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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 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. Spatial dependence was incorporated to the uncertainty estimates within individual stormwater drainages and results were interpolated to adjacent parcels. After 3–6 observation periods over 20 months, we found approximately 30% of the city area showed minimal litter accumulation at an 80% certainty level. The outputs provide a practical alternative for cities to determine compliance with stormwater trash regulations, update understanding of trash accumulation patterns, and iteratively adjust sampling designs in response to new observations. The methods described have been implemented as a web-based geospatial decision support tool to help stormwater managers target implementation actions and report progress to regulators. • An approach is presented to quantify uncertainty associated with assessments of urban trash loading to stormwater systems. • Outputs can be used to track trash mitigation effectiveness and prioritize areas for action. • The approach has been incorporated into web-based geospatial stormwater management platform. [ABSTRACT FROM AUTHOR]

With growing understanding of trash impacts on aquatic habitats throughout the world, cities increasingly face regulatory requirements to reduce trash inputs to local waterways and the ocean, but they often rely upon insufficient monitoring data to prioritize and measure trash reduction effectiveness. We present an approach designed to make urban trash monitoring more cost-efficient and align the data collected with critical information needs of cities. We quantified urban trash accumulation along roadsides using vehicle mounted cameras and a deep convolutional neural network model to identify trash in the imagery captured. We compared the trash detection performance of three different models, with the best performing model (Mask R-CNN) achieving 91% recall, 83% precision, and 77% accuracy using data collected along 84 road segments in two California Cities. Trash detection model outputs were interpreted via a statistical model to relate the proportion of image pixels identified as trash to measured trash volumes. The resulting model estimates explained 67% of the variance in measured trash volumes collected on roadsides, which is more than double the variance explained by walking visual assessments. With vastly more efficient data collection compared to the visual assessments, deep learning-based monitoring approaches can provide a stronger basis for understanding urban trash sources, changes over time, and cost-effective compliance with stormwater regulatory requirements. • An image-based approach is presented to quantify trash volumes on city streets. • The model successfully detected 91% of trash with a precision of 87%. • Outputs explained more than double the trash variance of visual surveys. • The demonstrated approach can provide substantial cost savings for trash surveys. [ABSTRACT FROM AUTHOR]