I have been writing alot lately!
SOME LATELY:
The Springing Jumping Punching Kicking Throwing Meditating Universal Spirit of the Martial Arts I grew up in America in the 1980s and 90s. My mother grew up in a small farming community in Osmond, Nebraska, and her family is mostly of German, Russian, and English descent. My dad, on the other hand, grew up in Alexandria Egypt before he moved to America in 1981. I was raised by these two very different people in a suburb between Dallas and Fort Worth, in northeast Texas, and it was here that I was introduced to martial arts. Irving, Texas is a city of cars and fast food restaurants and shopping malls, and growing up there I felt a sense of impermanence. However, in the shopping malls, between nail salons and laundromats, there are sometimes martial arts schools - tae kwon do, karate, kung fu, judo, and Brazilian jujitsu among others. As soon as my father realized that such places existed, my brother and I were enrolled in one of them. I learned tae kwon do from American YMCA teachers, from a Korean Master, and from Americans who had studied under Koreans. I watched Bruce Lee movies with my dad, and I remember thinking as a child that the 2 most important things to my dad must be religion and martial arts. He first fell in love with martial arts after seeing Arabic-dubbed movies of Bruce Lee in the 70s. His family couldn’t afford to pay for formal training, but that didn’t stop him - he would get books from the library and practice copying the stances, punches and kicks for hours in his room. Later, when he joined the Egyptian army, he practiced judo with his friends and entered into competitions in Cairo. I have also been introduced to judo as an adult, through this college course.
The martial arts have been practiced in a variety of forms throughout history and into the present, and all over the world in environments ranging from feudal Japan to the slavery plantations of Brazil in the 16th and 17th centuries, to the American suburbia of the present. If I were an evolutionary biologist, I might say that the evolution of the various martial art forms is an example of convergent evolution. In diverse and isolated environments, similar techniques and philosophies have developed, independently from each other, but into similar art forms. There are common threads that bind them together and nuances that distinguish them, that come from the particular flavors of the places and the people who have practiced them. I can identify a few of the common threads: respect for the individual and community, and respect for the ability of individuals and communities to funnel their energies into productive and rewarding results. A single person may be able to defeat 10, but it is the joint efforts of the community that make the achievement of the individual possible. Living amongst millions of people in a modern American city, it is easy to feel insignificant and unimportant. In cities, the individual is diminished and the sense of community is lost. Many daily interactions are superficial, neighbors are strangers, and the people are disconnected from the processes of life that sustain them – we do not build our homes, grow our food, gather our water, or educate our young. We rely on strangers for our most basic needs.
However, the human spirit is resilient, and communities spring up within the impersonal structure of the city. Connections are made, and humanity is not reduced to nothing. Sports, religions, music, common interests and philosophies draw people together. We may not have invented yoga, or judo, or kung fu, but we practice them as best as we can, and gain from the strong sense of individual and community that result.
One example of the convergent evolution of martial arts philosophies can be seen in the Japanese-born art of judo, and in the Brazilian martial art, capoiera. On the surface, these two martial art forms look very different. The practice of judo involves throwing and grappling, while capoiera is a fusion of music, dance, flips, kicks, blocks, attacks, and evasive gestures. However, at the heart of judo and capoiera, is the principle of energy control. The word judo literally means the gentle way. Jigoro Kano founded judo in 1882, after studying under many martial arts masters and applying his own theories about energy control to the ancient techniques of jujitsu. His innovations soon became the standard, and judo became more popular than its predecessor. Capoiera, on the other hand, was developed in Brazil in the 1500s, and is believed to be rooted in martial arts techniques developed in the African country of Angola. Slaves were not allowed to practice martial arts, and so they would play music and pretend to be dancing, while they were really practicing martial arts techniques. Capoiera sprung out the struggle of the Brazilian slaves to maintain their identities and individual strengths. The capoiera “dance” involves the fighters responding to the moves of each other, while following the rhythm of the song and the story being sung. The capoierista must respond to his opponent’s motions while following the music. This is similar to the gentle way of judo – the way of using the opponent’s energy against him, while conserving your own.
The practice of martial arts is universal. From the streets of the cities, to the huts of small villages across the world, under freedom and oppression, the resilience of the human spirit can be seen rising above difficult situations. One of these forms of resilience and resistance to oppression is the practice of the martial arts.
Introduction to Labreport: The Fluorimetric Determination of Quinine in Tonic WaterFluorescence is exhibited by molecules and atoms in a wide range of matrices, varying from simple to complex and including gas, liquid and solid phases. A molecule fluoresces when it relaxes from an excited electronic state to the ground electronic state without undergroing any changes in spin. However, changes in spin can occur when an electron is in an excited state, and such transitions, termed intersystem crossing, become more probable when the vibrational levels of the electronic states overlap. Transitions between singlet and triplet states are possible. A singlet state is one in which the electrons’ spins are paired, and a triplet state is one in which the electrons’ spins are unpaired. Allowed transitions can occur between ground and excited singlet states, and excited triplet states. There is no “ground” triplet state, because such a state would violet the law of quantum mechanics. Intersystem crossing leads to phosphorescence, and a decrease in the quantum yield. The quantum yield is the ratio of the molecules that actually fluoresce to the total number of molecules in the solution. The probability of intersystem crossing occurring and leading to phosphorescence can be decreased by reducing spin/orbital interactions in the solution. Spin/orbital interactions tend to cause intersystem crossing, and when molecules in the solution contain heavy atoms, spin/orbital interactions are increased.2 Therefore, to maximize the quantum yield and minimize phosphorescence in fluorescence studies, it is important to consider the solution containing the analyte molecule.
In the present study, 3 kinds of spectra are analyzed. A series of increasingly chloride quenched solutions, the calibration solutions, and the urine samples are all analyzed by means of fluorescence emissions. In addition to this, absorption and excitation spectra will be obtained for a 0.1 ppm quinine solution solvated in 0.5M H2SO4. The excitation spectra is obtained by recording the emission of light at a particular wavelength by a sample excited by wavelengths spanning a much larger range. It is similar to an absorption spectrum that records the absorbance of light of varying wavelengths after it has passed through the sample.
The instrumentation for fluorescence spectrometry is similar to other luminescence detecting instruments. The general setup consists of an intense light source, wavelength selectors, a sample container, a beam attenuator, transducers, a difference amplifier and a readout device. See Figure 1 below.(from reference 2) The one pictured below employs a double-beam optical system in order to account for the fluctuations in the power of the source over time. In the present study we have used a pulsed xenon arc lamp as the source. The pulse is created by a capacitor discharging at regular intervals, and thereby creating an ac signal in the transducer. AC signals are advantageous because they are easily amplified and transduced to other energy forms.
UV-vis spectrometryThe absorptions of 4 dyes, bromothymol blue, phenolphthalein, methyl red, and thymol blue were measured over a 400 nm wavelength range spanning the UV-visible spectrum, at 3 different pHs: 4.6, 8.0 and 9.8. This data was used, in conjunction with Beer’s law, and the measured absorbance of a universal indicator solution, to determine their concentrations in the universal indicator which was known to contain all 4 dyes. A 4x1200 matrix was constructed and solved with Matlab. Phenolphthalein was found to have the largest concentration, followed by bromothymol blue. The concentrations of the metal additives Manganese and Chromium in a steel sample were determined by the construction of calibration curves using permanganate ions and dichromate ions respectively. Manganese was found to have a concentration of 2.83 mM, whereas chromium’s concentration was calculated to be below 0: -0.00950 mM. The negative value indicates that either there was no chromium present, or our detection method was not sensitive enough to distinguish between signal and noise at such low concentrations. UV-vis spectrometry is a powerful technique because it allows for the determinations of complex solutions without the need for separations to be performed first.
Introduction
The quantitative analysis of multicomponent systems can be carried out by a number of spectrophotometric techniques. Spectrophotometric techniques differ based upon detection methods, excitation or emission spectrum frequencies, and whether the analyte molecules are absorbing, emitting or fluorescing. We will measure the UV-visible absorptions of our analyte molecules using an HP8435 UV-vis spectrometer in conjunction with a linear photodiode array detector. The spectrometer is actually measuring transmittance, which is essentially the amount of radiation that makes it through the sample. Absorption is the negative logarithm of transmittance, and is easier talk about.1 This instrumental setup allows for quick absorption measurements over a wide wavelength range, and eliminates the need to perform expensive or difficult separations. Instead of scanning over wavelengths mechanically and measuring absorbance over the wavelength range separately (as in older instruments) the linear photodiode array detector measures the absorptions from all wavelengths at the same time. All UV-vis spectrometers consist of the same basic components: a light source, a wavelength selector, a transparent sample holder (usually a cuvette), and the detector.2 The diffraction grating technique used in older instruments is inexpensive and simple, but due to the fact that each wavelength is scanned individually, measurements are time consuming. The photodiode array detector, although slightly more expensive, has the advantage of being quick and efficient. A 600 nm range of absorbance can be collected in a tenth of a second with a photodiode array.2 Radiation from a light source is passed through the sample, and then focused onto a holographic reflection grating before being sent on to the photodiode array (see figure A below). The photodiode array is made up of hundreds of diode elements. The resolution of the spectrometer is determined by the size of these diode elements in conjunction with the ability of the dispersion grating to separate the radiation, and normally gives resolutions of approximately 2 nm.
We are interested in determining the concentrations of chromium and manganese in a steel sample. The primary component of steel is iron, but other metals are added because of their ability to favorably change the physical properties of the steel.
Nearly 90% of the world’s total production of manganese is used in steelmaking due to its sulfur-fixing, deoxidizing, and metal alloying abilities.3 Stainless steel and steel rail road tracks contain manganese as an additive. The primary effect of adding chromium to metal alloys is that it increases the hardness and durability of those metals. For similar reasons, vanadium and tungsten are also common steel additives.3
In the present study, the quantities of chromium and manganese in steel are determined by the construction of calibration curves using solutions of KMnO4 and K2Cr2O4 at 525 nm and 440 nm respectively. These wavelengths are chosen because permanganate ions have their maximum absorption at 525 nm and chromium ions have their’s at 440 nm. Absorption is directly proportional to concentration and the pathlength of the cuvette by a factor called the molar absorptivity. The molar absorptivity is particular to the compound and the wavelength of the absorption. An understanding of the relationship between absorption, concentration and path length is central to the analysis of spectroscopic data, and can be written mathematically as Beer’s law. The fact that Beer’s law, A = ?bc, is additive is what allows for the simultaneous quantitative analysis of multiple compounds in solution. By making use of the absorptivities of the calibration solutions at both 440 and 525 nm, we are able to construct a pair of simultaneous equations that can be solved for the concentrations of Mn and Cr in the unknown steel sample4. We will also analyze a series of dye indicator solutions in order to determine the concentrations of 4 dyes in a universal indicator. Again, the additivity of Beer’s law will be used to calculate individual concentrations from solution. The universal indicator is known to contain bromothymol blue (BB), phenolphthalein (PT), methyl red (MR), and thymol blue (TB). All dye indicators contain functional groups called chromophores which emit light in the UV-visible range dependent upon their excitation states. In other words, the color of an indicator is nearly always pH dependent. The pH dependency of the color of the indicator molecules is what makes them useful to chemists. “Universal indicators” are easily made by simply combining strategic amount of indicators that are active in different pH ranges. The result is a solution that undergoes a series of color changes over a broad pH range. In this experiment, the absorption range is from 400 to 800 nm, and a series of calibration solutions at 3 different pHs will be used to determine the contents of the universal indicator solution.
Lead Leached From Lead Crystal GlasswareMetals and their alloys have been used by human beings, more or less haphazardly, for thousands of years in applications ranging from architecture to ammunition to the making of beauty products. One example is lead – the ancient Romans used it to build water pipes, sweeten wines, and brighten eye-shadows and lip-balms, and in modern times it has been used as an additive to paints, gasoline fuels, and quartz glass. The assumption throughout history has been that although lead may be dangerous in large quantities, small amounts are harmless. Only within the past 40 years have the health effects of continual exposure to dilute concentrations of lead been investigated. The evidence collected so far suggests that even tiny amounts of lead in the blood-stream may be dangerous.
Lead poisoning can cause physical and mental lethargy, difficulty thinking, insomnia, headaches, poor appetite, irrational behavior, and in the extreme case, seizures, coma or death. However, there are several difficulties associated with identifying low-level lead poisoning. One is that the early symptoms are quite general. For example, a child that is more frustrated and angry on a daily basis than would be considered normal, may be suffering the effects of low-level lead poisoning. The exact mechanism by which lead causes health problems is unknown, but scientists do know that lead interferes with neurotransmission and calcium-controlled intracellular messenger systems. Because the central nervous systems of children are still being formed, they are particularly susceptible to the effects of lead poisoning. Another problem is that the identification of trace elements in complex solutions has historically been carried out by analytical chemists, and not doctors. When doctors perform blood tests, they do not use techniques that can detect parts per million or parts per billion concentrations. Therefore, unless a person exhibits all of the extreme symptoms associated with the worst kind of lead poisoning, a doctor would have difficulty saying with certainty that the person is suffering from lead poisoning.
The subject of this inquiry is the leaching of lead from lead crystal glassware into food and beverages. Lead crystal usually contains 24 to 36% lead oxide, and is commonly used in the making of wine glasses and decanters. The first studies concerning the leaching of lead from lead crystal glassware were published in the late 1970s, and caused consternation in the crystal making industry. In 1976, the World Health Organization recommended sampling and testing methods and limits for the leaching of lead from glassware that contacts food or beverages. The test consisted of filling the crystal container with 4% acetic acid and letting it stand for 24 hours at room temperature. During the 24 hour time period, a concentration no greater than 5 ppm should be attained for small hollow-ware, and no more than 2.5 ppm for larger hollow-ware. The International Crystal Federation, setup in 1991 to investigate the problem, has since adopted standards for allowable levels of lead migration that are lower than those mandated by the WHO: 1.5 ppm for small hollow-ware and 0.75 ppm for large hollow-ware. In this inquiry I will test the amount of lead leached from a large Bohemia Czech Republic lead crystal container, containing 24% lead oxide, over times ranging from only 2 minutes to 1 hour. It has been shown in previous studies that leaching occurs very quickly, and detectable changes in the concentration of lead in acidic solutions are seen within minutes. I will need to employ a method that is capable of detecting 1 or less ppm.
There are several analytical techniques that are employed by chemists to quantitatively analyze very dilute concentrations of chemicals. Titrations are nearly as old as chemistry itself, and are an elegant example of an analytical technique for determining unknown amounts of chemicals in solution, but are not sensitive enough to detect parts per million or parts per billion concentrations. In order to detect such low concentrations, more innovative and tricky techniques are required. In the literature, 2 separate techniques have been used for the determination of lead leached from lead crystal glassware – one, atomic absorption spectroscopy, involves spraying the sample into a flame, and measuring the intensity of the absorption of the sample of a light beamed through the sample to a detector. Every element and molecule absorbs light of varying frequencies, dependent upon the electronic and mechanical energy levels available to the element or molecule. The absorption spectrum is like a fingerprint, whose size at a particular wavelength is directly related to the amount of molecule in the flame.
The other technique that has been used to quantify lead leached from lead crystal is anodic stripping voltammetry (ASV). ASV is one of the most sensitive analytical techniques available to chemists for environmental analysis of heavy metals because it concentrates the analyte onto a small surface. Metal cations from the solution are deposited onto an electrode for a specified period of time. Although the solution may be very dilute, the concentration of metal ions on the electrode is larger, allowing for accurate measurements. There are 3 steps in anodic stripping voltammetry. The first step involves holding the electrode at a low potential, so that cations in the solution will migrate towards it and form amalgams with the surface molecules. During the deposition step, the solution is stirred to enhance delivery of the analyte cations to the surface of the electrode, and induced current is channeled away to the reference electrode so that potential energy is held constant. The second step is a resting period during which stirring is stopped and the applied voltage is turned off and the solution is allowed to come to rest. During the third step the potential is turned on again and scanned in the positive direction so that the metal cations are stripped from the electrode and a current is measured. The current is proportional to the concentration of the metal in solution. I will use a mercury electrode and linear sweep voltammetry to measure lead leached from a lead crystal container into a 4% acidic acid solution, and will construct a calibration curve in order to determine the concentrations based upon the stripping current. I will take measurements of the acidic solution at 2 minutes exposure to the lead crystal, 5 minutes, 10 min, 30 min, and 1 hour and see if a mathematical relationship between time and lead-leached over the course of an hour can be constructed.
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Conclusion
In conclusion, the data I obtained is probably not worth anything, as it stands. If I had time and motivation, I would repeat the experiment to see if I could obtain reasonable results. Reasonable results being that the calibration curve would have a large positive slope, and that the integrated peak areas would increase with concentration, and that the stripping peak would occur at 380 mV instead of 380 mV. I used nanopure water to make the 4% acetic acid solutions, and nothing except for lead and acid should have been in the water. Obviously, something else was going on that caused me to measure peaks at 540 mV.
The concentrations of lead leached from the container are probably less than 1 ppm, but greater than 1 ppb, simply because the regulations mandated by the ICF require them to be in this range. Tests of the lead leached from lead crystal glassware by anodic stripping voltammetry found that in time periods under 1 hour, the amount of lead leached into the lead glassware is of this order.
Trash AuditA survey of the landfill-bound trash generated at the University of Texas at Austin in one business day has been performed in order to determine the composition of the waste stream, in particular to obtain information about recyclables in the waste stream. The data collected has been analyzed both qualitatively and quantitatively. Qualitatively, general trends have been discussed with respect to data sets that are made available to the public by UT’s Physical Plant. Quantitatively, I have attempted to determine whether or not the data from one day can be used to make conclusions about the larger time frames of months and years. I have used averages, percentages, and the chi-test as statistical tools for data analysis.
Introduction
On March 4th, 2005 I helped to compile a survey of the trash generated by our University in the course of a day. The trash was generated on Thursday March 3rd, and collected the morning of the survey by 2 dump trucks. 13.1 tons of compacted trash was dumped at the BFI landfill1 in northeast Austin and the area was zoned-off so that we could sort through the trash. I was one of 8 students who participated in the survey.2 After donning protective gear (see picture) we proceeded to rip open bags of trash and separate the contents. Because we were interested in determining the percentage of trash that is recyclable, we sorted it into the five categories which are currently recycled in Austin: cans, paper, plastics (1 & 2), glass and corrugated cardboard3. The remaining trash was lumped together. With more time and people we may have been able to better analyze the remaining trash. Most of it appeared to be food related, but we have no quantitative data to support this observation. Over the course of 4 hours, we sorted through 1,266 pounds of trash. Although 1,266 pounds is a lot, it is only a small fraction of the total waste stream produced by the University in one day. On March 3rd, it was 4.8% of the waste stream.
By definition, a survey samples a small fraction of the population, and broad conclusions are made based upon the assumption that the sample population is representative of the whole. The sorted trash from March 3, 2004, is a subset of a subset of many populations. We sampled the 13.1 tons of trash generated on March 3rd, which in turn, is a part of a larger population of trash. March 3rd was a Thursday, a weekday in March, part of the 2004-2005 fiscal year, and a school day during the spring semester. The trash generated by the University on that day would be different from any other day: but how different? A good estimation of the answer to this question can be obtained by looking at statistics from previous years and extrapolating to the present year. A more difficult question might ask about the variation in the percent composition of different components in the waste stream from day to day. Does it vary as widely as the sheer volume? Although we would like to make conclusions about the entire fiscal year based upon the data collected from one day, we probably cannot. Statistical analysis will help us to answer these questions.
Analysis
The first question I want to address concerns the variation in the sheer volume of trash produced by the University throughout the year, including the portion that is sent to the landfill, and the portion that is recycled. Data from the past 3 years is made available on the internet by the Physical Plant, concerning the volume of trash the university sends to the landfill each month, and also the volume of paper recycled4. Paper is the only item the university recycles. (Other items are recycled by a student-run organization, the UT Recycling Task Force). I have constructed the data graphically for clarity. The light purple bars represent the 2002/2003 fiscal year, the maroon bars represent the 2003/2004 fiscal year, and the yellow bars represent the current fiscal year, 2004/2005, for which data from the last part of the year is not yet available. It is easy to see a few general trends. First, more trash is produced at the beginning of the school year in September and October, than during any other time of the year. Production of trash decreases to a minimum during winter break, when there are no classes on campus, and increases when the spring semester begins. A small decrease is seen again in the summer, when fewer classes are offered and most students are off campus. The monthly trash weights span a range of almost 200 tons – the smallest number is from December 2003 when the waste stream was 254.8 tons, while the highest amount was in Oct. 2002, when the scales hit 458.5 tons.
Another observation is that for any particular month, there is little variation over the past 3 years.
So to answer the first question, there is certainly variation in the amount of trash that is produced throughout the year, and it seems to be dependent upon seasonal changes in campus activity. Therefore, even if March 3rd was an average March day, we could not assume that 13.1 tons of trash are produced every day on campus throughout the year.
The question about the variation in the composition of the waste stream throughout the year is more difficult to answer. I cannot answer this question for all of the components of trash, because data is simply not available. However, I can carry out one analysis and make careful guesses.
Data is available concerning the amount of paper recycled each month at the university. If the percentage of recycled paper with respect to the total trash generated is constant each month, then perhaps I can guess that the percent of paper sent to the landfill is also constant throughout the year. In other words, if the ratio of recycled paper to total trash is constant each month, then so is the ratio of “paper sent to the landfill” to total trash (total trash is waste sent to the landfill plus recycled paper.)
The following graph summarizes the available data for the percent of paper recycled on campus with respect to the total waste stream. They are weight percentages. Notice that although the variation isn’t enormous, there is variation. It seems that for at least the previous 2 fiscal years, the ratio of recycled paper to total trash begins a slow increase in the summer months and levels out in the fall and spring. Whether or not this is significant might be tested statistically, but the question is beyond the scope of this survey.
I am taking the null-hypothesis and assuming that the ratio of paper recycled to total waste produced is not different each month. Invoking the null hypothesis, my expected value for each month is the average of all the months in that fiscal year, and the chi-squared test can be used to determine the probability that the actual data is consistent with the null hypothesis. My expected value for each month is the average for the year. When I perform the chi-test, the actual data set consists of the actual percentages for each month, and the expected data set is simply the year’s average. Each month has the same expected value. For the current fiscal year, only the first 6 months are considered.
For all 3 years, the probability that my data is consistent with the null-hypothesis is above 80%, and therefore, according to the chi-squared test, the ratio of recycled paper to total waste produced does not change significantly from month to month.
If I assume that the ratio of “trashed paper” to total trash does not change from month-to-month, because the ratio of recycled paper to total waste does not change, then we might be able to extend the result we obtained for March 3rd to the entire year. Of course, this involves a new assumption, that March 3rd was a typical March day. However, I have already determined that even though the total amount of trash varies, the ratios vary slowly enough that we might reasonably ignore their variations in order to make estimates.
Conclusions
We sorted through 1,266 pounds of trash produced by the University of Texas at Austin on Thursday March 3rd, 2005. The sorted trash comprised approximately 5% of the total trash sent to the landfill for that day. The trash was sorted into 5 catagories: paper, glass, cans, plastics, and corrugated cardboard. Anything not fitting into these categories was lumped together. We found that 12% (by weight) was paper, 6% was plastic, 3% cans, 2% corrugated cardboard, and 1% glass. The remaining trash was 76% by weight of the total sorted trash. Of this, we estimate that 30 to 40% consisted of food-related waste. We noticed that there seemed to be hundreds to thousands of black plastic trash bags that were filled with only a few items. We estimate that at least 5% of the total volume comes from these plastic trash bags. Many of the trash cans on campus are emptied every day, even if there is nothing in them. We estimate that another 5% of the total volume consisted of paper towels used to dry hands in restrooms around campus.5
I have attempted to analyze the significance of the data we collected, in order to determine the validity of extending our results to larger time frames. Based upon a qualitative discussion, I concluded that the sheer volume of the waste stream varies too much from day to day, and month to month to say that the amount of waste produced on March 3, 2005 is typical of every day of the year. On a campus the size of UT Austin, there are literally hundreds of factors affecting the waste stream. For example, March 3rd was only 2 days before UT’s open house “Explore UT.” The entire university was preparing for a huge event, so there may have been more waste than usual that day.
I asked about how the composition of the waste stream varies throughout the year. Is it nearly constant, or does it fluctuate as largely as the sheer volume? I assumed the null-hypothesis and used a chi-test to see if I can assume that the monthly fluctuations are simply random, or small enough to be treated as random. I obtained a high probability that the data is consistent enough with the null-hypothesis.
Quantitative conclusions are often wrought with many difficulties, but qualitative conclusions can almost always be made with greater ease. With quantitative analysis experimental errors are compounded with estimates and approximations which can lead to data whose significance is questionable. Statistical tools such as the chi-test can help to elucidate which conclusions are plainly false, and which ones have a good chance of being reasonable. Errors in the collection of our data could have arisen from many sources. For example, some of the cardboard and paper was wet, and therefore the weights we obtained may have been too high. Also, the plastic containers and aluminum cans often had liquid in them. Although we emptied out every container we caught, we couldn’t have caught them all, and even a small amount of liquid weighs a lot in comparison to an empty bottle, or a sheet of paper or a can. Our hope was that all of our errors combined have the effect of canceling each other out. While liquids and water added excess weight to our determination, we could not possibly have separated all of the recyclable items from the 1,266 pounds of trash.
Qualitatively, we can say that about 75% of UT’s trash that goes to the landfill is probably not recyclable. Of the trash sent to the landfill, 25% could be recycled, and of the trash that could be recycled, but is sent to the landfill, approximately half is paper. The University of Texas at Austin has recycled paper since 1994, after the passing of Senate Bill 1340 in 1993 which mandated that state institutions recycle 40% of their waste streams. UT has either surpassed this goal, or nearly met it since the introduction of the paper recycling program. If the trash sent to the landfill if always around 60% of the total waste stream (100% being the landfill trash and the recycling paper combined), then the actual percentage of recyclables being sent to the landfill is only 15% of the total waste stream. Therefore, even if paper consists of 50% of the total waste stream, most of it is already being recycled. Cheers to UT Austin.
A more in-depth study would be needed if we want to know the true composition of the waste stream. However, it is interesting that even a tiny amount of data can give valuable information if it is properly analyzed.
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