An Analysis of the Polluted Floods in Jakarta, Indonesia

Jakarta, or, the Special Capital Region of Jakarta, is Indonesia's capital, in the millions. is home to 13 rivers that run through the city. These rivers flood every year, affecting roughly 40% of the city.   ⁽¹⁾  These rivers are polluted, and deemed nearly entirely unsafe to consume. This is happening in a city whose development is heightening demands for safe drinking water. Pollution from the urbanization and industrialism in Jakarta has caused large amounts of plastics to flow throughout the river water from the city center while human waste flows in from the outer parts of the province that live in poverty and have no access to proper plumping, or that have septic tanks that are leaking. This study will analyze qualitative and quantitative data in Jakarta on the basis of its local waters and how polluted they are. Just how polluted are Jakarta waters?

Biochemical oxygen demand (BOD) is a process or measure of how much oxygen microbial organisms need to break down organic compounds in water. These microorganisms used dissolved oxygen (DO) to complete this process. BOD essentially is a numerical representative of how much DO is being consumed in a local region of water. A high amount of BOD means there are low amounts of DO, and low amounts of DO can kill off local marine life and disrupt the ecosystem, causing more internal pollution. Human waste and industrial runoff can sharply increase BOD levels while microplastics cause harm to the way bacteria can degrade organic compounds in the water. Water treatment plants are a way to decrease BOD levels of water, however, in Jakarta, proper water treatment is severely lacking.  ⁽⁴⁾  

The Indonesian government has standards for which its water can be divided into 4 classes of cleanliness. Class 1 is little to no pollution, Class 2 is moderate pollution, and Class 3 and 4 and indicated as heavy pollution, starting at around a BOD level of 6 mg/L.  ⁽⁴⁾  In a study done in 2019 by Luo, P., Kang, S., Apip, et al., where spatiotemporal trend analysis was used to determine overall water quality across Jakarta via 44 water quality stations. (See Table 1)

The 44 water stations (pictured Figure 2.) are observing water from close to the city center, all the way to the outskirts of slums to ensure the most varied and accurate representation of data on water quality. The data shows that the different clusters of DO levels are more varied, with quite a few in the range of acceptable or moderate water quality, BOD levels however are above what is considered severely polluted across nearly every station of water. The average BOD frequency is around 3027 mg/L mark, which is far above the 12 mg/L parameter set for highly polluted water. Some places where the DO reaches <1 mg/L are places of extreme biological life loss due to scarce amounts of oxygen for wildlife, thus increasing the incidence of BOD in the localized area. Overall it showed a general inability of Jakarta water to support marine ecosystems and provide safe consumption for its residents. However, it should be noted a majority of the areas are seeing a decrease in BOD levels, as indicated by the 2nd graph of the top row this data gives insight into how much biological waste is polluting the rivers that cannot be dealt with either by the microbes in the water or the failure of treatment plants in supporting this process. Biochemical oxygen demand data is limited in the fact that is generally only a measure of organic pollutants, as things like plastics take no oxygen to decompose because plastic does not decompose in the natural sense, so water pollution by plastics and other related substances has to be taken into account of overall pollutants.

In general, though, quantitative data can give a greater or more specified and technical insight into a specific topic and allow people to critically think about what is being presented, so long as the data being provided was recorded and observed in good faith and under scholarly pretenses. This data for example statistically proves and allows a person to grasp just how polluted nearly every local river in Jakarta is severely polluted and unsafe for consumption or other uses like bathing. However, quantitative data generally lacks a tangible or emotional connection to what is being presented, rather gives the reader something to draw more precise and statistical conclusions from.

The capital of Jakarta has many residents finding alternative ways to get access to clean water due to how polluted it is. From one resident of Jakarta in an interview from 2024, "The river is shallow and a lot of the river hasn't been dredged. If there was enough dredging, floods could subside more quickly. Residents living next to the river also throw rubbish carelessly (into the river)." (2) Many residents of Jakarta have to buy water from their neighbors or private vendors at inflated prices due to no direct access to water. (5) Gaining access would require a pump, which would require a network to be installed, and for the land owners to install a network, at least 10 other properties must also want this. Along with this are the challenges of maintaining your pump as many clogs and leaks. Additionally, selling water privately is finable, thus the increased prices of water sold locally due to fear of having to recuperate losses from fines.

From resident accounts to how water is being managed by the government gives insight into how far locals in this community go to avoid their closest available water source due to fear of how dirty and unsafe it is. Additionally, the people of Jakarta face a higher incidence of contagious diseases that can spread via water, which couples with symptoms of diarrhea that those in poverty have to dispose of in the local rivers creating a cycle of disease and dirtier waters.

Qualitative data like this helps give the areas impacted a sense of tangibility and helps us experience a wider range of emotions to aid in forming our conclusions. Although nowhere near as analytical as quantitative data, qualitative data helps form essential connections by engaging (usually) with multiple senses that can help change how we view things or shape our perspective. For example, the data presented in quantitative gives a clear understanding of how polluted water is in Jakarta, but qualitative helps make the picture clearer and gives a sense of reality to it, like a photo of how dirty the water in Jakarta is in a colored photo (pictured left). However, a major downfall in qualitative data (which can be seen in some quantitative data too) is bias. Pictures could be altered, and news stories and interviews may be cut to push agendas or confirm biases. This could even be done unintentionally, for example by not getting enough of a sample size, whether that be interviewing enough people or taking pictures of only a select location. This could cause qualitative data to skew in certain directions and give a warped generalization to the reader about aspects of the topic as a whole.

Language-model-based chatbots like ChatGPT were used to assist in gathering ideas and information based on a general topic such as biochemical oxygen demand. ChatGPT and Elicit were also used to find videos that gave personal anecdotes by the people of Jakarta to help with qualitative analysis. Additionally, I asked ChatGPT to summarize quantitative and qualitative data.

Some key quantitative data it presented when asked to summarize quantitative data is listed below.

• Biochemical Oxygen Demand (BOD): The study observed that the average BOD levels across Jakarta's rivers consistently exceeded the Class III water quality standard of 6 mg/L set by the Indonesian government. This indicates a high concentration of organic pollutants, primarily from domestic wastewater.
• Dissolved Oxygen (DO): The average DO levels were found to be below the Class III standard of 3 mg/L, suggesting that the rivers are unable to support aquatic life effectively. Low DO levels are indicative of excessive organic matter decomposition, leading to oxygen depletion.
• Spatial Variation: The research highlighted significant spatial differences in water quality. Rivers passing through densely populated and industrial areas exhibited higher BOD and lower DO levels compared to those in less urbanized regions.
• Temporal Trends: Over the decade-long study period, there was a noticeable deterioration in water quality, with increasing BOD levels and decreasing DO concentrations, correlating with Jakarta's rapid urban growth and inadequate wastewater management infrastructure.

I think the points it brought up were informative and overall correct. I asked it to summarize data from one article, but it was able to accurately import somewhat hard-to-find data like the parameters of water quality set by the Indonesian government on things like BOD and DO levels. Additionally, it correctly sorted the classes and their respective amount of pollution. One thing it neglected to mention was how total suspended soils were a factor and instead chose to highlight everything but that. Overall the quantitative data provided is very useful, but needs to be fact-checked for inconsistencies, especially when it draws from things found outside what is being referenced.

When asked to summarize qualitative data, it struggled to not provide numerical facts such as percentages of failing water treatments, the total number of operations being run, and the total level of lack thereof. However, it was able to provide beneficial anecdotes about the pollution in the rivers such as "The rivers are heavily polluted by gray water from households and commercial buildings, as well as solid waste and fecal matter from slum areas. This pollution is exacerbated by inadequate waste disposal sites, leading residents to dispose of garbage into the rivers, a practice considered normal in some communities." and concludes that "These qualitative insights underscore the complex interplay of industrial, domestic, and systemic factors contributing to Jakarta's river pollution, highlighting the need for comprehensive waste management and pollution control measures."

As stated previously, qualitative data helps engage the reader with their emotions more so than quantitative data, and it is ironic qualitative data is something A.I. struggled with so much, constantly spewing out numbers and statistical data rather than personal accounts or things of the sort. Of course, all of this stems from which sources the A.I. draws from, if they are trustworthy, and if what the A.I. puts together is not just cohesive, but faithful to its source material.

Jakarta, Indonesia is a beautiful developing city with a myriad number of problems affecting the most vital source of life, water. The citizens and inhabitants of Jakarta struggle to get access to clean water, having to either pay their neighbor illegally selling theirs for an inflated price or pay the price of using water contaminated with plastics, industrial runoff, and an abundance of human waste. The rivers of Jakarta have unprecedented levels of biochemical oxygen demand due to the low levels of available dissolved oxygen to support marine life and break down organic compounds like human fecal matter. These factors make a majority of Jakarta's water unsafe and unfit for use, and only exacerbate the damages caused by floods. Using both qualitative and quantitative data helps form a full image of how poor the conditions so many in Jakarta are subject to and gives urgency to the need for change.