 54 13 2 1117 (1) 25 25 26 52 34 24 45 34 30 1117 (n) 23 18 28 51 16 15 42 20 28 1117011) 20 21 27 48 13 16 39 21 31 H17(IV) 13 17 17 61 21 34 64 27 15 1117(me 5 13 14 50 27 10 63 7 17 H17(V)1.5 1 8 22 43 37 8 60 9 5 H17(V) 3.5 4 46 45 17 57 1 19 37 1 1119 41 61 53 37 70 64 35 67 41 1121 64 69 68 28 61 35 5 65 62 [2001) 005 22 22 7 5 19 2 38 14 35 120(II) 1-1.5 17 32 6 4 36 18 48 19 13 121111) 33.5 18 36 5 10 42 22 56 25 10 120 21 20 1 3 40 9 50 48 12 122 10 2 10 41 2 47 55 23 21 127 60 49 64 21 59 29 11 63 59 .119 (1) 9 3 11 29 3 17 26 3 29 J19(II) 34 4 19 63 5 58 59 8 48 119011115 42 15 4 69 29 46 67 2 58 119(111)3.5 46 19 15 73 28 70 73 26 47 J19(111)6 51 40 39 71 24 67 72 4o 42 113 Table A-3: Non-Parametric (cont’d) Samples Sr Cd Pb Al Se Sc Ti Hg Ca .1190V)0.5 12 12 8 49 7 23 22 5 54 119091.51 38 11 3 65 26 42 53 4 53 Jl9(IV)3.5 44 27 16 72 43 69 71 10 46 Jl9(IV) 6.0 47 26 24 70 30 68 70 12 45 .121 50 9 42 46 10 45 24 33 52 .123 7 1 2 42 1 28 49 1 27 K20 24 7 12 18 11 36 47 30 20 K20([1)1.5 15 6 9 36 20 52 66 15 22 K22 8 5 36 60 4 55 65 42 18 K22(n)mp 19 10 40 67 8 63 68 41 25 M20 59 63 50 27 52 31 4 59 57 M24 0.5 61 58 66 25 50 21 14 58 55 M24 1.0 71 71 71 53 46 50 30 56 60 M24 3.0 6 45 55 13 38 1 1 9 47 8 N21 32 29 21 47 25 51 57 29 23 N2101)1.5 11 33 13 56 12 41 61 17 14 N23 31 30 29 39 14 33 46 28 34 0mm 0.5 26 14 35 45 33 53 36 36 33 022(II)1.5 16 24 18 9 35 14 17 22 4 022(n)3.s 35 50 33 54 48 43 29 39 6 022(II)4.5 49 72 49 59 55 48 15 46 19 022 58 67 54 64 47 57 34 51 39 024 36 16 34 44 6 30 37 31 24 P23 57 37 44 68 22 66 69 32 50 P25 43 31 31 26 15 12 20 16 40 P2501) 0.0.5 37 28 41 6 9 5 6 6 36 P2501) 1-1.5 40 38 47 11 18 4 2 18 38 P2501) 33.5 55 60 67 20 56 25 8 43 56 9240015 27 43 30 40 44 40 51 24 16 92401135 45 52 43 66 62 49 62 35 9 924* 28 23 23 33 45 27 40 11 11 926 39 44 38 8 39 19 3 49 43 '1‘27 72 73 70 58 71 73 58 60 69 U26 52 47 60 22 53 37 13 53 51 114 Apmndix B Table B-1: Result of T-RFLP Analsxs. Numbers are in Peak Height Sample 32 36 48 65 67 69 72 73 75 77 78 BS 65 93 67 B7 75 262 B9 112 C10 C16 73 52 82 C4 C6 C8 51 81 D17 59 - 58 D9 56 50 60 56 112 55 E16 102 55 259 E18 100 53 53 90 52 E8 137 50 249 614 54 618 85 H15 H170) 382 81 100 77 111701) 104 111701) 65 85 165 H17(IV) 82 H17(V)top 57 97 59 11170915 H17(V)3.5 H19 H21 83 70 95 93 120(m15 62 79 120 [22 119(1) 54 J 19(11) 68 11901015 96 67 J19(IV)0.5 .1 190V)1.5 53 59 .1 19(IV)3.5 80 99 .121 83 50 61 J23 280 93 K20 54 115 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 32 36 48 65 67 69 72 73 75 77 78 K20([1)1.5 K22 55 301 113 M24 1.0 106 333 706 92 N21 2653 59 51 125 66 N2101)1.5 52 53 99 397 N23 61 236 114 022 022(II)1.5 024 95 P23 62 54 P25 264 P25(II)0.5 79 100 P25(II)1.5 434 924 57 59 (22401)].5 59 60 926 137 51 022 116 86 116 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 79 80 82 83 85 87 89 91 93 95 97 B5 180 287 1 10 1002 586 1478 841 B7 50 80 542 663 368 967 299 B9 51 55 344 144 1033 183 C10 60 188 95 265 120 C16 798 186 312 150 294 327 C4 61 132 C6 103 108 13 14 C8 183 677 536 861 017 60 55 240 50 248 143 388 115 D9 219 114 73 417 410 2084 364 E16 274 413 105 790 519 1512 2420 E18 66 521 126 50 1446 622 1082 468 E8 50 144 143 1223 535 1211 458 614 287 122 260 158 309 61 618 126 115 183 112 832 367 685 191 H15 185 273 88 227 H170) 324 340 125 120 1586 776 1916 513 H1701) 56 95 75 670 245 841 247 H1701) 146 66 557 249 415 89 H17(IV) 66 767 189 101 1213 491 985 260 H17(V)top 190 70 396 161 341 100 H17(V)1.5 4770 1027 287 257 259 259 H17(V)3.5 1118 137 73 113 306 117 71 H19 60 71 145 67 285 88 H2] 93 163 300 105 186 1982 942 2134 468 120(n)15 315 56 615 328 873 319 120 87 306 122 312 106 122 762 2066 203 189 494 332 336 .1190) 831 3259 558 228 1939 781 1240 349 11901) 154 823 142 94 1069 461 772 177 J 19011)].5 88 179 123 63 136 599 .1190V)0.5 50 53 74 177 1497 570 1058 324 J19(IV)].5 78 108 70 162 1117 443 1081 221 Jl9(IV)3.5 84 121 85 210 90 376 .121 164 56 404 247 388 61 .123 54 105 377 51 1 412 84 K20 674 4722 1378 1478 51 1 732 117 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 79 80 82 83 85 87 89 91 93 95 97 K20([1)1.5 141 1806 580 460 217 416 101 K22 52 297 147 531 1041 833 M24 1.0 63 51 698 277 1238 391 160 N21 146 761 102 1227 873 2130 696 N21(II)1.5 58 50 215 149 497 374 650 N23 117 272 781 367 022 1079 90 84 94 02201)].5 75 82 72 3 13 53 024 5 1 66 499 2 1 5 762 279 P23 58 68 377 68 145 682 608 725 155 P25 61 149 299 136 50 P250005 83 109 411 559 195 P25(II)1.5 53 62 71 355 275 75 924 69 134 74 212 9240015 146 429 133 371 139 683 Q26 106 2815 133 590 448 434 121 U22 56 58 332 55 58 789 414 761 152 118 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 123 125 127 128 129 131 133 134 136 137 138 140 141 . B5 65 104 212 303 636 B7 146 56 103 57 136 420 675 B9 66 130 362 406 C10 64 86 C16 172 65 130 51 232 333 C4 79 72 C6 56 1285 C8 72 151 91 51 163 229 663 017 132 67 132 153 329 D9 127 92 145 54 220 302 E16 76 247 82 74 133 576 E18 134 343 170 276 145 718 887 E8 56 1 17 381 348 614 63 78 229 287 618 94 93 120 355 H15 58 123 338 280 281 H170) 316 107 103 218 566 H1701) 124 152 66 221 H17(II) 354 115 81 354 702 H17(IV) 243 65 155 142 383 H17(V)top 169 98 1 16 278 H17(V)1.5 105 521 51 68 68 H17(V)3.5 1 15 H19 56 53 51 192 H21 103 122 279 543 120(u)15 87 59 102 77 238 120 108 67 142 191 122 53 55 79 61 106 .1190) 83 174 111 214 592 .1] 9(11) 241 79 156 209 695 11901015 220 63 67 90 144 234 219 J19(IV)0.5 106 111 93 180 425 .1190V)l.5 236 117 169 163 810 .1190fl3.5 69 200 126 131 188 J21 139 232 106 55 101 2316 .123 282 136 124 408 645 K20 61 94 50 140 K20([1)1.5 64 51 122 K22 177 67 149 77 304 200 332 119 Table B-1: T-RFLP (cont’d). Numbers are in Peek Height Sample 123 125 127 128 129 131 133 134 136 137 138 140 141 M24 1.0 64 110 161 111 89 289 N21 146 69 105 85 328 N2101)1.5 82 155 54 123 138 321 130 N23 166 262 160 324 325 459 181 022 71 158 77 022(II)1.5 122 73 110 134 193 96 024 77 158 60 161 125 295 131 P23 62 112 50 150 403 P25 303 86 57 77 81 63 P2501)0.5 93 202 80 143 124 340 P250015 122 160 91 76 144 Q24 206 117 170 77 321 476 284 9240015 50 167 70 303 380 361 302 Q26 87 72 134 129 222 85 U22 50 222 147 389 569 120 Table B—1: T-RFLP (cont’d). Numbers are in Peak Height Sample 143 145 146 147 150 151 153 155 155 156 157 159 B5 525 271 1329 762 54 B7 156 429 890 756 B9 109 131 792 664 C10 75 93 59 225 126 C16 135 136 903 326 C4 164 125 C6 92 481 406 C8 132 527 832 790 413 017 442 117 666 472 D9 634 232 1183 715 438 E16 579 200 977 980 E18 737 356 2038 1006 E8 1277 538 187 747 331 614 142 197 112 415 135 618 275 92 817 396 H15 139 285 102 529 156 H170) 500 386 1127 741 61 H1701) 103 280 531 184 431 254 H1701) 305 249 196 1004 509 156 92 H17(IV) 126 429 206 769 386 1117mm 161 176 154 414 414 68 H17(V)1.5 118 87 H17(V)3.5 150 118 130 H19 57 164 50 257 233 HZ] 685 194 1085 479 1200015 168 460 442 395 59 75 120 78 153 64 418 253 122 94 252 86 .1190) 132 296 222 1107 831 11901) 181 200 1052 1030 J19(III)1.5 223 196 253 534 1130 168 .1 19(IV)0.5 192 151 657 399 .1 190V)1.5 744 244 1073 699 81 Jl9(IV)3.5 131 76 215 322 656 150 .121 195 369 1008 1112 .123 199 154 159 427 288 K20 267 62 265 159 79 97 121 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height .Salnple 143 145 146 147 150 151 153 155 155 156 157 159. K20([1)1.5 147 64 133 97 K22 159 147 162 410 325 79 M24 1.0 113 126 550 544 58 N21 445 354 849 680 238 238 142 N2101)1.5 130 91 157 410 596 70 N23 181 99 107 173 654 496 64 022 407 344 022(II)1.5 96 132 101 260 242 97 024 131 436 l 24 424 424 P23 66 198 723 628 66 P25 152 161 P25(II)0.5 199 1 19 395 550 P250I)1.5 62 233 343 Q24 284 297 249 229 825 597 QZ4(II)1.5 302 239 146 166 437 379 51 102 Q26 85 153 77 250 237 U22 237 224 1483 774 63 122 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 160 161 163 165 167 175 177 179 182 184 185 186 B5 85 B7 68 B9 C10 C16 264 247 100 C4 C6 56 C8 120 66 64 135 D17 67 D9 53 758 70 72 E16 78 52 E18 71 54 58 64 51 E8 129 62 G14 102 187 64 G18 75 147 H15 388 907 H170) 194 113 80 H1701) 57 120 220 H1701) 184 184 115 72 74 53 H170V) 90 60 126 H17(V)10p 111 179 66 H17(V)1.5 3690 56 H17(V)3.5 4209 H19 191 H21 79 IZlKIl)l.5 66 57 120 87 122 84 53 .1190) 92 179 J19(II) 87 161 64 51 11901015 197 250 J190V)0.5 212 50 163 J190V)1.5 718 113 75 99 J190V)3.5 107 107 .121 62 59 J23 51 114 620 65 63 K20 109 69 123 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 160 161 163 165 167 175 177 179 182 184 185 186 K20([1)1.5 59 57 K22 335 434 257 56 M24 1.0 287 511 483 171 70 66 N21 1134 58 71 55 N2101)1.5 58 399 N23 250 436 022 4938 152 54 254 022(II)1.5 101 179 024 164 337 P23 130 130 62 P25 127 189 P250I)0.5 68 221 P250015 56 201 69 59 924 82 133 55 9240015 71 495 79 Q26 79 99 U22 92 85 92 62 124 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 188 189 192 194 198 200 202 204 206 207 207 209 B5 314 296 399 399 328 B7 504 164 164 170 208 B9 109 94 94 127 141 610 65 60 95 72 616 694 71 71 C4 C6 C8 51 190 182 182 188 D17 342 76 76 138 D9 132 113 83 83 209 394 E16 152 658 333 471 234 E18 62 83 494 618 E8 168 152 152 614 182 72 54 618 215 276 250 250 256 140 H15 156 70 70 H170) 61 82 227 50 413 413 H1701) 94 105 105 128 H1701) 83 211 492 239 239 252 H170V) 58 195 192 192 220 H17(V)t0p 84 202 88 1 10 H17(V)1.5 271 H17(V)3.5 57 52 H19 165 130 130 68 H21 323 390 390 214 82 12001)].5 78 170 182 182 120 177 192 192 122 65 53 69 69 69 J190) 83 129 139 151 151 365 75 11900 339 191 191 226 J 19011)].5 1 16 74 93 74 74 J190V)0.5 86 175 175 54 J190V)1.5 119 63 184 184 J190V)3.5 122 J21 271 184 J23 52 247 89 136 109 K20 570 570 77 100 99 125 Table B-1: T-RFLP (cont’d). Numbers are in Peak Hei ht Sampk 188 189 192 194 198 200 202 204 206 207 207 209 K20([1)1.5 72 125 57 57 K22 168 87 217 217 M241.0 82 170 259 91 91 N21 146 83 167 167 52 N2101)1.5 214 163 163 120 N23 55 304 152 149 77 022 0220015 164 87 024 194 170 170 266 P23 210 75 147 147 140 60 P25 77 P250005 306 102 80 98 P250015 146 86 55 74 924 198 543 104 104 126 9240015 53 162 205 96 96 62 Q26 127 140 140 132 U22 232 665 179 179 248 126 Table B-1: T-RFLP (cont’d). Numbersare H t 211214215222224227229233235243247 263 73 66 185 53 64 124 124 54 127 Table B-1: T-RFLP (cont’d). Numbers are in Peak 211 214 215 222 224 227 229 233 235 243 247 M24 1.0 N21 58 93 N21 1.5 53 57 N23 73 55 022 1.5 024 P23 P25 128 Table B-1: T-RFLP (cont’d). Numbers are in Peak Height Sample 265 267 269 272 273 275 277 279 280 282 283 284 BS 396 97 60 323 B7 1090 256 261 209 436 303 B9 283 131 64 102 65 84 C10 147 110 C16 54 C4 219 C6 1097 344 155 80 52 C8 1759 384 67 81 D17 167 56 103 293 D9 444 157 61 197 225 51 E16 498 274 113 119 491 E18 245 63 75 1124 E8 50 999 G14 54 218 G18 294 81 147 433 H15 82 291 H170) 507 1173 H1701) 111 65 595 H1700 323 123 382 H17(IV) 223 88 723 H17(V)top 67 169 H17(V)1.5 218 H17(V)3.5 142 H19 91 50 144 H21 . 182 76 63 59 1010 12001)].5 66 63 338 120 57 153 122 63 97 1190) 513 124 557 J19(II) 650 171 11901015 354 146 62 J19(IV)0.5 260 58 75 155 J190V)1.5 395 72 72 65 221 Jl90V)3.5 706 213 66 66 148 J21 164 130 57 300 92 92 56 .123 112 87 K20 83 60 201 K20([1)1.5 87 K22 137 61 171 129 Table B-1: T-RFLP [cont’dL Numbers are in Peak Hei ht 265 267 269 272 273 275 277 279 280 282 283 284 m41.0 110 53 53 76 167 72 94 166 N21 219 188 N2101)1.5 212 83 74 57 57 78 N23 164 150 022 0220015 50 135 024 146 69 322 P23 533 158 82 52 52 150 P25 55 139 P25(Il)0.5 57 76 P25(II)1.5 72 119 4 93 215 0240015 75 61 340 Q26 103 222 U22 141 53 320 130 Table B-1: T-RFLP [cont’dL Numbers are in Peak He' ht Sample 286 288 290 292 293 293 295 297 298 299 301 302 B5 321 333 233 958 141 B7 154 78 344 114 B9 66 213 684 C10 77 83 226 C16 200 544 117 117 165 C4 51 C6 957 344 C8 130 63 159 D17 343 454 297 297 398 D9 253 475 E16 491 65 522 287 284 E18 1130 480 831 E8 755 173 1029 G14 512 133 53 618 309 81 261 189 189 116 H15 725 119 98 121 55 11170) 304 504 [11701) 502 171 259 H17(II) 430 575 216 H17(IV) 714 227 344 69 H17(V)top 211 272 110 57 H17(V)1.5 H17(V)3.5 160 289 H19 127 198 260 H21 718 249 232 192 192 583 [20001.5 468 71 64 221 527 54 120 199 244 201 122 88 421 77 J19(I) 417 181 293 58 58 J19(II) 153 453 99 J19(III)1.5 54 168 53 J19(IV)0.5 147 59 138 J190V)1.5 441 153 236 Jl9(IV)3.5 J21 56 91 50 J23 95 56 197 66 68 65 K20 206 113 117 232 K20([1)1.5 99 53 83 56 101 K22 375 88 182 109 206 135 90 92 131 Table B-1: T-RFLP (cont’d). Numbers are in Peak He' ht Sample 286 288 290 292 293 293 295 297 298 299 301 302 M24l.0 76 188 210 312 194 184 N21 205 87 173 535 52 N210015 66 311 215 N23 73 79 287 121 102 022 97 0220015 123 266 64 024 254 52 299 767 P23 143 241 102 P25 142 90 59 P25(II)0.5 67 94 86 77 136 65 P25(II)1.5 73 120 82 86 024 157 491 215 0240015 170 93 93 313 146 Q26 120 231 101 54 U22 258 60 676 133 132 Table B-1: T-RFLP (cont’d). Numbers are in Peak H t 304 305 309 31 36 318 320 323 361 365 73 133 Table B-1: T-RFLP (cont’d). Numbers are in Peak H t 305307308309311316318320323361365. M24 1.0 56 50 N21 50 N21 1.5 67 N23 022 1.5 P23 P25 134 Table B-1: T-RFLP (cont’d). Numbers are in Peak 385 400 402 404 407 417 420 422 424 426 427 429 70 67 77 76 61 68 70 135 Table B-1: T -RFLP (cont’dl. Numbers are in Peak H t 402 404 407 417 420 422 424 426 427 429 52 136 Apmndix C Table C-l: Sample Site ID Conversion Table Geochemical Microbial ' fl Geochemical r Microbial Sample ID Sample ID List Sample 11) List Sample ID List List 135 BSR J19(III)1.5 119 1.5 137* B7R 11901035 119 3.5 B9 B9R J19(III)6 119 6.0 C4 C4R J19(IV)0.5 .119 0.5 C8 C8R J19(IV) 1.5 119 1.5 C10 C10a Jl9(IV)3.5 119 3.5 C16 C163 J19(IV) 6.0 J19 6.0 D9 D9R J21 JZIR D17 Dl7aR J23 123 E8 E8R K20 K20a0.5R E16 E16bR K20([1)1.5 K20b 1.5 E18 El 8bR K22 K22d G14 6148 M24 0.5 M24 0.5 G18 G18R M24 1.0 M24 1.0 H15 HISbR M24 3.0 M24 3.0 H17 0) H178 N210015 N21a 1.5R 1117 00 Hl7a N23 N23 H17 010 H17a 0220015 022d 1.5 H17(IV) Hl7b 022 022a H17(V)top H17 0.5R 024 024R H17(V)1.5 H17 1.5 P23 P23R H17(V) 3.5 H17 3.5R P25 P25cR H19 H19R P25(II) 0.0.5 P25a 0.5 1121 H21R P2500 1-1.5 P25b 1.5 120001-15 120bR 0240015 Q2412 1.5 120 1201511 0240035 Q24b 3.5 122 IZObR (224* Q24dR .119 (1) 119a 0.5 026 Q26bR .11900 11915 0.5 U26 U26 137 Appendix D Table D-l: R-mode Analysis of Marithmic Multi-Elemental Data Eigenvalue Difference Proportion Cumulative ' Factor 1 8.39031837 3.19712197 0.3995 0.3995 Factor 2 5.19319640 3.7755133 1 0.2473 0.6468 Factor 3 1.41768309 0.2 l 53 8357 0.0675 0.7143 Factor 4 1.20229951 0.24213349 0.0573 0.7716 Factor Loadin Factorl Factor2 Factor3 Factor4 K -0.01542 0.41223 -0.01435 0.74759 Cr 0.81094 0.03176 0.53399 -0.30978 Mn 0.42141 0.51889 -0.15203 0.62333 Fe 0.23549 0.49664 0.03715 0.81798 Ba 0.77197 -0.03535 0.50992 0.09871 ML 0.28704 0.71095 -0.15137 0.39424 V 0.31052 0.70792 0.39065 0.273 69 Co 0.1 1679 0.82576 -0.06821 0.56013 Ni 0.29555 0.64316 0.42551 0.21518 Cu 0.84029 0.15073 0.58481 0.0878 Zn 0.86531 0.1 1902 0.44286 0.32564 As 0.71505 0.24215 0.36787 -0.28726 Sr 0.92939 0.36425 0.39321 0.12806 Cd 0.6484 -0.00792 0.87951 0.06341 Pb 0.82731 -0.01555 0.65296 0.12853 Al -0.01444 0.68992 -0.29492 0.61504 Se 0.47502 0.07689 0.86192 -0.03 808 Sc 0.43742 0.89873 0.13051 0.15093 Ti -0.39679 0.71639 -0.3424 0.343 34 2g 0.63517 0.01365 0.82085 -0.33038 Ca 0.85131 0.38337 0.10519 0.10047 . 3 Factor Scores Sample Factor] Factor2 Factor3 Factor4 B5 -1.5922 -1.06847 -0.21772 -0. 16454 B7 * -1 .43339 -0.46862 -0.46701 0.39123 B9 -2.11244 -1.39732 -0.48103 0.11808 C4 -1.5274 -0.27272 -0.41993 0.14354 C8 -1 .45971 -2.28628 -1.13212 0.67339 C 10 -1.321 -0.63 844 -0.93562 0.50492 C16 -0.42148 0.44589 -1.09353 0.59482 138 Table D-l: R-mode (cont’d! Factor Scum 7 , Sample Factorl Factor2 Factor3 Factor4 D9 -1.3544 -0. 1321 -0.3667 0.80865 D1 1 -0.86589 0.18125 -0.92381 0.47117 D17 0.51293 0.94612 0.0381 1 1.28652 E8 -0.83307 0.01533 -1.06918 0.95379 E16 -0.25518 0.46974 -0.22132 0.88773 E187 1.0251 1.86036 -0.71968 2.22374 G14 1.4877 0.93835 -0.99776 1.269 G18 -0.37615 -0.13307 -1.96389 1.3512 618(11) 0-0.5 -0.65464 -0.08891 -0.02799 -0.03983 G18(II) l-l.5 -1.99715 1.04137 0.09724 -0.63 835 G 180]) 3-3.5 -1.77337 1.07274 -0.54035 -0. 1327 H15 1.27073 1.18054 -0.59151 -0.99934 H17 (I) 0.94768 0.68053 0.36165 -0.65034 H17 (11) 0.95628 1.00163 0.74619 -0.78075 H 17 (11]) 0.91328 0.99485 0.79441 -0.781 1 H17 (IV) 1.35207 0.20436 0.35093 -0.3 8263 H17(V)top 1.42362 0.74533 0.77695 -1.64772 H17(V)1.5 1.44574 0.83031 0.18173 -1.67344 H17(V) 3.5 1.22181 1.78686 -1.60109 -0.8455 H19 -0. 13092 -1.0855 -1.45093 1.00822 H21 -1.5421 0.69626 -0.8697 0.15578 120(II) 0-0.5 0.67873 0.95525 0.86456 1.60742 120(II) 1-1.S 1.04264 0.74338 0.02301 2.0665 120(II) 3-3.5 1.22328 0.71551 -0.09456 1.79069 [20 1.05704 0.51097 0.07854 2.18089 [22 0.34659 -0.046 2.63686 0.39048 [27 -0.97635 0.5372 -0.57169 0.03298 J19 (1) 0.64376 0.89394 2.7165 0.94402 J19(II) 0.3148 -0.69258 1.88162 0.26845 J19(III)1.5 0.38715 -1.46942 1.80034 -2.309 J19(IH)3.5 0.56102 -2.66035 0.76213 -1.298 J19(III)6 -0.0211 -2.21138 -0.14707 -1.51913 JI9QWO.5 0.35721 0.14163 1.55198 0.25122 J19(IV) 1.5 0.31263 -0.79251 1.57889 -1.55677 J 19(IV)3.5 0.47163 -2.1 1834 0.69953 -1 .29292 J19(IV) 6.0 0.39371 -2.07998 0.61965 -1.30237 J21 -0.50069 -0.36773 0.82656 0.51047 J23 0.70036 0.21843 3.42826 0.2576 139 Table D-l: R-mode (cont’d) Factor Scores Sample Factor] Factor2 Factor3 Factor4 K20 0.85772 -0.00058 0.41325 0.82816 K20(II)1.S 1.46195 -0.65735 0.57622 0.88408 K22 0.57272 -0.57984 0.82191 0.55292 K22(II)top 0.43677 -1.33061 0.23783 -0.03917 M20 -1 . 14088 0.90586 -0.62336 0.25772 M24 0.5 -1 .30383 0.66573 -0.70498 -0.00784 M24 1.0 -1.82222 -0.14544 ~0.87176 0.01323 M24 3.0 0.14837 0.96544 -0.83994 0.80682 N21 0.77451 -0.49755 0.1631 1 0.81052 N2101)1.5 1.04007 -0.53607 0.19653 0.03335 N23 0.46399 0.06553 0.33156 0.5162] 022(II) 0.5 0.36707 -0.45108 0.05413 0.30779 022(II)].5 0.90551 0.53614 0.06228 0.23127 022(II)3.5 0.25482 -0.39849 -0.66925 -0.13706 022(II)4.5 -0.80649 -0. 15976 -1 .02566 -0.86747 022 -0.865 84 -0.64466 -0.84434 -1.07133 024 0.50772 0.27331 0.43675 0.7776] P23 -0.05766 -1.75209 0.02013 —] . 14597 P25 0.00908 0.86522 0.33869 -0.74762 P25(II) 0-0.5 0.19265 1.17736 0.58258 -0.70027 P25(II) 1-1.5 -0.1541 1.46733 0.29183 -0.99079 P25(II) 3-3.5 -1.09306 0.78536 -0.61833 -1.33187 Q24(II)].5 0.79231 -0.33268 -0.36799 -0.95267 Q24(II)3.5 0.07978 -0.64407 -1.08139 -1.52369 Q24* 1.02047 0.1007 -0.02508 -0.23326 Q26 -0.2876 0.94103 -0.62434 0.27183 T27 -1.61158 -1.07308 -1.35947 -l.]7334 U26 -0.641 12 0.65596 -0.78235 -0.49723 140 Apmndix D Table D-2: R-Mode Anaflsis of Lgprithmic Chromium Partitioning Data ‘ Eigenvalue it ' Difi'erence A Proportion ' Cumulative T Factor 1 4.44778368 3.7545 8309 0.7412 0.7413 Factor 2 0.69320059 0.21007208 0.1 155 0.8568 Factor 3 0.483 12851 0.3008745] 0.0805 0.9374 Factor 4 0.18225400 0.05724462 0.0304 0.9677 Factor 5 0.12500938 0.05638553 0.0208 0.9886 Factor 6 0.06862385 0.01 14 1.0000 A - Factor Loadings 7 f j * EX 0.20160 WAS 0.18466 ER 0.19835 MR 0.21219 0X1 0.18924 0X2 0.17294 1 7 Factor 866% I 7 l ‘ Sample Factor 1 B7 -2.02751 C4 -2.0847 C8 -2.09575 C16 -1.5612 D9 -1 .81218 E8 -1.74681 E16 -0.83028 E18 0.36219 G14 0.61268 G18IIO -0.99603 H15 0.51567 H171 1.37328 H1711 1 .33469 H1711] 1.27246 H 171V 1.04235 H17V 1.08438 H17V1 1.96631 H17V3 0.18051 H19 -0.91416 [20 -0.97625 [20110 -0.92069 [20111 -0.47478 [20113 -0.08821 141 Table D-2: R-Mode (cont’d) Factor Scores Sample Factor 1 122 0.2919] 127 -1.74626 J 191 0.67102 .1191] 0.29297 J 191111 1.10968 J 191113 1.00782 J 191116 0.8881] J191V0 1.07484 J 191V3 1.29105 .12] 0.05754 J23 0.6235 K20 0.75837 K2011] 0.7715 K22 0.33977 K2211top -0.013 1 8 M20 -1.03261 M240 -0.97763 M241 -] .2246 M243 -0.99533 N21 0.31 833 N23 0.74218 N21H] 0.70494 022 -0.245 022110 0.77392 02211] 0.82999 022113 0.13655 022114 -0.58467 024 0.3466] P23 0.03409 P25 0.38066 P25110 0.58965 P2511] 0.4625 P25113 0.14316 Q23 0.65254 Q241111 0.69563 0241113 0.] 1427 Q26 -0.87541 T27 -1 . 10707 U26 -0.5173 1 142 Appendix D Table D-3: R-Mode Ana sis of Non-Parametric Multi-Elemental Data. Eigenvalue Difference Proportion Cumulative Factor 1 9.06435040 3.8524372] 0.4316 0.4316 Factor 2 5.21 191319 3.84970060 0.2482 0.6798 Factor 3 1.36221259 0.384724] 1 0.0649 0.7447 Factor Loadings Factor] Factor2 Factor3 K -0.1 1637 0.47413 0.66507 Cr 0.85101 -0.02851 -0.07666 Mn 0.29722 0.40] 15 0.75072 Fe 0.1 1883 0.61038 0.60937 Ba 0.80634 -0.00427 0.36978 flg 0.03745 0.66295 0.45708 V 0.40857 0.7861 1 0.20832 Co 0.01795 0.87845 0.37936 Ni 0.48748 0.75996 0.21909 Cu 0.8736 0.19825 0.26663 Zn 0.80788 0.14262 0.55022 As 0.82942 0.14128 0.0633 Sr 0.81574 0.3093 0.48435 Cd 0.90804 0.08533 0.1 1075 Pb 0.91322 0.06086 0.24778 A1 -0.18033 0.77732 0.51332 Se 0.78306 0.15556 -0.06396 Sc 0.35155 0.86609 0.23694 Ti -0.53259 0.65796 0.07168 11g 0.87857 0.08125 -0.2184 Ca 0.64291 0.20855 0.5542 Factor Score; Sample Factor] Factor2 Factor3 B5 1.18439 1.05029 0.86453 87* 1.20978 0.4559 0.32726 B9 1.64663 1.16215 0.8023 C4 1.31327 0.18382 0.74778 C8 1.46617 1.51242 0.00207 C10 1.45829 0.83408 0241 7 8 C16 0.99837 -0.34542 -1.01905 D9 1.26131 -0. 12499 -0.01207 143 Table D-3: R-Mode (cout’d) Factor Scores Sample Factorl Factor2 Factor3 D1 1 1.20813 -0.04913 -0.50436 D17 -0.29481 -1.35573 -1.22449 E8 1.26017 -0.20755 -0.70906 E16 0.37366 -0.64086 -0.463 E18 -0.27321 -1.59011 -2.24501 G14 -0.54357 -0.67026 -2.16777 G18 1.1837 0.82153 -1.38344 (218(11) 0-0.5 0.45244 0.15246 0.46916 G18(II) l-l.5 1.28525 -0.84923 1.33835 G 18(11) 3-3.5 1.47238 -1.48939 0.84473 H15 -0.7433 -0.43 1 72 -0.0264 H17 (1) -0.92983 -0. 16293 0.17749 H17 (11) -1. 14972 -0.67901 0.5421 H17 (H1) -1.13484 -0.68275 0.61687 H17(IV) -1.28342 0.17398 -0.49446 H17(V1top -1.681 15 -0.2680] 0.56166 H17(V)1.5 -1.37641 -0.31849 0.68795 H17(V) 3.5 -0.06228 -0.72267 -1.03002 H19 1.00172 1.41679 -1.64221 H21 1.62003 -0.82372 0.46872 120(II) 0-0.5 -0.88832 -1 .30856 -0.98768 120(II) l-l.5 -0.68394 -1.00692 -1.84089 120(II) 3-3.5 -0.6954 -0.98757 -1.87556 [20 -0.6875 -0.84446 -2.16666 122 -1.05789 -0. 19803 0.03319 127 0.99025 -0.79887 0.40599 J19 (1) -1.30663 -1.31372 -0.4371 J 19(11) -1.07361 0.14348 0.80606 J19(III)1.5 -1.13499 1.31723 1.95755 J19(HI)3.5 -0.92848 1.97004 0.94529 J19(III)6 -0.03 796 1.89423 1.1045 J19(IV)0.5 -0.93 748 -0.7738 0.73042 J19(IV) 1.5 -1 . 12659 0.84709 1.76982 J 1 9(1V)3.5 -0.83481 1.69966 1.062 J19(IV) 6.0 -0.74685 1.78347 1.12565 J21 0.10457 0.31811 0.04173 J23 -1.51 134 -0.7752 0.46981 K20 -0.82654 -0.03412 -1.27832 144 Table D-3: R-ModeLcont’fid) Factor Scores Sample Factorl Factor2 Factor3 K20(11)1.5 -1 .27194 0.51078 -1.01 K22 -0.74974 0.38419 -0.69876 K22(11)top -0.33241 1.36696 -0.18509 M20 1.21921 -1.11787 0.332 M24 0.5 1.3222] -0.83359 0.47008 M24 1.0 1.59957 0.38732 0.58929 M24 3.0 0.39525 -0.95459 -1.61004 N2 1 -0.64759 0.75614 -1 .46852 N2101)1.5 -1.02176 0.95187 -0.59972 N23 -0.56747 -0. 19974 -0.17799 022(II) 0.5 -0.27557 0.79552 -0.45215 022(II)1.5 -0.83473 -0.73814 -0.68199 022(II)3.5 0.19416 0.99797 -0.32865 022(II)4.5 1.06633 0.81444 0.60218 022 0.99008 1.15924 0.91089 024 -0.61659 -0.56422 -0.57945 P23 -0.04129 1.76542 0.83213 P25 -0.30092 -1.26232 1 .17906 P25(11) 0-0.5 -0.50345 -].64879 0.99969 P25(11) l-l.5 -0.20417 -].78553 1.32636 P25(11) 3-3.5 0.98959 -0.70656 1.60423 QZ4(II)].5 -0.4777 0.85253 0.1053 Q24(II)3.5 0.38803 1.35618 0.51839 024* -0.79007 -0.10363 -0.22884 Q26 0.57656 -1.]7583 -0.15724 T27 1.51201 1.34613 1.08203 U26 0.84272 -0.63737 0.47315 Apmndix D Table D-4: R-Mode Analxsis of Non-Parametric Chromium Partitioning Data. Eigenvalue Difference Proportion Cumulative Factor 1 4.34851723 3.61425128 0.7248 0.7248 Factor 2 0.73426595 0.2195349] 0.1224 0.8471 Factor Loadings . i 5 Factor 1 EX 0.89646 WAS 0.80765 ER 0.87656 MR 0.93340 0X1 0.83331 OX2 0.74738 Factor Scores 7 B7 1.77999 C4 1.82981 C8 1.855 C16 1.49919 D9 1.66585 E8 1.63348 E16 0.90529 E18 -0.31683 G14 069203 618110 1.08837 H15 -0.46412 H171 -1.60665 H1711 -1.54128 H1711] -1.46676 H17IV -1.23627 H17V -1.20631 H17V1 -1.75854 H17V3 -0.0385 H19 1.02927 120 1.10012 120110 1.01259 1201]] 0.64579 120113 0.31 171 122 -0.2537 146 Table D-4: R-Mode {cont’d} Factor Scores 127 1 .60923 J 191 -0.83872 J 1 91] 025651 .1191111 -1.17295 .1 191113 -1 .1242 J 191116 -0.76426 J19IVO -] .0904] J 191V3 -1.2997 .12] 0.05775 J23 -0.75916 K20 -0.95432 K20H1 -0.91551 K22 -0.23566 K2211top 0.14344 M20 1.05764 M240 1 .00056 M241 1.11729 M243 0.9275 N21 -0.32518 N23 -0.58009 N21111 -0.79913 022 0.2938 022110 -0.67451 02211] -0.86578 022113 001854 022114 0.62998 024 -0.26887 P23 0.17409 P25 -0.25591 P25110 -0.47359 P2511] -0.25654 P25113 -0.0357] 023 -0.78351 0241111 -0.81257 Q24III3 -0.08987 026 0.98465 T27 1 .21604 U26 0.66381 147 Apflndix E Table E-l: -Mode Ana sis of arithmic Multi-Elemental Data 0 Eigenvalue Difl‘ereuce Proportion Cumulative Factorl 69.60580 67.6268 0.9535 0.9535 Factor2 1.979015 1.61207 0.0271 0.9806 Factor3 0.366947 0.12609 0.0050 0.9856 Factor4 0.240857 0.07160 0.0033 0.9889 Factor Loadings Variable Factorl Factor2 K -6.27539 6.37241 Cr -8.19455 6.01094 Mn -1.94088 3.57103 Fe -9.403 84 9.70892 Ba -4.63471 4.18813 Mg -8.74391 8.45295 V -2.04416 2.22672 Co -2.80623 1.78467 Ni -7.50985 4.71960 Cu 4.02396 2.76421 Zn -5.25416 4.54533 As 4.49015 -2.60248 Sr -1.83794 1.99634 Cd 3.92679 -3.45606 Pb 1.03603 0.3458] A] -9.261 12 9.26792 Se 0.39144 -0.27715 Sc -2.20681 1.0335] Ti -8.16477 7.27971 Hg 1.91245 -2.03088 Ca -7.52107 7.97225 148 Apgndix E arithmic Chromium Partitionin Data. Eigenvalue Ditfereuce Proportion Cumulative ‘ Factorl 48.22836 34.54668 0.7307 0.7307 Factor2 13.68168 11.12646 0.2073 0.9380 Factor3 2.555215 1.803815 0.0387 0.9767 Factor Loadings Variable Factorl Factor2 Crgx 0.00974 -0.75460 Crwis 1.72327 0.15527 Cry. 1.15308 0.33452 Cr"; 3.36249 1.44328 Cram 0.74440 -] .403 77 Croxz 2.261 16 0.22448 149 Appgndix E Table E-3: Q-Mode Analxsis of Non-Parametric Multi-Elemental _ Eigenvalue Difference» ' Proportion Cumulative Factorl 58.742293 52. 1 85 7689 0.8047 0.8047 Factor2 6.5565237 4.8809548 0.0898 0.8945 Factor3 1.6755689 0.3434371 0.0230 0.9175 Factor4 1.3321318 0.2525628 0.0182 0.9357 Factors 1 .0795690 0.0549844 0.0148 0.9505 F actor6 1.0245846 0.3369709 0.0140 0.9645 Fact0r Loadin * Variable Factor] Factor2 K 27.604 -16.316 Cr 73 .547 40.308 Mn 82.082 -123.345 Fe -26.807 54.196 Ba 51.165 -28.781 Mg -176.066 334.337 V 45.967 -19.387 Co 7.400 25.1 13 Ni 178.747 -97.441 C II 77.010 -80.590 Zn 77.144 -77.363 As 96.425 -130.797 Sr 24.041 33.692 Cd 58.627 47.748 Pb 89.437 -57.264 AI 18.712 -22.807 Se 100.308 -91.903 Sc 14.238 111.781 Ti -33.353 143.379 Hg 60.590 -31.865 Ca -13.867 164.133 150 ApEndix E Table E-4: -Mode Ana sis of Non-Parametric Chromium Partitionin Eigenvalue Difference Proportion Cumulative Factorl 62.738872 57.756104 0.8836 0.8836 Factor2 4.9821683 2.8755384 0.0702 0.9538 Factor3 2.1066299 1.2171417 0.0295 0.9835 7 7 . Factor Loading: Variable Factorl Factor2 Factor3 CLEX 1370.78 -45.465 -2233.21 CrWAs 1399.44 -243.177 -1972.00 Cl’i; 1385.22 -l94.605 -1989.9] CrM! 1449.44 —179.417 -2151.70 Crox1 962.05 -332.773 -1040.76 Croxz 4033 -232.994 1327.04 151 1|