What is the effect of pH levels on the net production, given by the change in dissolved oxygen, in Chlorella pyrenoidosa?

Graduated  cylinder for measuring pH buffer solution quantities and Chlorella pyrenoidosa sample quantities (uncertainty +/- 1mL)

Probe Ware for DO readings

650 mL +/- 10 mL of pH 6 buffer solution (potassium hydrogen phosphate) uncertainty of +/- 0.1 in pH fluctuation from pH 6. 130 +/- 5 mL per trial used.

650 mL +/- 10 mL of pH 7 buffer solution (Sodium Hydroxide-Potassium Dihydrogen Phosphate) uncertainty of +/- 0.1 in pH fluctuations from pH 7. 130 +/- 5 mL per trial used.

650 mL +/- 10 mL pH 8 buffer solution (Potassium Phosphate-Sodium Hydroxide) uncertainty of +/- 0.1 in pH fluctuations from pH 8. 130 +/- 5 mL per trial used.

650 mL +/- 10 mL pH 9 buffer solution (Boric Acid-Potassium Chloride-Sodium Hydroxide Buffer) uncertainty of +/- 0.1 in pH fluctuations from pH 9. 130 +/- 5 mL per trial used.

650 mL +/- 10 mL pH 10 buffer solution (Sodium tetraborate-Sodium Hydroxide) uncertainty of +/- 0.1 in pH fluctuations from pH 10. 130 +/- 5 mL per trial used.

3 thermometers for measuring system temperature and sample temperature readings

500 mL +/- 50 mL sample of Chlorella pyrenoidosa from SK and Boreal Laboratories ©.

1 Beaker capable of holding up to 50 mL of liquid (for distilled water placement)

45 mL distilled water (+/- 1mL)

Timer

Glass Stirrer

1 Beaker capable of containing 1L of liquid

Procedure

Set up dissolved oxygen probeware using instructions provided by Vernier © including calibrations at 100% and 0% Dissolved Oxygen.

Take 1mL of Chlorella pyrenoidosa sample received and use wet-plating procedure for microscope observations. Record any observations.

Fill a 50 mL beaker with 45 mL +/- 1 mL of distilled water to warm up DO probe before taking DO readings. Beaker should be filled slowly and slanted in order to prevent excessive O2 trapping.

Fill up 5, 150mL BOD bottles with 130 mL +/- 5 mL of pH 8 buffer solution (Potassium Phosphate-Sodium Hydroxide (these 5 bottles will be the control group with pH of environment phytoplankton sample was found in).

Repeat with 5 bottles used for each pH in experimental group, that is add 130 mL +/- 5 mL of pH 6,7, 9, and 10 buffer solutions to remaining 20 BOD bottles committing about 5 bottles per pH buffer solution. At end there should be 4 experimental groups and 1 control group with 5 trials each.

Make sure water level of each BOD bottle is the same +/- 0.1 cm in solution height (the volume of bottle is same so +/- 0.1 cm in height for bottle represents about 5 mL +/- 1 mL of solution)

Add 20 mL +/- 1 mL of Chlorella pyrenoidosa sample into each 150 mL BOD bottles (25 bottles total). Use glass stirrer to mix solutions of Chlorella pyrenoidosa and pH slowly for turbulence can increase DO.

Allow for a 5 minute equilibration period of pH in solutions. Place pH probe into BOD bottles to obtain pH readings of each pH buffer and Chlorella pyrenoidosa sample to make sure buffers are correctly set on pH values +/- 0.1 in pH scale.

Label all BOD bottles with corresponding pH values of solution. Set up timer with moment of initial DO level reading at t = 0 and set for 48 hours. Timer is set at uncertainty of +/- 0.1 seconds, but to account for the data collection and reaction times to notice time, uncertainty would be closer to about +/- 30 seconds)

Take readings of DO levels of all samples with probe ware at moment of set up (this is time t = 0 hours). Probe ware should be rinsed with distilled water and gently dried after each measurement to avoid cross contamination of samples. Qualitative observations should also be recorded.  Make sure DO probes are placed in middle of bottle to obtain more accurate readings representative of the complete solution. Use a nomogram to find the DO Saturation %, by using temperature recordings and DO concentration intersection point. Uncertainty in intersection and DO saturation % is about +/- 10%.

All BOD bottles are left unhampered for 2 days so that days so that all trials face the same external conditions. Therefore, external conditions are not major factors in the relativity of the results in when comparing the results.

After two days, measure and Dissolved Oxygen Content of each BOD bottle. Any observations are also recorded.

After second day of experiment (t = 48 hours), take all pH solutions into 1L beaker (must repeat process in order to get solutions into beaker) and dilute with water until pH of 7 is obtained. Then pour solutions down sink. Beakers and BOD bottles cleaned with soap and de-ionized water, materials returned to original places.

Data Analysis (Raw, Qualitative and Quantitative, and Processed Data)

DO Concentrations of each sample per reading per trial

Initial Dissolved Oxygen Concentrations (t = 0 hours)

pH 6, 7, 8, 9 and 10 buffer solutions with Chlorella pyrenoidosa samples (uncertainty +/- 0.1 mg/L)

Note: Net production can be defined as the gross production of the Chlorella pyrenoidosa sample – respiration rates of the Chlorella pyrenoidosa sample.  It must be stated, however, that when subtracting Initial and Final DO values for each data set, one obtains a relative change in DO levels. This change in DO levels must have already accounted for respiration levels in the system. Thus, respiration was assumed to be the same for each sample when obtaining final DO measurements so that change in DO is attributed to relative measures of net production.  

Qualitative Data:

Sample Calculations

Average DO Calculation (both DO concentration and DO saturation)

Generic Formula: (DO concentration/DO Saturationtrial 1 + DO concentration/DO saturationtrial 2 + DO concentration/DO saturationtrial 3 + DO concentration/DO Saturationtrial 4 + DO concentration/DO Saturationtrial 5)/5

Example: For Average DO Calculation for Final DO for solution of pH 6 containing Chlorella pyrenoidosa: ((3.4 mg/L +/- 0.1 mg/L) + (3.2 mg/L +/- 0.1 mg/L) + (3.1 mg/L +/- 0.1 mg/L) + (2.0 mg/L +/- 0.1 mg/L) + (3.9mg/L +/- 0.1 mg/L))/5 = 3.1 mg/L +/- 0.1 mg/L

Note: Average was rounded to nearest tenth due to average uncertainty of due to uncertainty of 0.1 mg/L

Change in DO Calculation:

Generic Formula: (Final DO concentration/DO saturation – Initial DO Concentration/DO Saturation)

Example: For calculation of trial 1 of solution of pH 6 containing Chlorella pyrenoidosa: ((3.4 mg/mL +/- 0.1 mg/L) – (8.5 mg/mL +/- 0.1 mg/L)) = -5.1 mg/L +/- 0.1 mg/L

Note: Change in DO Calculation was rounded to nearest tenth due to uncertainty of 0.1 mg/L

Conclusion

        In this experiment the net production, a general marker for the health of the phytoplankton Chlorella pyeronoidosa, was assessed by the change in dissolved oxygen concentration and, resultantly, the change in the percent oxygen saturation. The experimental supports the notion that a drastic increase and decrease of pH from an optimum pH value, does indeed yield a lower amount of net production in a closed system. In fact the largest deviations from the control pH (8), like pH 6 and 10, yielded the largest decrease in both DO concentration levels and percent DO saturation levels from final to initial. Thus, in this regard, one can support the hypothesis that a deviation from optimum pH value does yield a lower net production The experimental data does not support the notion that the optimal pH, measured at pH 8, results in the highest net production, as there seems to be a difference in the optimal pH value as interpolated by the graphs of both ways of assessing net production (change in O2 saturation and change in DO concentration), a pH value between pH 7 and 8. In short, as pH 8 was the control group, it was also seen that it had the optimum ∆DO saturation, the experimental groups seemed to be at a larger decline in the net production of Chlorella pyrenoidosa as they deviated from the control.

         In regards to the effect of deviation from the optimum pH on the net production, it seems that there is a decrease in net production. More specifically, the difference in the change in oxygen saturation from pH 8 (about -30% ∆DO saturation) to pH 9 (about -40% ∆DO saturation) is about -10% +/- 10%, the same as the average difference of change in oxygen saturation from pH 8 to pH 7 (also -40% ∆DO saturation), about -10% +/- 10%. Even more, the ∆DO saturation in pH 8 (about -30% +/- 10%) is about 30% higher than that of pH 6 (about  -60% +/- 10%∆DO saturation) and about 110% higher than that of pH 10 (about -140% +/- 10% ∆DO saturation). In short, the net production in relation to ∆DO saturation for pH values lower than 8 declines from -40% +/- 10% at pH 7 to -60% +/- 10% at pH 6 and with relation to ∆DO concentration declines from -2.7 mg/L +/- 0.1 mg/L to -4.8 mg/L +/- 0.1 mg/L. Thus the net production of acidic values relative to pH 8 (that is pH 6 and 7) is at a decline characterized by the quadratic equation, y = -0.1857x2 + 2.8114x - 10.854 (where y = percent ∆DO saturation), meaning that the rate of decline is increasing with the deviation from the pH 8, further supporting the hypothesis in terms of deviation from optimal pH level. Moreover, the net production in relation to ∆DO saturation for pH values higher than pH 8 also decrease from -40% +/- 10% at pH 9 to -140% +/- 10% at pH 10, resulting in a high rate of decline deviating from pH 8, but the highest decline at pH 10, supporting part 2 of the hypothesis that the higher the deviation, the lower the net production.

        However, as the experimental data and the graphs of pH vs. ∆DO saturation and pH vs. ∆DO concentration point out, there is not a symmetrical relationship in the pH deviations from pH 8 for net production in terms of ∆DO saturation and ∆DO concentration. There exists a larger decrease (about -110% +/- 10%) from pH 8 (-30% +/- 10%) to pH 10 (-140% +/- 10%) in ∆DO saturation than the decrease (about -30% +/- 10%) in ∆DO saturation of pH 8 (-30% +/- 10%) to pH 6 (-60% +/- 10%). This may have been due to the interpolated new pH optimum from the graph of pH vs. ∆DO saturation (corroborated by the graph of pH vs. ∆DO concentration). Thus the rate of decline of net production was greater in the alkaline pH values than that of the acidic pH values, and could have been attributed to natural selection as abiotic factors like CO2 absorption (higher due to greater amounts of CO2 in atmosphere) or acid rain (relatively rare phenomenon but increasing in rate) tend to decrease the pH of the natural freshwater environment of the Chlorella pyrenoidosa and even more accelerated in natural selection due to the r-selected growth of the single-celled phytoplankton. Therefore, while the hypothesis was supported in that the data showed a general decline with deviations from optimal pH level (researched at pH 8), it must be noted that the rate of decline in net production was not equal on both sides (increasing pH and decreasing pH from pH 8) resulting in an asymmetrical trend.

        While the trends support the hypothesis, the overall net production is negative. According to the equation for net productivity, that Gross Productivity – Respiration = Net Productivity, there would have to be more respiration than gross productivity to yield a net productivity value relative to both ∆DO saturation and ∆DO concentration. Each datum collected in final dissolved oxygen was less than the initial dissolved oxygen level, so results came out as -4.8 mg/L +/- 0.1 mg/L (at pH 6) , -2.7 mg/L +/- 0.1 mg/L (at pH 7), -3.2 mg/L +/- 0.1 mg/L (at pH 8), -3/2 mg/L +/- 0.1 mg/L (at pH 9), and -10.8 mg/L +/- 0.1 mg/L (at pH 10) for ∆DO concentration and thus in % ∆DO saturation. Perhaps respiration associated enzymes (say….) are more resistant in pH change tolerance (and even more so in acidic environments) than the enzymes associated with photosynthesis (the Rubisco enzyme, for example, has a pH optimum of about pH 8 given the average magnesium ion concentration in a given Chlorella pyrenoidosa). Cross contamination in obtaining cultured phytoplankton sample or in taking DO measurements could have resulted in decomposition of dead phytoplankton (if more pH change tolerant than Chlorella pyrenoidosa) which results in an increase in respiration and thus a decrease in net production.

        From this data comparison, it seems as if pH levels greater than pH 8 and less than pH 8 result in a decrease in net production but the rates of decline in net production are greater as pH becomes more alkaline and less when pH becomes more acidic. This is corroborated by the equation derived from the trend shown in the pH levels’ effect on the ∆ percent DO saturation, as a quadratic equation: -0.1857x2 + 2.8114x - 10.854. Moreover, the phytoplankton that experienced a pH closer to the natural or optimal pH of pH 8 became more productive as the optimal pH allowed for the phytoplankton to maintain homeostasis and grow at faster rates (meaning increased gross production and thus increased net production relative to the other pH values). When looking at the R2 (the correlation coefficient) values for the correlations between the trend graph and the data points, there seems to be a lower correlation (R2 = 0.9055) for the graph showing pH levels vs. ∆DO concentration than the correlation for trend and data points in graph showing pH levels vs. ∆ percent DO saturation (R2 = 0.9144). This may be because the change in percent DO saturation takes into account the temperature of the environment and its effect on the amount of dissolved oxygen able to be saturated into the solution (as given by the nomograph). Thus the pH 7 and 9 values that resulted in a higher ∆ DO concentration than that of pH 8, when converted to a ∆ percent DO saturation (thus taking into account temperature), were actually lower in ∆ percent DO saturation than ∆ percent DO saturation of Chlorella pyrenoidosa in pH 8 solution. In addition, the hypothesis that the optimal pH was 8 was rejected as the interpolated maximum according to the general trend was between pH 7 and pH 8. Moreover, the standard deviation helps in understanding that the relative variation in DO readings for each pH reading with respect to the mean (that is, 0.59 +/- 0.1 for pH 6 and then under or about 0.2 +/- for other pH values) portrays a large amount of variation. Thus the trend, although useful in its general understanding, may not as accurate to go by the equation (so we may not have enough information to reject or support the hypothesis in that the optimal pH would be 8). Thus the Chlorella pyrenoidosa samples in the control (at pH 8) group had the solution within the pH tolerance, in this case the optimum pH, to produce much more rapid growth and more net production than experimental groups that deviated from the optimal pH, so not all of them had conditions to support a healthy plant growth.

 Limitations and Weaknesses of Experimental Design

        Data involving the change in percent DO saturation (derived from the change in DO concentration) seem to correlate best with the net production. Though the hypothesis that larger change in pH from the pH optimum result in the loss of net production can be corroborated based on the results of this experiment, the question cannot still be answered for many questions still remain. For example, in order to fully answer the question of what effects does pH have on the net production of Chlorella pyrenoidosa, one may need to understand a trend over a larger pH scale (say, from pH 0 to pH 16). Since this experiment used pH values only about +/- 2 on the pH scale from pH 8, more data from a more diverse range of pH values (pH values of solutions containing Chlorella pyrenoidosa) will be needed to better support the hypothesis.

Moreover, the different colors of the buffers used (pH 10 buffer, for example, was a light blue color while pH 7 buffer was a light green) may have impacted the light frequency as all colors except for green for pH 7 and blue for pH 10 were absorbed in those situations, while all wavelengths of light were absorbed in the other solutions (which were clear). The effect may have been that Chlorella pyrenoidosa in the pH 7 buffer would be more productive than if they were put in a clear solution as now the wavelength of light was selected to yield a more direct accessibility to red and blue-violet light, resulting in an uncontrolled higher productivity for pH 7 buffer. Blue color in the pH 10 would have selected out the wavelength of light that were not blue-violet, reducing the chances for productivity in that wavelength of light, which resulted in an uncontrolled lower productivity (thus lower net production) for Chlorella pyrenoidosa in pH 10 buffer solution. Thus color differences among the solutions with Chlorella pyrenoidosa may thus be a confounding factor in understanding fully the impact of pH level on the net production in Chlorella pyrenoidosa. Thus using buffers that are clear (colorless) may eradicate this confounding variable and help fully understand the effect of pH level on the net production.

Data collection had played a smaller part as a potential confounding variable in terms of answering pH level vs. net production. Obtaining salinity and temperature measurements (as part of monitoring potentially confounding control variables) would disrupt the closed systems by introducting more oxygen diffusion in Day 1 (t = 24 hours) to the BOD bottles for uneven amounts of time. Thus the measurements could have been done such that the BOD bottles were given the same amount of exposure to oxygen as to allow for a more controlled data collection. During the second day, that is t= 48 hours, for the final dissolved oxygen data collection, it was noticed that the membrane cap was cracked (but the crack was very small, less than 0.1 cm +/- 1 cm). Of course this was noted only after it was noticed in the readings for trials #4 and 5 in pH 10, but the crack likely may have been there during all data collection and likely interfered with calibration. Thus the relatively large standard deviation may have been the result of the small crack, although not large enough of a change that it had impacted the overall data trend. Moreover,a repeat of this experiment’s procedure with properly functioning equipment would likely generate reliable data which could then be used to test the stated hypothesis.  

Possible Improvements

         A repeat of this experiments procedure using a larger array of pH values (say a range from pH 0 to pH 16) would likely generate more reliable general trends of pH values affecting net production in Chlorella pyrenoidosa. The experiment could be repeated with more trials with about 30 trials, which would allow for statistically significant data, according to the students t-test as it is the minimum number that will yield a reasonably suggestive confidence interval of about alpha = 0.01 (or 99% confidence). More accurate results in terms of understanding phytoplankton growth may be by trying to find buffers that do not have limiting nutrients as part of the chemical formula. For example, pH 8 buffer contains the limiting nutrient phosphate, which is known to result in an increased production of the phytoplankton growth and another confounding factor to the experiment. Thus, one cannot completely attribute the growth in net production to the pH optimum of pH 8 as the phosphate limiting nutrient could have increased the growth of the Chlorella pyrenoidosa sample. Thus, switching this pH buffer to a pH 8 buffer without phosphate, like Tris (hydroxylmethyl) aminomethane-Hydrochloric acid would allow for the eradication of the limitating nutrients as potentially confounding factors for net production. Moreover, it would be important to understand that as temperature was not controlled (rather only monitored) there is a confounding factor in that a general temperature increase results in the decrease of oxygen saturation. To obtain generally more reliable data, it would be good to control temperature (by perhaps warming up the BOD bottles with pH buffer solution to 300C to obtain equal initial and final temperatures) from final to initial DO measurements so that change in DO saturation more accurately reflects the net production of Chlorella pyrenoidosa. An extended time period (that is a longer time period for the experiment to run), perhaps with collection of data over intervals of 24 hours over a 96 hour period (or 4 days), would allow for a clearer image of the data trend because the Chlorella pyrenoidosa would have more potential growth and would undergo enough division to see very large changes in final and initial DO concentrations and thus percent DO saturations. Moreover, an extended period of time would allow for the trials to not be run simultaneously, averting possible issue of sample cross-contamination.

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