Methods for monitoring urban street litter: a comparison of municipal audits and an app-based citizen science approach

Abstract

Street litter and the plastic pollution associated with it is an economic and environmental health issue in municipalities worldwide. Most municipal litter data are derived from costly audits, performed by consultants at sparse intervals. Mobile phone apps have been developed to allow citizen scientists to participate in collecting litter data. Both municipal audits and citizen science datasets may be useful not only for informing municipal management decisions but also for increasing scientific understanding of litter dynamics in urban environments. In this analysis, we compare the spatial patterns and composition of litter in Vancouver, Canada, measured through professional municipal audits and with Litterati, a widely used citizen science app. While reported litter composition was consistent across methods, regression analysis shows that spatially, Litterati submissions were more highly correlated with human population patterns than with correlates of litter. We provide method recommendations to improve the utility of resulting data, such that these non-traditional, underutilized datasets may be more fully incorporated into scientific inquiry on litter.