A Faculty Member from the College of Education for Pure Sciences Publishes a Scientific Article on: Artificial Intelligence to Combat Water Pollutants (Addressing Pollution and Climate Change).
Asst. Lect. Alia Abdullah Hantoush from the College of Education for Pure Sciences published a scientific article on: Artificial Intelligence to Combat Water Pollutants (Addressing Pollution and Climate Change).
In it, she explained that water pollutants are among the most serious environmental challenges of our time, due to their direct impact on human health, ecosystems, and water security. Every person, in every country, on every continent will be affected in one way or another by climate change.
Climate change is caused by human activities that threaten life on Earth as we know it. With the rise in greenhouse gas emissions, climate change is occurring at much faster rates than expected. Its effects can be devastating and include extreme and changing weather patterns and rising sea levels. Here, artificial intelligence (AI) has emerged as an effective tool to support global efforts in monitoring, predicting, and addressing pollution in smart and sustainable ways.
In the water sector, AI enables early detection of pollution by learning natural patterns. For water quality in supply networks and treatment plants, and to detect subtle deviations before they reach consumers or ecosystems, computer vision helps analyze satellite imagery and spectral sensing to monitor harmful algae, turbidity, chlorophyll, and oil spills across vast areas.
Artificial intelligence works to predict pollution and its sources by collecting massive amounts of data from multiple sources, such as water quality sensors (pH, turbidity, heavy metals), environmental monitoring stations, weather data (rainfall, temperature, wind), and data on industrial and agricultural activities. After identifying the potential source of pollution, intelligent analysis is performed to determine whether the pollution originates from factories, sewage, agriculture (pesticides and fertilizers), or landfills. The sources are then ranked according to their level of risk.
Therefore, intelligent systems recommend site-specific treatments (such as biodegradation and phytoremediation) with adaptive adjustments to humidity, temperature, and nutrients to maximize removal. In waste management, vision systems also increase the accuracy of material sorting to improve recycling rates. Recycling. By doing so, the Industrial Internet of Things (IIoT) integrated with artificial intelligence (AI) reduces waste and emissions by monitoring environmental performance indicators in real time.
