Zacharias Daniel
Senior Thesis | 2025
Zacharias Daniel
Senior Thesis
Mr.
Ranney
Effects of Ocean Acidification on Humans
Ocean acidification is a pressing issue confronting the world. Ocean acidification, similar to global warming, is caused by carbon emissions. Oceans mainly absorb carbon dioxide through phytoplankton, a type of photosynthesizing organism representing more than half of the world's primary production. Through photosynthesis, they convert carbon dioxide in the ocean and atmosphere into organic matter. When phytoplankton die, they carry the atmospheric carbon dioxide to the bottom of the sea, which acts as a mechanism of carbon sequestration. These organisms are also incredibly important to aquatic ecosystems, as the transformation of carbon into organic compounds allows for elements to cycle through the ecosystem. As the density of carbon dioxide and other emission agents increase in the atmosphere, phytoplankton are affected in a multitude of ways: elevated temperature and pH forces migration to colder and less acidic/basic (depends which way pH is going) waters, increased upwelling due to temperature causes uncontrolled growth of phytoplankton, and nutrient deficits are created due to heat differentials at different subterranean altitudes. As the climate grows hotter, phytoplankton are undergoing much stress, which may lead to lower levels of carbon fixing and further warming the climate, similar to a domino effect. The ocean also absorbs carbon dioxide directly from the atmosphere. Absorbed carbon dioxide reacts with the seawater to
form carbonic acid (H2CO3), releasing hydrogen ions (H+) which then reacts with carbonate ions (CO3 -2) and a stable form of calcium carbonate (CaCO3) to form bicarbonate (HCO3 -). The acidity of a solution is determined by the amount of hydrogen ions, the more there are, the more acidic it is. As hydrogen is released through this reaction, the pH of the seawater lowers, and the ocean acidifies.
The research our group at the MIT Data Activism program carried out focused on discovering how ocean acidification immediately impacts human lives and developing ways to solve these problems in the short term. Issues that we identified included loss of necessary resources – such as carbonate for shell building organisms – leading to increased rate of predation, degradation of the marine environment with things such as coral reefs and fish generally being lower quality, and increase of carbon dioxide in the atmosphere leading to health risks such as asthma for humans. Most of these issues have already been investigated and are well understood, so our project focused on disparate impacts of ocean acidification on minority populations. To do this, we had to consider multiple ways to connect ocean acidification to direct problems for humans in the short term. The main avenue we considered to make this connection was the relationship between climate change and ocean acidification. As pollution levels increase, the ocean, a natural carbon sink, absorbs less and less of the emissions but, due to the amount of greenhouse gases it has already absorbed, the ocean acidifies and ecosystems are harmed. In order to stop ocean acidification, the problem has to be mitigated and the leak, climate change, has to be stopped. We found that many of the products of ocean acidification will impact
minority populations harmfully and our group also found ways to mitigate these impacts. Proximity to carbon capture facilities, seafood consumption among the US population, lower rates of access to green space and facilities central to combating climate change, and lower air quality in the area encompass a few reasons our research gave warning of the impacts of climate change and ocean acidification on minority populations.
One question that needs to be addressed is whether the reasons we gave warning of the impacts of climate change and ocean acidification are relevant. Ocean acidification is a pressing issue, and scientists are actively developing strategies to combat it. One promising approach is carbon capture. This method involves trapping carbon dioxide in storage containers to prevent it from entering the atmosphere or the ocean. By reducing the amount of carbon dioxide that dissolves into the ocean, carbon capture helps mitigate ocean acidification. Locally, the Massachusetts government is actively addressing ocean acidification through several initiatives. They are developing programs to monitor acidity levels in coastal regions and funding studies to better understand the effects of ocean acidification. Additionally, the Massachusetts government plans to host community events to raise awareness and encourage public involvement. State and local governments should care about these issues because ocean acidification impacts everyone. Raising awareness in the community is vital to inform individuals about how they can contribute locally. For example, keeping local rivers and streams clean helps reduce excess pollutants, which worsen environmental degradation. Addressing ocean acidification also requires tackling climate change, as the two issues are closely linked as previously
stated. Supporting effective legislation that addresses both ocean acidification and climate change is crucial at both the state and local levels, and advocating for policies that address these interconnected issues can lead to the passage of important bills and drive meaningful change.
As for seafood consumption the main axis we focused on in regard to seafood was the quality of the seafood people are eating and the demographics of people who eat seafood on a regular basis. As the ocean takes in carbon dioxide, the carbon dioxide reacts with water to form carbonic acid. The carbonic acid then reacts with calcium carbonate losing a hydrogen ion to form two bicarbonate ions leaving behind a calcium molecule that shellfish and other shell building fish cannot use. Another reason why we wanted to survey how often and who eats seafood was to assess the risk that paralytic shellfish poisoning will pose to humans in the coming century. Paralytic shellfish poisoning (PSP) is a lifethreatening syndrome commonly contracted from seafood that is contaminated with neurotoxins such as saxitoxins. Most symptoms are neurological and appear quickly within ten minutes to around three hours of consumption. They include tingling, numbness, burning of the perioral region, ataxia, giddiness, drowsiness, fever, rashes, staggering, abdominal pain, nausea, vomiting, and diarrhea. In some cases, severe respiratory arrest can occur within 24 hours of consumption. Most survivors recover fully however there is not an explicit antidote. Most patients are given supportive therapy as a method of recovery. Species of single-celled algae can increase in numbers through reproduction if the conditions are right. Ocean acidification allows for the conditions to favor algae production. The algae clump together to form blooms that can become far
more harmful as some single-cell algae have greater effects than others. Some species of single-cell algae can release harmful toxins. These toxins are absorbed into the tissue of shellfish. These toxins can kill the shellfish or be passed down to humans during consumption. The consumption of contaminated shellfish can cause PSP.
Our reasoning for including greenspace as a metric to assess the risk ocean acidification poses to humans was twofold. The first reason was that, as previously stated, with ocean acidification being a product of pollution and organisms of the hue green generally absorbing carbon, making sure that there is greenspace in every possible place is a necessity for reducing emissions and mitigating other risks of ocean acidification to humans. Secondly, studies have shown that having a lot of greenspace in the area in which somebody lives leads to lower risks for diseases like asthma and cardiovascular diseases like heart attacks. Though recent studies have shown that there isn’t a direct correlation, there is an indirect benefit to mental health and physical health. Because of the contradictory claims, we relied on a previous groups’ work on greenspaces in Boston and included a section on greenspace in our final report after looking at a couple studies and datasets on the subject.
Another topic that we decided to focus on was people who fish for a living and the seafood industry. The reason why we decided to focus on the fishing industry is because, as previously mentioned, fish and other sea life are heavily affected by ocean acidification, with paralytic shellfish poisoning and acidity being two among the many ways that the ocean is affected. We found
many datasets relating to the fishing and seafood industry with a large amount of Americans eating seafood and a high amount of people who rely on fishing for their livelihoods in Massachusetts and specifically shellfish with 70% of Massachusetts’ seafood industry being shellfish. Another way we decided to support the data was through a survey designed to get information on various topics that we were unable to get data online on.
Our research used a large dataset with over 150 entries of carbon capture facility data to analyze trends in regards to carbon capture. Our dataset focused on the US and did not specify the type of carbon capture. The first task we undertook was finding the data. To find our dataset, our whole group of more than 15 people spent at least three days of work or 24 hours. We found more than one dataset with more than one topic for exploration. From seafood consumption datasets to datasets that explored the demographics of fishing our datasets were varied and pointed to many ways through which ocean acidification can affect humans. To narrow down our topic range we decided to split up and to run some pandas and Python based data analysis on our respective datasets. The first topic that I will cover is carbon capture. Our carbon capture dataset was found on the Global Carbon Capture and Storage Institute website. The website had a report that listed a wide variety of currently running and in construction carbon capture facilities and gave general details on the state of carbon capture across the world. Our group was advised to geolocate our data to the Boston area but, with this dataset that was impossible to do because there wasn’t enough data in the area, so it was decided that our analysis would only include United States-based facilities. Our dataset then was cleaned up using pandas and Python. We used the dropna and
fillna pandas methods to fill in and get rid of blank rows. We also used a sorting method to find projects based in the United States. We also wanted to find a way to find demographic information about the areas surrounding carbon capture facilities as the technology is new and with the amount of money and uncertainty flowing into the field we felt it important to find out who is benefitting or being affected by the burgeoning industry. To find the demographic information we inputted our data into ChatGPT 4.0 and asked it to find the demographics of the towns and cities through the internet but GPT gave us almost completely inaccurate answers. To rectify this mistake, one of the mentors on our team found a longitude-latitude geo-coordinating module. After getting the coordinates for the facilities, we decided to ask ChatGPT 4.0 to get the town and city names, and this time it succeeded. From there, we manually inputted the data on demographics using the website data usa and ended up with a 146 row long dataset. From there, we did analysis on trends within the dataset. At that point, our time was limited, so we decided to limit our level of analysis. Our analysis used the diversity, population, rural vs urban, and poverty level statistics. We found that there was a correlation between carbon capture facilities and the level of poverty in the surrounding cities and towns. On average, around 16.4 percent of the population in the areas surrounding carbon capture facilities were below the poverty line compared to a national average of 11.5 percent. In addition to that statistic, we found that more than 58 percent of the populations in these areas identified as people of color. Over 9 million people live near carbon capture facilities and so our research underscored the importance of understanding how carbon capture will affect the United States.
The second topic I will cover is our seafood datasets and survey. Through a method similar to our method for finding the carbon capture dataset we spent the same amount of time looking for a dataset online. We found many datasets applicable to the data analysis we planned to do. Our first dataset was a dataset with hotspots indicating where the fishing industry and seafood industry will be affected by ocean acidification. As expected, Massachusetts and the northeast of the United States with a huge portion of its fishing industry dependent on shellfish had many hotspots. Another dataset we used to do our analysis was a dataset that cataloged seafood consumption rates in the United States. The dataset found that over 80 percent of Americans had eaten seafood in a 30 day period from the study. The fact that over 80 percent of Americans in this representative sample had eaten seafood within 30 days of the study indicates that seafood consumption is a matter of importance in considering the impacts of ocean acidification. The dataset also found that 65 percent of seafood is consumed “out of home” or at restaurants, a significant difference from other food expenditures with less than 50 percent consumed “out of home” suggesting that seafood eating habits are different from normal eating habits. Another dataset we found was a dataset looking at the percentage of the economy that is part of the seafood and fishing industry and will be affected. We did this because looking at the percentage of the economy and the amount of jobs that will be affected is a good way to measure ways that humans will be affected by ocean acidification. The dataset also looked at specific types of seafood that are consumed by Americans and because some sea life like shellfish are more affected by ocean acidification than other sea life like algae this study allows our work to gain a
level of specificity helping to narrow down the scope of what specific organisms need help. We also found a dataset regarding the demographics of fisher people. We figured that this dataset was important because we wanted to focus on the demographics of people who might be affected by ocean acidification. After establishing the importance of these datasets, we proceeded to visualize and analyze the datasets. Our first dataset we analyzed and visualized found that mostly the northeast was affected by ocean acidification with all hotspots being in the northeast and growing in frequency as you moved north. This suggests that there is an indirect correlation between colder waters and ocean acidification. Our second dataset and visualization didn’t really find anything as the dataset was from a study but we made a visual interpretation of the dataset. Our third dataset found that the race of fisher people was predominantly white with over 72 percent identifying as white and with the national percentage of people identifying as white alone at 58 percent the dataset showed that white Americans were overrepresented in the fishing industry. In the dataset, 13 percent of the fisher people identified as black matching the national statistic but almost all other demographics were underrepresented in the industry. Our dataset also found that most fisher people had gone to college for at least one year with about 58 percent of fisher people included in this statistic but, the national average is around 65 percent showing that fisher people on average have less education than the general United States population. As for annual household income, fisher peoples annual household income varied but was around the average annual household income in the United States though there was a significant population of fisher people, around 11 percent, with a
below twenty thousand annual household income. Finally we analyzed the fisher peoples ages and found that they were slightly older than the national average of 42.3 with more than half of the fisher population being aged over 45. We also conducted a survey of fisher people and analyzed and visualized the results which will be shown later in this paper.
As previously stated, we decided to investigate poor air quality as a factor that could lead to human harm due to ocean acidification. Air quality has been greatly affected by climate change and by extension ocean acidification. As carbon dioxide and other pollutants enter the atmosphere, the atmosphere becomes cluttered with these pollutants and that can affect air quality. Low air quality can lead to diseases like asthma and higher risk for chronic conditions. Because of these reasons we decided to make air quality an indicator that we paid attention to.
In this section there will be a full discussion of methods and ways in which we analyzed our datasets. All of our datasets, with the exception of the data we ourselves collected, was taken from the internet. Due to this fact, we usually had to account for the variety of ways that the data was presented to us. For example, more than one of our files came in PDF format and we had only learned to perform data analysis on a csv. The process to transform a PDF file to a csv required us to find a website that did the process and then we had to correct for any errors made by opening the excel file and manually inputting the data in. Long story short, because of the variety of the data we had a variety of different methods to convert our files into csv format. One method that more than one of our group used was AI or ChatGPT. We decided to use
ChatGPT to convert the files to csv format due to the complicated nature of the files we were getting, the possibility of failure in converting our files through conventional means, and ChatGPT’s transparency of code allowing us to understand what it did to transform the files. ChatGPT’s free model limits the amount of data analysis that you are able to do in one day and because we didn’t always give the correct input to get our desired output we relied often on a mentor’s ChatGPT Plus to get a good csv file. As previously mentioned, once we had a csv file, we filled and dropped null values and blank values. As for our analysis itself, we usually didn’t do complicated data analysis as our data wasn’t that complicated and did not have many columns and rows leading us to usually deal with uncomplicated numbers. With these uncomplicated numbers, we are able to perform basic data analysis like percentage analysis along columns very easily. Sometimes, though, with complicated datasets like the carbon capture dataset with opportunities for analysis along several of the columns the analysis needs to be filtered by location or other prerequisites to analysis of the dataset and specific columns. To do this we filter by specific columns and then create a new dataset containing only the rows that have attributes that we want in the specific column. We used this concept to its fullest in the carbon capture dataset. Initially, the dataset included non-United States locations and because we wanted to include only United States locations we filtered the dataset using this method. Finally, regarding the data visualizations, we mostly performed these for uncomplicated datasets as for uncomplicated datasets we were unable to communicate the full scope of the data through the simple visualizations we were able to do. The visualizations we defaulted
to were pie charts, bar graphs, and scatter plots. The use of each of these visualizations was based on what made the most sense for communicating the information and often data was visualized in more than one way.
In order to get a better understanding of the populations that we are trying to serve, we decided to conduct a series of surveys. One survey was on fisher people and the other survey was on the general population to gauge how much they know about ocean acidification and products of ocean acidification. Our fisher people survey found that out of the 28 people surveyed, all but three knew about ocean acidification, and out of those 17, every one of them but one knew about ocean acidification for more than 5 years. All but 4 of those people said their business has taken precautionary measures against ocean acidification, and all of them said they believe that their business can’t take any more measures against ocean acidification. The fisher people's responses to questions on whether they would support specific types of policy were more mixed. When asked ‘If the water quality monitoring network in MA was upgraded to include Ocean acidification(OA) monitoring, would you personally find this useful?’ 8 respondents said maybe 1 said no and the rest said yes.
When asked ‘Would you support legislation aimed at funding research into OA on commercially valuable species?’ 8 respondents also said maybe 1 said no and the rest said yes. When asked ‘Would you support legislation aimed at addressing the causes of OA?’ 9 respondents said maybe and the rest said yes. When asked ‘Would you support legislation aimed at improving OA monitoring along the Massachusetts coastline?’ all but 8
respondents said yes and the rest said maybe indicating an interest in monitoring ocean acidification. The impression given was that these fisher people would generally support policy addressed at ocean acidification but were a little wary of implementation. From these surveys, we concluded that there was a clear concern with ocean acidification coming from the fisher people and, though they were not unanimously in support of ocean acidification legislation, they seemed to generally agree that ocean acidification needs to be addressed on a policy level. Our second survey was much more broad with fifty-four respondents. Our first question asked for their consent in participating in the survey. Our second question asked about how much each respondent knew about ocean acidification. The majority of respondents said they did not know much about ocean acidification. Our third question asked whether the respondents knew anybody who had been affected by ocean acidification. The vast majority of respondents said they did not know anybody affected by ocean acidification with less than 10 respondents indicating they did know somebody affected by ocean acidification. Similar responses resulted when the participants were asked whether they knew about the effect of ocean acidification with less than 10 respondents saying yes. Our next question was ‘How do you think that ocean acidification might affect you and/ or your community?’ and was an open response question. We received varied responses but, generally, no respondent was able to give a nuanced and fully understanding answer, leading us to conclude that there is not much general knowledge regarding how ocean acidification will affect people. Our next question was ‘Are you satisfied with the amount of green spaces, trees, plants or other forms of nature in your neighborhood?’ and we asked the question
for the reasons mentioned earlier. 33 respondents answered no and the rest answered yes. Our next question was ‘How often do you eat seafood?’ for reasons previously mentioned. We found that most people ate seafood at least once a month if not once a week with 32 out of 55 respondents saying that they ate seafood once a week or once a month. Our next question was ‘What type(s) of seafood do you most often eat?’ for those that responded, twenty seven percent of them eat shellfish most often and the rest eat ‘fish’ most often. Our next question was ‘Have you seen a particular increase or decrease in seafood prices (specifically shellfish) at your local grocery store?’24 out of 51 respondents replied in the affirmative. Our final relevant question was regarding whether there has been a decrease in the quality of seafood with 9 respondents out of 51 respondents replying in the affirmative. The rest of our questions were questions on ethnicity.
The goal of our study was to determine whether there is a connection between ethnicity and background and the effects of ocean acidification. Our hypothesis was that there was a significant connection between these two variables but, we found that the waters were murkier than they seemed. Though there are some connections between the variables we were unable to conclusively determine that these two variables are connected. Still OA remains a pressing issue as it connects to many new and emerging fields such as Carbon Capture and marine biology. OA in general is still an issue that needs to be addressed and though our study did not shed any light on anything new regarding the effects of OA the study served as a reminder of the effects of OA. The conclusion of this study is that there are many nuances to the question of how OA effects humans and any one simple conclusion would fail to
capture all the details involved in understanding and answering this pressing question.