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I’I1I3II3 n33 1331;31' 1301:1133 3 31313 1111313333 6'1333'" 3333 éa. 1111133 “'1 111333131 III'III'II 13' " -' ~x_: - - I THESIS This is to certify that the thesis entitled THE CHARACTERIZATION OF MUNICIPAL SLUDGES FOR THIRTY-TWO VOLATILE, SEMI-VOLATILE AND PHENOLIC TRACE ORGANIC COMPONENTS presented by JOHN HENRY PHILLIPS has been accepted towards fulfillment of the requirements for _M.S.___degree in inning;— W 2% Major wager Date 9/22/81 0-7 639 llllllllllllllllllllllllllllllllllHllllllllllllllllllllllUll MSU 3 1293 10486 2275 RETURNING MATERIALS: Place in book drop to remove this checkout from LIBRARIES All-(gnu... your record. FINES wiII be charged if book is returned after the date stamped beIow. 3-0 ‘ 3‘ ‘ "'3’;- NEW? “1* £3 THE CHARACTERIZATION OF MUNICIPAL SLUDGES FOR THIRTY-Two VOLATILE, SEMI-VOLATILE AND PHENOLIC TRACE ORGANIC COMPONENTS By John Henry Phillips A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of M ASTER OF SCIENCE Department of Zoology Pesticide Research Center 1981 " v \....a . v‘ 1 ABSTRACT THE CHARACTERIZATION OF MUNICIPAL SLUDCES FOR THIRTY-TWO VOLATILE, SEMI-VOLATILE AND PHENOLIC TRACE ORGANIC COMPONENTS By John Henry Phillips Municipal sewage sludges from 238 communities were characterized for a group of volatile, semi-volatile and phenolic compounds. The development of analytical methodology for each class of compounds mentioned, as well as selected phthalates, nitrobenzenes, Chlorinated and non-chlorinated aromatic hydrocarbons, triarylphosphate esters and bases are discussed. Samples were extracted via the most efficient extraction technique. Cleanup of samples was achieved via acid/base partitioning, gel permeation chromatography, activated florisil and silica gel columns. Gas liquid chromatography and reverse phase high pressure liquid chromatography were used for separation of compounds. Various detectors were employed including gas Chromatography/mass spectroscopy for confirmation. Results were compiled according to various parameters including popula- tion, percent industrial input, treatment method, sludge type, percent solids and flow rate to determine their effect on the levels of components of interest. Statistically significant trends were found for all of the above parameters with the exception of flow rate. ACKNOWLEDGEMENTS I wish to express my gratitude to the technical direction and guidance of Dr. Matthew Zabik and Dr. Richard Leavitt of the Pesticide Research Center. Sincere thanks to the many individuals who worked on the project over the past two years in various analytical and statistical capacities. My greatest appreciation to my wife Ruth for typing this thesis and providing moral support throughout the duration of the project. ii Table of Contents I. Literature Review . . . . ....... A. Sludge Analysis . . B. Volatiles ........ . . . . . C. Phenols ...... D. Aromatic Hydrocarbons ....... E. Phthalates ....... . . . . . . F. Aryl Phosphates . . ........ G. Aromatic Amines . ........ 11. Introduction A. B. Scope Of Project . . . . . . Thesis Proposal . . . . . . . . . . III. Analytical Methods ........ G. . Overall Scheme . Glassware Preparation . Sampling Procedure ...... . . . Solids Determination ....... Volatiles and Semi-Volatiles Analysis . . I. Introduction ......... 2. Methods Used ........ a. Extraction Techique . . . b. Quantitation ....... 3. Quality Control . . . . . . . . ‘4. Mass Spectra ......... . Phenol Analysis .......... I. Introduction ......... 2. Methods Used ......... a. Phenol Extraction . . . . b. Cleanup . . . ..... c. Quantitation ....... 3. Quality Control - . ..... 4. Mass Spectra . . . ..... Other Methods . ......... l. Extraction .......... 2. BaseFraction- . . . . . . . . 3. Neutrals . . . ........ iii 0000000000 22 22 26 IV. v. Discussion . FPWPPOP’ h—l 0 Parameters Tested Statistics Effect of Percent Industrial. Input on. Organic Component Levels . . . Effect of Population on the Level of Organics in Sludge. . . . . Effect of Size of Treatment Facility on Levels of Selected Organic Components Effect of Sludge Type on the Levels of Selected Organics in Municipal Sludges . . . . Effect of Percent Solids on the Concentration of Selected Organic Components in Municipal Sludges Effect of Treatment Method on the Level of Selected Organic Components in Municipal Sludges , Recoveries from Sludge . I. The effect of percent solids and extraction technique on recovery. 2. The effect of sludge type on recovery. 3. The effect of phenol recovery from unpreserved samples over time. Conclusion . Bibliography iv 102 102 111 HQ 121 126 129 139 145 160 160 161 161 173 178 LIST OF FIGURES Figure Title Page 1 Municipal Waste Treatment Plants Sampled . . . . . . . . . . . 25 2 Overall Analysis Scheme . . . . . . . . . . . . . . . . . 29 3 Overall Analysis Scheme Continued. . . . . . . . . . . . . . . 30 4 Sampling- Problems and Remedies. . . . . . . 31 5 Volatile Extraction Techniques-Advantages and Disadvantages . . . 36 6 Volatile Extraction Scheme . . . . . . . . . . . . . #1 7 Gas Chromatographic Conditions - Acrylonitrile . . . . - . . . . 43 8 Gas Chromatographic Conditions - Early Eluting Volatiles - - . . . MI 9 Gas Chromatographic Conditions - Flame Ionization Detectable Volatiles . - . . . . . - . 45 10 Gas Chromatographic Conditions - Late Eluting Volatiles . - . . . #6 11 Mass Spectra for Selected Volatile Organics I . . . . . . . . . . 51 12 Mass Spectra for Selected Volatile Organics II . . . . . . . . . . 52 13 Mass Spectra for Selected Volatile Organics III. . . . . . . . . . 53 14 Mass Spectra for Selected Volatile Organics IV. . . . . . . . . . 54 15 Mass Spectra for Selected Volatile Organics V. . . . . 55 16 General Trends for Recovery of Phenols From Oven Dried Sludge . 6O 17 Non-volatile ACId/Base/ Neutral Partition . . . . . . . . . . 61 18 Liquid Samples Phenol ACId/ Basel Neutral Partition . . . . . . . 62 19 ACId/Base/ Neutral Partition. . . . . . . . . 64 20 Percent Recovery From a Buffer/SolvEnt Partition Of Selected Phenolic Compounds . . - . . - - . . . . 67 21 Percent Recovery From XAD-Z Resin of Selected Phenolic Compounds. . . . . . . . . . . . . . . . . . . . . 68 22 Phenol Cleanups . . . . . . . . . . . 69 23 Projected Percent Recovery From Activated FlorisiI Cleanup of Selected Organic Compounds - - - . - 70 2# Comparison of Gas/Liquid Chromatographic Separations of Phenols 71 25 Plate Efficiency vs. Mobile Phase Flow Rate - - - - - - - - - - 73 26 Comparison of Isocratic and Gradient Solvent System - - - - - - - 75 27 Key for Chromatographic Separations of Phenols- - - - - - - - - 76 28 Total Phenol Mix- HPLC Separation - - . - . - - - - - 77 29 Chlorinated Phenol Mix (Dilute)- HPLC Separation . . ~ - - - . 78 30 Chlorinated Phenol Mix- HPLC Separation - - - - . - - - . - 79 31 Methyl Phenol Mix- HPLC Separation . . . . . . . . . . . . . 8O 32 EPA Phenol Mix- HPLC Separation. . . . . . . . . . . . . . . 81 33 Electrochemical Oxidation of Phenol . . . . ; . . 82 314 Carbon Paste Electrode and Ag/AgCl Reference Electrode . . . . 83 35 Comparison Between the Two Most Popular Liquid Chromatographic Detection Systems. . . . . 85 36 Selectivity of Electrochemical Detection of Various POtentials. . . 86 Figure Title High Pressure Liquid Chromatograph System . Early Phenol Elutriates - HPLC System Late Phenol Elutriates - HPLC System - Mass Spectra of Selected Phenols I Mass Spectra of Selected Phenols II - Mass Spectra of Selected Phenols III - - Gel Permeation Chromatography Separations . Electrochemical Oxidation Mechanisms of Bases Activated Florisil Separation of Non-volatiles. . Triaryl Phosphate Esters Structure and GC Retention Triaryl Phosphate Ester Isomer Separation . Separation of Non-volatiles by Silica Gel . - Non-volatile Component Gas Chromatograph Conditions . Total Volatiles vs. Percent Industrial Input - Population 2, 000- 4, 499. . Total Phenols vs. Percent Industrial Inp::ut - Populations 2, 000-4, 999 - Total Phenols vs. Percent Industrial Input Hydroquinone vs. Percent Industrial Input Pentachlorophenol vs. Percent Industrial Input Total Volatiles vs. Percent Industrial Input - 1,3-Dichloropropene vs. Percent Industrial Input. Percent of Sites Above Detection Limit vs. Percent Industrial Input . . Total Phenols vs. Population . Total Phenols vs. population - 15- 4096 Industry Pentachlorophenol vs. Population . . . . Phenol vs. Population . . Total Volatiles vs. Population. . Percent of Sites Above Detection Limit vs. Population . Total Volatiles vs. Flow Total Phenols vs. Flow. Percent of Sites Above Detection Limit vs. FlOw Sludge Type Ranked by Total Volatile Concentration . . Sludge Type Ranked by 1,2-Dichloropropane Concentration . Sludge Type Ranked by m-Dichlorobenzene Concentration Sludge Type Ranked by Hexachloroethane Concentration . Sludge Type ranked by Tetrachloroethylene Concentration Sludge Type Ranked by 2,4-Dinitrophenol Concentration . Sludge Type Ranked by 2, 4 ,6-Trichlorophenol Concentration Sludge Type Ranked by Phenol Concentration. Sludge Type Ranked by Pentachlorophenol Concentration. Sludge Type Ranked by Hydroquinone Concentration . Sludge Type Ranked by 2,4-Dimethylphenol Concentration Sludge Type Ranked by Total Phenol Concentration Total Volatiles and Semi-volatiles vs. Percent Solids . Acrylonitrile vs. Percent Solids . Hexachloroethane vs. Percent Solids . . m-Dichlorobenzene vs. Percent Solids . Total Phenols vs. Percent Solids . . 4,6-Dinitro-O-cresol vs. Percent Solids . vi Page 87 88 92 93 94 96 97 98 99 100 100 101 117 117 118 118 119 119 120 120 123 123 124 124 125 125 127 127 129 I 133 133 . 134 . . 134 . . 135 . 135 . 136 . 136 . 137 . 137 . 138 . 138 . 141 . 141 . 142 . 142 . 143 . 143 Figure Title 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 Pentachlorophenol vs. Percent Solids . Phenol vs. Percent Solids . . . Treatment Method Ranked by Total Volatiles Concentration . Treatment Method Ranked by m- Dichlorobenzene Concentration . Treatment Method Ranked by 1,3-Dichloropropane Concentration. Treatment Method Ranked by Hexachloroethane Concentration- Treatment Method Ranked by Total Phenol Concentration . Treatment Method Ranked by 2,4-Dimethyphenol Concentration . Treatment Method Ranked by 4,6-Dinitro-o-cresol Concentration. Treatment Method Ranked by 2,4-Dinitrophenol Concentration . Treatment Method Ranked by Hydroquinone Concentration Treatment Method Ranked by Pentachlorophenol . Treatment Method Ranked by Phenol Treatment Method Raked by 2,4 ,6-Trichlorophenol Concentration. Battle Creek Wastewater Treatment Plant Flow Diagram . East Lansing Wastewater Treatment Plant Layout . East Lansing Wastewater Treatment Plant Flow Schematic Lansing Wastewater Treatment Plant Flow Sheet and LocationMap ........... Phenol vs. Log Time Hydroquinone vs. Log Time 2,4-Dinitrophenol vs. Log Time O-Cresol vs. Log Time . . p-Chlorophenol vs. Log Time 2,4-Dimethylphenol vs. Log time . O-Chlorophenol vs. Log Time. m-Chlorophenol vs. Log Time 4,6-Dinitro-o-cresol vs. Log Time 2,4-Dichlorophenol vs. Log Time . vii Page 144 144 149 149 150 150 151 151 152 152 153 153 154 154 155 155 156 156 168 168 169 169 170 170 171 171 172 172 LIST OF TABLES Table Title ' Page 1 Recovery Comparison Sample Number 218 . . . . . . . . . . . 37 2 Recovery Comparison Sample Number 225 . . . . . . . . . . . 38 3 Recovery Comparison Sample Number 228 . . . . . . . . . . . 39 4 Recovery Data of Volatile and Semi-volatile Organics . . . . . . 48 5 Physical Properties of Phenols I . . . . . . . . . . . . . . . . 56 6 Physical Properties of Phenols II . . . . . . . . . . . . . . . 58 7 Molecular Weights of Phenols . . . . . . . . . . . . . . . . . 65 8 The Dissociation of 2,4-Dichlorophenol. - - . . . . - - - - - . 65 9 Percent Recovery of Phenols - . - - - . . - . . - - . - - - - 90 10 Total Sites Computer Summary. . . . . . . . . . . . . . . . 104 ll Volatiles - Raw Computer Summary. . . . . . . 105 12 Ferric Chloride Phosphorous Removal Computer Summary . . . . 106 13 Population 10, 000- 49, 999 Computer Summary. . . . . . . . . . 107 14 Flow MGD 0.50-0.99 Computer Summary. . . . . . . . 108 15 Percent Solids - Volatiles 1. 00-1. 99 Computer Summary . . . . . 109 16 Percent Industry .001-0. 49 Computer Summary . . . . 110 17 Compound Levels for Battle Creek Waste Treatment Facility. . . 157 18 Compound Levels for East Lansing Waste Treatment Facility . . . I58 19 Compound Levels for Lansing Waste Treatment Facility . . . . . 159 20 Volatile Recoveries - Less Than 396 Total Solids. . . . . . . . . 163 21 Volatile Recoveries - 3-7% Total Solids. . . . . . . . . . . I63 22 Volatile Recoveries - Greater Than 796 Total Solids . . . . . . . 164 23 Recoveries for All Voaltile Samples . . . . . . . . I64 24 Non-volatile Recoveries - Less Than 1% Total Solids. . . . . . . 165 25 Non-volatile Recoveries - 1- 3096 Total Solids . . . I65 26 Soxhlet Recoveries of Non-volatiles—Greater Than 3096 TotaI Solids 166 27 Recoveries for All Non-volatile Samples . . . . . . . . . . . . 166 28 Volatiles Percent Recovery by Sludge Type . . . . . . . . . . . I67 29 Non-volatiles Percent Recovery by Sludge Type . . . . . . . . . 167 viii I. Literature Review A. Sludge Analysis Thanks to the Federation of Sewage, Sewage Works and Industrial Wastes Association, two fairly complete volumes on sewage treatment and utilization were compiled in the late forties. At this early date the Subcommittee of Utilization of Sludge as Fertilizer published a 120 page report on the soil requirements, sludge characteristics, treatment processes and economics of sludge application to agricultural lands (Pearse, 1945). Then in 1950, the Subcommittee on Chlorination of Sewage and Industrial Waste Practices released another report taking an indepth look at several waste chlorination practices and Chemical effects of chlorination (Gilcreas, 1950). Even though modern analytical techniques for the day were employed in both reports, the trace analytical capability which we have today had not yet been developed. The only reference in either paper to the potential hazards of organic toxicants in sludges concerned was the treatment of phenolics in effluents by Dow Chemical Company in Midland, Michigan. Actual analyses for organics were limited to the various forms of nitrogen present as nutrients. From this date forward, no analytical methods were published on the analysis of sludge for organic compounds until the late 19705. As a matter of fact, the literature is quite void Of any noteworthy strides in sewage or wastewater analysis prior to the early 19705. Before 1970, less than 100 different organic compounds had been identified in water let alone municipal sewage sludges. In' the early 19705, a split developed in the area of wastewater analysis. One group became primarily concerned with chlorination of wastewaters and the other with characterization of organics in wastewaters. The developments of both of these groups helped to pave the way for sludge 2 analysis. Some of the key workers in the effects of Chlorination include: Jolley, (I973, I975); Glaze et al. (1973); Glaze and Henderson (1975); Glaze and Peyton (1978); Carlson (1975); and Kopperman (1975). In a search for a better understanding of the effect of chlorination on organic components, new techni- ques were developed and utilized for trace component determinations. Various techniques also began to emerge once an effort was exerted toward the Characterization of organics in wastewater. Workers in this field included: Katz et al. (1972); Kleopfer 6c Fairless (1972); the Environmental Protection Agency (1973); Baird et al. (1974); Keith (1974, 1976); Manka (1974); Telliard (1977); Lichtenberg (1978); Jungclaus (1978); Cooper (1978); Mousa 6c Whitlock (1979); Walton 6c Eicemen (1978); Haller (I978); Narkis (1978); Lamparski (1978); Carter (1978); Waite (1978); Jenkins et al. (1978); Argamon (1978); and Hall (1978). Although a review of all these works would be too extensive and far reaching for the specifics of this paper, please note how the analysis of organic compounds in wastewaters bloomed about 1978. Much of the work during this time period involved either the analysis of phenols or chlorinated pesticides in municipal and industrial wastewaters. The largest amount of work completed to date involving sludge analysis has been concerned with heavy metal contamination (Keith, 1974). Several studies have shown the detrimental effects of high heavy metal concentration to both crops and mammals (Jelinek 6t Braude, 1978). The first modern techniques used in a comprehensive Characterization of organics in sewage effluents was by Garrison, Pope and Allen of the United States Environmental Protection Agency from 1971 to 1976 (Garrison et al, 1976). In this work, 80 specific volatile organic compounds were identified in 3 raw and treated domestic wastewaters primarily by G.C./M.S. This indeed was the first step toward the characterization of organics in sludge. In 1977 two publications discussed the management and effect of sludge used on land. The first was a National Science Foundation risk assessment of land application of municipal sludge (Jones 6: Fred, 1977). The National Science Foundation concluded that chemical contaminants "suspected" to be present in municipal sludge could have adverse effects on the environment as a result of land application practices. The second publication was from the Bureau of Foods, the Food and Drug Administration (Jelinek 6c Braude, 1977). In the FDA's study, levels of selected chlorinated pesticides, lead and cadmium were con- sidered with the following recommendations made: a. Sludges should not contain more than 20 ppm cadmium, 100 ppm lead or 10 ppm PCBs on the dry basis. b. In support of the limits proposed by the W 124/ NC 118 Committees Of Land Grant Colleges, the maximum total which should ever be added to an average soil (cation exchange capacity of 5-15) is 9 lbs. of cadmium/acre and 900 pounds of lead/acre. C. Crops which are customarily eaten raw should not be planted within 3 years after the last sludge application. (I. Crops such as green beans, beets, etc., which may contaminate other foods in the kitchen before cooking should not be grown on sludge- treated land unless the sludge gives a negative test for pathogens. e. Because sewage can be regarded as filth, food physically contami- nated with sludge can be considered adulterated even though there is no direct health hazard; hence, sludge should not be applied directly 4 to growing or mature crops where sludge particles may remain in or on the food. f. Commerical compost and bagged fertilizer products derived from sludges should be labeled properly to minimize any contamination of crops in human food chain which may result from their use. Richard Doyle (1978) from the University of Maryland completed a brief study under laboratory conditions on the effect of dairy manure sludge on pesticide degradation. This study was carried out with C-14 labeled pesticides. Doyle concluded that sludge application to soils can alter the rate of pesticide degradation, either accelerating or hindering biological activity depending upon conditions. Since this work was carried out totally in the laboratory instead of the field, more work is necessary before any applications can be made to environmental conditions. In 1979, a review was completed on the value and effects of sludges for agricultural use when contaminated with toxic elements (Sterritt, 1979). In 1979, interim methods for measurement of organic priority pollutants in sludges were produced by the U.S. Environmental Protection Agency Environ- mental Monitoring and Support Laboratory in Cincinnati, Ohio (E.P.A., 1979). This was the first serious attempt to extract, cleanup and quantitate a group of organic pollutants from sludge. Many extractions and Cleanup techniques were attempted, yet all met serious reproducibility problems, due to the sludge matrix. No data on percent recoveries of organics from sludge was presented in this paper. These methods were the basis for the E.P.A.‘s officially proposed guidelines for the analysis of priority pollutants in sludge (E.P.A., 1980). Once again, no recoveries were given; the data base was small and inter-laboratory testing was absent. S The Environmental Protection Agency began monitoring 50 publicly owned treatment works for the occurrence of organic priority pollutants in sewge influent, effluent and sludge in 1979. In October of 1979, a pilot study for this program was published which indicated large inconsistencies and extremely poor reproducibility in analysis techniques. Fortunately, since 1979 several other groups have initiated sludge analysis programs, each with there own methods of analysis. Hopefully, results from these undertakings will give us a better idea of the problems we face and the potentials for various sludge analysis techniques. To date, none of the previously mentioned studies have been completed. However, several have released interim reports on their progress (EPA, 1980; Jacobs dc Zabik, 1980). Here are ongoing studies in sludge analysis for organics: 1. Richard Rediske, Muskegon Wastewater Treatment Plant, Muskegon County Wastewater Management System, Michigan. An ongoing monitoring program for 30 organic pollutants in sewage influents, effluents and sludges. Sponsored by the Environmental Protection Agency. 2. Dr. Tom Clovengor, University of Missouri. Seventy-four municipal wastewater treatment facilities in Missouri. Study of the chemical compositions of municipal sewage sludges in Missouri. Sponsored by Missouri Department of Natural Resources. 3. Dr. William Glaze, Southern California. Analysis of reclaimed waste- water for priority pollutants. Department of Chemistry, North Texas University, Denton, Texas. 4. Vernon L. Stunp, Mid-Missouri Engineers Incorporated. Evaluation of Purifax Process in field operations. A comprehensive look at chlorinated organic compounds produced by a "super chlorination" 6 sludge treatment method. Sponsored by Basics in Flow Division of General Signal. Howard Feller, Burns and Roe Industrial Services Corporation. Fate of Priority pollutants in publicly owned treatment works. A compre- hensive study of 50 POTWs for priority pollutants. Linn Duling and Jillann Kobbee, Michigan Department of Natural Resources. The development of methods for the identification of potential toxic substances from various wastewater discharges in the state of Michigan. Michigan Department of Natural Resources through a Toxic Substances Control Act Cooperative Agreement with the U.S. Environmental Protection Agency. Dan Schelton, Crop and Soil Sciences, Michigan State University. Biodegradation of phthalates, cresols, monochlorophenols, methyl- benzoic acids, and chlorobenzoic acids in anaerobic sewage sludge. Sponsored by Office Of Toxic Substances, The Environmental Protec- tion Agency. Dr. Matthew Zabik, Presticide Research Center, Michigan State University. Analysis of 80 organic pollutants in 250 municipal sewage sludges throughout Michigan. Sponsored by the Michigan Department of Natural Resources and the Environmental Protection Agency. Dr. Matthew Zabik, Pesticide Research Center, Michigan State University. Field evaluation of the fate of hazardous organic Chemi- cals present in sewage sludge. Fate of organic priority pollutants in sludges under field application. Sponsored by the Michigan Depart- ment of Natural Resources. 10. Jackson Elington and Dr. Edo Pellizzari, United States Environmental Protection Agency, Environmental Research Laboratory, Athens, Georgia. "A comprehensive method for the analysis of organics on solids, sediments and sludge." EPA Contract No. 68-03-2994. 11. Dr. Zweidinger, U.S. Environmental Protection Agency, Environmen- tal Research Laboratory, Athens, Georgia. "Analytical procedures for proposed toxics in wastewaters and sludges." EPA Contract No. 68-03-2845. Very much in line with the type of work now being completed was a recent publication dealing with sludge stabilization and what effects this biological and chemical inner play has on the resulting organic composition (Hautenstein, 1981). Just as the 1970s were the era of blooming organic analysis in wastewaters, the 19805 are becoming the decade for development of sludge analysis techniques. Analysis techniques are an essential tool. Much work has yet to be done in order to characterize and determine the fate of potential xenobiotics in municipal sludges. B. Volatiles The only published work to date dealing with the determination of volatile organics in municipal sewage sludge is the Environmental Protection Agency's proposed methods for sludge analysis of organic priority pollutants released in March of 1980. This method suggests analysis via Bellar and Lichtenberg's "purge and trap" method. Samples are to be diluted when the percent solids is too great for accurate quantitation by this method. Since previous work in sludge analysis of volatile and semi-volatile organics is limited, I will highlight the advances made in the area of water analysis for these components. The most direct means of determining levels of a volatile organic com- pound in an aqueous medium is by "direct injection" onto a gas/liquid or high pressure liquid chromatographic column (Sugar 6: Conway, 1968; ASTM, 1973). This method is only possible when working with extremely clean samples at high levels with column packing and detectors that are inert to water vapor. Liquid/liquid solvent extraction using either high or low boiling solvents overcomes many of the problems faced with direct aqueous injection techniques (E.P.A., 1971, Duenbostel, 1973). Some of the problems which may plague the solvent extraction technique include erratic or low extraction efficiencies and in some instances solvent impurities. With the advent of capillary chromatography, solvent interference problems have been greatly reduced due to improved separation and resolution of peaks. Another widely used method for volatile analysis, especially in an industrial setting such as analysis of flavors, is the direct sorption of organics from water onto charcoal followed by solvent extraction, and concentration (Grob, 1973; Polak, 1974). This method, however, can suffer the same solvent contaminant 9 disadvantages as liquid/liquid extraction. Of course, some compounds are preferentially adsorbed into the charcoal, while others are not. The "head-space" or "head-gas" method of volatile analysis has been used for many years. For this technique a sample is sealed in a partially filled container and the volatiles allowed to partition into the gas phase which is directly injected via a gas tight syringe into a gas liquid chromatography (Dow Chemical, 1972). Recovery in this instance is dependent upon the partition coefficient of each compound, which must be known in order to calculate the concentration in the aqueous phase. This equilibrium can be forced in the favor of the gaseous phase by raising sample temperature, replacing the head-space with inert gas Of a lower density, or purging. In 1974, Bellar and Lichtenberg developed a very useful technique for volatile analysis which later was coined as the "purge and trap" method due to two of the phases in the analysis (Bellar 6t Lichtenberg, 1974). This technique used the principle of head-space analysis but improved the extraction efficien- cies by purging with an inert gas. In this way the partitioning continually proceeds in the direction of the less saturated gaseous phase. A porous glass frit is used to bubble nitrogen through the sample solution. The gas is then passed through a series of absorbants at ambient temperature to trap the organic constituents. After a sufficient volume of gas has swept through the sample and the specific organic compound(s) of interest has been concentrated on the absorbent, it is then quickly desorbed. The key to the desorption process is removal of the organic components of interest in a single concentrated slug onto a chromatographic column by raising the traping column temperature at a rapid rate. 10 In 1976, Steichen of the Good Year Tire and Rubber Company developed a head-space analysis technique for acrylonitrile and styrene sensitive down to the 0.5 and 1.0 ppm level respectively (Steichen, 1976). One year later workers at Dow Chemical devised a more sensitive colorometric method than had been used in the past for acrylonitrile determination in aqueous media (Hall at Stevens, 1977). This technique was based on the formation of a yellow colored acrylonitrile pyridine complex read by a spectrophotometer at 535 nm. Determination of volatile organic acids in municipal wastewater was per- formed via steam distillation onto a silicic acid column in 1977 (Narkis 8c Henfeld-Furie, I977). Volatiles were recovered from the silicic acid column by elution with n-butanol in chloroform and directly injected into a gas chromato- graphic column. This was the first attempt at characterizing the short chain organic acid content in sewage. In 1978 two methods surfaced for analysis of acrylonitrile by G.L.C. that were more sensitive, selective and reproducible than ever before (Marano et al, 1978; Pasquale, 1978). Both authors used Carbowax 20 M on Chromsorb W 60-80 mesh which allowed better recovery of acrylonitrile through the column and nitrogen phosphorous specific flame ionization detectors which achieved a 10 ppb level of detection. The Adolph Coors Company in conjunction with the University of Colorado, developed a modified "purge and trap" method for trace analysis of volatile organics in aqueous mediums (Peterson 6: Eiceman, 1978). In this method small compact cartridges of porous polymer sorbtion traps are used to collect the volatiles while sparging the sample with an inert gas. These cartridges were then directly inserted into a gas Chromatograph inlet system and heated for 11 desorption. This method is currently used in industrial hygene for analysis of volatiles in the work place. In 1978 a team of scientists from the Institute of Chemical Technology in Czechoslovakia looked at the efficiency of the purge and trap method previously described by Bellar and Lichtenberg (Vozhakova et al, 1978). In this study several variables were considered including the stripping vessel design, volatility and the effectiveness of various polymers in the concentration of organic compounds. A porous glass fritted system was found to give the greatest stripping efficiency and linearity over a wide concentration range in the case of less volatile components. Each volatile compound tested was shown to have unique recovery curves depending on purge time, concentration, temperature and rate of desorption from the porous polymer trap. Much work was completed on the analysis of halogenated volatile organics in drinking and waste water in 1979. Bellar and Lichtenberg described in detail a semi-automated purge and trap system for volatile analysis in drinking water. In this report purge efficiencies for various compounds were discussed in relation to purge volume and flow rate (Bellar and Lichtenberg, 1979). Comparisons were also made on levels of halogenated volatiles from various preservation techniques in aqueous matrices containing free chlorine over an 8-day time period (Kopfler et al, 1976). The results showed a rapid increase in some halogenated compounds over time when unpreserved, a gradual but linear increase when samples were stored at 4 C and only a slight increase in samples preserved with ferrocyanide. Both ferrocyanide and sodium thiosulfate proved effective in reducing continued chlorination when compared to non- preserved samples. 12 An article published in American Water Works Association discussed in detail a precise analysis technique of trihalomethanes by liquid/liquid extraction with pentane (Trussell et al, 1979). The results demonstrated that the technique was accurate, sensitive, reproducible, and workable for large numbers of routine samples. In another study a comparison of recoveries of trihalomethanes in drinking water was made between purge and trap and liquid/liquid'extraction with methylcyclohexane. Both methods showed comparable results with compen- sating advantages to both procedures. Overall, the liquid/liquid extraction technique maintained higher recoveries (Reding et al, 1979). Perkin Elmer Corporation described a newly designed head-space analysis mechanism for adaption to gas chromatography (Widomski, 1979). This technique solved the common problem of inadequate temperature control and non-repro- ducibility of many poorly designed systems. Purge and trap in conjunction with G.C./M.S. was applied to the E.P.A.'s list of 19 volatile priority pollutants for analysis in various types of waters. The method was tested successfully at the 5-50 u/L level and provided qualitative and semi-qualitative analysis (Pereira 6: Hughes, 1980). The purge and trap G.C.lM.S. priority pollutant scan has become the most popular method of analysis for environmental monitoring. Present E.P.A. recommended methods include purge and trap in conjunction with G.C./M.S. The E.P.A.'s Methods 1624 and 1625 combine a radio isotope labeled dilution with purge and trap G.C.IM.S. for volatile and semivolatile organic compounds (E.P.A., 1980, 1980). James Mieure from Monsanto undertook a survey of six techniques for low- level multi-component volatile organic techniques in water (Mieure, 1980). Included in the general overview were: static head-space analysis, purge and trap, solid sorbents, liquid/liquid extraction, steam distillation, and 13 semi-permeable membranes. Increased demand for purge and trap analysis techniques~ have resulted from governmental recommendations. Researchers from the Tekmar Company recently presented two papers taking an extensive look at the optimization of purge and trap parameters and design considerations for automatic sampling (Westendorf, 1981; Westendorf et al, 1981). C. Phenols Until 1980 no analysis of phenols from sludge had been reported. However, several important developments in the area of phenol analysis have been published. The past developments which have potential importance in sludge analysis include: sorption resins, derivitization techniques, organic acid G.L.C. columns, high pressure liquid chromatography coupled with ultraviolet, fluores- cence and electrochemical detection, preservation of phenols and gas chromato- graphy mass spectrometry. A dimethylbenzene polystyrene copolymer resin produced by the Rohm and Haas Company was shown to be effective in the sorption of substituted phenols (Paleos, 1969). Both the XAD-2 and XAD-7 resins were tested for adsorption from water of phenol, m-chlorophenol, O-nitrophenol, p-nitrophenol, 2,4- dichlorophenol and 2,4,6—trichlorophenol at concentrations up to 2.4 mol/liter. A more extensive study of the separation of nitrophenols on Amberlite XAD-2 resin was completed in 1972 (Grieser, 1972). Application of the XAD resin to chromatographic techniques for determination of phenols in water originated at Iowa State University (Chriswell, 1975). Later the XAD-2 resin was shown to be effective in separating a large group of chloro-, nitro-, and alkyl phenols via elution with buffers adjusted to various pHs. Sorption of phenol, o-cresol, m- cresol and 2.6-xylenol from water on a macroporous polymer and subsequent thermal desorption onto a G.L.C. column was demonstrated in 1979 by a group of Czechoslovakian scientists (Voznakova 6: Pope, 1979). XAD resins have been used for concentrating organics from wastewaters before and after treatment (Jolly, 1973; Glaze, 1981). Recovery efficiencies from XAD polymers are good for some compounds and poor for others. The reason for poor recoveries may be either poor adsorption (polar organics, such as natural organics after oxidation 14 15 with ozone or chlorine, low molecular weight acids and alcohols) or poor desorption (some long chain aliphatics and polynuclear aromatics). Throughout the years many procedures have been used for the derivitiza- tion and analysis of phenolic compounds by gas Chromatography (Seiber et al, 1972). Markedly improved resolution of derivatized methyl phenols was provided with the introduction to open tubular columns and eventually capillary columns (Hvivnak, 1971). In 1979 an extensive reference of phenolic trimethylsilyl derivatives was compiled according to their retention time on methyl and phenyl silicone G.L.C. columns (Mattsson 6: Peterson, 1977). This study attempted to draw a relationship between structure and retention of various phenolic deriva- tives. Two groups showed the use of derivatization in order to magnify sensitivity of certain phenols on a electron-capture detector (Lamparski 6c Nestrik, 1978; McCallum 6: Armstrong, 1973). Heptafluorobutyrylimidazole, pentafluorobenzoate and dimethyldichlorosilane were used as derivatizing agents for this extremely sensitive technique able to detect phenols in the part per billion range. The effectiveness of these two techniques was proven when both capillary chromatography and derivitization were combined in the analysis of a complex environmental matrix of coal-tar waste (Buryan et al, 1978). In an attempt to improve separation of phenolics by gas liquid chromato- graphy without derivitazation, several "organic acid" columns were developed (Supelco, 1978). A special high pressure liquid chromatographic column has also been introduced for better resolution of more complex organic acid mixtures Bio-Rad, 1980). High performance liquid chromatography is well suited for the analysis of phenolic components. A comparison between several normal-phase and reverse- phase systems demonstrated the specificity of each column. For example, alkyl 16 phenols were shown to have superior separation on a reverse phase C-18 column (Schabron, 1978). Good separation of E.P.A. priority pollutant phenols has also been demonstrated with a newly developed micropak 5u C18 column (Realin, 1981). Various detection systems have been coupled with H.P.L.C. in the past. Trace level detection of phenols including the priority pollutants has been shown to be achievable by direct ultraviolet detection (Bhatia, 1973; Realini, 1979). Fluorescence has been applied to phenol and alkylphenols demonstrating superior relative detectability over ultraviolet detection systems (Ogan 6c Kutz, 1979 6c 1981). A highly sensitive technique down to 0.4 ppb has been reported by using fluorescence spectroscopy and detecting Cerium 111 after phenols react with Cerium IV (Wolkoff dc Larose, 1974). Since 1952 the use of electrochemistry to monitor liquid chromatographic column effluent has been realized. The advent of amperometric detectors especially designed for liquid chromatography in leu of colorometric detectors has revolutionized this area (Kissinger, 1974, 1977, 1978). Electrochemical analysis is the most sensitive and selective method of detection for most easily oxidizable or reduceable components. Amperometric detection has been shown to be 100 to 1,000 times more sensitive than ultraviolet detection for various aromatic phenols and amines (Sternson 6r DeWitte, 1977). Application of amperometric electrochemical detection to the analysis of phenols in environ- mental samples has been wide-spread. Several recent papers have delt exten- sively with the exact parameters for the determination of various phenolics in water and wastewater (Armentrout et al, 1979; King, 1980; Mayer, 1981; Shoup, 1981; McCrory, 1981). 17 Quantitation and characterization Of phenols from complex environmental samples via gas chromatography mass spectrometry has been reported by several authors (Schmidt et al, 1974; Shackelford 6c Webb, 1979). An indepth study on preservation techniques for phenolic compounds in wastewaters by an Environmental Protection Agency team indicated the neces- sity of chemical preservation as well as storage at 4 C immediately upon sample collection (Carter 6t Huston, 1978). An exhaustive report on the environmental effects Of and accepted detection methods for all of the chlorophenol isomers was conducted by a group from the University of Wisconsin (Kozak et al, 1979). Kozak‘s report may very well be considered "out of date" in respect to the analytical techniques suggested due to the great stride in analysis over the past few years. D. Aromatic Hydrocarbons Much attention has been given to polychlorinated biphenyls over the past decade, yet many other chlorinated and non-chlorinated aromatic hydrocarbons have escaped exposure. An extensive review published by the Chemical Rubber Company examines in detail the chemistry of PCBs (Hutzinger, 1974). Several states have routinely analyzed sludges and effluents for PCB's as well as DDT, DDE, DDD, Endrin, Aldrin, Deildrin and Lindane due to their persistance in the environment (MDNR, 1969-81; Bergh 6c Peoples, 1977). These chlorinated hydrocarbons have been shown to be ubiquitous in the environment (Jones 6c Fred, 1977). 18 E. Phthalates The potential of phthalate ester plasticizers leaching into various contents stored in plastic containers is real (Jaeger 6t Rubin, 1970, 1972). Phthalates may also enter the environment through production of wastes both during the industrial process and due to the disposal of plastics. Several authors have assessed the environmental safety of phthalate ester exposure (Gledhill et al, 1980; Lawrence 6: Tuell, 1980). 19 F. Aryl Phosphates Aryl phosphates are routinely used as flame retardent plasticizers and in hydrautic fluids, both of which offer an avenue to environmental exposure. One publication has been released which discusses an analytical technique for analysis of triaryl phosphate esters in fish (Lombardo 6c Egry, 1979). Several reports have been published concerning the environmental impact of aryl phosphate esters (E.P.A., 1978; Howard 6: Deo, 1979). 20 G. Aromatic Amines Benzidine and 3,3-dichlorobenzidine are used in dyes and pigments; both have received much attention due to their potential carcinogenicity to humans (E.P.A., 1978, 1979). 3,3-dichlorobenzidine has also been shown to photodegrade to benzidine (Banerjee et al, 1978). A gas liquid chromatographic detection technique has been described for benzidine and other aromatic amines in the aquatic environment (Jenkins et al, 1978). Aromatic amines which can be easily oxidized are well suited for high pressure liquid chromatography and electro- chemical detection. Several publications have addressed the question Of aromatic amine determinations by liquid chromatographic electrochemical detection (Rice 6: Kissinger, 1979; Riggin 6: Howard, 1979; Shoup, 1980). 21 11. INTRODUCTION A. Scope of Project Sludge is a liquid or semi-solid waste which contains many contaminants removed from water during the treatment process, a large percentage of which consists of bacteria, fungi or other microbes which help to purify effluent. Sludge management is a problem faced by all municipalities in the United States. Municipal sewage sludges contain many nutrients which are valuable for soil enrichment and therefore one of their largest uses has been as a fertilizer in land application. Current methods of sludge disposal include: incineration, landfill, lagooning and land application, land application being the most economically feasible. Due to mismanagement or lack of proper monitoring of some sludge disposal systems, there has been a history of soil, plant, and ground water contamination from heavy metals, toxic organics and pathogenic bacteria. With the increased input of household chemicals and industrial effluents into municipal sewage systems, the problem of waste disposal has come into the forefront for federal, state and local regulatory agencies. Passage of the Federal Water Pollution Control Act in 1972 has caused a huge increase in the amount of sewage sludge for disposal. Under Michigan‘s Act 64, the "Hazardous Waste Management Act," sludges which contain hazardous organic materials present at concentrations from 1 to 1,000 ppm are designated "notification waste" and may subsequently be designated "hazardous waste." This designation tags the sludge as a potential environmental pollutant and must be disposed of properly and continually monitored by the wastewater treatment facility affected. Ultimately, the Michigan Department of Natural Resources must enforce and regulate the management of such wastes. 22 23 In order to manage wastes properly it is important to know if they contain hazardous materials and if so, in what levels, in order that they may be used safely, detoxified or disposed of via incineration or an approved landfill. For this reason the Michigan Department of Natural Resources and the United State Environmental Protection Agency contracted researchers from Michigan State University to characterize 250 sewage treatment plant sludges (Figure I). These sludges came from throughout the state and were analyzed for the following toxic substances or potential xenobiotics. PHENOLS PURGEABLES o-Chlorophenol Acrylonitrile m-Chlorophenol Chlorobenzene p-Chlorophenol p-Chlorotoluene o-Cresol O-Dichlorobenzene m-Cresol m-Dichlorobenzene p-Cresol p-Dichlorobenzene 2,3-Dichlorophenol 1,2-Dichloropropane 2,4-Dichlorophenol 1, 3-Dichloropropane 2,5-Dichlorophenol 1 ,3-Dichloropropene 2,6—Dichlorophenol Ethylbenzene 3,4-Dichlorophenol Hexachloro- 1 , 3-butadiene 3,5-Dichlorophenol Hexachloroethane 2,3-Dimethylphenol Pentachloroethane 2,4-Dimethylphenol Styrene 2,5-Dimethylphenol Tetrachloroethylene 2,6-Dimethylphenol 1,2,3-Trichlorobenzene 3,4-Dimethylphenol 1,2,4-Trichlorobenzene 3,5-Dimethylphenol 1,3,5-Trichlorobenzene 4,6-Dinitro-o-cresol 1,2,3-Trichloropropane 2,4-Dinitrophenol 1,2 ,3-Tr ichloropropene Hydroquinone Pentachlorophenol PHTHALATES Phenol 2,3,4,5-Tetrachlorophenol Butylbenzylphthalate 2,3,4,6-Tetrachlorophenol Diethylphthalate 2,3,5,6-Tetrachlorophenol Dimethylphthalate 2,3,4-Trichlorophenol Di-N-Butylphthalate 2,3,5-Trichlorophenol Di-N-Octylphthalate 2,3,6-Trichlorophenol Dioctylphthalate 2,4,5-Trichlorophenol ' 2,4,6-Trichlorophenol 3,4,5-Trichlorophenol 24 NITROBENZENES AROMATIC HYDROCARBONS 1Chloro-2,4-dinitrobenzene Biphenyl lChloro-Z,6-dinitrobenzene Hexachlorobenzene lChloro-3,4-dinitrobenzene Mercaptobenzothiazole 1Chloro-2-nitrobenzene Naphthalene 1Chloro-4-nitrobenzene Polychlorinatedbiphenyls 2,4-Dinitrotoluene 1,2,3,4-Tetrachlorobenzene 2,6-Dinitrotoluene l,2,3,5-Tetrachlorobenzene Nitrobenzene l,2,4,5-Tetrachlorobenzene Pentachloronitrobenzene BASES TRIARYL PHOSPHATE ESTERS Benzidine Cresyldiphenyl Phosphate 3,4-Dichloroaniline Tricresyl Phosphate 3,3'Dichlorobenzidine Trixylene Phosphate p—Nitroaniline Two hundred-thirty-seven sewage treatment plants throughout Michigan were sampled by the Michigan Department of Natural Resources (Jacobs 6L Zabik, 1980). These sludges were sampled from various stages of the treatment process which varied from city to city including raw sludge, primary aerobic digested, secondary aerobic digested, anaerobic digested, Purifax sludge, fil- tered, lagooned, and drying bed sludge. The Purifax sludge is specially treated via a patented super Chlorination process in order to decrease its active biological activity and subsequent odor. The sewage treatment plants sampled have imputs varing from totally residential to a large percentage in industrial sources. Community population may also vary from a few hundred to several million individuals, therefore each community usually treats its waste in a fairly unique manner which leads to a great diversity in treatment processes. 25 Municipal Waste Treatment Plants Sampled .0 . I . V C O . ’,‘D ‘k:’ 0 o O .0 o 00 ‘ O I o O . .- 0 . Q . . O :' , . . o ... . . o I . .9 ¢ . o g . .'CC . " O .. 1' 04“ . . . a" '0 .00 . 0...: ’ . ' ' o "o ' o 9 .. 1. .fl. . '1.» 1:h:'~: . C O .0 Q g... z 0’ . o 9 o O 0 One '/ . O . . O . O.‘4 II. 9 ‘. . o» 0 ° 1. ' Figure l B. Thesis Proposal In order to determine the content of organic priority pollutants in municipal sewage sludges it is necessary to develop accurate, precise analytical techniques which have not previously been available or well tested. This large volume and diversity of sludges to be analyzed for a wide spectrum of organics will in turn give a large data base by which new analytical techniques may be tried and tested. My purposes throughout this project was fivefold. 1. To develop analytical methods for the analysis of all selected organic compounds from municipal sewage sludges originating from various stages of the waste treatment process. To determine whether any of the municipal waste treatment plant sludges contain a group of volatile, semi-volatile and phenolic organic compounds in measurable levels. To determine if any statistically significant correlation exists be- tween various waste treatment processes and the distribution and levels of organic components analyzed for. To determine if any statistically significant correlation exists be- tween the size of the community (i.e., population served) and the levels of any organic components analyzed for. To determine if any statistically significant correlation exists be- tween the percentage industrial input into the sewage treatment facility and the levels of any organic components analyzed for. Once these five areas have been completed, answers to the following questions can begin to be answered. 1. What collection, extraction, cleanup and separation techniques work best for analysis of organic compounds from sludge? 26 27 How do the Characteristics of a sludge effect percent recovery of the components tested for? Which sludges contain any of the hazardous wastes tested for and at what levels? How does the waste treatment process effect these toxic organic components? How much of an effect does residential input have on the level of toxic substances analyzed for in sludge? How much effect does industrial input have on the levels Of toxic substances analyzed for in the sludge? III. ANALYTICAL METHODS At the onset of this project no analytical techniques for analysis of organics in sludge were available except "Interim Method" proposed by the Environmental Monitoring and Support Laboratory in Cincinnati, Ohio and these methods had not been well tested on large numbers and types of samples. Several techniques were tried and tested in order to determine the most cost- effective, accurate and reproducible analysis scheme possible. Municipal sludge contains 15-20 percent solvent extractable materials in the form of coal tars, petroleum fuels, lubricating oils, fatty acids and other biological materials, all of which contribute to interferences upon analysis. Therefore, an extensive extraction and Cleanup scheme was necessary in order to separate the interfering components from those which were to be analyzed. A. Overall Scheme The following pages present a flow diagram for the general analysis scheme used to separate, characterize and quantify all organic components of interest (Figure 2 and 3). B. Glassware Preparation All sample containers and teflon seals were detergent washed, rinsed three times each with tap water, distilled water, acetone and finally hexane. The glassware was allowed to dry in a 105°C oven for one hour and cooled in an area known to be free of organics. Bottles were sealed immediately after cooling to room temperature. Caution was taken not to heat teflon seals for more than one hour at 105°C preventing degradation of the silicone layer. 28 29 mSIUU cots-2:200 Seventeen—E" £300 cotmExEoU flux—92 NxIm Dazfiuu QEOU 0500 8: 33:”. cotmofificom EU floconm c232”. .9332th c289."— Eo< ocmxoonxu :33 530935 23353: .022 3:3 cotumhxm _ vmmm\_v_u< Ema; :2 8:8 m. 8:8 .88 .— 3.0.292 2323558 :8 cosmEEcoaoo 2.0m moi-emu 2:2..— - H 9 .3383 .2 .8 oowv Cm... 5:05. 02? Lens;- 25.3 8.32305 tee 5:00:00 035mm 6% 2:23 mZMEUm mHm>4..coz a 30 omszHezou mzmzum me>a..0..:0._0.0EA. 5.8050 2:03-255... m.m>.0:0 050: 3:30:00 50.5.00 3:30 500050050 80:20.30 :o.u00.:x0 c0008. 8.8050 2:03-23... n 08%.: 0.5808. coa0> 0 :03 m0 50.0.2000 :o.p.t0a :o.5.8\..8 00.200 0.5.). 500w050>00 5: 5:30:00 30.3.00 C03015CUUCOU OZ 00.00.50 80:38:50 :030w5a: 3:0 8.0505. 000% 000... mMU<.—.Z<>D 0 :03 m0 50.0.0000 5.0.0.0: :o.5.8\..8 000.8:00 8:5. m.m>.0:0 m:.E8:o0 05.... 00.00.50 80:38:50 :0B0wcsn... >.:O 50.5805. .0...:. Q00... 0:0 Owe-K. 00:00.05. 30... 3:30 :o.u0.:.:00:oU 0000:0050 20.0... 90:... 0:0 0mg 37 Table 1 Comparison of Extraction Techniques Sample Number 218 Liquid/ MDNR Techmar Spike Liquid Purge 6c Trap Purge 6c Trap Concentration 96 96 96 Purgeables l N Mg/l Recovefl Recovery Recovery Tetrachloroethylene .0003014 6.1 Possible Det. 1,2,3-Trichloropropene (1) 1.897 52.7 Detection 1,2,3-Trichloropropene (2) 1.897 63.3 Detection 1,2,3-Trichloropropane .5280 60. 7 Detection Pentachloroethane .01994 25.3 (Detected o-Dichlorobenzene . 5990 63.3 two p-Dichlorobenzene . 9724 64.7 unknown m-Dichlorobenzene .5060 42.7 volatile Hexachloroethane . 01012 13. 3 chlorinated 1,3,5-Trichlorobenzene .1016 97.3 compounds) 1,2,4-Trichlorobenzene .1654 100.0 1,2,3-Trichlorobenzene .1012 1 13.3 Hexachloro-l ,3-butadiene .00918 13. 7 Acrylonitrile * 2.286 66.0 Chlorobenzene 61.78 64.6 1 1.696 Ethylbenzene * 39.74 87.6 Styrene * 42.41 104.0 8.596 p-Chlorotoluene 64.25 89.0 12.096 Bromochloromethane .04491 85.3 1,2-Dichloropropane .05238 42.0 1,3-Dichloropropene (1) 20.28 105.4 Detection 1,3-Dichloropropene (2) 20.28 75.0 Detection 1,3-Dichloropropane .6792 60.4 2-Bromo-l-Chloropropane 2.387 28.9 *Techmar used a Hall electrolytic conductivity detector in the chlorine mode. Therefore, these compounds are not detectable. 38 Table 2 Comparison of Extraction Techniques Sample Number 225 Liquid/ MDNR Techmar Spike Liquid Purge 6c Trap Purge 6c Trap Concentration 96 96 96 Purgeables l N mg / 1 Recovery Recovery Recovery Tetrachloroethylene .0003014 1,2,3-Trichloropropene (1) 1.897 34.8 1,2,3-Trichloropropene (2) 1.897 66.2 1,2,3-Trichloropropane . 5280 78.5 Pentachloroethane .01994 74. 8 (Detected o-Dichlorobenzene .5990 56.5 two p-Dichlorobenzene .9724 32.5 unknown (No m-Dichlorobenzene .5060 22.6 chlorinated sample Hexachloroethane . 01012 40. 7 volatiles) given) 1,3,5-Trichlorobenzene .1016 69.9 1,2,4-Trichlorobenzene .1654 84.2 1,2,3-Trichlorobenzene .1012 103.8 Hexachloro-1,3-butadiene .00918 152.0 Acrylonitrile 2.286 59.8 Chlorobenzene 61.78 67.3 1 1.696 Ethylbenzene 39.74 87.6 Styrene 42.21 56.8 8.596 p-Chlorotoluene 64.25 58.4 Bromochloromethane .04491 1,2-Dichloropropane .05238 35. 8 1,3-Dichloropropene 20.28 101.6 1,3-Dichloropropane .6792 30.9 2- Bromo-l-chloropropane 2.387 81.8 39 Table 3 Comparison of Extraction Techniques Sample Number 228 Liquid / MDN R Techmar Spike Liquid Purge 6c Trap Purge 6c Trap Concentration 96 96 96 Purgeables l N mg / 1 Recovery Recovery Recovery Tetrachloroethylene .0003014 19.9 1,2,3-Trichloropropene (1) 1.897 1,2,3-Trichloropropene (2) 1.897 58.6 1,2,3-Trichloropropane . 5280 50. 9 (Sludge (Sludge Pentachloroethane .01994 59.1 too too o-Dichlorobenzene . 5990 58.7 thick thick p-Dichlorobenzene .9724 46.5 to to m-Dichlorobenzene .5060 8.8 analyze) analyze) Hexachloroethane .01012 53.9 1,3,5—Trichlorobenzene .1016 77.5 1,2,4-Trichlorobenzene .1654 79.8 1,2,3-Trichlorobenzene .1012 81.4 Hexachloro-l,3-butadiene .00918 26.7 Acrylonitrile 2.286 70.7 Chlorobenzene 61.78 78.1 Ethylbenzene 39.74 87.6 Styrene 42.21 73.4 p-Chlorotoluene 64.25 89.0 Bromochloromethane .04491 31.4 1,2-Dichloropropane .05238 37.1 1,3-Dichloropropene 20.28 67.3 1,3-Dichloropropane .6792 171.5 2- Bromo-l-chloropropane 2.387 65.9 2. Methods The extraction technique used was adapted from J.E. Henderson's liquid/liquid extraction for water and wastewater (Henderson, 1976; Figure 6). The technique consisted of quantitatively transfering an aliquot of sludge into a 60 m1 serum bottle, at which point it was diluted with organic free water whenever necessary. The dilution of extremely viscous samples improved extraction efficiencies as well as providing a partitioning of water soluble interferences from the solvent phase. a. Extraction technique Tests of various solvent mixtures showed that cyclohexane gave superior overall recoveries for this application. The extraction solvent may be selected specifically to improve recoveries of the components of interest. Short-chain aliphatic hydrocarbons would be preferentially extracted by pentane or hexane while methylene chloride is more specific for many chlorinated compounds. After 3 mls of cyclohexane were added, the serum bottle was sealed with a teflon septum allowing zero head-space. Next the sample was thoroughly mixed by hand for 5 minutes and then centrifuged at 2,000 rpm for 10-20 minutes. By venting the bottle with a small needle, the cyclohexane was drawn off via a syringe and placed in a screw capped vial. The sludge was then extracted again with a second 3 ml aliquot of cyclohexane introduced via a syringe through the septum. After both extracts were combined, anhydrous sodium sulfate was added to the extract to adsorb residual water. An aliquot of dried extract was then transfered to a small vial with a teflon septum allowing zero head-space. In cases where less than 6 mls of extract were recovered, the sample was brought up to a 6 ml volume in the screw-capped vial before addition of sodium sulfate. 40 41 MGDEmhzmo o 005w.“ mzmzum ZO~PU<¢bxm mma~h 42 b. mantitation Two instruments were used for quantitation: a Varian 3700 capillary gas Chromatograph and a Tracor 560 gas Chromatograph. Separation of the volatile and semi-volatile organic components was achieved via three columns: a 25 meter fused silica SE-30 capillary, a 3 meter stainless steel column packed with 1096 Apiezon L on 30/100 chrome WHP, and a 1 meter stainless steel column packed with 0.296 Carbowax on 60/80 mesh Carbopack. Detection was by electron capture for most halogen containing compounds, nitrogen phosphorous specific flame ionization for acrylonitrile and flame ionization for all others. While both Chlorobenzene and p-chlorotoluene contain an electrophilic atom their physical structure allows superior detection via F.I.D. as opposed to an E.C.D (Figures 7-10). All data was fed directly into a PDP-8 Digital Computer from all gas chromatographs and peaks were evaluated according to their area. This data was then transfered to the PDP 11/40 RSTS system for storage and manipulation. Using external standards, the data was fitted to a best degree polynomial and printed out in report form. 43 GAS/LIQUID CHROMATOGRAPHIC CONDITIONS FOR ACRYLONITRILE Acrylonitrile N.P.D. 1 METER STAINLESS STEEL 0.21 CARBOWAX on 60-80 MESH CARBOPACK INJECTOR - zao'c OVEN - 100 c DETECTOR - zso'c COLUMN FLOW - 4.0 mllmin. ue CHART - 1 cm/min. Figure 7 44 GAS CHROMATOGRAPHIC CONDITIONS FOR EARLY ELUTING VOLATILES E.C.D. 63m 3 METER STAINLESS STEEL 107. APIEZON L on 30/100 Oman VHF INJECTOR - 230°C OVEN - 100°C DETECTOR - 300°C COLUMN FLOW - 4.0 nil/min. N2 CHART - 0.5 inches/min. Bromchloromethane I ———— 1,3-D1chloropropene / Z-Bromo-l-Chloropropane / 1 , 3-Dichloropropane Figure 8 45 GAS CHROMATOGRAPHIC CONDITIONS FOR FLAME IONIZATION DETECTABLE VOLATILES ir_. F.I.D. 15 METER FUSED SILICA CAPILLARY COATING - SE-30 INFECTOR - 200°c OVEN - 65 C DETECTOR - 300°c COLUMN FLOW - 1 ml/min He CHART - 2 cut/min. Chlorobenzene zene r0810 [_ 1:. [ p-Chlorotoluene Figure 9 46 o. 0hsmflm )3 5 antqaaozotqguaua Z . J . I _Tu1 £17.. HT: DU. Tu..L IO .0» I H m p“ .. to a 030 u 000 . a 3_ d0 0 u m_ T. 0d 9 T. O 1.. g 3 l 00 . 4 O O. I .l ..L a O O 3 U. ... a. m. u 0 a a. m m a n a 3 ac W G. P a m .d. u 2 u m a a .c.e\ao . - em<=o «z .:.e\.a . - scam zzaqoo cocoa - uoeomeuo .:.s\6 m 00 o co. 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" u a . m o q— 00 . NO 5.0.... 2 .. .. . TM... 3.: / . NmN W. a. . N45. — x 0.2 m ozo~conouo.;u.ono 32miz x m Q “.3. ...w nigh; n NOW m a u s T... 14 .Nmm & .0 .0 0:. .z - N8. 00.:: mam: o.EOu< com ocm om. o~._ . o . n 0 o m 0 . - _ J-om q NO _———: 0..-: ... at a.z 3 . . .2 m «.2 .0....»2N 2.3.: n I. A 0:00:02000.:0.:-E a 2.. . No. I ....»4... U Amaztb m m x s .0 I. .... .Nmn 0 . .0 :b aNoo. 00m 00.:3 mam: 0.500< 8. A aqm L, om. . cum on . .p q a d n q q— 00 q ..-: ... m. 3¢1 . .... z 2.. 5.1.0.: u:o~=onouo.zu.o-m .oswvt .0 0.; “£13.23... 0:. .0 .t 00.:3 mam: 0.500< .0....3:. fit. a... 1 a 1 q 4 4 4 u d on 3.1 T «i z \ z m 0|0H0 _ / z 2 an .1: o.....=o.».0< an +1 ... mUHZ<0mC mAHH omhumqmm x00 H m0HZ awhumqmm mom m0.z<0mo mAHF cmHUmAmm KOm .03 0.0.9.0 3020.. :8 0a.... o.~ 2.0.0.. 0wcmco Em: 0.500; .m. E.Omu.E0m {030.3900 0....3 00.0.0.0 wee. 2m 2000.. c305 ESE—0.9.00 00.... 0......» n...: 3.50 0000.00 E00._o\m._0.m:.o .8050 0...; 00m 0.0.050 0000.00 5000 00:52 .03 0.00050 0.23 ...... .o 09:20 0.0m 3N 20.020 5 0.0.9.0 00:70.0; 00.23 oNN 0.0.9.00 c305 Em: 00:00:. ..mEm ...N 0.3.0.. 0.0.5.. Ew: 50002050.; n2 2.0.0.. 300000.50... Nm. 0.0.050 >000.» 0.23 .000... 0233 22.30 00.70.80: .20 =me w: 5:2 005 0 om 00 00.0.2.0... .020ch 0 mm .. 0.9.0.... .0 00.2009... .00.»..5 0 030... .o:0caob.c.n.-...~ .oc0_.x-s.~ .8055 .ocozaoeoEomEg .oc0saoeo.;um.30._.um.0.m.~ 3.0030962... 00.10....N .o:0fio.o.6.fi-n.s.~ 85.3826550 35.30.5200 .oc0caoeoEU-E 3.00.30.52.00 .ocucm 0:05.696»... 2. Methods a. Phenol Extraction Originally continuous soxhlet extraction with MeCl was the method of choice due to superior recovery of phenols under optimum conditions. Several variables can interact and affect recovery when sludge is dried in preparation for soxhlet extraction (Figure 16). While pH and oven temperature can be regulated, phenol concentration and organic content will be variable. Therefore, continuous liquid extraction was only used for solid samples and a liquid/liquid extraction method was adapted from the E.P.A (1980) proposed methods for all liquid samples. Those liquid sludge samples with less than 3096 solids were routinely basified and extracted three times with MeCl using centrifugation to break the emulsion. The sludge was then acidified and extracted wtih three aliquots of MeCl. Solid samples were dried completely and then soxhlet extracted for 24 hours with 200 ml of MeCl. This extract then proceeded through a continuous acid/base extraction scheme (Figures 17 and 18). b. Cleanup Sludges consist of 5-100% solids which can contain nearly 100% organics, 10-15% of which is solvent extractable material. Typical interferences present in municipal sludges include petroleum fuels, lubricating oils, asphalts, fats, fatty acids and detergents. Obviously a strenuous and complete cleanup procedure is required. Several cleanup methods may be appropriate: acid/base partitioning, separation by size with gel permeation chromatography, separation by polarity with florisil or silica gel columns, separation by pKa with pH buffer partitioning or separation by affinity to an XAD divinylbenzene polystyrene polymer. Preliminary tests were run with each cleanup technique in order to determine 59 60 GENERAL TRENDS FOR RECOVERY OF PHENOLS FROM OVEN DRIED SLUDGE 100% - , lo“ ‘ Z Recovery V 07. ’ 0% g;, 1002 Increasing Organic Content PPt PPm Increasing Phenol Concentration ACidiC WW, Basic Increasing pH 20°C WW 100°C Increasing Temperature Figure 16 61 WWI-AM ACID/WW PARTITDN 2b how Soxhlet exuaczion Med 1 Add 100 ml 0.1 N NaOH to 250 ml centrifuge bottle l Centrifuge Aqua- Extnct 100 ml MeCl — 2.me 10mm--- Aciditypr2—9m 300"“th tunnel I with 6N H250. : (uxm (~fibu) I l Extractvith 1‘30lele EmeOmlMeCI ...... \ l l \ I (ma) \ (AW' 1 \ «b ExamaritthOlelem Ami-nus manning— \ Nudity I l \ pH 2\ I mum \ \ (“End 1 \ \ 4' hue/Neutral Fraction \ \ Discard I Adm” y MMM.C. pH2~\ Extm)?mlMeCl.—. \ I Add 100 ml 0.1 N H250. (Am-an) to 300 ml mm hml\ \ l J. I \ \\ Discard (HOG) \ Aqua-firm 3| MYNPHIIZ EmeOmlMeCl _ mmxoomimum vith‘NNnOI-i l l I \ (Mn-u) (mum \ \ i w l \\ ‘\\ mmmioomminm \ mammal-c:— \ \ (MIG) m (w \ may pH-lz mun-cum \ \ mmmim_. \ (Am-cu) \ \ manna-c1.— (W N (Am) Discard ”Fraction q.__r Figure 17 62 LIQUID SAMPLES PHENOL ACID] BASE/ NEUTRAL PARTITION Liquid Sludge Sample 100 ml sample volume Basidify to szlZ with 6 N NaOH 1 Extract with 50 ml MeCl in 250 ml centrifuge bottle 1 Centrifuge 2,500 rpm 10 minutes I W Extract with 25 ml MeCl w Extract with 25 ml MeCl l Acidify to pH=2 with 6 N H2504 l 4v Extract with 50 ml MeCl ‘14 Extract with 50 ml MeCl l I Extract with 50 ml MeCl l Discard v Discard or save Base/ Neutral Fraction Figure w Acid Fraction 63 the best method or combination of methods. Acid/base extraction should be more appropriately referred to as acid/base/neutral partitioning, since neutral components are also partitioned. The most effective extraction is achieved by choosing an organic solvent with a favorable partition coefficient for compounds of interest. Figure 19 is a typical scheme showing the separation of 3 components: an acid, base and neutral compound. Separation by gel permeation chromatography was made possible with an Analytical Biochemistry Laboratories automatic Gel Permeation Chromato- graphy unit. Two 2.5 x 35 cm columns were filled with S-XZ biobeads, effective range 100-2,7OO M.W. Mobile phase consisted of 66.696 methylenechloride, 33.l+% cyclohexane pumped at a flow rate of 5 mls per minute. A test standard showed the phenols of interest eluting within a 250 ml range. Phenol was the first compound to elute at the 300 ml fraction and pentachlorophenol the last component to be removed after washing the column with 550 mls of elutriate. Therefore, the G.P.C. unit was set to discard the first 250 mls, collect the next 350 mls which included phenols, and discard another 250 mls. This allowed the early eluting large organic polymers, proteins and fatty acids, as well as later eluting small molecule interferences, to be isolated from the phenol fraction. Obviously phenols could also be separated from themselves on the basis of molecular weight by simply using a gel with a lower exclusion limit, such as S-X8 biobeads, with an effective molecular weight range under 1,000. 64 ocvncvm ocvncvm LG ocencvm OuQ engage—go Aeoduumum owmamv on onsmfle vczme< _oev;m “MHHNILLHMHHfl HHHHH.IIV 4|. All. .nzz NE .0 2o oczmc< .ococm ~22 .uc vczmc< 3ch n M H + .2 =0 mafixxxwueaeeeovuru ocmxoon>U 2.8832950 a=0auueuu ~euu=ozv u 2a cm: Q .5 +' O WEIJ u 2a o~= EZOEu—(L d<¢PDMZ\mW~om a.“ wad—Seam“ I - 9.: u... .... .. m 6.... on .... .. I 222228 882%.. 353mm .5 so u. .... u g zBEfia Fzmiomkmtsm < :9: £585. Emuema 68 N2: docoxa 6626-6.6 -ouuucdo-¢.~ flocosaahsuofi lac a one: Hm ousmfla Hones: moeoznoho~gu leuofizooucom nocuzaouousouuh tan a one: access ‘. .“ ‘ '-._'-~el--‘ w h c . 3745‘. . H N,‘ M‘uu—‘i-.--~ .. ‘11.; .-w- 7. '~‘-, fin...—-_._—— .. N , _ .r— .<—t-'~...1 w. .- W‘A.‘ V ' ~ 0:0:«3a0uvaz mGZDOAZOU Umaozmzm awkumqmm no szmm Nlomm>oum¢ szummm o.e~ on za ca 2. an n.o on an oauuudGOuoo< I _..._ __ E I an“. I mm 69 Phenol Cleanug Silica Gel Method 10 mm diameter column Oven burned silanized glass wool 10 grams of silica gel 80-100 mesh 4 cm anhydrous NaSOu Slurry pack with hexane Prerinse with 20 ml hexane Add sample 1. Elute with 20 ml hexane 2. Elute w/20 ml 15% toluene in hexane 3. Elute w/20 ml #096 toluene in hexane 4. Elute w/20 ml 75% toluene in hexane Florisil Method 10 mm diameter column Oven burned silanized glass wool 10 grams of activated florisil 70-80 mesh 550°C Add 2 ml of distilled H20 and mix well u cm anhydrous NaSOu Slurry pack with hexane Prerinse with 50 ml hexane Add sample ‘ 1. Elute with 50 ml hexane . Elute w/50 ml 696 ethyl ether in hexane . Elute w/50 ml 1596 ethyl ether in hexane . Elute w/50 ml 50% ethyl ether in hexane 5. Elute w/20 ml 15% 2-propenol in toluene . Elute w/50 ml 10% Acetone in MeCl . Elute w/50 ml acetonitrile in MeOH 2 3 a 5. Elute w/50 ml 10% MeCl in hexane 6 7 8. Elute w/50 ml acetonitrile Check all fractions especially 3 6c ll Check fractions 5-8 Figure 22 This type of polarity gradient will elute non-polar components first and the most polar ones last (Figure 23). c. Quantitation Many methods for quanitification of phenolic compounds are available ranging from simple colorometric tests to G.C. or L.C./M.S. The two most popular are gas/liquid chromatorgraphy and high pressure liquid chromatography. When using gas liquid chromatography (G.L.C.) several detection methods are possible such as flame ionization detection for non-halogenated species and electron capture detection for halogenated components. Three methods of separation of phenols by G.L.C. include derivitization, capillary columns or special organic acid columns such as SP-1240 DA (Figure 214). Each method has 70 mm ouzmwm dosage wax-2.51.30! access dong-5931.0 schist—its :3 a 95: toned—ounce.— uoeoaoouongouuh «a a one: dong—P coop—«crouch: IIIIIIII"'|I l:\\\\\\\\\\\\\\ l \\\\\\\\\\\\\\\\\\\\\\\ \ Road undo.— I “\v anaemia-.0» I I a . ... 8,5250 2.5sz 883mm "8 5o .52 is W .5 25.5 4825.: smith: 52.8.. - U :9: >588: Emumma 88mg..— 71 COMPAIRISON OF GAS CHROMATOGRAPHIC SEPERATIONS OF PHENOLS Phone's 191 Capillavxf 5330 '3 3h; 6 I --PHEN0L L - Z-CHLOROPHENOL I 4 A ,I I - 2,4-DICHL0R0PHEN0L ”“‘” - 2,4,6-TRICHL0ROPHEN0L - PENTACHLOROPHENOL u-CHLORo-S-METHYLPHENOL Z-NITROPHENOL Q-NITROPHENOL ZIQ-DINITROPHENOL 4,5‘DINITRO'O-CRESOL 2,4-DIMETHYLPHEN0L minetta Figure 24 72 certain trade-offs, therefore the most suitable technique for each situation must be chosen. High pressure liquid chromatography (H.P.L.C.) possess several key advan- tages over gas/liquid chromatography. No derivatization is needed; the system is compatable with aqueous matrices and is excellent for heat liable compounds with H.P.L.C. The inherent polarity of phenols can be used to ones advantage and limited cleanup is necessary. The question arises, whether to use normal or reverse phase chromatographic techniques. Though both work for phenols, it has been shown that reverse phase is slightly superior for the group of substituted phenols of interest here (Schabron, 1978). Reverse phase consists of a non-polar stationary phase and a polar solvent so that the stationary phase retains the most non-polar phenolic component and allows the polar ones to elute first, thus achieving separation. The optimum mobile phase for this system was a ratio of acetonitrile and .05 NaAc buffer adjusted to pH 3.0. In any chromatographic application, the key to improved separation is the reduction of the height equivalents per theoretical plate (HETP). HETP = 2 dp + s Dm/V + grdZV/Ds + wdeV/Dm HETP consists of four factors: the multipath effect, longitudinal diffusion, mass transfer effect of the stationary phase and the mass transfer of the mobile phase. To decrease HETP: a. Use uniform homogenous packing; b. Decrease the viscosity of the mobile phase c. Decrease the particle size of the support d. Use thin film bonded stationary phase e. Optimize the mobile phase velocity (Figure 25). mN unease A>v m~uzmHOHmmm whim mhm: 74 Gradient solvent programming in which the ratio of two or more solvents is changed over time can improve separation of early eluting peaks and shorten elution time for later peaks. It is also instrumental in determining an optimum mobile phase (Figure 26). Unfortunately, one of its disadvantages is poor reproducibility between runs, especially when using aqueous solvent systems. Flow gradient programming, however, can improve separations in much the same way as varying solvent ratios but with improved reproducibility. By reducing the mobile phase viscosity via a column oven, reproducibility and low system pressure can be maintained while programming at higher flow rates. Using two columns in series improves peak separation with a minimum amount of band broadening, but also greatly increased column back pressure due to increased resistance to longitudinal diffision. Once again by raising the mobile phase temperature with a column oven, acceptable pressure levels can be achieved (Figures 27-32). Note that nearly all chlorinated phenol isomers can be separated by reverse phase chromatography with the proper choice of phases, flows, temperature and solvent system. At the onset of this project all phenolic isomers listed previously were to be analyzed. However, due to the time consuming nature of such an analysis, the phenolic compounds were limited to the Environmental Protection Agency‘s priority pollutant list: m-Chlorophenol 2,#-Dinitrophenol o-Chlorophenol Hydroquinone p-Chlorophenol Pentachlorophenol o-Cresol Phenol 2,4-Dichlorophenol 2,4,6-Trichlorophenol 4,6—Dinitro-o-cresol 2,4-Xylenol 75 em ousmua t 3.32.: H. j ruhmrm Hzm>aom hzm~a<¢o 322.... CM 1\ P < d zmhmrm P2m>Aom mdoz~m m2m...w>m sz>aom Emacs—U 92¢ UnhS—Uomm n5 Emmy—3mg ~L 76 Key for Chromatographic Separation of Phenols Methyl Phenol Mix 1 - Hydroquinone 2 - 2,4-Dinitrophenol 3 - p-Cresol 4 - m-Cresol 5 - o-Cresol 6 - 3,4-Dimethylphenol 3,5-Dimethylphenol 2,3-Dimethylphenol 2,4-Dimethylphenol 0 - 2,5-Dimethylphenol I 2,6-Dimethylphenol 7- 3- 9- 1 1 Chlorinated Phenol Mix 1 - Hydroquinone 2 - Phenol 3 - o-Chlorophenol ll - p-Chlorophenol 5 - m-Chlorophenol 6 - 2,3-Dichlorophenol 7 - 2,6-Dichlorophenol - 2,5-Dichlorophenol 2,#-Dichlorophenol 3,4-Dichlorophenol 3,5-Dichlorophenol 2,3,5,6—tetrachlorophenol 13 - 2,3,6—Trichlorophenol Ill - 2,3,4-Trichlorophenol 15 - 2,¢l,5-Trichlorophenol l6 - 2,4,6-Trichlorophenol 17 - 2,3,5-Trichlorophenol l8 - 2,3,ll,6—Tetrachlorophenol I9 - 3,4,5—Trichlorophenol 20 - 2,3,4,5-Tetrachlorophenol 21 - Pentachlorophenol 8 9 E.P.A. Phenol Mix 1 - Hydroquinone 2 - Phenol 3 - 2,4-Dinitrophenol 1+ - o-Chlorophenol 5 - o-Cresol 6 - p-Chlorophenol 7 - m-Chlorophenol 8 - 2,l+-Dimethylphenol 9 - €4,6-Dinitro-o-Cresol 10 - 2,1l-Dichlorophenol 11 - 2,l+,6-Trichlorophenol 12 - Pentachlorophenol Total Phenol Mix 1 - Hydroquinone 2 - Phenol 3 - 2,ll-Dinitrophenol ll - p-Cresol 5 - m-Cresol 6 - o-Chlorophenol 7 - o-Cresol 8 - p-Chlorophenol 9 - m-Chlorophenol 10 - 3,4—Dimethylphenol 11 - 3,5-Dimethylphenol 12 - 2,3-Dimethylphenol 13 - 2,l+-Dimethylphenol 14 - 2,5-Dimethylphenol 15 - 2,6-Dimethylphenol 16 - l+,6--Dinitro-o—Cresol 17 - 2,3-Dichlorophenol 18 - 2,6-Dichlorophenol 19 - 2,5-Dichlorophenol 20 - 2,#-Dichlorophenol 21 - 3,#-Dichlorophenol 22 - 3,5-Dichlorophenol 23 - 2,3,5,6-Tetrachlorophenol 2t; - 2,3,6-Trichlorophenol 25 - 2,3,ll-Trichlorophenol 26 - 2,4,5-Trichlorophenol 27 - 2,4,6—Trichlorophenol 28 - 2,3,5—Trichlorophenol 29 - 2,3,4,6-Tetrachlorophenol 30 - 3,l+,5-Trichlorophenol 31 - 2,3,4,5-Tetrachlorophenol 32 - Pentachlorophenol Figure 27 77 TOTAL PHENOL MIX H.P.L.C. SBPERATION 23 Du Pont C-8 zzzc 2526 Du Pont C-18 78 Chlorinated Phenol Mix (dilute) H.P.L.C. Separation Whatman C-18 Du Pont C-18 Du Pont C-8 Figure 29 79 Chlorinated Phenol Mix H.P.L.C. Separation 1 2 3"v5 6,7 8-10 11 12 Hhatman C-18 16 13 15 17 1» 19 20 21 -> 18 1 2 3-5 5,7 a—1o 11 ”"17 12 13 11.,15 Du Pont C—18 1 2 3"!5 67 P10 1, 12 Du Pont C-8 13 16 17 in. 15 19 20 1a 21 + Figure 30 80 Methyl Phenol Mix H.P.L.C. Separation 3-“ 9 Du Pont C-8 N11 LV 10 Du Pont C-18 11 A ‘ Whatman C-18 Figure 31 81 Phenol Mix .C. separation r5» Du Pont C-8 Du Pont C-18 Whatman C-18 Figure 32 82 The three most common liquid chromatgraphic detectors are ultraviolet (U.V.), refraction index and electrochemical. Both U.V. and electrochemical detectors give good sensitivity, electrochemical being superior in most cases. The U.V. detector works on the principle that the phenol‘s benzene ring, a chromatophore, absorbs light energy (Bard 6c Lund, 1978; Figure 33). The electrochemical or amperometric detector detects on the basis of the phenols charateristically easily oxidizable hydroxyl group. Electrochemical Oxidation of Phenol OH R R OH -é- R R© R -e- _H+ OH R 00 Figure 33 When the phenol enters the electrochemical cell, it is oxidized by a carbon paste electrode and ionic species are then detected by a Ag/AgCl reference electrode (Kissinger, 1974; Figure 34). 83 Carbon Paste Electrode and Ag/AgCI Reference Electrode Reference LC Column '7 2 Auxillary a? 5 ‘ z a? g .6 Waste r__, W l—-—————4 1 cm 1 in. Figure 34 This brings up several important characteristics of electrochemical cell. 1. Amperometric detection is extremely sensitive to substituted phenols in the nanogram to femtogram range. 2. Amperometric detection is selective for easily oxidized or reduced compounds such as amines or phenols (Figure 35). a. This means background noise is reduced. b. Cell potential can be adjusted for specific components of interest (Figure 36). 84 3. The detector can be plagued by extraneous electrical interferences when working at high sensitivity ranges. This generally can be overcome by placing a grounded faraday cage over the cell. 4. Since electrochemical detection is just gaining popularity, solvents are not yet screened for impurities which are readily detected by oxidation or reduction. 5. The pH of the mobile phase can be critical. For phenols a pH approximately equal to 4.5 appears to be optimum. One can expect a change in the optimum oxidation potential of 50-60 millivolts per pH unit variation. The final detection system consisted of an Altex pump, Reodyne valve, Altex microprocessor-controller, Du Pont oven, Du Pont C-8 Zorbax ODS analytical column and a Whatman C-18 analytical column, all held at a constant temperature of 50 C (Figure 37). For early eluting components flow rate was held at 0.8 ml/min with a 2:3 ratio of .05 M NaAc buffer at pH 3.0 to acetonitrile (Figure 38). For late eluting peaks flow rate was 1.0 ml/min with a 6:4 ratio of NaAc buffer to acetonitrile as the mobile phase (Figure 39). 85 mm ousmfim 1 z . ...: 1%.: Av 1: .29 4 n Jo OEEEOmmaE E: emu ..>.D mzmemsm 2853mm Emaéooizoezo Spa: ”.528 So: oz... a: zmmEmm zomufiazou 86 SELECTIVITY OF ELECTROCHEMICAL DETECTOR AT VARIOUS POTENTIALS I [.20 VOLT WIN. Phone l 60 VOLT mm .IO mt WILL Figure 36 87 mesofioo w-u xenaom Eu mm x m.o 03F [A casaoooum rxxxxl doom a: sum: o>am> u.om um =m>o F acuuouoa HoUfiEwsuouuoon neon usaouwouosouso cacao; whommoum 5w“: 2mpm>m ouzauomox uoo>aom EARLY PHENOL ELUTRIATES HIGH PRESURE LIQUID CHROMATOGRAPHIC SYSTEM 88 Iouoqdoxotqotq-p‘z Iosaxo-o-ozltuIa-9‘v touaudtfiqzamta-v‘z Iouaqdoxorgg;2_______—::= IouaqdoonqQ-d I osazg— o 2- IouoqdoonqQ-o touaqdoxztuta-v‘z ouournboszu Figure 38 89 LATE PHENOL ELUTRIATES FROM HIGH PRESURE LIQUID CHROMATOGRAPHIC SYSTEM 2,4,6-Trichlorophenol Pentachlorophenol Figure 39 3. Quality Control Duplicates and internal standards of all compounds were randomly run on 1096 of the samples to determine percent recovery and maintain quality control (Table 9). Spikes were introduced into sludges consisting of less than 3096 solids prior to extraction via a methanol solution. Sludges with greater than 3096 total solids content were spiked with an identical methanol solution previous to continuous soxhlet extraction. Blanks of distilled deionized water were run periodically to check for cross contamination. A stock solution containing all phenols of interest at a concentration of 1,000 ppm was made up every 14 days. Dilutions of this stock solution were made in methanol and methylene chloride every two days for use as internal spikes or external standards, respectively. All standards were stored at 4°C in the absence of light when not in use. Table 9 Percent Recovery of Phenols 1-10096 Solids - - PERCENT RECOVERY - - Minimum Det. Standard Phenols Limit mg/kg Maximum Minimum Mean Deviation o-Chlorophenol . 03 130 . 0 0 . 0 55 . 0 41. 0 m-Chlorophenol . O3 160 . 0 11 . 0 83 . 0 35 . 0 p-Chlorophenol . 03 160 . 0 13 . 0 81. 0 33. 0 o-Cresol .03 110.0 0.0 52.0 34.0 2,4-Dichlorophenol . 03 160 . 0 3 . 9 87 . 0 43 . 0 2,4-Dimethylphenol . 03 160 . 0 3 . 3 61. 0 42 . 0 4,6-Dinitro-o-cresol . 06 160 . 0 0 . 0 56 . 0 53 . 0 2,4—Dinitrophenol . 18 100 . 0 3 . 4 44 . 0 38 . 0 Hydroquinone . 07 98 . 0 0 . 0 20 . 0 29 . 0 Pentachlorophenol . 03 150 . 0 52 . 0 72 . 0 40 . 0 Phenol .03 83.0 0.0 37.0 25.0 2,4,6—Trichlorophenol .06 150.0 0.0 73.0 49.0 90 4. Mass Spectra Substituted phenols generally yield a large molecular ion as their base peak with the characteristic loss of the hydroxyl group and its ring carbon (Budzikiewicz et al, 1967). The M-29 loss of CHO is common throughout many of the spectra shown in (Figures 40-42). Surprisingly, the loss of hydrogen from the hydroxyl group is not a common occurrence except in the case of hydroquinone. Methyl substituted phenols and some multi-chlorinated phenols demonstrate the predominate M-l peak as opposed to the parent ion. Spectra are predictable as a rule, yielding few surprises. Chlorinated phenols consistently produce daughter fragments which have lost CH3OClx or C2H5Clx groups. This trend is especially obvious in the mass spectra of pentachlorophenol, yet nearly absent for 2,4,6-trichlorophenol. 2,4,6- trichlorophenol is also an exception to the norm since it has M-Clz as its base peak and relatively little parent ion in the spectra. 91 92 mugs: mom: UHEOu< 00m OqN om— Gad oo o b p P b b P b D $11 . la a . . .# _ q _ I No m :m as .m :0 co 8 0.- f .. NmN a an H r~é. 3 .39: M a .1 Non m. 3 a u S :0 "u .-z . N2 ,1. Hommuolo oo_ .2 . No2 nude: moo: vascu< com com cod . om_ . I x L x x . . . N6 N2 m . P. 3 v... 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HQ "02 BNu “Unit 5...: a: .8. 95...... a}. .u .mendci HO :0 ..z Hocmzauoasoaolq.m No. «a HH mqozmzm Dmbomqmm mo cots—m can can coo Dom oov con DON oofi fi____1_______._ l I I I I I I I I l 3523 8:2 + 2020 flocozaoczz mute—:02 coo—uocbom £9.95 mm Un— moEuZmom “22228 23mm oumzomozm 1.3:. 8:232 m=O 9.5.8.80 *3 628.5 9.228: $8 5.: 632m .38 8885 :5: 85850 No 8E=_o> Spam 97 C1 C1 C1 C1 oxidation O Car—e Q o 3,3'-Dichlorobenzidine NH2 NH2 oxidation ’ p-nitroanaline ..e- N02 N02 Figure 44 3. Neutrals The neutral fraction was roto-evaporated down to 1 ml prior to cleanup via an activated florisil column. Ten grams of florisil activated overnight at 550°C was dry packed in a 10 ml diameter column plugged with oven burned silanized glass wool. A vibrator was used to achieve a consistent homogeneous packing. After topping the florisil with approximately 4 cm of anhydrous sodium sulfate, the column was pre-rinsed with 100 mls of glass distilled hexane and kept wetted while the sample was applied on the top of the column. At this point several solvent solutions ranging from nonpolar to polar were used to elute the column; certain fractions retained and others discarded as follows: ACTIVATED FLORISIL SEPARATION OF NON VOLATILES Elutriate Fraction Contents 50 m1 Hexane 1 Nonchlorinated aromatics 50 ml Hexane 1 Chlorinated hydrocarbons 50 ml 696 E.E. in Hexane 1 Chlorinated presticides Elutriate 50 ml 696 E.E. in Hexane 50 ml 1596 E.E. in Hexane 50 ml 15% E.E. in Hexane 50 ml 50% E.E. in Hexane 50 ml 50% E.E. in Hexane 50 ml 10% MeCl in Hexane 50 ml 10% MeCl in Hexane 50 ml 1096 Acetone in MeCl 50 ml 1096 Acetone in MeCl 50 ml 50% Acetonitrile in Methanol 50 ml of Acetonitrile Fraction Number 2 containing the triaryl phosphate esters were analyzed by gas liquid chromatography equipped with a nitrogen phosphorous specific flame ionization detector. A 3 meter glass column packed with 80/100 mesh gas chrom Q coated with 396 SE-30 was used to complete the separation. Hydrogen and air flow to the detector was 30 ml and 280 ml per minute respectively. A column flow of He was maintained at 25 ml/min while at a constant 200 C. Since each triaryl phosphate ester contains several isomers, samples were quantitated by taking total area for a group of peaks each with a specific 98 E12932 waste 2 2 2 waste waste waste waste Figure 45 retention time. Elution order was as follows: Contents Triaryl phosphate esters Triaryl phosphate esters Triaryl phosphate esters Phthalates Phthalates Phthalates 99 Triaryl Phosphate Esters Structure and Retention CH 8 3 CH~ P 3 QO/ 6 \O Q Superior separation of isomers was achieved with a capillary column. While a packed column separated the three triaryl phosphate isomers into thirteen peaks, capillary split them into 46 peaks (Figure 47). separation of all isomers present in each standard was possible; however, the excess peaks for monocresyldiphenylphosphate indicate impurity in at least this standard. Monocresyldiphenyl- phosphate Tricresyl phosphate Trixylene phosphate Figure 46 Retention Time 2.4 minutes 3.0 minutes 3.9 minutes 4.2 minutes Retention Time 5.3 mintues 5.6 minutes 6.1 minutes 7.0 minutes Retention Time 8.9 minutes 9.8 minutes 11.0 minutes 12.5 minutes Complete 100 Triaryl Phosphate Ester Isomer Separation # of Possible # of peak separations Isomers on capillary Monocresyldiphenylphosphate 3 9 Tricresylphosphate 18 l 1 Trixylenephosphate 36 27 Figure 47 The first fraction contains nonchlorinated aromatics such as biphenyl and napthalene as well as a mixture of chlorinated hydrocarbons. Since certain chlorinated aromatic hydrocarbons (PCBs) and chlorinated pesticides (DDD, DDE, DDT) co-chromatogram, it was necessary to separate them prior to analysis by gas liquid chromatography. A method has been described for the separation of polychlorinated biphenyls from DDT metabolites (Armour, 1970). This method uses silica gel deactivated with 3% H20 by weight and separates the DDT metabolites from PCBs via two elutions (Figure 48). After combining fractions Number 3 and Letters A and B and reducing these volumes to one milliliter, they are analyzed on 4 separate gas liquid chromatographic systems each specific for a particular component (Figure 49). In this way maximum sensitivity can be achieved for each compound of interest. Separation of Non-volatiles by Silica Gel Elutriate Fraction Contents 250 mls n-hexane A PCB's and similar compounds 200 mls Acetone:Hexane:Methylene chloride waste DDT metabolites 1:19:80 250 mls B Nitrobenzenes and Acetonitrile:Methylene chloride similar compounds 20:80 Figure 48 101 Non-volatile Component Gas Chromatograph Conditions Flame Ionization Detector Capillary SE-30, 15 meters Column Flow = 1.5 ml/min H2 Makeup = 28 ml/min H2 Air = 300 ml/min Oven temperature: Initial = 200°C for 3 min. Program rate = 10 per min. Final = 240°C for 15 min. Compounds of interest in order of retention time: Napthalene Biphenyl Dimethylphthalate Diethylphthalate Dibutylphthalate Butylbenzylphthalate Dioctylphthalate Di-n-octylphthalate N-P Flame Ionization Detector Capillary SE—30, 15 meters Hydrogen purge Hydrogen = 28 ml/min. Air = 280 ml/min. Column Flow = He at 28 ml/min. Oven = 180°C Injector = 220°C Compounds of interest in order of retention time: Nitrobenzene 1-Chloro-4-nitrobenzene l-Chloro-Z-nitrobenzene 2,6-dinitrotoluene 2,4-dinitrotoluene 1-Chloro-2,6-dinitrobenzene 1-Chloro-3,4-dinitrobenzene 6c 1-Chloro-2,4-dinitrobenzene Pentachloronitrobenzene 3-mercaptobenzothiazole Electron Capture Detector Capillary SE-30, 15 meters Column Flow = 1.5 ml/min H2 Makeup = 28 ml/min N2 Injector = 220°C Detector = 280°C Oven temperature = 200°C Compounds of interest in order of retention time: 1-Chloro-4-nitrobenzene l-Chloro-Z-nitrobenzene 1,2,4 ,5-tetrachlorobenzene 1,2,3 , 5-te trachlorobenzene 1,2 ,3 ,4-tetrachlorobenzene 1-Chloro-2,6-dinitrobenzene 1-Chloro-3,4—dinitrobenzene l-Chloro-Z ,4-dinitrobenzene He xachlorobenzene Pentachloronitrobenzene Polychlorinated biphyenyls Flame photometric Detector - Sulfur Mode 396 OV-101 on Qf-l, 60-80 mesh Hydrogen : 28 mllmin Air = 280 ml/min. Column Flow = He at 30 ml/min. Oven = 250°C Injector = 260°C Compounds of interest in order of retention time: 3-Mercaptobenzothiazole Figure 49 IV. Discussion A. Parameters Tested All sample sites were cataloged according to various parameters including: percent industrial input, population served, flow in millions of gallons per day, treatment methods used, sludge type and percent solids. This information was compiled and manipulated with the aid of a Digital PDP 11 computer. Sample sites were then grouped into categories, these sub-populations being greater than 1096 of the total population for each of the variables mentioned earlier. Once this was completed, any subgroup of sites for a specific parameter within a specific range could be listed showing the detection limit, number of sites above and below detection limit, average amount found, standard deviation, median value, and range of values for all compounds (Tables 10-16). By observing the number of sites greater than the detection limit, note that most compounds were very infrequently found above the detection limit. This means that many of the values for compounds of interest will be statistically insignificant when compared to one another. This also signifies that if a trend is seen for a certain compound or class of compounds, the trend may only apply to those sites which had measurable levels. Clearly the way to remedy this problem would be either to sample at several points along the treatment process stream or lower the limit of detection, depending on the parameter of interest. Several difficulties arise when comparing values which come from such a 'wide data base, with so many variables. Since samples were taken from various treatment stages—in communities with a broad spectrum of populations and industry—extremely large standard deviation can be expected. Over a 24-hour period, levels of various components may change drastically due to influx of 102 103 influent. General trends demonstrate that from 6:00-8:00 a.m. and 4:00-7:00 p.m. are high usage times while 11:00 p.m.-5:00 a.m. are low volume periods. The E.P.A.'s recently published Interim Report on 20 publically owned waste systems also showed as much as a fourfold variation in component levels depending solely on the day of the week on which the sample was taken, weekends tending to be lower (EPA, 1980). While these variations may have no effect whatsoever on the final sludge material, raw and intermediate sewage samples may vary as much as ten to twentyfold, depending upon the time and day on which the sample was taken. Clearly a 24-hour composite sample would be preferable to a grab sample at points in the process stream which had rapid turnover rates or poor mixing. Large numbers of samples will help to overcome some of the bias presented by variables. When trends are observed, these correlations may be used to decrease the bias of that particular variable upon other variables. Since we are dealing with so many values, a total of 4,416 individual measurements consisting of 138 treatment facilities tested for 32 organic parameters, it will not be possible to discuss every correlation possible. Therefore, only the most significant components will be referred to in this paper. Significance will be defined in relation to the station which has more than 10% of its values above the detection limit and those values ranging a minimum of two orders of magnitude. 104 f I...’ l .3. I" - °. ‘--~°V° ".!"'"L. 4.1.1.4--..) ~ ' 7'. ~- '- . 1" - n-v-fl r- N -.- “r r "-r‘r- T:f". (“It 4 ~ A. _. -_"-imr-v‘1"')’ :Jr Digit: 3 Uri I“: gut) .44.:{33- LIL-ICE L'LL'J'“'¢ 1.1.: fi'lto - i .... .. __ ,1 ' .. . _ I ,..,_.. T -., t N, . _ -.. LLHUJLmIrGAIiLW4 OHL¢,_3 1:. rs .os 14:. uthan. SITES: 1 2 7 4 5 _ 7 8 7 10 11 1: 13 14 15 16 17 18 IT 23 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 31 4o 41 4- 43 44 4s 4s 47 'E 49 so 5 52 53 54 SS 54 57 58 59 60 61 62 63 64 65 36 6, 68 69 7 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 $8 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 11; 117 118 119 12 121 122 123 124 25 126 1:7 128 129 130 13 132 33 134 135 136 37 38 139 140 141 142 143 144 145 146 147 148 149 15 151 152 153 154 55 156 157 158 5? 160 161 1-2 163 164 165 1‘6 167 168 16 170 171 172 173 174 175 176 77 178 179 180 181 82 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 L 5 206 207 208 209 210 211 212 213 214 215 216 21/ 218 219 220 221 222 223 224 225 226 227 228 229 230 231 23: 233 234 235 236 23 238 DETECT SITES 7 AVE AHT STAND MEDIAN RANGE COMPOUND NAME LIMIT DET LIH FOUND DEV VALUE HIGH LOU ACRYLONITRILE 4. 25/155 16. 19. 7. 2. 4. CHLOROBENZENE 60. 3/158 337. 441. 106. 846. 60. O-DICHLOROBENZEN 6. 15/215 89. 209. 16. 809. 6. H-DICHLOROBENZEN 5. 44/216 119. 327. 22. 1651. 6. P-DICHLOROBENZEN 10. 18/216 77. 151. 23. 633. 10. 1,2-DICL PROPANE 0.08 91/157 1.91 7.36 0.66 65.97 0.09 193-DICL PROPANE 0.5 40/158 18.0 50.7 3.2 308.9 0.6 193-DICL PROPENE 0.1 119/157 24.2 115.7 3.9 1231.6 0.1 ETHYLBENZENE 0.08 14/220 25.44 21.97 19.76 65.53 1.22 HEXCL-1’3BUTDIEN 3. 1/217 4. HEXACHLOROETHANE 0.05 40/217 0.67 2.58 0.16 16.48 0.05 PENTACHLORETHANE 0.4 5/199 2.7 3.7 1.3 9.2 0.4 STYRENE 90. 6/219 1338. 2249. 405. 5848. 99. TETRACLETHYLENEX 0.01 108/128 0.68 1.32 .29 12.18 0.01 1,2,3-TRICLRBENZ 1. 7/216 25. 56. 1. 152. 1. 1,2,4-TRICLRBENZ 3. 17/217 14. 12. 13. 51. 3. 1:395-TRICLRBENZ 50. 0/217 1,2,3-TRICLPROPA 4. 2/141 14. 7. 14. 19. 9. 1,2,3-TRICLPROPE 3. 21/137 23. 47. 6. 167. 3. O-CHLOROPHENOL 0.03 20/231 13.13 23.08 3.60 90.00 0.08 H-CHLOROPHENOL 0.03 16/231 9.22 23.79 0.89 93.33 0.12 P~CHLOROPHENOL 0.03 19/231 17.80 29.62 3.55 90.00 0.10 O-CRESOL 0.03 16/231 24.72 52.10 2.05 182.70 0.18 294-DICLPHENL 0.03 17/230 25.19 54.42 4.76 203.33 0.21 294-DIHETPHENL 0.03 41/231 6.52 14.88 2.19 86.67 0.09 496~DNOC 0.06 20/228 12.68 41.11 2.34 186.67 0.20 2:4-DINITROPHENOL 0.18 66/228 24.34 80.81 4.98 500.00 .27 HYDROQUINONE 0.07 61/229 8.2 28.59 2.55 223.33 0.14 PENTACHLOPHENOL 0.03 155/223 81.14 684.81 5.00 8494.62 0.17 PHENOL 0.03 178/229 9.22 28.51 2.02 287.50 0.05 29496~TRCLPHENOL 0.06 66/223 42.2 177.93 4.81 1333.33 0.19 TOTAL VOLATILES 191/191 122.47 510.37 13.66 5891.59 0.05 TOTAL PHENOLS 226/226 93.85 591.47 12.52 8513.15 0.09 TOTAL 236/236 188.99 735.97 37.98 8539.77 0.2 X CONC VALUES X 100 J2LAFILES RAU :UmrARr UF DATA FOR THE CFJNEENTRATICN "ALUE IN SITES‘ 2 10 19 20 133 20; 219 236 16 160 166 169 170 174 DETECT CDfiPOUHD HARE LIHIT ACRYLONITRILE 4. EHLC RUDEr'ZERE 60. F CHLDRU|OI-UENE 50. O- DI CHLDRDI:ENZEN 6. n D1 CI'LJR EN EN 5. F- D1: ILDROBENZEN 10. 1.2— DICL FRUFANE 0.08 1.3 DICL FROFANE 0.5 1.3 DICL F'ROFENE 0.1 EFHYLI:ENZENE 0.08 HE:CL~1.3DUTDIEN 3. HEXAEHLDRDETHANE 0.05 PENTACHLORETHANE 0.4 STYRENE 90. TETRACLETHYLENEX 0.01 1.2.3wTRICLRBENZ 1. 1.2.4- TRICLRDENZ 3. 1. 3.5 TRI VLFDENZ 50. 1.2.3 -TRICLFROF‘A 4. UnCHLORDPHENOL 0.03 H—CHLOROPHENOL 0.03 P-CHLORUPHENOL 0.03 O-CRESOL 0.03 2.4—DICLPHENL 0. 3 294~DIMETPHENL 0.03 496*DNOC 0006 2.4«DINITROPHENOL 0.18 HYDRDQUINONE 0.07 PENTACHLDPHENDL 0.03 F'HENOL 0.03 294.6 TRCLFHENOL 0.06 105 Table 11 I "T :5 U C) .L LISTED IELLU . HF ’RG DRY HEIGHT. 150 153 144 142 H? fin! RANGE TiIBFi 29. 5.01 26.2 107.3 20.04 160. 90.00 93.33 80.00 96.67 203.33 86.67 186.67 500. 00 223.33 110. 00 287.50 16.3 62 173 146 LOU u—uu---_—..—.-~-~—u~ C‘ Jo uu—-——-—--...-—u——--——-o---- —.--——~—.«v—-_n-.¢———-———-———-.-—-—..-——————~—.—-———-———_——-——u-uc—“——_— TOTAL VOLATILES TOTAL PHENOLS TOTAL 33 43 82 87 94 98 13 145 14 35 36 38 39 40 41 7 51 134 205 216 217 218 275 226 228 SITES . AVE AHT STAND fiEDIAN DET LIN FOUND DEV VALUE 7/30 12. 9. 7. /30 0/30 0/45 5/45 10. 4. 7. 1f45 21. 10/30 1.15 1.43 0.67 7/30 7.0 9.0 2. 21/30 18.7 30.5 .2 3/45 14.79 8.62 19.48 0/45 9/45 0.21 0.15 0.16 1/43 0.4 1/45 5848. 21/23 0.93 1.14 1.00 1/45 20 3/45 17. 10. 17. 0/45 1/25 19. 4/28 47. 76. 11. 7/45 21.71 34.76 4.52 3/45 32063 52061 4044 3/45 35.10 39. 84 21.33 3/45 34.82 53.58 5.16 3/45 109.11 99.67 119.2 12/45 11.52 24.39 2.60 5/45 40.12 81.96 3.46 10/45 69.96 154.62 8.56 14/45 22.84 58.30 4.19 28/43 19.01 29.10 5.57 35/44 21.74 53.44 5.90 8/43 8.10 5.89 9.00 77/37 182.80 965.43 10.63 44/44 79.61 253.23 20.74 46/46 23.19 893.62 34. —”“——~-gw-—~——.~—n——o—-—-~n-——-unnun--~——tun—...——--—-———--——--“--——-————-—-—--——-n—_———--fi X CONC VALUES X OPTION? 2 100 ' ‘ 1 c _ ‘1 -- n ..V ~ ~- lfir“'1 LI. ’ A .\ '4 u.- .. ‘__ ‘21. ‘ ’ 0 ~ fi L I v- 9‘- 7 I. “ v '4 1‘ ’1'." ..4-r L ,L...‘.. J! FERTF 1; CHLORIDE PHDSPHORUUS OF DATA FOR CONCENTRATION VALUES SUHHART IN THE 68 106 HEDIAN VALUE 6. 27. 23. 18. 0.76 F) c‘ 3.0..) H .’ 2.00 19.76 0.14 ..-—......—---——~-—————--—.——“...—.....——~—————-—-—.—-.—.--~.«—--—-—............ «---~---—_—-----ou-_.-—-.r-..—"~”---——n.-”~-.--.-“--_....-l_.--—----o_.._——----—‘—~—-‘—-.u-u anon—cu.-- Table 12 REHOVAL SITES LISTED BELOU. fiO/RB DRY UEIGHT. 11 12 15 17 19 26 58 6O 64 65 7O 72 73 35 136 137 144 146 1 201 203 208 219 225 SITES AVE AHT STAND DET L1H FOUND DEV 6/39 9. 9. 1/38 106. 1/38 108. 6/62 29. 14. 18/63 124. 343. 3/63 20. 7. 23/38 3.99 3.59 13/39 8.6 17.8 29/39 15.6 30.8 6/65 29.33 22.03 1/64 4. 14/64 .32 0.37 1/58 0.4 2/65 911. 365. 27/29 0.47 0.45 1/64 19. 8/64 16. 7. 0/64 0/35 7/36 5. 1. 6/67 11.87 18.83 4/67 8.51 16.17 4/67 24.74 36.05 4/67 22.57 43.30 2/67 62.88 79.69 10/67 9.00 12.74 4/66 6.43 4.68 20/67 12.78 24.60 22/67 2.05 2.01 44/63 30.50 91.31 48/67 .29 7.08 17/63 24.27 59.03 58/58 96.23 268.79 67/67 41.75 86.91 68/68 173.22 260.80 39 40 41 5 86 87 90 158 163 28 229 230 I?ArU3E HIGH LOU 27. 5. 53. 16. 1463. 6. 27. 14. 65.97 0.10 66.2 0.6 136.2 0.1 60013 493 .29 0.05 1169. 653. 1.00 0.02 78. 4. 7O 40 48.93 0.08 32.76 0.12 76.75 0.10 87.50 0.24 119.23 6.54 42.74 .28 11.25 .27 106.67 0.34 9.00 0.14 597.83 0.34 43.33 0.05 246.75 0.5 1567.78 0.05 604.24 0.19 0033 u—---‘—~—o—..--u-oo-u—u————-.-—--u----—..u—u———n~—_nu——~—_-.....n-oo—o—u-n—u-ou-—-—n———---nnum ———....-—-...no-—--—n----ou SITES: 1 2 4 5 6 42 43 45 46 0 55 93 96 103 119 124 167 168 76 187 198 237 DETECT COMPOUND NAME LIHIT ACRYLONITRILE 4. CHLOROBENZENE 60. PWCHLOROTOLUENE 50. O-DICHLOROBENZEN 6. fiuDICHLOROBENZEN 5. P’DICHLOROBENZEN 10. 192“DICL PROPANE 0.08 1.3“DICL PROPANE 0.5 193wDICL PROPENE 0.1 ETHYLBENZENE 0.08 HEXCL“193BUTUIEN 3. HEXACHLOROETHAHE 0.05 PENTACHLORETHANE 0.4 STYRENE 90. TETRACLETHYLENEX 0.01 192,3“TRICLRBENZ 1. 19294-TRICLRBENZ 3. 193.5“TRICLRBENZ 50. 192:3-TRICLPROPA 4. 1,2,3-TRICLPROPE 3. O-CHLOROPHENOL 0.03 H-CHLOROPHENOL 0.03 P~CHLOROPHENOL 0.03 O-CRESOL 0.03 274~DICLPHENL 0.03 294~DIHETPHENL 0.03 496*DNOC 0.06 294~DINITROPHENOL 0.18 HYDROGUINONE 0.07 PENTACHLOPHENOL 0.03 PHENOL 0.03 29496-TRCLPHENOL 0.06 TOTAL VOLATILES TOTAL PHENOLS TOTAL X CONC VALUES X 100 OPTION? 107 Table 13 POPULATION 10000-"49999 SUMMARY OF DATA FOR THE 40 SITES LISTED BELOU. 18“AIJ3-~81 CONCENTRATION VALUES IN HTS/RB DRY HEIGHT. SITES: 3 6 7 9 11 12 14 16 19 22 23 38 40 42 47 5 5 64 76 103 107 118 119 125 126 134 141 146 156 173 176 188 193 206 224 225 226 227 228 229 IIIETECT SITES I2? AUE AMT STAND MEDIAN RANGE COMPOUND NAME LIMIT DET LIM FOUND DEU UALUE HIGH LOU ACRYLONITRILE 4. 6/20 6. 1 . 6. 8. 5. CHLOROBENZENE 60. 1/21 106. F'--CHLOROTOLUENE 50. 1/21 108. D-DICHLOR‘ODENZEN 6. 3/36 27. 24 . 21 . 53. 7. H-DICHLOROBENZEN 5. 8/37 201 . 510. 19. 1463. 7. F‘wIIICHLOROBENZEN 10. 2/37 19. 2. 19. 27. 10. 1,2-DICL F'ROF'ANE 0.08 12/21 2.79 7.34 0.80 26.07 0.10 1’3-DICL PROPANE 005 81,21 604 900 206 2602 100 1:3-DICL F'ROF'ENE 0.1 18/21 24.9 48.0 4.1 189.9 0.3 ETHYLBENZENE 0.08 4/37 26. 12 23.74 19.76 60. 13 4.84 HEXCL~1 v3BUTDIEN 3. 1/37 4. HEXACHLOROETHANE 0.05 9/37 2.03 5.42 .28 16.48 0.07 F'ENTACHLORETHANE 0 . 4 2/35 1 . 1 1 . 0 1 . 9 O . 4 STYRENE 90. 0/37 TETRACLETHYLENEX 0.01 18/19 0.36 0.40 0. 12 1 .00 0.01 1,2,3-TRICLRBENZ 1. 1/37 1. 1,2,4-TRICLRBENZ 3. 4/3 9. 8. 5. 20. 4. 1 9395-TRICLRBENZ 50. 0/37 1 92.3-TRICLF'R0F'A 4. 0/19 1 p2p3-TRICLF'ROF'E 3. 1/19 6. O-CHLOROF'HENOL 0.03 3/39 18.07 26.83 5.03 48.93 0.26 H-CHLOROPHENOL 0.03 2/39 0.31 0.21 .31 0.46 0. 16 P—CHLOROF'HENOL 0.03 2/39 21.92 0.82 21.92 22.50 21.33 O‘CRESOL 0.03 4/39 0.42 0.34 0.29 0.93 0.18 294-DICLF'HENL 0.03 3/39 0.82 1.03 0.23 2.01 0.21 294-[IIMETF'HENL 0.03 4/39 1.35 1.29 1.30 2.71 0.10 496-DNOC 0.06 2/3 4.36 5.78 4.36 8.44 0.2 2’4-[IINITROF'HENOL 0.18 10/38 22.30 35.59 2.43 106.67 0.34 HYDROGUINONE 0.07 11/38 6.55 7.93 2.65 27.86 0.49 F'ENTACHLOF'HENOL 0.03 20/36 50.38 149.31 4.39 675.00 0.34 F'HENOL 0.03 29/38 11. 19 25.56 2.40 135.71 0.05 294 96~TRCLF'HENOL 0.06 12/36 54.87 160.54 3.65 562.50 0. 19 TOTAL VOLATILES 31/31 86.58 281.58 12.37 1567.78 0.09 TOTAL F'HENOLS 38/38 63.23 206.43 10.99 1260.00 0.33 TOTAL 39/39 130.43 312.63 38.82 1568.51 0.82 '--—._ _ ~-~--“---———“‘——-—~~-—~_-_—————--_-_-—-—.---~_——~—-——.—--—-——-o-.-—‘—““——~—-~*_---u--. * CONC VALUES x 100 OF'T I ON? 77.11 TL ' ,'iT-ua‘ JuflTF "1 - .. ...T e; dUHUL *'Tfr Tw- vo -- fl. 1... flu u FLOU MGD 0.50“O.99 SUMMARY OF DATA FOR THE CONCENTRATION VALUES IN SITES: 5 10 18 26 90 97 22 23 124 194 195 201 212 213 DETECT COMPOUND NAME LIMIT ACRYLONITRILE 4. CHLORODENZENE 60. P~CHLOROTOLUENE 50. U-DICHLOROBENZEN 6. H-DICHLOROBENZEN 5. P-DICHLOROBENZEN 10. 192*DICL PROPANE 0.08 173—DICL PROPANE 0.5 1,3-DICL PROPENE 0.1 ETHYLBENZENE 0.08 HEXCL~193BUTDIEN 3. HEXACHLOROETHANE 0.05 PENTACHLORETHANE 0.4 STYRENE 90. TETRACLETHYLENEX 0.01 1,2,3-TRICLRBENZ 1. 1,2,4-TRICLRBENZ 3. 19395~TRICLRBENZ 50. 1,2,3-TRICLPROPA 4. 1,2,3-TRICLPROPE 3. O-CHLOROPHENOL 0.03 H“CHLOROPHENOL 0.03 F""CHLOF\'OF‘HENOL 0.03 O—CRESOL 0.03 294-DICLPHENL 0.03 29 4~DIMETPHENL 0.03 4v6-DNOC 0.06 2’4-DINITROPHENOL 0.18 HYDROOUINONE 0. 07 PEPITACHLOPHENOL 0.03 F'HENOL 0. 03 2' 4 '6-TRCLF'HENOL 0.06 108 13*AUS"81 MEDIAN VALUE 16. 64. .73 4.2 3.9 43.26 ‘— .—--——-——----——-—n—-——D-“—”————“——-——----.-—_—-—--------.—~-oou—————“---*“~-~_-u—“n TOTAL VOLATILES TOTAL F'HENOLS TOTAL Table 14 SITES LISTED DELOU. MG/hG DRY HEIGHT. 48 53 59 67 6 71 9 52 155 164 172 173 SITES 3 AVE AMT STAND BET LIM FOUND DEV 2/27 19. 10. 1/27 846. 1/27 158. 2/35 16. 1. 7/35 102. 123. 2/35 64. 55. 19/26 0.85 0.66 7/27 14.5 24.0 19/26 16.3 40.4 3/35 37.40 31.47 0/35 6/35 0.67 0.47 1/32 .3 1/35 653. 15/22 0.54 0.53 1/35 19. 1/35 14. 0/35 0/26 5/24 38. 2. 2/35 0.75 0.90 2/35 1.53 1.18 2/35 9.65 12.10 4/35 22.59 43.27 2/35 22.18 31.04 8/35 3.07 3.84 2/34 6.42 6.83 11/34 3.69 4.34 8/34 4.61 9.53 25/36 10.37 22.26 27/35 15.21 38.93 9/36 42.30 81.04 31/31 107.73 206.82 34/34 38.95 65.73 36/36 129.55 200.68 78 80 8 87 33 189 ‘92 RANGE HIGH LOU 16. 5. 299. 9. 103. 26. 2.23 0.10 66.2 0.7 178.2 0.6 5.53 3.41 1.44 0.18 1.57 0.02 167. 4. 1.39 0.12 2.37 0.70 18.20 1.10 87.50 0.33 44.13 0.23 11.25 0.54 11.25 1.59 15.07 0.27 27.86 0.26 105.48 0.25 135.71 0. 2 246.75 0.48 848.16 0.10 260.26 0.48 857.34 0.21 -—~ -h----———-—-———-..—_—-—---~—--——.-_--——-----———--...-—a——.-_—-o—--——"—-”—_--~-—‘-~”O-- * CUBIC VALUES X 100 OF'TI ON? 109 Table 15 3; SOLID VOL81.00‘1.29 53UHMARY OF DATA FOR THE 26 SITES ISTED DELUU. 13WAU5181 CONCENTRATION. VALUES IN MG/NG DRY HEIGHT. SITES: 12 34 6 65 68 71 88 90 120 25 132 133 138 139 146 149 159 173 175 97 201 204 205 213 221 228 DETECT SITES I=~ AVE AMT STAND MEDIAN RANGE COMPOUND NAME LIMIT DET LIM FOUND DEV VALUE HIGH LOU ACRYLONITRILE 4. 5/23 13. 9. 8. 27. 6. CT'ILORODENZENE 60. 0/23 F‘--CHLOROTOLUENE 50. [’23 O—DICI-lLOROE-iENZEN 6. 2/26 32. 31 . 32. 53. 10. 1‘1"DI..LHLOROBENZEN 5. 6/26 267. 586. 26. 1463. 9. F‘-~DICHLOROBENZEN 10. 3/26 16. 10. 11 . 27. 10. 1 92~~DICL PROPANE 0. 8 14/22 1 .32 1.88 0.73 6.81 0. 10 1 93*DICL PROPANE 0.5 6/23 13.2 26.0 3.0 66.2 0.9 1 r3-DICL F'ROPENE 0.1 16/22 17.9 23.1 11.8 84.5 0.7 ETHYLBENZENE 0.08 2/26 48.25 24.44 48.25 65.53 30.96 HEXCL"193DUTDIEN 3. 1/26 4. HEXACHLOROETHANE 0.05 9/26 0.20 0.26 0.09 0.83 0.05 F'ENTACHLORETHANE 0.4 1/25 0.4 STYRENE 90. 1/26 99. TETRACLETHYLENEX 0.01 17/18 0.66 0.45 0.78 1 .30 0.03 1 9293-TF:ICLRDENZ 1 . 0/26 1 9294~TRICLRBENZ 3. 2/26 13. 10. 13. 20. 7. 1 9395-TRICLRBENZ 50. 0/26 1 92’3-TRICLPROPA 4. 0/19 1 ,293~TRICLPROPE 3. 8/22 7. 5. 6. 20. 3. O-CHLOROPHENOL 0.03 0/25 H~CHLOROPHENOL 0.03 1/25 0.46 F'-CHLOROPHENOL 0.03 1/25 90.00 O‘CRESOL 0.03 1/25 87.50 2'4-DICLPHENL 0.03 1/25 1.06 2,4-DIMETPHENL 0.03 5/25 5.28 4.00 5.25 11.25 0.76 496*DNOC 0.06 3/24 4.13 6.17 0.87 11.25 .27 2,4-DINITROPHENOL 0.18 5/24 14.99 17.28 9.93 45.24 1.83 HYDROQUINONE 0.07 4/25 11 .46 12.04 8.85 27.86 0.26 F'ENTACHLOPHENOL 0.03 16/24 26.20 32. 17 14.57 126.67 0.25 PHENOL 0.03 16/25 15.83 33.64 3.31 135.71 ‘ 0. 15 2! 4 v 6-TRCLPHENOL 0. 06 2/24 125.55 171 .41 125.55 246. 75 4.35 TOTAL VOLATILES 26/26 94.09 302.78 19.75 1567.78 .21 TOTAL PHENOLS 25/25 50.49 62.79 23.64 260.26 0.48 TOTAL 26/26 142.63 298.54 91.03 1568.51 0.21 -- .. .. “—--_---—~-n-_-—n*——_———-———-——-_-u-~-.-’—--——_——-----—--——--~——~-”——_—-u- * CONE VALUES x 100 OF'T I ON? 3; INDUSTRY .001“0.49 5 SITES hG/KO DRY c-- 5‘s.) 151 SITES tr:- IIET LIH LISTED “EIGHT. 110 Table 16 67 73 17 176 AVE AMT FOUND DELOU. DEV 18~AUS*81 103 188 MEDIAN VALUE 104 194 113 214 RANGE —- ————~o_—-—--—-_—-~n-—..--——----—-—n——————.-——-——‘.¢--_——.——.~..-¢—....-~o~ouu—u——-—-~---—-~----‘—uw .0.—......— nnnnn SZUMMARY OF DATA FOR THE IEONCENTRATION VALUES IN SITES: 3 26 34 48 129 132 133 142 150 216 217 218 236 DETECT CONPOUND NAfiE LIMIT ABNYLONITRILE 4. CkmOROBENZENE 60. F“~CHLOROTOLUENE 50. D-{dCHLORODENZEN 6. r1-DICHLOROBENZEN 5. F'—DICHLOROBENZEN 10. 1.92-DICL PROPANE 0.08 1 v3-DICL PROPANE 0.3 1..3~DICL PROPENE 0.1 ETHYLBENZENE 0.08 riEXCL~193BUTDIEN 3. riEXACHLOROETHANE 0.05 FSENTACHLORETHANE 0.4 STYRENE 90. ‘TETRACLETHYLENEX 0.01 1.2.3—TRICLRBENZ 1. 1 9294-TRICLRBENZ 3. 1,3.5—TRICLRBENZ 50. 1.2:3-TRICLPROPA 4. 1.2.3—TRICLPROPE 3. O-CHLOROF'HENOL 0.03 Pfi-CHLOROPHENOL 0.03 F“-CHLOROPHENOL 0.03 O-CRESOL 0.03 2 , 4-DICLF‘HENL 0.03 2' 4~IIIMETPHENL 0.03 4. 6—DNOC 0.06 2,4-DINITROF'HENOL 0.18 HYIWQOGUINONE 0.07 PENTACHLOF‘HENOL 0.03 F'HENOL 0.03 2, 4 , 6—TRCLF'HENOL 0. 06 1060 216. 412. 339. 210. 0.84 5.7 20.9 37.41 1.82 1.70 8.60 2.81 18.43 1.63 15.33 6.80 135.09 54.96 147.97 216. 412. 30. 0.66 3.6 4.7 40.35 0.09 .23 19. 1651. H 0~\JH H L": CD (4 L4 U! k] (4 (.4 Ch (.4 H b b O O U! I") H 4.67 4.00 22.50 6.67 44.13 4.33 43.33 16.19 675.00 287.50 2821.83 1260.00 2831.43 ..--.— h- m‘”-—”--—-—*_——----.-——”-----~-—-_--—--_——---—*—-----—-—-~--“nH”-fi- * CONC VALUES x 100 TOTAL VOLATILES TOT A L F'HENOLS TOT AL OPT I 0N? 521.75 225.81 517.43 B. Statistics In order to determine the significance of correlations between populations it is necessary to use acceptable statistical methodology. The common handle in comparing populations and their relationships between each other is their means. By grouping populations according to a variety of variables, the significance of these variables can be determined by observing their effect on their means. A systematic method was used to determine the most powerful statistical treat- ment for data (Gill, 1978). Therefore, the hypotheses in question when comparing two populations are: H3Pl = P2 VS- H;u1 xpz or H; up #2 or H; u1< P2 The simplest most powerful test to determine the correct assumption is possible only when the variances are "known" or large samples (r1, r2 >100) are used. This test is termed the 2 test. 1 = 671 -372)/(012/r1) +(022/r2) i 2141 /2 If variances are unknown and populations are small (r 1, r2 < 100), then a preliminary test may be instituted in order to determine the second most powerful statistical test. This is done by comparing the relationship between the estimators of sample variances according to the f distribution: f = 512/522 where S = J12: (Y1 - 'Y-fl/(n-l) when 51 > 52 f 01/2, ”1 ,v, 111 112 The result of this test will determine whether the unknown variances are equal or unequal. If H; 512 = $22 is accepted, the two sample t tests may be used in order to determine whether the hypothesis H; ”I — U2 is a correct or incorrect assumption. t =(y1- y2)/{SJ(1/r1) + (l/I‘EH where 5 = + \/[{§Yli2 -(?§yi)2/r1} + {£52124 gyifllrfl] /(r1 + r2 - 2) it Ol/Z,r1+r2--2 If, however, H; 51 1; 52 was proven in the preliminary f test, a second preliminary test statistic which follows the t distribution must be used in order to determine the next best course of action. This statistic will test the hypothesis: H; (01/111) 2 (02/112) vs. H;(01/l11) 5&(02/112) t ={(51/)71) - (52/92)}/,/{(51/§1)2 + 2(51/?1)4}/2r1 t{(52/)72)2 + 2(5252)“ /2r2 it “/2,” +r2-2 If equal coefficients of variation are determined to be a correct assump- tion and populations are of equal size (r 1 = r2) then the Lohrding test may be used. q = 2r{log(252) - log }J(§12 + 2512X§22 +2522) 59172 where 52 = ($51 + SSz)/(r1 + r2 - 2) for r> 30 X20”; If, however, equal coefficients of variation are an incorrect assumption after the second preliminary test or populations are not equal, then the Statistical test known as "Welch's solution" must be employed. This test is a relatively weak statistical test in comparison to the previous tests mentioned and therefore should only be used when variances, coefficients of variation and Pepulations are all unequal. 113 t' =61 - 9'2» «57%) + (522/17) with critical values it a /2,0 where o = (1 + g)2/{g2/(r1-1)+1/(r2 - 1)} and g = (512/r1)/(522/r2) C. Effect of Percent Industrial Input on Organic Component Levels It is extremely difficult to get a good handle on exactly what is the percent industrial input of a waste treatment facility. One may rank industrial input according to the number of industries in each community, but this tells us nothing of the amount of waste they contribute. One may survey each community and then calculate the percent of the sewage treatment plant's flow which all the industries combined contribute. However, this may be meaningless without knowing what types of waste each facility is contributing. Finally, one may calculate the percent industrial input according to volume and type of waste, but this is only as accurate as your statistics and can also be very biased. By obtaining a computer printout of the Michigan Department of Natural Resources Critical Materials Registry, the percent industrial input of all communities in Michigan were determined. The Critical Materials Registry contains all industries in the state of Michigan which discharge at least 10,000 gallons of waste per day. The Registry lists what critical materials are discharged, what the total volume of waste is and what waste treatment facility they discharge into. By totaling all of the industrial input volumes into a particular treatment facility and then dividing that total by the flow of the treatment facility, I arrived at a figure I will call "percent industrial input." Each parameter and its relationship to percent industrial input will be Presented in the form of bar graphs (Figures 50-57). Although we have no way of statistically testing the median, this can be a useful parameter in determining the distribution of values at a quick glance. Within each mean bar has been placed a hatched bar which signifies the median. In all cases the median is never greater than the mean which indicates the mean is always skewed by a few extremely large values. 114 115 All individual phenolic, volatile and semi-volatile components demon- strated a unique distribution when sludge levels were compared to industrial input. Notice how in the three examples given, hydroquinone, pentachlorophenol, and 1,3-dichloropropene, one outstanding value distorts the entire picture (Figures 51, 52, 514). Even when excluding these extreme variations from the mean no trend is observed. Apparently a few industries which discharge high levels of these materials are located over a wide spectra of communities. If, however, we look at a much broader picture such as the total phenolic content or total volatile and semi-volatile content in relation to percent industrial input, a trend does develop. Much to the author‘s surprise, as the industrial input increases the levels of these components decrease dramatically (Figures 50, 53). This may tell us that the large volumes of cooling waters and other nonorganic effluents which are received from large industries may dilute any organic critical material to such an extent that it is reflected in a decreased level of organic priority pollutants. It is often difficult .to look through a forest of variables and notice the trend of a single variable. If we remove one variable such as population, we may get a clearer picture of the effect of industrial input. As you will see in the next section, population also has an effect on the level of organic components of interest. Therefore, selecting only those sites which fall in a similar range of Populations and then grouping this sub-population according to percent industrial input, one can in essence remove the population variable by keeping it constant. Observe the much clearer trend in percent industrial input once this interferring Variable has been removed (Figures 56, 57). Another way of looking at the contribution of input is to observe the percent of sites with levels above the detection limit in relation to industrial 116 groupings (Figure 55). There is no trend in either the number of phenols or volatile input over a wide range of industrial inputs. This shows that the groupings were unbiased and further reinforces the fact that phenol and volatile levels (not the number of observations) decrease as industrial input increases. In an attempt to pinpoint sources of industrial input, the 60 sites with the most extreme variations from the mean were selected and compared to the £113. of industrial input that each site received. Although nearly all sites contained significant industrial input, only two of the sites were able to be matched with a reported discharge of a specific critical material. Thus, either many high-level industrial discharges are made without being reported to the Michigan Depart- ment of Natural Resources or the nonindustrial segment is responsible for discharging these components. In either case, the Critical Materials Registry is not a true representation of actual discharges of critical materials such as VOIatile and semi-volatile organics and phenols. 117 \ \ 90 Population 2,000-4,999 67‘ 90% In H 0.: —. DD '"'8 79 ‘§:: .. )V 9 o o >.4S > s I '3 no «H u¥ o~\ *- a 23' Y < 0.0 .001 2.0 12.00 25.00 0.0 1.99 11.99 24.99 100.00 Percent Industrial Input Figure 50 60 Population 2,000-4,999 45' K—-——90%'fi U as O "" 9790 ' 5 1’ so '5 >. .. ‘5 a a no 0.: b-st . v 90 a a 15 ‘_‘<:: ___I_ V 1\ E m 25.00 100.00 0.0 .001 2.0 12.00 0.0 1.99 11.99 24.99 Percent Industrial Input Figure 51 118 ace .ccc - m. E, — @— ._.l9 3. 1 . .P — P... H T»? m w w a m. g m G. I , G. a m EN .n _ ln— .... S. m T a.m. W1 w|._ E ..m. I— SN— m m.c t W - m.cm .... n E_ . E, m w u ~.c mm 5. ~.o.r 0/0 F 8 Fl. 9|l|¢ E NE .8 , .8 NH _ .2 - .8 \\\\\ & q d occ d u 1 0°° 0 0 0 0 0 S 0 S 8 6 4 2 6 4 3 1 exude: sac mxxue azufioz an: ux\un mmocosa _auoh ococmaaOuexz Figure 53 119 Hoe .59. m 0 ca .Nm. my a .Hm. 0/0 mm HN. m.o ~.e .mo .oH 0 Q 0. Q . co 0 s 0 S 0 7 S 2 1. usmwo: sun wx\we Hocosaououzuaucom Percent Industrial Input Figure S4 woo _ - —~yc. E. Q h -.. Q. g. “mm mo §. -cu m nu ,Q 1% .co “swam: sua_ux\wa mommuw~o> “much Percent Industrial Input Figure 55 120 Hcc E3. 0/0 , WI. % 1% E - um um. HM. — WM.° _ o/o Ru Ru “+N.o a.—x.mo * .OH a . . .-.cc 8 6 4 2 6 2 8 4 1 1 gamma: spa ua\ua ocod6hoouoasowo-m.~ Percent Industrial Input Figure 56 36 L, _\\.\\\\\ _\\.\\\\ _\\\\\\\\\ _.\\\\\\\\\ _\\\\\\.\ —\\R\\\\\ _.\\\\\.\\\\1 m~o=ogm m d 7 2 mmxxnxnxAxxcxmxnxuxmxv mo_auw~o>—V\\.\\\\\\ 8 1 one“; :owuuouoa c>oa< mOUmW HO HEQUHQA poo Ac. Nm. Hm. HN. m.o ~.o .mc 0°” .co Percent Industrial Input Figure S7 D. Effect of Population on the Level of Organics in Sludge Population was determined by the number of individuals which each waste treatment facility serves. Note that the unit of measurement used here was the number of individuals which input into the treatment facility, not "population equivalents." By plotting the levels of individual and total phenolic components against population groupings in the form of a bar graph, one can easily observe the effect of increasing the population (Figures 58-63). A single outstanding value of 8,495 mg/kg for pentachlorophenol in the l,000—l,999 individual grouping dramatically offsets the trend in the pentachlorophenol plot. It is apparent that residential input or some other variable associated with population has a stimulative effect on the input both of phenol and pentachlorophenol into the municipal wastewater treatment system. No other individual phenolic compound demonstrates any statistically significant trend. When all of the phenolic Co mpounds analyzed are placed together into a single plot, a trend develops. A Possible decrease in phenolic concentration with increased population is seen, except for one low mean in the 2,000-3,000 individual grouping. This may be due to the relatively small sample size of this group, only 1096, or the effect of other Variables. Population increase may or may not show an increase in the volume in industrial input into that community's sewage system. . Therefore, the two Variables may be either dependent, independent or interdependent. By holding a Variable such as percent industry constant, a better look at the effect of POpulation is possible (Figure 59). No significant correlations could be found when looking at individual Volatile and semi-volatile components. When all of the volatile and semi-volatile Components were combined into a single plot, a decreasing trend was observed in 121 122 the volatile component levels as population increased (Figure 62). No simple explanation for this arises except that some variable which is inversely related to population may control the level of volatile, semi-volatile, and most phenolic compounds. By observing the percentage of sites above the detection limit in relation to increasing population groupings, no trend is observed showing that the rate in occurrence of volatiles and phenols at measurable levels has no correlation to population (Figure 63). Total Phenols ”g/kg Total Phenols mg/kg Dry Wt. 123 Dry Wt 20 85% 150'4 . 1001 50! 0 K? m x: O N W U1 H U1 hi 0 O N Population in Thousands 8 Figure 58 15% to 40% Industrial Input 50 97.5% 38 1 25 . k 0 \ \\ \ O U) "' m 2 Population in Thousands Figure 59 002$ 124 100. r.u. : 4 z"! Ivvvvvu I I 75" | I l l ' l .. l I O | I g . a I I 'r—' O >. o .. .. —1 r——95.——a- l—| 3 a l 94% u: 00 .. .. I“, . £6 \ E as 25 I l a 88951 I __ l l l_... o m R m l3 D In N m 'I ru- 01 u D C N Population in Thousands 2 Figure 60 304 23* F—SS‘t—fi l .5 3 15 .. .-. >4 0 $4 5:: 5 DO 'F-— 9.x E 7 I—I ' [98% ——-I I—_ ‘ O .— N 9.; U1 0— U1 02 O N Population in Thousands 8 Figure 61 125 92% WI no 325 Q. II 4. .6 2 1 .6; sun wx\ue m0~HHG~O> #580? 81: camellia-Wan.“ to woo Population in Thousands Figure 62 .4/4///.... W/////... _ ..m J//_/// m fi/xI/// ..m H um D//// u _ .m 7/v// w .......W//// mugged—o>_ D was“; cowuuouoo o>ona mouam mo acouuom Figure 63 E. The Effect of Size of Treatment Facility on Levels of Selected Organic Components The parameter chosen to reflect the size of treatment facility was the designed flow of each facility, stated in millions of gallons per day. Increased designed flow capacities are based on two main variables, the population of the community and the volume of industrial effluent entering the sewage system. In essence we will be looking at the quantity of materials entering the treatment complex in comparison to organic component levels. While quantity is a fairly easily measurable parameter, one must remember that quality of influent is also an important parameter. One measurement of influent quality is termed the "population equivalent," which is based on two variables, volume and biological oxygen demand of the influent. We observed no trend in either volatile, semi-volatile or phenolic components (Figures 614-66). Since both population and industrial input can change indiscriminately of one another, no trend should be expected. The option of holding either population or industrial input constant becomes void since holding one variable constant would simply give us a plot of the other. Holding both population and percent industry constant and observing the effect of designed flow would be the ideal course to pursue. However, every time one variable is excluded, less information can be collected on the other variables. Thus not enough sites would be obtained to make a statistically significant correlation. At this point we can only conclude that the levels of selected organic pollutants tested for do not show a statistically significant correlation to design flow. In other words, pollutant levels may be independent of the size of the treatment facility. 126 127 4001 300‘ 97.5% W a). u-IH 133200. ex '35 >00 —-) 0-13 —L w\ woo £5100. J —— 97.5% .___I "—1 9 .° .° F .— .~ 5» g :3 O '— 04 U1 C D D .O O Flow in Millions of Gallons per Day Figure64 300. __i— 225- 88% :0. '852150- : o>. .:I-. r—J an: ~00 3i ,_ 35° 75. 9595 VI ‘— _ m n 4:— .—-—I °..= .= .= 1- s s“ '5 s O v— 04 U1 0 C O .6 Flow in Millions of Gallons per Day Figure 65 128 _\\\\.\\\\\\ _\\\\\\\\\\ I_\|\\\\\\\ 1\\\\\\\\. N\\\\\\\ c\\\\\\\\ _\\\\\\\\.\ \ mfiococc— a.m.... w\.\\ \ \ \\ q 0 3 3 2 — 5 7 0 1 awed; cowuoouoa o>on< moumw mo «coupon Hm Ho. u.o N.o H.o o.m o.w o.~ o.o .o 0 Flow in Millions of Gallons Per Day Figure 66 F. The Effect of Sludge Type on the Level of Selected Organics in Municipal Sludges Sludges were categorized by the Michigan Department of Natural Resour- ces into eight and nine classifications for volatile and non-volatile samples respectively. After each sample was grouped according to sludge type and the mean, standard deviation, median, and range determined for each compound, they were ranked according to increasing compound levels. Statistical analysis was performed among each grouping to determine their significance (Figures 67- 78). Obvious trends were observed for both volatile and phenol components. Phenols demonstrated the following overall order of sludge types from lowest levels to highest levels. Low Industrial ' —\ . . , Significant Chlorine OXIdation L Lime Stabilized I 4 Dried Significant Wet Air Oxidation L Imhoff a Aerobic Raw Anaerobic High Space indicates a statistically significant variation. Industrial sludges were actually only a very select group of samples consisting of an Air Force base, a hospital, a salt company, a sugar company, iron works, and a restaurant. 129 130 Chlorine oxidation is a process by which raw, primary or secondary sludges undergo oxidation reactions via the addition of chlorine gas at ambient tempera- ture and low pressures of 30-40 psi over a 24-48 hours time period. This oxidation process "stabilizes" the sludges by disinfecting the sludge and reducing its unpleasant odor. Pentachlorophenol was the only phenolic compounds which demonstrated high levels after chlorine oxidation Figure 75). I would avoid making the assumption that chlorine oxidation actually catalyzes the chlorina- tion of phenols since no high levels of intermediates are observed. Lime stablization is simply the addition of CaCO3 to sludge which raises the pH to approximately 12 and inhibits bacterial activity. Dried samples were generally quite low in phenols, no doubt due to volatilization and photodecomposition of phenolics in the drying process. Dried sludge material can arrive from a variety of sources, whether it be various types of digesters, settling tanks, or clarifiers. Frequently the wet sludge may be passed through a vacuum filter with the addition of various types of electrolyte polymers as dewatering agents prior to drying, incineration, or other processing. Wet air oxidation is a process by which organic sludges are "stabilized" via combustion. Organic matter in an aqueous suspension is placed in a specially designed reactor and at a high temperature and pressure (374 C, 3,200 psia) and is chemically oxidized by dissolved oxygen (Sawyer, 1967). This may be thought of as a large reactor specifically designed to promote chemical oxidation reactions. Imhoff cones are frequently used in older plants for the purpose of separating denser sludge materials from lighter effluents. Heavy organic matter and particulates sink to the bottom point of the cone by gravitation. 131 Raw sludge is generally that sludge which enters the treatment facility and is sampled prior to a treatment. The level of organics in this group should reflect the levels of components prior to any treatment, and should only be effected by dilution and mixing of other effluents in the sewer system. Aerobic sludges are those which have been treated by continual aeration of the material while being digested by aerobic organisms. Anaerobic sludges are those which are placed in an oxygen-free environment and allowed to digest with anaerobic organisms. Both of these environments have been shown to produce phenolic compounds as will be discussed later. Notice in particular that phenol shows rather low values for aerobic and anaerobic digester (Figure 74). Volatile and semi-volatile compounds also showed stratification throughout various sludge types. bow Industrial r ‘v Lime Stabilized Significant Chlorine Oxidation ' : Imhoff I -=Aerobic Significant Raw Wet Air Oxidation fiAnaerobic High Space indicates a statistically significant variation. You will note the similar order to that of the phenolics. Wet air oxidation appears to have a more profound affect on the volatile and semi—volatile components than the phenols. Since many reactions other than oxidation reactions are promoted at high temperature and pressure, it is quite feasible that 132 short chain hydrocarbons are being produced from larger molecular weight compounds. Tetrachloroethylene seems to be an exception to the general trend. In the case of chlorine oxidation its levels are found to be quite high in comparison to the other volatiles tested. It seems as in the case of pentachlorophenol, that the chlorine enhanced oxidation reaction tends to favor the production or non- degradation of multi-chlorinated totally unsaturated compounds. LowX :28 55:34 HighX =41 2:120 2:180 2:275 133 Total Volatiles lChlorine oxidation Lime stabilized Industrial Imhoff Aerobic 1 6‘ 2Anaerobic Raw 2Wet air oxidation 11 have 9596 but not 9896 confidence. 2Approximately 97.5% confidence. 33:24 2:.53 2:11 2:2.8 Figure 67 l ,Z-Dichloropropane Chlorine oxidation Lime stabilized lIndustrial Wet air oxidation 1 Aerobic Imhoff Raw 2Anaerobic 1I have 9096 but not 92% confidence. 2Approximately 95% confidence. Figue 68 Low X = 5.0 134 m-Dichlorobenzene Industrial Chlorine oxidation 1 6: 2Raw Lime stabilized Imhoff Aerobic 1Anaerobic 2Wet air oxidation 1I have 9596 but not 98% confidence. 2Approximately 90% confidence. 32:.10 2:.21 2:.49 52:1.2 Figure 69 Hexachloroethane Industrial 1Chlorine oxidation Imhoff Lime stabilized 1Raw 2Wet air oxidation 2 6‘ 3Aerobic 3Anaerobic 1I have 9596 but not 98% confidence. 2Approximately 93% confidence. 31 have 8896 but not 90% confidence. Figure 70 2:85 2:;n 2:81 2:5% 135 Tetrachloroethylene lLime stabilized 1Industr ial zhnhoff 2Wet air oxidation Aerobic 3Anaerobic 3 Raw Chlorine oxidation 1I have 90% but not 92% confidence. 21 have 88% but not 90% confidence. 3Approximately 90% confidence. 2=J8 2:28 2:98 2:170 x:5ao Figue 7l 2,4—Dinitrophenol Chlorine oxidation Wet air oxidation Aerobic Imhoff lLime stabilized Dried l 6‘ 2Anaerobic dustrial Raw 1I have 95% but not 98% confidence. 2Approximately 92.5% confidence. Figm'e 72 2:.15 2:2.4 2:83 2:18 2:54 2:125 l 36 2,4,6-Trichlorophenol Chlorine oxidation Industrial Lime stabilized lWet air oxidation 1 Dried 2Raw 31mhoff 2Anaerobic 3Aerobic 1Approximately 95% confidence. 2Approximately 95% confidence. 3Approximately 88.5% confidence. i=1.8 LOWX =Q.2 i=5.8 myxeao 2:19 Figure 73 Industrial lChlorine oxidation lAerobic Anaerobic Dried 2Lime stabilized Wet air oxidation zlmhoff Raw 1Approximately 92.5% confidence. 2Approximately 88.5% confidence. Figu'e 711 137 Pentachlorophenol 2 = .46 1Industrial 2 : 6.9 lDried Zlmhoff Low X = 16 2Lime stabilized )2 _._ 17 Wet air oxidation Aerobic High x : 19 3Raw 2 = MI 3 5‘ “Chlorine oxidation 3(- = 130 l*Anaerobic 1I have 98% but not 98.7% confidence. 21 have 95% but not 98% confidence. 31 have 88% but not 90% confidence. “Approximately 90% confidence. Figure75 Hydroquinone Low x : 1.8 lmdustriai Dried _ Imhoff X = 2.7 Chlorine oxidation Lime stabilized High X = 4.0 Anaerobic i z 9.6 l 9‘ errobiC Wet air oxidation 2 : 19 2Raw {Approximately 87.5% confidence. 21 have 88% but not 90% confidence. Figure 76 2 : .06 2 : 1.4 2 : 5.1 2 : 12.0 1 2 X! >-asem . mo_abaao> _ebca 375' 125‘ Percent Solids Figure 79 .uoo pm _n. - w.m _ .u.o xxx o.o m xxx 4 _ ...e 2\ n1 .8 —_ .u.e .moxxx fil N o u o — «MA\V\\\x\k\y\k\k\\\\\\\\. 111 I . . . 6.6 0 0 0 0 4 3 2 1 0 cameo: be: a\». oaaaeacc_xbu< Percent Solids Figure 80 142 poo law 90% $ 3 O‘ O 90% I U1 D I b O ~.o o.o 5001 375 5 125‘ O cease: be: a\we ocwsumouofizowxo: Percent Solids Figure 81 woo um Sh D ._ E l_“— L O J. C J 0 O k—1 99 2% 3‘.) O .... 1:. 3.0‘ «a no 1“ 2. cameo: be: ~\un econconouo~suaois .5 7 Percent Solids Fiure 82 143 :5 o 6 mm we 9 7% be _m m.c _IIII.,... a mu -..: O/O IN.M 8 w R ..e nNuNMHMHMHMHMHMHMm , . c.e m .0 na 0 0 0 0 0 o 4 3 2 1 cause: an: _\ua m_cceca aabce Percent Solids Figure 83 :5 oo 4 so .. No .. n 5 oh I He 9 “1 w.o so i‘m Nine ... LE H 8 \_ re \Nkmk\x\(\V\N\x\k\k\x\\\x\J\A\n\vxx\ d c d Ono 0 S o 5 AU m 7 5 2 banana be; ~\ue aomouo-o-opea:fia-c.e Percent Solids Figure 84 144 woo Iuoo ._..l -8 .H La oo ,5 o“ _ am too _ also a . _ INo I~o m s . L. "A d 9 5.: . .1 0 _“M aim mmmMHNn .m .b o. m 1A4 e an m.o do n. m.c .5 m e 2 1* E a m. m, 9 3.0 r .1 a.c _ m .... .. . B. m N.m do ~.m wl|1_9 E a 1— m 05 do .LWIlleo. ... w HI ... mVVAV\\\.\\fi\\.\\\\m\\x\.\\i\.x\x\\\\\k\\\ — o.o u q . .O W— 9 Aw no Mel 5 0 5 c m 7 5 2 0 m 7 S 2 0 usumo: um: H\wE usuwoz um: «\ua Hococaohoucoaucoa accuse Percent Solids Figure 86 H. The Effect of Treatment Method on the Level of Selected Organics in Municipal Sludges. A total of nine treatment method parameters were chosen from the 1980 revision of the Michigan Department of Natural Resources survey of wastewater treatment plants. These treatment parameters were the methods which had the greatest potential to affect the level of the selected organics in sludge. After grouping each sample according to the nine treatment categories, mean, standard deviation, median and range were computed for each group of sites. The most statistically significant compounds were then separated and their nine treatment categories ranked from lowest to highest concentration (Figures 87- 98). Statistical analysis was then completed on each grouping to determine significant variations. No two compounds showed the same ranking of treatment methods. However, some generalizations were able to be made between compounds of the same class. Volatile an'd semi-volatile Low Ferric chloride phosphorous removal Aerated grit removal -. Prechlorination capacity I——9 Non-aerated grit removal Grit classifier or washer Significant Circular secondary clarifier with skimmers Primary clarifiers with mechanical skimmers 9Conventional activated sludge Comminuter High Space indicates a statistically significant variation. Ferric chloride (FeCl3 or FeSOq) is routinely used by many treatment facilities in order to remove phosphorous from the sewage stream. Ferric 145 146 chloride is generally added to the primary or secondary clarifiers in small amounts (approximately 6 mg/l) in order to form an insoluble precipitate (iron monophosphide, FeP). Various types of grit removal systems are used in order to separate out extremely dense materials such as gravel. Some systems use an airflow in order to break surface tension and better separate grit from other materials. Still other facilities simply have gravity separators similar to Imhoff cones. After separation the grit may be either washed or classified or simply sent to land fill without further treatment. In the case of washing and classification the effluent or treatable portions will be returned to the process stream. These fractions may contain organic materials; however, as a rule they will be extremely minimal. Pre-chlorination capability refers to the process of purging raw sludge influent with chlorine gas C12 (approximately 10 mlg/l CI) in order to reduce the odor within the treatment facility. Clarifiers are large circular or rectangular tanks in which the sewage is circulated in order to allow the settling out of heavier organic sludge materials. Clarifiers are usually accompanied by surface skimmers which remove scum and foam from the tanks. Secondary clarifiers are always preceded by a primary clarifier which simply enhances the efficiency of effluent clarification. A comminuter is a device which mechanically pulverizes materials in the effluent prior to grit removal. The only exception to the generalized ranking given to volatiles as a whole Was hexachloroethane which was found at significantly lower concentrations in Sludges from those plants which used conventional activated sludge (Figure 90). 147 In general those plants with conventional activated sludge demonstrated statisti- cally significantly higher concentration of the volatile and semi-volatile organic compound tested. Activated sludges are those which have aerobic biological and bacterial activity, and therefore would be likely candidates for the production of volatile organic hydrocarbons. Non-volatile organics followed a somewhat different trend in treatment parameters. Non-volatiles Low Pre-chlorination capability Significant Ferric-chloride phosphorous removal |-————9 Aerated grit removal Grit classifier or washer ' 4. Conventional activated sludge Significant Primary clarifiers with mechanical skimmers l Comminuter sCircular secondary clarifier with mechanical skimmer Non-aerated grit removal High Space indicates statistically significant variation The only exceptions to this general trend are 2,4,6-trichlorophenol and 2,4- dinitrophenol (Figures 94, 98). Again significantly lower levels of 2,4-dinitro- Phenol are found in those plants with conventional activated sludge. It may be that the biota in these sludges degraded 2,4-dinitrophenol and hexachloroethane. Plants with grit classifiers or washers also demonstrated significantly reduced levels of 2,4,6—trichlorophenol (Figure 98). This is surprising since we WGuld expect to find little or no effect on the organic content from such a device. 148 In order to get a more exacting idea of how the treatment process affects the compounds of interest, three treatment plants were sampled from several points throughout the process stream. These samples were composited from over a 12-hour time period in order to get a more representative sample. Flow diagrams are included for each facility for the reader to get a better idea of the treatment sequence (Figure 99-102). In the Battle Creek sewage treatment process, an obvious reduction of organics occurs (Table 17). Liming appears to have a positive effect except for the freak appearance of pentachloroethane and 2,4-dinitrophenol after liming. One must remember that the sludge has been dramatically concentrated between the primary sludge and liming process. Waste activated sludge values indicate a general decrease in compounds tested except for acrylonitrile, trichloro- benzenes, 2,4-dimethylphenol and 2,4-dinitrophenol. The appearance of hydro- quinone in the filter cake is unexplainable. The East Lansing sewage treatment facility utilizes several large storage tanks for containment prior to filtration of sludge material. Sludge from these tanks show a decrease in volatile organics as well as increases in several chlorinated species (Table 18). Activated sludge samples from the East Lansing system show an increased level of pentachlorophenol. Once again 2,4-dimethyl- phenol and 2,4-dinitrophenol show up only this time in the ash lagoon. The general trend in the Lansing sewage system is a decrease in the organic compounds tested for until the wet air oxidation process (Table 19). After wet air oxidation levels of acrylonitrile, dichlorobenzenes, chlorinated Propanes and propenes, 2,4-dichlorophenol and phenol all increase. 1,2-dichloro- Propane and hexachloro-l,3-butadiene indicate an increase in concentration after Waste activation and 2,4-dinitrophenol appears in the filter cake sludge. 2 : 100 LOW X 2127 x : 145 High x : 155 2 : 190 149 Total Volatiles lFerric chloride phosphorous removal Prechlorination capability Aerated grit removal Non-aerated grit removal Grit classifier or washer Primary clarifiers with mechanical skimmers Comminuter Circular secondary clarifier with mechanical skimmer lConventional activated sludge 1No significant separation. 2:61 LOW X =12‘l 2 : 140 High x :157 2 : 230 Figure 87 m-Dichlorobenzene 1 6‘ 2Non-aerated grit removal Ferric chloride phosphorous removal Aerated grit removal Pre-chlorination capability Primary clarifiers with mechanical skimmers Circular secondary clarifier with mechanical skimmer Comminuter 2Conventional activated sludge lGrit classifier or washer 1I have 88% but not 90% confidence. 21 have 88% but not 89% confidence. Figure 88 150 l , 3—Dichloropropane Low X = 5.2 lGrit classifier or washer Aerated grit removal lNon-aerated grit removal Ferric chloride phosphorous removal '52 = 11 Circular secondary clarifier with mechanical skimmer 2Conventional activated sludge Pre—chlorination capability Primary clarifiers with mechanical skimmers High X : 23 2Comminuter 1I have 8896 but not 90% confidence. 2I have 8896 bu not 90% confidence. Figue 89 Hexachloroethane Low X = .25 1 5‘ 2Conventional activated sludge Grit classifier or washer .. Aerated grit removal X = -29 Ferric chloride phosphorous removal High X = .36 2 ‘5‘ 3Circular secondary clarifiers with mechanical skimmer Low X = 0.92 lPrimary clarifier with mechanical skimmer _ Comminuter X = 1-0 Primary clarifier with mechanical skimmer High X = 1.1 3Non-aerated grit removal 1Approximately 92.5% confidence. 2Approximately 90% confidence. 31 have 8896 but not 90% confidence. Figu'e 90 LOWX =37 lel‘l HighX=59 X=121 151 Total Phenols lAerated grit removal Grit classifier or washer Ferric chloride phosphorous removal Conventional activated sludge Circular secondary clarifier with mechanical skimmer 2Pre-chlorination capability 2Primary clarifiers with mechanical skimmer lNon-aerated grit removal 1I can have 90% but not 92% confidence. 2I can have 88% but not 90% confidence. LOW X = (4.6 X = 6.9 High X = 9.0 Figue 91 2,ll—Dimethylphenol lPre-chlorination capability Comminuter Primary clarifiers with mechanical skimmer Grit classifier or washer Circular secondary clarifier with mechanical skimmer Non-aerated grit removal Conventional activated sludge Ferric chloride phosphorous removal Aerated grit removal 1I have approximately 97.5% confidence. 21 have 8896 but not 90% confidence. Figue 92 LOW X = 3.1 X! ngh X = 6.4 152 4,6-Dinitro—o-cresol 1Primary clarifiers with mechanical skimmer Pre-chlorination capability Conventional activated sludge Comminuter Aerated grit removal Grit classifier or washer Circular secondary clarifier with mechanical skimmer 1 6‘ 2Ferric chloride phosphorous removal 2Non-aerated grit removal 1I have 92.5% but not 95% confidence. 2Approximately 92.5% confidence. LOWlel > mm: ACTIVATED awn \ nsrunu 1.1 1x50 m'V‘Tm ‘ LIQUOR (I 5WD“ ' ‘ A I ELO‘NER . BUILa nan-3— SEWAGE- FLOW — ~> -_ SLUDGE new ' -—-<)—- ' AIR ' ' BATTLE CREEK-mp 2 . _—>"’_ 2305:“ GGQESTO OPERATE . FLOW DIAGRAM ‘ I Figure 99 I; .. ... ... .r' ...—was EAST LANSING WWTP LAYOUT DOV unna- n-Il ”m1..- wmvmmH m "In '0! H Figure 100 156 EAST LANSING WWTP - FLOW SHEflATlC m arm-a mmmaunwv r mam-mut- Ina-rm W. mm $901.0“ om Afigflhi OICI‘I‘TIOI 70(li m:moIM-nuo~a wmnmum-mmnmm. Figure 101 Lansing 1am - Flousnaat I Location Hap C12 3:135; °'" Settled Samoa 1 storm ovarflou ' Mtcnt‘lon Bash: 4 I I 1 1 Screening: Grit to l Polyur to 0m Landfill 1 Hasvedlolcthvgg- fl - » Secondary ' rt II es Prtlnary : Clarifiers CI"I""’ ""“°" Rabid chlort out- ? k. Tanks Sand _ Conic: ”III. Clariftars T Return Acttvatad Sludge I (Prtlary Sl Bachvasn to South Pflnarlas As; to DU!) ‘ 1——I Vacm- ficimriiuv Scribner Flltars I Scum ksidua ' I In]: Scnbbtr 1 rate to LI 6? II | l 9““? g. - - _—- — — - - l 5“" '_"°" .1 Scnbbcr Hater 7° PM” ‘rmaq Clarifier ' Incinerator Senbbcr - :l-A-h- -J (D an s Figure 102 157 Table 17 Compound Levels for Battle Creek Wastewater Treatment Plant Volatiles Acrylonitrile Chlorobenzene p-Chlorotoluene o-Dichlorobenzene m—Dichlorobenzene p-Dichlorobenzene 1 ,2,-Dichloropropane 1,3—Dichloropropane 1,3-Dichloropropene Ethylbenzene He xachloro-l ,3-butadiene Hexachloroethane Pentachloroethane Styrene Tetrachloroethylene 1,2,3-Trichlorobenzene 1,2,4-Trichlorobenzene 1,3,5—Trichlorobenzene 1,2,3-Trichloropropane 1,2,3-Trichloropropene Phenols o-Chlorophenol m-Chlorophenol p-Chlorophenol o-Cresol 2,#.Dichlorophenol 2,4-Dimethylphenol 4,6—Dinitro-o-Cresol 2,4-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,4,6-Trichlorophenol Primary Sludge . 012 N.D. 1.1 .0021 .018 .030 N.D. N.D. N.D. .32 .000022 N.D. N.D. .145 .000041 .00027 .0011 .00044 N.D. N.D. guzzzzzzzzzz HmPPPPPPPPPP Before 1&2 . 0093 N.D. .514 .0023 .015 .029 .0010 .106 3J4 .77 N.D. N.D. N.D. .53 .00004 .000#2 .00077 .000116 N.D. buzzzzzzzzzz ““PPUPPUPUUP After Lim_e . 0085 N.D. N.D. .0012 .0079 .016 .016 .063 2J1 N.D. .000069 N.D. .000087 N.D. .0000116 N.D. .00006 .00027 N.D. N.D. .0222??? “999990 2 22 P P? Thickened Waste Activiated Filter Sludge Cake .0097 N.A. N.D. N.A. N.D. ' N.A. N.D. N.A. N.D. N.A. .0027 N A. .0019 N.A. .058 N.A. 1.2 N.A. N.D. N.A. N.D. N.A. N.D. N.A. N.D. N.A. N.D. N.A. 000029 N.A. 00024 N.A. .000” N.A. .00027 N.A. N.D. N.A. N.D. N.A. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. .057 N.D. N.D. N.D. .95 N.D. N.D. .lll N.D. .15 158 Table 18 Compound Levels for East Lansing Wastewater Treatment Plant Volatiles Acrylonitrile Chlorobenzene p-Chlorotoluene o-Dichlorobenzene m-Dichlorobenzene p-Dichlorobenzene 1,2-Dichloropropane 1,3-Dichloropropane 1,3—Dichloropropene Ethylbenzene Hexachloro-1,3-butadiene Hexachloroethane Pentachloroethane Styrene Tetrachloroethylene 1,2,3-Trichlorobenzene 1,2,#-Trichlorobenzene 1,3,5-Trichlorobenzene 1,2,3-Trichloropropane 1,2,3—Trichloropropene Phenols o-Chlorophenol m-Chlorophenol p-Chlorophenol o-Cresol 2,#-Dichlorophenol 2,0-Dimethylphenol 11,6-Dinitro-o-Cresol 2,#-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,4,6-Trichlorophenol Before Storage Tank .011 .00071 .00030 N.D. N.D. UPPPPPUP ZZZZZZZZ zaau, DWUO , #N After Storage 3% .0074 N.D. N.D. .0015 .0159 .0089 .00091 N.D. zzzzz UP??? N.D. .0000029 .00031 .0012 .00040 .0020 N.D. 'zzzzzzzzz 00000000 20_ 0“”0 Waste Sludge . 007a N.D. N.D. N.D. .0015 N.D. .0037 N.D. .30 N.D. 09b??? zzzzzz O O O _ O N h—n 222 990 222222222 1"?» .mePUPUUPPP Z 0 Activated Filter Cake not sampled N . A. % e>>>>ae>e>>e>eé>> ZZZZZZZZZZZZZZZZZZ 2222222 00000000 I Ooz U \D Ash 2&2 not 5 m 0) '0 led 1:; O zzzzzzzzzzzzzzzzzzz >>?>>>?>?????>??? 159 Table 19 Compound Levels for Lansing Wastewater Treatment Plant l1,6-Dinitro-o-Cresol 2,4-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,11,6-Trichlorophenol Waste Wet Blended Activated Air Filter Volatiles 351;! Sludge Oxidation Cake Acrylonitrile .023 .00074 .015 N.A. Chlorobenzene N.D. N.D. N.D. N.A. p-Chlorotoluene N.D. N.D. N.D. N.A. o-Dichlorobenzene .0059 .0017 .0037 N.A. m-Dichlorobenzene .033 .00098 .012 N.A. p-Dichlorobenzene .0117 .0037 .0113 N.A. 1,2-Dichloropropane .00094 .0020 .0016 N.A. 1,3-Dichloropropane N.D. N.D. .012 N.A. 1,3-Dichloropropene .82 N.D. 1.2 N.A. Ethylbenzene N.D. N.D. N.D. N.A. Hexachloro-l,3-butadiene N.D. . 00025 .000010 N.A. Hexachloroethane N.D. N D N.D. N.A. Pentachloroethane N.D. N D N.D. N.A. Styrene N.D. N D. N.D. N.A. Tetrachloroethylene . 00026 000011 .000043 N.A. 1,2,3-Trichlorobenzene .00053 .00030 N.A. 1,2,t1-Trichlorobenzene .0026 .00018 .00034 N.A. 1,3,5-Trichlorobenzene .0011 N.D. .0003 N.A. 1,2,3-Trichloropropane .00084 N D .0021 N.A. 1,2,3-Trichloropropene N.D. N D .015 N.A. Phenols o-Chlorophenol . N.D. m-Chlorophenol . N.D. p-Chlorophenol . N.D. o-Cresol . N.D. 2,4-Dichlorophenol . .05 2,11-Dimethylphenol . . 2V~2¥Z222ZZZ 0“”000000000 22222 9???? 26622222222? 93899¢PPPPPP uczzrzzzzzzz ”FPPFPPPPPPU ,Zxo D°° I. Recoveries from Sludge Ten percent of all samples analyzed were run in triplicate, a duplicate and one spiked. Duplicates were averaged together and subtracted from spiked values prior to determination of percent recovery. Spiked sample value - Average duplicate value = Amount recovered. in mg/l in mg/l in mg/l Amount Spiked x .625* in mg/l Amount recovered X 100 = 96 recovered mg/l in mg/l *(.625 = 5/8, the fraction retained from G.P.C.) l. The effect of percent solids and extraction technique on recovery. After grouping all recovery samples according to their percent solids, the mean, standard deviation and range were calculated for each compound in each classification. A total listing of all recovery sludges was also included under the same format (Tables 20-27). A clear downward trend can be observed in the recovery rate of all compounds as percent solids increases. The only exception to this is the non- volatile extraction of phenols from samples with greater than 3096 solids (Table 20). For these "solid" samples, a continuous soxhlet extraction technique was used, which demonstrates superior recoveries. The decrease in recovery from 1 to 1096 solids indicates the irreversible adsorption of organics to solids. Another factor which greatly effects extraction efficiency is the emulsification. The greater the percent solids, the more easily an emulsion forms and the more difficult it is to remove the emulsion. The ability to obtain an efficient solvent to sludge interface is critical in the partitioning process. For sludge samples 160 161 with large percentages of solid material the continuous 24-hour extraction technique with hot methylene chloride proves to offer the best extraction efficiency with the least variation. 2. The effect of sludge type on the recovm. Each recovery sample was grouped according to the type of sludge. Since the choice of samples to be spiked was a random process, not every sludge type was accounted for. Tables 28 and 29 provide a listing of mean recoveries for various sludge types for both volatile and non-volatile samples respectively (Table 28, 29). No obvious differences between sludge types can be observed. The dried sludge samples do run higher than other samples; however, this is undoubtedly a carry over from the extraction technique used. Only one industrial sludge sample was present among the spiked sludges. This sample demonstrated slightly poor recovery of dichlorobenzenes. Blank spots throughout the volatile's data indicate that the sample was not analyzed for that parameter. 3. The effect of phenol recovery from unpreserved samples over time. Since samples were not preserved upon collection, a simple test was run to see what effect lack of preservation had on the phenol content of unpreserved sludges. Other researchers have indicated the loss of phenols in wastewaters and the accumulation of phenolics in the stablization process (Hartenstein, 1981). A single grab sample was taken from two sites, an anaerobic digester and an aerobic digester. Each sample was well mixed and then equal volumes poured into nine identical sample bottles so that zero head space was achieved. Eight of the nine bottles in each case were spiked with the phenols of interest at a concentration of 100 mg/l and one bottle left as a background. After mixing the samples well they were stored at 4°C in a dark room. This simulates the routine 162 handling of samples. The background sample and one of the eight spiked samples were extracted immediately. All other samples were tested one at a time over 64 days (1,536 hours) on a log time scale. The results of the study can be seen in graphic form (Figures 103-112). All phenols tested, except for the nitrophenols, increased in concentration until approximately 96 hours. 2,l+-dinotrophenol and 4,6-dinitro-o-cresol only showed decreasing trends indicating rapid microbial degradation (Figures 105, 111). In general, aerobic samples tended to lag anaerobic plots and then quickly drop to zero. Since all samples were placed in an anaerobic environment after collection, we can postulate that it took some amount of time for the aerobic biota to make the transition to an anaerobic biota. We can see the rise and fall of various microbial populations over time in both the aerobic and anaerobic samples. More sample points should be taken and over a longer period of time in order to get a better picture of the actual microbial dynamics. Even though a much higher starting concentration of phenols was used than we would normally expect to find in sludge and not all samples have active aerobic or anaerobic populations, the results of this study give cause for alarm. We can say with surety that the concentration of various phenols changes over time in some sludge samples, even when stored at 4°C in a dark environment when not properly preserved. 163 Table 20 Liquid/Liquid Extraction Leas Than 3 Percent Total Solids Acrylonitrile Chlorobenzene p-Chlorotoluene o-Dichlorobenzene m-Dichlorobenzene p-Dichlorobenzene 1,2-Dichloropropane 1,3-Dichloropropane 1,3-Dichloropropene Ethylbenzene Hexachloro-l ,3-butadiene Hexachloroethane Pentachloroethane Styrene Tetrachloroethylene 1,2,3-Trichlorobenzene 1,2 ,4-Trichlorobenzene 1,3 , 5- Trichlorobenzene 1,2,3-Trichloropropane 1,2,3-Trichloropropene Standard 53231 EEEQEEEEl 45.0 45.0 95.0 16.0 93.0 21.0 78.0 20.0 60.0 35.0 70.0 24.0 39.0 4.4 76.0* 29.0 98.0 20.0 56.0* 52.0 66.0 32.0 88.0 31.0 93.0 25.0 42.0 25.0 97.0* 18.0 93.0 17.0 89.0 15.0 62.0* 21.0 Tabk:21 Range Liquid] Liquid Extraction 3-7 Percent Total Solids Acrylonitrile Chlorobenzene p-Chlorotoluene o-Dichlorobenzene m-Dichlorobenzene p-Dichlorobenzene 1,2-Dichloropropane l ,3-Dichloropropane 1 ,3-Dichloropropene Ethylbenzene Hexachloro—l ,3-butadiene Hexachloroethane Pentachloroethane Styrene Tetrachloroethylene l ,2,3-Trichlorobenzene l ,2,4-Trichlorobenzene 1 ,3,5-Trichlorobenzene 1 ,2,3-Trichloropropane 1,2,3-Trichloropropene Mesa 41.0 91.0 92.0 83.0 66.0 37.0 57.0 95.0 35.0 58.0 97.0 89.0 39.0 83.0 92.0 82.0 55.0 m Standard 18.0 27.0 23.0 17.0 32.0 0.0 15.0 21.0 tindicates statistically significant variation. Low 24.0 65.0 64.0 59.0 46.0 37.0 47.0 67.0 43.0 59.0 67. 20.0 81.0 80.0 67.0 51.0 Range .— O O OOOOOOO 1 64 Table 22 Liquid/Liquid Extraction Greater Than 7 Percent Total Solids Standard Range Mean Deviation 1_.o_w High Acrylonitrile 50 . 0 21. 0 29 . 0 80 . 0 Chlorobenzene 94 . 0 28 . 0 54 . 0 120 . 0 p-Chlorotoluene 88 . 0 41 . 0 40 . 0 140 . 0 o-Dichlorobenzene 71 . 0 50 . 0 36 . 0 100 . 0 m-Dichlorobenzene 89 . 0 54 . 0 31 . 0 140 . 0 p-Dichlorobenzene 30 . 0* 16 . 0 19 . 0 49 . 0 1,2-Dichloropropane 49 . 0 28 . 0 23 . 0 79 . 0 1,3-Dichloropropane 72 . 0 l8. 0 55 . 0 91. 0 1,3-Dichloropropene 73 . 0* 27 . 0 54 . 0 92 . 0 Ethylbenzene 90 . 0 37 . 0 39 . 0 130 . 0 Hexachloro-1,3-butadiene 21 . 0* l9 . 0 7 . 5 35 . 0 Hexachloroethane 66 . 0 20 . 0 58 . 0 90 . 0 Pentachloroethane 69.0* 0.0 69.0 69.0 Styrene 95.0 31.0 51.0 130.0 Tetrachloroethylene 46 . 0 42 . 0 26 . 0 85 . 0 1,2,3-Trichlorobenzene 67 . 0* 24 . 0 40 . 0 86 . 0 1,2,4-Trichlorobenzene 39 . 0* 15 . 0 22 . 0 50 . 0 1,3,5-Trichlorobenzene 53 . 0 20 . 0 30 . 0 65 . 0 1,2,3-Trichloropropane 53.0 5.2 50.0 59.0 1,2,3-Trichloropropene nu . ---- -..-- ..... Table 23 Licpid/Uquid Ext-action All Volatile Samples Standard Range Mean Deviation _Lo_w High Acrylonitr ile 43 . 0 20 . 0 26 . 0 80 . 0 Chlorobenzene 94 . O 19 . 0 54 . 0 150 . 0 p-Chlorotoluene 90 . 0 23 . 0 40 . 0 140 . 0 o-Dichlorobenzene 81 . 0 26 . 0 36 . 0 100 . 0 m-Dichlorobenzene 88 . 0 36 . 0 31 . 0 140 . 0 p-Dichlorobenzene 62 . 0 30 . 0 l9 . O 100 . 0 1,2-Dichloropropane 49 . 0 34 . 0 23 . 0 79 . 0 1,3-Dichloropropane 81 . 0 l4 . 0 71 . 0 91 . 0 1,3-Dichloropropene 73 . 0 27 . 0 54 . 0 92. 0 Ethylbenzene 93 . 0 23 . 0 39 . 0 130 . 0 Hexachloro-l ,3—butadiene 36 . 0 20 . 0 7 . 5 70 . 0 Hexachloroethane 62 . 0 19 . 0 43 . 0 93 . 0 Pentachloroethane 78 . 0 16 . 0 69 . 0 120 . 0 Styrene 93.0 21.0 51.0 130.0 Tetrachloroethylene 45 . 0 l6 . 0 26 . 0 63 . O 1,2,3-Trichlorobenzene 82.0 19.0 40.0 110.0 1,2,4-Trichlorobenzene 78.0 31.0 22.0 110.0 1,3,5-Trichlorobenzene 78.0 24.0 30.0 110.0 1,2,3-Trichloropropane 58. 0 19 . 0 38 . 0 83 . 0 1,2,3-Trichloropropene 33 . 0 15 . 0 22 . 0 50 . *lndicates statistically significant variation. 165 Table 24 Liquid] Liquid Extraction Less Than 1 Percent Total Solids o-Chlorophenol m-Chlorophenol p-Chlorophenol o-Cresol 2,4-Dichlorophenol 2,4-Dimethylphenol 4,6-Dinitro-o-cresol 2,4-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,4,6-Trichlorophenol Standard Mean Deviation 52.0* 42.0 85.0 36.0 85.0* 38.0 54.0* 35.0 92.0* 45.0 66.0* 50.0 57.0* 53.0 47.0* 34.0 14.0* 13.0 87.0* 20.0 44.0* 26.0 91.0* 48.0 *Indicates statistically significant variation. Table 25 r o 2 OOOOHOOOUOOm Range Liquid/Liquid Extraction 1-30 Percent Total Solids o-Chlorophenol m-Chlorophenol p-Chlorophenol o-Cresol 2,4-Dichlorophenol 2,4-Dimethy1phenol 4,6-Dinitro-o-cresol 2,4-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,4,6—Trichlorophenol Mean 38. 70. 56. 6. 46. 41. 11. 22. OOOOOOOO‘OOO \n \n O Standad 2221229 38.0 59.0 35.0 6.6 65.0 35.0 11.0 36.0 39.0 66.0 23.0 45.0 Low 0. 15. p.— \OO\UOOOW\»O\O Ch-PNOOOb-leOOO Range Hig 87.0 130.0 90.0 11.0 120.0 87.0 25.0 85.0 85.0 110.0 57.0 120.0 Soxhlet Extraction Greater Than 30 Percent Total Solids o-Chlorophenol m-Chlorophenol p-Chlorophenol o-Cresol 2,4-Dichlorophenol 2,4-Dimethy1phenol 4,6-Dinitro-o-cresol 2,4-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,4,6-Trichlorophenol 166 Table 26 Mean 4: 81. 83. 86. 66. 92. 69. 92. 72. 47. 81. 24. 59. * OOOOOOOOOOOO * Standard Deviation 22. 11. 5. 17. 14. 30. 54. 35. 35. 49. 23. 52. OOOOOOOOOVOO *lndicates statistically significant variation. Table 27 Low 43. 70. 76. 39. 73. 40. 1 . OOOO-L‘ChOOOOOO 1 3 0. l 0 0 Range Both Extraction Methods All Non-Volatile Samples o-Chlorophenol m-Chlorophenol p-Chlorophenol o-Cresol 2,4-Dichlorophenol 2,4-Dimethy1phenol 4,6-Dinitro-o-cresol 2,4-Dinitrophenol Hydroquinone Pentachlorophenol Phenol 2,4,6—Trichlorophenol E m m :5 0‘ g—n OOOOOOOOOO Standard 221m 41.0 35.0 33.0 34.0 43.0 42.0 53.0 38.0 29.0 40.0 25.0 49.0 r1 o E r—t— O O O O O O O O I ooooeowxooooo \h OONOWOWWOWP‘O Range High *— p— 0 000000000 Hig 130.0 160.0 160.0 110.0 160.0 160.0 160.0 100.0 89.0 150.0 83.0 150.0 167 Table 28 Volatiles Percent Recovery Raw Aerobic Anaerobic Wet Air Limed Industrial Sludge Digester Digester Oxidation Sludge Sludge Acrylonitrile 55 34 49 29 27 52 Chlorobenzene 72 100 94 91 120 110 p-Chlorotoluene 76 100 88 81 130 92 o-Dichlorobenzene 68 75 98 88 --- 36 m-Dichlorobenzene 25 71 110 -—- - - - 31 p-Dichlorobenzene 62 72 62 49 --- 19 1,2-Dichloropropane 38 -- - 69 --- - -- 23 1,3-Dichloropropane 60 --- 66 --- ..-- ..-- 1,3-Dichloropropene 68 --- 66 --- - -- - -- Ethylbenzene 88 100 92 94 120 88 Hexachloro-1,3-butadiene 55 21 35 32 --- --- Hexachloroethane 40 82 80 49 --- 130 Pentachloroethane 69 91 95 97 --- - -- Styrene 77 96 85 91 130 98 Tetrachloroethylene 18 38 54 57 --- .--— 1,2,3-Trichlorobenzene 96 95 76 86 --- 76 1,2,4-Trichlorobenzene 86 84 76 100 -—- 46 1,3,5-Trichlorobenzene 84 80 63 67 --- 65 1,2,3-Trichloropropane 7O --— 57 --- --- 59 1,2,3-Trichloropropene 54 —-- 36 --- - - - 28 Table 29 Non-Volatiles Percent Recovery Aerobic Anaerobic Imhoff Waste Dried Digestor Digestor Cone Activated Sludge o-Chlorophenol 56 46 58 46 80 m-Chlorophenol 57 78 84 100 79 p-Chlorophenol 92 73 95 76 88 o-Cresol 61 45 74 39 75 2,4-Dichlorophenol 35 97 75 1 10 90 2,4-Dimethy1phenol 29 74 42 48 63 4,6-Dinitro-o-cresol 37 71 23 12 120 2,4-Dinitrophenol 4 . l 47 12 10 87 Hydroquinone 29 17 34 4 . 3 44 Pentachlorophenol 51 92 46 78 73 Phenol 40 44 35 40 31 2,4,6-Trichlorophenol 76 94 81 100 66 Phenol in Relative Concentration Hydroquinone in Relative Concentration 168 500: 400.1 300 - 200 d 100 . T I I 1.0 10 100 1000 Time in Log Hours Figure 103 — Anaerobic - — — - Aerobic 100 - 1 l 100 1000 Time in Log Hours Figure 104 169 Anaerobic - - -- Aerobic 1000 l 100 coaucuucoucoo o>uuefiom ca Hocoeeonufiedcie.~ Time in Log Hours Figure 105 1000 '100 Time in Log Hours nu n. .u .u 0 0 0 0 I.» 3 2 l. sowuouucoucou o>uuoaom cu HoooHUIO 1.0 Figure 106 2,4-Dimethy1phenol in Relative Concentration p-Chlorophenol in Relative Concentration 170 ’ / 4‘ Anaerobic I / \ — - -- Aerobic 300 - x’ l ,r' 3’ I ’:” 1 ’ 3 ’ I ’ l 200 - ‘ l l l l l 100 - l l l l l l l l l 1.0 10 100 1000 Time in Log Hours Figure 107 100- U! C I 1 I l 1.0 10 100 1000 Time in Log Hours Figure 108 o—Chlorophenol in Relative Concentration m-Chlorophenol in Relative Concentration 500 u 400 - 300 a 200 - 100 - 171 Anaerobic - .. .. Aerobic 300 . N O O I ... O O l 1.0 1 1 10 100 1000 Time in Log Hours Figure 109 I l 10 100 1000 Time in Log Hours Figure 110 172 Anaerobic - - - - Aerobic 50 T a nu nu 1 cofiuauucwoccu o>wucamm ca Honmnoicicnuacanic.e 1000 100 1. Time in Log Hours Figure 111 1000 100 Time in Log Hours 1.0 100 u 50 q COuuwhuflwUCOU U>HUQHO¢ ca Hocecaonossueaie.~ Figure 112 Conclusion Since so little work has been done up to this point, any gain in understanding trace organic content of municipal sludges is an important advancement. Many problem areas in sludge analysis have yet to be ironed out. This handicap readily reveals itself in the dramatic variability of all data presented. The areas which require the greatest amount of work at this time include: 1. Uniform and representative sampling. 2. Sample preservation and stabilization. 3. Improved cleanup and separation techniques for solvent extractable materials. 4. Development of a quality control program to evalute analysis techni- ques. Although much research remains to be done in this area, state of the art methods are by no means impotent. As demonstrated by this study, all compounds of interest were detected at measurable levels from municipal sludge except for 1,3,5-trichlorobenzene (Table 10). Since 1,2,3-trichlorobenzene and 1,2,4-trichlorobenzene were detected we are no doubt restricted only by our detection limit rather than the analytical method. Many factors affect the statistical significance of data. As with most environmental testing we have an extremely large number of variables to control. The most prevalent variables are: 1. Large deviations in recovery 2. Large variations in sample concentration due to fluctuations of influent volume. 173 174 3. Dramatic changes in the chemical makeup of samples a. Percent solids b. Percent industrial input c. Biological composition d. Treatment process effects As in any multi-component system, each component will react according to its individual chemical, physical and biological characteristics. The data also reflects the individual uniqueness of each compound analyzed. Although like compounds followed similar trends, no two component levels were identical. For this reason it is safest to draw conclusions from over all trends or extremes. This is not to say that individual variation is unimportant, but rather to emphasize the most obvious variations. Therefore, the major trends for the organic components tested in municipal sludge are bolded and exceptions noted thereafter. No significant variation in the number of inputs (only the level of components) irregardless of the variable selected. We found no exception to this observation; however, there are many variables which have not yet been checked. The term "significant" also indicates that if each variable were analyzed completely independent of the others, a trend may be evident. An increase in the percentage of industrial input decreases the levels of organic components tested. Although this is an initially astonishing occurrence, it may be easily explained. Since industrial input was measured in volume of effluent entering the sewage system even if a facility is discharging a compound of interest, it is likely that the output will contain fairly low levels of these compounds. When 175 several industries input large volumes of effluent in the form of cooling waters, etc., the end result may be the dilution of components already present. Another method of determining the effect of industry would be to categorize industries not by the volume of effluent alone, but some other qualitative parameter as well. An increase in the number of individuals inputing into the waste treatment system decreases the levels of organic components tested. As population of a community grows, so does the percentage of biological organisms per volume of sewage. We have already seen the effect of both aerobic and anaerobic organisms on the levels of phenols in sludge. Phenol and pentachlorophenol do not follow the trend of decreasing levels with increasing population. Phenol is frequently used in a variety of household disinfectants. Pentachlorophenol may enter the municipal sewage system via its use as a general herbicide, or leaching from wood or other products in which it has been used as a preservative. While these two compounds may not enter the residential sewage system any more frequently than some of the other compo- nents of interest, they may be less quickly broken down via microbial degrada- tion. As the percentage of solids in the sludge increases, the levels of organic components tested decreases. Every compound evaluated followed this trend. Decrease in volatile and semi-volatile trace organics as percent solids increases may be due to adsorption to particles, poorer recovery with higher percent solids and loss from the waste processing stream. This reduction may be attributed to one or more factors: 1. Partitioning of certain organics from solid to liquid phase. 2. Volatilization as sludge progresses through process stream. 176 3. Microbial degradation. 4. Catalyzed chemical degradation on particle surfaces. Sludges which have been treated by chlorine oxidation and lime stabilization generally have lower levels of organic components tested than the mean. While chlorine oxidation and lime stabilization may have no direct effect on the level of trace organics, indirectly they may have a great effect. Both chlorine oxidation and lime stabilization act to inhibit microbial growth. When the transformation, degradation and production of organic compounds has been halted, the concentration of certain compounds can be reduced via volatilization and photo decomposition. Although dried sludge was not analyzed for volatile organics, one would expect the effect of component loss to be great as in the case of phenolics. Higher than normal levels of pentachlorophenol and tetrachloroethylene lead one to believe that these compounds are favored in the chlorine oxidation process. We have no indication that the production of multi-chlorinated components occur under these conditions. However, these conditions may lead to an enhanced environment for certain chlorinated components to survive. As seen in the recovery study with phenols over time, the biota has a large effect on the chemical composition of sludge. When organisms are allowed to grow with a large food source, many by-products and breakdown products will inevitably appear. Phenols, for example, were found at higher than average levels in raw, anaerobic and aerobic sludges. The nitrophenols which were found to decrease rapidly in the recovery study also are found in relatively low levels in conventional activated sludge. 177 Wet air oxidation also appears to have a stimulative effect on the level of volatile organics of interest. Since little biological activity occurs under the conditions of wet air oxidation, this observation could be the result of catalyzed chemical reactions. Sludges which have been treated via ferric chloride phosphorous removal or have been pre-chlorinated generally have lower levels of the organic components tested than the mean. Sludges which have been processed via conventional activated sludge or have passed through a comminuter generally have higher levels of the volatile and semi-volatile organics tested than the mean. Sludges which have been processed via a non-aerated grit removal chamber generally have higher levels of phenols tested than those which have passed through an aerated grit removal system. The last three generalizations demonstrate the effect of many of the mechanisms we have previously discussed, especially volatilzation and microbial activity. Although ferric chloride in small amounts will not directly inhibit bacterial growth, it does so indirectly by removing the phosphorous source from the microbial population via a precipitate. VI. Bibliography Biobliography American Society of Testing Materials. Tentative recommended practice for measuring volatile organic matter in water by aqueous-injection gas chromatography. Annual Book of ASTM Standards, Pt. 23, Water. 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