Dear students,
Please get your lab notebooks at the lab. Or else you'll have to claim it in Payatas.
Jay Lazaro
Tuesday, May 27, 2008
Thursday, May 15, 2008
UA&P EnvSci Summer 08-09 Lab Exam Pointers
The following are possible items for the lab practical exams
Each item should be completed within 10 min.
1. Calculate molarity, ppm, required grams, or required volumes.
2. Prepare, focus a slide to reveal gridlines, count cells. Count no less than 100 cells.
3. Create a carrying capacity model on Excel, with a given initial algae count, birthrate, deathrate, and carrying capacity. Time step = 0.1
4. Estimate birthrate of an actual culture by fitting the data to a (ready-made) carrying capacity model. Use the method of least squares to fit theoretical and actual counts.
5. Determine wind direction, temperature, relative humidity and CO2 concentration.
6. Plot (ready-made) lettuce and onion toxicity data, insert a regression line, and calculate the median inhibitory concentration from the equation of the line.
This is how the group will be graded.
1. Each member will draw an item at random and proceed to do the exercise within the allotted time.
2. For each item, you either get a perfect 1 or a 0. For person#1 the personal grade = p1.
3. Adding the items will give a group grade. Thus group grade, g = p1+p2+p3+p4+p5+p6
4. The INDIVIDUAL GRADE will be calculated as follows:
final personal grade of person#1= ((6*p1) + g)*100/12
If a group member is absent, his p1 = 0, and the group grade will include that number.
One group has only 5 members. The calculation is therefore,
final personal grade = ((5*p1) + g)*100/10
5. Notebooks will be collected just before you take the exam.
6. Group 1 will take the exam first, Group 6 last. While a group is taking an exam the others wait.
Arrange with me if you need to practice.
Good luck!
Each item should be completed within 10 min.
1. Calculate molarity, ppm, required grams, or required volumes.
2. Prepare, focus a slide to reveal gridlines, count cells. Count no less than 100 cells.
3. Create a carrying capacity model on Excel, with a given initial algae count, birthrate, deathrate, and carrying capacity. Time step = 0.1
4. Estimate birthrate of an actual culture by fitting the data to a (ready-made) carrying capacity model. Use the method of least squares to fit theoretical and actual counts.
5. Determine wind direction, temperature, relative humidity and CO2 concentration.
6. Plot (ready-made) lettuce and onion toxicity data, insert a regression line, and calculate the median inhibitory concentration from the equation of the line.
This is how the group will be graded.
1. Each member will draw an item at random and proceed to do the exercise within the allotted time.
2. For each item, you either get a perfect 1 or a 0. For person#1 the personal grade = p1.
3. Adding the items will give a group grade. Thus group grade, g = p1+p2+p3+p4+p5+p6
4. The INDIVIDUAL GRADE will be calculated as follows:
final personal grade of person#1= ((6*p1) + g)*100/12
If a group member is absent, his p1 = 0, and the group grade will include that number.
One group has only 5 members. The calculation is therefore,
final personal grade = ((5*p1) + g)*100/10
5. Notebooks will be collected just before you take the exam.
6. Group 1 will take the exam first, Group 6 last. While a group is taking an exam the others wait.
Arrange with me if you need to practice.
Good luck!
Sunday, May 11, 2008
UA&P EnviSci Summer 08-09 Water Toxicity Expt
Description of the Water Toxicity Experiment, 12 May 2008
The exercise is an example of the use of bioassays, i.e., tests that use live organisms.
The Payatas landfill in Quezon City is one of the Southeast Asia's biggest open landfill. It is 500 meters from the border of the La Mesa Watershed. Trash for the last 30 or so years have been dumped in this place to produce two mountains of trash covering 7 hectares and rising 7 or 8 stories above the level of the surrounding houses.
The trash produces a liquid called leachate. Leachate is a complex mixture of organic compounds and water, and possibly dissolved metals. It is toxic, and there is some danger that this liquid seeps into the groundwater. People who draw from contaminated deep wells might therefore be at risk for toxic effects.
We want to know two things. First, how do we measure the contamination? And second, knowing that, what are the risks to the populations surrounding the dump?
One way to measure contamination is to go to a chemical laboratory and have the components of the water analyzed. This gives exact amounts but has the disadvantage of being expensive and being selective only for the chemicals that are deliberately sought.
Another method is to use a bioassay. Although they have the disadvantage of not being able to identify the components of a sample, a toxic sample would cause a TOTAL toxic response on the test organism. It will simply reveal that a sample is toxic. But a bioassay is cheap, as it can make use of simple materials and test organisms that are readily sourced.
We used three test organisms: Lettuce (Lactuca sativa), Onion (Allium cepa) and a species of freshwater hydra (Hydra littoralis). Lettuce and onion were sourced from the supermarket, whereas Hydra was imported from the States and maintained at the laboratory.
To test for toxicity, we use a variation experiment; we use decreasing concentrations of the samples to check for a corresponding decrease in the toxic response. The toxic response we seek in the plants are inhibition of root growth; in hydra we look for death or abnormalities. Thus, at weaker and weaker concentrations of a toxic sample we should get longer and longer roots. The lengths should fall between the lengths obtained with clean water (negative control, or NC) and the lengths obtained with a maximal concentration of a positive control (copper for onion and zinc for lettuce).
What measure of toxicity is used and how is it obtained? Toxicity is measured by getting the average length of all roots per concentration of sample. Then all these averages are placed on a graph that shows average root length versus concentration. A line is then fit to these points using a mathematical procedure known as linear regression and the method of least sum of squares, which gives an equation for that best fit line. Then, using the equation of the line, we can compute for the concentration of sample that results in 50% inhibition of root growth, known as the median inhibitory concentration or IC50. Thus, if "clean water" gives a root length of 20 cm, and strong positive control gives 0 cm, then the IC50 of a sample would be the concentration required to give a root length of 10 cm. The IC50's can then be compared across all samples. Those with lower IC50's are the more toxic.
Now that we are able to measure toxicity, how are we going to measure risk? To be more specific, we are interested in knowing the direction in which the supposed leachate is flowing underground. Thus, we sample water from all directions around the dump (N, E, W, S) and then compare the IC50's of the samples. Given the terrain, we would expect to find more toxicity in the south wells, for example, but this will not always be the case.
Thus, this is what we must do:
1. Collect water samples from deep wells located north, east, west, and south of the Payatas dumpsite.
2. Dilute the samples and test them on lettuce, onion, and hydra.
3. After 3 days check for root growth inhibition in onion. After 4 days, check for death and abnormality in hydra. After 5 days, check for root growth inhibition in lettuce. Plot the data against concentration and determine IC50's by linear regression.
The exercise is an example of the use of bioassays, i.e., tests that use live organisms.
The Payatas landfill in Quezon City is one of the Southeast Asia's biggest open landfill. It is 500 meters from the border of the La Mesa Watershed. Trash for the last 30 or so years have been dumped in this place to produce two mountains of trash covering 7 hectares and rising 7 or 8 stories above the level of the surrounding houses.
The trash produces a liquid called leachate. Leachate is a complex mixture of organic compounds and water, and possibly dissolved metals. It is toxic, and there is some danger that this liquid seeps into the groundwater. People who draw from contaminated deep wells might therefore be at risk for toxic effects.
We want to know two things. First, how do we measure the contamination? And second, knowing that, what are the risks to the populations surrounding the dump?
One way to measure contamination is to go to a chemical laboratory and have the components of the water analyzed. This gives exact amounts but has the disadvantage of being expensive and being selective only for the chemicals that are deliberately sought.
Another method is to use a bioassay. Although they have the disadvantage of not being able to identify the components of a sample, a toxic sample would cause a TOTAL toxic response on the test organism. It will simply reveal that a sample is toxic. But a bioassay is cheap, as it can make use of simple materials and test organisms that are readily sourced.
We used three test organisms: Lettuce (Lactuca sativa), Onion (Allium cepa) and a species of freshwater hydra (Hydra littoralis). Lettuce and onion were sourced from the supermarket, whereas Hydra was imported from the States and maintained at the laboratory.
To test for toxicity, we use a variation experiment; we use decreasing concentrations of the samples to check for a corresponding decrease in the toxic response. The toxic response we seek in the plants are inhibition of root growth; in hydra we look for death or abnormalities. Thus, at weaker and weaker concentrations of a toxic sample we should get longer and longer roots. The lengths should fall between the lengths obtained with clean water (negative control, or NC) and the lengths obtained with a maximal concentration of a positive control (copper for onion and zinc for lettuce).
What measure of toxicity is used and how is it obtained? Toxicity is measured by getting the average length of all roots per concentration of sample. Then all these averages are placed on a graph that shows average root length versus concentration. A line is then fit to these points using a mathematical procedure known as linear regression and the method of least sum of squares, which gives an equation for that best fit line. Then, using the equation of the line, we can compute for the concentration of sample that results in 50% inhibition of root growth, known as the median inhibitory concentration or IC50. Thus, if "clean water" gives a root length of 20 cm, and strong positive control gives 0 cm, then the IC50 of a sample would be the concentration required to give a root length of 10 cm. The IC50's can then be compared across all samples. Those with lower IC50's are the more toxic.
Now that we are able to measure toxicity, how are we going to measure risk? To be more specific, we are interested in knowing the direction in which the supposed leachate is flowing underground. Thus, we sample water from all directions around the dump (N, E, W, S) and then compare the IC50's of the samples. Given the terrain, we would expect to find more toxicity in the south wells, for example, but this will not always be the case.
Thus, this is what we must do:
1. Collect water samples from deep wells located north, east, west, and south of the Payatas dumpsite.
2. Dilute the samples and test them on lettuce, onion, and hydra.
3. After 3 days check for root growth inhibition in onion. After 4 days, check for death and abnormality in hydra. After 5 days, check for root growth inhibition in lettuce. Plot the data against concentration and determine IC50's by linear regression.
Friday, May 9, 2008
UA&P EnSci Summer 07-08, Pointers for 2nd LE
Here are pointers for the second long exam.
Lab questions.
1. Calculate final ppm or molarity given a description of the preparation procedure.
2. (Just an example) Estimate the slope (m) of the line y=mx + 3 that best fits the three points (1,5), (2,15), and (3, 16). Slope m should be an integer.
3. Draw three growth curves with the same carrying capacity but having different growth rates: high, medium, and low.
4. Describe how to make a solution of X, 0.5X, 0.25X, and 0.125X of a given quantity.
5. Describe, step by step, how to transfer algae aseptically from an algal culture into a flask of clean, fresh, and sterile medium.
6. Draw a Hydra attached to the bottom surface of a culture well.
7. Draw an onion set-up in a shot glass for a toxicity experiment.
Lecture questions.
1. What kinds of problems generally do not have a technical solution and why?
2. The world's carrying capacity for humans probably exists, but it is very hard to estimate it in the long term. Explain why.
3. What basis is there in fact (true/false) for saying that man SHOULD (good/bad) care for the environment. Be very specific about those facts.
4. Leopold and Hardin would probably disagree on many points about how the population "problem" should be addressed.
5. When Miller talks about man being part of nature does he advocate a return to the Ancient's way of thinking? Explain.
6. McKibben probably disagrees that genetic modification will solve the food supply problem, while Tansley will probably say the opposite. Who will side with McKibben and who will side with Tansley--Hardin or Boulding--and why.
7. Convert the following NON-SCIENTIFIC statements into SCIENTIFIC statements and give one negating evidence for the SCIENTIFIC version of each question.
a. Detergents are bad for plant growth.
b. Children are God's gifts to their parents.
c. Thou shalt not covet thy neighbor's goods.
d. Thou shalt not kill.
e. You! Over there! Yes, you're cheating!
8. Ethics, a question of good, must ultimately be based not on what is good but on what is true. Explain.
9. The postmodern theory that "What is good is what is good for me" is illogical and absurd. Show where the logical error lies.
10. "We shouldn't cut too many trees because Aldo Leopold says so" is an example of a certain kind of argument that is valid and acceptable under certain conditions. Give two of those conditions.
Lab questions.
1. Calculate final ppm or molarity given a description of the preparation procedure.
2. (Just an example) Estimate the slope (m) of the line y=mx + 3 that best fits the three points (1,5), (2,15), and (3, 16). Slope m should be an integer.
3. Draw three growth curves with the same carrying capacity but having different growth rates: high, medium, and low.
4. Describe how to make a solution of X, 0.5X, 0.25X, and 0.125X of a given quantity.
5. Describe, step by step, how to transfer algae aseptically from an algal culture into a flask of clean, fresh, and sterile medium.
6. Draw a Hydra attached to the bottom surface of a culture well.
7. Draw an onion set-up in a shot glass for a toxicity experiment.
Lecture questions.
1. What kinds of problems generally do not have a technical solution and why?
2. The world's carrying capacity for humans probably exists, but it is very hard to estimate it in the long term. Explain why.
3. What basis is there in fact (true/false) for saying that man SHOULD (good/bad) care for the environment. Be very specific about those facts.
4. Leopold and Hardin would probably disagree on many points about how the population "problem" should be addressed.
5. When Miller talks about man being part of nature does he advocate a return to the Ancient's way of thinking? Explain.
6. McKibben probably disagrees that genetic modification will solve the food supply problem, while Tansley will probably say the opposite. Who will side with McKibben and who will side with Tansley--Hardin or Boulding--and why.
7. Convert the following NON-SCIENTIFIC statements into SCIENTIFIC statements and give one negating evidence for the SCIENTIFIC version of each question.
a. Detergents are bad for plant growth.
b. Children are God's gifts to their parents.
c. Thou shalt not covet thy neighbor's goods.
d. Thou shalt not kill.
e. You! Over there! Yes, you're cheating!
8. Ethics, a question of good, must ultimately be based not on what is good but on what is true. Explain.
9. The postmodern theory that "What is good is what is good for me" is illogical and absurd. Show where the logical error lies.
10. "We shouldn't cut too many trees because Aldo Leopold says so" is an example of a certain kind of argument that is valid and acceptable under certain conditions. Give two of those conditions.
UA&P EnSci Summer 07-08 Project One Summary
Let me describe the approach we took in the algae experiments.
1. We set out to ask two questions regarding an important environmental issue. The BIG question was: "Are detergents toxic to plants?" We made an assumption that toxicity may be measured by its effects on birthrate. The more specific question, then, was "Do detergents in the water decrease the growth rate of algae?" Given that question, the best general strategy was to do a variation-type of experiment where we manipulate the concentration of detergent in water and then check for a co-variation in growthrate.
2. But, how do we measure growth rate? And then, what growth rate are we talking about: the intrinsic growth rate or the actual growth rate? In principle, measuring the intrinsic growth rate should be easy: Count the algae at some posterior day (say day 2), get the difference between that count and the previous day's count (day 1), and divide that difference by the previous day's count. The value we get should be the same for whatever pair of days we choose, if the growth of algae is EXPONENTIAL.
But are the actual algae cultures growing exponentially? Exponential growth is obtained under the very best conditions, i.e., practically unlimited space and resources, and no competition. A flask of algae, however, is a small world; so small that we may reasonably expect that as algae become many, their growth rate would decrease because of competition and rapid depletion of resources. Therefore, the growth rate would change rather quickly with time; that is, it will not be constant. The exponential model would be imprecise; it would be difficult for us to choose an appropriate anterior and posterior date to make the calculation.
So, it would seem that a CARRYING CAPACITY model is more appropriate to describe the real situation in the flask. But the model, like all models, requires some assumptions before it can be precisely defined in a form that can be tested. One assumption is that the actual growth rate is inversely proportional to the current population count.
Now, an inverse proportion is a linear relation that is easy to describe, but is not necessarily true. We had to test if the model containing that assumption was realistic.
3. So, we set up a growth experiment. We put various algae in flasks and counted their growth everyday for up to 12 days and plotted the population as a function of time. We then compared the actual growth curve to a theoretical growth curve predicted by a carrying capacity model, defined below:
dQ/dt = Q0 x birthrate x (1 - Q/carrying capacity), where Q0 is the algal population count at time 0 (the initial count) and Q the population at any particular time.
The Q for various times was calculated using a numerical method implemented on Excel. Thus, there was a theoretical Q and its corresponding actual Q at every point in time measured.
4. How do we know the model works? Check how close the theoretical and actual Q values are. This was done by first fixing the carrying capacity as the estimated maximum value of Q (which can be seen by the plot of real Q's as they approach some kind of stable value), then trying different birthrates, then getting the sum of the squares of the difference between actual and theoretical Q's for all time points tested, then choosing the birthrate that gives the lowest sum of squares.
5. What happened was that in most cases--if not all--the selected birthrate gave theoretical values that very closely matched the actual values. Graphically, this means that the plot of the actual and theoretical growth curves coincided rather well. This result suggested that we may use the carrying capacity model to calculate intrinsic birthrate even when the actual birthrate changes with time.
6. Thus equipped with a reliable way to calculate birthrate, we proceeded by repeating the algal growth curve experiment with various concentrations of a detergent called sodium dodecyl sulfate, or SDS, added to the culture medium. Using the carrying capacity model, we were able to estimate birthrates for every flask in the experiment.
7. A look at the all these growth rates showed that when the SDS concentration was higher, the growth rates were lower. This result suggested that SDS concentration does bring down growth rate. We deduce from this that SDS is toxic to algae.
8. In short
a. We wanted to know if detergent lowers growth rate.
b. We needed a way to estimate growth rate given realistic conditions. That way was to use a model, the carrying capacity model, which included growth rate as a factor. We defined the model mathematically so that it could be tested in Excel. An experiment comparing actual population with theoretical population (the number predicted by the Excel model) showed that we can estimate growth rate rather nicely, as seen by the beautifully fitted curves.
c. Equipped with a reliable and realistic (and rather beautiful) model, we proceeded to estimate growth rate in cultures to which detergent was added. The results showed that growth rate does go down as detergent concentration goes up.
1. We set out to ask two questions regarding an important environmental issue. The BIG question was: "Are detergents toxic to plants?" We made an assumption that toxicity may be measured by its effects on birthrate. The more specific question, then, was "Do detergents in the water decrease the growth rate of algae?" Given that question, the best general strategy was to do a variation-type of experiment where we manipulate the concentration of detergent in water and then check for a co-variation in growthrate.
2. But, how do we measure growth rate? And then, what growth rate are we talking about: the intrinsic growth rate or the actual growth rate? In principle, measuring the intrinsic growth rate should be easy: Count the algae at some posterior day (say day 2), get the difference between that count and the previous day's count (day 1), and divide that difference by the previous day's count. The value we get should be the same for whatever pair of days we choose, if the growth of algae is EXPONENTIAL.
But are the actual algae cultures growing exponentially? Exponential growth is obtained under the very best conditions, i.e., practically unlimited space and resources, and no competition. A flask of algae, however, is a small world; so small that we may reasonably expect that as algae become many, their growth rate would decrease because of competition and rapid depletion of resources. Therefore, the growth rate would change rather quickly with time; that is, it will not be constant. The exponential model would be imprecise; it would be difficult for us to choose an appropriate anterior and posterior date to make the calculation.
So, it would seem that a CARRYING CAPACITY model is more appropriate to describe the real situation in the flask. But the model, like all models, requires some assumptions before it can be precisely defined in a form that can be tested. One assumption is that the actual growth rate is inversely proportional to the current population count.
Now, an inverse proportion is a linear relation that is easy to describe, but is not necessarily true. We had to test if the model containing that assumption was realistic.
3. So, we set up a growth experiment. We put various algae in flasks and counted their growth everyday for up to 12 days and plotted the population as a function of time. We then compared the actual growth curve to a theoretical growth curve predicted by a carrying capacity model, defined below:
dQ/dt = Q0 x birthrate x (1 - Q/carrying capacity), where Q0 is the algal population count at time 0 (the initial count) and Q the population at any particular time.
The Q for various times was calculated using a numerical method implemented on Excel. Thus, there was a theoretical Q and its corresponding actual Q at every point in time measured.
4. How do we know the model works? Check how close the theoretical and actual Q values are. This was done by first fixing the carrying capacity as the estimated maximum value of Q (which can be seen by the plot of real Q's as they approach some kind of stable value), then trying different birthrates, then getting the sum of the squares of the difference between actual and theoretical Q's for all time points tested, then choosing the birthrate that gives the lowest sum of squares.
5. What happened was that in most cases--if not all--the selected birthrate gave theoretical values that very closely matched the actual values. Graphically, this means that the plot of the actual and theoretical growth curves coincided rather well. This result suggested that we may use the carrying capacity model to calculate intrinsic birthrate even when the actual birthrate changes with time.
6. Thus equipped with a reliable way to calculate birthrate, we proceeded by repeating the algal growth curve experiment with various concentrations of a detergent called sodium dodecyl sulfate, or SDS, added to the culture medium. Using the carrying capacity model, we were able to estimate birthrates for every flask in the experiment.
7. A look at the all these growth rates showed that when the SDS concentration was higher, the growth rates were lower. This result suggested that SDS concentration does bring down growth rate. We deduce from this that SDS is toxic to algae.
8. In short
a. We wanted to know if detergent lowers growth rate.
b. We needed a way to estimate growth rate given realistic conditions. That way was to use a model, the carrying capacity model, which included growth rate as a factor. We defined the model mathematically so that it could be tested in Excel. An experiment comparing actual population with theoretical population (the number predicted by the Excel model) showed that we can estimate growth rate rather nicely, as seen by the beautifully fitted curves.
c. Equipped with a reliable and realistic (and rather beautiful) model, we proceeded to estimate growth rate in cultures to which detergent was added. The results showed that growth rate does go down as detergent concentration goes up.
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