Estimating and mapping cigarette butt littering in urban environments: A GIS approach

BackgroundCigarette butts are some of the most common form of litter in the World, causing severe environmental damage. Analysing spatial distribution of cigarette butts in the urban environment may lead to useful insights for further interventions to reduce this form of litter. In this study, we present a GIS-based methodology to estimate the density of cigarette butts across a large urban area.MethodsWe collected information about discarded cigarette butts in outdoor public spaces by systematic social observation in a diverse sample of areas in Madrid, Spain. We used these data to estimate the density of cigarette butts in public spaces around the entire city by performing GIS analyses based on Kernel Density Estimations. Last, we validated these measures using on-field observations in a set of locations across the city.ResultsHospitality venues and public transportation stops were the places with the highest concentrations of cigarette butts, followed by the entrances to educational venues and playgrounds. Central districts showed the highest amount of cigarette butts in contrast to peripheral ones. We found that our measure had good validity, with a correlation coefficient of 0.784.DiscussionThis is the first study estimating and mapping cigarette butt litter in a large urban area. We identified a set of outdoor public places with high concentrations of cigarette butts and found geographical unevenness in the distribution of this pervasive form of litter across the study area. Our findings demonstrate the ubiquitous nature of cigarette butts in the urban environment and the need for interventions to reduce its impact on both people's health and the environment.

» Author: Roberto Valiente, Francisco Escobar, Jamie Pearce, Usama Bilal, Manuel Franco, Xisca Sureda

» Reference: Environmental Research, Volume 183

» Publication Date: 01/04/2020

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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