Shining Light on Water Pollution Detection
Shining Light on Water Pollution Detection
Nikolas Fitzpatrick
Thomas Jefferson High School for Science and Technology
This article placed 2nd in the 2025 Teknos Fall Writing Contest.
As I slid the cuvette into the colorimeter during my Beer’s Law lab in my Chemistry I class, the cover snapped shut, and the results screen slowly loaded. At first, the number bounced about wildly before settling on a perfect middle number. Once again, I saw the solution’s results fit the underlying pattern. After applying Beer’s Law, I was surprised that such a simple equation could tell the exact amount of solute in a solution. After the lab, my teacher revealed a more interesting idea to the class. When you combine colorimetry and spectroscopy methods, you don’t just quantify a pollutant, you can identify it. I immediately thought of the applications these methods could have–think of a cheaper, more rugged design capable of screening drinking water for all those without a steady clean water supply.
Over 94 million people are at risk of drinking from well water that contains over 10 µg/L of arsenic [2]. Persistent contaminants like polyfluoroalkyl substances (PFAS) are synthetic chemicals that have been found in food, drinking water, waste water, and even humans’ blood systems. Prolonged exposure to PFAS can damage organs and endocrine systems [4]. Due to the sheer amount of pollutants that are found in drinking water, multiple screening methods have been developed to determine the best possible filtration systems. However, these measurement methods require multiple pretreatment steps, bulky equipment, and trained technicians. This makes these methods costly and difficult to use [3]. However, all these methods are still relatively inaccurate, failing to measure as accurately as 1 nM [1]. Cheaper, more efficient, and straightforward methods are important for the drinking water needs of the growing population.
Across the world, researchers are developing clever ways to bridge accuracy and simplicity. One spectroscopy study established a method of precipitating polyatomic ions to detect cyanide. Unlike previous methods, which used a one-to-one ratio of cations and anions, Ibnul et al. (2024) demonstrated an approach that purposely used excess silver cations to force complete formation of silver cyanide. This minimized competitive effects from other compounds that might affect results. The precipitate is captured on a membrane and analyzed by an infrared (IR) radiation spectrum, giving the researchers good silver cyanide detection and precision [3]. Even in the presence of other anions, such as sulfate, the amount of cyanide was still detectable because the IR band for cyanide stands apart from common interference. The use of excess cations ensures that, though interfering anions such as sulfate or chloride may exist in the water source, a precise measure of the contaminating anion can be easily obtained. The technique is conceptually simple, requires minimal reagents, and avoids much of the multi-step pretreatment typically used by labs.
The use of a reaction to detect nonmetal anion pollutants can also be used to detect metal particles. Cooray and Pullin (2022) used reverse flow injection analysis (rFIA) in their research for better iron detection. Flow injection analysis (FIA) involves a small sample being injected into a stream of carrier solution. Reagents are added to the solution, creating a colorful product that can be detected with a colorimeter. rFIA refines this by injecting reagents into a sample stream with precise timing to minimize interference and improve sensitivity. The researchers used ferrozine and iron to form a magenta colored compound “pollutant” that could be detected to a limit of 0.65 nM, better than conventional FIA methods that detect to a limit of 1 nM. It’s also possible for the rFIA device to be made from commercially available materials, making it possible for easy transport and setup.
With the success of colorimetry and spectroscopy in detecting pollutants, researchers have looked to other branches of these detection methods to detect contaminants. Zhao et al. (2022) developed a spectrophotometric method based on a microplasma converter that can detect substances. Spectrophotometry measures the amount of intensity of light that passes through a solution, while the microplasma detector uses trace amounts of highly ionized gases to convert certain substances into more easily detectable compounds. In their experiment, the researchers removed electrons from hydrogen sulfide, transforming it into sulfur dioxide. By turning the microplasma off, the sulfur dioxide could be quantified, and with the microplasma on, the mixture of both could be quantified, allowing for the quantification of the hydrogen sulfide without any impact from potential interference. The combination of spectrophotometry and visual colorimetry was an easily operable and effective method for the detection of pollutants in water. The device’s small size and simple use showed its potential for easy analysis anywhere.
In recent years, a new frontier has appeared. As instruments become more effective, interpreting richer spectral data becomes a bottleneck. With recent advancements in artificial intelligence (AI), Zhu et al. (2023) created an AI model that can correctly recognize 11 types of microplastics with 95% accuracy. AI models can recognize subtle spectral signatures across many classes and even automate identification. Pairing compact spectroscopy with AI programs offers another good route to fast, reliable screening.
Detecting contaminants and freshwater pollutants is easier than it has ever been before, and advances in spectroscopy and colorimetry methods let us both find and identify pollutants with growing speed and accuracy. If researchers and engineers continue to prioritize ruggedness, affordability, and possible automation, these methods can be scaled and adapted for many communities who lack proper infrastructure. Widespread deployment of compact and low cost detectors, paired with clear yet simple guidance, would make it possible to screen water sources more frequently. Treatment could be better implemented, and exposure to the many harmful chemicals we’ve put in our water could be avoided before it becomes a crisis. Practical colorimetry and spectroscopy methods could provide a better quality of life for millions.
References
[1] Cooray, A. T., & Pullin, M. J. (2022). Ferrozine colorimetry and reverse flow injection analysis (rFIA) based method for the determination of total iron in aqueous solutions at nanomolar concentrations. Journal of the Indian Chemical Society. https://doi.org/10.1016/j.jics.2022.100541
[2] He, Y., Liu, J., Duan, Y., Yuan, X., Ma, L., Dhar, R., & Zheng, Y. (2023). A critical review of on-site inorganic arsenic screening methods. Journal of Environmental Sciences, 125, 453-469. https://doi.org/10.1016/j.jes.2022.01.034
[3] Ibnul, N. K., Russell, J., Dennen, K., & Tripp, C. P. (2024). Quantification of free and weakly bound cyanide in water using infrared spectroscopy. Talanta, 266(1). https://doi.org/10.1016/j.talanta.2023.124939
[4] Rehman, A. U., Crimi, M., & Andreescu, S. (2023). Current and emerging analytical techniques for the determination of PFAS in environmental samples. Trends in Environmental Analytical Chemistry, 37, e00198. https://doi.org/10.1016/j.teac.2023.e00198
[5] Zhao, L., Zhou, J., Zhou, J., Lin, X., Huang, K., Jiang, X., Yu, H., & Xiong, X. (2022). A microplasma converter-based spectrophotometry and visual colorimetry for nonchromatographic speciation analysis of H2S/SO2 or S2-/SO3(2-) in environmental water samples. Microchemical Journal, 183. https://doi.org/10.1016/j.microc.2022.108100
[6] Zhu, Z., Parker, W., & Wong, A. (2023). Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging. Environmental Pollution, 337. https://doi.org/10.1016/j.envpol.2023.122548
