Chapter 1

The Problem

Introduction

The globally known problem of water pollution affected our health for a very long

time. Unsafe drinking water kills more people a year than war and every other form of

violence combined. Not only that, but less than 1% of Earth's freshwater was accessible to us.

Moreover, if no action were taken, the problem would only get bigger by 2050 (NDRC,

2021).

Locally, things were the same. The Philippines was accountable for the annual

discharge of 0.75 million tons of plastic debris into the country's water (Development Aid,

2023).

Water pollution detection plays a significant role in this global problem. However, its

process could have been faster. "Labs were used to identify water pollution by analyzing

small water samples for various contaminants" (Lenntech, 2021).

However, there was a method that could help make the process faster: Significantly

elevated electrical conductivity could indicate that pollution had entered the river. A measure

of electrical conductivity could not tell you what the pollutant was, but it could help identify

a problem that may harm invertebrates and fish (QMUC UK, 2023).

If this technology had been applied to a small device, i.e., a small and straightforward

semi-autonomous robot, then it could have drastically sped up the process of water pollution

detection, allowing future researchers to find safe water sources faster.

This ignited the researchers' interest in developing an invention that would detect

water pollution, maximizing the idea of electrical conductivity. Further, this undertaking
would allow the researchers to assess this concept and confirm the effectiveness of electrical

conductivity in detecting water pollution.

Conceptual Framework

The focus of this study involved creating a semi-autonomous robot that could detect

water pollution via conductivity measurements.

Based on Atlas Scientific (2021), any significant changes in conductivity could

indicate a drop in water quality. However, water also contained several other

elements/chemicals that affected water quality: sodium, magnesium, and calcium, to name a

few. More than that, whenever an increase or decrease in conductivity was measured on a

conductivity meter, it could signify the presence of pollutants in the water.

Therefore, if the researchers had tested the Aquabot's ability to detect polluted waters,

it could have directly led to the effectiveness of this concept.

 Conductance  Conductance  Effectiveness
Rating of Range(s) of of the Aquabot
Water Polluted Water in Detecting
Sample(s)  Conductance Water Pollution
Range(s) of
Unpolluted
Water

Figure 1. The Conceptual Framework of the Study

Objectives of the Study

This understanding aimed to create and test an autonomous aquatic robot utilizing

electrical conductivity for detecting water pollution. Below are the objectives required to be

completed.

1. To assess the effectiveness of electrical conductivity for water pollution

detection.

2. To apply the concept of electrical conductivity to a water pollution detecting

device.

3. Install and integrate the device into an aquatic, autonomous (or semi-

autonomous) robotic device.

Scope and Limitations of the Study

The scope of this study involved the creation, programming, and testing of an aquatic

robotic device that could be used to detect polluted and unpolluted waters. To program this

simple robot, the researchers utilized a development board, specifically, an Arduino Nano

programmed to be a conductivity measurement tool. This study only covered the extent of

water quality categorizing, efficiency testing, and robot programming. Moreover, it took five

months to complete at most.

Due to budget restrictions, the Aquabot's build quality was affected; therefore, the

Aquabot was only designed to detect water pollution in freshwater sources with slow water

currents.
 The availability of water samples for testing the Aquabot may also have been limited.

Therefore, the researchers may have resorted to creating "Polluted" and "Unpolluted" water

samples to mimic real-life scenarios with varying water quality.

It was important to remember that this concept was not an entirely definitive "water

pollution detector"; the main reason for this was that to detect water pollution fully, other

factors needed to be considered, such as turbidity, salinity, temperature, and pH levels (Atlas

Scientific, 2023).

The researchers intrinsically relied on conductivity measurement for water pollution

detection since it is a relatively simple concept and budget-friendly.

Significance of the Study

This study was significant in environmental conservation, public health, and

sustainable development. Water detection enabled us to respond promptly and implement

mitigation measures, thereby preventing further contamination. These actions were essential

to safeguard our water resources and ensure a healthier and more sustainable future.

This study was expected to exert a substantial impact on the following:

Communities. Having access to sources of clean water benefited communities.

Aquabot: Aquatic robot pollution detection guaranteed that contamination was quickly

discovered, enabling swift corrective action. This promoted environmental and public health

protection.

Government Agencies. The study helped with resource allocation by precisely

identifying contaminated locations. To maximize the impact of conservation measures,

government agencies, and NGOs could concentrate their clean-up efforts and resources where

they were most needed.

 Future Researchers. Researchers gained from the study's techniques and

technological advancements, especially future generations of scientists and engineers. Their

skills and expertise were improved by exposure to cutting-edge research methods and

practical applications, preparing them for future similar or more complex research projects.

Definition of Terms

To register and analyze the terms used in the study, the following are thus

defined:

Aquatic. Relating to water (Oxford Languages, 2023).

Aquatic Robots- Robots that sail, submerge, or crawl underwater (robotplatform,

2023).

Conductance. The degree to which an object conducts electricity is determined by the

ratio of the current flowing to the potential difference present. This was measured in Siemens

or ohms and was the reciprocal of the resistance (Oxford Languages, 2023).

Intrinsic. Belonging to a thing's fundamental constitution or nature (Merriam Webster,

2023).

PH. The measurement of hydrogen ion concentration in a water-based solution. More

hydrogen ions are present in a liquid with a lower pH, while fewer are in a liquid with a

higher pH (Medical et al., 2023).

Resistance: The degree to which a substance prevents the flow of an electric current

through it (Cambridge Dictionary, 2023).

 Salinity. The amount of salt dissolved in water (Britannica, 2023).

Temperature. Various scales, such as Celsius and Fahrenheit, indicate the degree of

hotness or coolness (Britannica, 2023).

Turbidity. The measure of the relative clarity of a liquid. When light was shone

through a water sample, the amount of light scattered by the substance in the water was

measured. This was an optical characteristic of water (USGS, 2023).

Chapter II

Review of Related Literature

This chapter reviewed the literature on AquaBot: An Autonomous Aquatic Robot

Utilizing Electrical Conductivity to Detect Water Pollution, which provided a basis for

interpreting the data gathered.

Water Pollution

Water pollution has degraded the water quality in rivers, lakes, streams, groundwater,

etc. Numerous pollution causes degraded water quality. Heavy metals, fecal coliform

bacteria, phosphorus, sodium, nitrogen, sediments, and dangerous bacteria and viruses were

all examples of water pollutants. Such water pollutants cause severe health problems (Khalid

& Khaver, 2019). Every year, millions of deaths and other illnesses are attributed to water

pollution (Landrigan et al., 2018).

There were many ways to measure water pollution, including utilizing conductivity

measurement. This method made it possible to determine whether the water was polluted and

whether any remedies should be added. Conductivity has measured a solution's propensity to

allow an electrical current to flow through it. The quantity of inorganic dissolved chemicals

that had an ionic charge (also known as salts) affected the electrical currents in water. In this

instance, the greater the ions and dissolved salt in the water, the greater the ability to conduct

electricity. Hence, as salinity increased in water, so did the electrical conductivity (EC).

Electrical conductivity measured the ability of water to conduct an electrical current, and it

was measured in microsiemens per centimeter (uS/cm), a unit of measurement for electrical

conductivity (Pomelo, 2022).

The electrical conductivity, which ranged from 0 to 50,000 uS/cm, was used to

determine how salty the water was. Seawater typically had a conductivity value of roughly

50,000 uS/cm, while freshwater typically ranged from 0 to 1,500 uS/cm. Natural streams

included small amounts of salt, which were essential for the growth of both plants and

animals. High salt concentrations in freshwater could negatively affect aquatic ecosystems

and impede human usage (Wedlock et al., 2018).

μS/cm Use
0 - 800 • Good drinking water for humans (provided there was no organic

pollution and not too much suspended clay material)

• Generally suitable for irrigation, though above 300μS/cm, some care had

to be taken, particularly with overhead sprinklers, which might have caused

leaf scorch on salt-sensitive plants.

• Suitable for all livestock

800 - 2500 • It could be consumed by humans, although most would have preferred

water in the lower half of this range if it were available.

• When used for irrigation, it requires special management, including

suitable soils, good drainage, and consideration of plant salt tolerance.

• Suitable for all livestock

2500 -10,000 • Not recommended for human consumption, although water up to 3000

μS/cm could have been consumed.

• Not usually suitable for irrigation, although water up to 6000 μS/cm

could have been used on salt-tolerant crops with special management

techniques. Over 6000 μS/cm, occasional emergency may have been

possible with care.

• When used for drinking water by poultry and pigs, the salinity should

have been limited to about 6000 μS/cm. Most other livestock could have

used water up to 10000 μS/cm

Over 10,000 • Not suitable for human consumption or irrigation.

• Not suitable for poultry, pigs, or any lactating animals, beef cattle could

have used water up to 17000 μS/cm, and adult sheep on dry feed could

have tolerated 23000 μS/cm. However, it was possible that waters below

these levels could have contained unacceptable concentrations of particular

 ions. Detailed chemical analysis should, therefore, have been considered

before using high-salinity water for stock.

• Water up to 50000 μS/cm (the salinity of the sea) could have been used

(i) to flush toilets, provided corrosion in the cistern could be controlled,

and (ii) for making concrete, provided the reinforcement was well covered.

Technology in Detecting Water Pollution

"The existing water pollution detection technologies included laboratory detection

technologies, remote sensing monitoring technologies, field quick detection technologies as

well as automatic and continuous monitoring technologies" (Zhang et al., 2019). Detecting

polluted water contaminants was a crucial step toward purifying the water and removing its

adverse health hazards.

Using hyperspectral imaging, a vast span of the electromagnetic spectrum was

covered by high-resolution photographs. Based on their distinctive spectral signatures, it

allowed for the identification and classification of water constituents. By studying the

reflected light from water bodies, researchers could gather valuable data regarding water

quality characteristics like chlorophyll-a concentration, turbidity, dissolved organic matter,

and algal blooms. Using aerial or satellite platforms, hyperspectral imaging could provide

extensive coverage and real-time monitoring capabilities (Ganesan, 2023).

For laboratory detection technologies, one of the technologies that could innovate the

place of traditional analytical systems was microfluidics. It enabled technology to perform

biological and chemical experiments at significantly reduced spatial scales. Most microfluidic

devices operated with conventional pumps and valves and relied on electronics for sensing,
which increased the size and price of the finished platforms and lessened the likelihood of

their commercialization (Saez, 2021).

For detecting technologies, spectroscopy was an alternate way of identifying

substances. Ultraviolet-visible (UV-Vis) spectroscopy offered an efficient method for both

qualitative analysis and quantitative identification of pollutants in a water environment. It

was based on the principle that different substances absorbed light at different wavelengths

and that the amount of light absorbed was proportional to the substance's concentration.

When intense monitoring of water resources was required, UV-Vis spectroscopy proved a

tool for online and in-person water parameter monitoring (Guo et al., 2020).

Furthermore, the smartphone sensor used DNA-magnetic particle technology,

including minuscule magnetic particles intended to seek out particular bacteria in a water

source. The sensor strip that attracted DNA-magnetic particles was inserted into the water

sample, and the sensor strip was then inserted into a device controlled by a smartphone app,

which then did an electrochemical measurement to find any bacteria in the water sample (Vu,

2018).

Related Studies

In one study by Regino et al. (2023), water samples from various rivers in Calasiao

were tested to determine the accuracy and precision of the Hydrabot. The readings were

compared to those from the Dagupan City Water District, and the results were verified using

Paired T-test. The Hydrabot was accurate and precise, as shown by the fact that neither the

computed standard deviation nor the average % relative error of the three samples on both

variables exceeded their respective thresholds. The study could be used in public barangay
offices to monitor communities' water quality. Using more sensors, like identifying specific

components and living organisms, helps to analyze water quality in-depth, as well as a larger

LCD screen, which can show the values of more sensors and their labels.

Similarly, a study conducted by Adarsh et al. (2021) showed that a robot could

monitor the water quality in a particular area, identify anomalies in the data, and detect oil

spills from shipwrecks and pipeline leaks. Local regulations could be created to prevent

pollution and raise awareness about the kind of components that wind up in the ocean or

other bodies of water using the data on water quality and the knowledge on contaminants

from the machine learning model. In other words, a robot with water quality monitoring

sensors, such as pH and electrical conductivity sensors, could address the ocean's oil spills.

A robotic eel called the Envirobot was intended to detect the source of water

pollution. It was part of a large project funded by the Swiss NanoTera Program and was

created by researchers at the École Polytechnique Fédérale de Lausanne (EPFL). The robot

had several sensors that enabled it to identify various forms of pollution, such as electrical,

chemical, and environmental disturbances in water. Engineers could alter the Envirobot's

length and composition because of its modular construction. Some modules contained tiny,

sophisticated chambers filled with water as the robot swam, and others featured conductivity

and temperature sensors. These chambers held bacteria, tiny crustaceans, or fish cells in the

form of miniature biological sensors. The sensors functioned by observing how these

organisms responded to the presence of water, which provided information about the water's

general toxicity and certain important pollutants (Cook, 2019).

A remotely controlled boat that could be used for water quality monitoring and

sampling was presented by Desouza et al. (2021). Based on their study, the boat was square-

framed and equipped with various sensors, including pH and electrical conductivity. A
Bluetooth module was incorporated into the water quality monitoring boat for remote

communication. Moreover, a GPS unit mounted on the boat enabled the user to obtain the

precise location of the boat and its contaminants. The PC and LED screen displayed the

results with their readings alongside the water's pollutants and restriction points.

The study of Chen et al. (2023) used biomimetic robotic fish for time monitoring and

automatic feedback to monitor dynamically several water quality parameters in aquaculture.

The robotic fish had sensors that measured water quality parameters such as temperature, pH,

dissolved oxygen, and turbidity. It was made to simulate the swimming motion of actual fish.

Remote controllability and programming of the robotic fish allowed for total coverage in the

aquaculture field. Real-time data on these water quality parameters could be obtained in this

way, allowing for improving fish growth and health while also optimizing the aquaculture

environment. Using 3D printing technology to build the robotic fish enabled the robotic fish

to be produced at a minimal cost.

Research Gap

Most earlier studies on evaluating water quality relied primarily on fixed sensors or

manual data collection techniques. This study distinguished by accepting the challenges of

integrating water pollution detection equipment into autonomous or semi-autonomous aquatic

robots.

This study stood out for its dedication to simplicity and achievement. Introducing

electrical conductivity into an aquatic robot offered a simple yet effective approach, in

contrast to numerous complicated and resource-intensive technologies utilized in water

pollution monitoring. This practicality and ease of use translated into greater accessibility for

a broader range of users, such as environmental agencies, researchers, and local populations.

The simplicity of the method and the autonomy it offered raised the possibility of

low-cost water pollution monitoring. This enabled regions and organizations with low

resources to efficiently identify issues about water pollution, which could be very useful for

them.

Considering all this, the simplicity and applicability of this study's approach to

detecting water pollution made it special. The benefits included efficiency, real-time

monitoring with autonomous robotics, simplified device development, and ease of operation,

making it a potential strategy for various applications. Due to its unique approach, the study

was well-positioned to considerably impact the fields of environmental management and

water pollution identification.

Chapter 3

Research Methods

This chapter entailed the methods and processes that showed the research design,

research environment, research specimen, data-gathering procedure, statistical treatment, and

ethical considerations in research. The researchers underwent experimental research to

complete this study's objectives.

Research Design
 This study utilized the actual experimental research design. Quantitative research

involves collecting data so that information can be quantified and subjected to statistical

treatment to support or refute "alternate knowledge claims" (Creswell, 2003). A proper

experimental design uses the scientific method to determine the relationship between a set of

variables in a research study regarding cause and effect. A control group that offered

trustworthy standard data to which the experimental results could be compared was a

necessary component of accurate experimental research. The researcher attempted to control

every other variable except for the independent variable, which was the one being

manipulated. In an experimental research design, the effects of the independent variable on

the dependent variable were gathered and analyzed for a relationship (Johannesson,2014).

The researchers utilized the quantitative research design for one main reason: the

Aquabot was programmed to measure the conductivity of a tested water sample or the water

it stayed afloat on in microsiemens/centimeter (µS/cm). Then, it classified the measured data

into two nominal categories labeled "non-polluted" (signified by the blue LED) and

"polluted" (signified by the red LED) in real-time.

The nominal categories in question were 0-2500 µS/cm for the "non-polluted"

category and >2500 µS/cm for the "polluted" category (Wedlock et al, 2018). The researchers

chose the actual experimental design to test the Aquabot's capabilities in detecting water

pollution across various water samples, using tap water as a control variable.

Research Environment
 This study was conducted at Mahayag School of Arts and Trades (MSAT) in Purok 3,

Poblacion Mahayag, Zamboanga Del Sur, near the Salug Valley River. This benefited the

researchers as they needed to take samples from the river for testing and utilize the aquatic

environment to test the Aquabot's capabilities. The location was strategic, especially for the

researchers, due to an open body of water requirement. Moreover, since MSAT's Campus

was situated nearby, data gathering, testing, and compiling were relatively efficient,

eliminating the need for long-distance travel between locations.

Research Specimen

This portion detailed the required specimen(s) to test the Aquabot’s effectiveness in

detecting pollutants present in water samples.

 Household tap water and river water samples collected from the Salug Valley River

were utilized to act as a basis. However, to simulate real-life conditions where "muddy" water

is present, a second set of these water samples was combined with garden soil.

The most common form of pollutants in the area were agriculture-grade chemicals

such as fertilizers, herbicides, fungicides, etc. The researchers chose chemicals/substances

like Ammonium Sulfate and isopropylamine glyphosphate salt ("Roundup" Herbicide). The

dried Bacillus Subtillis Strain ("Serenade" Fungicide) acts as "pollutants" during testing due

to their high concentration in easily polluting water sources and their attainability.

The term "polluted" was used throughout the procedure to name the water samples

combined with the mentioned chemicals.

Materials

The materials required for this study were divided into two portions: (1) For the

Aquabot's assembly, and (2) For the preparation of chemicals/substances needed to test the

Aquabot.
 Materials required in portion 1:

Quantity Materials

1× Arduino Nano

1× Mini Breadboard

4× Jumper wires

1× 1K resistor

2× 220-ohm resistors

1× Red led

1× Blue led

1× 9V battery

1× DC jack-9V connector

1× Female DC plug

Materials required in portion 2:

Quantity Materials
½ liter Tap water

1 liter River water

12× Disposable cups

½ cup Ammonium Sulfate

½ cup Roundup Herbicide

½ cup Serenade Fungicide

1 kg Garden soil

Procedure

The procedure will be divided into two parts: Part A will be the Aquabot's assembly

and programming. Part B will be Aquabot’s testing with the created samples.
Part A:

1. Prepared needed materials

2. Mounted the Arduino Nano into the mini breadboard

3. The components were assembled as shown in the diagram below.

4. Attach the USB cable to the Arduino and a computer.

4. Ran Arduino IDE on the computer and uploaded the following source code:

int analogPin = A0;

int raw = 0;

int Vin = 5;

float Vout = 0;

float R1 = 1000;

float R2 = 0;

float buffer = 0;
5. Detached the Arduino from the computer.

Part B:

1. Pour 1 cup of tap water into four disposable cups, then repeat the same process for

the river water.

2. Pour 1 cup of river water into four more disposable cups and combine 1 tbsp of

garden soil into each of the 4 cups.

3. Added ½ tbsp of Ammonium sulfate into one of the tap water cups, one of the

river water cups, and one with garden soil.

4. Repeated step 2 for the Roundup Herbicide and Serenade fungicide.

5. Dipped the exposed pins in each sample.

6. Then, the data was collected by which color of the LED lit up.

Data Gathering

Aquabot was programmed to measure the conductivity of a water sample using its

exposed pins and will light up a red LED if the water sample is polluted and a blue LED if

the water sample is unpolluted.

Numerically, if the water sample's conductivity is measured at 0-2500 µS/cm, the

water sample is unpolluted. Moreover, if the water sample's conductivity is greater than 2500

µS/cm, the water sample is polluted (Wedlock et al., 2018).

The gathered data in the procedure was put into two tables: Table 1 was for every

water sample without the garden soil combined. Table 2 was for the water samples with the

garden soil combined. This is because Table 2's water samples are intended to act as a mock

test for the Aquabot to mimic real-life scenarios with varying water quality. Because those
water samples have garden soil, it will make it more difficult for the Aquabot to detect any

pollutants in the "muddy water."

Chapter IV

Presentation, Analysis, and Interpretation of Data

This chapter showcases if the researchers were able to apply the concept of electrical

conductivity into a water pollution detecting device, install and integrate the device into a

small, aquatic, autonomous(or semi-autonomous) robotic device, and asses the effectiveness

of electrical conductivity for water pollution detection.

The resultant device created by the researchers can be described as an Arduino Nano

installed on a mini breadboard with components attached to designated pins. The device's

operation is simple: While the device is turned on, the user can dip the exposed pins in a

given water sample. Then, the device will measure the conductivity of the water sample and

light up a blue LED if the conductivity rating is under 2500 µS/cm and light up a red LED if

the conductivity rating is over 2500 µS/cm; this indicates that the water sample may have

potential pollutants present in it (Wedlock et al., 2018).

The procedure for testing the Aquabot was divided into two parts: for part 1, the

“Non-muddy water” test, the chemicals/substances were dispersed and diluted into 1 cup of

water and river water, respectively. For part 2, the “Muddy water” test, the

chemicals/substances were diluted in water and garden soil to simulate real muddy water

conditions.

Logically, the results are interpreted using two tables, one for each part. Discussions

are then presented accordingly.

 Table 1: “Non-muddy water” test results

Sample LED Color

Tap water Blue

Tap water +Isopropylamine Glyphosphate Red

Salt (“Round Up”)

Tap water + Dried Bacillus Subtilis Strain Red

(“Serenade Fungicide”)

Tap water + Ammonium sulfate Red

River water Blue

River water + Isopropylamine Glyphosphate Red

Salt (“Round Up”)

River water + Dried Bacillus Subtillis Strain Red

(“Serenade Fungicide”)

River water + Ammonium Sulfate Red

Table 1 presents the data gathered during phase 1 of testing,. As shown, the Aquabot

did what it was expected to do when testing the tap water, which was to light up the blue

LED to indicate that the sample’s conductivity was under 2500 µS/cm. Threshold. For the

ammonium sulfate and "Round up", the Aquabot managed to detect each chemical's presence

in the water. Moreover,Surprisingly, the same goes for the "Serenade fungicide," which is a

dried strain of bacteria and is not a definitive form of chemical.

 During testing, the blue LED lit up when the Aquabot was being tested with the plain

river water, which is unpolluted but certainly has minerals dissolved since it was collected

directly from the Salug Valley River. This indicates that the Aquabot is capable of detecting

conductive pollutants present in water while being insensitive enough to ignore the river

water's naturally occurring dissolved minerals.

Table 2: “Muddy water” test results

Chemical/Substance LED Color

Garden Soil (“Muddy Water”) Blue

Garden Soil + Ammonium sulfate Red

Garden Soil + Round-Up Red

Garden Soil + Serenade Red

Table 2 shows the data gathered during phase 2 of testing. When the garden soil

sample was tested, the Aquabot detected no trace of any chemicals/substances given by the

lit-up blue LED. This is intriguing, considering that garden soil will most likely have

minerals. However, this topic is beyond the scope of this study, and the researchers will not

discuss this any further.

Regarding the other chemicals/substances, since the Aquabot managed to detect their

presence, as given by the red LED, it is only logical for the Aquabot to detect these

substances in the simulated muddy water. Moreover, as expected, the Aquabot still managed

to detect said substances. However, to further discuss, one small detail the researchers did

manage to observe was on the garden soil mixed with the "Serenade" fungicide. The Aquabot

detected its presence around 3 seconds after the probes (exposed pins) were dipped into the
sample. This is unlike the other samples where the Aquabot detected their presence almost

immediately.

Chapter 5

Summary of Findings, Conclusions, and Recommendations

This chapter provides a summary of the data gathered throughout the study. Detailing

the findings and providing the conclusions and recommendations for future related studies.

Summary of Findings

This study aimed to determine the effectiveness of the Aquabot in detecting water

pollution utilizing the concept of electrical conductivity. Throughout the study, the

researchers mainly explored the first objective: To Assess the effectiveness of electrical

conductivity for water pollution detection. The following is the summary of findings based on

gathered data.

1. Assess the effectiveness of electrical conductivity for water pollution detection.

Based on the results, the Electrical Conductivity is effective for water pollution

detection but only to some extent; it could detect the pollutants present within the tested

water samples. The "Pollutants" in question were common chemicals found within the

researchers' area: a standard fertilizer (Ammonium Sulphate), a herbicide ("Roundup"), and a

dried bacteria strain-based fungicide (“Serenade”). Thus proving Electrical Conductivity's

effectiveness in detecting water pollution.

Conclusion

The results above have led the researchers to conclude that the Aquabot, utilizing

electrical conductivity for water pollution detection, effectively detects water pollution in

most situations. In the researcher's case, it was in water samples that simulated real-life

situations where water pollutants would be present. However, the Aquabot may not be able to

detect water pollution in harsh, unforgiving environments such as fast-current

rivers/waterways because the researchers did not test this since it was beyond the scope of the

study.

Nonetheless, the Aquabot was able to do what it was created to do: detect watto er

pollution via electrical conductivity and effec,tive it provedg to the researchers' testing.

Recommendations

The study found that Electrical conductivity is capable of detecting water detection to

a certain extent. Thus, the following recommendations are at this moment presented:

1. Due to the limitations in the availability of chemicals that could act as water

pollutants, it would be advisable to test further Electrical Conductivity's capability

in detecting the presence of various other pollutants that might be present in water.

2. Test Electrical Conductivity’s effectiveness in detecting water pollution by

gathering samples from truly polluted water sources.

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