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Drinking Water Detection
Chapter 10
Drinking Water Detection Water is one of the fundamental resources required for the preservation of planetary bio-diversity. The availability of clean water is essential for human survival and directly impacts the quality of human life across the planet. Nearly 97.5% of water on earth is constituted of salty ocean waters, which cannot be put to use without expensive and energy-intensive desalination techniques. Glaciers and ice caps are huge repositories of fresh water comprising almost 2.15% of all the water on earth but are not easily accessible for immediate application. Only a small amount of water (0.65%) in the form of rivers, lakes, and underground aquifers is available to us for direct utilization (Shiklomanov, 2000). The global demand for fresh water has increased at an alarming rate, primarily due to the exponential rise in population, which has led to aggressive agricultural and industrial expansion. Fresh water withdrawals have increased globally at 1% per year since 1980 to cater to the growing needs of society. The irrigated agriculture sector accounts for most of the fresh water withdrawal (70%) and use, followed by the energy (15%) and industrial manufacturing (4%) sectors. Such overexploitation combined with unabated deforestation has led to the disruption of carefully balanced ecosystems, whereby natural means of replenishment of fresh water sources have been hampered. Almost 38% of irrigated areas globally still depend on groundwater. Aggravated by poor irrigation techniques, this has accelerated the decline of water quality in most communities. In addition, due to a lack of strict policies, untreated and unregulated agricultural and industrial runoffs are degrading the conditions of existing surface and groundwater sources through further contamination. The entire scenario has been exacerbated by climate change, which has disrupted weather patterns across the world, causing irregularities in the hydrological cycle and threatening the collapse of major ecosystems. The uneven distribution of freshwater resources across the world has had the most impact in the developing countries of Africa, Asia, and the Middle East, where inadequate access to clean and safe drinking water has turned into the most potent threat to the quality of life. Nearly two-thirds of the global population inhabit areas associated with economic and physical water scarcity. Recent reports indicate that an estimated 750 million people lack access to improved drinking water sources, while nearly 1.8 billion others use a Water Quality Monitoring and Management. https://doi.org/10.1016/B978-0-12-811330-1.00010-7 © 2019 Elsevier Inc. All rights reserved.
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source of water that is fecally contaminated; 82% of those who lack access to improved drinking water sources reside in rural areas or in minimal resource settings. Ingestion of such contaminated water has led to the prevalence of waterborne diseases like diarrhea and cholera, causing approximately 2 million deaths annually, with a majority of them being children under the age of five. Naturally, proactive efforts are necessary to mitigate the challenges of contaminated drinking water sources in limited resource settings through water treatment and water quality monitoring techniques. The challenges are further elevated due to the dearth of appropriate infrastructure and connectivity in such communities to carry out traditional purification and monitoring processes. Hence, an active branch of research has delved into the development of effective and sustainable point-of-use devices to improve access to clean and safe drinking water and consequently improve the quality of life in these marginalized communities. Microbial contamination of drinking water sources is one of the leading causes of waterborne diseases, which begs the need of developing efficient water monitoring systems to improve worldwide living standards. Even in low concentrations, the pathogens in water are detrimental to the well-being of the human population. Hence, there is an urgent need for developing efficient water monitoring systems that will improve global living standards. Conventional methods of pathogen detection (Lazcka et al., 2007) are efficient and provide an accurate estimation of contaminants in water. Both developing and industrialized countries are challenged with a wide range of water contaminants ranging from traditional compounds such as heavy metals, fluorides and deadly waterborne pathogens like Escherichia coli (E. coli) to emerging micropollutants such as endocrine disrupters and nitrosoamines. Extensive research in this field has brought about a wide variety of effective processes to treat contaminated water, ranging from chemical disinfection (Krasner et al., 2006), photocatalysis (Liou and Chang, 2012), and different forms of membrane filtration (Basri et al., 2011), to the use of nanoparticles (Dankovich and Gray, 2011) to bring about disinfection and coagulation-flocculation (Jung et al., 2015). Conventional methods of water disinfection and decontamination are targeted toward large systems and hence are energy intensive, requiring considerable capital infusion and engineering expertise. The infrastructure required for such elaborate systems is unavailable in most of the developing countries. Additionally, intensive chemical treatment creates unwanted contamination (like disinfection by-products) of treated water. Additionally, the developing nations suffer from diverse socioeconomic and political constraints that require a broader approach incorporating sustainable water resource management. Hence, recent research has focused on reducing chemical treatment through engineered natural systems for drinking water production and reduction of residual chemicals from distribution systems. An area of particular interest in this aspect is the exploration of the available nature-based solutions to the water purification problem.
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10.1 WATER-QUALITY INDEX The crucial role of water as the trigger and sustainer of civilizations has been witnessed throughout human history. But until as late as the 1960s, the overriding interest in water has been regarding its quantity. Except in manifestly undersirable situations, the available water was automatically deemed utilizable water. Only during the last three decades of the 20th century was the concern for water quality strongly felt, so that now water quality has acquired as much importance as water quantity. How do we express water quality of the same stream? The quality may be good enough for drinking but not suitable for use as a coolant in an industry. It may be good for irrigating some crops but not good for irrigating some other crops. It may be suitable for livestock but not for fish culture. One way to describe the quality of a given water sample is to list out the concentrations of everything that the sample contains. Such a list would be as long as the number of constituents analyzed and that may be anything from the 20-odd common constituents to hundreds! Moreover, such a list will make little sense to anyone except well-trained water-quality experts. How to compare the quality of different water sources? It can’t be done easily by comparing the list of constituents each sample contains. For example, a water sample containing six components in 5% higherthan-permissible (hence objectionable) levels—pH, hardness, chloride, sulfate, iron, and sodium—may not be as bad for drinking as another sample with just one constituent—mercury—at 5% higher-than-permissible. Water-quality indices seek to address this vexing problem. Water-quality indexes (WQIs) aim at giving a single value to the water quality of a source on the basis of one or the other system that translates the list of constituents and their concentrations present in a sample into a single value. One can then compare different samples for quality on the basis of the index value of each sample. WQIs may have gained currency during the last three decades, but the concept in its rudimentary form was first introduced >150 years ago—in 1848—in Germany where the presence or absence of certain organisms in water was used as an indicator of the fitness or otherwise of a water source. Since then various European countries have developed and applied different systems to classify the quality of the waters within their regions. These water classification systems usually are of two types: (1) Those concerned with the amount of pollution present, and (2) Those concerned with living communities of macroscopic or microscopic organisms. Rather than assigning a numerical value to represent water quality, these classification systems categorized water bodies into one of several pollution classes or levels. By contrast, indexes that use a numerical scale to represent gradations in water-quality levels are a recent phenomenon, beginning with Horton’s index in 1965.
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WQIs (Abbasi and Abbasi, 2012) can be formulated in two ways: one in which the index numbers increase with the degree of pollution (increasing scale indexes) and the other in which the index numbers decrease with the degree of pollution (decreasing scale indexes). One may classify the former as “water pollution indexes” and the latter as “water-quality indexes.” But this difference is essentially cosmetic; “water quality” is a general term of which “water pollution,” which indicates “undesirable water quality” is a special case.
10.1.1 Horton’s Index Horton set for himself the following criteria when developing the first-ever modern WQI (Horton, 1965): (1) The number of variables to be handled by the index should be limited to avoid making the index unwieldy. (2) The variables should be of significance in most areas. (3) Only such variables for which reliable data are available, or obtainable, should be included. Horton selected 10 most commonly measured water-quality variables for his index, including dissolved oxygen (DO), pH, coliforms, specific conductance, alkalinity, and chloride. Specific conductance was intended to serve as an approximate measure of total dissolved solids (TDSs), and carbon chloroform extract (CCE) was included to reflect the influence of organic matter. One of the variables, sewage treatment (percentage of population served), was designed to reflect the effectiveness of abatement activities on the premise that chemical and biological measures of quality are of little significance until substantial progress has been made in eliminating discharges of raw sewage. The index weight ranges from 1 to 4. Notably, Horton’s index did not include any toxic chemicals. The index score is obtained with a linear sum aggregation function. The function consists of the weighted sum of the subindices Ii divided by the sum of the weights Wi and multiplied by two coefficients M1 and M2, which reflect temperature and obvious pollution, respectively: Xn Wi Ii M1 M2 (10.1) QI ¼ Xi¼1 W i i¼1 Horton’s index is easy to compute, even though the coefficients M1 and M2 require some tailoring to fit individual situations. The index structure, its weights, and rating scale are highly subjective as they are based on the judgment of the author and a few of his associates. Horton’s pioneering effort has been followed up by several workers who have striven to develop less and less subjective but more and more sensitive and useful water-quality indexes.
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10.1.2 Brown’s Index Brown et al. developed a water-quality index similar in structure to Horton’s index but with much greater rigor in selecting parameters, developing a common scale, and assigning weights (Brown et al., 1970). This effort was supported by the National Sanitation Foundation (NSF). For this reason, Brown’s index is also referred to as NSF-WQI. A panel of 142 persons with expertise in water-quality management was formed for the study. The panelists were asked to consider 35 parameters for possible inclusion in the index. They were free to add to the list any parameter of their choice. Each parameter was to be assigned one of the following choices: “do not include,” “undecided,” or “include.” The panelists were asked to rank the parameters marked as “include” according to their significance as a contributor to the overall quality. The rating was done on a scale of 1 (highest) to 5 (lowest). The responses of the panel were brought to the knowledge of every member of the panel and the members were allowed to review their individual judgment in the light of the full panel’s response. Finally, the panelists were asked to select not >15 parameters that they considered to be the most important. The complete list of parameters arranged in decreasing order of significance, as determined by average rating of the panel, was presented to each member. The panelists were asked to assign values for the variation in the level of water quality produced by different concentrations of the parameters selected as previously. The concentration-value relationship of each parameter was obtained in the form of a graph. These graphs were produced by the panelists to denote curves which, in their judgment, best represented the variation in level of water quality produced by possible measurements of each respective parameter. The judgment of all the respondents was averaged to produce a set of curves, one for each parameter. For pesticides and toxic elements, it was proposed that, if the total contents of detected pesticides or toxic elements (of all types) exceed 0.1 mg/L, the water-quality index would be automatically registered zero. The panelists were asked to compare relative overall water quality using a scale of 1 (highest) to 5 (lowest) for the finally selected parameters. Arithmetic mean was calculated for the ratings of experts. To convert the rating into weights, a temporary weight of 1.0 was assigned to the parameter which received the highest significance rating. All other temporary weights were obtained by dividing the highest rating by the individual mean rating. Each temporary weight was then divided by the sum of all the temporary weights to arrive at the final weight. The index originally proposed by Brown et al. has the form WQI ¼
9 X i¼1
wi Ti ðpi Þ
(10.2)
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where p is the measured value of the ith parameter and Ti is the quality rating transformation (curve) of Pthe ith parameter value pi, wi is relative weight of the ith parameter such that 9i¼1 wi ¼ 1. Brown’s index represents general water quality. It does not recognize and incorporate specific water functions such as drinking water supply, agriculture, industry, etc. Related to this difficulty has been an apparent tendency for some respondents to be heavily influenced in their judgment of a parameter’s suitability for inclusion in a WQI by factors such as data availability and existing analytical methodologies for measuring the various parameters.
10.1.3 Prati’s Implicit Index of Pollution This index was developed by Prati et al. on the basis of water-quality standards (Prati et al., 1971). The concentration values of all the pollutants were transformed into levels of pollution expressed in new units through mathematical expressions. These mathematical expressions were constructed in such a way that the new units were proportional to the polluting effect relative to other factors. In this way, even if a pollutant is to be present in smaller concentrations than other pollutants, it still will exert a large impact on the index score if its polluting effect is greater. In the first step, water quality was classified vis a vis all the parameters based on water quality standards. In the second step, one pollutant was taken as reference and its actual value was considered directly as reference index. In the third step, mathematical expressions were formed to transform each of the values of the other pollutants into subindices. This transformation took into account the polluting capacity of the parameters related to a selected reference parameter. In the construction of these functions, the analytical properties of various curves were used to ensure that the resulting transformation would be applicable not only to small values of pollutant concentrations but also to those exceeding class V. Accordingly, the subindices are generated. The index was computed as the arithmetic mean of the 13 subindexes. The index ranges from 0 to 14 (and above) and was applied by Prati et al. to data on surface waters in Ferrara, Italy.
10.1.4 Dinius’ Water-Quality Index This index broke new ground in the sense that through it an attempt was made to design a rudimentary social accounting system that would measure the costs and impact of pollution control efforts. In this sense, Dinius’ WQI is a forerunner of the “planning” or “decision-making” indexes. Eleven parameters were selected. Like Horton’s index and the NSF-WQI, it had decreasing scale, with values expressed as a percentage of perfect water quality which corresponds to 100%. Like Prati’s indexes, the subindexes in Dinius’ index were developed from a review of the published scientific literature. Dinius examined the water quality
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described by various authorities to different levels of pollutant variables, and from this information generated 11 subindex equations. The index was calculated as the weighted sum of the subindexes (Otto, 1978), like Horton’s index, and the additive version of the NSF-WQI: WQI ¼
11 1X wi Ii 21 i¼1
(10.3)
The weights ranged from 0.5 to 5 on a basic scale of importance. On this scale, 1, 2, 3, 4, and 5 denote, respectively, very little, little, average, great and very great importance. The sum of the weights was 21, which is the denominator in the index equation. The index was applied by Dinius on an illustrative basis to data on several streams in Alabama in the United States.
10.1.5 Bhargava’s Index This is one of the first reported indexes by an Asian author, and addresses the issue of drinking water supply. To develop the index, Bhargava (1985) identified four groups of parameters. Each group contained sets of one type of parameters. The first group included the concentrations of coliform organisms to represent the bacterial quality of drinking water. The second group included toxicants, heavy metals, etc., some or all of which have a cumulative toxic effect on the consumer. The third group included parameters that cause physical effects, such as odor, color, and turbidity. The fourth group included the inorganic and organic nontoxic substances such as chloride, sulfate, foaming agents, iron, manganese, zinc, copper, total dissolved solids (TDS), etc. The variables, with their maximum allowable contaminant level, CMCL (as per the US Environmental Protection Agency), and the subindexes worked out by Bhargava, which include the effects of concentrations of different parameters and their weightage. The subindexes were aggregated as follows: " #1=n n Y fi (10.4) WQI ¼ i¼1
in which fi(Pi) is the sensitivity function of the ith variable, and n is the number of variables considered. The index was applied to the raw water quality data at the upstream and downstream of river Yamuna at Delhi. The author suggested that the public drinking water supplies should have a WQI larger than 90.
10.1.6 A “Universal” Water-Quality Index Boyacioglu took into consideration the water-quality standards set by the Council of European Communities (EC 1991) (Boyacioglu, 2007), the Turkish water
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pollution control regulations, and other scientific information to select 12 water-quality parameters as the most representative for drinking water quality. They set three classes of water representing excellent, acceptable, and polluted categories. To assign weights to the water-quality variables, the following factors are taken into account: l
l
Chemical parameters had a lower weight than microbiological parameters, because microbial contaminants belong to the greatest health impact category Higher weight was given to those parameters which are of known health concern
The temporary weights ranged from 1 to 4 on a basic scale of importance. On this scale 1, 2, 3 and 4 denote, respectively, little, average, great and very great importance. Each weight was then divided by the sum of all weights to arrive at the final weight factor. The index is given by UWQI ¼
n X
wi Ii
(10.5)
i¼1
where wi is the weight for ith parameter, and Ii is the subindex for the ith parameter. The index value in the range of 0–<25 represents poor quality, 25–<50 marginal quality, 50–<75 fair quality, 75–<95 good quality and, above it, excellent quality.
10.2 METHOD OF DRINKING WATER DETECTION 10.2.1 Multiple-Tube Fermentation Technique The technique of multiple-tube fermentation (MTF) has been used for over 80 years as a water quality monitoring method (Rompre et al., 2002). The method consists of inoculating a series of tubes with appropriate decimal dilutions of the water sample. Production of gas, acid formation or abundant growth in the test tubes after 48 h of incubation at 35°C constitutes a positive presumptive reaction. Both lactose and lauryl tryptose broths can be used as presumptive media. All tubes with a positive presumptive reaction are subsequently subjected to a confirmation test. The formation of gas in a brilliant green lactose bile broth fermentation tube at any time within 48 h at 35°C constitutes a positive confirmation test. The fecal coliform test can be applied to determine total coliforms (TC). The results of the MTF technique are expressed in terms of the most probable number (MPN) of microorganisms present. This number is a statistical estimate of the mean number of coliforms in the sample. As a consequence, this
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technique offers a semiquantitative enumeration of coliforms. Nevertheless, the precision of the estimation is low and depends on the number of tubes used for the analysis: for example, if only 1 mL is examined in a sample containing 1 coliform/mL, about 37% of 1-mL tubes may be expected to yield negative results because of the random distribution of the bacteria in the sample. But if five tubes, each with a 1 mL sample, are used, a negative result may be expected <1% of the time. Many factors may significantly affect coliform bacteria detection by MTF, especially during the presumptive phase. Interference by high numbers of noncoliform bacteria, as well as the inhibitory nature of the media, have been identified as factors contributing to underestimates of coliform abundance. The MTF technique lacks precision in qualitative and quantitative terms. The time required to obtain results is higher than with the membrane filter technique that has replaced the MTF technique in many instances for the systematic examination of drinking water. However, this technique remains useful, especially when the conditions do not allow the use of the membrane filter technique, such as turbid or colored waters. MTF is easy to implement and can be performed by a technician with basic microbiological training, but the method can become very tedious and labor intensive since many dilutions have to be processed for each water sample. However, it is also relatively inexpensive, as it requires unsophisticated laboratory equipment. Nevertheless, this method is extremely time-consuming, requiring 48 h for presumptive results, and necessitates a subculture stage for confirmation which could take up to a further 48 h.
10.2.2 Membrane Filter Technique The membrane filter (MF) technique is fully accepted and approved as a procedure for monitoring drinking water microbial quality in many countries (Rompre et al., 2002). This method consists of filtering a water sample on a sterile filter with a 0.45-mm pore size that retains bacteria, incubating this filter on a selective medium, and enumerating typical colonies on the filter. Many media and incubation conditions for the MF method have been tested for optimal recovery of coliforms from water samples. Among these, the most widely used for drinking water analysis are the m-Endo-type media in North America and the Tergitol-TTC medium in Europe. Coliform bacteria form red colonies with a metallic sheen on an Endo-type medium containing lactose or yellow-orange colonies on Tergitol-TTC media. Other media, such as MacConkey agar and the Teepol medium, have been used in South Africa and Britain. However, comparisons among the media have shown that m-Endo agar yielded higher counts than MacConkey or Teepol agar. To enumerate fecal coliforms (FC), the filters can be incubated on an enriched lactose medium (mFC) at a temperature of 44.5°C for 24 h.
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Enumeration of TC by membrane filtration is not totally specific. When MF is associated with m-Endo media containing lactose, atypical colonies that are dark red, mucoid, or nucleated and without a metallic sheen may occasionally appear. Atypical blue, pink, white, or colorless colonies lacking sheen are not considered as TC by this technique. Furthermore, typical colonies with a sheen may be produced occasionally by noncoliform bacteria and, conversely, atypical colonies (dark red or nucleated colonies without sheen) may sporadically be coliforms. Coliform verification is therefore recommended for both types of colonies. With the acceptance of MF as a technique of choice for drinking water monitoring, questions regarding interference with coliform detection and the accuracy of the enumeration have arisen. The presence of high numbers of background heterotrophic bacteria was shown to decrease coliform recovery by MF. Excessive crowding of colonies on m-Endo media has been associated with a reduction in coliform colonies producing the metallic sheen. The predominant concern about MF is its inability to recover stressed or injured coliforms. A number of chemical and physical factors involved in drinking water treatment, including disinfection, can cause sublethal injury to coliform bacteria, resulting in a damaged cell unable to form a colony on a selective medium. Exposure of bacteria to products like chlorine may result in injury and increased sensitivity to bile salts or to the replacement surface-active agents contained in some selective media. Some improvements in the method have increased detection of injured coliform bacteria, including the development of m-T7 medium formulated specifically for the recovery of stressed coliforms in drinking water. The high number of modified media in use is a reflection of the fact that no universal medium currently exists that allows optimal enumeration of various coliform species originating from different environments and present in a wide variety of physiological states. A significant advantage of the MF technique over the MTF method is that, with MF, the examination of larger volumes of water is feasible, which leads to greater sensitivity and reliability. MF also offers a quantitative enumeration comparable to the semiquantitative information given by the MTF method. MF is a useful technique for the majority of water quality laboratories, as it is a relatively simple method to use. Many samples can be processed in a day with limited laboratory equipment by a technician with basic microbiological training. Nevertheless, since this method is not sufficiently specific, a confirmation stage is needed, which could take a further 24 h after the first incubation period on selective media.
10.2.3 Enzyme Substrate Method The biochemical tests used for bacterial identification and enumeration in classical culture methods are generally based on metabolic reactions. For this reason, they are not fully specific, and many additional tests are sometimes
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required to obtain precise confirmation. The use of microbial enzyme profiles to detect indicator bacteria is an attractive alternative to classical methods (Rompre et al., 2002). Enzymatic reactions can be group, genus- or speciesspecific, depending on the enzyme targeted. Moreover, reactions are rapid and sensitive. Thus, the possibility of detecting and enumerating coliforms through specific enzymatic activities has been under investigation for many years now. β-D-Glucuronidase is an enzyme that catalyzes the hydrolysis of β-Dglucopyranosiduronic derivatives into their corresponding aglycons and D-glucuronic acid. Although this bacterial enzyme was discovered first in E. coli, its relative specificity for identifying this microorganism was not apparent until Kilian and Bulow surveyed the Enterobacteriaceae and reported that glucuronidase activity was mostly limited to E. coli. β-D-Glucuronidase-positive reactions were observed in 94%–96% of the E. coli isolates tested, while Chang et al. found a higher proportion of β-D-glucuronidase-negative E. coli (a median of 15% from E. coli isolated from human fecal samples). In contrast, β-Dglucuronidase activity is less common in other Enterobacteriaceae genus, such as shigella (44%–58%), salmonella (20%–29%) and yersinia strains and in flavobacteria. β-D-Galactosidase catalyzes the breakdown of lactose into galactose and glucose and has been used mostly for enumerating the coliform group within the Enterobacteriaceae family. Chromogenic and fluorogenic substrates produce color and fluorescence, respectively, upon cleavage by a specific enzyme. These substrates have been used to detect the presence or the activity of specific enzymes in aquatic systems. The use of these substrates has led to improved accuracy and faster detection. Methods for detection or enumeration may be performed in a single medium, thus bypassing the need for a time-consuming isolation procedure prior to identification.
10.2.4 Ion Chromatography Ion chromatography (or ion-exchange chromatography) is a chromatography process that separates ions and polar molecules based on their affinity to the ion exchanger. It works on almost any kind of charged molecule—including large proteins, small nucleotides, and amino acids. The two types of ion chromatography are anion-exchange and cation-exchange. It is often used in protein purification, water analysis, and quality control. Water-soluble and charged molecules such as proteins, amino acids, and peptides bind to moieties that are oppositely charged by forming ionic bonds with the insoluble stationary phase (Nachod and Jack, 2013). The equilibrated stationary phase consists of an ionizable functional group where the targeted molecules of a mixture to be separated and quantified can bind while passing through the column—a cationic stationary phase is used to separate anions and an anionic stationary phase is used to separate cations. Cation exchange
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chromatography is used when the desired molecules to separate are cations and anion exchange chromatography is used to separate anions. The bound molecules then can be eluted and collected using an eluant which contains anions and cations by running higher concentration of ions through the column or changing pH of the column. One of the primary advantages of the use of ion chromatography is only one interaction involved during the separation as opposed to other separation techniques; therefore, ion chromatography may have a higher matrix tolerance. However, there are also disadvantages involved when performing ion-exchange chromatography, such as the constant evolution of the technique, which leads to inconsistency from column to column (Neubauer, 2009).
10.2.5 Gas Chromatography Gas chromatography (GC) is a common type of chromatography used in analytical chemistry for separating and analyzing compounds that can be vaporized without decomposition. Typical uses of GC include testing the purity of a particular substance, or separating the different components of a mixture. In some situations, GC may help in identifying a compound. In preparative chromatography, GC can be used to prepare pure compounds from a mixture (Pavia, 2005). In gas chromatography, the mobile phase is a carrier gas, usually an inert gas such as helium or an unreactive gas such as nitrogen. Helium remains the most commonly used carrier gas in about 90% of instruments although hydrogen is preferred for improved separations. The stationary phase is a microscopic layer of liquid or polymer on an inert solid support, inside a piece of glass or metal tubing called a column. The instrument used to perform gas chromatography is called a gas chromatograph. The gaseous compounds being analyzed interact with the walls of the column, which is coated with a stationary phase. This causes each compound to elute at a different time, known as the retention time of the compound. The comparison of retention times is what gives GC its analytical usefulness. Gas chromatography is in principle similar to column chromatography, but has several notable differences. First, the process of separating the compounds in a mixture is carried out between a liquid stationary phase and a gas mobile phase, whereas in column chromatography the stationary phase is a solid and the mobile phase is a liquid. Second, the column through which the gas phase passes is located in an oven where the temperature of the gas can be controlled, whereas column chromatography has no such temperature control. Finally, the concentration of a compound in the gas phase is solely a function of the vapor pressure of the gas. Gas chromatography is also similar to fractional distillation, since both processes separate the components of a mixture primarily based on boiling point differences. However, fractional distillation is typically used to separate components of a mixture on a large scale, whereas GC can be used on a much smaller scale. Gas chromatography is also sometimes known as
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vapor-phase chromatography (VPC), or gas-liquid partition chromatography (GLPC). These alternative names, as well as their respective abbreviations, are frequently used in the scientific literature. Strictly speaking, GLPC is the most correct terminology and is thus preferred by many authors (Pavia, 2005).
10.3 STANDARD OF DRINKING WATER Drinking water should have a taste determined by the presence of physiologically necessary salts of calcium, magnesium, sodium, and potassium in corresponding concentrations without which metabolism in the human organism is impossible. What optimal parameters should the water possess to be completely safe from the biological and physiological points of view? For the first time in the world in 1853, in Brussels, at the International Congress of Hygienists, an attempt was made to set a standard for drinking water, which would include only nine controllable components. Among them there were vitally important substances such as magnesium, calcium, total mineralization (determined mainly by the presence of salts of sodium and potassium), and the content of oxygen in the water, sulfates, chlorides, nitrates, and ammonium. However, the age of industrialization, fast development of the agroindustrial complex, and the emergence of megapolises with compact settlements of people, whose number dramatically increased, all began. In connection with the worsening of the ecological state of surface and underground sources, the issue of water quality control, used by people for drinking purposes, became more acute. As a result of a low quality of drinking water, real threats to the sanitary-epidemiological situation in various regions of the planet cropped up. And only early in the 20th century did the first standard for drinking water appear in the United States. After almost a century after adoption in Brussels of the first recommendation for drinking water quality, a document of the World Health Organization (WHO) was published, which today, at the beginning of the 21st century, proposes to bring under regulation 95 indexes, of which 26 are toxic substances, whose presence in drinking water is undesirable. The US standard regulates 102 indexes in drinking water, where complete absence is required for as many as 35 toxic indexes.
10.3.1 EPA Drinking Water Standards Most facilities interpret the “potable, uncontaminated” water requirement for ordinary animals as water that meets, at a minimum, the Environmental Protection Agency (EPA) drinking water standards for human consumption. The EPA is responsible for the National Primary Drinking Water Regulations, which are health-related standards that establish the maximum contaminant levels (MCLs). MCLs are the maximum permissible level of a contaminant in water delivered to users of a public water system. MCLs are enforceable under the Safe Drinking Water Act. The EPA has also set unenforceable maximum contaminant
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level goals (MCLGs) at levels where no known or anticipated adverse effects on health occur and that allow an adequate margin of safety. The enforceable MCL is set as close to the MCLG as reasonable, taking into consideration the costs and treatment techniques available to public water systems. Health advisories provide information on contaminants that can cause human health effects and are known or anticipated to be in drinking water. Health advisories are guidance values based on noncancer health effects for different durations of exposure (e.g., 1-day, 10-day, longer-term, and lifetime).
10.3.2 USP Pharmaceutical Water Standards It is useful to learn about pharmaceutical water standards to see what can be applied to animal drinking water. This is especially true for bacterial contaminants. It is impossible to achieve absolutely sterile water in any piped water system, but automated watering systems can (with appropriate design and operation) achieve the quality of USP Purified Water and can approach USP Water for Injection. The United States Pharmacopeia Convention is a private, not-for-profit organization that sets standards for drugs, devices, and diagnostics. It publishes two compendia (summary documents). The US Pharmacopeia (USP) contains standards for drug products. The National Formulary (NF) sets standards for drug excipients (inert substances used as carriers or dilutants). The USP monograph lists two waters that are prepared in bulk form: Purified Water (PW), often called USP Purified Water to distinguish it from other purified waters, and Water for Injection (WFI).
10.3.2.1 USP Purified Water (PW) Purified Water is described in the USP 23 monograph as follows: “Purified Water is water obtained by distillation, ion-exchange treatment, reverse osmosis, or other suitable process. It is prepared from water complying with the regulations of the U.S. Environmental Protection Agency (EPA) with respect to drinking water. It contains no added substances.” Microbial quality: Regarding the bacteriological purity of PW, the monograph (legally enforceable section) states only that PW must comply with the EPA regulations for drinking water. The EPA regulations only specify limits for coliform bacteria. In the informational section of the USP 23, which deals with action guidelines for the microbial control of ingredient water, it says: “A total microbial (aerobic) count that may be used for source drinking water is 500 colony-forming units (cfu) per mL. A general guideline for Purified Water may be 100 cfu/mL.” These numbers for cfu/mL are only advisory guidelines that represent recommended alert/action limits, not reject levels. The informational section also suggests that the microbial action limits for PW should be based on the
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intended use of the water and the nature of the product being made. It recognizes that microbial limits for PW require being defined on a case-by-case basis. USP 23 Supplement 5, effective since November 1996, specifies the method for total bacteria counts. It states “Heterotrophic Plate Count of a 1-mL sample, using Plate Count Agar at an incubation temperature of 30 to 35°C for a 48-hour period (minimum).” There is some controversy “starved” bacteria in highpurity water. Chemical quality: Effective November 15, 1996, the former inorganic chemistry tests (for calcium, sulfate, chloride, ammonia, and carbon dioxide) were replaced with a three-stage conductivity test. The conductivity limit is pH-dependent but, for example, at pH 7.0, conductivity should be <5.8 microSiemen/cm (μS/cm). The former test for oxidizable substances was replaced with a total organic carbon (TOC) limit of 0.05 mg/L. TOC is an indirect measure of organic molecules present in water measured as carbon. The new tests allow continuous in-line monitoring of water using instrumentation rather than lab work.
10.3.3 Lab Animal Drinking Water Standards for chemical quality: (1) EPA Regulated Chemical Contaminants. The requirement for “potable, uncontaminated” drinking water for “ordinary animals,” as stated in the Guide, can be interpreted to mean that animal drinking water should, at a minimum, comply with the EPA’s Primary (health-related) Drinking Water Regulations for human consumption. This is similar to the requirement that states USP Purified Water must be prepared with water complying with the EPA regulations. (2) pH. The Guide specifically mentions pH as something that may necessitate periodic monitoring. The reason for setting a low limit is that low pH water is corrosive and can dissolve plumbing components. This is especially a concern when water contacts brass and copper piping systems where copper, zinc, and lead can dissolve into the animal’s drinking water. Reasons for setting a high limit are that high pH can promote hardness scale precipitation (see number 3 “Hardness” following) and that chlorine disinfection is not as effective at high pH. See the Edstrom Industries document Forms of Chlorine in Water (MI-4148) for more information. Some facilities acidify animal drinking water to pH 2.5–3.0 in order to kill Pseudomonas and other common water bacteria. (3) Hardness. The reason for setting an upper limit on hardness is that hard water can cause calcium carbonate scale deposits in automated watering systems, which can lead to drinking valve leaks and other operational problems. According to the Water Quality Association, water is considered “hard” when the measured hardness exceeds 120 mg/L. Just knowing the hardness level of water is not enough to predict if it will cause scaling problems. A better predictor is the Langelier saturation index (LSI). When the LSI (which is calculated from pH, total dissolved solids, calcium hardness, and alkalinity) is greater than zero, water will have a tendency to scale. (4) Total
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dissolved solids or conductivity. Total dissolved solids (TDS) and conductivity both indicate the total inorganic mineral content of drinking water. Either of these tests can be used to monitor the consistency of quality from water purification processes (such as reverse osmosis), which remove inorganic contaminants from water. The typical conductivity of reverse osmosis (RO) water ranges between 1 and 100 μS/cm, depending on the conductivity of the supply water. Usually, conductivity is measured with in-line sensors. (5) Disinfectants. An automated drinking water system may contain residual disinfectants from the public drinking water supply or additional disinfectants may be injected into animal drinking water to control bacterial growth. The EPA has proposed/ tentative maximum contaminants levels for these common disinfectants: chloramine proposed MCL ¼ 4 mg/L; chlorine proposed MCL ¼ 4 mg/L; chlorine dioxide tentative MCL ¼ 0.8 mg/L. Standards for microbial quality: In order to establish microbial quality guidelines for animal drinking water, it is useful to understand the guidelines and regulations for EPA drinking water and for USP PW and WFI. (1) Specify treatment techniques. EPA regulations specify treatment techniques (filtration, disinfection, etc.) in lieu of specific maximum contaminant limits for most microbial contaminants in drinking water. Because of the complexity and cost of testing for waterborne microorganisms, routine examination of water for pathogens is not feasible. Even when specific pathogens are examined, a negative result may be due to the inadequacy of the testing method. Or, it may indicate a safe water quality at that moment when the water was sampled, but it does not guarantee that safe water can be expected 1 h before or after testing. If this type of guideline were adapted for laboratory animal drinking water, a facility may, for example, specify reverse osmosis water containing a minimum concentration of disinfectant in lieu of specifying maximum concentrations of bacteria, viruses, and pathogenic protozoa. (2) Set limits for certain specific microorganisms. The EPA’s MCL for coliform bacteria is less than one per 100 mL and the unenforceable MCLG for Giardia lamblia, Legionella, and viruses is zero. (3) Set limits based on use of water. USP Purified Water guidelines suggest an action/alert limit for total microbial counts at 100 colonyforming-units/mL, but it also recognizes that microbial limits should be defined on a case-by-case basis. Just as the bacteria count limits for USP PW should be based on the intended use of the water, microbial limits for animal drinking water may need to be based on the requirements for the animals or the study. Perhaps there need to be separate recommendations for different animals and study types.
REFERENCES Abbasi, T., Abbasi, S.A., 2012. Water Quality Indices. Elsevier, Amsterdam. Basri, H., Ismail, A.F., Aziz, M., 2011. Polyethersulfone (pes)–silver composite UF membrane: effect of silver loading and PVP molecular weight on membrane morphology and antibacterial activity. Desalination 273 (1), 72–80.
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Bhargava, D.S., 1985. Water quality variations and control technology of Yamuna River. Environ. Pollut. 37 (4), 355–376. Boyacioglu, H., 2007. Development of a water quality index based on a European classification scheme. Water SA 33 (1), 101–106. Brown, R.M., McClelland, N.I., Deininger, R.A., Tozer, R.G., 1970. A water quality index—do we dare? Water Sew. Works 117, 339–343. Dankovich, T.A., Gray, D.J., 2011. Bactericidal paper impregnated with silver nanoparticles for point-of-use water treatment. Environ. Sci. Technol. 45 (5), 1992–1998. Horton, R.K., 1965. An index number system for rating water quality. J. Water Pollut. Control Fed. 37 (3), 300–306. Jung, C., Son, A., Her, N., Zoh, K.D., Cho, J., Yoon, Y., 2015. Removal of endocrine disrupting compounds, pharmaceuticals, and personal care products in water using carbon nanotubes: a review. J. Ind. Eng. Chem. 27, 1–11. Krasner, S.W., Weinberg, H.S., Richardson, S.D., Pastor, S.J., Chinn, R., Sclimenti, M.J., Onstad, G.D., Thruston, A.D., 2006. Occurrence of a new generation of disinfection byproducts. Environ. Sci. Technol. 40 (23), 7175–7185. Lazcka, O., Campo, F.J.D., Munoz, F.X., 2007. Pathogen detection: a perspective of traditional methods and biosensors. Biosens. Bioelectron. 22 (7), 1205–1217. Liou, J., Chang, H., 2012. Bactericidal effects and mechanisms of visible light-responsive titanium dioxide photocatalysts on pathogenic bacteria. Arch. Immunol. Ther. Exp. 60 (4), 267–275. Nachod, F.C., Jack, S., 2013. Ion Exchange Technology. Academic Press, Amsterdam. Neubauer, K., 2009. Advantages and disadvantages of different column types for speciation analysis by LC-ICP-MS. Spectroscopy 24 (11), 30–33. Otto, W.R., 1978. Environmental Indices: Theory and Practice. Ann Arbor Science Publishers Inc., Ann Arbor, MI. Pavia, D.L., 2005. Introduction to Organic Laboratory Techniques: A Small Scale Approach. Cengage Learning, Boston. Prati, L., Pavanello, R., Pesarin, F., 1971. Assessment of surface water quality by a single index of pollution. Water Res. 5, 741–751. Rompre, A., Servais, P., Baudart, J., De-Roubin, M.R., Laurent, P., 2002. Detection and enumeration of coliforms in drinking water: current methods and emerging approaches. J. Microbiol. Methods 49 (1), 31–54. Shiklomanov, I.A., 2000. Appraisal and assessment of world water resources. Water Int. 25 (1), 11–32.
Drinking Water Detection Water is one of the fundamental resources required for the preservation of planetary bio-diversity. The availability of clean water is essential for human survival and directly impacts the quality of human life across the planet. Nearly 97.5% of water on earth is constituted of salty ocean waters, which cannot be put to use without expensive and energy-intensive desalination techniques. Glaciers and ice caps are huge repositories of fresh water comprising almost 2.15% of all the water on earth but are not easily accessible for immediate application. Only a small amount of water (0.65%) in the form of rivers, lakes, and underground aquifers is available to us for direct utilization (Shiklomanov, 2000). The global demand for fresh water has increased at an alarming rate, primarily due to the exponential rise in population, which has led to aggressive agricultural and industrial expansion. Fresh water withdrawals have increased globally at 1% per year since 1980 to cater to the growing needs of society. The irrigated agriculture sector accounts for most of the fresh water withdrawal (70%) and use, followed by the energy (15%) and industrial manufacturing (4%) sectors. Such overexploitation combined with unabated deforestation has led to the disruption of carefully balanced ecosystems, whereby natural means of replenishment of fresh water sources have been hampered. Almost 38% of irrigated areas globally still depend on groundwater. Aggravated by poor irrigation techniques, this has accelerated the decline of water quality in most communities. In addition, due to a lack of strict policies, untreated and unregulated agricultural and industrial runoffs are degrading the conditions of existing surface and groundwater sources through further contamination. The entire scenario has been exacerbated by climate change, which has disrupted weather patterns across the world, causing irregularities in the hydrological cycle and threatening the collapse of major ecosystems. The uneven distribution of freshwater resources across the world has had the most impact in the developing countries of Africa, Asia, and the Middle East, where inadequate access to clean and safe drinking water has turned into the most potent threat to the quality of life. Nearly two-thirds of the global population inhabit areas associated with economic and physical water scarcity. Recent reports indicate that an estimated 750 million people lack access to improved drinking water sources, while nearly 1.8 billion others use a Water Quality Monitoring and Management. https://doi.org/10.1016/B978-0-12-811330-1.00010-7 © 2019 Elsevier Inc. All rights reserved.
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source of water that is fecally contaminated; 82% of those who lack access to improved drinking water sources reside in rural areas or in minimal resource settings. Ingestion of such contaminated water has led to the prevalence of waterborne diseases like diarrhea and cholera, causing approximately 2 million deaths annually, with a majority of them being children under the age of five. Naturally, proactive efforts are necessary to mitigate the challenges of contaminated drinking water sources in limited resource settings through water treatment and water quality monitoring techniques. The challenges are further elevated due to the dearth of appropriate infrastructure and connectivity in such communities to carry out traditional purification and monitoring processes. Hence, an active branch of research has delved into the development of effective and sustainable point-of-use devices to improve access to clean and safe drinking water and consequently improve the quality of life in these marginalized communities. Microbial contamination of drinking water sources is one of the leading causes of waterborne diseases, which begs the need of developing efficient water monitoring systems to improve worldwide living standards. Even in low concentrations, the pathogens in water are detrimental to the well-being of the human population. Hence, there is an urgent need for developing efficient water monitoring systems that will improve global living standards. Conventional methods of pathogen detection (Lazcka et al., 2007) are efficient and provide an accurate estimation of contaminants in water. Both developing and industrialized countries are challenged with a wide range of water contaminants ranging from traditional compounds such as heavy metals, fluorides and deadly waterborne pathogens like Escherichia coli (E. coli) to emerging micropollutants such as endocrine disrupters and nitrosoamines. Extensive research in this field has brought about a wide variety of effective processes to treat contaminated water, ranging from chemical disinfection (Krasner et al., 2006), photocatalysis (Liou and Chang, 2012), and different forms of membrane filtration (Basri et al., 2011), to the use of nanoparticles (Dankovich and Gray, 2011) to bring about disinfection and coagulation-flocculation (Jung et al., 2015). Conventional methods of water disinfection and decontamination are targeted toward large systems and hence are energy intensive, requiring considerable capital infusion and engineering expertise. The infrastructure required for such elaborate systems is unavailable in most of the developing countries. Additionally, intensive chemical treatment creates unwanted contamination (like disinfection by-products) of treated water. Additionally, the developing nations suffer from diverse socioeconomic and political constraints that require a broader approach incorporating sustainable water resource management. Hence, recent research has focused on reducing chemical treatment through engineered natural systems for drinking water production and reduction of residual chemicals from distribution systems. An area of particular interest in this aspect is the exploration of the available nature-based solutions to the water purification problem.
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10.1 WATER-QUALITY INDEX The crucial role of water as the trigger and sustainer of civilizations has been witnessed throughout human history. But until as late as the 1960s, the overriding interest in water has been regarding its quantity. Except in manifestly undersirable situations, the available water was automatically deemed utilizable water. Only during the last three decades of the 20th century was the concern for water quality strongly felt, so that now water quality has acquired as much importance as water quantity. How do we express water quality of the same stream? The quality may be good enough for drinking but not suitable for use as a coolant in an industry. It may be good for irrigating some crops but not good for irrigating some other crops. It may be suitable for livestock but not for fish culture. One way to describe the quality of a given water sample is to list out the concentrations of everything that the sample contains. Such a list would be as long as the number of constituents analyzed and that may be anything from the 20-odd common constituents to hundreds! Moreover, such a list will make little sense to anyone except well-trained water-quality experts. How to compare the quality of different water sources? It can’t be done easily by comparing the list of constituents each sample contains. For example, a water sample containing six components in 5% higherthan-permissible (hence objectionable) levels—pH, hardness, chloride, sulfate, iron, and sodium—may not be as bad for drinking as another sample with just one constituent—mercury—at 5% higher-than-permissible. Water-quality indices seek to address this vexing problem. Water-quality indexes (WQIs) aim at giving a single value to the water quality of a source on the basis of one or the other system that translates the list of constituents and their concentrations present in a sample into a single value. One can then compare different samples for quality on the basis of the index value of each sample. WQIs may have gained currency during the last three decades, but the concept in its rudimentary form was first introduced >150 years ago—in 1848—in Germany where the presence or absence of certain organisms in water was used as an indicator of the fitness or otherwise of a water source. Since then various European countries have developed and applied different systems to classify the quality of the waters within their regions. These water classification systems usually are of two types: (1) Those concerned with the amount of pollution present, and (2) Those concerned with living communities of macroscopic or microscopic organisms. Rather than assigning a numerical value to represent water quality, these classification systems categorized water bodies into one of several pollution classes or levels. By contrast, indexes that use a numerical scale to represent gradations in water-quality levels are a recent phenomenon, beginning with Horton’s index in 1965.
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WQIs (Abbasi and Abbasi, 2012) can be formulated in two ways: one in which the index numbers increase with the degree of pollution (increasing scale indexes) and the other in which the index numbers decrease with the degree of pollution (decreasing scale indexes). One may classify the former as “water pollution indexes” and the latter as “water-quality indexes.” But this difference is essentially cosmetic; “water quality” is a general term of which “water pollution,” which indicates “undesirable water quality” is a special case.
10.1.1 Horton’s Index Horton set for himself the following criteria when developing the first-ever modern WQI (Horton, 1965): (1) The number of variables to be handled by the index should be limited to avoid making the index unwieldy. (2) The variables should be of significance in most areas. (3) Only such variables for which reliable data are available, or obtainable, should be included. Horton selected 10 most commonly measured water-quality variables for his index, including dissolved oxygen (DO), pH, coliforms, specific conductance, alkalinity, and chloride. Specific conductance was intended to serve as an approximate measure of total dissolved solids (TDSs), and carbon chloroform extract (CCE) was included to reflect the influence of organic matter. One of the variables, sewage treatment (percentage of population served), was designed to reflect the effectiveness of abatement activities on the premise that chemical and biological measures of quality are of little significance until substantial progress has been made in eliminating discharges of raw sewage. The index weight ranges from 1 to 4. Notably, Horton’s index did not include any toxic chemicals. The index score is obtained with a linear sum aggregation function. The function consists of the weighted sum of the subindices Ii divided by the sum of the weights Wi and multiplied by two coefficients M1 and M2, which reflect temperature and obvious pollution, respectively: Xn Wi Ii M1 M2 (10.1) QI ¼ Xi¼1 W i i¼1 Horton’s index is easy to compute, even though the coefficients M1 and M2 require some tailoring to fit individual situations. The index structure, its weights, and rating scale are highly subjective as they are based on the judgment of the author and a few of his associates. Horton’s pioneering effort has been followed up by several workers who have striven to develop less and less subjective but more and more sensitive and useful water-quality indexes.
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10.1.2 Brown’s Index Brown et al. developed a water-quality index similar in structure to Horton’s index but with much greater rigor in selecting parameters, developing a common scale, and assigning weights (Brown et al., 1970). This effort was supported by the National Sanitation Foundation (NSF). For this reason, Brown’s index is also referred to as NSF-WQI. A panel of 142 persons with expertise in water-quality management was formed for the study. The panelists were asked to consider 35 parameters for possible inclusion in the index. They were free to add to the list any parameter of their choice. Each parameter was to be assigned one of the following choices: “do not include,” “undecided,” or “include.” The panelists were asked to rank the parameters marked as “include” according to their significance as a contributor to the overall quality. The rating was done on a scale of 1 (highest) to 5 (lowest). The responses of the panel were brought to the knowledge of every member of the panel and the members were allowed to review their individual judgment in the light of the full panel’s response. Finally, the panelists were asked to select not >15 parameters that they considered to be the most important. The complete list of parameters arranged in decreasing order of significance, as determined by average rating of the panel, was presented to each member. The panelists were asked to assign values for the variation in the level of water quality produced by different concentrations of the parameters selected as previously. The concentration-value relationship of each parameter was obtained in the form of a graph. These graphs were produced by the panelists to denote curves which, in their judgment, best represented the variation in level of water quality produced by possible measurements of each respective parameter. The judgment of all the respondents was averaged to produce a set of curves, one for each parameter. For pesticides and toxic elements, it was proposed that, if the total contents of detected pesticides or toxic elements (of all types) exceed 0.1 mg/L, the water-quality index would be automatically registered zero. The panelists were asked to compare relative overall water quality using a scale of 1 (highest) to 5 (lowest) for the finally selected parameters. Arithmetic mean was calculated for the ratings of experts. To convert the rating into weights, a temporary weight of 1.0 was assigned to the parameter which received the highest significance rating. All other temporary weights were obtained by dividing the highest rating by the individual mean rating. Each temporary weight was then divided by the sum of all the temporary weights to arrive at the final weight. The index originally proposed by Brown et al. has the form WQI ¼
9 X i¼1
wi Ti ðpi Þ
(10.2)
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where p is the measured value of the ith parameter and Ti is the quality rating transformation (curve) of Pthe ith parameter value pi, wi is relative weight of the ith parameter such that 9i¼1 wi ¼ 1. Brown’s index represents general water quality. It does not recognize and incorporate specific water functions such as drinking water supply, agriculture, industry, etc. Related to this difficulty has been an apparent tendency for some respondents to be heavily influenced in their judgment of a parameter’s suitability for inclusion in a WQI by factors such as data availability and existing analytical methodologies for measuring the various parameters.
10.1.3 Prati’s Implicit Index of Pollution This index was developed by Prati et al. on the basis of water-quality standards (Prati et al., 1971). The concentration values of all the pollutants were transformed into levels of pollution expressed in new units through mathematical expressions. These mathematical expressions were constructed in such a way that the new units were proportional to the polluting effect relative to other factors. In this way, even if a pollutant is to be present in smaller concentrations than other pollutants, it still will exert a large impact on the index score if its polluting effect is greater. In the first step, water quality was classified vis a vis all the parameters based on water quality standards. In the second step, one pollutant was taken as reference and its actual value was considered directly as reference index. In the third step, mathematical expressions were formed to transform each of the values of the other pollutants into subindices. This transformation took into account the polluting capacity of the parameters related to a selected reference parameter. In the construction of these functions, the analytical properties of various curves were used to ensure that the resulting transformation would be applicable not only to small values of pollutant concentrations but also to those exceeding class V. Accordingly, the subindices are generated. The index was computed as the arithmetic mean of the 13 subindexes. The index ranges from 0 to 14 (and above) and was applied by Prati et al. to data on surface waters in Ferrara, Italy.
10.1.4 Dinius’ Water-Quality Index This index broke new ground in the sense that through it an attempt was made to design a rudimentary social accounting system that would measure the costs and impact of pollution control efforts. In this sense, Dinius’ WQI is a forerunner of the “planning” or “decision-making” indexes. Eleven parameters were selected. Like Horton’s index and the NSF-WQI, it had decreasing scale, with values expressed as a percentage of perfect water quality which corresponds to 100%. Like Prati’s indexes, the subindexes in Dinius’ index were developed from a review of the published scientific literature. Dinius examined the water quality
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described by various authorities to different levels of pollutant variables, and from this information generated 11 subindex equations. The index was calculated as the weighted sum of the subindexes (Otto, 1978), like Horton’s index, and the additive version of the NSF-WQI: WQI ¼
11 1X wi Ii 21 i¼1
(10.3)
The weights ranged from 0.5 to 5 on a basic scale of importance. On this scale, 1, 2, 3, 4, and 5 denote, respectively, very little, little, average, great and very great importance. The sum of the weights was 21, which is the denominator in the index equation. The index was applied by Dinius on an illustrative basis to data on several streams in Alabama in the United States.
10.1.5 Bhargava’s Index This is one of the first reported indexes by an Asian author, and addresses the issue of drinking water supply. To develop the index, Bhargava (1985) identified four groups of parameters. Each group contained sets of one type of parameters. The first group included the concentrations of coliform organisms to represent the bacterial quality of drinking water. The second group included toxicants, heavy metals, etc., some or all of which have a cumulative toxic effect on the consumer. The third group included parameters that cause physical effects, such as odor, color, and turbidity. The fourth group included the inorganic and organic nontoxic substances such as chloride, sulfate, foaming agents, iron, manganese, zinc, copper, total dissolved solids (TDS), etc. The variables, with their maximum allowable contaminant level, CMCL (as per the US Environmental Protection Agency), and the subindexes worked out by Bhargava, which include the effects of concentrations of different parameters and their weightage. The subindexes were aggregated as follows: " #1=n n Y fi (10.4) WQI ¼ i¼1
in which fi(Pi) is the sensitivity function of the ith variable, and n is the number of variables considered. The index was applied to the raw water quality data at the upstream and downstream of river Yamuna at Delhi. The author suggested that the public drinking water supplies should have a WQI larger than 90.
10.1.6 A “Universal” Water-Quality Index Boyacioglu took into consideration the water-quality standards set by the Council of European Communities (EC 1991) (Boyacioglu, 2007), the Turkish water
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pollution control regulations, and other scientific information to select 12 water-quality parameters as the most representative for drinking water quality. They set three classes of water representing excellent, acceptable, and polluted categories. To assign weights to the water-quality variables, the following factors are taken into account: l
l
Chemical parameters had a lower weight than microbiological parameters, because microbial contaminants belong to the greatest health impact category Higher weight was given to those parameters which are of known health concern
The temporary weights ranged from 1 to 4 on a basic scale of importance. On this scale 1, 2, 3 and 4 denote, respectively, little, average, great and very great importance. Each weight was then divided by the sum of all weights to arrive at the final weight factor. The index is given by UWQI ¼
n X
wi Ii
(10.5)
i¼1
where wi is the weight for ith parameter, and Ii is the subindex for the ith parameter. The index value in the range of 0–<25 represents poor quality, 25–<50 marginal quality, 50–<75 fair quality, 75–<95 good quality and, above it, excellent quality.
10.2 METHOD OF DRINKING WATER DETECTION 10.2.1 Multiple-Tube Fermentation Technique The technique of multiple-tube fermentation (MTF) has been used for over 80 years as a water quality monitoring method (Rompre et al., 2002). The method consists of inoculating a series of tubes with appropriate decimal dilutions of the water sample. Production of gas, acid formation or abundant growth in the test tubes after 48 h of incubation at 35°C constitutes a positive presumptive reaction. Both lactose and lauryl tryptose broths can be used as presumptive media. All tubes with a positive presumptive reaction are subsequently subjected to a confirmation test. The formation of gas in a brilliant green lactose bile broth fermentation tube at any time within 48 h at 35°C constitutes a positive confirmation test. The fecal coliform test can be applied to determine total coliforms (TC). The results of the MTF technique are expressed in terms of the most probable number (MPN) of microorganisms present. This number is a statistical estimate of the mean number of coliforms in the sample. As a consequence, this
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technique offers a semiquantitative enumeration of coliforms. Nevertheless, the precision of the estimation is low and depends on the number of tubes used for the analysis: for example, if only 1 mL is examined in a sample containing 1 coliform/mL, about 37% of 1-mL tubes may be expected to yield negative results because of the random distribution of the bacteria in the sample. But if five tubes, each with a 1 mL sample, are used, a negative result may be expected <1% of the time. Many factors may significantly affect coliform bacteria detection by MTF, especially during the presumptive phase. Interference by high numbers of noncoliform bacteria, as well as the inhibitory nature of the media, have been identified as factors contributing to underestimates of coliform abundance. The MTF technique lacks precision in qualitative and quantitative terms. The time required to obtain results is higher than with the membrane filter technique that has replaced the MTF technique in many instances for the systematic examination of drinking water. However, this technique remains useful, especially when the conditions do not allow the use of the membrane filter technique, such as turbid or colored waters. MTF is easy to implement and can be performed by a technician with basic microbiological training, but the method can become very tedious and labor intensive since many dilutions have to be processed for each water sample. However, it is also relatively inexpensive, as it requires unsophisticated laboratory equipment. Nevertheless, this method is extremely time-consuming, requiring 48 h for presumptive results, and necessitates a subculture stage for confirmation which could take up to a further 48 h.
10.2.2 Membrane Filter Technique The membrane filter (MF) technique is fully accepted and approved as a procedure for monitoring drinking water microbial quality in many countries (Rompre et al., 2002). This method consists of filtering a water sample on a sterile filter with a 0.45-mm pore size that retains bacteria, incubating this filter on a selective medium, and enumerating typical colonies on the filter. Many media and incubation conditions for the MF method have been tested for optimal recovery of coliforms from water samples. Among these, the most widely used for drinking water analysis are the m-Endo-type media in North America and the Tergitol-TTC medium in Europe. Coliform bacteria form red colonies with a metallic sheen on an Endo-type medium containing lactose or yellow-orange colonies on Tergitol-TTC media. Other media, such as MacConkey agar and the Teepol medium, have been used in South Africa and Britain. However, comparisons among the media have shown that m-Endo agar yielded higher counts than MacConkey or Teepol agar. To enumerate fecal coliforms (FC), the filters can be incubated on an enriched lactose medium (mFC) at a temperature of 44.5°C for 24 h.
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Enumeration of TC by membrane filtration is not totally specific. When MF is associated with m-Endo media containing lactose, atypical colonies that are dark red, mucoid, or nucleated and without a metallic sheen may occasionally appear. Atypical blue, pink, white, or colorless colonies lacking sheen are not considered as TC by this technique. Furthermore, typical colonies with a sheen may be produced occasionally by noncoliform bacteria and, conversely, atypical colonies (dark red or nucleated colonies without sheen) may sporadically be coliforms. Coliform verification is therefore recommended for both types of colonies. With the acceptance of MF as a technique of choice for drinking water monitoring, questions regarding interference with coliform detection and the accuracy of the enumeration have arisen. The presence of high numbers of background heterotrophic bacteria was shown to decrease coliform recovery by MF. Excessive crowding of colonies on m-Endo media has been associated with a reduction in coliform colonies producing the metallic sheen. The predominant concern about MF is its inability to recover stressed or injured coliforms. A number of chemical and physical factors involved in drinking water treatment, including disinfection, can cause sublethal injury to coliform bacteria, resulting in a damaged cell unable to form a colony on a selective medium. Exposure of bacteria to products like chlorine may result in injury and increased sensitivity to bile salts or to the replacement surface-active agents contained in some selective media. Some improvements in the method have increased detection of injured coliform bacteria, including the development of m-T7 medium formulated specifically for the recovery of stressed coliforms in drinking water. The high number of modified media in use is a reflection of the fact that no universal medium currently exists that allows optimal enumeration of various coliform species originating from different environments and present in a wide variety of physiological states. A significant advantage of the MF technique over the MTF method is that, with MF, the examination of larger volumes of water is feasible, which leads to greater sensitivity and reliability. MF also offers a quantitative enumeration comparable to the semiquantitative information given by the MTF method. MF is a useful technique for the majority of water quality laboratories, as it is a relatively simple method to use. Many samples can be processed in a day with limited laboratory equipment by a technician with basic microbiological training. Nevertheless, since this method is not sufficiently specific, a confirmation stage is needed, which could take a further 24 h after the first incubation period on selective media.
10.2.3 Enzyme Substrate Method The biochemical tests used for bacterial identification and enumeration in classical culture methods are generally based on metabolic reactions. For this reason, they are not fully specific, and many additional tests are sometimes
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required to obtain precise confirmation. The use of microbial enzyme profiles to detect indicator bacteria is an attractive alternative to classical methods (Rompre et al., 2002). Enzymatic reactions can be group, genus- or speciesspecific, depending on the enzyme targeted. Moreover, reactions are rapid and sensitive. Thus, the possibility of detecting and enumerating coliforms through specific enzymatic activities has been under investigation for many years now. β-D-Glucuronidase is an enzyme that catalyzes the hydrolysis of β-Dglucopyranosiduronic derivatives into their corresponding aglycons and D-glucuronic acid. Although this bacterial enzyme was discovered first in E. coli, its relative specificity for identifying this microorganism was not apparent until Kilian and Bulow surveyed the Enterobacteriaceae and reported that glucuronidase activity was mostly limited to E. coli. β-D-Glucuronidase-positive reactions were observed in 94%–96% of the E. coli isolates tested, while Chang et al. found a higher proportion of β-D-glucuronidase-negative E. coli (a median of 15% from E. coli isolated from human fecal samples). In contrast, β-Dglucuronidase activity is less common in other Enterobacteriaceae genus, such as shigella (44%–58%), salmonella (20%–29%) and yersinia strains and in flavobacteria. β-D-Galactosidase catalyzes the breakdown of lactose into galactose and glucose and has been used mostly for enumerating the coliform group within the Enterobacteriaceae family. Chromogenic and fluorogenic substrates produce color and fluorescence, respectively, upon cleavage by a specific enzyme. These substrates have been used to detect the presence or the activity of specific enzymes in aquatic systems. The use of these substrates has led to improved accuracy and faster detection. Methods for detection or enumeration may be performed in a single medium, thus bypassing the need for a time-consuming isolation procedure prior to identification.
10.2.4 Ion Chromatography Ion chromatography (or ion-exchange chromatography) is a chromatography process that separates ions and polar molecules based on their affinity to the ion exchanger. It works on almost any kind of charged molecule—including large proteins, small nucleotides, and amino acids. The two types of ion chromatography are anion-exchange and cation-exchange. It is often used in protein purification, water analysis, and quality control. Water-soluble and charged molecules such as proteins, amino acids, and peptides bind to moieties that are oppositely charged by forming ionic bonds with the insoluble stationary phase (Nachod and Jack, 2013). The equilibrated stationary phase consists of an ionizable functional group where the targeted molecules of a mixture to be separated and quantified can bind while passing through the column—a cationic stationary phase is used to separate anions and an anionic stationary phase is used to separate cations. Cation exchange
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chromatography is used when the desired molecules to separate are cations and anion exchange chromatography is used to separate anions. The bound molecules then can be eluted and collected using an eluant which contains anions and cations by running higher concentration of ions through the column or changing pH of the column. One of the primary advantages of the use of ion chromatography is only one interaction involved during the separation as opposed to other separation techniques; therefore, ion chromatography may have a higher matrix tolerance. However, there are also disadvantages involved when performing ion-exchange chromatography, such as the constant evolution of the technique, which leads to inconsistency from column to column (Neubauer, 2009).
10.2.5 Gas Chromatography Gas chromatography (GC) is a common type of chromatography used in analytical chemistry for separating and analyzing compounds that can be vaporized without decomposition. Typical uses of GC include testing the purity of a particular substance, or separating the different components of a mixture. In some situations, GC may help in identifying a compound. In preparative chromatography, GC can be used to prepare pure compounds from a mixture (Pavia, 2005). In gas chromatography, the mobile phase is a carrier gas, usually an inert gas such as helium or an unreactive gas such as nitrogen. Helium remains the most commonly used carrier gas in about 90% of instruments although hydrogen is preferred for improved separations. The stationary phase is a microscopic layer of liquid or polymer on an inert solid support, inside a piece of glass or metal tubing called a column. The instrument used to perform gas chromatography is called a gas chromatograph. The gaseous compounds being analyzed interact with the walls of the column, which is coated with a stationary phase. This causes each compound to elute at a different time, known as the retention time of the compound. The comparison of retention times is what gives GC its analytical usefulness. Gas chromatography is in principle similar to column chromatography, but has several notable differences. First, the process of separating the compounds in a mixture is carried out between a liquid stationary phase and a gas mobile phase, whereas in column chromatography the stationary phase is a solid and the mobile phase is a liquid. Second, the column through which the gas phase passes is located in an oven where the temperature of the gas can be controlled, whereas column chromatography has no such temperature control. Finally, the concentration of a compound in the gas phase is solely a function of the vapor pressure of the gas. Gas chromatography is also similar to fractional distillation, since both processes separate the components of a mixture primarily based on boiling point differences. However, fractional distillation is typically used to separate components of a mixture on a large scale, whereas GC can be used on a much smaller scale. Gas chromatography is also sometimes known as
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vapor-phase chromatography (VPC), or gas-liquid partition chromatography (GLPC). These alternative names, as well as their respective abbreviations, are frequently used in the scientific literature. Strictly speaking, GLPC is the most correct terminology and is thus preferred by many authors (Pavia, 2005).
10.3 STANDARD OF DRINKING WATER Drinking water should have a taste determined by the presence of physiologically necessary salts of calcium, magnesium, sodium, and potassium in corresponding concentrations without which metabolism in the human organism is impossible. What optimal parameters should the water possess to be completely safe from the biological and physiological points of view? For the first time in the world in 1853, in Brussels, at the International Congress of Hygienists, an attempt was made to set a standard for drinking water, which would include only nine controllable components. Among them there were vitally important substances such as magnesium, calcium, total mineralization (determined mainly by the presence of salts of sodium and potassium), and the content of oxygen in the water, sulfates, chlorides, nitrates, and ammonium. However, the age of industrialization, fast development of the agroindustrial complex, and the emergence of megapolises with compact settlements of people, whose number dramatically increased, all began. In connection with the worsening of the ecological state of surface and underground sources, the issue of water quality control, used by people for drinking purposes, became more acute. As a result of a low quality of drinking water, real threats to the sanitary-epidemiological situation in various regions of the planet cropped up. And only early in the 20th century did the first standard for drinking water appear in the United States. After almost a century after adoption in Brussels of the first recommendation for drinking water quality, a document of the World Health Organization (WHO) was published, which today, at the beginning of the 21st century, proposes to bring under regulation 95 indexes, of which 26 are toxic substances, whose presence in drinking water is undesirable. The US standard regulates 102 indexes in drinking water, where complete absence is required for as many as 35 toxic indexes.
10.3.1 EPA Drinking Water Standards Most facilities interpret the “potable, uncontaminated” water requirement for ordinary animals as water that meets, at a minimum, the Environmental Protection Agency (EPA) drinking water standards for human consumption. The EPA is responsible for the National Primary Drinking Water Regulations, which are health-related standards that establish the maximum contaminant levels (MCLs). MCLs are the maximum permissible level of a contaminant in water delivered to users of a public water system. MCLs are enforceable under the Safe Drinking Water Act. The EPA has also set unenforceable maximum contaminant
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level goals (MCLGs) at levels where no known or anticipated adverse effects on health occur and that allow an adequate margin of safety. The enforceable MCL is set as close to the MCLG as reasonable, taking into consideration the costs and treatment techniques available to public water systems. Health advisories provide information on contaminants that can cause human health effects and are known or anticipated to be in drinking water. Health advisories are guidance values based on noncancer health effects for different durations of exposure (e.g., 1-day, 10-day, longer-term, and lifetime).
10.3.2 USP Pharmaceutical Water Standards It is useful to learn about pharmaceutical water standards to see what can be applied to animal drinking water. This is especially true for bacterial contaminants. It is impossible to achieve absolutely sterile water in any piped water system, but automated watering systems can (with appropriate design and operation) achieve the quality of USP Purified Water and can approach USP Water for Injection. The United States Pharmacopeia Convention is a private, not-for-profit organization that sets standards for drugs, devices, and diagnostics. It publishes two compendia (summary documents). The US Pharmacopeia (USP) contains standards for drug products. The National Formulary (NF) sets standards for drug excipients (inert substances used as carriers or dilutants). The USP monograph lists two waters that are prepared in bulk form: Purified Water (PW), often called USP Purified Water to distinguish it from other purified waters, and Water for Injection (WFI).
10.3.2.1 USP Purified Water (PW) Purified Water is described in the USP 23 monograph as follows: “Purified Water is water obtained by distillation, ion-exchange treatment, reverse osmosis, or other suitable process. It is prepared from water complying with the regulations of the U.S. Environmental Protection Agency (EPA) with respect to drinking water. It contains no added substances.” Microbial quality: Regarding the bacteriological purity of PW, the monograph (legally enforceable section) states only that PW must comply with the EPA regulations for drinking water. The EPA regulations only specify limits for coliform bacteria. In the informational section of the USP 23, which deals with action guidelines for the microbial control of ingredient water, it says: “A total microbial (aerobic) count that may be used for source drinking water is 500 colony-forming units (cfu) per mL. A general guideline for Purified Water may be 100 cfu/mL.” These numbers for cfu/mL are only advisory guidelines that represent recommended alert/action limits, not reject levels. The informational section also suggests that the microbial action limits for PW should be based on the
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intended use of the water and the nature of the product being made. It recognizes that microbial limits for PW require being defined on a case-by-case basis. USP 23 Supplement 5, effective since November 1996, specifies the method for total bacteria counts. It states “Heterotrophic Plate Count of a 1-mL sample, using Plate Count Agar at an incubation temperature of 30 to 35°C for a 48-hour period (minimum).” There is some controversy “starved” bacteria in highpurity water. Chemical quality: Effective November 15, 1996, the former inorganic chemistry tests (for calcium, sulfate, chloride, ammonia, and carbon dioxide) were replaced with a three-stage conductivity test. The conductivity limit is pH-dependent but, for example, at pH 7.0, conductivity should be <5.8 microSiemen/cm (μS/cm). The former test for oxidizable substances was replaced with a total organic carbon (TOC) limit of 0.05 mg/L. TOC is an indirect measure of organic molecules present in water measured as carbon. The new tests allow continuous in-line monitoring of water using instrumentation rather than lab work.
10.3.3 Lab Animal Drinking Water Standards for chemical quality: (1) EPA Regulated Chemical Contaminants. The requirement for “potable, uncontaminated” drinking water for “ordinary animals,” as stated in the Guide, can be interpreted to mean that animal drinking water should, at a minimum, comply with the EPA’s Primary (health-related) Drinking Water Regulations for human consumption. This is similar to the requirement that states USP Purified Water must be prepared with water complying with the EPA regulations. (2) pH. The Guide specifically mentions pH as something that may necessitate periodic monitoring. The reason for setting a low limit is that low pH water is corrosive and can dissolve plumbing components. This is especially a concern when water contacts brass and copper piping systems where copper, zinc, and lead can dissolve into the animal’s drinking water. Reasons for setting a high limit are that high pH can promote hardness scale precipitation (see number 3 “Hardness” following) and that chlorine disinfection is not as effective at high pH. See the Edstrom Industries document Forms of Chlorine in Water (MI-4148) for more information. Some facilities acidify animal drinking water to pH 2.5–3.0 in order to kill Pseudomonas and other common water bacteria. (3) Hardness. The reason for setting an upper limit on hardness is that hard water can cause calcium carbonate scale deposits in automated watering systems, which can lead to drinking valve leaks and other operational problems. According to the Water Quality Association, water is considered “hard” when the measured hardness exceeds 120 mg/L. Just knowing the hardness level of water is not enough to predict if it will cause scaling problems. A better predictor is the Langelier saturation index (LSI). When the LSI (which is calculated from pH, total dissolved solids, calcium hardness, and alkalinity) is greater than zero, water will have a tendency to scale. (4) Total
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dissolved solids or conductivity. Total dissolved solids (TDS) and conductivity both indicate the total inorganic mineral content of drinking water. Either of these tests can be used to monitor the consistency of quality from water purification processes (such as reverse osmosis), which remove inorganic contaminants from water. The typical conductivity of reverse osmosis (RO) water ranges between 1 and 100 μS/cm, depending on the conductivity of the supply water. Usually, conductivity is measured with in-line sensors. (5) Disinfectants. An automated drinking water system may contain residual disinfectants from the public drinking water supply or additional disinfectants may be injected into animal drinking water to control bacterial growth. The EPA has proposed/ tentative maximum contaminants levels for these common disinfectants: chloramine proposed MCL ¼ 4 mg/L; chlorine proposed MCL ¼ 4 mg/L; chlorine dioxide tentative MCL ¼ 0.8 mg/L. Standards for microbial quality: In order to establish microbial quality guidelines for animal drinking water, it is useful to understand the guidelines and regulations for EPA drinking water and for USP PW and WFI. (1) Specify treatment techniques. EPA regulations specify treatment techniques (filtration, disinfection, etc.) in lieu of specific maximum contaminant limits for most microbial contaminants in drinking water. Because of the complexity and cost of testing for waterborne microorganisms, routine examination of water for pathogens is not feasible. Even when specific pathogens are examined, a negative result may be due to the inadequacy of the testing method. Or, it may indicate a safe water quality at that moment when the water was sampled, but it does not guarantee that safe water can be expected 1 h before or after testing. If this type of guideline were adapted for laboratory animal drinking water, a facility may, for example, specify reverse osmosis water containing a minimum concentration of disinfectant in lieu of specifying maximum concentrations of bacteria, viruses, and pathogenic protozoa. (2) Set limits for certain specific microorganisms. The EPA’s MCL for coliform bacteria is less than one per 100 mL and the unenforceable MCLG for Giardia lamblia, Legionella, and viruses is zero. (3) Set limits based on use of water. USP Purified Water guidelines suggest an action/alert limit for total microbial counts at 100 colonyforming-units/mL, but it also recognizes that microbial limits should be defined on a case-by-case basis. Just as the bacteria count limits for USP PW should be based on the intended use of the water, microbial limits for animal drinking water may need to be based on the requirements for the animals or the study. Perhaps there need to be separate recommendations for different animals and study types.
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