NOTE TO USERS
The original manuscript received by UMI contains indistinct, slanted and or Iight print. All efforts were made to acquire the highest quality manuscript from the author or school.
Microfilmed as received.
This reproduction is the best copy available
UMI
The Impact of Paper Additives on Activated SIudge
Gregory Wade Keech
A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science
Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto
O Copyright by Gregory Wade Keech 1997
395 Wellington Street 395. rue Wellington Ottawa ON K I A ON4 Ottawa ON K1A ON4 Canada Canada
Your Ble Voire reférence
Our file Notre reldrsnce
The author has granted a non- exclusive licence allowing the National Library of Canada to reproduce, loan, distribute or sel1 copies of this thesis in microform, paper or electronic formats.
The author retains ownership of the copyright in this thesis. Neither the thesis nor substantial extracts fkom it may be printed or otherwise reproduced without the author's permission.
L'auteur a accordé une licence non exclusive permettant à la Bibliothèque nationale du Canada de reproduire, prêter, distribuer ou vendre des copies de cette thèse sous la forme de microfiche/filrn, de reproduction sur papier ou sur format électronique.
L'auteur conserve la propriété du droit d'auteur qui protège cette thèse. Ni la these ni des extraits substantiels de celle-ci ne doivent être imprimés ou autrement reproduits sans son autorisation.
Canada
Gregory W. Keech, Masters of Applied Science, 1997 Department of Chemical Engineering, University of Toronto
ABSTRACT
The primary objective of this research was to identiq paper additives with a potential to
cause adverse effects on an activated sludge effluent treatment system, using a screening method
based on batch respirometry. The screening of twenty different paper additives used at Abitibi-
Consolidated's Iroquois Falls Mill revealed that three basic paper dyes (Orange 3, Violet 5, Red
B) one cleaner/solvent and a microbiocide are the most toxic to the respiring biomass in the
effluent treatment system. Polymeric additives, such as drainage and retention aids had no
observable impact on the biomass. Aithough the oxygen uptake rate (OUR) of the biornass was
decreased by up to 61 % by Orange 3 paper dye, at ten times the worst expected dose (750
mg/L), significant inhibitions are not expected under normal operations in the paper mill.
Qualitative microscopic examinations, showing toxicity to rotifers and protozoa, substantiated the
screening results and in some cases revealed that subtle toxic effects which occur at low doses are
not accompanied by an observable decrease in OUR.
Continuous pilot plant testing confirmed the vaIidity of the batch respirometric screening
protocol. A seven day exposure to 200 mg/L of Orange 3 paper dye resulted in a 53 % decrease
of the specific oxygen uptake rate in the aeration basins, and the disappearance of al1 rotifers and
protozoa. A complete recovery quickly followed the removal of paper dye from the treatment .
system feed. Removal of paper dyes by adsorption ont0 the biomass is believed to be an
important toxicity removal mechanism.
ACKNOWLEDGEMENTS
1 would like to express my sincere appreciation to those who contributed to the completion of this
work:
Professor D. Grant Allen for his supervision, encouragement, patience, and sense of
humour throughout this project.
Dr. P. Whiting for his leadership, initiative and coaching in getting this program started,
and for his CO-supervision, and assistance throughout this project.
Mr. Bill Sheffield, of Abitibi-Consolidated Inc. for his leadership and initiative in getting
this program started at Iroquois Falls.
Abitibi-Consolidated Inc. and NSERC for financially supporting this project.
The employees of Abitibi-Consolidated, Iroquois Falls, ON for their acceptance, patience,
assistance and advice throughout this project.
My wife, Joanne, for her tireless love, support, patience and encouragement throughout
this project.
ABSTRACT . . . . . . . . . . . .
ACKNOWLEDGEMENTS . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . 111
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TABLE OF CONTENTS iv
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LIST OF FIGURES vii
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LIST OF TABLES ix
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . INTRODUCTION 1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . 1 Hypothesis and Objectives 2
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Outline of Report 2
. . . . . . . . . . . . . . . . . . . . . . 2 . BACKGROUND AND LITERATURE REVIEW 4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Mechanical Pulping 4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Paper Making 5
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Paper Additives 5
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 4 Wastewater 7
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Effluent Treatment 8
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Respirometry 12
. . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Applications of Respirornetiy 15
. . . . . . . . . . . . . . . . . . . 2.7 Effects of Paper Additives on Activated Sludge 18
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . EXPEFUMENTAL APPROACH 22
3.1.1 Selection of Chernicals and Concentrations . . . . . . . . . . . . . . . . 22
3.1 .2 Respirometer Screening Test Set-up . . . . . . . . . . . . . . . . . . . . 23
. . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Standard Screening Met hod 2 6
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . 2 Pilot Plant Experiments 2 7
. . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Pilot Plant Configuration 2 7
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Operational Strategy 3 2
3.3 Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
. . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Total Suspended Solids 34
3 .3 .2 Oxygen Uptake Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.3 Ammonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.4 Phosphate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.5 Nitrite & Nitrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
. . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.6 Total Dissolved Carbon 3 6
. . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.7 Siudge Volume Index 3 6
. . . . . . . . . . . . . . . . . . . . . . . 3.3.8 Biochemical Oxygen Demand 36
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . 4 Data Analysis 37
. . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Respirometer Screening Data 37
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.RESULTS 39
. . . . . . . . . . . . . . . . . . . . . . . . . . . 4 .1 Batch Respirometry Screening 39
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Nature of Results 39
. . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Screening Test Results 43
. . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 .3 Dose-response curves - 4 8
. . . . . . . . . . . . . . . . . . . . . . . . 4.1 . 4 Other Measured Paramet ers 50
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Pilot Plant Results 54
. . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Steady State Operations 54
. . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Perturbation Strategy 57
. . . . . . . . . . . . . . . . . . . . . 4.2.3 Effect of Short Term Shock Load 57
S . L.. . Y&&""% W& Y I L C Y a... Y" Y J V . .YY.L . V.. . . . . . . . . . .
. . . . . . . . . . . . 4.2.5 Control Systern Deterioration
. . . . . . . . . . . . 4.2.6 Experimental System Recovery
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 . DISCUSSION -72
. . . . . . . . . . . . . . . . . . . . . 5.1 Standard Respirometry Screening Protocol 72
. . . . . . . . . . . . . . . . . 5.1.1 Characteristics of Oxygen Uptake Curves 72
5.1.2 Reproducibility of Screening Results . . . . . . . . . . . . . . . . . . . . 75
. . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Sensitivity of Screening Tests 75
. . . . . . . . . . . . . . . . . . . . . . 5.1.4 Biases and Precision in Results 76
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Toxicity of Paper Additives -78
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Pilot Plant Operation 79
. . . . . . . . . . . . . . . . . . . . . . . . 5.4 Predicting Upsets with Respirornetry 81
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Significance of Research 84
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 . CONCLUSIONS 85
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 . RECOMMENDATIONS 87
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 . REFERENCES 88
APPENDICES :
Appendix A: Standard Screening Procedure
Appendix B: Raw Data
Appendix C: Sample Calculations
Appendix D: Respirometer Calibration Checks
Figure 2.1 :
Mill .
Figure 2.2 :
Figure 3.1 :
Figure 3.2:
Figure 3.3 :
. . .
Figure 3.4 :
. . .
Figure 4.1 :
Figure 4.2:
Figure 4.3 :
Figure 4.4:
Figure 4.5 :
Figure 4.6:
Figure 4.7:
Figure 4.8 :
Figure 4.9:
Figure 4.1 O:
Figure 4.1 1 :
Figure 4.12:
Figure 4.13 :
Figure 4.14:
Fiçure 4.15:
Figure 4.1 6:
Figure 4.1 7:
Figure 4.18:
Wastewater Sources and Treatment in an Integrated Mechanical Pulp and Paper
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Heterotrophic Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Detail of Reactor Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . -24
Schematic Representation of Respirometer Configuration . . . . . . . . . . . . 25
Schematic Diagram of Full Scale Activated Sludge Effluent Treatment System
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Schematic Diagrarn of Pilot Plant Activated Sludge Effluent Treatment System
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
The Effect of Whey on Respiration Rate . . . . . . . . . . . . . . . . . . . . -40
The Effect of Carbarnate Biocide on Respiration Rate . . . . . . . . . . . . . -40
Replication of Conditions Within An Experiment . . . . . . . . . . . . . . . . 41
. . . . . . . . . . . . . The Effect of Orange 3 Paper Dye on Respiration Rate 45
The Effect of Blue 5 Paper Dye on Respiration Rate . . . . . . . . . . . . . . . 45
The Effect of A Polyamide Polymer on Respiration Rate . . . . . . . . . . . -46
. . . . . . . . . . . . Eflect of Orange 3 Paper Dye on Respiration Rate (DR) 47
. . . . The Effect of Pre-exposure to Orange 3 Paper Dye on Respiration Rate -47
. . . . . . . . . . . . . . . . A Dose-Response Curve for Orange 3 Paper Dye 49
. . . . . . . . . . . . . . . . . A Dose-Response Cuwe for a CleanerISolvent 49
. . . . . . . . Biomass Concentration During Exposure to Orange 3 Paper Dye 51
. . . . . . . Substrate Concentration During Exposure to Orange 3 Paper Dye -51
. . . . . . . . . . . . . . . . . . pH During Exposure to Orange 3 Paper Dye 52
. . . . . . . . . . Pilot Plant Operating Data - Mixed Liquor Suspended Solids 64
. . . . . . . . . . . . . . . Pilot Plant Operatinç Data - Sludçe Volume Index 64
. . . . . . . . . . . . . . . Pilot Plant Operating Data - Treated Emuent BOD 65
. . . . . . . . . . . . . Pilot Plant Operating Data - First Aeration Basin SOUR 65
. . . . . . . . . . . Pilot Plant Operating Data - Second Aeration Basin SOUR 66
.
Figure 4.20:
Figure 4.21 :
Figure 4.22:
Figure 4.23:
Figure 4.24:
Figure 4.25:
Figure 4.26:
Figure 4.27:
Figure 4.28:
Figure 4.29:
.
Pilot Plant Operating Data . Treated Effluent TDC . . . . .
Pilot Plant Operating Data . Arnmonia Residual . . . . . . .
Pilot Plant Operating Data . Feed TDC . . . . . . . . . . .
Full Scale Operating Data . Feed BOD . . . . . . . . . . .
Full Scale Operating Data . Mixed Liquor Suspended Solids
Full Scale Operating Data . Sludge Volume Index . . . . . .
Full Scale Operating Data . Treated Efnuent BOD . . . . .
Full Scale Operating Data . 1/3 HRT SOUR . . . . . . . . .
Fu11 ScaIe Operating Data . Full HRT SOUR . . . . . . . .
FuH Scale Operating Data . Treated Effluent TSS . . . . . .
Table 3.1:
Table 4.1 :
Table 4.3 :
Table 4.4:
Table 4.5 :
Table 4.6:
Table 4.7:
Table 4.8:
Table 4.9:
Operating and Design Conditions for Full Scale and Pilot Plant Treatment Systems
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
. . . . . . Reproducibility ofTest Results Using 750 mg/L Orange 3 Paper Dye 43
. . . . Summary of Key Parameters for Pilot and Full Scale Treatment Systems 55
Respirometric Comparison of Pilot Plant Biomasses During Steady State . . 55
Respirometric Cornparison of Full Scale and Pilot Plant Biomass Response to
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orange 3 Paper Dye 56
Effect of Orange 3 Paper Dye at Two Biornass Concentrations . . . . . . . . . 57
Impacts to the Pilot Plant fiom Extended Dye Exposure Measured By Normal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Operating Parameters 59
Arnmonia, Nitrite and Nitrate Concentrations in Pilot Plant Systems . . . . . . . 60
Respirometnc Effect of Extended Dye Addition to Pilot Plant Biomass . 62
AST
ATP
BOD
CO2
COD
CSTR
EQ
F/M
H*O
HRT
H Y S
G o
KOH
MLSS
N
OUR
P
SGWD
SOUR
SV1
RAS
ThOD
TDC
TOC
TMP
TSS
Activated Sludge Treatment
Adenosine Triphosphate
Biochernical Oxygen Demand (mg/')
Carbon Dioxide
Chernical Oxygen Demand (mg/L)
Completely Stirred Tank Reactor
Equalizat ion
Food to Microorganism Ratio
Water
Hydraulic Retention Time (days or hours)
High Yield Sulphite
Concentration Causing 50 % OUR Inhibition (rng/L)
Potassium Hydroxide
Mixed Liquor Suspended Solids
Nit rogen
Oxygen Uptake Rate (mg/Lh)
Phosphorus
Stone Groundwood
Specific Oxygen Uptake Rate (rndgh)
Sludge Volume Index (mL/g)
Return Activated Sludge
Theoretical Oxygen Demand ( m a )
Total Dissolved Carbon (m&)
Total Organic Carbon ( m a )
Thermomechanical Pulp
Total Suspended Solids (mg,/L)
Since January 1996, federal legislation requires pulp and paper mill effluent to be
nontoxic, to have a biochemical oxygen demand (BOD) less than 12.5 kg/tonne of paper
produced and to contain less than 18.75 kg total suspended solids (TSS) per tonne of
paper produced. Biological treatment systems, such as the activated sludge process, are
widely used as secondary treatment systems to purifi pulp and paper mil1 wastewater.
A major upset in the activated sludge treatment system could result in
noncompIiance with these federal regulations, requiring a complete or partial shutdown of
the mill. It is therefore desirable to understand how the pulp and paper making process
can affect the performance and stability of the effluent treatrnent systern. For example,
Abitibi-Consolidated Inc. uses the activated sludge process to treat the waste water fiom
their integrated mechanical pulp and paper mill in Northern Ontario. They have observed
regular process upsets of their biotreatment system which coincide with the intermittent
production of numerous specialty paper products.
The manufacture of specialty grade paper from a mechanical pulp furnish requires
the use of several chernical additives. These additives, used in various combinations, help
to produce the spectrum of properties desired in the finished paper product. Typical paper
additives include azo dyes, modified starches, inorganic minerals and synthetic polymers.
These chernicals affect paper properties such as colour, strençth, density, and surface
properties. The potential downstream impact of paper additives on a biological treatment
system is not ciear. One potential way to assess the impact on a biological system is to
measure the effects of these chemicals on the oxygen uptake rate (OUR), offline, using a
respirometer.
1.1 Hypothesis and Objectives
The hypothesis of this research was that some chemicals used in the paper making
process may affect the activated sludge treatment system.
The specific objectives of the project were to:
Identifj, potentially probiematic chemicals through a screening process
utilizing respirometry
Determine cntical operating concentrations below which no respirometnc
impact is observed
Operate a pilot plant to test the application of respirometry as a screening
tool and investigate long term effects and physical manifestations of paper
making chemicals
This research was carried out at Abitibi-Consolidated's Iroquois Falls Mill using whole
mil1 effluent and biomass from the activated sludge (secondary) effluent treatrnent plant.
1.2 Outline of Report
The following chapter presents a backçround and literature review of pulp and
paper making, biological treatment and respirometry. Chapter 3 outlines the experimental
approach and methods employed for this research. The results of both respirometry and
pilot plant experiments are presented in Chapter 4. Chapter 5 discusses the significance of
the results and draws conclusions and recommendations which are summarized in
Chapters 6 and 7. Four appendices contain raw data, calibration data, sample calculations,
and fùrther experimental details.
This chapter begins with a brief explanation of mechanical pulping and paper
making. A short description of the additives used in specialty papers is also given. Next,
the physical and chemical nature of mil1 effluents, and their biologicaI treatment are
highlighted. Finally, the theory and applications of respirometry are discussed.
2.1 Mechanical Pulping
There are many processes used to produce pulp fiom wood. The processes range
from totally mechanical as in the stone groundwood (SGWD) and thermomechanical
pulping (TMP) processes t o totally chemical as in the Kraft process, with a number of
variations in between, one being the high yield sulphite (HYS). The type of process used
depends on the desired end paper properties and the source of fibre available. Shorter,
mechanically rendered, fibres make a weaker, yet opaque sheet ideally suited for
newspapers. Chemical pulps, with their longer fibres, make strong paper bags or fine
printing paper. Only TMP will be explained in this report because it is the pulping process
used at Abitibi-Consolidated's mil1 in Iroquois Falls, Ontario. For a comprehensive
treatise on pulp and paper making refer to Smook (1994).
The TMP process relies on mechanical energy through a series of refining
screening, and cleaning processes to separate wood chips into individual fibres. Under
optimal conditions, 95% of the raw wood can be converted into paper fibre. Wood chips
enterinç the plant are prescreened before being steamed at atmospheric pressure. The
chips are then washed, drained and steamed again before being fed to the primary refiner.
The refiner consists of two rapidiy rotating discs which shred the chips into fibre under
conditions of increased temperature and pressure. A secondary refiner completes the
initial refining process. M e r unwinding in a latency tower, the fibres are screened and
rescreened in preparation for paper making. The rejects are then fùrther processed
through a reject refiner and then reject screens.
2.2 Paper Making
Paper is produced on increasingly larger and faster machines. A thin jet of dilute
(0.5-1 .O wt%) pulp impinges ont0 a rapidly moving forming fabric and ont0 a forrning
table. Water is continuously drained fiom the fibre mat as it moves dong on the wire,
until it can support its own weight (20 wt%). The sheet is then pressed between two rolls
to further remove water (40-50 wt%). The paper is transported through a series of s t e m
heated dryer cans using a felt fabric. The surface of the sheet is then smoothed or
calendered by passing through a series of vertical rolls before being wound ont0 a reel.
2.3 Paper Additives
Many different chemicals may be added to the papermachine fbrnish depending on
the desired qualities in the finished product. These paper additives include chernicals such
as alum, sizing agents, mineral fillers, starches, dyes, drainage aids, defoamers, retention
aids, pitch dispersants, slimicides, and corrosion inhibitors.
Sizing is used to reduce wetting by aqueous solvents. Alum is ~enerally used in
conjunction with a rosin sizing agent to precipitate a layer of ahminum resinate molecules
ont0 the surface of the sheet. This layer renders the paper partially hydrophobic, thereby
slowing water penetration. Another sizing agent used is alkyl ketene dimer (AKD) which
forms a stable ester linkage with the cellulose fibres.
Interna] strength may be imparted to the sheet via a modified starch or gum.
Fibre-fibre bonds are reinforced, thereby increasing burst strength and reducing linting.
Wet strength resins may also be employed to bind fibres and fines with nonhydrolyzable
bonds.
Fillers are used to improve the optical and physical properties of the paper. The
sheet becomes denser, softer, brighter, smoother and more opaque because small voids
between fibres are filled. However, the strength of the sheet is somewhat reduced by the
use of fillers. Common fillers are clay, calcium carbonate, talc, and titanium dioxide.
Dyes change the colour of the paper. The absorption of a dye depends on the
chemical nature of the dye, the capillary pore structure of the fibre and the nature and
polarity of the fibre surface. Principal types of dyes are acid, basic and direct. Basic dyes,
the most important type in paper colouring, are salts of coloured bases. There are several
chemical groupings of basic dyes such as triphenylmethane, azo, and methine, with
considerable variations in their physical and dyeing properties. Basic dyes are stable to
most biological attack, but some microorganisms have been isolated which can effectively
degrade azo dyes under aerobic conditions. [Wong and Yuen, 19961. Recent work by
Pasti-Grigsby et al. (1996) showed that introduction of lignin-like fragments into azo dye
molecules niay greatly enhance their biodegradability.
2.4 Wastewater
Pulp and paper mil1 effluent contains soluble organic material which must be
removed before discharge to the environment. There are several ways to measure the
concentration of organic material in a wastewater, including biochemical oxygen demand
(BOD), chemical oxygen demand (COD) and total organic carbon (TOC). The most
widely used parameter of organic pollution is the five-day BOD (BOD,) which measures
the arnount of dissolved oxygen used by microorganisms during the biochemical oxidation
of organic matter [Metcalf and Eddy, 19911. The COD is the amount of oxygen required
to chernically oxidize the organic material. BOD is an indicator of the biodegradable
organic concentration, whereas COD encompasses al1 the organic material. The ratio of
the BOD to COD is therefore a measure of the biodegradability of the wastewater. While
BOD and COD indirectly quanti@ organic material in terms of oxygen requirements, TOC
measurements quanti@ wastewater strength directly in terms of organic carbon. Al1 three
methods are general indicators of organic material and say nothing about the concentration
of particular organic compounds. Most of the organic matter in the mil1 effluent cornes
from the pulping process, with the average TMP plant producing 28-30 kg of BOD per
tonne of pulp. The organic matter from the TMP process is composed mainly of simple
wood sugars with small amounts of lignin and resin and fatty acids (RFAs).
The RFAs make up only a small fraction of the total biodegradable organic content
of the wastewater, but contribute considerably to the overall toxicity [Lo et al., 19941.
Toxicity is the property of a material that produces a harmfùl effect upon a bioloçical
system [Landis and Yu, 19951. Any chemical can exhibit harmhl effects (eg. death) if the
dose is high enough., The toxicity of pulp and paper mil1 effluent is assessed based on the
percent mortality of rainbow trout. For a wastewater to be nontoxic, more than 50% of
the fish must survive in undiluted wastewater for 96 hours. Effluent characteristics Vary
between mills and even within a mill due to the species and age of wood being pulped
[Kovacs and Voss, 19921.
The paper mill also discharges a Stream called white water, which is a very dilute
(0.01 wt%) slurry of fines and paper additives not retained in the sheet. There is a large
volume of water discharged fiom the typical mechanical pulp and paper miIl. Typically,
fiom 20 to 40 cubic metres of water are discharged for every tonne of paper produced. Al1
contaminated process streams in the mil1 must be treated before being discharged to the
receiving water body.
2.5 Emuent Treatment
Mill effluent passes through a primary and secondary treatrnent system before
release to the environment (Figure 2.1). The primary system consists of a clarification
unit, where settleable solids are removed and thickened before incineration or landfilling.
M e r clarification, the effluent pH is adjusted to between 8 and 9 for biodegradation in the
secondary treatment plant. In nature, the organic matter in the wastewater would be
decomposed as aquatic microorçanisms consumed it as food. This process would cause
an accompanying decrease in dissolved oxyçen levels in the water body, which in turn rnay
lead to mortality or speciation changes among the aquatic population.
The secondary treatment plant sirnulates the natural biological purification process
under accelerated controfied conditions. In the aeration basin, a concentrated broth of
microorganisms (activated sludge) feed on the organic matter in the effluent, while they
are supplied with necessary arnounts of oxygen and nutrients (eg. N, P). The microbial
population inside the biological treatment plant forms a complete food chain. At the
bottom of the food chain, bacteria thrive on the soluble organic matenal. Protozoa feed
on fiee floating bacteria, helping to reduce turbidity. At the top of the food chain, rotifers
and nematodes put the finishing touch on water treatment as they forage through
biological fiocs. As the mass of microorganisms grows on the food in the mil1 effluent,
they convert soluble organic material to more (insoluble) microorganisms, carbon dioxide
and water. In this manner, the activated sludge process found at many pulp and paper
mills accomplishes in one day what might take a week in a river or lake.
The secondary clarifier allows the activated sludge to be separated, by flocculation
and gravity sedimentation, from the purified water. Most of the settled microbes are
returned to the head of the secondary treatment plant (return activated sludge) to repeat
the process, while a fraction are wasted to maintain a constant biological inventory.
Nontoxic treated water, with low BOD, suspended solids and turbidity is discharged to
the environment without fùrther processing.
Both the aeration basin and secondary clarifier are essential for the treatment
system to fùnction. Any factor which affects either the biological assimilation of organic
matter or solids separation will hinder system performance [Jenkins et al. 19931. The
effluent from the TMP process is easily deçraded in the secondary treatment system with
greater than 95% BOD and 100% toxicity removal.
The performance and stability of the biological treatment system is highly
dependent upon mil1 operations. Factors such as temperature, pH and concentration of
various organic constituents in the mil1 effluent affect the biological treatment process
and the types of microorganisms it sustains. Even subtle changes in effluent
characteristics will cause dramatic changes in the population dynamics over time. An
excess of filamentous bacteria ofien interferes with solids separation, and could ultimately
lead to treatment system failure. Filamentous bacteria thrive under conditions of low
dissolved oxygen, very high or very low FM (mass of food entering the treatment plant
per day divided by the mass of microorganisms under aeration), and nutrient deficiency
[Jenkins et al., 19931.
If a potentially toxic chemical enters the mill sewer system, secondary treatment
performance is threatened. The chemical or its partial degradation products may be
inhibitory to the activated sludge and 1 or biota in the receiving water body, reducing
toxicity and BOD removal efficiency. For example, the secondary treatment plant at the
Iroquois Falls mill often experiences upsets of foaming and dispersed growth indicative of
a mild toxic shock. A mild toxic shock can kill the vulnerable protozoan population,
allowing bacteria to multiply unchecked [Patoczka et al. 19881. The resulting
predominance of bacteria will increase effluent turbidity and oxygen uptake rates.
Intermittent chemical usage in the mill presents a potential hazard to secondary
treatment. Many species of bacteria can degrade toxic orçanic compounds, afier suficient
acclimation [Katz and Weber, 19851. Acclimation may require anywhere from a few days
to several weeks of constant exposure [Tabak et al., 198 11, [Tabak et al. 19921, [Tabak
and Barth, 19781. If a background level of the specific compound is not maintained, the
ability to tolerate that compound may be lost [Melcer and Bedford, 19861. Paper
additives are used for short time intervals and may not reappear in the system for weeks.
Aithough they are present in relatively low concentrations, they can play a significant role
in the treatability of the mill effluent. One way to measure the treatability of the mill
effluent is through respirometry.
2.6 Respirometry
Respirometry is a technique for the continuous measurement of the oxygen uptake
of microorganisms. Oxygen uptake rneasurements provide a fast, reliable indication of
microorganism activity. Many other techniques have been developed to measure biomass
activity, such as: measuring dehydrogenase activity using trïphenyl tetrazolium chloride
(TTC), measuring reductor activity and measuring adenosine triphosphate (ATP)
concentration. [Suschka and Ferreira, 19861
Among respirometric techniques, the most common are manometnc and direct
oxygen uptake rneasurements. Direct measurements of oxygen uptake are accomplished
using a dissolved oxygen probe. Direct techniques rnay become oxygen limited and are
not suited for extended tests. Manometric respirometers, such as the one employed in this
work, maintain a constant oxygen concentration inside the reactor, by adding known
quantities of pure oxysen as it is consumed by the respirinç biomass.
Equation 2.1 illustrates how a respiring biomass oxidizes and assimilates a simple
-
carbon source.
Biomass + Nutrients + CH,O + 0, -. CO, + H20 + Energy + More Biomass [2-11
In a closed reactor system the pressure in the head space would remain approximately
constant, as carbon dioxide (CO, ) production approximately balanced oxygen (O,)
consumption. The ratio of CO, production to O, consumption depends on the reaction
stoichiometry which is a firnction of the substrate and the state of the biomass. In the
respirometer, the carbon dioxide is removed fiom the head space with a strong alkali like
potassium hydroxide (KOH) according to the following reaction:
2KOH (s) + CO, (g) - &Co3 (s) + H 2 0 (1) [2-21
The resultant pressure drop triggers the release of a discrete pulse of pure oxygen into the
head space of the reactor, reestablishing the original pressure. By these means, the
respirometer can track oxygen consumption with time.
Under aerobic conditions, heterotrophic organisms carry out respiration to
produce energy fiom carbon (substrate) and oxygen, as shown in Figure 2.2. The energy
is stored in the cells as adenosine triphosphate (ATP). ATP energy may be chamelled into
the synthesis of new cells or used to repair existing ones. Oxygen is consumed mainly
through exogenous respiration (the carbon source comes from outside the cell). A small
amount of oxygen is also consumed durinç autodigestion or endogenous respiration,
where the cells beçin to metabolise their own ce11 components. If al1 the organisms are
capable of using the carbon sources in the wastewater, then endogenous respiration is
neçligible, and the oxygen consumed is directly related to the degradation of the substrate
in the wastewater. Hence, batch respirometry tests can be used to measure the effects of
a chemical on the treatability of a wastewater, and thereby estimate the impact o n the
performance of a fidl scale treatment system.
Respirometry has been shown to be one of the most sensitive techniques for
deterrnining the effects of toxic materials on biological treatment processes [Beach et al,
19911. Toxicity tests using respirometry fa11 into two general types. In the first type of
test, a sludge in endogenous respiration is exposed to a potentially toxic chemicaI. This
procedure really measures the toxicity to the sludge respiration rate. In the second type of
toxicity test, the biomass is exposed to a potentially toxic chernical in a typical substrate
matrix. This procedure measures the effect on the substrate respiration rate [Ros, 19931.
Bacteria exhibit maximum sensitivity to an inhibitor while actively metabolizing a substrate
[King and Dutka, 19861, the magnitude of which is dependant on the substrate
concentration [Hartmann and Laubenberger, 1 9681; [Volskay and Grady, 1988 and 19901
and type [Ros, 19931. Thus, studies which only examine effects of chemicals on the
endogenous respiration rate are not really applicable for predicting effects on biological
treatment systems.
2.6.1 Applications of Respirometry
Respirometry got its start in the mid 1920s [Beach et al, 19911 and has found
broad application to wastewater treatment for many decades. Jenkins (1960) provides a
comprehensive review of the use of respirometers and their applications to water pollution
problems. Applications range from simple short-term BOD tests to biotreatability and
toxicity studies. For example, respirometry was used to evaluate the biodegradability of
wastewaters containing emulsified specialty chemicals used in the paper industry.
Biodegradability was ranked based on the removal of chernical oxyçen demand by the
- -
respiring biomass [Colvin et al, 19911.
A great deal of work has been done in modelling of the activated sludge process
and several respirometric protocols have been developed to estimate biokinetic constants
for models based on the Monod equation [Monod, 19491. Rozich and Gaudy (1992)
describe in detail how respirometric models may be used for the design and operation of
activated sludge treatment systems.
Respirometry has been successfùlly employed to study the inhibition potential of
several individual organic compounds. Tabak et aL(1992) looked at the effect of different
organic molecules on unacclimated biomass. They were able to develop a method to
predict how different fûnctional groups influenced the rate of biodegradation. For
example, the addition of an alcohol group decreased the rate, but adding an acid group to
benzene doubled the rate of biodegradation. Geating (1981) found that the percent
biodegradation decreases with increasing molecular weight, and steric hindrances due to
branching and bulky side chains.
Therien et al. (1984), used respirometry to study the effect of different alcohols on
activated sludge, and found that there is a cntical concentration where inhibition begins.
Alcohols enhanced the oxygen uptake rate when they were dosed below a critical
concentration and the sludge returned to the previous endogenous respiration rate. When
an alcohol was present above the critical concentration, the endogenous rate was
decreased and eventually fell to zero, indicatinç that the affect on the biomass was
irreversible. The critical concentration decreased with increasing carbon chah length, an
observation that they attributed to the lipophilic nature of the alcohol. As chain length
increases, alcohols are increasingly able to interact with the lipid ce11 membrane. At low
concentrations, .these interactions are favourable, increasing transport across the
membrane. At very high concentrations, membrane function is altered such that the
biomass can no longer metabolize the substrate.
Many factors such as biomass concentration, acclimation, biomass age, complexing
agents, and toxicant sorption on biomass can affect test resutts. VoIskay and Grady
(1990) found that most chernicals caused more severe inhibition at low substrate
concentrations, suggesting that high substrate concentrations rnay mitigate toxic effects.
The effects of some inhibitors change with exposure time [Dutton et al., 19861; [King and
Dutka, 19861. Consequently, batch tests typically neglect the affects of acclimation and
do not detect chronic effects [ Patoczka et al., 19881. Certain chemicals (eg 2,4 -
dinitrophenol) may uncouple oxidative phosphorylation when present below their
threshold level, stimulating oxygen uptake rate without substrate removal [Voet and Voet,
19901. Many toxic substances are biodegradable at low concentrations (eg. phenol) but
inhibit their own degradation at higher concentrations, while others (eg. copper) are not
degraded at all, but stimulate enzyme tùnction at low concentrations [Ros, 19931.
Selvakumar and Hsieh (1988) found that biosorption of organic chemicals on activated
sludge can be modelled by the Freundlich adsorption isotherm. The extent of adsorption
is a function of the biomass and the octanol-water partition coefficient of the chernical.
Gaudy et al. ( 1 988), used batch oxyçen uptake measurements to get kinetic
constants for biomass exposed to phenol. Usinç these kinetic parameters, they were able
to accurately predict the behaviour of a continuous lab scale activated sludge system.
Gaudy et al. (1990) again showed how respirometry can be used to mode1 growth on toxic
and nontoxic wastes.
Respirometry has been used to predict full scale treatrnent performance. Tur et al
(1 990) found that online oxygen uptake measurements provide an early warning for
operational upsets. The OUR is very sensitive to changes in wastewater characteristics
and showed a dramatic change when toxic substances entered the treatrnent system.
Colvin et al (1 99 1 a) successfully applied a process mode1 calibrated with respirometry to
predict full scale activated sludge performance. Ros (1 993) found a good corelation
between OUR inhibition in batch tests and COD and BOD removal in a pilot plant study.
A practical methodology was developed by Gaudy et al., (1988a) using batch respirometry
for predicting the critical operating range of systems treating inhibitory wastes. They
successfblly predicted washout rates for an activated sludge system using data collected
fiom batch growth studies.
2.7 Effects of Paper Additives on Activated Sludge
The effect of DTPA (diethylenetriaminepentaacetic acid) on activated sludge was
investigated by Larisch and Duff (1996). The chelating agent, DTPA, was found to inhibit
the OUR of biomass metabolizing pulp mil1 effluent. The effect was mitigated through
excess metal ion addition, suçgesting the inhibition is related to chelation. A continuous
lab scale activated sludge plant was also operated. BOD removal eficiency was decreased
by 39% at 0.875 g/L of DTPA. A full recovery of the treatment system was observed
immediately after DTPA addition ceased. At no time was toxicity removal impaired as
assessed using the Microtox assay.
Pagga and Brown (1986) investigated the biodegradability of eighty-seven dyes.
They concluded that the dyes are not degradable by aerobic bacteria and that colour
removal is most iikely from physical adsorption ont0 the biological floc.
Brown et al. (1 98 1) developed a screening test to assess the impact of dyes on
aerobic wastewater bacteria. Using unacclimated biomass in the presence of a synthetic
wastewater, dyes were screened for their potential to affect the respiration rate. A
screening concentration of 100 mg/L represented ten times the expected concentration in a
typical wastewater. If a dye decreased the OUR to less than 80% of the control, it was
fùrther investigated to estimate the concentration which would cause a 50% inhibition
versus the control ( IC,, ). They screened 202 dyes, including acid, direct, disperse,
reactive, miscellaneous and basic dye types. The basic dyes were found to be the most
toxic. Of 30 basic dyes tested, 3 had an IC,, of 1-10 mgL, 15 had an IC,, of 10-100
mg/L and 12 had an IC,, greater than 100 mg/L. It was demonstrated that exposure time
to the dye affected the test results, but that the relationship was different for each dye.
After a three hour contact tirne, the OUR was measured over a ten minute period using a
dissolved oxygen probe. The sensitivity of the screening test was also dependent on
biomass concentration; lower concentrations were slightly more sensitive. Based on
convenience and applicability, a biomass concentration of about 1600 mg/L was used for
al1 screening tests.
Milanova et al. ( 1 997) studied the effect of various paper dyes on activated sludge,
luminescent bacteria (microtox assay), and rainbow trout. Screening of activated sludge
employing a method (International Standards Organization, 1986) sirnilar to Brown et al.
(1 98 1 ), yielded IC,, values fiom 20-350 mg/L with TMP mil1 biomass, and 150 - 1000
mg/L with Kraft mil1 biomass. Certain dyes caused stimulatory responses with activated
sludge at low concentrations (ca. 150 mg/L), but inhibitory responses at higher
concentrations (ca. 985 mgL). Milanova et al. postulated that stimulation may be due to
metabolism of the dyes by some microflora. Rainbow trout toxicity testing (96 hour)
revealed that 50% mortality occurred at dye concentrations below 0.5 mg/L. Microtox
assays showed a similar sensitivity to paper dyes, with IC,, values of less than 1 .O mgL.
It was assumed that dye concentrations in untreated wastewaters fiom newsprint tinting
would not exceed 1 .O mg/L based on typical water consumptions, an average dye usage of
200 ghonne, and 90 % retention of the dye in the paper.
In a related study, Milanova and Sithole (1 997) propose a method for estimating
the concentration of newsprint dyes in wastewater, based on sorption by strong cation
exchange resins and analysis by HPLC (high pressure liquid chromatography).
Unrnodified silica was found to be the most effective for adsorbing dyes fiom distilled
water, but only strong cation exchange resins were adequate for wastewater applications.
Bonding to paper fibre is so strong that dyes cannot be leached fiom newsprint using hot
water, implying that dye adsorbed to suspended solids should remain fixed in a biological
effluent treatment system.
An extensive investigation by Moebius and Demel (1983) involved the screening
of 48 chernicals used in paper production. The biodegradability of the various additives
were characterized accordinç to their BOD, : COD ratio. Oniy starch and rosin sizing
showed any significant biodegradability, with ratios of 0.74 and 0.30 respectively, Serial
diiutions of standard BOD, tests were compared to the theoretical oxygen demand
(ThOD) to determine the concentration where inhibition begins. Dehydrogenase activity
was also measured, using TTC, to determine the toxic limiting concentration for each
additive.
In a later paper, Moebius and Demel (1985) describe lab scale pilot plant
experiments on selected additives. They demonstrated that many additives cause a
decrease in the performance of the biological treatment system as measured through
elevated effluent BOD, and COD. These effects were always accornpanied by a marked
decrease in the protozoan population, and in some cases increased filamentous growth
was observed. The biomass eventually adapted to the chemical, with a corresponding
increase in performance. Unadapted sludge returned to normal 1-5 days d e r addition
was stopped. Moebius and Demel concluded that paper additives cause inhibitory effects
on activated sludge at concentrations comrnonly found in wastewaters, and that the
magnitude of the effect depends on the condition of the sludge, the exposure time and
concentration of the chemical.
A two-phase experimental approach was used to achieve the primary goals of this
study: to investigate the effect of paper making chernicals on activated sludge using
respirornetry, and to evaluate the merit of respirometry as a screening tool using a
continuous pilot scale activated sludge treatment system. Details of the approach follow
in four sections. The first section descnbes the rationale for, and details of respirornetry
screening. The second section describes the physical nature of the pilot plant and
operational strategy. Finally, analytical techniques and data analysis are presented.
3.1 Respirometry Screening
3.1.1 Selection of Chernicals and Concentrations
It was not realistic to screen every chemical used in Abitibi-Consolidated's
Iroquois Falls mill. Instead, only those chemicals judged to have the greatest potential for
adverse effects were chosen. This decision was based primady on the nature of the
chemical with some consideration given to the quantity and frequency of usage. A
chemical usage/consumption survey was conducted and material safety data sheets
(MSDS) studied to select a list of 20 priority chemicals.
Based on the results from the survey and historical effluent data, a worst case end-
of-pipe concentration was calculated, assuming al1 chemical used went to the process
sewer. It is recognized that a hiçh percentage of paper additives are retained in the sheet
and thus this type of calculation provides an inflated final efluent concentration for the
chernical under consideration. A fluorescent dye tracer study was also conducted to verie
that the above approach could be used to approximate the final concentration of a
chemical in the wastewater feeding the biological treatment plant.
Effluent was collected and fiozen from the inlet t o the biological treatment system
while the paper mil1 was producing standard white newsprint. This procedure ensured a
representative, reproducible food source with a minimum of paper making additives, for
use in the respirometry screening experiments.
At the time of effluent sample collection the pulp fiirnish was approximately a
70:30 ratio of stone groundwood and high yield sulphite pulps. Al1 screening was
completed using this wastewater before the thermomechanical pulp plant replaced the two
existing pulp mills. Selected chemicals were subsequently re-screened using wastewater
from the new TMP plant to determine if BOD source had any influence on test results.
The chemicals were screened in the respirometer at ten times the estimated final
effluent concentration. If no effect was observed dunng screening, then normal practices
should prove quite safe. Two chemicals which caused more than a 30% decrease in the
OUR vs. the control (identical conditions, no chemical) were evaluated at lower
concentrations to determine a dose-response relationship.
3.1.2 Respirometer Screening Test Set-up
In the Comput-RX 00-240 respirometer (N-CON systems, Larchmont, NY,
USA), four (500 mL) reactors can be operated simultaneously. Each reactor receives
independent oxygen delivery from a compressed oxygen source, regulated to 70 kPa.
L9
Figure 3. I shows the, design o f the reactor cap / CO, trap assembly. As shown in the
schematic (Figure 3.2) a persona1 computer equipped with data acquisition software (N-
CON systems) and an input/output (110) interface coliects and logs al1 oxygen
consumption and experimental conditions.
Oxygen Source
Figure 3.1 : Detail of Reactor Assembly
Compresed Oxygen
Figure 3.2:
Pressure Regulator
I l O Interface
O Personal Compute
So lonoid Valve
Temperature Controlled Water Bath
Differential Pressure 1 Swi tch
Schematic Representation of Respirometer Configuration
L"
A standard method (Appendix A) was developed and tested for screening
chemicals using batch respirometry. In general, al1 four reactors received identical
amounts of wastewater and biomass. Two reactors received a known amount of a single
paper additive. This set up allows for cornparison both among and between the two
controls and the two test samples, and provides a built in test of significance for any
differences between control and test samples. The precision with which the test was
performed and day-to-day biomass variability is accounted for with this interna1 standard
and check.
3.1.3 Standard Screening Method
The ideai screening test would mirror ail conditions of the full scale effluent
treatment system. Unfortunately, in the interest of experimental practicality,
reproducibility and convenience, the developed screening test contains some compromises
from this ideal. Further details of the standard procedure can be found in Appendix A.
Biomass was obtained from the return activated sludge (RAS) line of the hl1 scale
treatment system each day and aerated for 2 hours to ensure stable endogenous
respiration. The frozen wastewater was thawed, filtered and adjusted to pH 8.0 prior to
use to eliminate TSS, ensuring a homogeneous, reproducible substrate source.
Wastewater and biomass were blended such that the total suspended solids (TSS) in the
respirometer reactor was approximately 2000-2500 m a . This biomass concentration,
coniparable to the mixed liquor suspended solids (MLSS) concentration used in the
aeration basin of the full scale treatment plant, provides a linear OUR well within the
oxygen delivery capacity of the respirometer, at substrate to biornass (FM) ratios typically
encountered in the treatment system.
Each test was lefi in the respirometer for 20 hours for experimental convenience
and to simulate the relative exposure to chernicals endured during the hydraulic retention
time in the full scale system. Al1 reactors were submerged in a water bath controiled to 25
h 0.1 OC, which falls into the low end of the range of operating temperatures (25-35 OC) of
the fiill scale treatment system.
3.2 Pilot Plant Experiments
3.2.1 Pilot Plant Configuration
The pilot plant, located in a 12 m trailer at the base of the full scale aeration basin,
was configured to emulate the f ù H scale treatrnent system as much as possible. In fact, the
pilot plant used in this study was originally used to gather effluent treatrnent data for the
design of the present fbll scale activated sludge system (Figure 3.3) in Iroquois Falls.
Figure 3.4 is a schematic representation of the final pilot plant configuration.
Pri mary Effluent - Feed
ph Adjust (Lime)
w Clarifier b
A RAS
I Ammonia Phosphoric
Acid
Feed to Pilot Plant
Treated Emuent
Waste Biomass
Figure 3.3: Schematic Diagram of Full Scale Activated Sludge Effluent Treatment System
In Table 3.1, some key operating parameters are compared and contrasted for the
pilot plant and full scale treatment systems. Most importantly, the two systems share the
same biological foundation and treat the same wastewater. The contrast (0.8 d vs 1.6 d)
in hydraulic retention times (HRT) did not make any difference in final effluent quality, as
measured by biochemical oxygen demand (BOD).
Table 3.1: Operating and Design Conditions for Full Scale and Pilot Plant Treatment
One limitation of the pilot plant configuration, for making cornparisons with fidl
scale operating data, is the lack of flexibility for sampling across the treatment process. In
the full scale treatment system samples may be taken anywhere around the circular basin
as the wastewater is treated in a semi-plug flow regime. In the pilot plant, samples may
only be taken in either of the two (CSTR) aeration basins.
Perhaps the bigçest challenge in pilot plant operations was maintaining a
continuous and stable flow throuçh small diameter hoses. The pilot plant was gravity fed
through a flexible poly(vinyl)chloride (PVC) hose (2.5 cm ID) from the overhead, pH
Systems
System
Full Scaie
Pilot Plant 1
Pilot Plant 2
Temp (OC)
32.6
24.8
25.6
HRT (dl
variable 1.5 - 1.7
fixed 0.8
fixed 0.8
pH
6.9
7.3
7.3
SludgeAge (dl
variable 6.6 - 10.8
fixed 7
fixed 7
D.O. (mgL)
4.9/4.3
4.7 / 4.9
4.3 / 5.8
N Source
NH, , Urea
Urea
Urea
P Source
Phosphonc Acid
Phosphonc Acid
Phosphonc Acid
adjusted feed of the full scaie system. The flow rate to the pilot plant was adjusted using a
bal1 valve at the inlet to the equalization (EQ) basin. Flow was set at 20 L h i n to
rninimize the chance of plugging in the PVC hose (50 meters long), to decrease the
retention time in the EQ basin, and provide a good sampling of the primary treated
effluent fiom the mill. It was very important to keep the flow rate of the feed to the EQ as
stable as possible because nitrogen (N) and phosphorus (P) dosing was established based
on 20 L/minute of wastewater and a B0D:N:P ratio of 100:5: 1. Plugged hoses were the
leading cause of operational upsets, in the form of overfiowing aeration basins or a starved
EQ basin (overdosed nutrient s).
The EQ basin (polyethylene, 100 cm diameter, 1 10 cm high, overfiow at 800 L)
was agitated with an outboard mixer (Greey Mixing Equipment Ltd., Toronto, ON) and
aerated to promote good mixing of nutnents, to strip off any noxious gases, and to
oxygenate the wastewater before mixing with the return activated sludge (RAS). By
adding nutrients to the EQ basin, a single nutrient addition system could be used for both
treatment trains. The majority of the feed to the EQ overflowed into a sewer connected to
the fùll scale treatment system.
The first aeration basin (polyethylene, 75 cm diameter, 250 cm high, level control
at 550 L) in each train was fed from the EQ basin using a Moyno Pump (Robbins & Myers
Inc., USA, Model No. 22502 ) at a rate of 1 Llminute through polyethylene tubing (13 cm
ID). Immediately before entering the first aeration basin, the feed was combined with the
RAS pumped (Robbins Bc Myers Inc., USA, Model No. 32003) from the underflow of the
clarifier (PVC, 60 cm diameter, 60 cm cylinder, 60 cm cone, 200 L volume). The
wastewater flowed fiorn the first aeration basin to a second identical basin, and to the
clarifier in each train through polyethylene tubing (25 cm ID) without firther pumping.
Flushing ports, allowing a fresh water hose (350 kPa) to be connected, were located
between each tank for routine flushing of al1 hoses.
Nutrient and chernical addition (experimental system during perturbation) was
achieved using metering pumps (Alldos Pumps, USA, Modei Nos. M201-8027 & M.01-
4014). Nitrogen was supplied as a urea solution (150 g urea/L). Phosphonis was
supplied as dilute phosphoric acid (30 times dilution of 75% H3P04 ). Air was supplied by
an electnc air compressor (575 V, 20 kW, 212 m3/h, 620 kPa) regulated to 150 kPa. The
flow rate into each aeration basin and the EQ basin was controlled using bal1 valves.
Moisture (oil in water emulsion) was continuously rernoved from the air header using a
standard liquid separator. Coarse bubble aeration was achieved using a coiled, perforated
garden hose, anchored to the bottom of each tank with a stainless steel ring.
3.2.2 Operational Strategy
Hydraulic retention time (approx 18 hours) was controlled by feeding each systern
fiom the overflowing EQ basin at a constant rate of 1 O00 mllminute. Biomass was
recycled at the same rate for each system. Usually, a rate of 800 Wrninute presented a
good compromise between sludge thickening and line plugçing problems. The depth of
sludge blanket was maintained at approximately 60 cm in each clarifier. Nutrient dosing
was simplified by addition to the well mixed EQ basin.
A sludge age of seven (7) days was chosen. This was controlled by wasting one
seventh of the biological inventory each day. Wasting was achieved by draining directly
from the aeration basins four (4) times per day. Solids losses in the final effluent (10 %)
and clarifier inventory (8 %) were ignored, for simplicity. Air flow rates were not
measured, but adjusted (seldomly) to maintain a comparable dissolved oxygen
concentration between the aeration basins in each system (Table 3.1). There was no pH
control within the pilot plant. The pH of the feed was controlled by the full scale activated
sludge treatment system operator.
Foarning and foam trapping were problems. Foam was manually removed by
skirnrning off the top of each aeration basin. Every effort was made to keep foam removal
equal between the two systems. Defoamer (Sentinel 365) was also added to effectively
control foam in al1 but the worst of cases. Foam trapping is of concem because it allows
material to accumulate at the aidwater surface, and may promote the growth of unwanted
microorganisrns, such as Nocardia spp. [Jenkins et al., 19931. Manual removal of scum
and floating sIudge was also regularly required fiom each of the clarifiers.
Twice per day, samples from each of the four aeration basins, the EQ basin, and
the final treated effluent from each system were taken for analysis. Samples were analyzed
for pH, TSS, TDC, OUR, phosphate ion, ammonia, and settleability. Occasionally, nitrite
and nitrate concentrations were measured in each treatrnent system. The health of each
culture was also assessed on a qualitative basis using microscopic analysis. Key criteria
were relative abundance (eç. lots, few) of different orçanisms and floc structure (eg
smallltiçht, large/loose). Occasionally, the biomass froni each system was compared with
one another using batch respirometry.
3.3 Analytical Methods
3.3.1 Total Suspended Solids
Total suspended solids (TSS) is a measure of the concentration of suspended
material in a liquid. Standard Method 2540 D [APHA, 19921 was used to quanti6 the
TSS in the aeration basins and treated effluent from the full scale and pilot plant activated
sludge treatment systems, and the TSS in batch respirorneter tests. The term MLSS
(mixed liquor suspended solids) simply refers to the TSS measured in an aeration basin,
and is an estimate of the concentration of biomass under aeration.
3.3.2 Oxygen Uptake Rate
The oxygen uptake rate (OUR), a measure of the respiration rate of the
microorganisms in an activated sludge, was determined according to Standard Methods
27 10 B [APHA, 19921. The rate of decrease in dissolved oxygen (D.O.) concentration in
a mixed liquor sample was measured using a 300 rnL BOD bottle (Wheaton, Millville, NJ,
USA), a YS1 Model 58 D.O. Meter (YS1 Inc., Yellow Springs, Ohio, USA), a YS1 Model
5730 D.O. Probe (YS1 Inc., Yellow Springs, Ohio, USA) and a magnetic stirrer (Fisher
Scientific, Cat No. 14-5 1 1-1) Samples of mixed liquor were taken fiom the full scale
treatrnent system at approximately 113 the HRT (hydraulic retention time) and at the
outfall of the aeration basin. Pilot plant mixed liquor samples were taken from each of the
aeration basins. Units of OUR are milligrams of oxyçen per litre per hour (mgLh). If the
TSS (MLSS) of the sample is known, a specific (mass normalized) OUR or SOUR
(mdgh) is usehl for comparing respiration rates between biomass samples of diEerent
concentrations.
3.3.3 Ammonia
Arnmonia residual was measured in the aeration basins and treated effluent of the
full scale and pilot plant treatment systems, using an ion selective electrode, ISE,
(Accument, Fisher Scientific Cat 13-620-505) and a pWion meter (Accument Mode1 25)
according to Standard Methods 4500-MI3 F [APHA, 19921. Arnmonia concentration
(reported as mg of NB,) is important for assessing nutrient dosing in activated sludge
systems.
3.3.4 Phosphate
Phosphate (PO, ") was measured in the aeration basins and treated effluent of the
full scale and pilot plant treatment systems using the EPA approved HACH Method 8048
and a HACH DR 2000 spectrophotometer (HACH Co., Loveland, CO, USA). Phosphate
residuals (reported in mg PL) are important for assessing phosphorus dosing in activated
sludge systems.
3.3.5 Nitrite & Nitrate
Nitrite (NO, -) and nitrate (NO,-) anions (reported as mg N/L) were determined
usinç an ion chromatograpb (Dionex DX-100, Toronto, ON) as per Standard Method
4 1 1 O C [APHA, 19921. Nitrite is an intermediate formed in the oxidation of ammonia
nitrogen to nitrate by a two stage bacterial process.
3.3.6 Total Dissolved Carbon
Total dissolved carbon (TDC) was determined by rneasuring the total carbon of
the filtrate for a particular sample. Standard Method 53 10 B [APHA, 19921 was
followed, using a Dohrmann DC- 190 Total Organic Carbon Analyzer (Rosemount
Analytical Inc., Santa Clara, Cq USA). Liquid samples were vapounzed and combusted
in a quartz tube over a platinum/alumina catalyst (680 OC). The carbon dioxide
concentration of the combustion gases was subsequently determined by infiared
absorption. Reported in mg C L , TDC is a collective rneasure of al1 the possible soluble
carbon sources available to rnicroorganisms, but cannot differentiate between
biodegradable and non-biodegradable carbon.
3.3.7 Sludge Volume Index
The sludge volume index (SVI), a useful measure of the bulk of a settled sludge,
was determined according to Standard Methods 27 10 D [APHA, 19921. The bulkier the
sludge, the higher the SVI, reported in mL/g. A low SV1 suggests a sludge which settles
quickly and compacts well, which is a desirable characteristic of an activated sludge
system.
3.3.8 Biochemical Oxygen Demand
Biochemical oxygen demand (BOD or BOD,) is a five day biological test which
estimates the amount of oxyçen required to bioloçically stabilize a wastewater. The five-
day BOD of the feed and treated effluent of the hl1 scale and pilot plant systems was
determined according to Standard Methods 52 10 B [APHA, 19921. BOD is a colIective
and indirect measure of the organic material present in a wastewater, and is reported in mg
oxygen /L.
3.4 Data Analysis
3.4.1 Respirometer Screening Data
The cumulative oxygen uptake registered by each of the four reactors was recorded every
thirty minutes during each experiment for a period of 20 hours. From the group of al1
respirometer experiments, 20 sets of data, each one pertaining to a different chemical
compound were selected for analysis. Appendix B contains raw data from these twenty
experirnents. The oxygen uptake is quite linear fiom about one hour to seven or eight
hours into the test. Regression analysis was performed (over the linear range) on the raw
data to produce an oxygen uptake rate (OUR) for each reactor in each set.
To determine the effect of chernicals on the oxygen uptake rate, the percent
difference was calculated using the average OUR value of the controls and the average
OUR value of the tests, within a given experimental mn, according to equation 3.1.
% Difference = OUR of Control - OUR of Test x 1 O0 [3-1] OUR of Control
The statistical significance of chemical induced respiration inhibition was tested
using a t-test (method 1) and an analysis of variance (ANOVA) test (method 2). An
alternative method of analysis (method 3) was also applied to the data.
The t-statistic (Equation 3.2) was calculated by dividing the difference of the two
average OURS (the numerat or in Equation 3.1 ) by the square root of the sum of the
average variances for the controls and tests. The variances in the denominator of
Equation 3.2 are the averages of the individual variances of the regression lines frorn each
pair of reactors. The degrees of freedom are 4(n-2) where n is the number of observations
for each regression. If the t-statistic was greater than the critical t-value, the difference
between control and test values was said to be significant at the 95 % confidence level.
t-statistic = (OUR of Control - OUR of Test) (Var.of Control + Var.of Test)"
Method 2, uses an ANOVA calculation and an F-test to compare the difference
among like reactors to the difference between reactor pairs. The dope calculated for each
reactor is treated as a single point estimate of the OUR. The F-statistic (Equation 3.3) has
the F-distribution with a-1 and a(b-1) degrees of fieedom, where a is the number of
treatments (control treatment plus test treatment), equal to two, and b is the number of
replicates, also equal to two. If the F-statistic was larger than the critical F-value for 1
and 2 degrees of freedom, the difference between control and test values was said to be
significant at the 95 % confidence level.
F = Mean Sauare of the variation between treatments Mean Square of the variation within treatments
In method 3, the average and standard deviation of the differences between
controls was calculated. Using this information, a 95 % confidence interval was
calculated, usinç the t-distribution with 20 degrees of freedom. If the difference between
the average control OUR and the average test OUR was greater than this confidence
interval, the difference was said to be significant.
The experimental investigation was carried out in two distinct phases, employing
batch respirometry to screen chemicals for their impact on activated sludge and
continuous flow pilot plant testing to examine chemical effects on an operating treatment
system. The results are also separated into two sections. Batch respirometry screening
results, with a description of the nature of results, detailed results and dose-response
curves are outlined in the first section. Next, the pilot plant results are presented, with
details on steady state operations, perturbations through chemical addition and system
recovery.
4.1 Batch Respirometry Screening
4.1.1 Nature of Results
The respirometric method was designed to show the influence of paper additives
on the respiration rate of activated sludge (biomass). One way to measure the validity of
this method is to compare the method's response using compounds with known effects.
The addition of a food source, whey, greatly increased the oxygen uptake rate (OUR) of
biomass in endogenous respiration (Figure 4.1). Conversely, the OUR of biomass
exposed to a srnall amount of toxin (also in the presence of a food source) is much lower
than the control containing only biomass and mil1 effluent (Figure 4.2).
No Whey
w
5 10 15 Time (h)
Figure 4.1: The Effect of Whey on Respiration Rate Cumulative oxygen uptake as a tùnction of time for respiring biomass from the fùll scale activated sludge treatrnent system. The oxygen uptake of the biomass in endogenous respiration is greatly increased with the addition of the food source, whey (7200 ma).
10 Time (h)
Figure 4.2: The Effect o f Carbarnate Biocide on Respiration Rate Cumulative oxyçen uptake as a fùnction of time for respiring biomass from the full scale activated sludçe treatment system. The oxyçen uptake rate of the biomass is g e a t l y reduced with the addition of 10 mg& o f a toxic carbarnate biocide.
. a The experimental results are best viewed graphically as a plot of cumulative
oxygen demand ( m a ) vs. time (h). Data were recorded every 30 minutes, for a penod
of 20 hours in each of four reactors. Reactors were calibrated and tested against known
oxygen demands (as per manufacturers suggestions) to ensure high quality data. Oxygen
consumptions registered by reactors were within 2% of the standard oxygen demand
(Appendix D), with excellent agreement between reactors. When al1 four reactors were
set-up as controls, the resulting oxygen uptake curves were often indistinguishable (Figure
4.3).
-
O 2 4 6 8 10 12 14 16 18 20
Tirne (h)
Figure 4.3: Replication of Conditions Within An Experiment Cumulative oxygen uptake as a fünction of time for respirinç biomass frorn the fÙ11 scale activated sludçe treatment systern. Precision is good when al1 reactors are prepared identically accordinç to the standard screeninç method.
. -
For chernical screening tests, two reactors were set up as controls (containing only
biomass and mil1 effluent) and two reactors were set up as tests (containing biomass and
mil1 effluent and known amount of one paper additive). This set up allows for a built in
test for equipment malfùnctions (eg. a leak). Precision within reactor pairs of 5 % or 2
mg/Lh was routinely achieved.
In general, oxygen uptake curves consisted of a brief lag period (0-2 hours), an
extended linear region (6-8 hours) of rapid oxygen uptake and an endogenous respiration
penod of very low OUR. Cumulative oxygen uptake data usually began to diverge after
the linear region, as small differences between reactors were amplified. Data analysis was
always restricted to the initial linear region, which was selected by visual inspection of the
data.
Results of batch respirometry screening tests were quite reproducible. Table 4.1
shows results from four different mns over a three month period. During this time subtle
changes in the biomass occurred, in addition to the pulp fiirnish changing to
thermornechanical pulp (TMP) fiom stone groundwood (SGWD) and high yield sulphite
(HYS) pulps. Note that the absolute values of the OUR are not identical, however,
relative values for tests and controls (i.e. percent inhibition) within a run agree very well
with one another.
Table 4.1: Reproducibility of Test Results Using 750 mg/L Orange 3 Paper Dye
4.1.2 Screening Test Results
The impact of paper additives on respiration rate covered a spectrum from
stimulatory to inhibitory. The screening data for 20 different chernicals is presented in
Table 4.2. In general, Test vs. Control OUR deviations of greater than 10 % were found
to be statistically significant.
A total of 13 different basic dyes were screened. The Orange 3 dye was the most
inhibitory, causing up to a 61% decrease in respiration rate vs. the control containing no
dye (Figure 4.4). Conversely, exposure to Blue 5 dye caused a stimulatory response of
53% above the control respiration rate (Figure 4.5). The basic dyes screened were
approximately 10-40 O h acetic acid. This readily deçradable organic acid was also
screened, at the appropriate wastewater concentration, with no observable impact.
Test Run
1
2
3
4
OUR of
Control
mw 21.8
31.3
15.8
1 1 4 . 5
OUR of
Test
(mgl 'w
8.5
13.8
6.4
1 6 . 3
Percent
Inhibition
(W 61
56
59
1 57 1
Death 1 Recovery
(Y esNo)
No
Yes
Yes
Yes I
Emuent Type
(pulp fiirnish)
HYS/SGWD
HYS/SGWD
HYS/SGWD
TMP
19- OÇL l 8- OOOI
l
I I
i
'Slol3ea~ aqg âuouia uo!sga~d ~ood 01 anp 8u!isa~a~ isaâ8ns sqs!Jaav sqnsax paL 3upams J ~ @ W O J ! ~ S ~ ~ J O Oeruutn~ :z*p q q a l
N A A A N N N N A N N A A
(aueqlauilhuaqclul) l a l o ! ~ 3!s@ (a~nwu i oze) z a6ue~o qseg 01 ~el !w!s
taha ~aded) c w ! ~ (aAa ~aded) a6ue~o
N A A A A N N A A
1 €8- SC- ZZ Z O Z- Ç-
s 1- A A A A
~ a ! j ! x d o 3!u126~0 a p p o ! q o ~ a ~ j
~ausal=) asod~nd p ~ a u a 3 lua6v Gu!z!s
u W ! l u V p!y aOeu!e~a pui? uo!lualad
l u e s ~ a d s ! ~ q ~ y d pue p ! ~ uo!jua~aa M a ~ a d ~ d ) a PW (aha ~aded) 3 P ~ U
OS1 1 O1 009 OOP L1
OOP O€ 1
06ç 1 OCP
8 1- 99 ZP- ÇP-
(aha Jaded) g a6ueJo (aAa ~aded) ç a6ut?~o
(aha ~ a d ~ d ) a P ~ H (ana ~aded) ç W!A
~auihlod apyehlod :,!uo!le=, ap!:,o!q paseq aleweq~e:,
alelhxoqla louaqdlhuou 'auauouq-p uo!slnwa az!s u!so~ quo!le=i
Jaweoj!lue paseq JaleM Jawr(lod quo!le:, au!uier(lod
aM(o~palaAlod xalel quo!les ( a ~ n l x ! ~ ) P C patl ~ ! s ~ a ( a ~ n l x ! ~ ~ ) 69 ~ a t l 3 ! s ~
01b OL8 OOP 08s
(auiylayy) 09 a 6 u ~ ~ o qseg (au!q)apj) €9 a6ue~o qseg
(au!uw) ZI PW 3!seg (aueqlauilhuaqd~l) c lalo!n qseg
5 10 15 Time (h)
Figure 4.4: The Effect of Orange 3 Paper Dye on Respiration Rate Cumulative oxygen uptake as a fùnction of time for respiring biomass fiom the fidl scale activated sludge treatment system. The respiration rate of the biomass is significantly reduced with the addition of 750 mg/L of Orange 3 paper dye.
Time (h)
Figure 4.5: The Effect of Blue 5 Paper Dye on Respiration Rate Cumulative oxygen uptake as a fùnction of time for respiring biomass from the full scale activated sludçe treatment system. Oxygen uptake is stimulated by the addition of 750 mg/L of Blue 5 paper dye.
Several polymers were also screened using the standard method. Polymers caused
no impact on the respiration rate of the activated sludge (Figure 4.6).
Time (h)
Figure 4.6: The Effect of A Polyamide Polymer on Respiration Rate Cumulative oxygen uptake as a fùnction of time for respiring biomass fiorn the hl1 scale activated sludge treatment system. The superposition of the two curves suggests that high doses (1 150 mg/L) of a polyamide polymer did not affect the oxygen uptake rate.
Severe toxic shocks, as in Figure 4.7, were ofien followed by an apparent recovery
of the biomass several hours after initial contact. In Figure 4.7, the initial OUR of the dye
containing samples is severely depressed, yet, afier 15 hours the OUR increased
dramatically. This deathlrecovery phenomenon was observed in 3 out of 4 replicates with
Orange 3 dye (Table 4. I ) and in low dosage (1 O pprn) experiments using a carbarnate
biocide (Figure 4.2).
To explore the possibility of long term efTects, biomass from the experiment shown
in Figure 4.7 was used as the inocuium for a subsequent experiment. Four reactors were
again setup: 2 containing biomass from the previous controls, plus mil1 effluent, and 2
containing biomass from the previous Orange 3 dye test (Figure 4.7), plus mill effluent.
As shown in Figure 4.8, the preceding toxic shock did not hinder the ability of the biomass
to metabolize dye-fiee mill effluent. The metabolic activity of the two biomass
1 1 t I l
O 5 10 15 20
Time (h) Figure 4.7: Effect of Orange 3 Paper Dye on Respiration Rate (DR) Cumulative oxygen uptake as a function of time for respiring biornass from the fiil1 scale activated sludge treatment system. Toxicity from Orange 3 paper dye (750 mg/') is usuaily followed by an apparent recovery after about 15 hours.
O 5 1 O 15 20 Time (h)
Figure 4.8: The Effect of Pre-exposure to Orange 3 Paper Dye on Respiration Rate Cuniulative oxyçen uptake as a function of tinie for respiring biomass from the full scale activated sludge treatment system. The preceding toxic shock (Figure 4.7) does not affect the ability of the biomass to metabolize dye-free effluent.
4.1.3 Dose-response curves
A few chemicals showed a particular propensity for toxicity to activated sludge. A
graphical tool for determining critical concentrations below which no adverse effects are
observed was developed. Dose-response curves were constructed for the Orange 3 dye
(Figure 4.9) and a general purpose cleaner/solvent (Figure 4. IO), by plotting the percent
inhibition vs. the control at several chemical concentrations. Both curves show an
insignificant stimulatory effect at very low levels, however, past a critical level, toxicity
increased rapidly before asymptotically approaching an upper limit. Interestingly, the
inflection point for both curves is at nearly the same concentration (1 50 mg/L), suggesting
a similar toxicity of both active constituents. Even at extremely high concentrations,
100% mortality was not achieved. This is an indication of the plethora of rnicroorganisms
comprising an activated sludge.
Since screening tests were carried out at 10 times the worst case effluent
concentration, the actual concentration of these chemicals entering the biological
treatment plant should fa11 well within the no response area of the curves.
Dye Concentration (mg/L)
Figure 4.9: A Dose-Response Curve for Orange 3 Paper Dye Respiring biomass from the full scale treatment plant was used. At a critical concentration the toxicity (percent inhibition to OUR) begins to increase rapidly with increasing dye concentration.
-10 : Concentration of Cleaner (mg/L)
Figure 4.10: A Dose-Response Curve for a Cleaner/Solvent Respiring biomass from the hll scale treatment plant was used. At a critical concentration the toxicity (percent inhibition to OUR) beçins to increase rapidly with increasing cleaner concentration.
4.1.4 Other Measured Parameters
Other measurable parameters, such as total dissolved carbon (TDC) and total
suspended solids (TSS) can be used to quanti@ the effects of chernicals on activated
sludge, however, with much less precision than respirometry. The standard method
employed for chernical screening using respirometry also included measuring TSS, TDC
and pH before and after each expenment. High (1500 mg&) TSS concentrations were
used to rninimize the relative growth of the biomass during the expenment, so it is not
surprising to see little or no change in this parameter. Also, due to the error in TSS
determinations (10 %), the small changes that were observed are statistically insignificant.
Since there is no simple, direct measurement for the many soluble substrates in pulp mil1
effluent, TDC was chosen as a surrogate for the sum of soluble carbon sources. The TDC
method employed routinely measures concentrations with a relative standard deviation of
2 %.
During a single respirometer screening expenment using Orange 3 dye (Figure
4.7), TSS, TDC and pH data were collected with time. The control (no dye) shows no
significant change in TSS (Figure 4.1 1) during the first 15 hours of the experiment. The
dye containing reactor shows a steady decline in TSS during this same time period. The
dissolved carbon level (Figure 4.12) was quickly reduced by the control, whereas there
was essentially no removal in the dye samples. There was a corresponding pH drop
(Figure 4.13) in the control durinç this initial period of high metabolic activity, not seen in
the dye sample.
T ,-, No Dye
1200 ! 1 I 1 1 1 1
O 5 10 15 20
Time (h)
Figure 4.11: Biomass Concentration During Exposure to Orange 3 Paper Dye Total suspended solids (TSS) as fùnction of tirne during a batch respirometer test (Figure 4.7) using 750 mg/L of Orange 3 paper dye.
5 10 15 20 Time (h)
Figure 4.12: Substrate Concentration Durinç Exposure to Orange 3 Paper Dye Total dissolved carbon (TDC) as a fùnction of tirne during a batch respirometer test (Figure 4.7) usinç 750 m d L of Orange 3 paper dye.
O 2 4 6 8 10 12 14 16 18 Time (h)
Figure 4.13: pH During Exposure to Orange 3 Paper Dye pH as a fiinction of time during a batch respirometer test (Figure 4.7) using 750 mg/L of Orange 3 paper dye.
As shown in Figure 4.7, after 15 hours there was a "recovery" where the OUR of
the dye samples increased greatly. There was no change in the TSS of the dye sample,
while in the control endogenous respiration was occurring, resulting in a decrease in TSS.
The results show that two different series of events was O C C U ~ ~ ~ in controls and test
samples. In the control there was the normal cycle of metabolism of substrate followed by
endogenous respiration. In the samples with dye there appeared to be very limited
metabolic activity followed by a high leveI of metabolism.
Conclusions drawn merely from comparisons between initial and final TSS or TDC
data are dependent on the test duration and may be misleadinç. For example, after 14
hours there was a measurable difference in TSS between dye and no dye samples (Figure
4.1 l), suggesting that the dye may have had an adverse effect on growth. M e r 18 hours,
one might surmise that the dye had little or no effect on growth. In the case of TDC, it is
impossible to distinguish between the original carbon present and rnetabolic intermediates
or end products produced during the test period. It is also unclear whether spurious data
points are due to biological processes or random measurement errors. The respirometric
method employed here is more direct, accurate and therefore a more appropriate measure
of the impact of chemicals on activated sludge.
Microscopic examinations were also performed before and afier each experiment.
Higher life forms such as rotifers, and ciliates (protozoa) are excellent indicators of
toxicity due to their size, sensitivity and position in the food chah. Selective toxicity to
these indicator organisms would not result in a measurable change in the OUR of the
biomass due to their small relative abundance in a normal activated sludge. Significant
bacterial toxicity would be required to bring about the large OUR inhibitions observed
with some chemicals (eg. Orange 3 dye).
Qualitative rnicroscopic results substantiated the respirometer data and even
suggested subtle effects not observable with respirometry. Inhibitive chemicals showed an
obvious toxicity towards indicator organisms. In some cases, toxicity to higher life forms
was apparent although not observable through respirornetry, suggesting only a mild
toxicity to the biomass. Bacterial toxicity, although quite likely based on the respirometry
results, was not discernable through microscopic inspection. Microscopic examinations
are a fast, accurate measure of chernical toxicity to hiçher organisms.
4.2 Piiot Plant Results
4.2.1 Steady State Operations
The dual train activated sludge pilot plant was operated for a period of three
months before any perturbation was attempted. Most of the monitored operating
parameters fluctuated somewhat fiom day to day, but both systems behaved in a similar
and predictable fashion. Figures 4.14 through 4.22 show trends in operating data for key
parameters for both control and expenmental systems fkom day 60 to day 110.
Cornparison with analogous operating data trends fiom the full scale plant (Figures 4.23 to
4.29) reveals that the pilot plant and full scale system fiinctioned similarly, but not without
some significant differences (eg. specific oxygen uptake rate). Figures 4.14 through 4.29
are presented at the end of the pilot plant results, beginning on page 64. A summary
(average, standard deviation, and relative standard deviation) of key operating parameters
for the pilot plant and full scale treatment system is presented in Tabie 4.3.
Batch respirometry was performed during steady state operation to compare the
two pilot treatment systems. The OURs fiom five batch respirometer tests during steady
state operation are summarized in Table 4.4. The differences between average OURs are
less than 3 mg/Lh, and are about the same as among reactor pairs. Biomass fiom each
pilot system was added on a volumetric basis and the TSS of the biomass was not
rneasured for these five runs. Therefore, different arnounts of biomass may have been
added if the concentrations of the biomass samples were different. This should be
considered when comparing the OURS of the two systems from these batch respirometer
tests.
Table 4.3:
-
System
Full Scale
PP Control
PP Expt'l
System
Full Scale
PP Control
PP Expt'l
System
Full Scale
PP Control -
PP Expt'l
Summary of Key Parameters for Pilot and Full Scale Treatment Systems
MLSS
Final Effluent BOD 1 First Basin & 1/3 HRT SOUR
SVI
Average
238 1
Second Basin & Full HRT SOUR 1
Average
151
- - --
Final Effluent TSS
Std Dev
150
R.S.D.
6
Std Dev
58
R.S.D.
42
45
94
Average
12
5
6
Average
6
17
18
Table 4.4: Respirornetric Comparison of Pilot Plant Biomasses During Steady State
R. S.D.
39
StdDev
5
2
6
StdDev
1
5
4
Experimental OUR (mg/Lh) 1
R.S.D.
22
29
24
R.S.D.
16
29
24
Average
30
36
3 1
Average
5
12
12
D ~ Y
79
StdDev
O. 8
3.4
2.7
Control OUR (mg/Lh)
23.8
StdDev
16
19
22
R.S.D.
55
53
72
Biomass from the pilot plant was also exposed to Orange 3 dye to compare its
response to previous screening results using biomass from the fùll scale system. Oxygen
uptake inhibitions at 750 and 200 r n f ~ doses of Orange 3 dye (Table 4.9, suggest that
the pilot plant biomass is more sensitive to Orange 3 dye than the full scale biomass. Note
that the amount of biomass used in the pilot plant respirometry screening tests was
significantly (63 %) lower than that used in the standard chemical screening test.
Table 4.5: Respirometric Comparison of Full Scale and Pilot Plant Biomass Response to Orange 3 Paper Dye
1 Dye Concentration (mg/L) 1 Full Scale Inhibition (%) 1 Pilot Plant Inhibition (%) 1 1 1 (Standard Method) 1 (Non-standard Method) (
The lower biomass concentration (non-standard) was chosen to account for the
increased activity of the pilot plant biomass and may have influenced the sensitivity of the
screening results. To investigate this hypothesis chemical screening tests were repeated
for Orange 3 dye at 100 mg&, using the standard and non-standard amounts of full scale
biomass. As shown in Table 4.6, a 4 % stimulation under standard conditions (biomass
concentration approx. 1500 mg/L) becomes a 27% inhibition under non-standard
conditions (biomass concentration approx. 550 mg&), indicating that biomass
concentration does indeed affect the sensitivity of screening results.
Table 4.6: EEect of Orange 3 Paper Dye at Two Biomass Concentrations
4.2.2 Perturbation Strategy
To test the validity of batch respirometer tests, Orange 3 dye was added to the
experirnental train of the continuous flow pilot plant. At the outset, the addition of
Orange 3 dye was to be lirnited to a constant mass by varying dose and exposure time, in a
series of perturbations. For example, one perturbation could be 750 mg/L for 2.4 hours
and another could be 75 mgL for 24 hours. However, due to a scheduied one week mil1
shutdown, only two perturbations could be performed. The first experiment was a short
term, high concentration exposure and the second was a long term, lower concentration
exposure. The total mass of dye added was not held constant between the two
perturbations.
Dye Concentration (mg&)
1 O0
4.2.3 Effect of Short Term Shock Load
The first perturbation of the experimental system was performed on day 85.
Orange 3 dye was metered into the feed for 2.4 hours to produce an influent concentration
of 750 m a . The purpose of this experiment was to explore the threat of a large dye spi11
in the miIl process sewer. The dye concentration of 750 mg/L was selected for its
dramatic effect in batch respirometer tests.
High Concentration
OUR Inhibition (%)
-4
Low Concentration
OUR Inhibition (%)
27
Inspection of the operating trends fiom the pilot plant (Figure 4.14 to 4.22) reveals
that addition of the Orange 3 dye had no measurable effect on treatment system
performance. The only qualitative observations were increased orange foam and orange
treated effluent for a few days after the short dye addition.
4.2.4 Effect of Extended Dye Addition
For the second perturbation of the experimental system, Orange 3 dye was added
to produce a feed concentration of 200 mg/L fiom day 93 to day 100. The concentration
of dye was chosen by examining the dose-response curve (Figure 4.9). At 200 mg/L
Orange 3 dye is above its critical toxicity threshold and should produce a very noticeable
effect without completely decimating the microbial population. Addition of 200 m g L of
Orange 3 dye decreased the pH of the feed by 1.5 units. It also increased the TDC of the
influent by approximately 60 mg/L, of which 40 mg/L is non-biodegradable and 20 rngL
is biodegradable (based on 25% acetic acid). The BOD, of the influent was found to
increase by about 50 m a , which is consistent with the assumed proportion of acetic acid
in the dye.
The second dye addition brought about some observable changes in system
performance, which are summarized in Table 4.7. As shown in Table 4.7, and
accompanying Fiçures 4.14 to Figure 4.22, al1 operating parameters, with the exception of
MLSS (Figure 4.14) and SV1 (Figure 4.15) were adversely affected by extended exposure
to the Orange 3 paper dye.
Table 4.7: Impacts to the Pilot Plant fiom Extended Dye Exposure Measured By Normal Operating Parameters
1 Final Emuent TDC (mg/L) 1 Adverse 1
O perating Paramet er
hmss (ma) Sm (mL/g)
Final Efliuent BOD (mg/L)
SOUR (m&)
Final Effluent TSS (rng/L)
1 Ammonia Residual (mg/L) 1 Adverse 1
Effect (Adverse, None, Positive)
None
None
Adverse
Adverse
Adverse
Within eight hours of dye addition, the foam on the first aeration basin changed
fiom the usual frothy brown foam to a loose sudsy foam, suggesting that a toxic shock
had occurred. Microscopic examinations confirmed that al1 higher life had disappeared
from the perturbed system by the third day. The remaining single celled bacteria, with
their associated pinpoint floc, contributed to elevated effluent TSS (Figure 4.19). Sample
filtration also became dificult due to extracellular slime produced by the biomass, under
these toxic conditions. A thick sludge layer formed on the clarifier, requiring constant
attention to prevent plugçing of hoses.
The SOUR in the experimental system also showed a corresponding decline as dye
was continuously metered into the wastewater feed. A 72% SOUR decrease (3 standard
deviations fiom the mean) in the tirst aeration basin, and a 52% SOUR decrease (2
standard deviations from the mean) in the second aeration basin occurred on the third day
(Day 96) of dye addition (Figures 4.1 7 and 4.1 8). Both systems displayed increased
SOURs towards the end of the experimental period.
After two days of dye addition, the solids in the settlometer began rising and
falling, as tiny (nitrogen ?) bubbles carried flocs to the surface. This apparent de-
nitrification continued throughout the addition penod and ceased after the dye was turned
off Although this phenomenon undoubtedly contributed to increased effluent TSS, the
sludge volume index (Figure 4.15) was not significantly affected.
The ammonia residuals (Figure 4.2 1) were lower and the nitnte and nitrate leveIs
much higher in the perturbed system than the control dunng dye addition. As shown in
Table 4.8, the total nitrogen residual (measured in the second aeration basin) was twice as
much in the perturbed system as in the control system on Day 96.
Table 4.8: Arnmonia, Nitnte and Nitrate Concentrations in Pilot Plant. Systems
Day 1 Ammonia (mg NL) 1 Nitrite (mg NIL) 1 Nitrate (mg NIL) 1 Total N (mg NL)
54
5 5
64
65
66
80
96
Control
0.1
0.8
0.8
8.4
0.5
0.0
4.2
Expt'l
o. 1
1.5
2.6
12.8
0.4
0.0
O. 8
Control
0.0
0.6
0.3
2.1
0.0
0.0
O. 8
Expt'l
0.0
0.4
0.6
1.6
0.0
0.0
7.6
Control
0.0
0.0
1.7
O. O
0.0
0.0
O. 7
Control
o. 1
1.4
2.8
10.5
O. 5
0.0
5.7
Expt'l
0.0
0.0
1.7
0.0
0.0
0.0
2.4
Expt'l
o. 1
1.9
4.9
14.4
0.4
0.0
10.8
- -
The colour of the biomass and treated effluent darkened continuously during dye
addition, indicating that the biomass may have been continuously adsorbing dye
throughout the seven day perturbation. The colour of the discharge changed from the
usual yellow/brown through Orange 3 and finally to a cola colour. At the end of one week
of dye addition, the biomass and effluent were the same dark cola colour.
The effluent TDC increased from 100 mg/L to 200 mg& over the course of dye
addition (Figure 4.20), but influent TDC was only increased by 60 mg/L. Taking into
consideration the 40 mg& of inert TDC (dye) added does not totally account for this
increase. The effluent BOD (Figure 4.16) also increased from 6 m g L to 30 mgL during
dye addition.
Respirorneter testing during dye addition to explore changes in the ability of the
perturbed biomass to metabolize dye-free wastewater, was inconclusive due to equipment
malfùnction. Every 24 hours, for eleven days (day 93-1 O3), biomass was sampled fiom
each pilot system and tested using filtered primary effluent in the respirometer.
Unfortunately, a leak in one reactor cap caused poor precision of the respirometer for
several days, until it was replaced. Only five days (day 99-1 03) of data are shown in Table
4.9 due to these precision problems. Due to differences in the consistencies of biomass
samples from each system and a volumetric addition basis for respirometer tests, each pair
of reactors did not contain an equivalent amount of biomass. Therefore, only the specific
(mass normalized) OURS of the two biomasses could be directly compared. Comparison
of results calculated usinç OURS vs. SOURs (Table 4.9) from batch respirometer tests,
illustrates the importance of this information. The 25 % decrease in SOUR on day 99,
coupled with the relative increase on subsequent days suggests there may have been some
negative impact on the respiration rate of the biomass (when exposed to dye-free
wastewater) as a result of dye addition. However, these results are inconclusive due to
the lack of data earlier in the dye addition period.
Table 4.9: Respirometric Effect of Extended Dye Addition to Pilot Plant Biomass
1 Day 1 Difference in OUR vs. Control (%) 1 Difference in SOUR vs Control (%) 1
4.2.5 Control System Deterioration
A series of events led to the unstable operation of the control system after the dye .
addition period. Normal pilot plant operation involved the manual removal of foam fiorn
aeration basins, to prevent foam (and biomass) from accumulating on basin surfaces.
Usually the character of the foam on each system was similar enough that solids losses
from this procedure did not cause the MLSS of the two systems to differ significantly.
However, due to the dilute nature of the foam on the experimental system as a result of
dye addition, foam removal was stopped on both systems (to avoid masking changes in
MLSS due to dye toxicity). Unfortunately, manual foam removal frorn the aeration basins
had been keeping a suspected Nocardia population in the control system at Iow levels.
Over the course of the perturbation experirnent, the Nocardia problern worsened,
such that after the dye addition had stopped, the stability of the control system was
jeopardized. Symptoms of upset in the control system included a thick viscous foam on
aeration basins, a scum on the clarifier, elevated effluent TSS (turbidity) and BOD and a
deterioration of settling properties including de-nitrification. Foam removal was re-
initiated in the control system, but the foarning problem was persistent. Thus, comparison
between the two systems, post dye addition, is weak. However, companson of the
perturbed system with steady state operating conditions reveals that the system appears t o
have fùlly recovered.
4.2.6 Experimental System Recovery
The experimental system made a fast recovery after dye addition ceased. Within
three days (dye washed out) higher life was re-establishing itself, helping to bring down
the number of dispersed bacteria. There was an associated decrease in effluent TSS and
the OUR returned to normal levels. The system appeared to be perfonning better now
than it had since start-up. The net effect of the extended dye addition was like starting
with a new sludge. There appeared to be no long term effects from dye addition.
Figure 4.14: Pilot Plant Operating Data - Mixed Liquor Suspended Solids Mixed liquor suspended solids (MLSS or TSS) shown are averaged values for the two aeration basins in each treatment train. I/O, 1 and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
4000 T ExpPI
E I - 2000 t 1500
i- I
A A A Contro8 1 O00 +
I I l 0 I O 500
Control I
O
Ctrl Avg = 260
I 1 1 1 , !
1 1 1 1 8 l 3 1 1
D ~ Y Expt'l Avg = 2 1 6
60 65 70 75 80 85 90 95 100 105 110
Ctrl Avg = 2968 Days Expt'l Avg = 3070
Figure 4.15: Pilot Plant Operatiiig Data - Sludge Voluine Index Sludge volume index (SVI) data shown pertains to the biomass in the second aeration basin of each treatment train. I/O, 1 and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
u E
30 - E 8 25 --
C
60 65 70 75 80 85 90 95 100 105 11
D ~ Y Ctrl Avg = 5 Expt'l Avg = 6
Figure 4.16: Pilot Plant Operating Data - Treated Effluent BOD Soluble biochemical oxygèn dernand (BOD) data shown for the treated effluent fiom each treatment train. VO, 1 and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
O ~ 1 I I 1 L
60 65 70 75 80 85 90 95 100 105 110
D ~ Y
Ctrl Avg = 17 Expt'l Avg = 1 8
Figure 4.17: Pilot Plant Operating Data - First Aeration Basin SOUR The specific oxygen uptake rate (SOUR) in the first aeration basin is shown for each treatment train. I/O, 1 and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
Ctrl Avg = 12 Expt'l Avg = 12
Figure 4.18: Pilot Plant Operating Data - Second Aeration Basin SOUR The specific oxygen uptake rate (SOUR) in the second aeration basin is shown for each treatment train. I/O, 1 and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
Ctrl Avg = 36 D ~ Y Expt'l Avg = 31
Figure 4.19: Pilot Plant Operating Data - Treated Effluent TSS The total suspended solids (TSS) of the treated emuent iç shown for each treatment train. 110, I and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
Ctrl Avg = 105 Expt'l Avg = 107
Figure 4.20: Pilot Plant Operating Data - Treated Effluent TDC The total dissolved carbon (TDC) of the treated effluent is shown for each treatment train. The peak on Day 90 corresponds with an air compressor failure lasting 6 hours. I/0, i and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
60 65 70 75 80 85 90 95 1 O0 1 05 110
Ctrl Avg = 4 D ~ Y Expt'l Avg = 4
Figure 4.21: Pilot Plant Operating Data - Ammonia Residual The ammonia (NH3) residual is shown for the second aeration basin of each treatment train. The peak on Day 90 corresponds with an air conipressor failure lasting 6 hours. I/O, 1 and O (arrow heads) indicate the start (1) and stop (O) of dye addition periods.
I
O i I I I I 1 1
1 I I I
I 1 1 1 1 I l I
60 65 70 75 80 85 90 95 1 O0 105 1 I I
D ~ Y Avg = 653
Figure 4.22: Pilot Plant Operating Data - Feed TDC The total dissolved carbon (TDC) of the wastewater feeding both treatment trains is shown. It was assumed that the TDC of the wastewater feeding the fidl scale system was identical.
= 1200 -
. 400 - E 1 PI = 200 - O O m ---- O ------ +---- I
75 80 85 90 95 1 O0 1 05 60 65 70
D ~ Y Avg =7
Figure 4.23: Full Scale Operatinç Data - Feed BOD The biochemical oxygen demand (BOD) of the wastewater feeding the full scale treatment system is shown. It was assurned that the BOD of the wastewater feeding the pilot plant was identical.
O I , 1 ! 1 1 I 1 1
1 1 1 I 1 1 I i
60 65 70 75 80 85 90 95 100 105 Ili
D ~ Y Avg =2381
Figure 4.24: Full Scale Operating Data - Mixed Liquor Suspended Solids The mixed liquor suspended solids (MLSS or TSS) is shown for the aeration basin of the full scale treatment system.
1 ---- p- O - 1 I
60 65 70 75 80 85 90 95 1 O0 1 05 1
D ~ Y Avg =151
Figure 4.25: Full Scale Operating Data - Sludge Volume Index The sludge volume index (SVI) is shown for the aeration basin of the fùll scale treatrnent system.
I I 1 1
1 l , I 1 , I 1 1 !
60 65 70 75 80 85 90 95 100 105 110
D ~ Y Avg =12
Figure 4.26: Full Scale Operating Data - Treated Effluent BOD The biochemical oxygen demand (BOD) is shown for the treated effluent from the fidl scale treatment systern.
Avg = 6
Figure 4.27: Full Scale Operating Data - 113 HRT SOUR The specific oxygen uptake rate (SOUR) is shown at approximately 113 the hydraulic retention time (HRT) in the aeration basin of the fidl scale treatment system.
D ~ Y Avg = 5
Figure 4.28: Full Scale Operating Data - Full HRT SOUR The specific oxygen uptake rate (SOUR) is shown at the fidl hydraulic retention time (HRT) in the aeration basin of the full scale treatment system.
D ~ Y Avg =30
Figure 4.29: Full Scale Operating Data - Treated Effluent TSS The total suspended solids (TSS) is shown for the final treated effluent of the fidl scale treatment system.
5.1 Standard Respirometry Screening Protocol
5.1.1 Characteristics of Oxygenuptake Curves
The shapes of the oxygen uptake curves, in addition to the relative OURS of
controls and test samples, provide information on the nature of the biomass during the
screening experiments. The results show that there are several variations in the shape of
the plots of cumulative oxygen uptake versus time. The typical oxygen uptake curve (eg.
Figure 4.4) consists of a brief lag period, a steep linear region (constant OUR) and a
period where the OUR gradually decreases. This characteristic shape is a result of the
natural metabolic cycle of the microorganisms in the activated sludge.
The microorganisms first need to synthesize digestive enzymes before substrate
can be oxidized, resulting in a lag period. Although the duration of the lag period has the
potential to infer chemical-induced respiratory impacts, it is often artificially extended due
to data recording only at 30 minute intervals.
The constant OUR region corresponds to substrate oxidation by the respiring
organisms. After a period of 8-10 hours the oxygen uptake rate usually starts to decrease,
indicating that the soluble food sources have been depleted, and the biomass is engaging in
endogenous respiration.
In some results (Figure 4.2, Figure 4.5, Figure 4.7) the oxygen uptake rate
increases, rather than decreases afler a period of several hours. There are many reasons
why the OUR may increase with time: çrowth of new biomass, additional carbon sources
available, acclimation to toxic compounds, removal of inhibitory compounds, or other
stimulation mechanisms. In Figure 4.2, the OUR of the control and the test sample
increases afler the linear region. It is iikely that growth is responsible for this increase in
OUR since about one third the standard biomass concentration was used, and the effect
was observed in both controls and tests.
In the case of Blue 5 dye (Figure 4 .9 , it appears as though the addition of the dye
acted as a food source. It is known that the paper dyes screened in this work are not
biodegradable [Pagga and Brown, 19861, yet compared to the control containing no Blue
5 dye, the OUR of the dye spiked sample is 53% greater during the linear region and
further diverges with time. This type of exponential OUR would indicate that growth is
taking place, since the OUR increases with the mass of respiring organisms. Examination
of the TSS and TDC data (Appendix B) before and after the experiment show no
significant differences between the controls and dye samples, indicating that the significant
increase in oxygen consumption in the dye sample did not accompany a significant
increase in growth or substrate removat.
Blue 5 dye may act to uncouple oxidative phosphoiylation, as do many chemicals
when present below their toxic threshold levels. Oxidative phosphorylation, the process
by which ADP is converted to ATP through an electron transport chain, is normally tightly
coupled to oxygen consumption. When this couplinç is removed, through the action of
certain toxic compounds, the organism loses control of respiration rate and begins
metabolizing their own cell coniponents [Patoczka et al., 19881, without producing ATP.
The molecular structure of basic dyes are similar to the known uncoupler,
carbonylcyanide-p-trifluoro-methoxyphenylhydrone, suggesting a possible structure-
activity relationship. This is one shortcoming of respirometry. Relying only on the
oxygen uptake curves may lead to the belief that Blue 5 dye is a good food source or a
way to boost treatment system performance, when in fact blue dye could lead to treatment
system failure.
In most experiments using Orange 3 dye (eg. Figure 4.7), there is a dramatic
decrease in the OUR before a sudden increase after about 15 hours. This observation has
been called a deathhecovery phenomenon because it would appear that afier being
severely inhibited the biomass makes a remarkable recovery and begins respiring at a
greater rate than the control. The first notion may be that afler an initial decline in
biomass activity, the biomass becomes acclimated to the toxic dye and begins to normally
metabolize substrate. Several studies [Tabak et al., 198 1, Tabak et al., 1992, and Tabak
and Barth, 19781 suggest that acclimation to toxic substances may take days or even
weeks, and this scenario does not account for the greater than normal OUR that followed
the toxic shock. Microscopic examination shows that there is a rapid kill of higher life
forms, implying some degree of bacterial toxicity which corresponds to the OUR
inhibition. When the OUR begins to increase there is a bloom of bacteria and only
carcasses of protozoa and rotifers. The most probable explanation is that dead cells lyse
and are subsequently consumed by the surviving organisms. Another factor which aids
bacterial çrowth is that physical adsorption, which is thought to be an important removal
mechanism, may reduce the free dye to a sub-toxic concentration. Thus, the surviving
single-celled bacteria may consume soluble organic material without dye-induced
inhibition, o r predatory action by higher life forms.
5.1.2 Reproducibility of Screening Results
Four replicates were performed using Orange 3 dye over a period of several
months. The results show that the percent inhibition of the biomass was consistently
about 55-60 % at 750 mgL of Orange 3 dye. Repeated tests using mil1 effluent with the
new TMP fùrnish imply that the source of the BOD has no bearing on the impact of the
chemical on the respiration rate of the biomass. Determining the linear region for
obtaining the OUR can be subjective. Ideally, an algorithm could be used for getting
reproducible results with different testers.
5.1.3 Sensitivity of Screening Tests
The sensitivity of the screening tests is a fbnction of the applicability of
respirometiy and the experimental design of the standard protocol. It is well established
that respirometry is a valid and sensitive technique for measuring changes in biomass
activit y. The standard screening met hod was also designed (eg . temperature, test
duration) to be applicable to conditions encountered in a biological treatment system. In
addition, the controls and tests could be paired within an experimental run. The between-
control and between-test difference was compared to the difference amonç controls and
amonç tests. This makes the screening test internally consistent because of the built-in
check for experimental errors or equipment problems. That is, it is unlikely that one would
make the same error in both controls or both tests, or that a leak in one reactor would go
unnoticed. Analysis becornes more sensitive because there are twice as many observations
for each set of conditions. For each run, the experimental precision is apparent and the
significance of results can almost be determined by inspection.
The sensitivity to a paper dye during respirometer screening was dependent on the
amount of biomass present. At lower biomass concentrations, the toxicity of dyes
increased significantly, suggesting that toxicity can be reduced through physical adsorption
to biomass, and that the key to sensitivity is the rnass of material available to adsorb
toxins. Inert material may also act as a buffer for toxic upsets, if the toxin is highly
physically adsorbed. In this case, the toxin is a cationic paper dye and the inert material
is anionic paper fibres, so physical removal is expected to be efficient and imeversible. To
present the most realistic measure for a chernical's potential t o influence respiring
microorganisms, a standard screening procedure should use biomass concentrations as
close t o fùll scale conditions as possible, without exceeding the OUR limitations of the
respirometer.
5.1.4 Biases and Precision in Results
Comparison of the differences between like reactors for the 20 sets of screening
data (Table 4.2) suggest that there was a slight (3 %) bias in the results (Control
I>Control2, Test ]>Test 2). This is not surprising since the control and test reactors
always occupied the same position in the respirometer. This system was chosen for
experimental convenience and to improve troubleshooting of equipment malfunctions.
Randomization of reactor location would eliminate this problem, without affecting the
conclusions regarding the significance of chemical-induced effects.
An estimate of the precision of the screening method can be made by comparing
the 20 pairs of controls in Table 4.2. Differences between controls were typically less than
3 mg/Lh (95% confidence) with an average difference of 0.8 mgLh and a standard
deviation of 1.3 mg/Lh.
There are several ways in which observed chernical effects can be examined for
significance. A t-test (method 1) using errors in estimating the OUR provides the largest
degrees of fieedom, and a very sensitive test since these errors in the dope tend to be
quite small (0.05-0.1). However, the error associated with preparing identical reactor
contents is much larger than the error in measuring the OUR for a given reactor. An
analysis of variance (ANOVA), method 2, to compare differences among like reactors to
differences between reactor pairs, treats the estimated OUR as a single point. The
difficulty with this rnethod is that with 1 and 2 degrees of fieedom it is a very weak test.
Nonetheless, even this insensitive test indicates the significance of differences caused by
the main problem chemicals.
The ANOVA could be made sensitive by increasing the number of replicates for
tests and controls. This could be achieved by either investing in a larger respirometer to
accommodate more samples per run, or by using a consistent biomass source (chemostat)
so that experiments on one day could be compared to those performed on another.
An alternative, and preferred, treatment (method 3) States that three criteria should
be met for screening test results to be meaningful. First, the average OUR of the controls
should be about 30 m a h , so that experimental error (<3 rng/Lh) is less than 1 O % of the
measured OUR. Secondfy, the screening test should be repeated if a difference of greater
than 3 mg/Lh between either the controls or the tests occurs. Lastly, in order for a
chernical effect to be considered significant, the difference between the average control
and the average test OUR must be greater than 3 mg/Lh and be greater than twice the
between-control difference.
5.2 Toxicity of Paper Additives
Respirometer screening and pilot plant results indicate that paper additives are
toxic at very high concentrations, but unlikely to cause significant effects under normal
operations. The worst offender was Orange 3 dye, causing a 61 % inhibition to the
oxygen uptake rate at a concentration of 750 mgL. An inhibition of this magnitude
represents a severe toxic shock that would likely cause failure of the treatment system.
However, this screening concentration represents ten times the estimated worst case
effluent concentration, assuming no dye is retained in the paper.
Further screening showed that at 75 mg/L there was no negative impact on the
respiration rate of activated sludge. If one further considers that approximately 80 % of
the paper dyes used become part of the finished paper product (BASF, 1997), it becomes
clearer that the activated sludge system at iroquois Falls is in no danger from toxicity due
to normal dye consumption in the paper mill.
During the course of this research, the bioloçical effluent treatment system has
experienced many extended exposures to paper dyes without loss in treatment
performance. However, there are always some subtle changes in the treatment system (eç.
coloured foam, slight dispersed growth) but without specific and explicit documentation
these observations are not captured in routinely monitored process parameters. Hence,
significant inhibitions seen in respirometer tests (at ten times the normal concentration)
suggest that there are small impacts to the biomass at lower doses, yet, not observable
with the screening test.
Milanova et al. (1 997) reported toxicities to activated sludge comparable to that
found in this work for selected paper dyes, and similarly concluded that normal effluent
concentrations should be non-inhibitory. Brown et al. (1981) reported IC,, values of 10-
100 mgL for the three dyes found here to the most toxic: Orange 3, Violet 5 and Red B.
These concentrations suggest that oxygen uptake inhibitions may occur under normal mil1
operation. Since the same chernicals were found to be toxic, the difference in the results
may be due to the source of the biomass: Milanova et al. used biomass from a TMP d l ,
Brown et al. used biomass from a municipal treatment plant. The biomass used by Brown
et al. was probably more sensitive to inhibition by paper dyes because it was totally
unacclimated. Moebius and Demel(1985) found the normal effluent fiom various paper
mills to be toxic to unacclimated biomass fiom a municipal treatment system.
5.3 Pilot Plant Operation
The pilot plant was operated to emulate the full scale treatment system as much as
possible, and despite some different operating procedures, it is believed to have been a
valid indicator of full scale system response. The pilot plant was a continuous system with
comparable design and operating parameters to the ful l scale system. Biomass from the
fbll scale system was used to inoculate the pilot plant, and both systems treated the same
wastewater. Cornparison of the operating data trends between the 611 scale system and
the pilot systems indicates that aside from being less stable, the pilot scale treatment
systems were comparable to the full scale operation.
The biggest observed difference between pilot plant and full scale operation was
the apparent elevated bioactivity level of the pilot plant, as evidenced by the SOURs. The
fbll scale treatment system has historically had a low SOUR (5.0 mgfgh), compared with
the pilot plant biomass (12 mglgh). The most probable explanation is that the two
populations had comparable activity levels, but the pilot plant samples were taken in an
area of higher metabolic activity. This hypothesis could be venfied by comparing the
endogenous respiration rates of the two biomasses.
The activity (SOUR) of a biomass can be an indirect measure of the amount of
inert material (eg. paper fibre) present. One possible explanation for these SOUR
differences between treatment systems, is the amount of paper fibre entering the treatment
system as carryover from primary effluent treatment. Based on average solids loadings
and wasting rates of the full scale system, the biomass may be as much as 43 % paper
fibres by weight. It is known that sorne floatable TSS (paper fibres) were removed from
the feed of the pilot plant to prevent plugging of the equalization basin overflow.
However, considering the mass of material actually removed, and that required to make a
significant difference, it is highly unlikely that this had any impact on either the sensitivity
or the inert fraction of the pilot plant biomass.
The sludge age in the pilot plant was controlled to seven days by wasting a known
volume directly from the aeration basins each day. Approximately another 10 % of the
inventory was wasted each day through effluent suspended solids. Ignoring the inventory
in the clarifier (8 %) during wasting helped to offset effluent solids losses, such that the
actual sludge age was probably quite close to seven days. The sludge age in the full scale
system was allowed Vary, but averaged 8.7 2.1 days during pilot plant operation.
The hydraulics of the fidl scale system have never been modeled, but it was
believed that the pilot plant operation of two CSTRs in series would provide a close
approximation.
5.4 Predicting Upsets with Respirometry
The screening method employing batch respirometry was able to predict process
upsets in the continuous flow pilot plant and is therefore applicable to fidl scale treatment
systems. The principle upsets predicted by respirometry are a dramatic decrease in
SOUR and a microbial population shift as higher life forms are killed and single celled
bacteria dominate.
There were no observable changes in the pilot plant after the short term exposure
to dye. There may have been some small toxicity as the RAS and dye were mixed, but
upon entering the first aeration basin, dilution and adsorption would have dropped the dye
concentration considerably. During such a short time, only about 10 % of the biomass
would corne into contact with the desired dye concentration. A better approach would
have been to consider the dye loading required to produce the desired free dye
concentration.
--
Continuous exposure to Orange 3 dye during the second pilot plant perturbation
resulted in a 72 % and a 52 % SOUR decrease in the first and second aeration basins by
the third day (system saturation). This inhibition is comparable to batch testing with pilot
plant biomass at Iow biomass concentrations (56 %), but is twice as great as effects fiom
batch testing with full scale biomass at standard biomass concentrations (27 %). The
increased response by the pilot plant biomass during continuous flow exposure may be due
to saturation of the biomass by the dye, which does not occur in batch tests. This
hypothesis could be tested using successive batch tests with Orange 3 dye.
There were also other impacts on treatment system performance, not predicted by
respirornetry. Effluent solids losses increased due to dispersed growth and the nature of
the pinpoint floc produced fiom this growth condition. The settling characteristics of the
sludge also appeared to deteriorate as suspected de-nitrification caused biomass to float in
the clarifier. Foaming, indicative of toxicity, also increased after extended dye exposure.
This same type of foam has been observed at the full scale plant start-up and afier
extended dye consumption in the paper mill.
Ammonia residual decreased while nitritelnitrate increased. The reduced amrnonia
residual is due to rapid growth of new bacteria and the nitritelnitrate can be explained by
lysis of dead cells. Pagga and Brown (1986) suççest that the dyes are not broken down
by activated sludçe organisms and probably do not themselves release nitrogen. The
origin of this nitroçen is not known, but it is believed to be due to increased lysis
associat ed wit h dye toxicity . Elevated nitri telnitrate levels may lead to emuent toxicity or
denitrification with an inversion of the secondary clarifier leading to excess solid losses.
The final effect of the Orange 3 dye on the pilot plant, not predicted by
respirometry, is the dramatic darkening of the biomass and the final treated effluent. This
observation is indicative of the extensive adsorption of dye ont0 biomass. Sludge
darkening is often observed in the fùll scale system especially afler producing black
construction paper, but never to the extent seen in pilot experirnents. Slight colouration of
the treated effluent is alsoobserved in the hl1 scale system, a rerninder of the recalcitrant
nature of paper dyes.
Respirometer screening tests with polymers used in paper making indicated that
these large inert molecules did not impact the respiration rate of the biomass. Microscopic
examination confirrned that there was indeed no observable toxicity fiom these
compounds. If one considers the molecular weight (ca. 1-2 million g/mol) of these
macromolecules, it is unlikeiy that they would be bioavailable, and would therefore be
inactive as toxicants. However, one possible impact on activated sludge, not addressed by
the respirometric screening procedure, is.floatation of the biomass during aeration. Large
quantities of high molecular weight polymers causing flocculation and excessive air
entrainment could hinder treatrnent system performance. In extreme circumstances, the
biological inventory in the treatment system could accumulate on the surface, as in a
dissolved air floatation clarifier, reducing BOD and toxicity removal across the system and
creating the potential for a significant biological washout due to hindered settling in the
secondary clarifier.
5.5 Significance of Research
The development of a valid chemical prescreening test, based on batch
respirometry, is a powerfitl tool for the stable operation of a biological effluent treatment
system. Fact based decisions, based on test results, cm be made regarding the use of
chemicals in the mill (eg. which chemicals, when and how much) and the potential for
process upsets in the event of a chemical spill. Screening test results which indicate a
potentia1 for system perturbation can be followed by challenging chemical suppliers to
provide equivalent, yet, less toxic products. The production of certain paper products,
which necessarily require large amounts of chemical additives, may be scheduled to allow
advance fortification (eg. through increased biological inventory) of the effluent treatment
system. Ideally, al1 chemicals on the mill premises should be screened for their potential
impact on the activated sludge effluent treatment system.
1. Batch respirometry is a valid method for screening chemicals for their impact on an
activated sludge effluent treatment system. The method developed imposes minimal
technician time requirements (2 hours) and has been shown to yield reproducible results.
The test is sensitive enough to detect chemical-induced changes of 10 % to the respiration
rate of the treatment system biomass .
2. Respirometric measurements have some disadvantages. Physical changes in the
biomass (eg. settling characteristics) are not detected by measuring oxygen uptake rates,
and can only be evaluated with a continuous pilot plant treatment system. Conclusions
based exclusively on respirometry may be misleading if a chemical causes stimulation of
the oxygen uptake rate through uncoupling of oxidative phosphorylation.
3. Qualitative microscopic examinations, as part of the standard screening test, are
valuable in assessing the toxicity of chemicals. Toxicity to indicator organisms (eg.
rotifers and protozoa) can detect subtle effects not observed with respirometry and may
help to explore possible uncoupling effects.
4. Some chemicals are toxic to activated sludge at high concentrations, but are non
toxic at normally expected miIl effluent concentrations. Of paper additives, paper dyes
were found to be most toxic with Orange 3, Violet 5, and Red B causing the highest
inhibitions to the respiration rate of the biomass. Other process aids, such as a general
purpose cleaner/solvent and a microbiocide also exerted a siçnificant toxicity to activated
sludge.
5 . Adsorption of dyes by biomass is an important toxicity removal mechanism.
Sensitivity to dyes increases with decreasing biomass concentration and may possibly
increase with extended dye exposure as the biomass becomes saturated and adsorption
less effective.
6. Screening results, using the standard screening procedure based on respirometry,
should meet certain criteria:
i) the magnitude of the oxygen uptake of the controls should be approximately 30
mg/Lh so that experimental errors are less than 10 % of the measured OUR
ii) the difference among like reactors should be less than 3 mg/Lh
iii) to be significant, the difference between the average control and the average
test must be greater than 3 mg/Lh and be greater than twice the between-control
difference
7. Final, treated effluent may be toxic due to dye carryover or elevated nitrite
concentrations associated with paper dyes. Toxicity testing was not performed on the
effluent from the pilot plant, but the dyes are known to be recalcitrant and toxic to
rainbow trout. Daphrria magna, a commonly used organism in toxicity testing, is quite
sensitive to elevated nitrite concentrations, which increase when significant amounts of
dyes are present in the treatment system.
Experimental work is recommended in the following areas, to further explore how
chernicals can affect an activated sludge effluent treatment system.
i) develop semi-quantitative rnicroscopic tracking for subtle effects to biomass
ii) expand screening test to include air entrainment and settling interactions (sequencing
batch reactor or shake flask) so that polymers (eg. flocculants) can be accurately assessed
iii) investigate contribution of adsorption on screening results
iv) investigate the utility of a simple oxygen probe / BOD bottle screening test
APHA, Standard Methods for the Examination of Water and Wastewater, 1 8th Ed., Arnerican Public Health Association, American Works Association, and Water Environment Federation, Washington, DC, 1 992.
BASF, Private communication with Craig Leavitt, 1997.
Beach, M.I., Jacquez, R.B., Cadena, F., Shah, A., A Computenzed Respirometric Method for Determinhg Inhibition Potential of Wastewaters, 45th Purdue Industrial Waste Conference Proceedings, Lewis Publishers Inc., 487, 1991
Brown, D., Hitz, H.R., Schafer, F., The Assessment of the Possible Inhibitory Effect of Dyestuffs on Aerobic Waste-Water Bacteria Experience with a Screening Test, Chemosphere, 10, 245, (1 98 1).
Colvin, R.J., Rozich, A.F., Hough, B.J., Gaudy, A.F., Use of Respirometry to Evaluate the Biodegradability of Emulsified Specialty ChemicaI Products, 45th Purdue Industrial Waste Conference Proceedings, Lewis Publishers Inc., 477, 199 1.
Colvin, R.J., Rozich, A.F., Gaudy, A.F., Martin, J., Application of A Process Mode1 Calibrated with Respirometry to Predict Full-Scale Activated Sludge Performance, 45th Purdue Industrial Waste Conference Proceedinas, Lewis Publishers Inc., 50 1, 199 1 .
Demel, I., Moebius, C.H., Toxic Inhibitions in Paper Mill Wastewaters. Part I., Wochenbl. Papierfabr. 11 1,95, (1983).
Dutton, R.J., Bitton, G., and Koopman, B., Rapid Test for Toxicity in Wastewater Systems, Toxic. Assess. h t . o., 1, 147, (1986).
Gaudy, A.F., Rozich, A.F., Garniewski, S., Moran, N.R., Ekambaram, A., Methodology for Utilizing Repirometric Data to Assess Biodegradation Kinetics, 42nd Purdue Industrial Waste Conference Proceedings, Lewis Publishers Inc., 573, 1988.
Gaudy, A.F., Rozich, A.F., Colvin, R., Lowe, W., Practical Methodology for Predicting Critical Operating Range of Biological Systems Treating Inhibitory Substrates, J. WPCF, 60, 77, (1 988a).
Gaudy A.F., Ekambaram, A., Rozich, A.F., A Respirometric Method for Biokinetic Characterization of Toxic Wastes, 43rd Purdue Industrial Waste Conference Proceedinrr;~, Lewis Publishers Inc., 35, 1989.
Gaudy, A.F., Ekambaram, A., Rozich, A.F., Colvin, R.J., Cornparison of Respirometrk Methods for Determination of Biokinetic Constants for Toxic and Nontoxic Wastes, 44th Purdue Industrial Waste Conference Proceedings, Lewis Publishers Inc., 393, 1990.
Geating, J., Literature study of the biodegradability of chernicals in water, U.S.E.P.A. Report Number EPA-60012-8 1-1 75, (1 98 1).
Grady, C .P.L., Lim, H. C., Biological Wastewater Treatment : Theorv and Applications, Marcel Dekker, Inc., New York, 1980.
Hartmann, C., Laubenberger, G., Toxicity Measurements in Activated Sludge, J. Sunit. Engng. Div., h o c . Am. Soc. Civ. E17gr~. , 94, 247, (1 968).
Jenkins, D., The Use of Manometric Methods in the Study of Sewage and Trade Waters, In: Waste Treatment, P.C.G. Isaac (ed.), Pergamon Press, Oxford (1 960).
Jenkins, D., Richard, M.G., Daigger, G.T., Manual on the Causes and Control of Activated Sludge Bulking; and Foaminq, 2nd Ed., Lewis Publishers Inc., Chelsea, 1993.
Katz, E., and Weber, W.J., Discussion, Wat. Sci. Tech., 18, 1280, (1985).
King, E.F., and Dutka, B.J., Respirornetric Techniques, Britton, G., and Dutka, B.J. (eds.), in Toxicity Testin Using Microorganisms, Vol I., CRC Press, Inc., Boca Raton, Florida, 1986.
Kovacs, T.G., Voss, R.H., Biological and Chernical Characterization of Newsprintlspecialty Mill Effluents, Waler Res., 26, 77 1, (1 992).
Landis, W.G., Ming, H.Y., Environmental Toxicolow Impacts of Chemicals Upon Ecological S~stems, Lewis Publishers Inc., 1995.
Larisch, B.C ., DuK S. J.B., Effect of DTPA on the Activated Sludge Treatment of TCF and ECF Kraft Pulping Effluents, Proceedinas of the 83rd Annual Meeting, Technical Section CPPA, A143, 1997.
Lo., S.N., Lavallee, H.C., Rowbottom, R.S., Meunier, M.A., Zaloum, R., Activated Sludge Treatment of TMP miIl emuents, Part 1, Tol,>i Jowwnl, 77, 167, (1 994).
Lo., S.N., Lavallee, H.C., Rowbottom, R.S., Meunier, M.A., Zaloum, R., Activated Sludge Treatment of TMP niill effluents, Pan 2, 7Oppi Joiri~nl. 77, 179, (1 994).
Melcer, H., and Bedford W.K., Removal of Pentachlorophenol in Muncipal Activated Sludge Systems, Presented 59th WPCF Conf., Los Angeles, California, 1986.
Metcalf & Eddy, Inc., Wastewater Engineering Treatment, Disposal, and Reuse, 3rd Ed., McGraw-Hill, Inc., New York., 1991.
Milanova, E., and Sithole, B.B., A Simple Method for Estimation of Newsprint Dyes in Effluents and Their Migration from Paper Samples, Ta* Jourmai, 80, 121, (1 997).
Milanova, E., Wood, S., Sithole, B.B., Toxicity of Newsprint Dyes to Rainbow Trout and Activated Sludge, Tappi Jounzal, 80, 1 13, (1 997).
Moebius, C.H., Demel, I., Toxic Inhibitions in Paper Mill Wastewater. Part 2., Wochenbl. Papierfabr. 113, 797, (1 985).
Monod, J., The Growth of Bacterial Cultures, A. Rev. Microbiol., 3, 371, (1949).
Newman, M.C., Quantitative Methods in Aquatic Ecotoxicoloe;v, Lewis Publishers Inc., Chelsea, 1995.
Pagga, U., Brown, D., The Degradation of Dyestuffs: Part II Behaviour of Dyestuffs in Aerobic Biodegradation Tests, Chemosphere, 15,479, (1 986).
Patoczka, J, Pulliam, G.W., Chowning, G.L., Determination of Toxicity Thresholds of Industrial Wastestreams to Activated Sludge Process Using Fed Batch Reactor, 43rd Purdue Industrial Waste Conference Proceedings, Lewis Publishers Inc., 5 1, 1989.
Patsi-Grisby, M.B., Burke, N.S., Gosczynski, S. and Crawford, D.L., Transformation of azo dye isomers by Strepkmyces C h r ~ ? ? ? ~ f i d ~ ~ i l ~ Al 1 ., Appl. Environ. Mzcrobioi. 62, 18 14, (1 996).
Ros, M., Respirometv of Activated Sludge, Tehnomic Publishing Co., Lancaster, PA., 1993.
Rozich, A.F., and Gaudy, A.F., Design and Operation of Activated Sludge Processes Using Respirometry, Lewis Publishers Inc., Chelsea, Michigan, 1992.
Selvakumar, A., Hsieh, H., Removal of Organic Compounds by Microbial Biomass, 43rd Purdue Industriai Waste Conference Proceedings, Lewis Publishers Inc., 275, 1989.
Smook, G.A., Handbook for Pulp & Paper Technolo.&s, 2nd Ed., Angus Wilde Publications Inc., Vancouver, BC, 1994.
Spieçel, M.R., Probability and Statistics, Schaum's Outline Series, McGraw-Hill, Inc., NY, NY, 1991.
Suschka, J., and Ferreira, E., Activated SIudge Respirometric Measurements, Waf. Res., 20, 137, (1986).
Tabak, H.H., and Barth, E.F., Biodegradability of Benzidine in Aerobic Suspended Growth Reactors, J. WPCF, 50, 552, (1978).
Tabak, H.H., et al., Biodegradability Studies with Organic Priority Pollutant Compounds, J. W C F , 53, 10, 1503, (1981).
Tabak, H.H., Gao, C., Desai, S., and Govind R., Development of Predictive Stmcture- Biodegradation Relationship Models with the use of Respirometrically Generated Biokinetic Data, Wat. Sei. Tech., 26, 763, (1992).
Therien, N., LeCalve, P., Jones, P., A Respirometric Study of the Influence of Aliphatic Aicohols on Activated Sludge, Wat. Res., 18, 905, (1984).
Tur, M.Y., Tseng, L., Chou., H., Fang., H., Hsia, J., Application of Oxygen Uptake Rate in Managing An Activated Sludge Process for Treating Refinery Wastewater, 44th Purdue Industrial Waste Conference Proceedings, Lewis Publishers Inc., 495, 1990.
Voet, D., and Voet, J.G., Biochemistry, John Wiley & Sons, Inc., NY, NY, 1990.
Volskay, V.T., and Grady C.P.L., Toxicity of Selected RCRA Compounds to Activated Sludge Microorganisms, J. W C F , 60, 1850 (1 988).
Volskay, V.T., and Grady, C.P.L., Respiration Inhibition Kinetic Analysis, Wat. Res. 24, 863 (1990).
Wong, P.K. and Yuen, P.K., Decolorkation and Biodegradation of Methyl Red by Klebsiella pneun~orîiae RS-13, Wat. Res., 30, 1736, (1 996).
APPENDIX A:
STANDARD SCREENING PROCEDURE
METHOD.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..A- 1
DETAILS.. . . . . . . . . . . . . . . . . . . . . . . . . , DETAILS................................, , . DETAILS................................, . DETAILS................................, . . . . . . . . . . . . . . . . . . . . . . . . . . . . .A-2
Standard Method For Chernical Screening Using Respirometry
Emuent Preparation: Thaw 800 mL effluent sample in a warm water bath Filter with Whatman 934-AH glass fibre filter papers Adjust pH to 8.0 Prepare appropriate dilution and determine TDC Store at 25 OC until needed
Biomass Preparation: Sarnple 800 mL of biomass from RAS pump (let sample line flow for one minute) Measure and record pH initially and at end of aeration Determine TSS using 25 mL sample volume Aerate with Stone sparger for two hours Examine microbiology
Test Sample Preparation: Determine volumes of effluent and RAS for target MLSS Add stir bar to each of four respirometer bottles Add 10 KOH pellets to traps Add effluent to each of four, 400 mL beakers Add predetermined volume of chernical to two of these beakers Check and adjust pH to 8.0 (as required) for chernical containing beakers Add biomass to each beaker such that final volume is 250 mL Remove 50 nir, from each beaker and place in 250 rnL beakers Transfer remaining 200 mL to respirometer bottles Place uncapped bottles in respirometer for 15 minutes to equilibrate temperature Measure and record pH of samples in 250 mL beakers Determine actual TSS for each 250 rnL beaker using 25 rnL sample volume Save filtrate in 100 mL beaker, prepare dilution and determine TDC M e r temperature equilibration, cap respirometer bottles and start tests M e r test, promptly remove bottles from respirometer Transfer contents to 250 mL beakers Measure and record final pH Examine microbioloçy of samples and controls Determine final TSS in each bottle using 25 mL sample volume Save, dilute, and determine TDC of filtrate as before
Appendis A: Siandard Screcning Procedure
Experimental DetaiIs
The following advice is intended for anyone wishing to carry out the standard respirometer
procedure outlined above.
It is not necessary to use a fiozen effluent source, unless comparison of screening results
for a single chernical are required over a significant time period, or there is concern that chernicals
present in the current wastewater are inhibitory.
The pH may be adjusted to between 7 and 8 depending on the current operating
conditions of the treatment plant. Small changes in pH should not affect test results. Be sure to
adjust the pH of the chemical / effluent mixture before adding the biomass, otherwise chemical
induced effects may be contiised for pH effects. The pH meter used was a Radiometer
Copenhagen PHM 82 Standard pH Meter with an Accumet pH electrode (Fisher Scientific,
Toronto, ON). A O. 1 M solution of NaOH (Sodium Hydroxide, ACS Grade, BDH Inc.,
Toronto, ON) was used to adjust the pH.
Usually a 10x dilution is sufficient for the Dohrmann DC-190 Total Organic Carbon
Analyzer (Rosemount Analytical, Santa Clara, CA, USA). Calibrate the instrument using a 100
mg C L KHP (Potassium Hydrogen Phthalate, AnalaR Grade, BDH Inc., Toronto, ON) solution
(dissolve 0.2126 g of KHP and make up to 100 mL). Store standard in refngerator at 4 O C . Use
deionized water (Milli-Q or equivalent) as the blank. Three replicates for each sample, with a
water rinse in between samples çives satisfactory results.
Aerate biomass usinç an aquarium air pump (HARTZ Canada Inc., St. Thomas, ON) and
a Stone sparger (20 mm), mixing with a maçnetic stirrer is not essential but it ensures more
Appèridis A: Standard Scrcening Procedurc
uniform aeration of the sample.
When examining the microbiology, prepare at least three slides, to get a representative
sampling of the culture. On a Iow power (100-400x), examine the size of the flocs and their
shape, record the abundance (lots, few, none etc.) of rotifers and protozoa. Use Higher
magnification for positive identification of filament types, and for recording the abundance of
dispersed single celled bacteria. An excellent resource book is "Manual on the Causes and
Control of Activated Sludge Bulking and Foaming", 2nd Edition, by David Jenkins, Michael G.
Richard and Glen T. Daigger, Lewis Publishers, Inc., Chelsea, MI, U S 4 1993.
When diluting the RAS and effluent, it is convenient to stick to a standard volume ratio. If
the RAS concentration is fairly consistent at about 7000-8000 mg& use 70 mL of RAS and 180
mL of effluent. An increased proportion of RAS may be used if the MLSS of a particular
treatment system is targeted. T h e higher the RAS, the less sensitive (but more realistic) the test
will be. That is, more dramatic effects will be observed with a lower RAS concentration.
Experiment with varying amounts of RAS and effluent to find a balance between convenience, a
sufficiently high OUR (approx. 30 mg/Lh) and applicability to a real treatment system.
T h e mass of KOH (Potassium Hydroxide, ACS Grade, BDH Inc., Toronto, ON) added to
the reactor traps is not critical. Ensure that there is sufficient KOH to consume the CO, produced
during respiration.
The amount of time the samples are left in the water bath before the start of the test, will
affect test results. The relationship between exposure time t o chemicals and observed toxicity is
complicated and different for each chemical, each orçanism and different experimental conditions.
Appendis A: Standard Screening Procedure
Fifieen minutes is usually enough time for the samples to reach the temperature of the water bath,
if the lab temperature is fairly (h2 OC) close to water bath temperature. If thermal equilibnum is
not achieved before the start of the test, erratic and erroneous oxygen consumption will be
observed as the contraction or expansion of the gases in the reactor head space creates a negative
or a positive reactor pressure.
Prompt removal of samples is essential to prevent damaging the sensitive differential
pressure switches inside the respirometer. Continuing carbon dioxide removal without pressure
stabilization by oxygen will result in sufficient negative pressure inside the reactor to damage the
differential pressure switches.
Welding grade oxygen was used for the Comput-RX 00-240 Respirometer (N-CON
Systems Inc., Larchmont, NY, USA).
After calibration (follow manufacturers suggestions), test the calibration by draining a
known volume from a burrette (sealed at the top) connected to a reactor port inside the
respirometer. The manufacturer suggests a precision of 10 %, but 1-2 % can be routinely
achieved. The precision of the respirometer is essential (agreement between reactors), the
accuracy of the results is secondary (agreement with a standard).
Appcndis A: Standard Screening Procedure
APPENDIX B:
RAW DATA
OXYGEN UPTAKE DATA
TSS, TDC, pH DATA
Appendix B: Raw Data
Red A Avg Chem (mg OZL)
O 15.2
29.85 45.05
Avg Ctrl (mg 0ZL)
O 13.95 28.8
44.65
Time (h)
O 0.5
1 1.5
Test 1 (mg 0ZL)
O 14.8 29.5 44.3
Control 1 (mg OUL)
O 20 35 51
Test 2 (mg OZL)
O 15.6 30.2 45.8
Control 2 (mg 02fL)
O 7.9
22.6 38.3
Appendix 6: Raw Data
Appendix B: Raw Data
Orange 3 Time
(h) O
0.5 1
1.5
Control 2 (mg OZL)
O 4
16.8 28.7
Control 1 (mg 0ZL)
O 8.2
20.5 33.7
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
Test 1 (mg OZIL)
O O
2.2 7.5
266.9 271 273
276.1 278.1 281.2 283.2 284.3 286.3 288.4 290.4 291.4
Test 2 (mg 0ZL)
O O
3.2 9.5
255.6 258.6 261.6 264.6 266.5 268.5 270.5 272.5 273.5 275.5 277.4 278.4
Avg Ctrl (mg 02lL)
O 6.1
18.65 31.2
Avg Chem (mg OZL)
O O
2.7 8.5
97 99.1
101.3 103.5 105.6 106.7 109.9
11 1 il 3.2 11 5.3 11 8.5 120.7
96 97
99.1 101.3 102.3 104.4 106.5 108.6 11 0.7 112.9
115 11 7.1
261.25 264.8 267.3
270.35 272.3
274.85 276.85 278.4 279.9
281.95 283.9 284.9
96.5 98.05 100.2 102.4
103.95 105.55
108.2 109.8
111.95 114.1
116.75 118.9
Appendix B: Raw Data
Control2 (mg OUL)
O O
7.9 21 -8 35.7 48.6 62.4 76.3 91.2 1 05
11 5.9 128.8 140.7 152.6 164.5 176.4 189.2 201 -1
214 225.9 238.8 250.7 263.6 275.5 287.3 299.2 310.1
322 332.9 342.8 353.7 363.6 373.5 383.5 393.4 403.3 41 3.2 422.1
432 440.9 450.81
Violet 3 Test 1
(mg OZL) O O
5 -4 15.1 25.9 34.5 44.2 53.9 64.7 75.4 84.1 92.7
1 02.4 11 1
120.7 130.4 140.1 149.8 1 59.5 169.2
180 190.7 201 -5 21 2.3 224.2
236 248.9
264 279.1 295.3 311.5 324.4 336.2 348.1 359.9 370.7 382.6 393.4 405.2
41 6 426.8,
Time (h)
O 0.5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
10.5 11
11.5 12
12.5 13
13.5 14
14.5 - 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 I 201
Control 1 (mg OZL)
O 2
13.3 25.6 37.8 48.1 60.3 72.6 85.9
100.2 1 10.4 121.7 132.9 144.2 1 55.4 166.7 177.9 189.2 201.4 212.7 223.9 234.2 246.4 257.7 268.9 280.2 291.4 302.7 31 2.9 324.1 334.4 343.6 353.8
364 373.2 382.4 391.6 399.8
409 41 8.2 426.41
Test 2 (mg 0ZL)
O O
1 0.5 21.1 31.6 42.2 50.6 62.2 72.8 85.4 94.9
104.4 115
125.5 136.1 147.7 158.2 169.8 181.4
193 203.6 213.1 222.5
232 242.6 254.2 266.8 278.4
290 301.6 312.2 322.7 333.3 343.8 354.4 363.9 373.4 382.9 392.4 400.8 409.21
Avg Ctrl (mg OZL)
O 1
10.6 23.7
36.75 48.35 61.35 74.45 88.55 102.6
113.15 125.25 136.8 148.4
159.95 171 -55 183.55 195.15 207.7 219.3
231.35 242.45
255 266.6 278.1 289.7
300.75 312.35 322.9
333.45 344.05 353.6
363.65 373.75 383.3
392.85 402.4
410.95 420.5
429.55 438.61
Avg Chem (mg OZL)
O O
7.95 18.1
28.75 38.35 47.4
58.05 68.75 80.4 89.5
98.55 108.7
11 8.25 128.4
139.05 149.15 159.8
170.45 181.1 191.8 201.9
21 2 222.15 233.4 245.1
257.85 271.2
284.55 298.45 31 1.85 323.55 334.75 345.95 357.15 367.3
378 388.15 398.8 408.4
41 81
Appendix B: Raw Data
Time (h)
O 0.5
1 1.5 2
2.5 3
3.5 4
4.5 5
5.5 6
6.5 7
7.5 8
8.5 9
9.5 10
10.5 11
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5
Test 2 (mg OZL)
O 16.7 36.5 54.2 71.9 89.6 107.3 122.9 139.6 155.2 170.9 185.4 201.1 21 5.7 232.3 251.1 270.9 289.6 31 0.5 329.2 349
368.8 388.6 408.4 429.2 450.1 470.9 492.8 513.6 535.5 555.3 577.2 598
618.8 638.6 659.5
Control 1 (mg OBL)
O 13 30 44 58 72 85 97 110 121 133 144
155.9 167.9 178.9 190.9 201 -9 21 2.9 224.9 235.9 246.9 257.9 268.9 280.9 291 -9 304.9 31 6.9 329.9 342.9 354.9 367.9 379.9 393.9 405.9 41 7.9 430.9
Avg Ctrl (mg 021L)
O 14.35 31.2 45.6 58.95 72.85 86.25 97.65 1 10.5 121.45 132.85 144.25 156.05 167.5 177.9 189.8 200.7 21 1.1 223
233.4 244.8 256.2 266.6 278.5 289.4 301.8 314.2 326.6 339.5 351.85 364.25 376.15 389.55 401.95, 413.85 426.25
Avg Chem (mg OZL)
O 17.3 38.25 57.65 76.5 95.4
114.75 132.05 150.4 167.2 185.55 204.4 225.45 245.4 266.85 289.45 312.5 336.1 361.3 384.85 409.5 434.15 459.35 485.05 51 0.2 536.45 562.65 589.95 616.7 644
669.7 696.95 724.25 750.95 777.2 805.05
Control 2 (mg OUL)
O 15.7 32.4 47.2 59.9 73.7 87.5 98.3 11 1
121 -9 132.7 144.5 156.2 167.1 176.9 188.7 199.5 209.3 221.1 230.9 242.7 254.5 264.3 276.1 286.9 298.7 31 1.5 323.3 336.1 348.8 360.6 372.4 385.2 398
409.8 421.6
Blue 5 Test 1
(mg 021L) O
17.9 40
61 .l 81.1 101.2 122.2 141.2 161 -2 179.2 200.2 223.4 249.8 275.1 301.4 327.8 354.1 382.6 412.1 440.5 470
499.5 530.1 561.7 591.2 622.8 654.4 687.1 719.8 752.5 784.1 816.7 850.5 883.1 91 5.8 950.6
Appendix 6: Raw Data
Time (h)
O 0.5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
1 0.5 11
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5
Brown 4 Test 1
(mg 021L) O
12.6 32.7 52.7 71.7 91.7
11 1.7 130.7 149.6 167.6 185.5 204.4 222.4 241.3 263.5 285.6 309.8 334.1 357.3 382.6 406.8
431 456.3 482.7
508 534.3 561 -7 589.1 61 7.6 648.1 677.6 708.2 740.9 773.5
Control 1 (mg 02JL)
O 12 29 45 60 74 88
1 O1 113 1 26 138
148.9 160.9 171.9 183.9 194.9 206.9 21 7.9 229.9 241.9 252.9 264.9 275.9 287.9 300.9 31 2.9 325.9 338.9 350.9 363.9 376.9 389.9 402.9 -- 414.9
Test 2 (mg OZL)
O 12.5 31 -3
50 67.7 86.5
104.2 121 -9 138.6 154.2 169.8 185.4
200 21 5.7 231.3
248 266.7 284.4 303.2 321.9 340.7 359.4 377.1
398 41 6.7 436.5 457.4 477.1
498 51 8.8 539.7 560.5 581.3 602.2
Avg Ctrl (mg OZ/L)
O 9.95 25.8
41.15 55.05 68.45 81.85 94.25
105.65 117.55 128.95 139.8 150.7 161.6
172.55 183.45 194.85 205.75 217.15 228.05 238.95 249.9 260.8 272.7 284.1
296 307.9 320.3 331.7 344.1 356.5 368.4 381.3 392.7
17 17.5
18 18.5
19 19.5 20
Control 2 (mg OZL)
O 7.9
22.6 37.3 50.1 62.9 75.7 87.5 98.3
109.1 1 19.9 130.7 140.5 151 -3 161.2
172 182.8 193.6 204.4 21 4.2
225 234.9 245.7 257.5 267.3 279.1 289.9 301.7 31 2.5 324.3 336.1 346.9 359.7
Avg Chem (mg 02/L)
O 12.55
32 51.35 69.7 89.1
107.95 126.3 144.1 160.9
177.65 194.9 211.2 228.5 247.4 266.8
288.25 309.25 330.25 352.25 373.75
395.2 416.7
440.35 462.35 485.4
509.55 533.1 557.8
583.45 608.65 634.35 661.1
687.85 405.1
417.95 429.8 442.7 454.6
467 479.9
715.65 744.5 773.9
802.25 831.1
861.55 890.9
427.9 440.9 453.8 466.8 479.8 492.8
382.3 395
405.8 41 8.6 429.4 441.2
505.81 454
808.3 845.2 882. i 91 7.9 954.8 993.8
623 643.8 665.7 686.6 707.4 729.3
1031.7 750.1
Yellow 4
Appendix 6: Raw Data
Appendix B: Raw Data
Violet 5 Avg Ctrl (mg 02fL)
O O
9.95 23.35 38.25 54.1 66.5-
78.45 90.8
102.25 114.15 125.05
136.9 148.3 159.7 170.6 182.5 193.9 205.3 21 6.2 227.1 238.5 250.4 261 -8 273.2
284.65 296.05 307.45 318.85 330.25 341.15 352.55 364.4 375.3 386.7 398.6 409.5 420.4
431.75 442.65 455.05
Avg Chem (mg 02/L)
O O
7.35 17.3
28.85 41.4 49.8
57.65 65.5 71.8 78.6 85.4
91.1 5 97.95 103.2
108.95 11 4.7 120.5
126.25 132
737.25 141.95 148.25
154 159.8 165.5
171.85 177.55 184.9 191.7
198.55 205.85 213.75 221.55 229.95 238.9
247.25 256.2 265.6 274.5
285
Time (h)
O 0.5
1
Test 1 (mg 0ZL)
O O
10.5
Test 2 (mg 02lL)
O O
4.2
Control 1 (mg 02/L)
O O
15 13.5
25 37.5 46.9 55.2 63.6 69.8 77.1 84.4 90.6 97.9
103.1 109.4 1 15.6 121 -9 128.i 1 34.4 139.6 144.8 151.1 157.3 163.6 169.8 176.1 182.3 189.6 196.9 204.2 211.5 21 8.8 227.1 235.4 243.8 252.1 260.5 269.8 278.2 288.6
1.5 2
2.5 3
3.5 4
4.5 5
5.5 6
6.5 7
7.5 8
8.5 9
9.5 10
10.5 11
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
Control 2 (mg OUL)
O O
4.9 3 1 48 65 79 92
105 117 130 142
153.9 165.9 177.9 188.9 200.9 212.9 223.9 234.9 245.9 257.9 269.9 281.9 293.9 305.9 31 7.9 329.9 340.9 352.9 362.9 373.9 384.9 395.9 406.9 41 7.9 427.9 438.9 448.8 459-8 471.8
15.7 28.5 43.2
54 64.9 76.6 87.5 98.3
108.1 $1 9.9, 130.7 141.5 152.3 164.1 174.9 186.7 197.5 208.3 219.1 230.9 241.7 252.5 263.4 274.2
285 296.8 307.6 31 9.4 331 -2 343.9 354.7 366.5 379.3 391.1 401.9 41 4.7 425.5
- - - P T -
438.3
21.1 32.7 45.3 52.7 60.1 67.4 73.8 80.1 86.4 91.7
98 103.3 108.5 113.8 119.1 124.4 129.6 134.9 139.1 145.4 150.7
156 161.2 167.6 172.8 180.2 186.5 192.9 200.2 208.7
21 6 224.5
234 242.4 251.9 261 -4 270.8 281.4
Appendix B: Raw Data
Avg Ctrl (mg OZL)
O 18.35 35.25 51 -1 66.5 80.9 94.3
107.7 120.6
132.95 144.85 157.25 169.15 180.55 192.45 203.85 21 5.25 226.65 238.05 249.45 261.35 272.25 283.65 295.05 306.95 317.85 329.25 342.15 354.05 365.95 378.35 390.25 402.65 414.55 426.4 438.8
451.65 463.55 476.45 488.85 501.75
Test 2 (mg 02/L)
O 18.8 32.3 44.8 56.3 66.7 76.1 85.4 94.8
103.1 110.4 118.8 126.1 133.4 140.6 146.9 153.1 159.4 166.7 172.9 179.2 185.4 191.7
199 206.3 213.6 221.9 230.2 237.5 244.8 253.2 260.5 268.8 277.1 284.4 292.7 301.1 309.4 317.8 326.1 335.5
Avg Chem (mg 02lL)
O 17.3 30.9 43.5
55 65.5
74.95 84.35 93.25 101.6
108.95 117.35 124.7 131 -5 138.8 145.1
151.35 157.7 164.5
170.75 176.55 182.8
189.65 197
203.8 21 1.15 218.95 226.8
234.15 241.5
249.35 256.2
264.55 271.85 279.2
287.05 295.45
303.3 31 1.7
319.55 328.45
Time (h)
O 0.5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
1 0.5 11
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
Control2 (mg OuL)
O 15.7 29.5 43.2
56 67.8 79.6 91.4
103.2 115
125.8 137.6 149.4 160.2
172 182.8 194.6 205.4 217.2
228 239.8 250.6 262.4 273.2
285 295.8 306.6 319.4 330.2
342 352.8 364.6 376.4 387.2
399 410.8 423.5 434.3 447.1 458.9 471.7
Control 1 (mg 0ZL)
O 21 4 1 59 77 94
1 09 124 138
150.9 163.9 176.9 188.9 200 -9 212.9 224.9 235.9 247.9 258.9 270.9 282.9 293.9 304.9 316.9 328.9 339.9 351 -9 364.9 377.9 389.9 403.9 41 5.9 428.9 441 -9 453.8 466.8 479.8 492.8 505.8 55 8.8 531.8
Red B Test 1
(mg 02/L) O
15.8 29.5 42.2 53.7 64.3 73.8 83.3 91 -7
100.1 107.5 11 5.9 123.3 129.6
1 37 143.3 149.6
1 56 162.3 168.6 173.9 180.2 187.6
195 201.3 208.7
216 223.4 230.8 238.2 245.5 251.9 260.3 266.6
274 281.4 289.8 297.2 305.6
31 3 321.4
Appendix B: Raw Data
Appendix B: Raw Data
Avg Ctri (mg OZL)
O 18.35 35.2
52.05 66.45
81.3 95.2
108.1 122
134.4 146.25 158.15 169.55 180.95 191.35 202.3 214.2 225.1 235.5 246.9 258.3
269.25 281.65 293.55 305.95 318.4
332.25 344.2 357.6
369 381.4 393.8 405.2 417.1 428.5 439.9 451.3 462.7 473.6 485.5 496.4
Test 2 (mg OuL)
O 17.7 32.3 44.8 57.3 69.8 81 -3 91 -7
103.1 1 13.6
124 134.4 144.8 155.2 165.6 177.1 188.6
199 21 0.4 221.9 234.4 246.9 260.5
275 288.6 304.2 320.9 337.5 356.3 371.9 386.5
399 41 2.6 427.1 441.7 457.4
473 489.7 504.2 520.9 535.5
Time (h)
O 0.5
1 1.5
2 .. 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
10.5 11
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
Avg Chem (mg OuL)
O 15.7 30.4
42.95 55
67.55 78.6
89.05 100.55 111.05 121.55 132.55
143 154
164.45 176.55 188.6
200.15 21 2.7 225.3 238.9
252.55 267.8
283.45 299.2 316.5
334.85 350.55
366.8 380.9
395.05 408.15
421.8 435.9 451.1
466.35 481 -5 498.3
512.95 530.25 545.45
Control 1 (mg OZIL)
O 20 37 55 70 86
1 O1 115 130 143
155.9 168.9 180.9 192.9 203.9 215.9 227.9 239.9 250.9 262.9 274.9 287.9 301.9 314.9 329.9
- --
344.9 360.9 374.9 390.9 404.9 41 8.9 432.9 444.8 458.8 470.8 482.8 494.8 506.8 51 8.8 530.8 541.8
Control 2 (mg OUL)
O 16.7 33.4 49.1 62.9 76.6 89.4
101 -2 114
125.8 136.6 147.4 158.2
169 178.8 1 88.7 200.5 21 0.3 220.1 230.9 241.7 250.6 261 -4 272.2
282 -- -
291.9 303.6 31 3.5 324.3 333.1 343.9 354.7 365.6 375.4 386.2
397 407.8 41 8.6 428.4 440.2
451
Orange 6 Test 1
(mg 02L) O
13.7 28.5 41 -1 52.7 65.3 75.9 86.4
98 108.5 11 9.1 130.7 141.2 152.8 163.3
1 76 188.6 201.3
21 5 228.7 243.4 258.2 275.1 291.9 309.8 328.8 348.8 363.6 377.3 389.9 403.6 41 7.3
431 444.7 460.5 475.3
490 506.9 521.7 539.6 555.4
Appendix B: Raw Data
Appendix 8: Raw Data
Control 2 (mg OZL)
O 15.7 33.4 48.2 63.9 78.6 92.4
106.1 119.9 132.7 145.4 159.2
171 182.8 194.6 205.4 218.2 229.9 241.7 253.5 265.3 277.1 287.9 299.7 310.5 321.3 332.1 343.9 355.7 368.5 380.3 392.1 404.9 417.6 428.4 440.2
451 463.8 474.6 485.4 496.2
Time (h)
O 0.5
1 1.5
Red D Test 1
(mg 02lL) O
16.9 33.7 48.5 63.2
78 91.7
105.4 119.1 131 -7 145.4 159.1 171.8 186.5 200.2 21 3.9 229.7 246.6 263.5 280.3 299.3 31 7.2
332 348.8 364.6 380.4 395.2
410 423.7 439.5 452.1 466.9 480.6 493.2 506.9 51 9.6 531 -1 544.8 556.4 569.1 580.7
Control 1 (mg 0ZL)
O 18 37 53
Teçt 2 (mg OZL)
O 17.7 33.3 47.9 62.5 76.1 89.6
103.1 116.7 129.2 142.7 156.3 168.8 182.3 194.8 208.4 222.9 238.6 253.2 267.7 283.4
299 31 3.6 329.2 342.8 357.3 371.9 385.5 400.1 41 3.6 427.1 440.7 454.2 466.7 479.2 491.7 504.2 51 7.8 529.2 541.7 553.2
Avg Ctrl (mg OZL)
O 16.85 35.2 50.6
66.45 81.8 96.2
110.55 124.95 138.85 152.15 166.55 178.95 191.35 203.75 215.65 229.05 242.4 255.3 268.2 281.6
294 305.9 31 8.8 331.7 344.1 356.5 369.4 381 -8 395.2 407.6
419.95 432.35 445.2 457.1
469 480.4 493.3 504.2 51 5.6 526.5
Avg Chem (mg OZL)
O 17.3 33.5 48.2
62.85 77.05 90.65
104.25 11 7.9
130.45 144.05
157.7 170.3 184.4 197.5
21 1.15 226.3 242.6
258.35 274
291 -35 308.1 322.8
339 353.7
368.85 383.55 397.75 41 1.9
426.55 439.6 453.8 467.4
479.95 493.05 505.65 517.65 531 -3 542.8 555.4
566.95
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
Y
7 7.5
8 8.5
9 9.5 10
10.5 11 -
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
69 85
1 O0 115 130 145
1 58.9 173.9 186.9 199.9 212.9 225.9 239.9 254.9 268.9 282.9 297.9 310.9 323.9 337.9 352.9 366.9 380.9 394.9 407.9 421.9 434.9 447.8 459.8 472.8 485.8 497.8 509.8 522.8 533.8 545.8 556.8
Appendix B: Raw Data
Appendix B: Raw Data
Avg Chem (mg OZIL)
O 22.55
43 60.8 78.1 94.3
110.05 125.75 139.9
154.55 168.7 181.3
193.85 206.45
21 9 230.55 242.6 254.1
265.65 -
277.15 289.25 302.35 316.45 331.15 344.75 357.3
371.45 384.55 398.2 411.3 423.9
436.45 449.05 460.55 473.65 485.2
497.25 508.75 520.3
530.75 542.85
Retention and Drainage Aid
Avg Ctrl (mg 0ZL)
O 21.3
42.1 5 59.95 77.3 92.7
108.55 124.4
138.25 153.6
167.45 180.35 192.75 205.1 217.5 229.4 241.3 252.7 264.6 -
276
Time (h)
O O. 5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5
Control 1 (mg OZIL)
O 2 1 42 59 77 92
108. 123 137
151.9 t 65.9 178.9 190.9 202.9 214.9 226.9 238.9 249.9 261.9 272.9
10 10.5
11 11 -5
12 12.5
13 13.5
14 14.5
15 15.5
16 16.5
17 17.5
18 18.5
19 19.5
20
Control2 (mg 0211)
O 21.6 42.3 60.9 77.6 93.4
109.1 125.8 139.5 155.3
169 181.8 194.6 207.3 220.1 231.9 243.7 255.5 267.3 279.1
283.9 295.9 309.9 322.9 336.9 349.9 362.9 376.9 389.9 402.9 414.9 426.9 438.9 450.8 462.8 473.8 485.8 496.8 507.8 51 8.8 530.8
Test 1 (mg 02L)
O 25.3 46.4 64.3 82.2
98 113.8 129.6 144.4 159.1 172.8 185.5 198.1 210.8 223.4
235 247.7 259.2 270.8 282.4
290.9 303.6 317.4 333.1 346.9 359.7 374.4 388.2 401.9 414.7 427.5 440.2
453 465.8 478.6 491.3 504.1 51 5.9 527.7 539.5 551.3
Test 2 (mg OZIL)
O 19.8 39.6 57.3
74 90.6
106.3 121.9 135.4
150 164.6 177.1 189.6 202.1 214.6 226.1 237.5
249 260.5 271 -9
295.1 308.8 323.5 338.3
352 364.6 378.3
392 405.7 419.4 432.1 444.7 457.4
469 482.7 494.3 506.9 518.5 530.1 540.6 553.3
283.4 295.9 309.4
324 337.5
350 364.6 377.1 390.7 403.2 415.7 428.2 440.7 452.1 464.6 476.1 487.6
499 510.5 520.9 532.4
287.4 299.75 313.65
328 341.9 354.8
368.65 382.55 395.9 408.8 421.2
433.55 445.95 458.3 470.7
482.55 494.95 506.35 517.75 529.15 541.05
Appendix 6: Raw Data
Avg Chem (mg OZL)
O 11.55 18.9
26.15 33.5
40.85 47.1 5
54.5 61.85 68.65 75.95 82.8 89.6 96.4
103.75 110.55 117.85
125.2 132.05
139.4 146.7
154.05 161 -4 169.2 177.1 185.5
193.35 202.25 21 0.6
217.45 223.2
228.45 234.75 239.95 245.75 251.5
256.75 261.95 267.8
273.55 278.75
Test 2 (mg 02/L)
O 11.5 18.8
26 33.3 40.6 46.9 54.2 61 -5 67.7
75 81 -3 88.6 94.8
102.1 108.3 1 15.6 122.9 129.2 136.5 143.8 151.1 158.4 165.6 172.9 181 -3 188.6 196.9 205.2 212.5 21 7.7 222.9 229.2 234.4 239.6 244.8
250 255.2 260.5 265.7 270.9
Anitfoarn
Test 1 (mg 02iL)
O 11.6
19 26.3 33.7 41.1 47.4 54.8 62.2 69.6 76.9 84.3 90.6
98 105.4 112.8 120.1 127.5 134.9 142.3 149.6
1 57 164.4 172.8 181 -3 189.7 198.1 207.6
21 6 222.4 228.7
234 240.3 245.5 251.9 258.2 263.5 268.7 275.1 281.4 286.6
-
Time (h)
O 0.5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
10.5 11
11 -5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
Avg Ctii (mg OZL)
O 9.9
17.35 24.3
31.25 38.2 45.1
52.05 59.5
66.45 72.85 79.3
86.25 93.2
100.15 107.05
114 120.95 127.9
134.85 142.25 149.15 157.1
164.55 172.45 t 80.4 188.8
196.75 204.2
210.15 21 5.1
220.55 226
230.95 235.9
241.85 246.3
251.75 256.2
261.65 267.15
Control 1 (mg OUL)
O 11 19 26 33 40 47 54 6 1 68 74 8 1 88 95
102 1 09 116 123 130 137 145
151.9 159.9 167.9 175.9 183.9 192.9 200.9 207.9 21 3.9 21 8.9 223.9 229.9 234.9 239.9 245.9 249.9 255.9 259.9 264.9 270.9
Control 2 (mg 0 2 L )
O 8.8
15.7 22.6 29.5 36.4 43.2 50.1
58 64.9 71.7 77.6 84.5 91.4 98.3
105.1 112
11 8.9 125.8 132.7 139.5 146.4 154.3 161.2
169 176.9 184.7 192.6 200.5 206.4 21 1.3 21 7.2 222.1
227 231 -9 237.8 242.7 247.6 252.5 258.4 263.4
Appendix 8: Raw Data
Avg Chem (mg 0ZL)
O O
0.5 8.4
17.3 25.65
35.1 44.05
53.4 61.85 70.75 78.55 86.45 94.3
101.1 108.45 1 15.3
121.55 128.4
134.65 140.95 146.7
153 159.3 166.1
171.85 178.7
185.45 191.25 198.05 204.85 21 1.7
217.95 225.3
Time (h)
O O. 5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6 -5
7 7.5
8 8.5
9 9.5 10
1 0.5 11
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5
Sizing Test 1
(mg OZL) O O O
7.4 16.9 25.3 34.8 44.3 53.7 62.2 71 -7
79 87.5 94.8
102.2 109.6
117 123.3 130.7
137 143.3 149.6
156 162.3 169.7
176 183.4 190.7 197.1 204.4 211.8 219.2 225.5 232.9
17 17.5
18 18.5
19 19.5
2 0
Control 1 (mg OUL)
O O 2 9
76 24 3 1 39 45 53 60 66 73 80 86 92 99
105 111 117 123 129 135 141
146.9 152.9 158.9 165.9 170.9 177.9 183.9 189.9 196.9 203.9
Agent
Test 2 (mg 02/L)
O O 1
9.4 17.7
26 35.4 43.8 53.1 61.5 69.8 78.1 85.4 93.8 1 O0
107.3 113.6 11 9.8 126.1 132.3 138.6 143.8
150 156.3 162.5 167.7 -
1 74 180.2 185.4 191.7 197.9 204.2 210.4 21 7.7
Control2 (mg 02/L)
O O O
6.9 14.7 22.6 29.5 37.3 44.2 51.1
58 63.9 70.8 77.6 83.5 89.4 96.3
102.2 108.1
t 14 1 19.9 125.8 131.7 137.6 143.5 149.4 155.3 161.2 167.1
173 178.8 184.7 191.6 198.5
21 0.9 21 7.9 225.9 232.9 239.9 245.9 250.9
Avg Ctrl (mg 0211)
O O 1
7.95 15.35 23.3
30.25 38.1 5 44.6
52.05 59
64.95 71 -9 78.8
84.75 90.7
97.65 103.6
109.55 115.5
121.45 127.4
133.35 139.3 145.2
151 .15 157.1
163.55 169
175.45 181 -35 187.3
194.25 201.2
205.4 21 1.3 21 9.1
226 232.9 240.8 247.6
241.3 249.8 258.2 265.6
274 283.5 291.9
224 231.3 237.5 244.8 252.1 259.4 266.7
208.15 214.6 222.5
229.45 236.4
243.35 249.25
232.65 240.55 247.85 255.2
263.05 271.45 279.3
Appendix B: Raw Data
Avg Chem (mg 02lL)
O 2.1 6.8
11.5 16.75
22 27.25 32.5
37.75 42.45 48.2
53.45 59.25 65.55 72.3
79.1 5 87.55 96.45
109.05 122.15 136.8
151.45 165.6
180.25 193.9
208.05 221.65 234.7
246.25 256.2 267.7
277.65 287.65 297.6 308.6 31 9.1
329.05 338.5
347.85 357.3
365.75
Avg Ctrl (mg OUL)
O 8.45
18.35 27.75
37.2 46.6 55.5
63.95 72.35 80.3
88.25 95.65 103.1 110.5
117.95 124.9
131.85 138.3 745.2 152.1
158.55 165.5
171 -95 178.85
184.8 191.75 198.2
204.65 210.1
216.05 222
227.4 232.85 238.3
244.25 249.2
254.65 260.1
265.55 270.5
275.95
Cleaner Test 2
(mg 021L) O
4.2 8.3
12.5 17.7 21.9 27.1 32.3 36.5 40.6 45.8
51 56.3 61.5 67.7
74 81.3 89.6
101.1 113.6 129.2 144.8 159.4
174 188.6 203.2 216.7 230.2 242.7 253.2 264.6
275 284.4 294.8 305.3 31 5.7 326.1 335.5 344.8 354.2 362.6
General Purpose
Test 1 (mg 0 Z L )
O O
5.3 1 0.5 15.8 22.1 27.4 32.7
39 44.3 50.6 55.9 62.2 69.6 76.9 84.3 93.8
103.3 117
130.7 144.4 158.1 171 -8 186.5 199.2 212.9 226.6 239.2 249.8 259.2 270.8 280.3 290.9 300.4 31 1.9 322.5
332 341.5 350.9 360.4 368.9
Control 2 (mg OZL)
O 7.9
17.7 27.5 36.4 46.2
55 63.9 71 -7 80.6 87.5 95.3
103.2 11 1
1 17.9 124.8 131 -7 138.6 145.4 152.3 159.2 166.1
173 179.8 185.7 192.6 199.5 206.4 21 2.3 21 8.2 224.1 229.9 235.8 241.7 247.6 252.5 258.4 264.3 270.2 275.1
281
Time (h)
O 0.5
1 1.5
2 2.5
3 3.5
4 4.5 "
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
1 0.5 II
11.5 12
12.5 13
13.5 14
14.5 15
15.5 16
16.5 17
17.5 18
18.5 19
19.5 20
Control 1 (mg OUL)
O 9
19 28 38 47 56 64 73 80 89 96
1 03 110 118 125 132 138 145
151.9 1 57.9 164.9 170.9 177.9 183.9 190.9
- 196.9 202.9 207.9 21 3.9 219.9 224.9 229.9 234.9 240.9 245.9 250.9 255.9 260.9 265.9 270.9
Appendix B: Raw Data
Avg Ctrl (mg 021L)
O 2
3.95 5.95 8.45 10.9 13.4
15.85 18.85 21.8 24.8
28.75 32.7 36.7
42.15 47.1
53.05 60.45
. 67.4 75.85 83.25 89.2
95.1 5 102.1
109.05 116
123.9 131.85 140.75 149.65 159.55 169.45 180.35 190.3 201.2 210.6
220.55 230.45 240.35 249.75
Biocide Test 2
(mg 02lL) O O O O 1
2.1 3.1 4.2 5.2 6.3 7.3 8.3 9.4
10.4 11.5 11.5 12.5 13.5 14.6 15.6 16.7 18.8 19.8 20.8 22.9
25 27.1 29.2 32.3 35.4 39.6 43.8
49 55.2 62.5 69.8 78.1 87.5 93.8
99
Avg Chem (mg OZL)
O O O O
0.5 1 .O5 1.55 2.1 2.6
3.1 5 4.2 5.2
5.75 6.8
7.85 8.4 9.4
10.45 11.5
12.55 13.6 15.2 16.2 17.8
19.35 21.45 23.55 26.2 29.3
32.45 36.65 41.4
46.65 52.9
60.25 67.55 76.45 84.85 90.65 95.85
Carbarnate
Test 1 (mg OZL)
O O O O O O O O O O
1 .l 2.i 2.1 3.2 4.2 5.3 6.3 7.4 8.4 9.5
10.5 11.6 12.6 14.8 15.8 i 7.9
20 23.2 26.3 29.5 33.7
39 44.3 50.6
58 65.3 74.8 82.2 87.5 92.7
Time (h)
O 0.5
1 1.5
2 2.5
3 3.5
4 4.5
5 5.5
6 6.5
7 7.5
8 8.5
9 9.5 10
10.5 11
11.5 12
12.5 13
13.5 74
14.5 75
15.5 16
16.5 17
17.5 18
18.5 19
19.5
Control 1 (mg OZL)
O 3 5 7
10 12 14 17 20 23 26 30 34 38 43 48 54 6 1 68 76 84 90 96
1 03 11 O 117 125 133 142
150.9 160.9 170.9 181.9 191 -9 202.9 212.9 222.9 232.9 242.9 251.9
Control2 (mg OZL)
O 1
2.9 4.9 6.9 9.8
12.8 14.7 17.7 20.6 23.6 27.5 31.4 35.4 41 -3 46.2 52.1 59.9 66.8 75.7 82.5 88.4 94.3
101.2 108.1
115 122.8 130.7 139.5 148.4 158.2
168 178.8 188.7 199.5 208.3 218.2
228 237.8 247.6
Appendix B: Raw Data
1 T ~ C I TSS 1 DH 1 . - - I . - -
1 Initial 1 Final 1 Initial 1 Final 1 I
lnitial 1 Finri 1
Green 1
Violet 3
Orange 3
Blue 5
Control 1 f est 1 Test 2 Control 2
Appendix B: Raw Data
PH TDC Initial
7.05 7.02 7.02 7.06
TSS Initial
1263 1475 1634 1285
Final 4.59 6.39 6.41 4.58
Initial 2344 2376 2248 2320
Final 943
1419 1385 1098
Final 2288 2268 21 32 2376
Brown 4
Yellow 4
Violet 5
Red B . .-- - 1 TDC 1 TSS 1 ni4 1
1 - - - m r. .
Final 1 Initial 1 Final 1 Initiai 1 Final 1
Oranae 5
Appendix B: Raw Data
-
6.35 5.95
6 6.28
Control 1 Test 1 Test 2 Control2
1096 1357 1362 1 276
7.82 7.88 7.95 7.83
745 1207 1231 91 9
2788 2884 2640 2648
31 72 2864 2756 3024
Red C
Red D 1 TnC 1 TSS 1 DH 1 . -- I . -- I - -
initial 1 Final 1 Initial 1 Final 1 Initial 1 Finri 1 - - - - - - - - - .. - - - - Control 1 1 042 724 2548' 2944 8.09 6.68 Test 1 1444 997 2448 2884 8.16 6.94 Test 2 1460 1067 2492 281 6 8.14 6.9
Organic Opacifier
Appendix B: Raw Data
Retention Aid and Pitch Dispersant
Control 1 Test 1 Test 2 Control 2
pH Initial
7.82 7.77 7.79 7.83
Control 1 Test 1 Test 2 Control 2
PH
Final 6 -43 6.45 6.5
6.43
TDC
Initial 8.01 7.98 7.99 8 .O2
TSS
TSS Initial
1 043 1188 1233 1195
Final 6.52 6.48 6.46 6.51
Initial 261 2 2728 2588 2636
TDC
Initial 1804 2092 1964 21 28
Final 785 874 897 896
Final 3220 31 32 31 64 31 28
Initial 1092 1138 1180 1184
Final 2072 2328 1 984 1 744
Final 737 377 430 371
Drainage and Retention Aid 1 TDC 1 TSS 1 DH 1
Antifoam TDC TSS pH
Initial Final Initial Final Initial Final Control 1 1106 1 057 1180 1384 7.99 7.1 Test 1 1123 1081 1192 1500 8 7.1 Test 2 1186 1080 1256 1324 8 7.07 Control2 1 244 1029 1200 7.99 7.06
Control 1 Test 1 Test 2 Control 2
Sizing Agent
Carbarnate Biocide
Initial 1120 1279 1 254 1181
Control 1 Test 1 Test 2 - Control 2
General Purpose Cleaner
Appendix B: Raw Data
Initial 8.16 8.1 5 8.16 8.16
Control 1 Test 1 Test 2 Control 2
Final 6.58 6.56 6.56 6.57
Final 2992 31 04 3072 3056
Final f Initial
TDC
737 850 828 843
Initial 1091 1162 1157 1136
2560 2632 261 6 2664
TSS Final
982 1 107 1055 1 083
PH TDC
Initial 1148 1128 1252 1196
pH
Initial 8.14 8.38 8.45 8.1 5
TSS Initial
1163 1192 1 224 1104
Final 1272 1352 1276 1388
Initial 7.95
7.8 7.8
7.98
Final 7.05 6.92 6.9
6.99
Initial 1360 1280 1336 1268
Final 898
1090 916 91 9
Final 7.2
6.87 6.84 7.1 5
Final 1432 1184 1152 1512
APPENDIX C:
SAMPLE CALCULATIONS
METHOD EXPLANATION
METHOD RESULTS
A sarnple calculation is shown for Orange 3 paper dye using data contained in Appendix B
Step 1 Perfom regression analysis for each reactor The dope of the best fit line is the OUR in rnglLh
Step 2 Calculate the avefage OUR for the controls and the average OUR for the tests CaLlate the percent difference using equation 3.1 % Dierence = Average OUR of Controls - Average OUR of Tests x 100
Average OUR of Control
Tests of Significance of Difference belween average OUR values
Method 1 Use a t-test. which uses the error in the dope of the best fit line as the error estimate
Calculate the variance in each slope by squaring the standard enor (SE) of each slope
Calculate the average variance for the controls, and the average variance for the tests The t-statistic is given by equation 3.2 t-statistic = Average OUR d Controls - Average OUR of Tests
(Average Variance of Cacitrds + Average Variance of Te~ts)~(112)
the degrees of freedorn are 4(n-2), where n is the number of observations for the dope look up the critical t-value in a t-table for the 95% confidence level if the calculated t-statistic is larger thah the the critical t-value, the difference between the average control and the average test is significant
Method 2 Use analysis of variance (ANOVA), which tests whether the diiference within controls and within tests is significantly d i r e n t frorn the difference between the average control and the average test if the calculated F-statistic is larger than the critical F-value, the diRerence between controls and tests is significant
Method 3 Compare OUR of Controls if greater than 3 rng/Lh difference repeat Compare OUR of Tests if greater than 3 rnglLh difference rspeat Compare Average OUR if greater than 3 rnglLh difference AND of Control and Test greater than 2 x difference between
controls significant
Resuits of Step 1 The OUR is the "coefficient of X Variable 1"
Control 1 Regression perfomed time 2h-7h SUMMARY OUTPUT
Regression Statistics Multiple R 0.999617715 R square 0399235577 Adjusted R Sqi 0.999150641 Standard Error 1 .O65676624 Observations 11
ANOVA df SS MS F Significance F
Regression 1 13360.64809 13360.64809 11 764.58593 2.44297E-15 ~e i idua l 9 1 0.221 1.135666667 Total 10 13370.86909
Coefficients Standard E m r t Stat Pvalue L o w r 95% Upper 95% L o w r 95.0% Upper 95.0% lntercept 3.620909091 0.969281208 3.735664181 0.004656556 1.428240992 5.81357719 1.428240992 5.81357719 X Variable 1 22.04181818 0.203216559 108.464676 2.44297E-15 21.5821 1004 22.50152633 21.5821 1004 22.501 52633
Control 2 Regression performed time 2h-7h SUMMARY OUTPUT
Regression Statistics Multiple R 0.9995971 12
Appendix C: Sarnple Calculations
AaJUSleO rC 3qi V.YYY 1 worv Standard Enor 1 . O ï l 202477 Observations 11
ANOVA di SS MS F Signikance F
Regression 1 12808.80909 12808.80909 11 162.60651 3.09381E-15 Residual 9 10.32727273 1 .147474747 Total 10 12819.1-
Coefficients Standard Emr t Stat P-value Lcwer 95% Upper 95% Lowr 95.0% Upper 95.0% lntercept -0.83636364 0.974307223 -0,85841 88 0.41 2938401 -3.040401 378 1.3676741 06 -3.040401 38 1.3676741 O6 X Variable 1 21.58181818 0.204270298 105.6!532371 3.09381E-15 21.1 1972631 22.04391005 21.1 1972ô31 22.04391005
Test 1 Regression petfornid time 2h-7h SUMMARY OUTPUT
Regtession Stetistic5 Multiple R 0.997463483 R Square 0.994933401 Adjusted R Sqi 0.994370445 Standard E m r 1 .O661 8834 Observations 11
ANOVA di SS MS F Significancs F
Regression 1 2009.036455 2009.036455 1767.339402 1.21592E-11 Residual 9 10.23081818 1.136757576 Total 10 2019.267273 p.
Coefncients StanderdEm tStat P-velue L o w 95% Upper 95% Loiver 95.0% Upper 95.0% t ntercept -2.41 727273 0.969746637 -2.49268483 0.034270707 -4.6109937 -0.223551 75 -4.61 09937 9.22355175 X Variable 1 8.547272727 0.203314139 42.03973599 1.21592E-11 8.087343841 9.007201 614 8.087343841 9.007201614
Test 2 Regression performed tirne 2h-7h SUMMARY OUTPUT
R ~Quare 0.996371679 Adjusted R Sqi 0.995968532 Standard Enor 0.884724462 Observations 11
ANOVA df SS MS F Signiiïcance F
Regression 1 1934.524455 1934.524455 2471 -485992 2.70459E-12 Residual 9 7.044636364 0.782737374 Total 10 1941.569091
Coefficients Standard Emr t Stat P-velue Lowr 95% Upper 95% Lower 95.0% Upper 95.0% l ntercept -0.43363636 0.804697012 -0.538881 54 0.603040868 -2.253988862 1.38671 61 34 -2.25398886 1.38671 61 34 X Variable 1 8.387272727 0.168710335 49.71 40422 2.70459E-12 8.005623143 8.768922312 8.005623143 8.768922312
Results of Step 2 Average OUR of Controts 21.8 Average OUR of Tests 8.5
% Difference = 21.8-8.5 x100 21.8
Tests of Significance Method 1
Var of Ctrl 1 Var of Ctrl 2 Var of Test 1 Var of Test 2 0.04129697 0.041 726354 0.041 336639 O.Oî8463t 77
Appendix C: Sarnple Catculations
~verage Var of Tests 0.034899908
t-statistic = 21.8 - 8.5 (0.041 5 + 0.0349)A(1 12)
- - 48.1
degrees of freedorn is 4(n-2) where n is 11 degrees of freedom is 36
t-critical for 36 tif, 95 % confidence 2.04
Since t-sîaüstic > tcritical, conclude that the difierence between controls and tests is significant
Method 2 df Formula degrees of freedom (df) wiîhin a-1 a=2 b=2 between a(b1)
F-critical for 1 and 2 df is 18.5 at the 95 % confidence level
OUR of Controll will be defineci as C l OUR of Control2 will be defineci as C2 OUR of Test 1 will be defined as T l OUR of Test 2 will be deiïned as T2
Calculate the grand total (Tg) Calcluate the total for the controls (Tc) Calculate the total for the testsot) Calculate the total variance (v) Calculate the ûetwwn treatment variance (vb) Catcuiate the within treatment variance (wv) Calculate the mean square for between treatments (sb) Calculate the mean square for within treatments (sw) Determine F-statistic Compare with F-critical(l8.5) Judgement
Answer 1 2
(Cl+C2+Tl +T2) 60.56 (Cl +C2) 43.62 f l l + n ) 16.93 (Cl "2+C2"2+Tl "2+T2A2)-TgA2h 178.1 9 flcA2+TtA2)lb - fgA2/ab 178.m v-vb 0.12 vbla-1 178.08 w/(a(b-1) 0.06 SWSW 3002.98
larger signficant
For theoretical assistance refer to standard statistics book (Ret Probability and SWistics by Murry R. Spiegel, Schaum's Outline Series, McGraw Hill. Inc., NY, NY, 1991)
Method 3 C l 4 2 0.5 ok Tl-T2 0.2 ok Ctrl- Test 13.3 significant
Appendix C: Sample Calculations
Data Analysis Method 1 - Respirometer Screening Data
Data Analysis Method 2 - Respirometer Screening Data
Data Analysis Method 3 - Respirometer Screening Data
APPENDYS, D:
RESPIROMETER CALIBRATION CHECKS
CALIBRATION CHECKS
Reactor Burette Reading Respirometer Reading D-ifference Average
Appendix D: Respirometer Calibration Checks
APPLIED A IMAGE. lnc - - - 1653 East Main Street - -. , Rochester, NY 14609 USA -- -- - - Phone: il 6/482-O3OO -- -- - - Fax: 71 61288-5989
O 1993. Applied Image. Inc.. All Righls R e s e ~ e d
