By Scout Nelson
In South Dakota, water quality management is critical due to the state's extensive natural water resources. Recent findings indicate that over 70% of the state's stream miles and more than half of its lakes fail to meet U.S. EPA water quality standards. Addressing these issues efficiently is crucial given the vast landscape and economic constraints.
Xufei Yang, an assistant professor at South Dakota State University and an Extension environmental quality engineer, is at the forefront of developing a cutting-edge solution. Yang's project involves an artificial intelligence-powered, non-contact method for quickly assessing surface water quality using both imagery and olfactory data.
Traditional vs. New Methods: Traditionally, water quality assessment has required physical water samples and on-site measurements, which are often costly and labor-intensive. With Yang's innovative approach, these challenges are addressed by employing hyperspectral cameras and gas sensor arrays, also known as "e-noses." These devices detect visual and scent markers from water bodies without direct contact.
Technological Integration and Team Expertise: The integration of hyperspectral imagery and scent data aims to overcome the limitations posed by water turbidity that affects visual analysis.
A neural network algorithm will analyze the combined data, improving accuracy and efficiency. Yang collaborates with SDSU colleagues ZhengRong Gu and Sushant Mehan, who bring expertise in sensor development and water quality monitoring, respectively.
Project Validation and Future Prospects: The team will test this system with 150 surface water samples across eastern South Dakota. Successful validation could streamline the process of identifying problematic water samples, significantly reducing the time and cost associated with traditional methods.
Looking ahead, Yang envisions applying this technology to other types of water assessments, such as analyzing livestock wastewater and groundwater. This innovative approach not only promises to enhance current methodologies but also paves the way for broader applications in environmental monitoring, potentially making South Dakota a leader in sustainable water resource management.
Photo Credits:gettyimages
Categories: South Dakota, Livestock