Dissolved Measurements in Aquatic Environments: The E! ects of Changing Temperature and Pressure on Three Technologies

Corey D. Markfort* and Miki Hondzo University of Minnesota–Twin Cities

Dissolved oxygen (DO) is probably the most important  measurement of DO is one of the most frequently used and parameter related to water quality and biological habitat in one of the most important of all chemical variables investigated aquatic environments. In situ DO are some of the T in aquatic environments (Wetzel, 2001). Dissolved oxygen most valuable tools used by scientists and engineers for the evaluation of water quality in aquatic ecosystems. Presently, we data provide valuable information regarding the biological and cannot accurately measure DO concentrations under variable biochemical reactions in aquatic ecosystems. For example, DO can temperature and pressure conditions. Pressure and temperature be used to measure how environmental factors aff ect aquatic life and infl uence polarographic and optical type DO sensors compared the capacity of aquatic ecosystem to produce and decompose organic to the standard Winkler titration method. ! is study combines matter. Consequently, DO is often used as an indicator of aquatic laboratory and fi eld experiments to compare and quantify the accuracy and performance of commercially available macro ecosystem health. While a signifi cant number of studies have been and micro Clark-type oxygen sensors as well as optical sensing conducted on the diff erent applications of DO sensor technologies, technology to the Winkler method under changing pressure most have focused on the steady-state operating characteristics of DO and temperature conditions. Field measurements at various sensors and on questions related to steady-state measurements (Daly lake depths revealed sensor response time up to 11 min due to et al., 2004; Johnson et al., 2007). However, when using sensors to changes in water temperature, pressure, and DO concentration. Investigators should account for transient response in DO quantify the amount and distribution of DO in a water column or sensors before measurements are collected at a given location. along a transect, these sensors are exposed to a variety of changing We have developed an eff ective model to predict the transient conditions, including changes in water temperature, pressure, and response time for Clark-type oxygen sensors. ! e proposed DO. As each of these variables aff ects the measurement of DO, it is procedure increases the accuracy of DO data collected in situ often diffi cult to measure steady-state conditions. for profi ling applications. Among the diff erent electrochemical oxygen sensor technologies, known as Clark-type, two general classes of sensors exist: macro- scopic (centimeter scale) and microscopic (micrometer scale) (Glud et al., 2000; Taillefert et al., 2000; Daly et al., 2004; Johnson et al., 2007). Macroscopic sensors are available on many commercially produced sensor suites. In contrast, DO microsensors are available primarily for research use, as most researchers construct their own sensors, although recently these sensors have become commercially available (Unisense A/S). Information on operation theory and DO electrochemical sensor performance under a variety of conditions has been discussed in several reviews (Hitchman, 1978; Gnaiger and Forstner, 1983; Oldham, 1994; Glud et al., 2000). ! e functioning principle of a Clark-type sensor is DO diff uses from the surrounding solution through a permeable membrane, KCl solution, and fi nally is consumed electrochemically at a (Fig. 1) (Gnaiger and Forstner 1983). ! e resulting electric current between the cathode and an anode is measured, Copyright © 2009 by the American Society of Agronomy, Crop Science and the magnitude of the current is calibrated to the known con- Society of America, and Soil Science Society of America. All rights reserved. No part of this periodical may be reproduced or transmitted centration of oxygen in the sample solution. Fluorinated ethylene in any form or by any means, electronic or mechanical, including pho- propylene (FEP) polymer membranes are most commonly used on tocopying, recording, or any information storage and retrieval system, commercial macroscopic sensors, whereas silicone polymer is com- without permission in writing from the publisher. monly used on microsensors (Gundersen et al., 1998). ! e fl ux of Published in J. Environ. Qual. 38:1766–1774 (2009). oxygen is a function of the of DO in solution and doi:10.2134/jeq2008.0197 Received 28 Apr. 2008. St. Anthony Falls Lab., Dep. of Civil Engineering, Univ. of Minnesota-Twin Cities, *Corresponding author ([email protected]). Minneapolis, MN. © ASA, CSSA, SSSA 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: DO, dissolved oxygen.

1766 Fig. 1. The Clark-type sensor schematic with transport model for macro Clark-type sensor response to changing dissolved oxygen (DO) concentration in the aquatic environment. An example of a sudden increased change in ambient DO concentration. Membrane di! usion is the limiting transport factor that characterizes sensor response. the resistance through the various media including the diff usive no oxygen fl ux through the sensor, unlike polarographic sensors, boundary layer at the tip of the sensor. Most sensor systems which is a benefi t for steady-state sampling. However, if mea- are equipped with an external stirrer so DO is not depleted in suring in the presence of DO gradients the sensor must reach the diff usive boundary layer at the sensor tip. For macroscopic equilibrium with the ambient solution before an accurate signal Clark-type sensors, it is feasible to neglect the resistance in the is measured causing a delayed response. electrolyte due to a two orders of magnitude diff erence between When collecting water column DO profi le data, it is im- the diff usion coeffi cients of the electrolyte (10–5 cm2 s–1) and in portant to consider the time constant, or sensor response the membrane (10–7 cm2 s–1) (Hitchman, 1978). Unlike mac- time, to determine the spatial resolution that can be resolved roscopic sensors, the fl ux model for microsensors must include and overall measurement accuracy. Most manufacturers pro- the electrolyte resistance due to a greater relative thickness of the vide time constants for their sensors (Table 1). For example, electrolyte media compared to the membrane thickness (Gun- Unisense’s specifi cation for the OX-25 micro-DO sensor, used dersen et al., 1998). Berntsson et al. (1997) reported several en- in this study, is a response time (t90) of 0.5 s, the time it takes vironmental factors that aff ect stable polarographic sensor signals to reach 90% of the fi nal reading. Aanderaa’s specifi cations, for including: membrane transport properties, electrolyte properties, the Optode, state the t63 (the time it takes to reach 63% of the cathodic properties, and the DO boundary layer thickness at fi nal signal) as 25 s for the oxygen sensor and 30 s for the tem- the membrane. Each of these factors is aff ected by temperature perature sensor. ! e Hach-Hydrolab DO sensor specifi cations change over time, the most important being membrane trans- state a response time for their standard Clark-type DO sensor port properties. However, none of the above studies reported the and temperature sensor as 60 s (Table 1). Time series analysis response time of Clark-type oxygen sensors under transient tem- of measured DO concentrations can be used to determine the perature and pressure conditions. response time for a particular sensor. Optical DO sensors employ dynamic luminescence quench- ! e focus of this paper is to investigate the eff ects of chang- ing technology, and are relatively new to aquatic research (Gruber ing temperature and pressure on DO measurements. ! is et al., 1993; Glud et al., 1999; Glazer et al., 2004). To measure study includes four diff erent methods for measuring DO, DO concentration, molecules must diff use through a silicone which are all available commercially: (i) macro Clark-type membrane and come in contact with the luminophore matrix sensor with a mechanical stirrer (Hach-Hydrolab), (ii) micro in the sensing foil. Fluorescence is aff ected by the amount of Clark-type sensor (Unisense microsensor mounted on a self- oxygen present, and the decay time is a function of the phase contained autonomous microstructure profi ler [SCAMP]), angle of the signal received by a photo diode. ! e Optode sys- and (iii) macroscopic optical sensor (Aanderaa-Optode). ! e tem provides temperature compensated oxygen concentration, standard Winkler method is used for calibration of sensors and and tests of these sensors have shown good long-term stability for baseline comparison of the measurements. ! ese methods (Wolfbeis 1991; Glud et al., 1999; Stokes and Somero 1999). To are introduced with a description of their basic operation along make these sensors more rugged for fi eld measurements and to with an assessment of their limitations in terms of measured protect them from interference (e.g., direct sunlight), an optical transient temperature and pressure under fi eld and laboratory isolation membrane is installed over the sensing foil. Due to this conditions. A method for determining DO measurement re- membrane, these sensors experience many of the same delayed sponse time under variable conditions is developed for macro response characteristics the polarographic sensors have, related Clark-type oxygen sensor technology, which is the most fre- to transport limited diff usion of oxygen across a membrane. It is quently used sensor by monitoring agencies and researchers. commonly accepted that, unlike polarographic technology, opti- Users can determine the response time of the sensor as a func- cal sensor technology is not stir sensitive because sensors do not tion of a change in temperature and pressure while conducting consume oxygen while operating (Klimant et al., 1995). ! ere is water column DO measurements.

Markfort & Hondzo: Dissolved Oxygen Measurements in Aquatic Environments 1767 Table 1. Manufacturers’ speci" cations for oxygen sensors. Published response time Sensor DO Temperature Published accuracy Source/contact Micro Clark- type 90% within 0.5 s 0.007 s n/a Unisense A/S (non-pulsed) www.unisense.com Hydrolab macroscopic Clark-type <1min <1min ± 0.2 mg/L Hach Environmental (non-pulsed) www.hydrolab.com Oxygen optode dynamic ! uorescence 63% within 25 s 30 s <8 $mol/L (0.26 mg/L) or 5% whichever Aanderaa Data Instruments quenching is greater (Lifetime-based detection) www.aanderaa.com Winkler titration n/a ± 0.05 mg/L (experimentally determined) (wet chemistry method)

where i is the measured electrical current, i is current at time, t, i Materials and Methods t 0 is current at t = 0, i" is the fi nal equilibrium current, and # is the Clark-type Response Model Development dimensionless time constant for an arbitrary starting point. ! e ! e diff usion of oxygen through the membrane is the most function with respect to # is compared to the measured current signifi cant factor contributing to the delay in sensor response. To ratio, for consecutive DO measurements to implicitly determine model this eff ect, the transient diff usion of oxygen in polymer the membrane diff usion coeffi cient from the experimental data: membranes must be characterized. FEP polymer membranes are Dtm t= 2 commonly manufactured at a thickness of approximately 0.025 xm [3] mm. ! e permeation of solute through polymers is related to the dissolution of a permeant in the polymer and diff usion of the where t is the time interval between consecutive measurements dissolved permeant. ! e product of diff usivity, D and , and xm is the membrane thickness. ! e molecular diff usion S determines the permeability of the membrane, coeffi cient for the membrane installed on the macro Clark- P = DS [1] type DO sensor was estimated for various conditions from experimental fi eld data using this method (Fig. 2). Koros et al. (1981) and Pasternak et al. (1971) experimentally Diff usion time for the transport of DO through the mem- quantifi ed the diff usion and permeability of oxygen through brane can be modeled by the square of the membrane thickness FEP Tefl on polymer for the temperatures range of 20 to 80°C, divided by the membrane diff usion coeffi cient, and developed the Arrhenius relationships for permeability, 2 * xm solubility, and diff usivity of oxygen through FEP polymer t = Dm [4] membranes. ! e relationships as a function of temperature are E éùE -HD D = D exp éùD P = P exp êúP S = S exp éùs m0ëûRT , m0ëûRT , m0ëûRT , where where #* is the diff usion time for DO transport. ! e D , P , and S are the diff usion, permeability, and solubility m m m diff usion coeffi cient for a membrane in solution is Dm = KPm D P S CSww coeffi cients of the membrane respectively, 0, 0, and 0 are the where K = = and is the distribution coeffi cient of the pre-exponential coeffi cients, E , E , and !H are the activation CSmm D p s concentration of DO in the solution and in the membrane. energies of diff usion and permeation and the heat of solution C is the DO concentration in water at the membrane-water respectively, R is the gas constant (8.3144 × 10–3 kJ K–1 mol–1) w interface and C is the DO concentration in the membrane and T is temperature in Kelvin (K). Because environmental m at the same interface (Fig. 1). ! is relationship for K is temperatures often occur below the published range, equivalent to the ratio of of DO, in water and in experimental data were analyzed to extend the relationships of the membrane. ! e solubilities are determined by membrane D , P , and S , using the data from Koros et al.’s (1981), down m m m properties temperature and pressure. Rewriting Eq. [4] by to 5.oC. substituting the relationships for D and K gives, Hitchman (1978) developed a transient model for oxygen m Sx2 transport through a membrane, which we will use to analyze t* = mm experimental data and develop a new predictive model for tran- PSmw [5] sient response in Clark-type sensors. Two boundary conditions are assumed for this model: (i) the cathode is eff ectively located Equation [5] provides an estimate of solute transport time at the membrane—electrolyte interface causing the DO con- through a membrane in solution as a function of temperature, centration to be zero at x = 0 and (ii) the oxygen concentration membrane thickness, and atmospheric pressure. ! e solubility of oxygen in fresh water can be obtained from the thermodynamic at x = xm is equivalent to that in the bulk solution of the aquatic environment (Fig. 1). ! e transient model used to analyze the properties of the atmosphere-water system by Henry’s law and experimental data is, characterized empirically by Benson and Krause (1980 and 1984) and can be found in most water quality modeling texts, it0 - i 2 F()t = = 1- 2exp(- p t ) for example, Chapra (1996). i¥ - it [2]

1768 Journal of Environmental Quality • Volume 38 • July–August 2009 Field Experiments A fi eld study was conducted at Square Lake in Washing- ton County, Minnesota (45°9´29´´ N, 92°48´24´´%W). Data were collected to determine DO sensor response time by in situ fi eld measurements. ! e investigation involved collecting DO and temperature vs. depth measurements during August, September, and October 2004. Sampling was conducted in the northwest quadrant of the lake where the maximum lake depth is approximately 15 m. Dissolved oxygen sampling in the lake included the use of the optical DO sensor (Optode), macro Clark-type DO sen- sor (Hydrolab), and micro Clark-type DO sensor (Unisense) mounted on a SCAMP. ! e Optode measured temperature Fig. 2. Plot of experimentally determined membrane di! usion coe# cient, D for FEP using the current ratio method, Eq. [4]: and DO. ! e Hydrolab measured depth, temperature, and m data from October pro" le, Depth = 14.47 m, xm = 0.025 mm and DO. ! e SCAMP measured temperature, depth, and DO (Fig. –8 2 –1 Dm = 4.5 × 10 cm s . 3). Winkler samples were also collected at discrete depths. Each source. Following the protocol outlined in Standard Methods for sensor was calibrated according to manufacturers specifi cations the Azide modifi cation, all samples were fi xed and then analyzed to using the Winkler method. ! e Optode and Hydrolab were determine the DO concentration (APHA, 1998). Standard statis- rigged together and lowered to sample depths (2, 6, 8, 10, 12, tical error analysis was performed to determine the accuracy con- and 14 m), corresponding to the discrete water sample depths. fi dence for each sample analyzed. Because all samples were drawn At each depth, sensors were held for 20 min to allow their mea- from the same source and within a relatively short time period, the surements to reach steady-state equilibrium. temperature and DO concentration were identical in each sample ! e SCAMP microstructure profi ler was released into the and the only deviation in data collected occurred from errors pro- water where the profi ler descends at a 45-degree angle away from duced during titration. ! e mean value was 7.10 mg L–1 and the the boat to avoid sampling turbulent wake structures induced 95% confi dence interval was ± 0.05 mg L–1. by the descent or by the boat. Once the probe was at the pre- scribed maximum depth, an expendable weight was automati- Results and Discussion cally released and the probe ascended vertically at approximately 10 cm s–1 sampling at 0.01 s per measurement (1 measurement Macro Clark-Type and Optical Dissolved Oxygen Sensor per millimeter). Ten profi les were collected for each fi eld day for Time Series Data comparison with the profi les collected with the other methods. ! e time series data collected with the Optode and Hy- Sensor Response Using Autocorrelation Analysis drolab systems for August, September, and October were ana- Statistical autocorrelation analysis was conducted on the lyzed (Fig. 4). ! e transient part of these data, the fi rst 10 min time series of DO data. A correlogram is a plot of the auto- of each step, was used as input for statistical autocorrelation correlation function against the time-lag, #, and is useful in analysis to determine the response time of the sensors under determining if successive observations are correlated. Any variable pressure and temperature conditions. ! e remaining trends that exist in the mean value of the signal were removed half of the data, for each step (the stable part of the signal) was to obtain an unbiased time series. Integrated over time, the averaged and synthesized into points plotted vs. depth used to autocorrelation function gives the integral time scale, which compare the consistency of each system normalized to the sur- face readings (DO/DO ) to remove any calibration error (Fig. is a measure of time over which the signal is highly correlated surf with itself. ! is method is used to determine the response time 5). Plots of discrete points for Optode and Hydrolab include of DO sensors associated with variable depth and temperature their respective manufacture’s published error limits and are from the lake profi les. ! e response time determined from au- segregated into upward and downward profi ling measurements tocorrelation analysis is based on 99% of the fi nal stable read- to show where diff erences occurred. ! e micro DO sensor data are plotted as a continuous line, due to the rapid sampling rate, ing (t99). ! e statistics were performed on the raw DO sensor signal to isolate the response time of the DO sensor from that representing a characteristic upward measured profi le. ! e spa- of the temperature sensor response time. tial resolution of the SCAMP thermistor in this case is about 1 mm. ! e Unisense micro-DO sensors are specifi ed to have a Winkler Method Accuracy response time of <0.5 s (Table 1). ! is corresponds to a spatial resolution of about 5 cm when profi ling at 10 cm s–1. A repeatability study was conducted for the Winkler Method Although the sensors were calibrated at the same conditions, to determine the confi dence interval for DO samples collected DO concentrations were not always in agreement. ! e initial sur- by this investigator to determine the precision of the method in face concentration measured in the September profi le with the this study. Under laboratory conditions 20 300-mL distilled wa- Winkler Method was 8.38 mg L–1. ! e optical DO sensor devi- ter samples were collected in BOD bottles from a single fi ltered ated by (+) 0.27 mg L–1 (3.2%), the macro Clark-type DO sensor

Markfort & Hondzo: Dissolved Oxygen Measurements in Aquatic Environments 1769 Fig. 3. Self-contained autonomous microstructure pro" ler (SCAMP) with micro dissolved oxygen (DO) sensor and micro temperature sensors: (Left) assembled for " eld sampling, in upward mode, (Right) Close-up of the temperature and micro DO sensors as well as the pressure compensation chamber containing connectors, " lled with oil and covered by a rubber sheath. ber profi le the surface Winkler measurement was 7.75 mgL–1. ! e optical DO sensor deviated by (+) 0.02 mg L–1 (0.3%), the macro Clark-type DO sensor deviated by (–) 0.01 mg L–1 (0.1%) and the micro Clark-type sensor deviated by (–) 0.07 mg L–1 (0.9%). In the August profi le, normalization of the data caused all the data to collapse to the Winkler observed concentrations except for the macro Clark-type sensor when on the return to the surface. In the September profi le, normalization provided little benefi t to under- standing the scatter of the data. However, in the October profi le, data collapse well after normalization. Macro Clark-Type Response Autocorrelation analysis of the time-series data from the

Macro Clark-type DO sensor gave response time (t99) for September and October ranging from 65 to 261 s. ! is sen- sor responds faster than the Optical DO sensor for the same conditions. However, the response time found here should be taken into account when conducting hydrographic profi les as it is not common for an investigator to wait 4.5 min on a single measurement point. Optical Sensor Response Autocorrelation analysis of the data from the Optical DO sensor for August, September, and October showed a range of

response time (t99) from 46 s to 668 s. ! e Optical DO sensor response time is notably long likely due to the optical isolation membrane included with the commercial model. Because the Op- tical DO sensor does not consume oxygen, it has a much longer response time. Contrary to the Clark-type sensors that consume oxygen and have a constant partial pressure of DO equal to zero Fig. 4. Time-series data for September 2004 lake pro" le used to analyze at the cathode, the Optical DO sensor has a variable boundary transience of sensor measurement: (A) Hydrolab, (B) Optode. condition that changes asymptotically as oxygen diff uses toward deviated by (–) 0.02 mg L–1 (0.2%) and the micro Clark-type sen- or away from the luminophore matrix. ! is causes the diff usion sor did not deviate from the Winkler measurement. For the Octo- process to take much longer, as theoretically the system can never

1770 Journal of Environmental Quality • Volume 38 • July–August 2009 Fig. 5. Lake dissolved oxygen (DO) pro" les. Dissolved oxygen measurements normalized to surface measurements to remove any minor inconsistencies in calibration. Discrete points for Optode and Hydrolab are averaged from stable time-series data and are set apart for upward and downward pro" ling. (A) August, (B) September, (C) October. reach true steady state. Another variable that has a large eff ect in beaker and allowed to reach approximate equilibrium with the the Optical DO sensor diff usion process, and therefore response aerated water. ! e sensor was then carefully transferred to the bea- time characteristics, is the diff usive boundary layer that exists at ker of anoxic water. ! e sensor was not wiped dry so air could not the membrane-water interface as most optical sensors do not have directly interact with the membrane during the transfer. ! e sen- any stirring mechanism to ensure adequate surface renewal. sor was inserted into the water very slowly to minimize movement To investigate the importance of the boundary layer thickness of the water in the beaker. ! e sensor was then left to equilibrate for the Optical DO sensor response time, a laboratory test was without outside movement. ! is process took approximately 43 performed in which two beakers of water, one aerated and the min to equilibrate to 99% of the fi nal value. ! e t90 for this ex- other devoid of oxygen, were held at equal temperature approxi- periment was 450 s. ! e sensor manufacturer reports 63% of the mately 23°C. ! e Optical DO sensor was placed in the aerated fi nal value occurs in approximately 25 s. For this experiment, at

Markfort & Hondzo: Dissolved Oxygen Measurements in Aquatic Environments 1771 Macro Clark-type Response Time Prediction Model Many factors, described above, aff ect transient response in sensors. Membrane transport is the limiting and largest aff ect for macroscopic Clark-type sensors, but many other factors also infl uence response rates. Because these factors are numerous and often sensor specifi c, it is diffi cult to accurately model each. As an alternative, a calibration factor, C, is used to characterize these other variables. ! e resulting fi nal response time model is: Sx2 t'=C mm PSmw [6]

Calibrating the response time model to experimental data Fig. 6. Laboratory test of Optode response verses stirring sensitivity. produces a calibration coeffi cient, C = 1.34, for our macro Clark- Time series plot of measurements normalized by the initial type sensors with a continuous current. ! e response time model dissolved oxygen (DO) saturated measurement and either placed for temperatures 5 to 40°C, are in the range of about 300 to 30 s, in stagnant, anoxic water or placed in gently stirred anoxic water and allowed to reach equilibrium. respectively. ! e model is also plotted for the range of membrane thicknesses measured to show the range of response times that data 25 s approximately 46% of the fi nal reading was attained (Fig. 6). might fall into for the full range of environmental temperatures at Alternatively, an experiment was performed in contrast to the stag- standard atmospheric pressure (Fig. 7). ! is calibration coeffi cient nant water response test where the procedure was followed exactly is specifi c to the Hydrolab macro Clark-type sensor used in this as stated previously, but this time the sensor was manually stirred study. However, the model provides a good approximation for in the beaker at a slow rate, about a half cycle per second. For this other sensors of similar type. Also, because the response time test, there was a dramatically faster response presumably due to the model provides 99% of the fi nal signal, minor variations in sensors much smaller boundary layer thickness at the membrane-water and therefore calibration coeffi cients should be within acceptable interface. In this test it took 78 s to achieve 99% of the fi nal signal limits of accuracy for many investigators. and the t90 value was only 38 s. For this experiment, approximately 80% of the fi nal reading was attained at 25 s. Model Coe! cients ! ere is no standard method for determining sensor re- Hitchman (1978) described a method for determining dif- sponse time and because asymptotic diff usion is the controlling fusion coeffi cients based on experimental data as explained us- process for the Optical DO sensor, the boundary layer is very ing Eq. [2]. Plotted with the data from Koros et al. (1981), up- important and any variation in boundary layer thickness will dated activation energies and model coeffi cients were obtained result in very diff erent measures of response time. Other factors by regression to provide transport properties at temperatures as that may aff ect the response time include any variation in the low as 5°C for FEP polymer (Fig. 8). ! e pre-exponential coef- optical isolation membrane transport properties over time, as –9 –3 –1 fi cient for solubility is S0 = 7.97 × 10 mg cm atm , and the well as the condition of the luminophore matrix. Biofi lm for- heat of solution is !H = –31.58 kJ mol–1. ! e pre-exponential mation on the outside of the membrane surface will also have –3 2 –1 –1 coeffi cient of permeability, P0 = 1.102 × 10 cm s atm , and an eff ect on transport properties. –1 the activation energy of permeability, EP = –25.5 kJ mol . ! e phenomenon of the boundary layer thickness aff ecting Two additional factors that aff ect the transport model are response time was also observed in the fi eld from the oxygen pro- liquid absorption and membrane thickness. According to Ya- fi les collected at Square Lake. Two diff erent profi les were selected suda and Stone (1966), a hydrophilic membrane has a perme- to determine how mixing aff ected Optical DO sensor response. ability of an order of magnitude higher when transporting DO Two transient steps were selected from September and October, from two liquid phases vs. two gaseous phases. Silicone mem- attempting to keep all other conditions similar. For September, branes are hydrophilic and are used with microsensors and the the selected step was at 10.23 m depth with a change in DO of Optical DO sensor. ! e FEP polymer membranes used with –1 about 7.8 mg L . ! e October step selected was at 9.24 m depth the Macro Clark-type DO sensor are hydrophobic and trans- –1 with a change in DO of about 8.8 mg L . ! e rate of turbulent port oxygen at the same rate in liquid and in gas. & kinetic energy dissipation ( ) was used as a measure of stirring. Because the estimate of the diff usion coeffi cient from the Using the fast response temperature measurements by SCAMP, current ratio method is based quadratically on the membrane & was estimated as reported by various investigators (e.g., Krocsis thickness, Eq. [4], it is essential to determine this parameter ac- et al., 1999). In the September example, the Optode response curately. Tests were performed to determine membrane thick- time (t ) was 294 s with a corresponding & = 6.4 × 10–4 cm2 s–3. 90 ness on many new and previously installed membrane fi lms. A For the October example, the response time was 666 s with a Mitutoyo micrometer (no. 293–768.30) with an accuracy of & –4 2 –3 corresponding = 2.3 × 10 cm s . ! ese data support the 0.001 mm was used to measure membrane thickness. It was laboratory experiments showing that mixing can have a large im- found that the macro Clark-type DO sensor membranes have pact on response time for optical type oxygen sensors. an initial thickness of 0.025 mm and an average installed thick-

1772 Journal of Environmental Quality • Volume 38 • July–August 2009 Fig. 7. The response time prediction model is plotted for a range of membrane thicknesses along with experimental, transient Clark-type sensor raw dissolved oxygen (DO) data from Square Lake analyzed using autocorrelation. The response time is plotted against temperature.

Fig. 8. The Arrhenius plot of temperature dependent di! usion coe# cient (Dm) for FEP polymer membranes. Data from Koros et al. (1981) and from analysis of transient data for macroscopic Clark-type sensor taken in the " eld during September and October 2004. ness of approximately 0.024 mm. When installed correctly, factors, for example, structural deformation of the sensor may membranes should not be stretched excessively over the sensor change the transport rates enough to signifi cantly aff ect the as the membrane will rebound which causes errors in the cali- sensor readings and response. A fi eld example, demonstrating bration. Because the membrane is an elastic polymer and the the application of the response time model, Eq. [6], is provided rubber o-ring used is not a perfect anchor, the membrane will in the supplemental material (Appendix A). return from a stretched condition to close to the original thick- ness. However, one membrane, used in the experiments, was Conclusions measured with a thickness as low as 0.020 mm near the center, Dissolved oxygen is one of the most important environmen- showing the importance of this variable. tal variables, still diffi cult to measure accurately. One important Pressure at depth below the water surface may also aff ect the artifact of DO measurement, sensor response time, is shown to thickness of the membrane. Polymer compression is a function be an important factor to consider when conducting fi eld mea- of hydrostatic pressure and the material property of modulus surements. We developed a method to calculate the response of elasticity for the membrane. From the relationship of mem- time of macroscopic Clark-type DO sensors that are commer- brane thickness versus depth (i.e., compression force), the rela- cially available. Field measurements at various lake depths re- tive change in membrane thickness is approximately 0.20% at vealed sensor response time up to 11 min. Sensor response time 100 m depth. ! erefore, hydrostatic pressure force does not ap- is primarily a function of oxygen transport across the membrane. preciably aff ect the membrane thickness for the range of depths It is important to balance stirring sensitivity and response time found in most lakes. At larger depths, the pressure and other when selecting a membrane for Clark-type oxygen sensors.

Markfort & Hondzo: Dissolved Oxygen Measurements in Aquatic Environments 1773 ! ick membranes provide small stirring eff ect, while thin mem- Engineering/Math. branes provide shorter response time. Also, membrane thickness Daly, K.L., R.H. Byrne, A.G. Dickson, S.M. Gallager, B.J. Perry, and M.K. Tivey. 2004. Chemical and biological sensors for time-series research: infl uences response time quadratically, while stirring sensitivity Current status and new directions. Mar. Technol. Soc. J. 38:121–143. is linearly related. Membrane material is also very important. Glazer, B.T., A.G. Marsh, K. Stierhoff , and G.W. Luther. 2004. ! e dynamic Low diff usion conductivity produces small stirring eff ect while response of optical oxygen sensors and voltammetric electrodes to temporal changes in dissolved oxygen concentrations. Anal. Chim. Acta high diff usion coeffi cient provide a shorter response time. Ide- 518:93–100. ally, the best membrane has a high diff usion coeffi cient but a Glud, R.N., J.K. Gundersen, and N.B. Ramsing. 2000. Electrochemical low solubility for oxygen. ! e Optical DO sensor is primarily and optical oxygen microsensors for in situ measurements. p. 19–73. In J. Buffl e and G. Horvai (ed.) In situ monitoring of aquatic systems: used for extended monitoring at a single location and has shown Chemical analysis and speciation. John Wiley & Sons, New York. good long-term stability for this use, however some investigators Glud, R.N., I. Klimant, G. Holst, O. Kohls, V. Meyer, M. Kuehl, and J.K. may use it for profi ling applications and must consider longer Gundersen. 1999. Adaptation, test and in situ measurements with O-2 response times as shown in this study. microopt(r)odes on benthic landers. Deep-sea Res. I 46:171–183. Gnaiger, E., and H. Forstner. 1983. Polarographic oxygen sensors: Aquatic and physiological applications. Springer-Verlag, New York. Acknowledgments Gruber, W.R., I. Klimant, and O.S. Wolfbeis. 1993. Instrumentation for ! is work is supported by the Graduate Assistance in Areas optical measurement of dissolved oxygen based on solid state technology. of National Need (GAANN) fellowship program provided by SPIE 1885:448–457. Gundersen, J.K., N.B. Ramsing, and R.N. Glud. 1998. Predicting the signal the U.S. Department of Education through the Department of O-2 microsensors from physical dimensions, temperature, salinity, of Civil Engineering at the University of Minnesota. Support and O-2 concentration. Limnol. Oceanogr. 43:1932–1937. for experimental equipment was provided by the National Hitchman, M.L. 1978. Measurement of dissolved oxygen. John Wiley & Sons, New York. Center for Earth-surface Dynamics (NCED), a Science Johnson, K.S., J.A. Needoba, S.C. Riser, and W. Showers. 2007. Chemical sensor and Technology Center funded by the Offi ce of Integrative networks for the aquatic environment. Chem. Rev. 107:623–640. 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1774 Journal of Environmental Quality • Volume 38 • July–August 2009