Order 1223333: To what extent have human activities impacted the water quality in 5 waterways?
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MaxiW17512_SummativeProductivityLab2.pdf
Summative Productivity Lab Maxi Widjojo IB S1 ESS
Mrs. Henard
Research Question (RQ): What is the effect of Temperature on productivity at the Southern shore of Lake Borodun?
Variables:
Variable(s):
Independent: Temperature of the water extracted from Lake Borodun
Dependent: Oxygen concentration in the water from the lake
Raw Data: Displays the data of initial amounts of oxygen concentration in Lake Borodun in dark and light based on the given temperatures.
Month: January March May June July September November
Temperature (Celsius)
8 12 16 17 18 15 9
Initial amount of dissolved oxygen: (mg/l)
13.22 12.81 12.44 12.12 11.83 12.8 12.45
Trial 1: (Light) 14.65 17.00 17.21 18.08 17.91 17.12 16.23
Trial 2: (Light) 14.21 16.26 17.34 18.00 17.88 17.34 16.21
Trial 3: (Light) 14.44 16.55 17.68 17.86 17.77 17.15 16.11
Trial 4: (Light) 14.35 16.69 17.7 17.88 17.42 17.78 16.38
Trial 1: (Dark) 11.67 10.97 10.78 10.61 10.42 11.21 10.14
Trial 2: (Dark) 11.88 11.11 10.56 10.79 10.10 10.88 10.56
Trial 3: (Dark) 11.92 11.28 10.72 10.65 10.44 10.56 10.72
Trial 4: (Dark) 11.65 11.07 10.32 10.37 10.57 10.38 10.32
Processed Data Table: Displays the relationship between the amount of productivity and the given temperatures. Sample average calculation for 12℃℃ = (12℃℃ for light): 17.00 + 16.26 + 16.55 + 16.69 = 66.5
= 66.5 / 4 = 16.63
Month: January March May June July September November
Temperature (Celsius)
8 12 16 17 18 15 9
Initial amount of dissolved oxygen: (mg/l)
13.22 12.81 12.44 12.12 11.83 12.80 12.45
Trial 1: (Light) 14.65 17.00 17.21 18.08 17.91 17.12 16.23
Trial 2: (Light) 14.21 16.26 17.34 18.00 17.88 17.34 16.21
Trial 3: (Light) 14.44 16.55 17.68 17.86 17.77 17.15 16.11
Trial 4: (Light) 14.35 16.69 17.78 17.88 17.42 17.78 16.38
Average (Light): mg/l
14.41 16.63 17.53 18.00 17.75 17.35 16.23
Trial 1: (Dark) 11.67 10.97 10.78 10.61 10.42 11.21 10.14
Trial 2: (Dark) 11.88 11.11 10.56 10.79 10.10 10.88 10.56
Trial 3: (Dark) 11.92 11.28 10.72 10.65 10.44 10.56 10.72
Trial 4: (Dark) 11.65 11.07 10.32 10.37 10.57 10.38 10.32
Average (Dark): mg/l
11.78 11.12 10.60 10.61 10.38 10.76 10.44
Processed data table for the relationship between temperature and productivity: Sample calculations for data table below: NPP: Light - Initial E.g. for 8℃℃: 14.41 - 13.22 = 1.19 R: Dark - Initial E.g. for 8℃℃: GPP: NPP + R E.g. for 8℃℃:
Temperature (℃) NPP (Net Primary Production)
R (Respiration) GPP (Gross Primary Production)
8 1.19 -1.44 -0.25
12 3.82 -1.69 2.13
16 5.09 -1.84 3.25
17 5.88 -1.51 4.37
18 5.92 -1.45 4.47
15 4.55 -2.04 2.51
9 3.78 -2.01 1.77
Calculations for processed data table for average oxygen concentrations according to varying temperatures: (mg/) Sample average calculation for 12℃℃ = (12℃℃ for light): 17.00 + 16.26 + 16.55 + 16.69 = 66.5
= 66.5 / 4 = 16.63 Light:
1. (8℃ for light): 14.65 + 14.21 + 14.44 + 14.35 = 57.65 = 57.65 / 4 = 14.41
2. (12℃ for light): 17.00 + 16.26 + 16.55 + 16.69 = 66.5
= 66.5 / 4 = 16.63 3. (16℃ for light): 17.21 + 17.34 + 17.68 + 17.78 = 70.13
= 70.13 / 4 = 17.53 4. (17℃ for light): 18.08 + 18.00 + 17.86 + 17.88 = 71.82
= 71.82 / 4 = 18.00 5. (18℃ for light): 17.91 + 17.88 + 17.77 + 17.42 = 70.98 = 70.98 / 4 = 17.75 6. (15℃ for light): 17.12 + 17.34 + 17.15 + 17.78 = 69.39 = 69.39 / 4 = 17.35 7. (9℃ for light): 16.23 + 16.21 + 16.11 + 16.38 = 64.93 = 64.93 / 4 = 16.23
Dark:
1. (8℃ for dark): 11.67 + 11.88 + 11.92 + 11.65 = 47.12 = 47.12 / 4 = 11.78
2. (12℃ for dark): 10.97 + 11.11 + 11.28 + 11.07 = 44.43
= 44.43 / 4 = 11.12
3. (16℃ for dark): 10.78 + 10.56 + 10.72 + 10.32 = 42.38 = 42.38 / 4 = 10.60
4. (17℃ for dark): 10.61 + 10.79 + 10.65 + 10.37 = 42.42
= 42.42 / 4 = 10.61
5. (18℃ for dark): 10.42 + 10.10 + 10.44 + 10.57 = 41.53 = 41.53 / 4 = 10.38
6. (15℃ for dark): 11.21 + 10.88 + 10.56 + 10.38 = 43.03
= 43.03 / 4 = 10.76
7. (9℃ for dark): 10.14 + 10.56 + 10.72 + 10.32 = 41.47 = 41.47 / 4 = 10.44
Calculations for the NPP, R, and GPP: Sample calculations NPP: Light - Initial E.g. for (8℃℃): 14.41 - 13.22 = 1.19 NPP (Net Primary Production) Calculations:
1. (8℃): 14.41 - 13.22 = 1.19
2. (12℃): 16.63 - 12.81 = 3.82
3. (16℃): 17.53 - 12.44 = 5.09
4. (17℃): 18.00 - 12.12 = 5.88
5. (18℃): 17.75 - 11.83 = 5.92
6. (15℃): 17.35 - 12.80 = 4.55
7. (9℃): 16.23 - 12.45 = 3.78
R (Respiration) calculations: R: Dark - Initial E.g. for (8℃℃): 11.78 - 13.22 = -1.44
1. (8℃): 11.78 - 13.22 = -1.44
2. (12℃): 11.12 - 12.81 = -1.69
3. (16℃): 10.60 - 12.44 = -1.84
4. (17℃): 10.61 - 12.12 = -1.51
5. (18℃): 10.38 - 11.83 = -1.45
6. (15℃): 10.76 - 12.80 = -2.04
7. (9℃): 10.44 - 12.45 = -2.01 GPP (Gross Primary Production) calculations: GPP: NPP + R E.g. for (8℃℃): 1.19 + (-1.44) = -0.25
1. (8℃): 1.19 + (-1.44) = -0.25
2. (12℃): 3.82 + (-1.69) = 2.13
3. (16℃): 5.09 + (-1.84) = 3.25
4. (17℃): 5.88 + (-1.51) = 4.37
5. (18℃): 5.92 + (-1.45) = 4.47
6. (15℃): 4.55 + (-2.04) = 2.51
7. (9℃): 3.78 + (-2.01) = 1.77
Graph: Temperature vs amount of productivity
Data analysis:
Based on the graph and processed data above, it is evident that there is a positive correlation towards the amount of productivity and the temperature in the Southern Shore of the Lake Borodun. The productivity found based on the given temperatures was through the use of the formulas of GPP, NPP and R. This allowed the calculations for the processed data, however the data used for the graph was only using the calculations for the net primary productivity (NPP). Based on the numbers, the productivity throughout the experiment ranges from around 1-6. Looking at the data we can infer that as the temperature rises so does the amount of productivity. This is a positive relationship between the temperature and the productivity, as seen on the graph, the x-values go up, which in this case is the temperature, so does the y-value, which is the productivity from the lake. For example at (8℃) the NPP is 1.19, while at (18℃) the NPP is 5.92, which is why it is understood that there is more productivity found in higher temperatures. The reasoning behind the increase in the productivity as the temperature gets higher is due to the fact that the plants in the lake would be exposed to more sunlight, thus allowing them to photosynthesize, creating a larger amount of dissolved oxygen (DO2) in the lake. On the other hand, as the temperatures get colder, the plants in the lake are exposed to a significantly lower amount of sunlight than higher temperature weather, which limits the amount of sunlight exposure the plants in the lake receive, thus lowering the plants ability to photosynthesize. This brings us to the conclusion that the increasing temperatures throughout the this experiment have been evident to display that they show more productivity than lower temperatures.
Conclusion: Declining fish stocks have been an arising environmental issue that has been affecting those from lower-class societies as they have a very limited source of protein due to the shortage of fish. With the improved fishing technology, and increased number of fishing boats, the corporations behind the operations have claimed a very significant amount of the fish, depriving many people with less fortunate backgrounds by limiting their access to a proper nutrition. This has has had a major impact on people with impoverished backgrounds as they now have a shortage on a staple source of protein, as many lakes and oceans have been exploited by fishing boats for it’s resources. The data collected above, has been evident to show the increase in temperature and how it has been highly beneficial towards the productivity of the fish. Relating back to the research question: What is the effect of Temperature on productivity at the Southern shore of Lake Borodun? From the data we can conclude that there is indeed an impact on the productivity of the fish based on different temperatures, as the data has been evident to show that there is a positive correlation between the increasing temperatures and the amount of productivity in the lake. The data has shown that the productivity is highest in July, as the weather holds it’s highest temperature then, which will result in the fish being highly populated in the area due to the algae productivity being high as well, making July the month best for fishing. Overall the data collected was accurate, as there was no outliers due to human error in the lab. However, the data did hold two outliers, but that was due to the low temperatures. Furthermore, this lab has brought us to answer the research question, as the data collected has proven the relationship between temperatures and productivity in the lake.
Evaluation:
Strengths: Weaknesses: Opportunities for further studies / limitations:
Sufficient data is a strength in this lab, as it has created a clear perspective to view the relationship between temperature and productivity to see whether or not the trend was consistent throughout the trials.
The data collected was only from the first days of the month, which is a weakness because there would be no information on the activity of the productivity for the rest of the month, causing the data to possibly show inaccuracy when stating the productivity throughout the months.
This would be a limitation on the research of declining fish stocks, as it would not provide information on the productivity throughout the month, but only on one day of the month, which would possibly create inaccurate data on the most productive months for the fish. This could be an opportunity for further study, as water samples could be taken once a week and then averaged at the end of the month to be more accurate. This would allow more information on the productivity of the lake, to give a better idea on what the productivity would look like throughout the month, as opposed to just taking data from one day of the month and
presuming it remains consistent for rest of the month.
The lab was conducted using high technology items, which is a strength because it ensured that the data was accurate and allowed minimal risk for systematic errors to be conducted throughout the data collection.
The lab displays data from 7 months out of a year, and leaves out 5 months, which could possibly resort to inaccuracy in calculating the most productive month, as there is no information on the temperature nor productivity in the left out months.
This would be a limitation as there would be insufficient data to display the most productive month, as the data collected had left out 5 months in the year. This would make the data not as accurate as it is only collecting data from 7 months, and there would be no information on the temperature and productivity for the other 5 months. However this is also an opportunity for further study, as data could be collected for all 12 months to get a better understanding on the amount of productivity there is throughout the whole year. It would also allow a more clear perspective on which month is more productive, as there would be data on every month, as opposed to only 7 months.
Solution to the environmental problem of declining fish stocks:
The data above suggests a relatively accurate solution towards the problem of declining fish stocks, as it provides information on which months are most productive and the best for fishing. The data provides sufficient information to see the trend line on the relationship between temperature and net primary productivity (NPP), however it holds some limitations on the accuracy of the most productive month.
The data displayed only shows 7 months in the year which leaves us oblivious towards the information on the productivity and temperatures for the remaining 5 months. This is would impact the data as the months left out would have had a positive effect on the data by showing more information. It also could possibly have displayed a more productive month than July, which is the most productive month out of the 7 months the data was collected in. Another limitation on using this current method would be the fact that the data was only collected on the first day of the month, which leaves a lack of information on the productivity of the rest of the month, as the productivity is likely to fluctuate and not remain consistent due to constant temperature changes.
Despite the current limitations in the current method, with improvements made, this could most
definitely be an option to be considered as a solution to the environmental problem of declining fish stocks. If the method were to be slightly altered in ways such as taking data from every week of the month and then averaging it out, as opposed to taking data one day of the month and presuming it remains
consistent throughout the rest of the month. By improving this part of the method, it would allow more accuracy to be ensured as the productivity levels for each month would be more precise, due to the fact that there would be more information on the fluctuation of the productivity. Additionally, including the months that were left out, would also give more insight on the productivity levels throughout the year, which would be more beneficial for analyzing the most productive months, as there would be a lot more information to consider.
Furthermore, by changing the method slightly to fulfill the opportunities for further study, this
method would be very beneficial towards analyzing the most productive months as it would be a very informative source for analyzing the months individually to see the peak for fishing times. With the use of the data collected above it would give insight on the months which are economical to fish in, allowing fishermen to still benefit despite the environmental problem of declining fish stocks. By using this method after going through several alterations, this could be highly beneficial to use for fishing, as it provides information on productivity and the relationship it holds with temperature, which could be useful as well, as they could also go fishing based on the daily temperatures.
