Report about The Effect Of Light Intensity On Oxygen Production

Abstract The purpose of our experiment was to visualize the effect of light intensity on oxygen production. In the capacity of a test subject we used algal cultures. For these researches we used the Winkler method of titration. Our experiment results showed that there is an effect of light intensity on oxygen production.   Our research has a great significance for agriculture, biochemistry and biophysics. Introduction Photosynthesis is the metabolic process by which plants trap solar energy, convert it to chemical energy, and store it in the bonds of organic nutrient molecules, such as glucose. Nearly all types of plants and algae, as well as some protists and bacteria, are capable of photosynthesis (Govindjee, 1990). Once the photosynthetic organisms we call autotrophs used light energy to generate sugars and other organic nutrients, they can break them down again for their own cellular energy needs. Animals, fungi, and most microbes (the three groups are heterotrophs), of course, use autotrophs, or organisms that eat autotrophs as food. Photosynthesis uses carbon dioxide, water, and energy to build glucose, instead of breaking down glucose to carbon dioxide and water and in the process releasing energy: Carbon dioxide + Water + Light energy   Ã‚  Ã‚  Ã‚  Ã‚   Glucose + Oxygen. Photosynthesis consists of the â€Å"light-trapping† phase, which requires sunlight, and the sugar-building phase, which can proceed whether or not light is present (Postlewait Hopson, 1990, p. 104-112). It is estimated that between 70% and 80% of the oxygen in the atmosphere is produced by marine plants. The oceans cover about 71% of our planet and land is only about 29%. Algae produce about 330 billion tons of oxygen each year. Algae produce oxygen during the daytime, which in turn provides oxygen for fish and other underwater organisms, microorganisms and plants. On the flip side, algae use oxygen at night, and too much algae can deplete the waters oxygen. Marine plants are also used as food. Algae are very important ecologically because they are the beginning of the food chain for other animals. Phytoplankton, a single-celled type of algae, is eaten by small animals called zooplankton (mostly crustaceans, such as tiny shrimp) that drift near the surface of the sea. The zooplankton is in turn fed upon by larger zooplankton, small fish, and some whales. Larger fish eat the smaller ones. At the top of the open-water food web may be fish-eating birds, seals, whales, very large fish s uch as sharks or bluefin tuna, and humans. Researchers from the Baylor University established the exact cause of the high toxicity of representative Chrysophyta Prymnesium parvum. It turned out, the degree of toxicity produced by these algae substances affect pH of the water of lakes and rivers. The higher pH implicates more toxic waste products of some algae. The discovery is of particular interest because, as we know, the algae are able to regulate the pH of the habitat. In laboratory tests, scientists were able to establish that the toxic activity of algae in water pH 8.5 increased by more than 5 times in comparison with this activity at pH 6.5. For measuring dissolved oxygen we used the Winkler method of titration. In 1888 the Hungarian Lajost Winkler proposed a titremetric method to measure dissolved oxygen in waters. The Winkler Method is a technique used to measure dissolved oxygen in freshwater systems. Dissolved oxygen is used as an indicator of the health of a water body, where higher dissolved oxygen concentrations are correlated with high productivity and little pollution. This test is performed on-site, as delays between sample collection and testing may result in an alteration in oxygen content (Abril, 2000). Materials and Methods Procedure: Reagents List: 2ml Manganese sulfate; 2ml alkali-iodide-azide; 2ml concentrated sulfuric acid; 2ml starch solution; Sodium thiosulfate. Procedure: Filling a 300-mL glass Biological Oxygen Demand (BOD) carefully stopper bottle brim-full with sample water. 10 bottles were used. Adding 2mL of manganese sulfate to the collection bottle by inserting the calibrated pipette just below the surface of the liquid immediately. Adding 2 ml of alkali-iodide-acide reagent in the same manner. Stoppering the bottle with care to be sure no air is introduced. Mixing the sample by inverting several times. Checking for air bubbles. If oxygen is present, a brownish-orange cloud of precipitate or floc will appear. Mixing the sample by turning it upside down several times after settling of this floc to the bottom. Adding 2 ml of concentrated sulfuric acid via a pipette held just above the surface of the sample. Stoppering and inverting several times to dissolve the floc. At this point, the sample is fixed and can be stored for up to 8 hours if kept in a cool, dark place. As an added precaution, squirting distilled water along the stopper, and capping the bottle with aluminum foil and a rubber band during the storage period. In a glass flask, titrating 201 ml of the sample with sodium thiosulfate to a pale straw color. Titrating by slowly dropping titrant solution from a calibrated pipette into the flask and continually stirring or swirling the sample water. Adding 2 ml of starch solution; a blue color forms. Continuing slowly titrating until the sample turns clear. As this experiment reaches the endpoint, it will take only one drop of the titrant to eliminate the blue color. The concentration of dissolved oxygen in the sample is equivalent to the number of milliliters of titrate used. Each ml of sodium thiosulfate added in steps 6 and 8 equals 1 mg/l dissolved oxygen. Cell numbers were counted in each laboratory section at time zero. Cultures were then placed in incubators at 25, 37, and 45⠁ °C for 24 hours, and counted again. Our null hypothesis here is that there is no effect of temperature on bacterial growth. For the identification of bacteria growth we used the following steps: Step 1 calculating the growth rate by subtracting initial cell numbers from final cell numbers. Step 2 calculating the mean (Ã… ¶) growth rate for each temperature treatment. Step 3 calculating ÃŽ £Y which is the sum of all observed values within a treatment. Step 4 calculating ÃŽ £Y2, square each observation within a treatment, then add them all together. Step 5 calculating the sum of squares for each treatment. This is ÃŽ £Y2 (ÃŽ £Y)2/n, where n = the number of replicates. In this case n = 10 (10 lab sections). Step 6- calculating variance within each treatment. This is sum of squares/n , again where n = the number of replicates. In this case n = 10 (10 lab sections). Step 7- calculating the standard deviation within each treatment. This is the square root of variance. Step 8 Creating a graph for effects of temperature on bacterial growth. Step 9 calculating within-groups Sum of Squares. This is the sum of each individual treatment groups Sum of Squares. Step 10 calculating the sum of group means (ÃŽ £Ã… ¶). Step 11 calculating ÃŽ £Ã… ¶2, square each treatment mean, then add them all together. Step 12 calculating Sum of Squares for treatments (SStreat) = [ÃŽ £Ã… ¶2 (ÃŽ £Ã… ¶)2/k] x n, where k = number of treatment groups (here 3) and n = number of replicates (here 10). Step 13 calculating degrees of freedom associated with SSwithin [=k(n-1)] and the degrees of freedom associated with SStreat = k-1. Step 14 calculating Mean Square Error for within treatments (MSwithin) and Mean Square Error for between treatments (MStreat). Step 15 calculating F for the appropriate degrees of freedom. Here, for 2 and 27 degrees of freedom. F = MStreat / MSwithin. Results   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   25⠁ °C   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   37⠁ °C 45⠁ °C mean (Ã… ¶) = 0,975   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   1,76 -0,75 standard deviation = 0,205438555 0,153101274 0,220861948 The results showed us that algae’s growth at 25⠁ °C was from 0 to 0.9. At 35⠁ °C it increased to 1.6. And finally at 45⠁ °C it decreased to -0.7. If we look at the tables of critical values of F associated with different alpha values, and if we use 2 and 24 degrees of freedom (27 degrees of freedom is not listed), we find the critical value of alpha of 0.01 = 99.45. Because our F-value is 714, which is much higher than 99.45, we can say that the probability of the null hypothesis being true, based on our data, is less than 0.01. By convention, we generally reject a null hypothesis if p (the probability of it being true) is less than 0.05. Our result is that temperature did have a significant effect on bacterial growth (F2.27 = 714, p 0.01). References John H. Postlewait, Janet L. Hopson (1990). Modern biology. Austin, TX: Holt, Rinehart, and Winston. Abril 2000, modified by M. Hesselswe 2001, A.H. Nielsen 2007 Govindjee, W.J. Coleman â€Å"How plants make oxygen† Scientific American 262 (February 1990): 42-51.