By: Minghe, Solange, Leo, Ava, and Meg
INTRO:
The earth's surrounding air is composed of 21% oxygen and enters water through the process of slow diffusion and aeration. The temperature and the dissolved oxygen (DO) level have an inverse relationship; when temperature increases, the DO level decreases. Due to the increase in temperature increases the vibration of O2 molecules, weakens the molecular interaction between water and oxygen, increasing the energy for O2 escape. Another factor that will affect the DO level is the microorganisms habitat in the surface of the water will increase the DO level due to photosynthesis from phytoplankton, algae, and other aquatic plants can also produce dissolved oxygen. Dissolved oxygen is then depleted via chemical oxidation and respiration by aquatic organisms and the decomposition of organic materials present in the water.
In the case of data collected in the little sipp and Waquoit bay, the analysis will focus on verifying the relationship between temperature and dissolved oxygen levels. Any anomalies that does not align with the theory being hypothesized will be explained in evaluation to the sampling errors and other variants.
METHODOLOGY:
Students a part of the Sea Education Association participating in the summer pre college program were given the opportunity to explore local salt marshes. They were instructed to collect data on various water quality aspects for example temperature, and dissolved oxygen. Students used dissolved oxygen tests, and YSIs to gather said information. Ysis are aquatic internets that are able to read salinity and temperature of a body of water. Dissolved oxygen tests are used to analyze the percent of oxygen that have been dissolved in the water. Students collected water samples in a glass vial after properly acclimatizing it to ensure the best results from their titration. Students noticed upon organizing and analyzing the collected data it was observed that, there appeared to be no consistent relationships between data variables. This was further proven by the use of a graphing tool through desmos, all the R values presented a weak or negative correlation. The observed lack of relationship could indicate poor handling or collection of data. Thus a select group of students have taken on the task of understanding the potential causes for the weak correlation observed in data collected in the salt marshes.
Data collected from 3 respective sites helped in creating linear, nonlinear fits and r-value for trend interpretation. Error analysis based on percentage uncertainty and standard deviation by the help of excel and python coding can help validate the data.
RESULTS:
Upon analyzing the data that was collected and formatting in desmos, students observe that the graphs did not follow the expected relationship between dissolved oxygen and temperature. The expected relationship is as temperature increases the level of dissolved oxygen decreases, due to the fact that warm water molecules move at a faster rate, consequently pushing the oxygen in the atmosphere. The trend that the students observed was a linear correlation, as temperature increased so did the level of dissolved oxygen. This disconnect can be linked to poor collection of data done by students or potential influence of environmental phenomena.
Analysis
Little sipp 6.26
Correlation coefficient: 0.307
The correlation coefficient value indicates that the relatedness between dissolved oxygen and temperature change is relatively moderate
Standard Deviation of Dissolved Oxygen (DO): 1.455 mg/L
Percentage Uncertainty of Temperature (T): 4.82%
Percentage Uncertainty of dissolved oxygen (DO): 21.86%
This lower percentage uncertainty indicates less relative variability in the temperature measurements.
Little sipp 7.2
Correlation Coefficient (r): 0.4945
The correlation coefficient value indicates that the relatedness between dissolved oxygen and temperature change is relatively strong
Standard Deviation of Dissolved Oxygen (DO): 2.502 mg/L
Standard Deviation of Temperature (T): 1.155 °C
Percentage Uncertainty of Dissolved Oxygen (DO): 42.26%
This high percentage uncertainty indicates a significant relative variability in the dissolved oxygen measurements.
Percentage Uncertainty of Temperature (T): 4.75%
This lower percentage uncertainty indicates less relative variability in the temperature measurements.
Waquoit 7.3
Correlation Coefficient (r): 0.076
The correlation coefficient value indicates that the relatedness between dissolved oxygen and temperature change is relatively weak
Standard Deviation of Dissolved Oxygen (DO): 1.107 mg/L
Standard Deviation of Temperature (T): 1.336 °C
Percentage Uncertainty of Dissolved Oxygen (DO): 14.58%
This percentage uncertainty indicates moderate relative variability in the dissolved oxygen measurements.
Percentage Uncertainty of Temperature (T): 5.13%
This lower percentage uncertainty indicates less relative variability in the temperature measurements.
Data Set 1 and Data Set 2 both indicate some degree of positive correlation between dissolved oxygen and temperature, with Data Set 2 showing a stronger correlation.
Data Set 3 shows a very weak correlation, indicating that temperature might not be a significant factor in this set.
The nonlinear fits generally provide a better representation of the data compared to linear fits, indicating that the relationship between temperature and dissolved oxygen may not be strictly linear.
CONCLUSION & EVALUATION:
Possible experimental errors could cause the observed inconsistent trend presented in our data. For instance, incorrect handling time lag presumably caused an increase in microbial activity that altered the dissolved oxygen content. Also, at all stages, steps must be taken to ensure that oxygen is neither introduced to nor lost from the sample. Any incorrect input or output of oxygen content would disturb the final result. The main cause of error in data was the lack of consideration of the consumption and release of oxygen by the organisms around the marsh areas. Additionally, variations in environmental conditions such as temperature, salinity, and light exposure during sampling could have contributed to discrepancies in the data. These factors can significantly influence the levels of dissolved oxygen and should be carefully monitored and recorded. In summary, while the current data provides valuable insights, recognizing and addressing these potential sources of error is crucial for improving the reliability of future measurements. By adhering to stricter handling protocols and considering environmental and biological factors, we can obtain more consistent and accurate data on dissolved oxygen levels in marsh areas.
Little sipp 6/26
Little Sip 7/2
Waquoit 7/3
Combined data
