Tanveer Singh
First Year BTech Student, Plaksha University
According to the reports by WHO, more than 1 million people die every year due to lack of access to safe water, and every 2 minutes a child dies from a water or sanitation-related disease. That means unsafe water and poor sanitation kill more people each year than many wars and epidemics combined, even though the technology to prevent most of these deaths already exists.
So, what holds these technologies back from reaching the people who need them most? Often, the barrier is not invention itself, but intelligent selection and deployment—choosing the right solution, for the right contaminant, in the right place.
To understand this, imagine you have spilled a liquid and now to mop it up—what will you use? A sponge that soaks up the spill and makes the surface clean. In water treatment, scientists use their own kind of sponge, called an adsorbent—a material that absorbs and traps unwanted chemicals or heavy metals from polluted water. In adsorption, contaminant molecules in water attach to the surface of these solids through physical forces or chemical interactions, gradually coating the adsorbent rather than remaining dissolved in the water.
Here the story becomes more complex. There is not just one sponge, but many. Activated carbon purifies drinking water, biochar (a charcoal-like material made from plants) captures pollutants, zeolite acts like a molecular sieve that lets only certain molecules pass, and activated alumina is used in villages in Durgapur, Rajasthan to soak up fluoride from groundwater. Each of these adsorbents has its own pore structure, surface area, cost, and efficiency. So, every year, scientists are not only producing such different materials; they are also facing a harder question: when you have many possible “sponges”, which one should you choose first? With so many adsorbents on the shelf, the real struggle is deciding where to start.
So, how do we solve this problem of choosing the right “sponge” from so many? Well, have you ever organized your favourite music playlist, changing the queue of the songs you want to play most based on your priorities and not using the same playlist for every mood or occasion? Surprisingly, the same logic used to sort and play songs can help in a place that you might not expect like selection and utilization of adsorbents. For instance, imagine you have a playlist of 30 songs and you want to create the perfect lineup of 10 songs for a road trip. You would probably use an app feature to rapidly sort the songs based on parameters like favourites, play count, or genre. In the same way, scientists use sorting algorithms to choose the most preferable adsorbent based on certain parameters to evaluate how quickly they remove impurities from water.
This approach becomes clearer when examined in practice, particularly when compared with traditional methods of adsorbent selection. In traditional practice, researchers would spend hours or even days testing one adsorbent after another, searching the lab results to check which material removes contaminants most effectively from a given sample of water: a slow process that is not ideal in cases where safe water is needed urgently. Algorithms boost this process by treating each adsorbent like a “song” in a playlist and ranking them according to multiple criteria such as removal efficiency, cost, and number of reuse cycles. With common tools like Excel, scientists can input experimental data into a dataset file and quickly generate an ordered list of materials through a trained model of finding the most suitable adsorbent for a specific contaminant and situation made by using Python—a programming language, turning what was once trial and error into a systematic, data-driven selection. One powerful application of this approach is in choosing iron-based adsorbents for arsenic-contaminated groundwater, such as in parts of Bangladesh where millions rely on tubewells as their primary source of water. By using algorithmic ranking on field data from different filter designs—like iron-amended ceramic filters or zero-valent iron household units—researchers can identify which configuration delivers the highest arsenic removal at the lowest operating cost in that area. Studies show that optimized iron filters can consistently reduce arsenic concentrations below national safety limits while reducing treatment cost by 70%. This efficiency leads to 20–30% reduction in people exposed to dangerous arsenic levels in some affected regions of Bangladesh. Moreover, economical choices allow for increasing accessibility of clean water to rural or underserved places, cutting gaps of poverty and illness. Thus, algorithms have shaped water treatment from slow and costly methods to fast and cost-effective—highlighting the vital role of advancement of technology in terms of these algorithms.
This efficiency in water treatment is not only faster and cheaper but also better for the planet. By having adsorbents with specific properties through algorithm-driven methods, researchers can increase the number of times adsorbent materials like activated carbon and zeolite can be reused, reducing the need to manufacture new adsorbents and cutting waste generation. When optimized through such analysis, these materials can be regenerated by processes like thermal reactivation or chemical washing and used for four to five treatment cycles instead of being discarded after a single use, which can lower waste and related carbon emissions by as much as 50–60% in some systems or regions. At the same time, this technological shift is being driven forward by younger generations: students and early-career scientists around the world are applying coding, sensors, and data analysis to local water problems, from innovators like Gitanjali Rao in the United States, who developed a lead detection device using carbon nanotube sensors and app-based processing, to student groups in places like Gujarat that use Python to analyse data and choose effective adsorbent combinations such as biochar with activated alumina for fluoride removal in their village wells. Together, these data-driven and youth-led approaches show that with the right tools, knowledge, and creativity, meaningful advances in clean water and environmental sustainability can emerge even from classrooms and small community projects.
Despite these potentials, there are some major challenges that need to be solved to implement this. Many places have incomplete data on local pollutants or available adsorbents, making algorithm choices less reliable. Another problem is adaptability as the solution designed for one local condition might not work for another region. Thus, current technology requires an increased amount of open-source databases and making a common global research platform where researchers can collaborate and share their findings about adsorbents and their usage. Furthermore, enhancing the algorithms through which decisions have been made will not only increase the efficiency of providing clean water but also improve well-being, paving the way towards environmental and social healthiness.
In the end, algorithms for water treatment are not just about numbers and efficiency—they are about people. When we combine smart modelling with the right adsorbents, we can turn scattered test results into practical designs that help expand safe drinking water to even a fraction of the 2.2 billion people who still lack it today; if fully scaled, such improvements in water, sanitation, and hygiene could help prevent up to 1.4 million deaths every single year. Making this happen means working differently—openly sharing adsorbent data where anyone can find it, teaching people to test their own water, building transparent models that everyone can use and improve; and letting every experiment in a lab, classroom, or village well add to a growing pool of shared knowledge that turns clean water from something we talk about into something we actually deliver.
