Peer-reviewed publication in the Journal of Computers, Environment and Urban Systems
Urban stormwater runoff is among the most significant sources of trash delivery to waterways, degrading aquatic habitats and contributing to oceanic trash gyres across the globe. Municipal water quality permits that require the elimination of trash inputs to stormwater systems employ visual trash assessments on city streets to demonstrate litter reduction progress. We present a novel method to increase the utility of these assessments by quantifying their degree of certainty at a granular spatial scale via Bayesian credibility intervals. Using data collected in the City of Salinas, California, we illustrate how the outputs can be used to determine effective trash controls and prioritize areas for management actions.