Anthropogenic litter cleanups in Iowa riparian areas reveal the importance of near-stream and watershed scale land use

摘要

Volunteer cleanup operations collect large datasets on anthropogenic litter that are seldom analyzed. Here we assess the influence of land use in both near-stream and watershed scale source domains on anthropogenic litter concentration (standing stock, kg km(-1)) in riparian zones of Iowa, USA. We utilized riparian litter concentration data on four classes of anthropogenic litter (metal, recyclable, garbage, and tires) from volunteer cleanup operations. Anthropogenic litter data were tested for correlation with near-stream and watershed scale land uses (developed, road density, agricultural, and open lands). Road density (road length/area) and developed land use (% area) were significantly correlated to anthropogenic litter, but agricultural (% area) and open lands (% area) were not. Metal objects correlated to near-stream road density (r = 0.79, p = 0.02), while garbage and recyclable materials correlated to watershed scale road density (r = 0.69, p = 0.06 and r = 0.71, p = 0.05 respectively). These differences in the important spatial scales of land use may be related to differences in transport characteristics of anthropogenic litter. Larger, denser metal objects may be transported more slowly through the water-shed/channelized system and thus, dependent on more proximal sources, whereas smaller, less dense garbage and recyclable material are likely transported more rapidly, resulting in concentrations that depend more on watershed scale supply. We developed a linear regression model that used near-stream road density and the total amount of observed litter to predict an average anthropogenic litter density of 188 kg km(-1) and a standing stock of 946 t in all Iowa streams (4th Strahler order). The techniques employed in this study can be applied to other professional and volunteer litter datasets to develop prevention and cleanup efforts, inform investigations of process, and assess management actions. (C) 2019 Elsevier Ltd. All rights reserved.