Tao Wen, assistant professor in the Department of Earth and Environmental Sciences, has been awarded a grant from Earth Science Information Partners to further develop and improve an algorithm, called GeoNet, that automatically integrates and analyzes high-frequency surface water quality data to detect surface water contamination in river networks. As a case study, Wen will apply GeoNet to Pennsylvania river networks using high-frequency specific conductance data to estimate the incidence of water contamination caused by unconventional oil and gas development processes such as directional drilling and hydraulic fracturing.

According to Wen, this project will be the first hydrology-informed geospatial analysis tool capable of automatically analyzing multiple datasets including stream network and high-frequency water quality to detect stream water quality impairments potentially related to human activities.

“GeoNet could largely simplify and speed up the processes of data integration and analysis in water quality research and water management,” says Wen. “The outcome from this project can be used as a showcase of the best practice of performing data-driven studies in the field of geoscience.”

In addition, Wen says the improved GeoNet algorithm can be used to detect stream water quality pollution in other geographic regions to guide the design of more efficient stream monitoring networks.

Results from Wen’s project will be disseminated through journal articles and statistical computing platforms including R package and the R Shiny app. The R Shiny app will be hosted on the Syracuse University computing cluster, and will become accessible in January 2022 via this link: https://geonet.syr.edu/.