by Shanmugam Ganesan and Byeongchan Kang, American International School Chennai (AISC)
Abstract
This study aims to identify and discover water bodies of high nitrate levels across Chennai. Doing so is important because in excess, nitrate has been shown to be toxic to aquatic organisms and humans. Using an Arduino Uno Wifi Rev 2 connected to a Vernier Nitrate Ion-Selective Electrode, nitrate levels from seven samples of freshwater in Chennai were retrieved. These data points were compared to a reference point of 50 ppm to deduce whether their condition was toxic or acceptable. All lakes and rivers proved to have a nitrate concentration greater than the reference point, indicating that freshwater in Chennai is likely dangerous to aqualife and low-income families, who may rely upon these sources of water
Keywords
Nitrate; Freshwater; Arduino; Nernst Equation; Vernier Nitrate Ion-Selective Electrode
CONTEXT
Causes
Many countries face issues with nitrate contaminated freshwater. Primarily, this is a result of fertilizer and mistreated sewage contamination in water sources.
Since the Green Revolution between the 1950s and 1960s, farmers have increasingly relied upon fertilizers to boost crop yields. In addition to other few types of fertilizers, nitrogen-based ones have been popular. When used appropriately and with a limit, nitrate does not pose a great threat to the environment. However, when used excessively, to a point beyond which soils can withstand, nitrogen chemicals permeate through soil to groundwater. Soil erosion and runoff also cause nitrate contamination of nearby fresh and salt waters (“Nitrogen and Water Completed”, 2018).
In regards to sewage systems, when communities dispose of foods high in nitrate and certain kinds of waste like urine, the nitrate level of sewage water increases. If mistreated, or if a leakage is present, this water may spread to and contaminate groundwater. Often, this is an issue found in urban areas. For instance, the Chennai Metropolitan Water Supply & Sewerage Board (CMWSSB) states that in March of 2018, 82% of the urban city of Chennai was covered under the sewage system (CMWSSB: Sewerage System, 2018). However, according to the Comptroller and Auditor General (CAG) report of India, “[a]s of March 2019, only 52% of the sewage in the [Chennai Metropolitan Area] was being collected through the sewage system, leaving 48% uncollected.” Of the collected sewage, only 88% was being treated before being let out (Indian Audit and Accounts Department, 2020). These statistics demonstrate the relevance and importance of managing functional sewage systems in Chennai.
Other causes of high levels of nitrate in freshwater include mistreatment of animal waste—those produced by cows or dogs in the city, for example—and poorly designed septic systems. Although, it is to be noted that septic systems are not common in Chennai (TNN, 2016).
Consequences
Excess amounts of nutrients in aquatic ecosystems, like nitrate in combination with phosphorus, result in a biological process called eutrophication. Sufficient levels of nitrate in aquatic ecosystems are not harmful: algae and other aquatic plants rely on nitrates as a source of nutrients. However, excess levels of nitrate drastically increase the size and population of algae and other aquatic plants.
Growth may exceed to a point where algae covers the surface of the water, restricting sunlight and by consequence, inhibits the process of photosynthesis. In turn, underwater plants, as well as algae, die. When this occurs, bacteria consume the decaying organisms; such a process invokes aerobic respiration—in which bacteria use oxygen in addition to food for energy—releasing carbon dioxide inside the water. Ultimately, the activities of these bacteria deplete the level of dissolved oxygen in the aquatic ecosystem, creating an anoxic “dead” zone in the water. If fishes fail to migrate out of this zone, they die (“5.7 Nitrates,” 2012).
The accelerated process of eutrophication as a result of nitrate may damage the economic livelihood of fishermen. As fishes migrate and die, fishermen’s supply reduces and with that, so does their income. Beyond this, firms in the secondary sector that process fish will also have less fish to process, resulting in less revenue.
As it relates to human health, nitrate, when consumed in high concentrations in water, can produce adverse effects, primarily on infants. Moreover, excessive nitrate restricts the degree to which red blood cells carry oxygen, potentially creating a lack of oxygen, causing methemoglobinemia or “blue baby syndrome” (“Nitrate in Drinking,” 2021). As the name suggests, one of the main symptoms of this condition is the presence of a blue-ish skin tone on the infant. But in addition to this, methemoglobinemia has been shown to cause seizures, lethargy, difficulties in breathing, and in rare cases, death. Other consequences of high-nitrate consumption include an increased risk of cancer and thyroid disease (“Blue Baby,” 2018).
STUDY AREA
Limitations of Past Reports
Although researchers have published several reports on the issue of nitrate contamination in freshwater, the ones we identified each featured some limitations.
First, the Department of Chemistry at Anna University conducted a study on wells using a spectrophotometer where they found that approximately 50% of the samples they tested possessed a high concentration of nitrate (Selvaraj, Rengaraj & Murugesan, Velayutham & Lakshmanan, Elango & T, Elampooranan, 1996). Our study differs from this as it tests water from lakes and rivers, not necessarily wells; additionally, this study may have outdated data, as it was published in 1996.
Second, in 2014, the State of Environment and Related Issues in Tamil Nadu uploaded their findings on the water quality of various bodies of water across the state. It revealed that a number of samples featured high levels of nitrate (“Pollution Database,” 2014). While this report was relatively comprehensive, not all samples were tested for nitrate. Our study places more emphasis on this data point specifically.
Third, in the November of 2021, Sajil Kumar published his report on nitrate where his team explored the quality of water in the districts of Coimbatore and Tirupur. It found that 37% of the collected samples had an unhealthy concentration of nitrate (Sajil Kumar, P.J. & Kuriachan, Lemoon, 2021). While this study is up to date, ours differs from it as we focus on an entirely different city of Tamil Nadu state: Chennai.
Fourth, the “Impact of Solid Waste Effect on Groundwater and Soil Quality” written by N.Raman and D.Sathiya Narayanan, measured the groundwater and soil qualities without the parameter of nitrate level (Raman, Nishant & Narayanan, 2008). It also focused its research on the areas around the Solid Waste Landfill Site in Pallavaram, a district in Chennai, while our study tested samples from bodies of water throughout the city.
From all of this data, it is clear that nitrate pollution, at the time of data collection, was an issue across Tamil Nadu and likely Chennai.
Our Motivation
With this study, we sought to identify points of danger across bodies of water in Chennai. Nitrate has its effects on society, aqualife, and the economy, and so, it is all the more important to have current and relevant findings to not only spread awareness, but bring such an issue to the attention of local authorities. Thus, we researched the nitrate levels of different fresh water bodies around the Chennai city, as indicated in figure 2.
MATERIALS AND METHODS
Water Sample Collection
A total of 9 water bodies throughout the north, east, south, and west of Chennai, inclusive of areas in the outskirts, were selected. Using bottles, a sufficient amount of water was collected from each. In response to less accessible locations, a bucket pulley system—where a rope was tied on to
the end of a half-cut bottle—was used to reach the body of water. Here, after the bucket was launched into the water over any walls or obstacles, the rope was used to pull back the samples, which were then transferred into test-tubes prior to data collection.
Technology and Set-up
An Arduino Uno WiFi Rev 2, a microcontroller board capable of connecting to motors, sensors, and various external systems with a wifi unit, was used to extract the data collected by a Vernier Nitrate Ion-Selective Electrode. This nitrate sensor is a membrane-based electrode, which when submerged in a solution, measures the voltage of nitrate ions. These voltage values are dependent on the concentration of nitrate ions in solution, and so, can be used to deduce nitrate level with the Nernst equation. This report will delve more into this subject in the next section.
The Vernier sensor is compatible with both the Arduino integrated development environment (IDE)—the programming software used for Arduino—and LoggerPro 3, a data analysis software linked with Vernier. The LoggerPro 3 is capable of reporting data from Vernier’s Electrode. So, to verify the reliability of our Arduino, we corroborated its data with those retrieved by the LoggerPro 3.
The Arduino Uno WiFi Rev 2 was programmed to analyze the nitrate content detected by the Vernier sensor and transmit the collected data to our real time, online Firebase database, a platform capable of storing data points.
Data Collection
V=E0+m(ln C)
the Nernst Equation*, where V is the measured voltage, E0 is the standard potential for the combination of the two half cells, m is the slope, and C is the concentration of the measured species
The Nernst equation* was used to determine the mathematical relationship between the voltage value collected by the Vernier sensor and the concentration of nitrate in the aqueous solution.
We used the values collected by the Logger Pro from 1 ppm and 100 ppm solutions as our two known points when developing this mathematical equation. According to the data, the voltage value from 1 ppm was 2.260 V and the value from 100 ppm was 1.786 V. Using the straight line slope formula, it was deduced that for voltage, the slope is -0.103 and E0 was provided as 2.260 by the Vernier user manual.
This formula required concentration as an input and determined the voltage as an output. For the purposes of this experiment, the opposite was required. Thus, we found the inverse of the equation as follows.
As the Vernier sensor retrieved V, the voltage level of the sample, the above equation was used to determine C, concentration of nitrate.
Challenges and Our Methods
Our sensor, being a membrane-based one, is recommended to be used within 12 months of manufacture. The sensor was borrowed from our school and considering that it was purchased in 2018, the values the sensor collected were not always accurate, reliable or precise. Moreover, when trying to find a systematic difference between reported values and known concentration, there were no consistent precise factors. For example, on some days, the reported value was 2.7 times greater than the tested, whereas on others, it may have been 3.5 times greater. For this reason, a singular systematic error could not be identified, and the certainty of our data reduced.
*The Nernst equation was first introduced by a German chemist named Walther Hermann Nernst in 1888. It is often used to calculate the cell potential of electrochemical cells (“Nernst Equation, 2022). Cell potential refers to the potential difference—the difference between electric potential in voltage— between two half cells.
Consistently, however, when we tested highly concentrated samples, the program returned higher values than when relatively less concentrated samples were tested. Similarly, when we tested less concentrated samples, the program returned lower values than when relatively more concentrated samples were tested. As a result, it was still possible to deduce relative nitrate levels—the program consistently differentiated samples with more or less nitrate concentration given that testing occurred in a similar time frame—through setting a binary constant with which to compare other samples against. This binary constant was our reference point of 50 ppm (according to our research, this concentration is most certainly dangerous for human consumption, and most probably dangerous for an array of aquatic life). That is, we created and measured a 50 ppm sample of nitrate with the sensor, and compared that reported value with all of the reported nitrate values of the water samples. Through this, we determined whether a water sample had a nitrate concentration greater than or less than 50 ppm.
Another challenge was that as the sensor was submerged in a solution, the values continually became more and more accurate: this created variation within a sample in itself. To reduce inconsistencies within our findings, the time each sample was tested for was kept at five minutes as a controlled variable. After five minutes of submerging the sensor in each water sample, data was collected for one minute. The table below showcases an average of these detected values
| Water Samples* | Averaged Data (ppm) | 50 ppm Reference Point (ppm) | Percent Difference from the 50 ppm Reference Point (ppm) |
| Perungudi Lake | 93.45 | 13.93 | + 570.85% |
| Velachery Lake | 37.26 | 13.93 | + 167.48% |
| Buckingham Canal | 36.51 | 13.93 | + 162.10% |
| Madipakkam Lake | 38.51 | 13.93 | + 176.45% |
| Porur Lake | 22.53 | 13.93 | + 61.74% |
| Adyar River at Wellness Walking Track | 13.25 | 7.33 | + 80.76% |
| Retteri Lake | 7.45 | 7.33 | + 1.64% |
DATA EVALUATION
Sensor Error
Our Vernier sensor is old and potentially damaged. Unfortunately, due to resource constraints, we were unable to purchase a new sensor. This caused fluctuations in our findings, where on different days, the sensor reported different values for the same sample.
This indicates that no data point can be taken with absolute certainty: as the time frame of testing increases, the sensor becomes increasingly inconsistent across samples. This is even true for our binary point. For example, while we may have found that the sensor translates a 50 ppm sample to approximately 14 ppm, the binary point may have changed to 13 ppm if we tested for it a day later.
Even though we limited testing time to an hour or less per day, variations may have still occurred.
Personal Error
Personal errors may have also skewed the results, albeit only marginally. This is because the Vernier sensor was supposed to be submerged in each and every sample for 5 minutes. Due to imperfect reaction time, though, samples may have been tested for a slightly longer or shorter duration.
Sample error
Water samples from the Buckingham Canal, Madipakkam Lake, Porur Lake, and Wellness Walking Track were all collected one day after rainstorms. Since nitrate concentration in rainwater most likely differs from that of our freshwater samples, this may slightly skew our results. This suggests that the collected data might not accurately reveal nitrate levels of Chennai freshwater bodies for an entire year, especially when there is little to no rainfall.
Additionally, the data of this report would be more comprehensive in evaluating the severity of nitrate level in Chennai if more fresh water samples had been collected and recorded in the northern and southern part of Chennai.
RESULTS AND DISCUSSIONS
From viewing this data, all tested samples contained a higher concentration of nitrate than the reference point, 50 ppm. This firmly suggests that these sources of freshwater, and most probably others in Chennai as well, are unsafe to drink: the World Health Organization puts the maximum limit of nitrate on safe drinking water as 10 ppm (World Health Organization, 2011). The point of toxicity for fish, however, is much more contested. While some aquariums find that chronic exposure to nitrate levels above 30 ppm is harmful (PetMD, 2019), other organizations argue 44 ppm (Camargo JA, Alonso A, Salamanca A, 2005), and others 80 (“Water Treatment, 2022). Results are uncertain.
Regardless, it is clear that the conditions of water sources in Chennai are not ideal, especially for lakes and rivers that are significantly greater than the binary point. Moreover, 5 out of 7 of the data points report an increase of over 80% in ppm from the binary point, indicating that the majority of our samples have a high likelihood of being unsafe for aquatic life.
While the nitrate level from the Retteri Lake is relatively less, it seems like it is an outlier among the freshwater samples in Chennai that were tested. In fact, excluding outliers (Perungudi and Retteri Lake), our samples averaged a 129.71% positive difference in reported value from the binary point. The same argument can be applied to Perungudi Lake, which had an increase of 570.85%.
Despite some outliers, statistics from these water samples demonstrate that in Chennai, nitrate level is a prevalent issue. Most likely, the root of the problem can be traced back to factors such as sewage disposal, mistreatment of animal waste, and poorly designed septic systems.
CONCLUSION
While there are numerous aspects that determine water quality for humans and aquatic organisms, this lab report focuses on the nitrate levels of seven different fresh water bodies around Chennai. Vernier’s nitrate sensor and the Arduino microcontroller helped acquire the nitrate concentrations from these samples. With this data, it is evident that nitrate levels are excessive in many bodies of freshwater in Chennai, and it is likely many other cities face a similar issue. While finding nitrate concentrations can be an expensive process, the technology used in this investigation is more affordable. In future, the Arduino can be used in experiments in other locations as a relatively low-cost method of identifying dangerous water bodies.
Ultimately, the lab report highlights the severity of nitrate contamination in Chennai, and indicates its possible causes and consequences. Thus, authorities like the CMWSSB should take more
steps to ensure sewage is treated properly and fully as feasible. Doing so reduces nitrate levels, benefiting not only the aquatic ecosystems but also society at large.
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