Guarding Our Water Resources: Tackling Nutrient Threats

Nutrient Overload: A Looming Threat

In this critical age of water resource management, understanding the implications of nutrient overload is of great importance. Nitrogen and phosphorus, important yet frequently disregarded pollutants, pose substantial threats to the integrity of both surface and groundwater. Their uncontrolled accumulation is leading to excessive growth of algae and plants known as the Eutrophication process which disrupts the balance of the ecosystem and harms aquatic life. Indeed, given the increasing human impact and changing climate patterns, safeguarding our vital water sources from excess nutrients has become a top concern.

In the quest to decrease nutrient pollution, existing solutions often fall short. Traditional approaches, such as sampling campaigns and numerical models, grapple with inconsistent data and complex parameters. This leaves a critical gap in our understanding and management of nutrient transport processes in agricultural watersheds.

Machine Learning Algorithms: Revolutionizing Nutrient Management

A promising solution using machine learning (ML) algorithms, a powerful tool to better understand nutrient pollution processes, is being applied by Dr.’s Jana Levison (Doody Family Chair for Women in Engineering), Ahmed Elsayed, Andrew Binns, Pradeep Goel and their research team. They used ML models to tackle the complexity of transport processes by processing diverse datasets and parameter variability. Indeed, it reveals the intricate web of interdependence between process variables, shedding light on the dominant factors for optimal nutrient management. Using ML has also offered the advantage of evaluating the impact of intermediate variables on output, bringing clarity to the nutrient transport process.

Insights from ML: Optimizing Nutrient Management

In this study the data collection has been performed by skilled graduate and undergraduate students at the research site, followed by complex testing of ML classification algorithms. By harnessing geochemical, physical, climate, and field condition parameters, these algorithms classified nutrient concentrations in surface water with unprecedented accuracy. The results stand as a testament to the potential of ML algorithms in not only identifying nutrient exceedances but also in providing crucial insights for the formulation of effective nutrient management strategies.

Funding Acknowledgement: This research is funded and supported by the Ontario Ministry of the Environment, Conservation and Parks (OMECP),the Municipality of North Middlesex, the Municipality of South Huron, and the Ausable Bayfield Conservation Authority.

Reference: A. Elsayed, S. Rixon, J. Levison, A. Binns, and P. Goel, “Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed,” J. Environ. Manage., vol. 345, p. 118924, Nov. 2023.