An Economical Approach For Detecting Water Contamination At Homes- Preventing A Public Drinking Water Crisis

Summary

Contamination in water poses a serious and mostly undetected threat. It is necessary for people to have an economical option to test the water they drink in their own homes. The goal of this research is to develop an economical device to detect contaminants like lead and fluoride that exceed the EPA guidelines. Devices that can be used at home to detect these levels are unavailable in the market.

Molecular bonds absorb energy and vibrate when exposed to infrared light. The vibrating bonds scatter the light that is transmitted through them depending on the frequency of the transmitted light. The amount of scattering is unique to each kind of molecule and varies with the number of molecules. These concepts were used to build a device that can detect concentrations of lead and fluoride in a water sample. The device was initially calibrated using known lead and fluoride stock solutions. Using deep data analysis on the calibration data, a relationship between the scattering and the concentration of each toxin was established. The device is paired with an iPhone application to display the results. This device provided fairly accurate measurements for each sample, providing an affordable method to test the water supply at homes.  In the future, I intend to extend testing to other toxins like mercury, add more wavelengths of light to improve the device's accuracy and develop a social sharing capability to the iPhone app to help share the water quality results with the world.


 

Question / Proposal

There has been a widespread failure to detect drinking water contaminants. In many situations, the contamination levels are almost double the EPA limits—the EPA limit for lead in drinking water is 15 ppb. A study done at Virginia Polytech Institute discovered the average lead levels in the water supply to be more than 27 ppb (Rucker, 2015). Lead poisoning is a severe health condition, and health effects include impaired cognition, hearing problems, delayed puberty, and behavioral disorders (Cafasso, 2016). Similarly, fluoride concentrations beyond standards cause dental and skeletal fluorosis.


Although there are several ways that people can test for the presence of toxins (like lead and fluoride) in water, the existing methods are either expensive or inaccurate: spectrometers cost $3,000-$5,000 (complexity of usage makes it unsuitable for homes), colorimeters cost $600-$1000 to purchase, outsourced lab tests cost around $100 for each sample (results often take more than 10 days), and store-bought test kits are inaccurate and can only measure much higher concentrations of toxins (lead test kits measure in ppm whereas EPA guidelines are in ppb). Therefore, there is a need for people to have an inexpensive and reliable method to test their drinking water.


The purpose of my research was to provide a low-cost technology that can be used in homes to
measure various toxins in water. A device using the concepts of infrared molecular
vibration, Raman scattering, and Surface-Enhanced Raman Scattering (SERS) was constructed
and combined with traditional spot tests and an iPhone application to detect water contamination.

Research

A combination of infrared molecular vibration, Raman scattering, SERS, and spot testing can be used to determine the content of various toxins in spot test samples of water.

Infrared Molecular Vibrations:

Molecules can absorb infrared wavelengths, causing the covalent bonds within the molecule to vibrate. Atoms move towards and away from each other at specific frequencies.

Raman Scattering:

Raman spectroscopy, a molecular spectroscopy technique used for molecular fingerprinting and deep study of molecular structures. Being highly selective, Raman spectroscopy allows for the identification and differentiation of molecules and chemical species that are very similar.

Surface Enhanced Raman Scattering (SERS):

Surface Enhanced Raman Spectroscopy is a technique that enhances Raman Scattering. Raman scattering usually is very weak and difficult to detect using low-cost sensors.  Silver produces the strongest SERS effect, and colloidal silver can be used as a substrate. (Cyrankiewicz, 2007)

Molecular bonds absorb energy and vibrate when exposed to infrared light. The vibrating bonds scatter light transmitted through them and cause frequency shift depending on the frequency of the transmitted light (Raman Scattering). If various frequencies of light are passed through an excited molecule, each frequency of light will scatter differently. Therefore, a molecular fingerprint can be created for a toxin by measuring and plotting the scattering of various frequencies of light. The molecular fingerprint may be used to detect the toxin in a water sample.

Spot Test:

Combined spot test and diffuse reflectance spectroscopy using filter paper are widely used for
toxin detection (Luiz, 2015). Spot tests are created by mixing 3 drops of distilled water and 1 drop of indicator solution on a porous support. The indicator reagent is from a store purchased testing kit like “First Response Lead Test Kit” for lead testing and Fluoride Low Range Checker Reagent for fluoride testing. 

Testing Device:

The testing device consists of 2 parts: A data capturing device using Arduino Uno microprocessor connected to a light sensor, LED Neopixel Ring, IR LEDs, and a Bluetooth module (see figure below), and an iPhone app which processes the color sensor values obtained and calculates the lead content in the spot test sample.

Data Capturing Device:

The data capture device primarily consists of Arduino Uno, a microcontroller board based on the ATmega328P, connected to a TCS3200 color sensor to capture color characteristic changes with the application of Red, Blue and Green filters. The microcontroller is also connected to Adafruit 12 LED Neopixel Ring for generating light with different frequencies, IR LED lights to create molecular vibrations and HM-10, a Bluetooth module, to communicate to iPhone app. All the components are housed in an Arduino box with an opening for color sensor and IR lights.

iPhone App:

The iPhone app has a simple user interface to scan and connect to the Bluetooth device. On a successful connection, the user initiates the test and upon initiation, the app sends a message to the data collection device to begin testing.

 

 

 

 

Method / Testing and Redesign

Stock Solutions-

Lead:

Lead stock solutions were prepared with lead nitrate purchased from Home Science Tools. Stock solutions of 10 ppb, 20 ppb, 30 ppb, and 50 ppb were prepared. Spot tests were created for each solution. Each sample was dried for two hours and then prepared for testing. A control sample spot test was also prepared using distilled water instead of the lead stock solutions.

Fluoride:

Fluoride stock solutions were prepared with sodium fluoride fine powder purchased from an online store. Stock solutions of 8 ppm, 6 ppm, 4 ppm, and 2 ppm were prepared
using distilled water and sodium fluoride. Fluoride Low Range Checker HC Reagent was used as the reagent in the spot test. 

A control sample spot test was also prepared using distilled water instead of the lead and fluoride stock solutions.

SERS:

Colloidal silver 30ppm was purchased from Amazon.com and used for Surfaced Enhanced
Raman Scattering (SERS). All the spot tests were recreated by adding 2 drops of Colloidal silver to study the SERS effect.


Each spot test sample was placed at the opening of the data capture device and covered with a dark board or coversheet to block room light. Various readings of the sensor (in pulses) with Red, Green, and Blue (RGB) filters were then taken of each spot test sample.

To initiate the testing, a button is pressed in the iPhone app and a signal is sent to the device to begin. When the device receives the notification through Bluetooth, it generates light with 6 frequencies sequentially using the Neopixel LED ring: Red (700 nm), Yellow (575 nm), Green (540nm), Cyan (515 nm), Blue (460 nm), and Magenta (425 nm). The sensor then applies red, green, and blue color filters and takes several readings of the reflected light from the spot test (for every frequency of light that is shined). After these readings are taken, the IR LED lights are then turned on and the spot test paper is exposed for 5 minutes. The IR LED lights are turned off and the process of generating 6 frequencies of light and taking sensor readings is repeated. 

The pulse variation data were obtained by finding the absolute value difference between the readings before IR LED exposure and the pulse readings after IR LED exposure. These pulse variations were used to calibrate and determine the lead and fluoride content.

Results

All data is stored in CSV format and analyzed using R Studio by utilizing various R libraries. The goal is to identify the light frequency at which a noticeable pulse variation or frequency change occurs for a particular toxin.

Fluoride-

The following plot demonstrates how the frequency change is noticed for each light frequency and by the concentration of fluoride in parts per million(ppm). The plot also shows the frequency change captured by each filer Blue (B), Green(G) and Red(R).

It is clear that Blue light (460 nm) has significant pulse variation when the red filter is used. The following plot shows the same information when the SERS substrate is used.

 

 

Since higher pulse variations were obtained by adding the SERS substrate, the readings with the SERS substrate were considered for further analysis.

The following plots establish a relationship between pulse variation (frequency change) of Blue light (460 nm) and concentration of fluoride when the red filter is used.  Three models were created: Linear, Quadratic and Cubic.

Bellow: statistical details of each model from R libraries “tidr” and “broom”:

 

 

Models can be selected based on the accuracy needed. The quadratic model was picked for
calibration because it is a good balance between the linear model, which has lower precision, and
the cubic model, which is too complex. The R squared value is 0.96 and the p-value is 0.037. The formula "y = 8.738e-07x^2 + -4.400e-04x + -2.938e-01" is used to calculate fluorine concentrations.

 

Lead-

The following plot demonstrates how the frequency change is noticed for each light frequency and by the concentration of lead in parts per billion(ppb). The plot also shows the frequency change captured by each filer Blue (B), Green(G) and Red(R).

It is clear that Magenta light (425 nm) has significant pulse variation and has a measurable pattern when blue filter is used. The following plot shows the same information when SERS substrate is used.

It is noticed that by adding SERS substrate the pulse variation shifted to Cyan light (515 nm) when Blue filter is used. Since higher pulse variations were obtained by adding SERS substrate, readings with SERS substrate were considered for further analysis. The following plot displays frequency change when blue filter is used.

The following plots establish a relationship between pulse variation (frequency change) of Cyan light (515 nm) and concentration of lead when the blue filter is used.  Three models were created: Linear, Quadratic and Cubic.

Below:  the statistical details of each model from R libraries “tidr” and “broom”:

 

Models can be selected based on the accuracy needed. The linear and quadratic models are very
close to each other, but the linear model has better p-value. Hence linear model was picked for
calibration. The R squared value is 0.98 and the p-value is 0.001. 

The formula "y = 0.068849x + 22.4085" is used to calculate lead concentrations.

 

Conclusion

Cost Comparision-

The manufacturing cost of the device based on the best retail prices and usage of pro components will be less than $26. The mass production cost will likely be much lower than the calculated cost of production.

 

 

Conclusions-

  • It is demonstrated that IR molecular vibration and Raman Scattering causes variation in the light characteristics with the amount of lead and fluoride contents in samples.

  • The variation in the light characteristics of different wavelengths of light can be captured using a sensor, and a mathematical model can be established.

  • A simple device using open source electronics can be constructed and paired with a smartphone application to provide a very economical solution for lead and fluoride measurements in water samples.

  • This solution provides a very good option for many households to have a lead and fluoride testing device in their own homes.

Future Research-

  • Add more wavelengths of light to get better accuracy. Also, study on adding wavelengths in nonvisible light and sensors capturing those wavelengths.

  • Research on better quality sensors so that multiple readings could be avoided.

  • Extend the testing to other toxins like Mercury, Pesticides, etc.

  • Develop social sharing capability to iPhone application to help share water quality results across the country.

 

About me

My name is Meghana Avvaru and I am from Nashua, NH. I love the environment and nature. 

I am also a music aficionado. Playing piano is one my most favorite pastimes, and I have been playing since I was nine years old. I also love playing tennis. I am the captain of my high school's girls varsity tennis team and I compete annually in USTA New England tournaments.

I have loved math and science all of my life, and I have always been inspired by incredible scientists and inventors such as Albert Einstein and Nikola Tesla who have made extraordinary impacts on the world.

I specifically became interested in creating ways to use technology for solving real-world environmental problems a few years ago. I learned of specific instances of water and food contamination that had affected people all over the world, and I became motivated to find technologically-advanced solutions.

I plan to study in the field of environmental engineering and computer science in post-secondary education. Researching in the field of environmental engineering would also be a very likely career path in my future, and I intend to dedicate myself towards untangling some of the most pressing environmental problems.

Winning the Google Science Fair would mean that my research for detecting toxins in water can easily reach communities around the world who unknowingly suffer daily from water contamination. This would mean the world to me. 

 

Health & Safety

Hazardous chemicals that were used for experimentation include lead nitrate, the indicator reagent from the First Alert Lead Test Kit, sodium fluoride, and the Fluoride Low Range Checker. These chemicals may cause pain and redness if in contact with the skin and/or eyes. During the experimentation, safety procedures were implemented. Gloves, safety goggles, and lab apron were worn at all times during experimentation. Reagents and chemicals were stored in separate, covered plastic bins. The lead and fluoride stock solutions were disposed of safely through the Nashua Wastewater System. Filter papers from spot tests were safely disposed through solid waste (trash).

Bibliography, references, and acknowledgements

B&WTek, n.d. Theory of Raman Scattering. [Online]
Available at: http://bwtek.com/raman-theory-of-raman-scattering/

Cafasso, J., 2016. "Lead Poisoning." Healthline. Healthline Media. [Online]
Available at: http://www.healthline.com/health/lead-poisoning#Symptoms2

Cyrankiewicz, M., 2007. Study of SERS efficiency of metallic colloidal Systems. Journal of Physics: conference series.

Hach, 2017. Pocket Colorimeter™ II, Lead Test (LeadTrak®). [Online]
Available at: http://www.hach.com/pocket-colorimeter-ii-lead-test-leadtrak/product?id=7640445228

Holmen, A., 2015. Everything You Need to Know About Colour Sensors. [Online]
Available at: http://www.instructables.com/id/Everything-you-need-to-know-about-colour-sensors/

Jang, Y. H., 1996. Infrared Spectroscopy.. [Online]
Available at: http://www.wag.caltech.edu/home/jang/genchem/infrared.htm

Jungreis, E., 1985. Spot Test Analysis. In: s.l.:John Wiley & Sons, pp. 46-47.

Luiz, V. H. M. L. M. S. Ã. P. M. L. P. a. H. R. P., 2015. Rapid Determination of Lead in Progressive Hair Dye Lotion by Spot Test/Diffuse Reflectance Spectroscopy with a Paper Platform. Journal of the Brazilian Chemical Society .

Mosier-Boss, P. A., 2017. Review of SERS Substrates for Chemical Sensing. MDPI.

Nedelkovski, D., 2016. Arduino Color Sensing Tutorial – TCS230 TCS3200 Color Sensor. [Online]
Available at: http://howtomechatronics.com/tutorials/arduino/arduino-color-sensing-tutorial-tcs230-tcs3200-color-sensor/

Qingyong Lang, L. L. K. C., 2008. Spectrophotometric Determination of Lead.. [Online]
Available at: http://www.pharmtech.com/spectrophotometric-determination-lead

Reusch, W., 2013. Infrared Spectrometry.. [Online]
Available at: https://www2.chemistry.msu.edu/faculty/reusch/virttxtjml/Spectrpy/InfraRed/irspec1.htm

Rucker, P., 2015. This Is How Toxic Flint’s Water Really Is. [Online]
Available at: https://www.washingtonpost.com/news/wonk/wp/2016/01/15/this-is-how-toxic-flints-water-really-is/

Stellarnet, 2017. Price List. [Online]
Available at: http://www.stellarnet.us/price-list/

Xu, L., Yan, S.-M., Cai, C.-B. & Yu, a. X.-P., 2014. Nondestructive Discrimination of Lead (Pb) in Preserved Eggs (Pidan) by Near-Infrared Spectroscopy and Chemometrics. Journal of Spectroscopy, Volume vol. 2014, p. 7.