Water Quality Monitoring and Management: Basis, Technology and Case Studies

By Daoliang Li and Shuangyin Liu

Water Quality Monitoring and Management: Basis, Technology and Case Studies presents recent innovations in operations management for water quality monitoring. It highlights the cost of using and choosing smart sensors with advanced engineering approaches that have been applied in water quality monitoring management, including area coverage planning and sequential scheduling. In parallel, the book covers newly introduced technologies like bulk data handling techniques, IoT of agriculture, and compliance with environmental considerations. Presented from a system engineering perspective, the book includes aspects on advanced optimization, system and platform, Wireless Sensor Network, selection of river water quality, groundwater quality detection, and more.

It will be an ideal resource for students, researchers and those working daily in agriculture who must maintain acceptable water quality.

• Discusses field operations research and application in water science
• Includes detection methods and case analysis for water quality management
• Encompasses rivers, lakes, seas and groundwater
• Covers water for agriculture, aquaculture, drinking and industrial uses

• Language: English
• Publisher: Academic Press
• Release date: Oct 11, 2018
• ISBN: 9780128113318

Daoliang Li

Dr. Daoliang Li is Professor of Agricultural Engineering at China Agricultural University and Director of the National Innovation Center for Digital Fishery, Key Laboratory of Smart Farming for Aquatic Animal and Livestock, Ministry of Agricultural and Rural Affairs. He has held visiting positions at the University of California, Davis (USA), University of Bedfordshire (UK), Wageningen University (Netherlands), and IOSB Fraunhofer (Germany). He obtained his BA from Shandong Agricultural University and PhD from China Agricultural University’s College of Engineering. Dr. Li’s research has mainly focused on intelligent information processing, smart sensors/instruments, and robots for aquaculture and fish farming. He is the founder and editor-in-chief of Elsevier’s International Journal of Information Processing in Agriculture and has published 12 books, including Water Quality Monitoring and Management (Academic Press, 2018).

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patents.

Preface

Daoliang Li

The introduction of smart sensors, wireless sensor networks for water quality monitoring, systems and platforms for innovative sensing, water quality evaluation methodology and actuating technologies, together with improved information and communication technologies, has been disruptive, leading to new approaches that must be disseminated and learned by the involved stakeholders. As a result, traditional water quality monitoring methods have been supplemented with advanced information technology features, such as route planning, bulk data handling, and precision agriculture management, from a systems engineering perspective.

Although these advanced water quality monitoring features have been adequately described and analyzed in general industrial production and management textbooks, the water quality of agricultural and aquaculture management must be viewed in a different way from machinery management in the industrial domain. Compared with the industrial setting, the agricultural and aquaculture domain is subject to a greater impact from the environment and the inherent uncertainty and risk (e.g., aquatic organism growth or weather conditions) that characterize any aquaculture farm process. In general, risky decisions are the norm for water quality management.

Furthermore, according to the current trends of increased sustainability concerns in production systems, there is a high need for the targeted audience to become aware of the connection between decision making in agricultural water quality monitoring management and the corresponding environmental impact.

To that effect, this book aims at bridging the gap in the disseminated knowledge on operations management for water quality monitoring during the last several decades. It complements traditional aspects of the specific topic (e.g., cost of using and choosing smart sensors) with advanced engineering approaches that have been applied recently in water quality monitoring management, such as area coverage planning and sequential scheduling.

The primary target audience consists of undergraduate and postgraduate students of agricultural science, electronic information engineering, computer science and technology, and electrical engineering and automation, and engineering faculties worldwide involved in the discipline of agricultural engineering (in some universities this is also called biosystems engineering or agricultural and biological engineering). Note that there are hundreds of such departments around the world.

This book is organized into 13 chapters: Chapters 1–3 thoroughly cover the state-of-the-art technologies of sensing, communicating, and platforms for constituting a perfect water quality monitoring and management system; Chapters 4–6 systematically review the topics of algorithms and methodologies for water quality evaluation, prediction, and early warning; Chapters 7–13 cover actual research work in the context of various detection practices for river water quality, lake water quality, sea water quality, drinking water quality, groundwater quality, aquacultural water quality, and industrial water quality.

In detail, Chapter 1 focuses on sensors in water quality monitoring; this chapter comprises further subchapters covering probe material and sensing theories for pH, ORP-Redox potential, dissolved oxygen, turbidity, chlorophyll, blue-green algae, conductivity, and ammonia measurement.

Chapter 2 focuses on wireless sensor networks in water quality monitoring; this chapter comprises further subchapters covering wireless sensor network category, structure, self-organizing, multihop routing, communication protocols, power consumption, standardization, and fault diagnosis, in addition to state-of-the-art technologies of medium access control, time synchronization, wireless wide area networks (WWANs), wireless local area networks (WLANs) and systems on chip. The practical applications are also discussed.

Chapter 3 focuses on systems and platforms for water quality monitoring; this chapter contains practical research on system design, hardware platform design and software development, as well as practical cases and applications for aquacultural water quality management, maricultural water quality management, urban water supply, hydrological management, and wastewater management.

Chapter 4 focuses on water quality evaluation; this chapter introduces theoretical foundation and research issues of water quality evaluation and then presents the decision-making method, index evaluation method, and artificial intelligence evaluation method for water quality evaluation. Finally, practical applications of river water quality evaluation, rural drinking water evaluation, and freshwater aquaculture pond management are reviewed.

Chapter 5 focuses on water quality prediction; this chapter first introduces a mechanism model and nonmechanism model for water quality prediction. The mechanism modeling methods SWAT and MIKE11 are presented, and nonmechanism modeling methods like neurofuzzy inference, artificial neural networks, support vector regression, extreme learning machine, and deep learning algorithms are introduced. The case of dissolved oxygen prediction is illustrated further; in this case, wavelet analysis deals with data preprocessing, least square support vector regress is employed as the modeling method, and Cauchy particle swarm optimization further optimizes model parameters. The results are also presented in this chapter.

Chapter 6 focuses on water quality early warning; this chapter discusses the warning level, space and time, wireless system, and sensor system for early warning, and then presents research issues in decision support systems for early warning.

Chapter 7 focuses on detection of river water quality. This chapter provides detailed illustrated physical parameters and chemical parameters for river water quality detection, and different detection methods for river water quality detection and river water quality standards are discussed.

Chapter 8 focuses on water quality detection for lakes; in this chapter, the detection indicators involving contaminant and noncontaminant indicators are discussed and the main detection methods and processes for these indicators are also introduced briefly. In addition, the detection standards are reviewed.

Chapter 9 focuses on seawater quality detection; this chapter illustrates the basic index of sea water detection in the view of the physical, chemical, and microbial indicators. Then a detailed introduction to the detection step is presented, and detection methods, titration, gravimetric analysis, spectroscopic analysis, microbial detection, radioactivity analysis, radioactivity analysis, and chromatography are discussed.

Chapter 10 focuses on drinking water quality detection; in this chapter, a professional water quality index related to drinking water is introduced. Further, the detection method of multiple-tube fermentation, membrane filter, enzyme substrate, ion chromatography, and gas chromatography are presented. Finally, typical drinking water quality standards are illustrated.

Chapter 11 focuses on ground water quality detection; in this chapter, detection indices for ground water quality are introduced. Further, typical detection methods and detection steps for ground water quality are presented, and detection standards in different nations are contrasted with each other.

Chapter 12 focuses on aquacultural water quality monitoring; in this chapter, water quality parameters including general parameters, pathogenic parameters, toxic heavy metals, and toxic compounds are introduced. Then monitoring methods and detection steps are reviewed and, finally, normal index and standards are discussed.

Chapter 13 focuses on the detection of industrial water quality; this chapter overviews the characters and components of industrial water quality. The general index, pollution degree, and biological index of industrial water quality is presented, as well as the estimation methods for each indicator. Further, state-of-the-art technologies of automatic detection systems for industrial water quality are examined in detail.

I wish to thank many colleagues and students who were involved in completing this book: Cong Wang (Chapter 1); Hao Yang (Chapter 2); Jianhua Bao (Chapter 3); Yingyi Chen (Chapter 4); Shuangyin Liu (Chapter 5); Huihui Yu (Chapter 6); Zhen Li (Chapter 7); Jiaran Zhang (Chapter 8); Zhenbo Li, Yaoguang Wei, Ruichao Xiao, and Yinghao Wu (Chapter 9); Xuehua Zhao (Chapter 10); Yinfeng Hao, Zheng Miao, and Peng Wang (Chapter 11); Liang Wang (Chapter 12); Tan Wang and Xuehua Zhao (Chapter 13). We are grateful to those at Elsevier Global Book Production, with a special acknowledgment to Bharatwaj Varatharajan and Sheela Bernardine Josy for communicating on the publishing issues. We are also grateful to the National Nature Science Foundation of China (Grant No. 61571444) for providing research support for this book.

Chapter 1

Sensors in Water Quality Monitoring

Abstract

Even minute changes in the water characteristics that form its quality can jeopardize lives and industries that depend on water. To preserve its quality, accurate monitoring of water parameters such as conductivity, pH, salinity, temperature, dissolved oxygen and turbidity is crucial. This chapter offers a broad selection of water quality sensors that cater to both simple spot sampling requirements and complex unattended monitoring projects. It addresses current measurement techniques of pH, turbidity and clarity, oxidation reduction, dissolved oxygen, chlorophyll, and algae, among others.

Keywords

Water quality; Water quality sensor; Water sampling; Water monitoring; Turbidity; Turbidity measurement; Turbidity units; pH measure

The chemical, physical and biological characteristics of water combine to form what is called water quality. Even minute changes in these characteristics can jeopardize the lives and industries that depend on water. To preserve its quality, accurate monitoring of water parameters such as conductivity, pH, salinity, temperature, dissolved oxygen (DO), and turbidity is crucial. This chapter describes a broad selection of water quality sensors that are suitable for both simple spot sampling requirements and complex unattended monitoring projects.

1.1 pH Measurement and Value

1.1.1 pH and How to Measure It

Knowing the pH value of a solution or fluid is fundamental in many chemical and analytical tasks and its measurement often determines any follow-up measurements. Measuring pH can seem to be trivial, which is why pH measurements are often not questioned. But to make a useful pH measurement, close attention must be paid to the measurement details. To determine pH accurately and avoid errors, you must first be familiar with the basics of pH measurement.

1.1.2 What Does the pH Value of a pH Measurement Mean?

The water molecule has the property of dissociating into two ionic components in aqueous solutions (Covington et al., 1985).

   (1.1)

The H+ ion is termed a hydrogen ion or proton, and the OH– ion is the hydroxide ion. The pH value describes the activity of hydrogen ions in aqueous solutions, typically on a scale of 0–14. Based on this pH scale, liquids are characterized as being acidic, alkaline or neutral; a solution which is neither acidic nor alkaline is neutral, which corresponds to a value of 7 on the pH scale. Acidity indicates a higher activity of hydrogen ions and a pH measurement value lower than 7. Alkaline solutions are characterized by a lower hydrogen ion activity or higher hydroxide ion activity, respectively, and a pH measurement value above 7.

The pH scale is logarithmic. A difference of one pH measurement unit represents a 10-fold, or 10 times, increase or reduction of hydrogen ion activity in the solution. This explains how a solution's aggressiveness increases with the distance from the neutral point.

One of the keys to understanding pH measurements is the term activity; because the activity is temperature dependent, it is not the same as the solution's concentration. Activity, a, is defined as the product of the activity coefficient y, which is always smaller than 1, and the actual concentration c of the concerned compound (a = y × c).

Activity is the effective concentration of a chemical compound, or more precisely its particles in the solution. In a real solution the activity is always smaller than the actual concentration. This is true because it is only in an ideal (infinitely thinned) solution that the soluted particles do not affect each other. In this case they are spread apart, because many molecules of the solvent are between them. The difference between activity and concentration becomes apparent in real solutions of ions, because ions interact with each other as a result of their electrical charge. To describe or calculate the characteristics of a solution as exactly as possible, the activity and not the concentration must be used in the mass action law.

1.1.3 How Do I Measure the pH Value?

The pH value can be measured using electrochemical measuring systems, litmus paper, or indicators and colorimeters. The easiest way to take a pH measurement is to use litmus paper or a colorimeter. The advantage of this type of pH measurement is that the pH range is well known and the methods are easy to apply. Unfortunately, in many cases litmus paper and colorimeters are not accurate enough to make high-quality pH measurements, because the pH value transition point depends on the user.

Another pH value measurement possibility is amperometry (Stredansky et al., 2000). The advantage of amperometry as a pH measurement method is that it is simple to use. In amperometric pH measurements hydrogen generation occurs on a noble metal. When combined with a less noble metal, a power distributing galvanic cell is formed. Because hydrogen ions are generated, the cell's current depends on the pH value. The disadvantages of this method are that differences in the sample composition create very large errors in pH measurements and the method cannot deliver dependable results in extremely concentrated acids and bases, due to effects related to the pH glass membrane.

In special cases, the pH value measurement can be made using conductometry (Jacobs et al., 1992). With this method any membrane effects are minimized because of the measurement technique. The advantage of this pH measurement method is that it is relatively easy to use. The disadvantage is that a conductivity measurement measures all ion activity, not just hydrogen ion activity. Additionally, this pH measurement is only reproducible and safe at low ion concentrations.

A relatively new method for pH value measurement is the ion selective field effect transistor (ISFET) (Duroux et al., 1991). Briefly, the ISFET is a transistor with power source and drain, divided by an isolator. This isolator (gate) is made of a metal oxide where hydrogen ions accumulate in the same way as an electrode. The positive charge that accumulates outside the gate is mirrored inside the gate by an equal negative charge generated. Once this happens, the gate begins to conduct electricity. The lower the pH value, the more hydrogen ions accumulate and the more current can flow between source and drain. The ISFET sensors, similar to glass pH electrodes, act according to the Nernst equation. The advantage of an ISFET is that it is very small. The actual field effect transistor (FET) is only 0.2 mm². The disadvantage of using an ISFET for pH measurements is that they have comparatively short durability and low long-term stability, with a typical use life cycle being in the range of weeks.

The most common method of pH value measurement is the use of pH measurement electrodes, like the IoLine series from SI Analytics. These pH measurement devices are electrochemical sensors that consist of a measuring electrode and a reference electrode. The pH measurement electrode is made of special glass which, due to its surface properties, is particularly sensitive to hydrogen ions. The pH measurement electrode is filled with a buffer solution with a pH value of 7. When placing the pH measurement electrode into a test solution, the change in voltage is measured by the pH electrode by comparing the measured voltage to the stable reference electrode. This change is recorded by the pH meter, such as the pH 3110 Field pH Meter, Lab 850 Benchtop pH Meter, or ProLab 1000 Professional Bench Top pH Meter (Global Water Products) and converted into the pH measurement value displayed.

Of all these pH value measurement methods, currently the best one is the use of pH electrodes. There is no other pH measurement system that provides better reliability, accuracy, and speed of pH measurement across the complete pH range. The minimal disadvantage of using glass pH electrodes for this pH measurement method is the fact that glass electrodes are delicate and need to be handled with care. This disadvantage is overcome by using gel-filled pH electrodes in applications where the electrodes must be more robust.

1.2 ORP-Redox Potential Measurement for Water Quality

1.2.1 What Is ORP?

ORP stands for oxidation-reduction potential, which is a measure, in millivolts, of the tendency of a chemical substance to oxidize or reduce another chemical substance.

1.2.1.1 Oxidation

). Substances with multiple oxidation states can be sequentially oxidized from one oxidation state to the next higher. Adjacent oxidation states of a particular substance are referred to as redox couples. In the following case, the redox couple is Fe+ 2/Fe:

   (1.2)

This chemical equation is called the half-reaction for the oxidation, because, as will be seen, the electrons lost by the iron atom cannot exist in solution and must be accepted by another substance in solution. So the complete reaction involving the oxidation of iron will have to include another substance, which will be reduced. The oxidation reaction shown for iron is, therefore, only half of the total reaction that takes place.

1.2.1.2 Reduction

Reduction is the net gain of electrons by an atom, molecule, or ion. When a chemical substance is reduced, its oxidation state is lowered. As was the case with oxidation, substances that can exhibit multiple oxidation states can also be sequentially reduced from one oxidation state to the next lower oxidation state.

The chemical equation (Eq. 1.3) is the half-reaction for the reduction of chlorine:

   (1.3)

The redox couple in this case is Cl2/Cl− (chlorine/chloride).

Oxidation reactions are always accompanied by reduction reactions. The electrons lost in oxidation must have another substance as a destination, and the electrons gained in reduction reactions have to come from a source.

When two half-reactions are combined to give the overall reaction, the electrons lost in the oxidation reaction must equal the electrons gained in the reduction reaction.

   (1.4)

   (1.5)

   (1.6)

In the reaction shown in Eq. (1.6), iron (Fe) reduces chlorine (Cl2) and is called a reductant or reducing agent. Conversely, chlorine (Cl2) oxidizes iron (Fe) and is called an oxidant or oxidizing agent.

1.2.1.3 The Nernst Equation for ORP

The ORP of a general half-reaction can be written in terms of molar concentrations as follows:

   (1.7)

   (1.8)

Hypochlorous acid (chlorine in water) provides a useful example of the Nernst equation:

Nernst Equation (25°C)

Examining the hypochlorous acid/chloride equation shows some important properties of ORP:

•The ORP depends upon the concentrations of all the substances in the half-reaction (except water). Therefore, the ORP of hypochlorous acid depends as much on the chloride ion (Cl−) and pH (H+) as it does on hypochlorous acid.

•The ORP is a function of the logarithm of the concentration ratio.

•The coefficient that multiplies this logarithm of concentration is equal to − 59.16 mV, divided by the number of electrons in the half-reaction (n). In this case, n = 2; therefore, the coefficient is − 29.58. With a 10-fold change in the concentration of Cl−, HOCl, H+ will only change the ORP ± 29.58 mV.

•There is no specific temperature dependence shown. Temperature can affect an ORP reaction in a variety of ways, so no general ORP temperature behavior can be characterized, as is the case with pH. Therefore, ORP measurements are almost never temperature compensated.

When checking the influence of an individual substance in the half reaction, the Nernst equation can be partitioned into individual logarithms for each substance, and the contribution of that substance can be calculated over its expected concentration range.

1.2.2 Measurement of ORP

An ORP sensor consists of an ORP electrode and a reference electrode, in much the same fashion as a pH measurement (ASTM D1498-14, 2014).

1.2.2.1 The ORP Electrode

The principle behind the ORP measurement is the use of an inert metal electrode (platinum, sometimes gold), which, due to its low resistance, will give up electrons to an oxidant or accept electrons from a reductant. The ORP electrode will continue to accept or give up electrons until it develops a potential, due to the built-up charge, which is equal to the ORP of the solution. The typical accuracy of an ORP measurement is ± 5 mV.

Sometimes the exchange of electrons between the ORP electrode and certain chemical substances is hampered by a low rate of electron exchange (exchange current density). In these cases, ORP may respond more strongly to a second redox couple in the solution (like dissolved oxygen). This leads to measurement errors, and it is recommended that new ORP applications be checked out in the laboratory before going on-line.

1.2.2.2 The Reference Electrode

The reference electrode used for ORP measurements is typically the same silver-silver chloride electrode used with pH measurements. In contrast with pH measurements, some offset in the reference is tolerable in ORP since, as will be seen, the mV changes measured in most ORP applications are large.

In certain specific applications (e.g., bleach production), an ORP sensor may use a silver billet as a reference, or even a pH electrode.

1.2.3 The Application of ORP

Due to its dependence upon the concentrations of multiple chemical substances, the application of ORP for many has been a puzzling and often frustrating experience. When considering ORP for a particular application, it is necessary to know the half-reaction involved and the concentration range of all the substances appearing in the half-reaction. It is also necessary to use the Nernst equation to get an idea of the expected ORP behavior.

ORP is often applied to a concentration measurement (chlorine in water, for example) without a clear understanding of all the factors involved. When the equation for the ORP of a hypochlorous solution (in the previous section) is considered, the problems associated with a concentration measurement can be outlined:

•The ORP depends upon the chloride ion (Cl−) and pH (H+) as much as it does hypochlorous acid (chlorine in water). Any change in the chloride concentration or pH will affect the ORP. Therefore, to measure chlorine accurately, chloride ion and pH must be measured to a high accuracy or carefully controlled to constant values.

•To calculate hypochlorous concentration from the measured millivolts, the measured millivolts will appear as the exponent of 10. The typical accuracy of an ORP measurement is ± 5 mV. This error alone will result in the calculated hypochlorous acid concentration being off by more than ± 30%. Any drift in the reference electrode or the ORP analyzer will only add to this error.

•Any change in the ORP with temperature is not compensated, further increasing the error in the derived concentration.

In general, ORP is not a good technique to apply to concentration measurements. Virtually all ORP half reactions involve more than one substance, and the vast majority have pH dependence. The logarithmic dependence of ORP on concentration multiplies any errors in the measured millivolts.

1.3 Measuring Dissolved Oxygen

1.3.1 What Is Dissolved Oxygen?

Dissolved oxygen refers to the level of free, noncompound oxygen present in water or other liquids. It is an important parameter in assessing water quality because of its influence on the organisms living within a body of water. In limnology (the study of lakes), DO is an essential factor second only to water itself. A DO level that is too high or too low can harm aquatic life and affect water quality.

Noncompound oxygen, or free oxygen (O2), is oxygen that is not bonded to any other element. DO is the presence of these free O2 molecules within water. The bonded oxygen molecule in water (H2O) is in a compound and does not count toward DO levels. One can imagine that free oxygen molecules dissolve in water much the way salt or sugar does when it is stirred.

1.3.1.1 Dissolved Oxygen and Aquatic Life

DO is necessary to many forms of life, including fish, invertebrates, bacteria and plants. These organisms use oxygen in respiration, similar to organisms on land. Fish and crustaceans obtain oxygen for respiration through their gills, while plant life and phytoplankton require DO for respiration when there is no light for photosynthesis. The amount of dissolved oxygen needed varies from creature to creature. Bottom feeders, crabs, oysters, and worms need minimal amounts of oxygen (1–6 mg/L), while shallow-water fish need higher levels (4–15 mg/L).

Microbes such as bacteria and fungi also require DO. These organisms use DO to decompose organic material at the bottom of a body of water. Microbial decomposition is an important contributor to nutrient recycling. However, if there is an excess of decaying organic material (from dying algae and other organisms) in a body of water with infrequent or no turnover (also known as stratification), the oxygen at lower water levels will get used up quicker.

1.3.1.2 Dissolved Oxygen Saturation

Two bodies of water that are both 100% air-saturated do not necessarily have the same concentration of DO. The actual amount of DO (in mg/L) will vary depending on temperature, pressure and salinity.

First, the solubility of oxygen decreases as temperature increases. This means that warmer surface water requires less DO to reach 100% air saturation than does deeper, cooler water. For example, at sea level (1 atm or 760 mmHg) and 4°C (39°F), 100% air-saturated water would hold 10.92 mg/L of DO. But if the temperature were raised to room temperature, 21°C (70°F), there would only be 8.68 mg/L DO at 100% air saturation.

Second, DO decreases exponentially as salt levels increase. That is why, at the same pressure and temperature, saltwater holds about 20% less DO than freshwater.

Third, DO will increase as pressure increases. This is true of both atmospheric and hydrostatic pressures. Water at lower altitudes can hold more DO than water at higher altitudes. This relationship also explains the potential for supersaturation of waters below the thermocline—at greater hydrostatic pressures, water can hold more DO without it escaping. Gas saturation decreases by 10% per meter increase in depth due to hydrostatic pressure. This means that if the concentration of DO is at 100% air saturation at the surface, it would only be at 70% air saturation 3 m below the surface.

In summary, colder, deeper fresh waters have the capability to hold higher concentrations of DO, but due to microbial decomposition, lack of atmospheric contact for diffusion and the absence of photosynthesis, actual DO levels are often far below 100% saturation. Warm, shallow saltwater reaches 100% air saturation at a lower concentration, but can often achieve levels over 100% due to photosynthesis and aeration. Shallow waters also remain closer to 100% saturation due to atmospheric contact and constant diffusion.

If there is a significant occurrence of photosynthesis or a rapid temperature change, the water can achieve DO levels over 100% air saturation. At these levels, the dissolved oxygen will dissipate into the surrounding water and air until it levels out at 100%.

1.3.1.3 Calculating DO From Percent Air Saturation

To calculate DO concentrations from air saturation, it is necessary to know the temperature and salinity of the sample. Barometric pressure has already been accounted for as the partial pressure of oxygen contributes to the percent air saturation. Salinity and temperature can then be used in Henry's law to calculate what the DO concentration would be at 100% air saturation. However, it is easier to use an oxygen solubility chart. These charts show the dissolved oxygen concentration at 100% air saturation at varying temperatures and salinities. This value can then be multiplied by the measured percent air saturation to calculate the dissolved oxygen concentration:

1.3.2 Dissolved Oxygen Measurement Methods

There are three methods available for measuring DO concentrations. Modern techniques involve either an electrochemical or optical sensor. The DO sensor is attached to a meter for spot sampling and laboratory applications or to a data logger, process monitor or transmitter for deployed measurements and process control.

The colorimetric method offers a basic approximation of DO concentrations in a sample. There are two methods designed for high-range and low-range DO concentrations. These methods are quick and inexpensive for basic projects, but limited in scope and subject to error due to other redoxing agents that may be present in the water.

The traditional method is the Winkler titration. While this method was considered the most accurate and precise for many years, it is also subject to human error and is more difficult to execute than the other methods, particularly in the field. The Winkler method now exists in seven modified versions, which are still used today.

1.3.2.1 Measuring Dissolved Oxygen by the Sensor Method

The most popular method for DO measurements is with a DO meter and sensor. While the general categories of DO sensors are optical and electrochemical, electrochemical sensors can be further broken down into polarographic, pulsed polarographic, and galvanic sensors. In addition to the standard analog output, several of these DO sensor technologies are available in smart sensor platforms with a digital output.

A DO sensor can be used in the lab or in the field. The DO sensors can be designed for biochemical oxygen demand (BOD) tests, spot sampling or long-term monitoring applications. A DO meter, water quality sonde or data logging system can be used to record measurement data taken with a DO sensor.

As DO concentrations are affected by temperature, pressure, and salinity, these parameters need to be accounted for. These compensations can be done manually or automatically with a DO meter or data-logging software. Temperature is generally measured by a thermistor within the sensor and is acquired by the meter or data logger without prompting. Many DO meters include an internal barometer, and data-logging systems can be set up with an external barometer or water level sensor for pressure measurements. Barometric pressure can also be manually input as altitude, true barometric pressure, or corrected barometric pressure. Salinity can be measured with a conductivity/salinity sensor and automatically compensated for, or approximated and manually input as shown in Table 1.1.

Table 1.1

Calibration and operating procedures can vary between models and manufacturers. An instruction manual should be referenced during the measurement and calibration processes.

1.3.2.1.1 Optical Dissolved Oxygen Sensors

Optical DO sensors measure the interaction between oxygen and certain luminescent dyes. When exposed to blue light, these dyes become excited (electrons gaining energy) and emit light as the electrons return to their normal energy state (Mcdonagh et al., 2001). When DO is present, the returned wavelengths are limited or altered due to oxygen molecules interacting with the dye. The measured effect is inversely proportional to the partial pressure of oxygen. While some of these optical DO sensors are called fluorescent sensors, this terminology is technically incorrect. These sensors emit blue light, not ultraviolet light, and are properly known as optical or luminescent DO sensors. Optical DO sensors can measure either the intensity or the lifetime of the luminescence, as oxygen affects both.

An optical DO sensor consists of a semipermeable membrane, sensing element, light-emitting diode (LED) and photodetector. The sensing element contains a luminescent dye that is immobilized in sol-gel, xerogel, or other matrix. The dye reacts when exposed to the blue light emitted by the LED. Some sensors will also emit a red light as a reference to ensure accuracy. This red light will not cause luminescence but will simply be reflected back by the dye. The intensity and luminescence lifetime of the dye when exposed to blue light is dependent on the amount of DO in the water sample. As oxygen crosses the membrane, it interacts with the dye, limiting the intensity and lifetime of the luminescence. The intensity or lifetime of the returned luminescence is measured by a photodetector and can be used to calculate the DO concentration.

The concentration of DO (as measured by its partial pressure) is inversely proportional to luminescence lifetime as shown by the Stern-Volmer equation:

   (1.9)

Io = intensity or lifetime of dye luminescence without oxygen

I = intensity or lifetime of luminescence with oxygen present

kq = quencher rate coefficient

t0 = luminescence lifetime of the dye

O2 = oxygen concentration as a partial pressure

This equation accurately applies at low DO concentrations. At high concentrations, this measurement is nonlinear. This nonlinearity comes from the way oxygen interacts in the polymer matrix of the dye. In polymers, dissolved gases show a negative deviation from Henry's law (which determines partial pressure). This means that, at higher concentrations, oxygen solubility in the dye matrix will follow the modified Stern-Volmer equation:

   (1.10)

Io = intensity or lifetime of dye luminescence without oxygen

I = intensity or lifetime of luminescence with oxygen present

A, B, b = Stern-Volmer and nonlinear solubility model quenching constants

O2 = oxygen concentration as a partial pressure

The use of this equation requires inputting predetermined sensor constants (Io, A, B, b), which are specific to each new or replacement sensor cap.

Optical DO sensors tend to be more accurate than their electrochemical counterparts and are not affected by hydrogen sulfide or other gases that can permeate an electrochemical DO membrane. They are also capable of accurately measuring DO at very low concentrations.

Optical DO sensors are ideal for long-term monitoring programs due to their minimal maintenance requirements. They can hold a calibration for several months and exhibit little (if any) calibration drift. These DO sensors also do not require any warm-up time or stirring when taking a measurement. Over a long period of time, the dye degrades and the sensing element and membrane will need to be replaced, but this replacement is very infrequent compared to electrochemical sensor membrane replacement. Luminescence lifetime-measuring sensors are less affected by dye degradation than intensity-measuring sensors, which means that they will maintain their accuracy even with some photodegradation.

However, optical DO sensors usually require more power and take two to four times longer to acquire a reading than an electrochemical DO sensor. These sensors are also heavily dependent on temperature. Luminescence intensity and lifetime are both influenced by ambient temperature, though most sensors will include a thermistor to automatically correct the data.

1.3.2.1.2 Electrochemical Dissolved Oxygen Sensors

Electrochemical DO sensors can also be called amperometric or Clark-type (Park et al., 2007) sensors. There are two types of electrochemical DO sensors: galvanic and polarographic. Polarographic DO sensors can be further broken down into steady-state and rapid-pulsing sensors. Both galvanic and polarographic DO sensors use two polarized electrodes, an anode and a cathode, in an electrolyte solution. The electrodes and electrolyte solution are isolated from the sample by a thin, semipermeable membrane.

When taking measurements, DO diffuses across the membrane at a rate proportional to the pressure of oxygen in the water. The DO is then reduced and consumed at the cathode. This reaction produces an electrical current that is directly related to the oxygen concentration. This current is carried by the ions in the electrolyte and runs from the cathode to the anode. As this current is proportional to the partial pressure of oxygen in the sample, it can be calculated by the following equation:

   (1.11)

id = current produced

F = Faraday's constant = 9.64 × 10⁴ C/mol

Pm(t) = permeability of the membrane as a function of temperature

A = surface area of