A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. Within the proximity of the New York–New Jersey–Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act’s 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). characteristics, such as urban land, are often associated with impaired microbial water quality. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments.
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