Geographical Information System: Seasonal Dynamics of Microbial Indicators and Antimicrobial Resistance Patterns in Suburban and Agricultural Row Crops Freshwater Stream Environment
Type of Presentation
Poster Session
Location
University Library
Start Date
4-9-2026 2:00 PM
End Date
4-9-2026 3:15 PM
Abstract
Bacteria are indicators of water quality in freshwater ecosystems and are influenced by urbanization, such as hospitals, wastewater treatment plants, runoff from roads, and veterinary facilities. These pollution sources introduce fecal-indicator bacteria, opportunistic pathogens, and antibiotic-resistant bacterial strains. They alter microbial community structure, nutrient inputs, and the dissemination of antibiotic-resistant genes. Monitoring bacterial abundance provides insight into ecosystem health, anthropogenic impact, and public health risk associated with freshwater ecosystems. Eight streams were classified as suburban or agricultural row crops, each based on the land use. Seasonal water and sediment samples were collected at headwater, midstream, and downstream locations to quantify hierarchical microbial indicators, including total Fungi, aerobic bacteria, Enterobacteriaceae (pathogenicity), total coliforms (FIB), and Escherichia coli (FIB). Spatial statistical analyses were conducted to assess relationships between watershed characteristics and microbial abundance. Microbial contamination is predicted to intensify downstream due to cumulative watershed inputs from the number of hospitals, veterinary clinics, and wastewater treatment plants. Specifically, I hypothesize that watersheds characterized by higher developed land percentages and elevated population density will exhibit significantly increased microbial abundance, affecting fecal indicator bacteria and fungi counts. Seven GIS data layers are used to quantify potential sources of contamination: Digital Elevation Models (DEM), land cover (LandSat), streams, human population density (Tiger Files), and potential point sources. Preliminary results suggested that there was a high microbial load across all streams, signifying the potential for increased microbial contamination from both point and non-point sources in the suburban and agricultural areas.
Identify Grant
Funded in part by the NSF Optimization Computing Grant
Faculty / Staff Sponsor
Dr. John Yunger
Presentation File
wf_no
Geographical Information System: Seasonal Dynamics of Microbial Indicators and Antimicrobial Resistance Patterns in Suburban and Agricultural Row Crops Freshwater Stream Environment
University Library
Bacteria are indicators of water quality in freshwater ecosystems and are influenced by urbanization, such as hospitals, wastewater treatment plants, runoff from roads, and veterinary facilities. These pollution sources introduce fecal-indicator bacteria, opportunistic pathogens, and antibiotic-resistant bacterial strains. They alter microbial community structure, nutrient inputs, and the dissemination of antibiotic-resistant genes. Monitoring bacterial abundance provides insight into ecosystem health, anthropogenic impact, and public health risk associated with freshwater ecosystems. Eight streams were classified as suburban or agricultural row crops, each based on the land use. Seasonal water and sediment samples were collected at headwater, midstream, and downstream locations to quantify hierarchical microbial indicators, including total Fungi, aerobic bacteria, Enterobacteriaceae (pathogenicity), total coliforms (FIB), and Escherichia coli (FIB). Spatial statistical analyses were conducted to assess relationships between watershed characteristics and microbial abundance. Microbial contamination is predicted to intensify downstream due to cumulative watershed inputs from the number of hospitals, veterinary clinics, and wastewater treatment plants. Specifically, I hypothesize that watersheds characterized by higher developed land percentages and elevated population density will exhibit significantly increased microbial abundance, affecting fecal indicator bacteria and fungi counts. Seven GIS data layers are used to quantify potential sources of contamination: Digital Elevation Models (DEM), land cover (LandSat), streams, human population density (Tiger Files), and potential point sources. Preliminary results suggested that there was a high microbial load across all streams, signifying the potential for increased microbial contamination from both point and non-point sources in the suburban and agricultural areas.