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The Problem With Statistics – It’s Not The Statistics, But How We Abuse Them

Here’s a COVID-19 statistic – interpret it as you will…

A 23 June 2020 United Press International (UPI) headline in Health News (proclaims: “Less than half a population needs COVID-19 infection for herd immunity, study says.

The report goes on to state: “The modeling study found that herd immunity potentially could be achieved with about 43 percent of the population being immune, as opposed to the 60 percent estimate derived from previous models.” This is based on modelling work done by a member of the University of California-Riverside faculty. As I read the article my thoughts again turned to the observation about lies, damned lies and statistics (variously ascribed to Samuel Clemens, Benjamin Disraeli, and various other mid-19th century sources).

What population?

I’m not quibbling with the model used to compute the statistic, but do have an issue with how the article’s writer used it (note: having been misquoted on occasion, I cannot say that the statistic that appeared in the UPI article captured the cited investigator’s intent accurately. My issue is about granularity – the scale or level of detail present in a set of data or other phenomenon. I illustrate my point in figure 1. All of the images include New York City ranging from a satellite image (least granular) to an aerial photo of a single building on the northeast corner of 96th Street and 5th Avenue (most granular).

The 43 % statistic cited above is meaningless unless it incudes a statement about granularity. If applied globally, it ignores the possibility that in some countries, the majority of the population might be immune while in others, the percentage of immune individuals might be substantially less than the 43 % threshold for herd immunity. Moving across the granularity spectrum, will it be sufficient to consider 43 % immunity for an entire city, or will 43 % of the residents of each building need to be immune?


Fig 1. Granularity – moving from left to right, the images become more granular – provide a more detailed view of New York City.

 

Nowhere in the article was there any indication of the geographic area within which herd immunity would be achieved once 43 % of the population was immune to COVID-19. The result is a misleading article. Note that is possible to focus too closely on the details – as in missing the forest for the trees. My personal object lesson was having focused on a sea anemone (size ∼10 cm wide by 15 cm tall) while a whale swan directly over my head (figure 2 – not actual photos of the 1975 event). As I came out of the water, people asked if I had photographed the whale. I responded: “What whale?”


Figure 2. Missing the forest for the trees, or the whale for the anemone.

 

Herd Immunity and Physical Distancing

Guidelines from the Centers for Disease Control (CDC) and World Health Organization (WHO) indicate that we should maintain physical spacing of at least 6 ft (~2m) for other people to prevent transmission of the SAR-CoV-2 virus from communicable individuals to susceptible ones. If there are a group of people in a room – say a restaurant on a New York city block on which more than 43 % of the residents are COVID-19 immune – how will that affect physical distancing requirements? Based on the statistics cited in the UPI article, I have no idea. Apparently, nor does anyone else. There are simply insufficient data from which to draw an objective conclusion.

Statistics Abuse – There’s the Rub

There’s an old joke about a duck hunter who fires his shotgun twice at a duck flying overhead (figure 3). His first shot flies past the duck, ∼1 m ahead of the bird and the second misses by the same distance behind it. The hunter proclaimed that one average (the midway point between the two shots) the duck was killed – except that it wasn’t (note: no ducks were harmed in the retelling of this statistics tale). Statistics is a branch of mathematics that provides elegant tools for distilling large amounts of data into useable form. That’s the science. The art is in marrying statistical analysis to other observations and logical thinking. Statisticians are the first to caution users to recognizes that their calculations are always in the context of probabilities. What is the probability that an apparent pattern (relationship) is simply random? What is the probability that a seemingly random pattern hides an important relationship? What is the impact of interpreting the statistics incorrectly?


Fig 3. One average the duck was shot. Statistically, the average of two volleys, equidistant in front of and behind the duck, would result in a kill.

 

What does this all mean?

Since my last post in May, epidemiologists and other public health experts have been trying their best to refine models for risks related to exposure to SAR-CoV-2, contraction of COVID-19, and alternative measures for ending the pandemic. In that in that post, I discussed risk versus hazard and the concept of acceptable risk. Within our free society, some citizens believe exposure to SAR-CoV-2 is an acceptable risk and have decided that no precautions are necessary. Recent spikes in the morbidity rate (i.e., number of new cases per 100,000 people in a given area) have reflected the wisdom (better: lack thereof) of ignoring the imperfect science. Presumably, at some point in the next few months, populations in many areas of the U.S. will approach the percent immunity targets identified in the UPI article. At that point, the risk of non-immune individuals contracting the disease will fall to a level that elected officials and business leaders deem acceptable. Will they be right or is acceptable risk in the eyes of the beholder?

I’m writing this to stimulate discussion, so please share your thoughts either by writing to me at fredp@biodeterioration-control.com or commenting to my LinkedIn post. Also, on 29 July at noon, Eastern Daylight Time, Dr. John Howell, Dr. Neil Canter, Mr. Bill Woods, and I will participate in an STLE webinar panel discussion on COVID-19 risk in the machine shop work environment.

SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2 – the virus that causes COVID-19) persistence in metalworking fluids

Does the SARS-CoV-2 virus persist in Water-Miscible Metalworking Fluids?

Over the past two months, I have received quite a number of emails and phone calls asking if water-miscible metalworking fluids (MWFs) were likely to be a source of SARS-CoV-2 virus exposure for machinists and others working in machine shops.

My short answer is that nobody really knows. I know that this answer is not particularly reassuring, but the test methods needed to test MWFs and MWF mists for SARS-CoV-2 in there types of samples do not yet exist. For companies and institutions developing test methods to detect SARS-CoV-2 the first priority has been identifying infected individuals. Given that most transmission seems to be via inhalation of aerosol droplets that carry virus particles, and that the aerosols of primary concern are those produced when someone sneezes, coughs, or speaks, investigating virus persistence in fluids was initially considered to be a less critical need.

However, for those working in the manufacturing sector, there is a history of adverse health effects – primarily allergies – caused by MWF aerosol exposure. Also, COVID-19 can be transmitted by touching a SARS-CoV-2 contaminated surface (i.e., contaminating the hands with viruses) then bringing the hands to the face. The virus can then be inhaled or gain entry through the eyes. If SARS-CoV-2 persists in MWFs, then machinists whose hands are in contact with the fluid and who then touched their face are at increased exposure risk. Additionally, machinists handle the parts that are to be machined. According to the European Centre for Disease Prevention and Control, SAR-CoV-2 an persist on copper surfaces for up to 4h, cardboard for 24h, and plastic or steel surfaces for up to three days. This means that there are ways COVID-19 can be transmitted at metalworking facilities.

Can we reasonably use what we know to assess the risk?

I believe that we can use the guidance provided by the Centers for Disease Control, (CDC) to minimize the incremental risk to machinists. Note that I am addressing incremental risk – that is the risk over and above our risk of contracting COVID-19 from our other activities. We are all at risk, however, all of the epidemiological studies that have been reported to date agree that social distancing reduces risk. To understand the incremental risk, we need to understand a few concepts:

Risk

Risk is a function of hazard + exposure (R = H + E – Figure 1). This means that the most hazardous substance poses no risk if exposure is zero. All of the clinical and epidemiological studies that have been published since the first reports of COVID-19 in Wuhan, China last November indicate that SARS-CoV-2 virus is quite hazardous. Although the number of virus particles needed to cause a COVID-19 infection is not known, the ease with which the disease spreads from infected individuals to susceptible victims, the severity of many non-lethal infections, and apparent mortality rate (percentage of people who have contracted clinically reported infections and who ultimately die from the disease) demonstrate that SARS-CoV-2 is hazardous. Consequently, until a SARS-CoV-2 vaccine is developed, the primary means of reducing disease risk is isolation.

Fig 1. Venn diagram illustrating the relationship between hazards, exposure, and risk.

In many respects, the risks encountered at manufacturing facilities are identical to those related to the general population’s activities. For example, most people walk outdoors, handle doorknobs, groceries, appliances (computer, TV, smart phones, etc.), and generally expose themselves in countless ways. As depicted in Figure 2, this (blue ellipse) is our non-MWF facility exposure. For those who work at manufacturing facilities, there is some incremental exposure (red ellipse in Figure 2). Note, this is not to scale. We do not know the actual incremental risk.

Fig 2. Venn diagram illustrating incremental exposure of machinists and others at metalworking facilities.

Acceptable Risk

Risk is an objective concept. You can compute it if you know the hazard and the exposure (direct contact). Acceptable risk is purely subjective. The chances of dying in a plane crash are 1 in 11 million (0.000009 %) and of dying in a bathtub are 1 in 840,000 (0.0001 %). However, fear of flying represents an unacceptable risk to more people than fear of bathing does. Throughout the world today, we see the impact of differing opinions regarding risk acceptability playing out. At one extreme, people have placed themselves in complete isolation. At the other, people are ignoring all COVID-19-related personal hygiene and social distancing guidance. There is no broad consensus on the appropriate balance between measures to reduce the exposure risk and those taken to sustain the economy. One both sides of the argument, hysteria tends to take precedence over objective risk assessment. Intelligent, honest people can reasonably disagree on what constitutes an acceptable SARS-CoV-2 exposure risk. I will steer clear of that argument here but will note that as the COVID-19 pandemic has illustrated, risks rarely exist in isolation. Reducing one risk can easily increase another risk. In the case of COVID-19, decreasing the disease risk has increased the poverty risk for many people.

Viruses

Viruses are sub-microscopic (i.e., can been seen through an electron microscope but are too small to be seen through a light microscope – as seen in Figure 3, viruses are ∼0.001 times the size (volume) of bacteria and ∼0.000001 times the size of human cells). They contain genetic material enveloped in a coat. More than 6,000 different viruses have been identified (no doubt a tiny fraction of the different types of viruses that exist). Some – including SARS—CoV-2 – contain ribonucleic acid (RNA) and others contain deoxyribonucleic acid (DNA) as their genetic material. Virus coats can be protein or protein and lipid (Figure 4 shows the SARS-CoV-19 structure). Viruses can persist (i.e., remain infectious) but cannot multiply outside of susceptible (host) cells. Most viruses can only attack specific types of cells. The infection process starts with one or more viruses attaching to sites on the host’s cell wall. For SARS-CoV-2 viruses, the spike protein attaches to a cell. The virus then injects its genetic material into the host cell and the virus’ genes hijack the host cells’ genes – redirecting them to produce new viruses. Once the host cell is full of newly manufactured virus particles, it breaks open (lyses) to release the viruses into the environment surrounding it. If there are no susceptible cells to infect, a virus will eventually decompose. This is the basis for the persistence testing. When 3 days persistence is reported, that means that although the number of infectious viruses is decreasing from the moment they are deposited onto a surface, it takes 3 days for the number has decreased to below the test method’s detection limit (the detection limit is the minimum number/value that can be measured by a given test method).

Fig 3. Size scale – atoms to frog eggs.

Fig 4. SARS-CoV-2 virus schematic. A complex coat encapsulates the virus’ RNA.

Detecting viruses

Viruses are cultured by inoculating a layer of susceptible cells (i.e., a tissue culture) with a specimen containing viruses. As they infect the tissue culture cells, the viruses create clear zones – plaques – each of which contains billions of individual virus particles – virions (Figure 5). Viruses isolated by culture testing can then be used to develop other test methods. The most common methods are immunoassays (detect the presence of antibodies to specific viral antigens) and genetic tests (see my January 2018 What’s New posts for more detail explanations of antigen and genetic test methods).

At present the lower detection limit for SARS-CoV-2 virions is ∼2,700. A sneeze droplet from an infected person can carry millions of virions. That makes it relatively easy to detect the virus on contaminated surfaces or on a nasal swab sample. If that same sneeze droplet lands in 1 mL of fluid, the number of virions in that droplet are diluted 50,000-fold. As the ratio of the fluid volume into which someone has sneezed, coughed, etc. increases, so does the dilution factor and the difficulty of detecting viruses in the contaminated fluid. Consequently, to be detected in fluids (water, MWF, etc.) virus particles must first be concentrated. This concentration step is easier with fluids that have few contaminants (for example, potable water) than with complex, contaminant loaded fluids like MWFs. Consequently, it might be months or years before methods are developed to detect and quantify SARS-CoV-19 virus particles in MWFs.

Risk Assessment

Clearly, without data, assessing the risk of COVID-19 infection due to exposure in metalworking facilities is an exercise in speculation. However, because of the pandemic-related epidemiological studies that have been done for the general public and at food processing facilities, there is a basis for an educated guess.

Bioaerosol Exposure

Social distancing is the most effective way to reduce exposure. The general CDC guidelines apply equally well to personnel working in machine shops. Although mist collection systems have reduced MWF mist exposure, and the incidence of reported clusters of industrial asthma and other respiratory diseases has plummeted since the 1990s, when mist collection systems were installed at many metalworking facilities, there remains some question about how well mist collectors capture sub-micron diameter, bioaerosols. It is likely that there remains some risk of bioaerosol exposure, but there are insufficient data to define that risk. Generally speaking, recirculating MWFs act as bioaerosol reservoirs (i.e., the source) and MWF system biofilms act as MWF microbial contamination reservoirs. There have not been any reported studies of virus loads in MWF aerosols or virus presence or persistence in MWFs, so it is difficult to predict SARS-CoV-19 persistence in MWFs.

Some studies have been done to evaluate the COVID-19 risk to wastewater treatment plant operators. It has been reported that SARS-CoV-19 can persist for “2 days at 20°C, at least 14 days at 4°C, and survive for 4 days in diarrheal stool samples with an alkaline pH at room temperature.” (source: https://www.waterra.com.au/_r9550/media/system/attrib/file/2200/WaterRA_FS_Coronavirus_V11.pdf). Given that MWFs are alkaline and that the temperature of recirculating MWFs typically ranges between 30 °C (86 °F) and 37 °C (100 °F) it is likely that the virus will persist for 2 to 7 days in MWFs. Consequently, there is a risk that workers can be exposed to virus particles in MWF mist droplets.

Contact exposure

As noted above, the SARS-CoV-2 virus can persist on steel surfaces for up to 3 days. Consequently, handling parts that have become contaminated with virus particles within the previous 3 days poses an infection risk.

Risk Mitigation

Social distancing

Workers are typically standing shoulder to shoulder at food processing facilities where COVID-19 clusters have been reported. The distance between machines at metalworking facilities is more conducive to social distancing. Keeping at least 1.8 m (6 ft) distance between workers substantially decreases the risk of transmission among workers.

Mist control

Reduced mist exposure translates to reduced risk. If enclosures remain closed for at least 30 sec after MWF fluid flow is stopped, then the risk of mist inhalation decreases substantially. Equally important is mist collection system maintenance. To operate effectively, mist traps and reservoirs must be kept clean. Their surfaces should be disinfected after each leaning. High-efficiency particulate air (HEPA) filters installed at mist collect exhausts must be changed with sufficient frequency to prevent filters from becoming a source of bioaerosol exposure. Effective facility ventilation – including air flow and relative humidity control – will reduce virus persistence.

Personal protective equipment (PPE)

The role of appropriate PPE, properly worn and maintained, in preventing respiratory disease and dermatitis has been well documented. Workers likely to be exposed to MWF aerosols should wear air filtration masks that will prevent virus inhalation (i.e., meet or exceed capabilities of N-95 masks). Other masks help to remind individuals not to touch their face and trap aerosol droplets that they produce but do little to prevent them from inhaling virus particles that are in the air. Non-porous gloves can prevent direct contact with viruses that are on part surfaces. However, surgical gloves are likely to tear quickly when used to handle tools, machines, and parts. Recognizing that SARS-CoV-2 particles can persist on glove surfaces for several days, it is important to disinfect gloves with a hand sanitizer before removing them.

Personal hygiene

It seems that a substantial percentage of people with COVID-19 infections never show symptoms. However, these individuals can infect others. Effective personal hygiene practices can mitigate the disease transmission risk. Effective measures, as detailed by the CDC (see link above) include keeping hands away from the face and washing hands frequently – after each time a person touches any surface that might be contaminated with the SARS-CoV-19 virus. Applying a hand sanitizer can be an effective alternative to constant washing. The standard metalworking facility personal hygiene practices that have been advocated for decades also apply here. Workers should wear clean shop clothes. Street cloths should no be worn in the metalworking facility and work cloths should be cleaned by an industrial laundry service. Personnel should not eat, drink, or smoke before having washed hands thoroughly. Individuals should wash hands both before and after using the lavatories.

Bottom Line

Because workers are exposed to MWFs and parts, there is some incremental risk of SARS-CoV-2 exposure associated with working at metalworking facilities. Given that in contrast to food processing facilities there have been no reported COVID-19 clusters at machine shops, the incremental risk is likely to be small. Still, there are steps that owners, managers, and workers can take to minimize workplace-related incremental risk. Taking these measures can help maintain productivity while protecting workers from unnecessary COVID-19 risk. From the moment of birth to the moment of death, our lives are risk-laden. It is impossible to reduce risk to zero. However, by remaining mindful of potential sources of exposure and taking precautions to avoid bioaerosol inhalation, metalworking industry stakeholders can minimize the risk of workplace exposure.

Stay safe, productive, and healthy! Please send your comments and questions to me at fredp@biodeterioration-contol.com.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 36 – Connecting the Dots, Part 4

Why do I sometimes detect heavy microbial contamination but no evidence that it is causing problems to my fuel system?

Explanations that I’ve provided in the last three articles provide some of the answers to this question. In this post I’ll address another issue – microbial activity. The various microbiological test methods I’ve described in this blog series have provide information about either whether microbes are present or what sorts of microbes are present. General culture tests and rapid methods determine whether microbes are present (see Parts 12, 13, 14 ). Typically, the raw data are assigned attribute scores – negligible, moderate or heavy contamination. This information is sufficient for routine condition monitoring. Selective growth media, enzyme-linked immunoabsorbent assays (ELISA – See Part 16) and genomic tests (see Part 17detect types of microbes (bacteria versus fungi, acid producers, sulfate reducers, etc.). Genomic testing can identify the types of microbes that are present more accurately and precisely that other methods can. However, none of these methods provide information about what microbes are doing.

Although methods can help determine what microbes are doing exist, they require more technical expertise than the previously mentioned tests. Moreover, the cost per test is still quite expensive ($500 to $1,000 per test). However, these emerging methods will be the tools we microbiologists will use to answer the question: “Why do I sometimes detect heavy microbial contamination but no evidence that it is causing problems to my fuel system?” and its complementary question: “Why do I sometimes observe substantial evidence of biodeterioration, but detect negligible to moderate microbial loads?”

Proteomics – the study of proteins

Proteins are long chains of amino acids. Proteins in cells are either enzymatic or structural. Enzymes are large protein molecules that function as cells’ machinery. The receive raw materials (i.e., nutrients) and produce products (i.e., cell building blocks). Proteomics uses analytical tools to determine what proteins are present in the sample. Each type of protein is only produced when the gene(s) that code for it are active. So, proteomics tells use which genes as active and which aren’t. This information can provide important clues as to what fuel system conditions might cause a nominally benign population to become aggressive.

Metabolomics – the study of what cells produce

As noted above, enzymes produce metabolites. Some metabolites are used to build new cells, others are used to maintain healthy, living cells, and many are excreted into the environment as waste products, signal molecules (this is how cells communicate) or external functional molecules (for examples include biosurfactants and extracellular polymeric substance – EPS, biofilm matrix). Metabolomic testing attempts to identify all of the metabolites produced by a microbial community. Indirect biodeterioration is caused by reactions between metabolites and the environment (i.e., fuel, fuel additives, and fuel system surfaces – in contrast, direct biodeterioration occurs when microbes use fuel or fuel additives as food). Metabolomics promises to tell us whether contaminant populations are producing metabolites that contribute to biodeterioration.

How do these omic tests help me answer my questions about the relationship between bioburden and biodeterioration?

Figure 1 illustrates the relationship among different types of microbiological tests. Each more sophisticated technology provides more detailed information about what is going on in the infected system.

Fig 1. Drilling down – the relationship between microbiological tests and information about biodeterioration processes.

In some respects, this is like zooming in using satellite imagery (figure 2). The high-altitude image (figure 2a) confirms that there is a land mass west of the Atlantic Ocean. This is analogous to microbiological tests used for routine condition monitoring. The next lower altitude image (figure 2b) shows that there are cities on this land mass. This is similar to the information obtain from genomic testing. Zooming in further, we see the general street layout of a city (Manhattan, NY in figure 2c). Knowing the street layout is similar to knowing which genes are active. As we approach ground level (figure 2d) we can see that the small dark spot just above the map pin in figure 2c is actually a reservoir (the Jacqueline Kennedy Onassis Reservoir) that is bordered by a path. Like metabolomic data, this image provides clues as to how the city functions.

Fig 2. Using satellite imagery (Google Earth) to get detailed infromation about a location – a) high-altitude image shows U.S. coastline from Massachusetts to Maryland; b) closer view showing contours of most of New Jersey, Southeastern New York, and Southwestern Connecticut; c) lower-altitude view reveals Manhattan’s streets and the rivers that make Manhattan an island; d) low altitude view shows Jacqueline Kennedy Onassis Reservoir in Central Park.

State of the Art

We know that not all microbial contamination poses the same risk, but we don’t know why some populations cause more damage or cause damage more aggressively than others.

Before 2010, there were few reports of genomic tests on fuel system samples. The few pre-2010 studies depended on culture testing to recover microbes for genomic testing. Consequently, our understanding of fuel microbiology was based on culture test results. During the past decade, microbiologists have exploited genomic test methods to better understand the types of microbe present in fuel systems and the relative abundance of different types of microbes in a given population (microbiome). Methods for performing proteomic and metabolomic tests on fuel and fuel associated water samples are in development. It will be several more years before these methods can be used with confidence. These methods promise to tell us why sometimes microbial contamination is a greater problem than it is at other times.

Because we do not yet have a full understanding of the relationship between bioburden levels (i.e.: negligible, moderate and heavy) and biodeterioration risk, we set conservative limits. The expense of acting when moderate or heavy microbial contamination is detected is a fraction of the cost of failing to control microbial contamination adequately. A colleague once estimated that the cost of taking a commercial aircraft out of service and disinfecting its fuel system was approximately $2 million U.S. What’s the cost of an aircraft falling out of the sky after fuel can no longer reach the engine? The relationship is similar for land-based emergency generator systems. Corrective maintenance actions to control microbial contamination typically cost between $500 U.S. and $2,000 U.S. When emergency generators fail to operate, the cost can easily exceed $100,000 U.S./min – not to mention the potential for catastrophic loss of lives (hospitals), equipment (nuclear power plants), or both.

 

The relationship between microbial contamination and fuel or fuel system damage is variable. BCA’s Microbial Audit collects climate, engineering, maintenance, and different types of test data (gross observations, physical, chemical, and microbiological) in order to assess given fuel system’s biodeterioration risk. As I observed at the beginning of this post, sometimes I find heavy microbial loads but no evidence of damage. At other times, even though microbiological tests are negative, the other results indicate severe biodeterioration is occurring. Until we have sufficient proteomic and metabolomic data from which to develop better diagnostic models, the prudent approach will be to continue to rely on the existing control limits recommended in guidance documents such as the Energy Institute’s Guidelines for the Investigation of the Microbial Content of Liquid Fuels and for the Implementation of Avoidance and Remedial Strategies and the International Air Transportation Association’s Guidance Material on Microbiological Contamination in Aircraft Fuel Tanks. A third document – Guidelines on Detecting, Controlling, and Mitigating Microbial Growth in Oils and Fuels Used at Power Generation Facilities (Energy Institute) – is in press. I’ll share details once the document has been published.

The details

For more details about understanding the relationship between microbiology test data and fuel or fuel system biodeterioration, please contact me at either fredp@biodeterioration.control.com or call 609.306.5250.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 35 – Connecting the Dots, Part 3

Refresher from Parts 1 and 2: What do Microbiology Test Results Mean?

In my January and February Fuel & Fuel System Microbiology articles, I addressed two reasons why microbiology data do not always agree with other indicators of fuel or fuel system biodeterioration. Part 33 covered dilution effects. Although direct degradation of fuels can easily be demonstrated in 1 L jars with fuel over water, when a tank has more than 1 m3 (264 gal) of fuel over traces of water, the impact that microbes have on fuel near the fuel-water interface is undetectable because the affected fuel is diluted by the unaffected fuel. I followed the dilution effect discussion with an explanation of the non-uniform distribution of microbes in fuel systems (see Part 34). Negative (i.e., below detection limit – BDL) microbial test results might indicate that there weren’t many microbes in the sample but provide no guarantee that there were no microbial contamination hot spots elsewhere in the system. In today’s post I’ll discuss differences among microbiology test methods.

Do My Microbiology Test Results Tell Me Conclusively Whether Microbes are Damaging My Fuel System?”

The answer is still: no. Most often when I detect heavy microbial contamination in fuel systems, I also see evidence of biodeterioration. However, sometimes I don’t. There are time when I recover heavy microbial loads in my sample, but I find no evidence of damage. On other occasions, all of my non-microbiology observations indicate the biodeterioration processes are damaging the fuel, the fuel system, or both, but I’m unable to detect a significant bioburden. When I run several different microbiology tests, I reduce the chances of my failing to detect microbes when they are present in the sample.

Why Don’t All Microbiology Test Methods Give the Same Results?

Different Test Methods Measure Different Properties

Consider a block of clay. Figure 1 illustrates three different measurement methods that can be used to determine how much clay there is. We can measure its length, width, and height to compute the block’s surface area. We can weigh the block to determine its mass. We can press the block into a graduated beaker to determine its volume (which – yes – was can also compute from the three linear measurements). All three approaches are valid, although each can be more appropriate than the others, depending on what you intend to do with the information. The same is true for microbiology test methods.


Fig 1. measuring a block of clay – a) surface area; b) mass; c) volume.

Most people responsible for fuel quality stewardship need microbiology test results that indicate whether or not corrective action is needed. The do not need a detailed description of the types of microbes present.

Microbiology Test Methods

There are three general types of microbiology test methods: direct count, culture, and chemical.

Direct Counts

Theoretically, direct count methods detect 100 % of the microbes present. However, dispersed water droplets and particulates can be incorrectly counted as microbes. Special stains can be used to determine whether the cells present are active or inactive. Organism-specific stains can also be used to determine whether microbes of concern are present. The main disadvantages for petroleum industry folks are the equipment (a good microscope), technical skill, and the amount of time (labor intensity) required to do direct counts.

Culture

Culture tests require cells to proliferate (i.e., multiply) in a liquid (broth) or on a solid/semisolid growth medium. Proliferation in broth media is detected either by an increase in the growth medium’s turbidity or a dye’s color change. Figure 2 illustrates two types of both test kits. In figure 2a, the dye in the growth medium turns red when microbes proliferate. The number of days between inoculation and color change is used to estimate the bioburden in the original sample. Figure 2b illustrates a broth test for acid producing bacteria (APB). Typically, a series of three to five vials is inoculated – each being a 10x dilution of the one before it. If APB grow in the broth, its color will change from red to yellow. Each vial is scored positive or negative. The sample’s APB population density is computed based on the most diluted vial in which the color changed.

Fig 2. Broth culture media – a) general medium in which red color develops as cells proliferate; b) differential medium for APB, ed dye turns yellow as proliferating microbes produce acid.

As illustrated in figures 3a and 3b, microbes form colonies when the proliferate in or on solid media. ASTM D6469 (Standard Practice for Enumeration of Viable Bacteria and Fungi in Liquid Fuels—Filtration and Culture Procedures begins with a filtration step to trap microbes onto a membrane filter. The filter is placed onto a solid, nutrient agar growth medium. Proliferating microbes from colonies on the membrane’s surface (figure 3a). For ASTM D7978 (Standard Test Method for Determination of the Viable Aerobic Microbial Content of Fuels and Associated Water Thixotropic Gel Culture Method (figure 3b) a small inoculum is added to a semi-solid growth medium. Colonies develop as red circles. A commonly used culture test for detecting sulfate reducing bacteria (SRB) uses a vial filled with a selective, semi-solid growth medium that turns black if sulfate reduction occurs – i.e., when proliferate.

Fig 3. Microbial growth on or in solid/semi-solid media – a) colonies on a filter membrane; b) colonies in a thixotropic gel; c) selective growth medium for SRB turns black as SRB proliferate.

Culture testing is relatively easy to perform, but detection depends on two important factors. First, only microbes able to proliferate in the nutrient medium used will be detected. Second, detection depends on a microbe’s ability to proliferate in the medium in the time frame prescribed by the test method.

Proliferation is population growth. One cell divides to become two, two to four etc. There are thousands of different growth medium recipes. Each is designated to support the proliferation of some types of microbes. General media can be used to grow diverse microbes. Selective media might support the growth of a single type of microbe. Regardless of intent, there is no single growth medium on which all microbes can grow. This challenge is made even more difficult because some microbes require oxygen while others only proliferate in oxygen-free environments. Other factors such as pH, salinity, temperature, and others determine which microbes will proliferate. Consequently, microbes that are healthy and active in the system from which they were sampled might not be able to proliferate under the test conditions used for culturing them. In the 1970s and 80s when I was able to feed microbes radiolabeled nutrients, I routinely observed high levels of metabolic activity (for example microbes using C14-glucose to produce C – carbon dioxide, the end product of mineralization) in samples from which culture data on several different types of growth media were BDL.

Inability to use the nutrients provided in the incubation environment is on issue. Generation time is the other. Generation time is time between cell divisions. Figure 4 illustrates this concept. During the first generation, one cell divides to produce two daughter cells. It takes 30 generations to accumulate enough cells to form a visible colony (i.e. a mass of cells with a diameter ≥0.08 mm) or 20 generations to make a broth visibly turbid. If a culture test is ended after 72h (3 days), only microbes with generation times ≤ 2.4 hours will be detected. Known microbe generation times range from 15 min to 30 days. Consequently, slower growing microbes that can proliferate in a given nutrient medium are likely to go undetected. Culture test results that might be positive after a week or two are erroneously scored as BDL.

Fig 4. Proliferation – each cell division cycle is one generation. It takes 30 generations for one cell to proliferate into a visible colony.

The generation time issue illustrates what I consider to be the primary factor that makes culture testing suboptimal for condition monitoring. If data are not available for several days after testing begins – not to mention delays between sampling and testing – necessary corrective actions are delayed. This delay is unlikely to cause substantially more biodeterioration in fuel systems but it will increase the cost of predictive maintenance (PdM – see Fuel & Fuel System Microbiology Part 5). 

Chemical Testing

All of the microbiology test methods fall into this broad category. I’ll discuss three categories here: adenosine triphosphate (ATP), enzyme-linked immunosorbent assay (ELISA), and genomic testing.

ATP is the primary energy molecule in all living cells. Bacterial cells contain 1 x 10-15 g (1 fg) of ATP per bacterial cell and ∼100 fg per fungal cell. Consequently, ATP concentration ([ATP]) is roughly proportional to the microbial population density in a sample. The original ATP test method – developed for testing water – proved to be unsuitable for brines and complex fluids such as fuels, lubricants, and oilfield produced waters. The protocol that was ultimately developed into ASTM D7687 Standard Test Method for Measurement of Cellular Adenosine Triphosphate in Fuel and Fuel-associated Water With Sample Concentration by Filtration is the only ATP test method that is not affected by these interferences (full disclosure – after 30 years of struggling to overcome the interference issue, I was involved with developing ASTM D7687). The D7687 ATP test only detects metabolically active cells. However, a variation of the basic test can be used to make dormant cells active and thereby determine whether a dormant population poses a future risk to the fuel system from which the sample was collected. Another test variation permits differentiation between bacterial and fungal contamination. The ASTM D7687 test can be completed in 5 min to 10 min and performed anywhere (I often run the test out of the back of my SUV). Because of its speed, precision, and accuracy, it is my preferred routine monitoring test method. Generally, ATP and culture test results agree. The ATP results from ∼15 % of 1,000s of samples I’ve tested indicate heavier contamination loads than those indicated by culture. Approximately 5 % of the time culture tests indicate heavier contamination. In the former case, D7687 is likely detecting microbes that would not proliferate under the culture test conditions used. In the latter case, culture testing most likely recovered microbes that were dormant in the sample but recovered once exposed to the culture medium.

Fig 5. ASTM D8070 LDF – a) LFD before use, showing two red lines – left line is control and right line is test; b) after specimen has been applied and given change to wet the test line – right line has disappeared, indicating presence of target antigen.

The ASTM D8070 test kit has six LDFs on a panel – two each for bacteria, total fungi, and the fungus Hormoconis resinae. One LDF of each pair detects moderate (33 μg ⁄mL to 166 μg ⁄mL) antigen concentrations and the other detects heavy (>166 μg/mL) concentrations. Like ATP testing ASTM D8070 can be completed in < 10 min. Although ASTM D8070 detects both active and dormant cells without differentiation between them, its results agree well with those obtained using ASTM D7687 ASTM D7687 s semiquantitative – given results in one of three ranges: <33 μg ⁄mL, ≥33 μg ⁄mL to 166 μg ⁄mL, and >166 μg/mL. Determining whether the test line has fully disappeared can be somewhat subjective. Decades ago, it was thought that H. resinae was the most common diesel fuel contaminating microbe. Although this has subsequently been disproven, the test kit manufacturer retains the H. resinae LFD – I suspect for sentimental reasons. D8070 is best used as a quick tool for determining if high [ATP] is due to bacterial, fungal or both types of microbes, rather than as a primary condition monitoring test.

Genomics is the branch of science focused on investigating genetic molecules – particularly deoxyribonucleic acid – DNA. In microbiology, genomic test methods use DNA extracted from a sample to determine what types of organisms are present. The key here is extraction. In order to get at their DNA, cells must first be broken open (lysed). If certain types of microbes in the samples are resistant to lysis, they won’t be detected. Consequently, the 80 % detection estimate for genomic testing assumes that in environmental samples, approximately 20 % of the cells present will not lyse. As lysing method improve, so should genomic test detecting percentages.

Currently, there are two general types of genomic tests used. Quantitative polymerase chain reaction qPCR) and next generation sequencing (NGS). Both methods use the enzyme DNA polymerase to generate millions of copies of the DNA molecules present in the original sample. qPCR methods are used to quantify specific genes, such as the dsrB gene present in all sulfate reducing bacteria. NGS looks at either 16S ribonucleic acid (RNA – bacteria), 18S RNA (fungi) or whole genome sequencing. Where qPCR can quantify the number of cells (actually copies of the target gene), NGS can provide a profile of all of the different types of microbes present. The great advantage of genomic testing is that the methods detect culturable and non-culturable microbes and identify the types of microbes present. Depending on the method used, detection can be fairly general (i.e., APB, SRB, etc.) or specific (i.e., H. resinae, Pseudomonas aeruginosa). I am currently evaluation the relationship between qPCR for total prokaryote (bacteria) and [ATP] (watch this blog for updates). Although field qPCR tests have recently become available, the level of information provided is more than that needed for routine condition monitoring. For now, genomic testing is best used as a follow-up diagnostic tool. NGS data is best used to prioritize target microbes qPCR test development.

Correlation Versus Agreement

A correlation is a linear relationship between two parameters. A good example of this is the relationship between two test methods use to measure bioburdens in a series of dilutions of an original sample. In this example, the types of organisms present is constant but the number of cells/mL varies linearly with the dilution factor. Figure 6a shows how each parameter decreases with increasing dilution factor. Figure 6b shows that the correlation between the two parameters is linear across the tested range.

Fig 6. Correlation between culture and ATP test results form serial dilutions of a heavily contaminated sample – a) each parameter plotted as a function of Log10 dilution factor; b) Log10 CFU/mL plotted as a function of Log10 [ATP].

In contrast, agreement refers to the likelihood that two parameters will lead to the same attribute score. For example, except for genomics, all of the microbiology parameters I’ve discussed in this article have recommended attribute scores: negligible, moderate, and heavy/high. Because each parameter measures a different aspect of the bioburden, agreement is more relevant than correlation. For example, in the ATP and ELISA test comparison mentioned above, results from 128 samples were compared. For 108 of the samples, both methods yielded the same attribute scores (84 % agreement). This degree of agreement indicates that both methods are providing reliable indications of fuel bioburdens.

Bottom Line

There is no best method for microbiology testing. Each serves an important purpose. Each has advantages and disadvantages relative to the others for different purposes. For example, although culture testing involves a long delay between initiating the test and having results, it is the only approach that provides isolated microbes for further study. Figure 7 is a Venn diagram illustrating the relationships among the different methods I’ve discussed in this post. Except for direct counts, which include erroneous counting of inanimate particles and water droplets, all of the methods detect some portion of the total microbiome. Note also the considerable overlap – i.e., agreement – among the methods. Still, there are regions where the circles do not overlap. It is in these regions that results from different methods are likely to not agree.

Fig 7. The relationships among microbiology test methods. The size of each circle indicates the estimated portion of the total microbiome detected. Disagreement among test results occurs where circles do not overlap.

Based on my field experience, I personally prefer ASTM D7687 for routine microbial contamination, condition monitoring testing. If I am doing diagnostic work, I’ll run additional microbiology tests to supplement high [ATP] results. Historically, I’ve run differential culture tests on samples with high [ATP]. As qPCR become less expensive, I’m beginning to replace differential culture with genomic testing. As I’ve written in previous blogs, as long as consensus standard test methods are being used, the relationship between microbiological data and other fuel and fuel system test data is more important than the relationships among different microbiology test results.

In Part 4 I’ll discuss the impact of specific microbial activities on the link between bioburden and biodeterioration.

The details

For more details about understanding the relationship between microbiology test data and fuel or fuel system biodeterioration, please contact me at either fredp@biodeterioration.control.com or 01 609.306.5250.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 34 – Connecting the Dots, Part 2

Refresher from Part 1: What do Microbiology Test Results Mean?

In January’s Fuel & Fuel System Microbiology article I led with this question and commented that it is actually a double question. In one sense, it is asking: “Do my microbiology test results tell me conclusively whether microbes are damaging my fuel or fuel system?” In another sense, the question means: “Why don’t the results from different fuel microbiology test kits always agree?” I am then asked why often, even when microbiological test data indicate that there is heavy biocontamination present, the fuel does not seem to be affected. In today’s post – the second of three on this topic – I’ll discuss the relationship between microbiological test results and system damage.

Do My Microbiology Test Results Tell Me Conclusively Whether Microbes are Damaging My Fuel System?”

As I wrote, last month, the short answer is no. Keep in mind, all three of these posts about whether detected microbial contamination invariably signals biodeterioration is happening. This is different from the situation in which there are numerous indications of system biodeterioration, but microbiological test results are negative. I’ll revisit that issue in a future post.

Fuel System Biodeterioration

Biodeterioration is any damage caused by organisms. In fuel systems, the most common forms of biodeterioration are biofouling and microbiologically influence corrosion (MIC).

Biofouling is the result of microbes and the slime they produce (i.e., extracellular polymeric substance – EPS – the primary material in biofilms (see Part 15 for a refresher on biofilms) accumulating on system surfaces. When biomass accumulates on filters or screens, it restricts product flow. Figure 1 shows photographs of a dispenser filter, dispenser strainer, and leak detector strainer – each of which has become fouled with biomass.

Fig 1. Biofouling – a) dispenser filter; b) dispenser strainer; c) leak detector strainer.

Biofouling can also cause other problems including valves sticking or failing to close completely. When biofouling accumulates on the surface of an automatic tank gauge’s (ATG’s) water float (Figure 2a) the impact will depend on the biofilm. If the biofilm is filled with gas pockets, the float will be lighter than normal and will float within the fuel – giving a false signal that bottoms-water is present when it is not (Figure 2b). Conversely if the EPS is loaded with rust particles, the water float will be heavier than normal. It will rest on the tank bottom, even when 2 cm to 3 cm bottoms-water as accumulated (Figure 2c)

Fig 2. ATG water float – a) fouling on float’s surface; b) gas pockets in biofilm lift float into fuel-phase; c) rust particles in biofilm weigh-down float, preventing from floating above bottoms-water.

Biofilms coating vehicle fuel gauges will cause the gauges to give inaccurate readings.

Note that all of these biofilm accumulation zones are on system components. Fuel systems can have substantial bioburdens in tank bottom samples, but no biofouling. Although the possibility of fouling increases with increased bioburden in fuel tank bottom samples, detection of substantial microbial loads in fluid samples doesn’t necessarily mean that fouling has occurred. The only way to know for certain whether biofouling has occurred is by direct inspection of the fuel system components that are likely to become fouled.

Microbiologically influenced corrosion (MIC) includes any from of material damage that is caused either directly or indirectly by microbes. Most commonly, MIC is related to metallic components, but polymeric materials are also susceptible to MIC.

Contamination Detection

Connie Francis recorded Where the Boys Are as the title track for the 1961 movie of the same name. The next several paragraphs could be titled Where the Microbes Are. Microbial contamination is not a fuel property. Unlike fuel properties, the distribution of microbes in fuel systems is non-uniform (heterogeneous). The heterogeneous distribution of microbial contamination makes it difficult to collect a sample that is guaranteed to contain microbes – even if microbial contamination is present in the fuel system.

In my fuel microbiology courses I recount a lecture I heard as an undergraduate. My professor was part of the team tasked with developing a reliable test method for determining whether there was life on Mars. One member of the team suggested using a camera that would scan the horizon for signs of life. The device would scan 15 ° of arc at a time, completing a 360 ° scan each hour. The counterargument – illustrated in figure 3 – was that large life forms (elephants in figure 3) might be present but missed entirely because they continually moved out of the camera’s line of sight.

Fig 3. Not detecting the elephants – a) elephants are to the east while camera is pointing west; b) elephants are to the west while camera is pointing east. A researcher viewing the camera’s photo record would conclude that there are no elephants in the area photographed!

Now consider a 0.5 L (0.13 gal) sample collected from the bottom of a 38,000 (38 m3, 10,000 gal) tank. The sample represents 0.001 % of the total liquid volume in the tank. Similarly, a bottom sample from the bottom of a tank with a 31 m2 (31,000 cm2, 334 ft2) surface area draws in fluid, sludge, and sediment form a 3 cm to 5 cm radius. That represents 0.02 % of the total bottom surface area. Figure 4 illustrates how a two bottoms samples, taken from spots just a few cm apart, can have substantially different bioburdens.

Fig 4. UST bottom – biomass density heat map. Green zones have negligible biomass accumulation. Red zones have > 5 mm thick masses. Numbered blue circles are points from which bottom samples were collected. Distance between #1 and #3 ≈ 0.25 m (10 in). Microbial loads: #1 – below detection limits; #2 – moderate bioburden; #3 – heavy bioburden.
This is why I argue that a sample that yields negative microbiological test results provides much less information than one that yields positive results. You can get negative test results from samples taken in tanks suffering from severe biodeterioration damage. The converse is also true: it’s possible to detect substantial bioburdens in systems that show no indication of biodeterioration. In the latter case, the microbiology data triggers further checks. The cost of performing these checks is a fraction of the cost of post-failure corrective maintenance (i.e., tank replacement, site remediation, etc.).

Bottom Line

Fuel system samples used for microbiological testing are meant to be diagnostic – not representative. To be reliably diagnostic, samples must come from locations most likely to harbor microbes. This is can be impractical (if not impossible). Consequently, samples from systems with substantial fouling, MIC, or both can have negligible detectable bioburdens. Conversely, it is not uncommon for systems from which samples have apparently heavy bioburdens to have no biodeterioration symptoms. In Connecting the Dots – Part 3, I’ll write about why test results from different microbiology methods can lead to different conclusions. As I was writing today’s blog I decided to add a Part 4 – the impact of specific microbial activities on the link between bioburden and biodeterioration.

The details

For more details about understanding the relationship between microbiology test data and fuel or fuel system biodeterioration, please contact me at either fredp@biodeterioration.control.com or 01 609.306.5250.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 33 – Connecting the Dots, Part 1

What do Microbiology Test Results Mean?

This is actually a double question that I hear quite often. In one sense, I’m asked: “Do my microbiology test results tell me conclusively whether microbes are damaging my fuel or fuel system?” In another sense, the question means: “Why don’t the results from different fuel microbiology test kits always agree?” Today’s post is the first of three in which I’ll write about how to make sense of microbiology data.

Do My Microbiology Test Results Tell Me Conclusively Whether Microbes are Damaging My Fuel or Fuel System?”

In a word: No. Let’s first consider fuel biodeterioration. In Part 2, I’ll write about system (infrastructure) biodeterioration. I’ll wait until Part 3 to explain why the results from different microbiology tests do not always agree.

Fuel Biodeterioration

In small microcosms, it is fairly easy to see the following fuel properties change due to biodeterioration:

  • Oxidative stability – decreases
  • Octane or cetane number – decreases
  • Carbon number distribution (simulated distillation curve) – shifts towards more complex molecules and molecules with more carbon atoms.
  • Corrosivity – increases
  • Total acid number – increases
  • Particulates – size and total number both tend to increase.

The larger the total fuel volume, the less likely these symptoms will be detectable. This is because the affected fuel is a minuscule fraction of the total fuel volume. Because the affected fuel is diluted in unaffected fuel, changes to the affected fuel become immeasurably small. Moreover, in high throughput systems, the contact zone between microbes and fuel is short – too short to give microbes time to attack the fuel. In long term storage systems, analysis are more likely to see differences between bottom fuel samples and those taken higher in the fuel column.

In addition to the dilution effect, in most fuel systems stratification and hydrodynamics create three primary zones. Figure 1 illustrates how a typical high throughput tank has three such zones.

Fig 1. UST hydrodynamic zones. Bottom 2 cm to 5 cm are stagnant. In the overlying 2 cm to 5 cm, turnover rate increases with distance from bottom. Above the transition zone the turnover rate is approximately the same throughout the tank. In a retail UST, this can be ≥5 turnovers/week.

This underground storage tank (UST) receives at least five deliveries per week. Consequently, fuel turnover in most of the tank is ≥5x/week. In most tanks the bottom several cm are stagnant (outside fill line scour zones). At the bottom of this zone, free-water and particulates accumulate so that they will not be drawn into the submerged turbine pump (STP) with clean product. Between the high turnover and stagnant zones there’s a transition zone. At the interface between this zone and the stagnant zone, turnover rates are negligible (see graph in Figure 1). As the distance from the tank bottom, the turnover rate increases until it is nearly the same as the high turnover zone. The transition zone is typically 2 cm to 5 cm thick, depending on the distance of the suction line’s inlet from the tank bottom.

The stagnant bottom-zone – where free-water, sludge, and sediment accumulate – is one of the regions where heavy bioburdens are most likely to be found. This is true for nearly all fuel tanks. When the fuel turnover rate is faster than 1x/month, fuel properties are unlikely to be affected by bottom-zone bioburdens. Thus, microbial test results indicating the need for immediate corrective action won’t be reflected in the results of tests run to determine whether the fuel is in specification. The longer fuel is stored (for example in emergency generator system fuel tanks, where fuel can be stored for years), the more likely it is that microbial activity will affect the fuel’s properties.

Notwithstanding the difficulty linking high bioburden results with fuel biodeterioration, bottom sample gross observations can be very helpful. Figure 2a shows a three-phase bottom sample. An invert emulsion zone (rag layer – fuel droplets dispersed in water) is most often a symptom of biodeterioration activity. When the rag layer adheres to the sample bottle’s surface (as in Figure 2b), you can be confident that the invert emulsion was caused by microbial activity. Microbes can produce detergent molecules called biosurfactants. Biosurfactant production can be tested quite easily. Place 5 mL of bottoms-water and 5 mL of fuel into a 50 mL polypropylene centrifuge tube and shake vigorously for 30 sec. Let the mixture stand for 15 min. If the phases separate cleanly, the results are negative (Figure 3a). If microbes have produced biosurfactant, a stable emulsion will form (Figure 3b).

 

Fig 2. Fuel separated from water by rag layer – a) invert emulsion stalactites (suspended from fuel into water) and stalagmites (extending up from bottom); b) rag layer adhering to sample bottle wall (highlighted by yellow dashed line).

Fig 3. Testing for biosurfactant production; two tubes 15 min after shaking – a) fuel and water have separated completely – no evidence of surfactant in sample; b) stable emulsion remains – microbial population has produced sufficient biosurfactant to produce this emulsion.

The details

For more details about understanding the relationship between microbiology test data and fuel or fuel system biodeterioration, please contact me at either fredp@biodeterioration.control.com or 01 609.306.5250.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 32 – FUEL SYSTEM DISINFECTION REVISITED

Questions from a colleague

Today’s post was inspired by a text message I recently received from a colleague. His message contained two questions. First, he asked whether fuel tank microbicide treatment could select for resistant microbes. Second, he asked about how best to treat microbially contaminated fuel systems.

I addressed microbicide selection in Part 21 (July 2018) and treatment strategies in Part 22 (August 2018). Today’s post will include some content from these two earlier posts as I focus on the questions of microbicide resistance and effective dosing strategy.

The Short Answer

  • Microbes in fuel systems can become microbicide resistant.
  • The two most common reasons microbes become resistant are:
    • – Underdosing
    • – Inadequate exposure period (soak-interval).
  • To minimize the risk of a system developing resistant microbes:
    • – Use the maximum permissible dose when treating systems
    • – Don’t expect microbicide treatment alone to disinfect heavily infected fuel systems
    • – Repeat treatments until microbial population is adequately controlled.

What is Microbicide Resistance?

The news has contained an increasing number of reports about antibiotic resistant microorganisms (ARMs). If you are not familiar with ARMs, I recommend this Centers for Disease Control (CDC) webpage for an excellent overview of the issue. Briefly, ARMs are strains of disease-causing microbes (pathogens) that have mutated successfully to become tolerant (if not 100 % impervious) to a broad range of antibiotics. Almost invariably, antibiotic resistance is due to successful mutations. A mutation is a change in a cells’ genetic code. This change is passed along from one generation to the next. A successful mutation is one that enables the mutant to compete favorably against its non-mutant neighbors. In bacterial populations approximately one cell per million is a mutant but not all mutations are successful. When exposed to sub-lethal antibiotic concentrations, more cells within the target population are able to mutate successfully.

Remember that the generation times (time in which the population doubles) for bacteria range from 0.5h to several days. If an antimicrobial substance is repressing the growth of non-mutant cells but not mutants, it does not take long for the mutants to become the dominant population. Voilà, the treatable microbes have been replaced by resistant one.

Industrial microbicide (microbicide) resistance is a bit more complicated. There is still plenty of debate on which of two mechanisms is most common. There is considerable evidence that mutation – just as for ARMs – is one of the ways microbial populations become microbicide resistant. However, there is also evidence that by selectively killing the fastest growing microbes, microbicides can select for slower growing ones. This is a case of eliminating the competition. Non-oxidizing microbicides – i.e., all of the ones used for fuel-treatment – target cell components (i.e., enzymes, genetic material, cell wall molecules, etc.). Typically, non-oxidizing microbicides are most effective against rapidly growing microbes – the faster you eat, the more poison you ingest; the more poison you ingest, the faster you die. This means dormant and slowly growing microbes tend to be more bioresistant than their metabolically active neighbors. Dormant microbes (sometimes called persister cells) are similar to bacterial endospores – they are inactive – but do not have the definitive spore structure that characterizes endospores (figure 1).

Fig 1. Bacillus subtilis, spore stained, photomicrograph. Endospores appear as hollow, blue spheroids. Vegetative (i.e., metabolically active) cells appear as solid, violet rods.

Figure 2 illustrates the how slow growing microbes can become the dominant contaminant population after microbicide treatment kills all of the faster growing microbes. Before treatment, the slow growers (red cells) are a minor part of the total population dominated by fast-growers (blue cells) (figure 2a). Treatment kills all of the fast-growing cells but leaves most of the slow-growers intact (figure 2b). With their competition eliminated, the slow-growers eventually become the dominant population (figure 2c). Since these microbes were microbicide-resistant originally, they develop into a microbicide-resistant, contaminant population.

Fig 2. Effect of microbicide treatment on microbial population profile – a) fast-growing (blue) cells dominate before treatment; b) microbicide treatment kills the fast-growing cells while most of the slow-growers survive; c) without competition from fast-growers, slow-growers proliferate and become the dominant population over time.

Note, in this case, the resistant microbes were never fully susceptible to the microbicide used. They did not mutate in response to the treatment. Note also, resistance happens when a cell that has been exposed to a normally toxic concentration of the microbicide survives. As I’ll discuss below, microbicide treatment can be ineffective if the chemical does not come into contact with cells.

Insufficient Microbicide Concentration Impact

Figure 3 (this is a copy of figure 3 from Part 22) illustrates the impact of underdosing. The critical concentration is the minimum dose at which a microbicide has some kill effect. At concentrations less than the critical concentration, microbicides trigger increased metabolic activity and cell proliferation. The maximum permissible concentration for the microbicide used in figure 3 is 1,000 ppm (vol). At ≥600 ppm (60 % maximum) it does a good job of killing the target microbes. At 200 ppm it has no effect and at concentrations <200 ppm it stimulates growth (i.e. % Inhibition is <0). This phenomenon – called hormesis – doesn’t mean the population is healthy. The population is actually working hard to counter the poison’s effect. If you’ve ever seen Arsenic and Old Lace, you’ve seen an hormesis case study. There was a time when people used tonics containing arsenic as stimulants. Low doses seemed to increase user’s vigor, but higher doses – not surprisingly – were lethal.

Fig 3. Microbial population response to different microbicide doses (hormesis).
Insufficient Exposure Period Impact

In fuel systems, microbes are either suspended (planktonic – figure 4a) or embedded (sessile) within biofilms (figure 4b). Microbicide treatment typically makes short work of planktonic cells. This is because each cell is exposed to a lethal dose (figure 4c). However, microbicides are unable to reach cells that are embedded within a biofilm’s extracellular polymeric matrix (EPS – figure 4d). Note that cells near the EPS-bulk fluid interface are killed but those deep with the EPS matrix are protected. Figure 4 illustrates how a single treatment, using an effective microbicide, can fail to disinfect a system.

Figure 4. Microbicide efficacy against planktonic and sessile microbes – a) planktonic bacteria in bottoms-water; b) sessile bacteria in biofilm (EPS) on surface under bottoms-water; c) same as (a) but after microbicide dosing; all cells are exposed to the microbicide equally; d) same as (b) but after microbicide dosing; cells deep within the biofilm are not exposed to the treatment.

The commonly used fuel-treatment microbicide manufacturers recommend 24h to 48h soak periods. Given that most contamination develops on the bottom third of tank surfaces, dosing just before a fill should provide the recommended amount of contact time. This will effectively kill the planktonic population and cells within biofilms that are <2 mm (1/8 in) thick. For thicker biofilms, multiple treatments will be necessary. As illustrated in figure 5 (copied from Post 22) each time you add microbicide it will kill the microbes in, and disperse the EPS from, the biofilm’s outer surface. If microbicide is being used alone, it can require three or more treatments – each delivered three to five days after the preceding dose. In figure 5, three treatments were needed to achieve tank wall disinfection.

Fig 5. Using microbicide to disperse biofilm – a) biofilm accumulation on a surface; b) first biocide dose penetrates into the biofilm partially, causing some biofilm material to slough off; c) second biocide dose treats most of the remaining biofilm; d) third does disinfects surface; e) after effective treatment, surface is biofilm-free.

Does Disinfection Make Matters Worse?

Microbicides are developed to kill microbes, not to clean system surfaces. Best practice is to combine microbicide treatment with physical and chemical surface cleaning. If a system has substantial biofilm accumulation on its surfaces, microbicide treatment is likely to release chucks of biofilm and cells that will rapidly plug filters. For details on how to deal with this issue, refer to Part 23 on post-treatment system cleanup.

If linking microbicide treatment with system cleaning is not practical, the alternative is to treat repeatedly until:

  •    a) Masses of dispersed biofilm are no longer plugging filters, and
  •    b) Microbiological test results are below detection levels (negative).

The Details

For more details about disinfecting fuel systems, please contact me at either fredp@biodeterioration.control.com or 01 609.306.5250.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 31 – MATCHING THE SAMPLING TOOL WITH THE SAMPLING OBJECTIVE

Dixon Pumps Fuel System Broadcast Emails

The folks at Dixon Pumps dixon@dixonpumps.com routinely send out broadcast emails about fuel system maintenance. Today’s article was inspired by their 31 October 2019 email: Water Removal Basics, by Patrick Eakins. After the fourth paragraph, Mr. Eakins has an action list. The first action he recommends is:

“1. Determine the volume of phase (e.g. free water, ethanol) at the bottom of the tank. This can be accomplished by using a fuel sampler. First take a sample on the very bottom of the tank, then at 1-inch increments until you determine where the phase ends and the fuel begins.”

The phrase: “using a fuel sampler” caught my attention and will be this article’s focus. Spoiler alert: In Fuel Microbiology Part 30, I wrote about fuel system sampling. In this article be covering some – but not all – of the same material.

What Sampler?

The Dixon Pump article advises folks to use a sample but makes no mention of what kind of sampler is best for determining the height of free-water (or phase-separated ethanol and water) in underground storage tanks. The problem with an open statement like this is that the samples obtained by different types of samplers tell different stories.

Bacon Bomb Samplers

The Bacon bomb is probably the best, currently available bottom sample. Part 30, figure 2a shows a photo of a chrome-plated Bacon Bomb sampler. Figure 1, is a photo of a Bacon Bomb with a clear, polymeric cylinder (the cylinder is the sampler’s primary container). To make it easier to clean, the cylinder is threaded at each end so that the cap and bottom can be removed. The cap and plunger each have a hole for inserting a ring clip. To facilitate lowering and retrieving the sampler into tanks, a sounding tape can be attached to the cap’s ring. A secondary line can be attached to the piston’s ring for sampling above the tank’s floor (when the sampler is at the desired depth, the secondary line is pulled for approximately 30 sec to allow the sampler to fill. It is then released so that the piston is sealed against the sampler’s inlet).

Fig 1. Bacon Bomb sampler.

Figure 2 illustrates what happens when a Bacon Bomb sampler is used to collect a bottom sampler. The piston rests against the sampler’s inlet as it is lowered through the fuel column (figure 2a1 and 2b1). When the piston contacts the tank bottom, it is pushed up to open the inlet (figure 2a2 and 2b2). Hydrostatic pressure from the fuel column forces fluid into the sampler. When the Bacon Bomb is lifted off of the tank bottom, the piston will drop back into place – sealing it closed with the sample retained inside the cylinder. If there is no water or sludge present, the sampler will fill with fuel (figure 2a3). However, if bottoms-water is present, it will be the first fluid to enter the sampler. Thus, if there was 500 mL or water, and the Bacon Bomb’s capacity was 500 mL, the sample would be all, or nearly all water (figure 2b3). This would not be an accurate means for estimating the tank’s water level.

Fig 2. Bottom sample collection using a Bacon Bomb sampler. a) No water on tank bottom: 1) as sampler is lowered through fuel, the piston’s seat rests against the sampler bottom’s inlet, preventing fluid from entering; 2) when the piston touches the tank bottom and sample continues to fall, the inlet is opened and fluid enters – driven by the force of the fuel column’s hydrostatic pressure; 3) as the sampler is lifted off the tank bottom, the piston once again falls to reseal the sampler’s inlet – retaining the sampled fluid. b) Bottoms-water present: 1) same as a1; 2) any bottoms-water and sediment are pushed into sampler before any overlying fuel can enter; 3) same as a3, but now sampler is filled with water instead of fuel.

What does this mean in practical terms? Take a look at figure 3. Three, 500 mL Boston round bottles were filled from Bacon bomb samples collected from a tank bottom. The first sample (figure 3a) captured 450 mL bottoms-water and 50 mL of diesel fuel. If this had been used to estimate the water level, one might have concluded that the tank bottom was covered with a 5 in (13 cm) high water-layer. All 490mL of 500 mL from the second Bacon bomb sample (figure 3b) was bottoms-water. Was there actually 6.7 in (17 cm) of bottoms-water? The third sample (figure 3c) was mostly fuel. Three successive Bacon bomb samples from the same spot were sufficient to pull most of the water out of the tank. Water paste had shown that at the fill-end, the tank had 0.5 in (1.2 cm) of water.

Fig 3. Three successive Bacon bomb samples form one sampling point. A, b, and c were the fist, second, and third samples, respectively.

Bailer Samplers

Bailer samples are normally used to collect fluids from monitoring wells. Figure 4a illustrates how monitoring wells are placed around underground storage tanks. Note that the bottom of the well is porous and at a depth below the water table. After sample collection, the contents of the bailer sampler are layered – reflecting the layering of fuel over ground water in the well (figure 4b – normally the sampler will contain only water). Figure 4c shows the primary components of a bailer sampler. There are numerous bailer designs. For sampling fuel tank bottoms, the sampler must be fabricated form fuel-compatible materials. Also, as shown in figure 4c, the bailer should have a flat bottom.

Fig 4. Monitoring wells and bailer samplers. a) schematic showing location of a monitoring well near a UST; b) bailer sampler retrieved from monitoring well – dark fluid next to ruler is leaked fuel that was captured in monitoring well; c) bailer sampler showing its key parts.

To collect bottom samples, the bailer is slowly lowered through the fuel column (figure 5a1 and 5b1) until it stands vertically on the tank floor (figure 5a2 and 5b2). Because it is not sealed as it descends through the fluid, it analogous to collecting a soil core sample (figure 6). Most bailer samplers have a ball that floats inside the cylinder as the sample is lowered, then settles to the base and seals the inlet as the sampler is raised (figure 5a3 and 5b3). As shown in figure 5c, just as with a soil core sample, the bailer sample reflects the profile of water, rag layer, and bottom fuel much as they are layered in the tank’s bottom. There is typically ∼0.25 in (∼1 cm) between the bailer’s bottom and the inlet. Consequently, bailer samplers are not good for collecting bottom sludge and sediment samples. However, they are useful for estimating bottoms-water height. The tank from which the figure 5c sample was taken had ∼3.5 in (∼9 cm) bottoms-water and 0.75 in (1.9 cm) thick rag layer. The water height in the sampler agreed well with that determined using water paste.

Fig 5. Bottom sample collection using a bailer sampler. a) No water on tank bottom: 1) as sampler is lowered through fuel, ball floats above sampler inlet; 2) when sampler comes to rest on tank bottom, the ball sinks to the inlet; 3) as the sampler is lifted off the tank bottom, the ball seals the sampler’s inlet – retaining the sampled fluid. b) Bottoms-water present: 1) same as a1; 2) water fills the sample to the level of bottoms-water in the tank; 3) same as a3, but now sampler has fuel over water; c) fuel tank bailer sample.
Fig 6. Soil core sampler. a) core sampler pressed into soil; b) soil core, showing three soil horizons: a – organic surface zone, b – surface soil, and c – subsoil.

Other Samplers

ASTM Practice D4057 describes other fuel samplers. However, none of these are useful for collecting true bottom samples.

Best Practice for Determining Height of Bottoms-Water Layer

As I explained in my previous fuel microbiology post, the best way to determine bottoms-water height is by coating either a sounding sick or bob with water paste and lowering it into the tank. The water will react with the paste to change its color. Because tanks are rarely level, best practice is to test for water at two – preferably three- points: fill end, ATG (automatic tank gauge well, and fill end).

The details

For more details about fuel tank bottom sampling and water accumulation determination, please contact me at either fredp@biodeterioration.control.com or 01 609.306.5250.

REMEMBERING A MENTOR AND A MENSCH – PROFESSOR EUGENE D. WEINBERG 1922 TO 2019

This morning, while reading the Fall 2019 issue of Indiana University Alumni Magazine, I was saddened to read Gene Weinberg’s name in the list of recently deceased IU faculty and staff.
Professor Emeritus Eugene D. Weinberg died on 08 March – less than a week after having celebrated his 97th birthday. Gene was the first academician to have had a profound effect on my life’s path. I know that his memory will be a blessing to all of us who had the privileged and pleasure of knowing him.

I first met Professor Weinberg in 1966 – a few weeks into my first semester at IU. My initial plan was to have been a math major, but within a month, I began to rethink that plan. Having been tinkering with microbiology since my parents made the mistake of presenting me with a microscope for my eighth birthday, I decided to explore the possibility of changing majors to microbiology. In late October 1966, I visited with Professor Weinberg in his Jordan Hall office to explore my options. He advised me that the courses that I was taking were perfectly aligned with those that would be part of a microbiology major. He contacted my original, math department advisor and agreed to become my faculty advisor. From that date through my graduation in June 1970, Gene was always available to offer guidance and to facilitate my efforts to perform extracurricular studies under various Microbiology Department professors. Although I never saw it, I have no doubt that Gene’s letter of recommendation helped me to get accepted into graduate school and receive a full fellowship for my studies at University of New Hampshire.

Gene’s research interest was in medical microbiology. Knowing that my passion was microbial ecology, while I was taking his course in Medial Microbiology, he encouraged me to make my class project ecologically focused. When I went home for Thanksgiving, 1968, I took a suitcase full of sterile, 100 mL glass bottles with me. One the Friday after Thanksgiving, I drove to the Delaware River’s source. From there, and at various bridges located at 50 mi intervals – ending at the Delaware Memorial Bridge, I used a fishing pool, jury-rigged sampling setup to collect samples from each bank and the middle of the river. I then carried the full bottles back to Bloomington (good thing this was before there were suitcase weight limitations or TSA) where I proceed to run culture tests and biochemical taxonomic profiles on each type of microbe that I had detected. I rationalized this survey effort by noting that there was a possibility that the taxonomic profiles along the river’s length might have been indicative of public health risks.

I didn’t realize it at the time, but that project marked the start of my career as a microbial ecologist. I did realize from the outset that Gene was a supportive, encouraging mentor. When others might have said: “you can’t do that!” Gene would always tell me that I had a great idea, asked me if I had thought about various details – which of course I hadn’t, and suggest research papers that might help me to refine my thoughts. Gene was one of perhaps four mentors whose influence shaped my career as a microbiologist. I feel most fortunate for having known him and have benefited from his wisdom, his kindness, and his mentorship.

You can find Gene’s full obituary article at https://www.hoosiertimes.com/herald_times_online/obituaries/eugene-weinberg-phd/article_f86ed715-7dde-5789-9916-40d1e0fb0bfe.html.

FUEL & FUEL SYSTEM MICROBIOLOGY PART 30 – looking for samples in all the right places

What samples are most useful for microbiological testing?

Earlier this week a colleague asked me to prepare a short piece about collecting samples from fuel systems when the intention was to perform microbiological tests. My initial response was to refer her to ASTM Practice D7463 Manual Sampling of Liquid Fuels, Associated Materials and Fuel System Components for Microbiological Testing and my recently published chapter on sampling in ASTM Manual 1, 9th Edition. My colleague responded that she was really looking for a two-page summary that she could share with her customer who wanted to monitor their fuel systems from microbial contamination. Today’s post provides that summary.

The right stuff…

I first addressed sampling in Fuel & Fuel System Microbiology Part 2 (December 2016) and discussed sample perishability in Fuel & Fuel System Microbiology Part 6 (January 2017), but have not previously addressed sampling directly in this posts. Two key principles lie at the heart of sampling for microbiological testing:

   1) 1. Fuel & Fuel System Microbiology Part 2Samples are diagnostic – not representative, and

   2) 2. Microbial communities develop at interfaces.

What’s a diagnostic sample?

Microbiological sampling is unique in that the objective is to capture a sample from a location that is most likely – within a fuel system – to harbor microbes. Our intent is to diagnose the risk of microbes causing damage (biodeterioration) to either the fuel or fuel system. This is in stark contrast to the more common objective of collecting a representative sample – one that we can use to determine whether the product is fit for its intended use. Consequently, I use the term diagnostic to differentiate microbiology samples from fuel samples.

What is an interface?

Interfaces are zones where two or more components of a system come into contact with one another. Figure 1 illustrates the interfaces found in fuel systems:

  • Fuel-vessel – the surface of tanks and other system components that are in contact with fuel.
  • Fuel-water – the surface at which fuel and fuel-associated water meet. The primary fuel-water interfaces are between fuel and bottoms-water, and between fuel and biofilms (slime layers) coating system surfaces.
  • Fuel-headspace – in fixed roof tanks, the fuel’s surface that is in contact with the tank’s air/vapor zone (ullage)
  • Water-vessel – areas of direct contact between fuel-associated water or biofilm and system surfaces.
  • Water-sediment (sludge/sediment) – the top surface of any sludge or sediment layer hat has accumulated on the tank bottom.
  • Sludge/sediment- vessel – the interface between sludge or sediment and tank bottom.
  • Vapor-vessel – exposed surfaces in a fuel tank’s ullage zone.

Fig 1. Fuel system interfaces.

The best fuel system microbial contamination diagnostic samples come from tank bottoms or interfaces. In practical terms, these are typically tank drain or bottom grab samples.

Sample collection – bottom drain

Supplies

  • Absorbent spill pads
  • Alcohol – methanol or ethanol liquid or wipes
  • Bottle, clear glass, Boston round, or HDPE, wide-mouthed, 500 mL.
    Note: Clear glass makes it easier to observe phase, particulates, etc. However, analytes, such as adenosine triphosphate (ATP) can adsorb onto glass – making HDPE the preferred container material for samples to be tested for ATP.
  • Bucket, 5 gal (20 L)
  • Funnel
  • Gloves, surgical
  • Rags, shop

Procedure

  •    1. Place absorbent spill pads on ground around drain to ensure that any spillage or splashing will be captured by pads.
  •    2. Don gloves to protect hands and to reduce risk of contaminating sample with microbes from your skin.
  •    3. Use alcohol to wipe down exposed surfaces of bottom-drain and funnel.
  •    4. If there is sufficient space between ground (floor) and drain, place sample bottle into bucket and place bucket under drain.
  •    5. Remove cap from sample bottle, place wide-end of funnel under drain and narrow-end into sample bottle.
  •    6. Open drain and fill sample bottle approximately 75 %.
  •    7. Close drain, remove funnel from sample bottle, replace cap, and label sample bottle with:
          a. Sample source identification
          b. Sample collection date and time
          c. Identity of sample collector
  •    8. If sample is not going to be tested immediately, place in ice of refrigerator.

Sample collection – bottom grab

  • Absorbent spill pads
  • Alcohol – methanol or ethanol liquid or wipes
  • Bottle, clear glass, Boston round, or HDPE, wide-mouthed, 500 mL.
    Note: Clear glass makes it easier to observe phase, particulates, etc. However, analytes, such as adenosine triphosphate (ATP) can adsorb onto glass – making HDPE the preferred container material for samples to be tested for ATP.
  • Bucket, 5 gal (20 L)
  • Funnel
  • Gloves, surgical
  • Sampler – Bacon bomb or bailer (figure 2)
  • Sounding tape

Fig 2. Bottom samplers – a) Bacon Bomb; b) bailer.

Procedure

  •    1. Place absorbent spill pads on ground around drain to ensure that any spillage or splashing will be captured by pads.
  •    2. Don gloves to protect hands and to reduce risk of contaminating sample with microbes from your skin.
  •    3. Use alcohol to wipe down the sampler and funnel.
    Note: If multiple samples are being collected, and the previous sample contained visible sludge, sediment, or both, use clean fuel to rinse out the sampler before disinfecting its internal surfaces.
  •    4. Place sample bottle into bucket.
    Note: This serves two purposes: 1) it reduces the risk of spillage onto ground around sampling bottle; and 2) it shields sample bottle from the view of those who are not directly involved in the sampling process – this is particularly important when sampling retail site underground storage tanks.
  •    5. Attach sampler to sounding tape and lower the sampler into the tank until it touches the tank’s bottom but remains vertical.
    Note: Follow standard fuel handling safety precautions to ensure that the sounding tape is properly grounded and that there is no risk of sparking.
    Note: Best practice is to first determine the height of any free-water in the tank (figure 3).


    Fig 3. Using water-detection paste to determine height of free-water in tank-bottoms – a) sounding plumb-bob; b) sounding stick. Both devices had been coated with white, water-detection paste that had turned purple on contact with water.
  •    6. Remove cap from sample bottle, place narrow-end of funnel into sample bottle.
  •    7. Recover sampler and place it over funnel.
  •    8. Drain contents of sampler into sample bottle (figure 4).

    Fig 4. Transferring bottom-samples to sample bottles – a) draining Bacon Bomb sample into glass bottle; b) draining bailer sample into HDPE bottle.
  •    9. Remove funnel from sample bottle, replace cap, and label sample bottle with:
          a. Sample source identification
          b. Sample collection date and time
          c. Identity of sample collector
  •    10. If sample is not going to be tested immediately, place in ice of refrigerator.

Sample handling

Best practice is to keep samples chilled (40  2 F; 5  1 C) and to begin microbiological testing within 4h after collection Fuel & Fuel System Microbiology Part 6 explains sample perishability. Samples that have been kept chilled can be tested reliably for up to 24h after collection. The total level of microbial contamination and types of microbes present in the sample are increasingly likely to change as sample age beyond 24h. This makes the test results less likely to reflect conditions inside the tanks from which the sample was originally collected. Consequently, the risk of either failing to detect heavy microbial contamination or incorrectly concluding that actually had negligible contamination when sample was heavily contaminated, increases with sample aging. Microbiological tests like ASTM Method D7687 for ATP are easy to run in the field, immediately after sample collection. Using this type of test eliminates the risks caused by sample aging.

The details

This brief explanation of sampling procedures will get you started on the right path. However, circling back my opening comments, I recommend using ASTM Practice D7464 for detailed, step-by-step sampling instructions, and referring to my sampling chapter in ASTM Manual 1 for a full discussion of the considerations that should be taken into account when deciding when and when to collect samples for microbiological testing. I also address sampling in considerable detail in BCA’s six-module fuel microbiology course. For more details about this course, please contact me at either fredp@biodeterioration.control.com or 01 609.306.5250.

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