Abstract
Anthropogenic litter is omnipresent in terrestrial and freshwater
systems, and can have major economic and ecological impacts. Monitoring
and modelling of anthropogenic litter comes with large uncertainties due
to the wide variety of litter characteristics, including size, mass, and
item type. It is unclear as to what the effect of sample set size is on
the reliability and representativeness of litter item statistics.
Reliable item statistics are needed to (1) improve monitoring
strategies, (2) parameterize litter in transport models, and (3) convert
litter counts to mass for stock and flux calculations. In this paper we
quantify sample set size requirement for riverbank litter
characterization, using a database of more than 14,000 macrolitter items
(>0.5 cm), sampled for one year at eight riverbank
locations along the Dutch Rhine, IJssel and Meuse rivers. We use this
database to perform a Monte Carlo based bootstrap analysis on the item
statistics, to determine the relation between sample size and
variability in the mean and median values. Based on this, we present
sample set size requirements, corresponding to selected uncertainty and
confidence levels. Optima between sampling effort and information gain
is suggested (depending on the acceptable uncertainty level), which is a
function of litter type heterogeneity. We found that the heterogeneity
of the characteristics of litter items varies between different litter
categories, and demonstrate that the minimum required sample set size
depends on the heterogeneity of the litter category. More items of
heterogeneous litter categories need to be sampled than of heterogeneous
item categories to reach the same uncertainty level in item statistics.
For example, to describe the mean mass the heterogeneous category soft
fragments (>2.5cm) with 90% confidence, 990 items were
needed, while only 39 items were needed for the uniform category metal
bottle caps. Finally, we use the heterogeneity within litter categories
to assess the sample size requirements for each river system. All data
collected for this study are freely available, and may form the basis of
an open access global database which can be used by scientists,
practitioners, and policymakers to improve future monitoring strategies
and modelling efforts.