Submitted and accepted for the Symposium of Contemporary Conservation Practice (SCCP) 2016. Nominated for the KZN Premier's Award 2016.

1. Counting and sizing microplastic fibres, the accurate and easy way
Kunnen, T.H., Gerber, G., Moodley, G.K. and Robertson–Andersson, D.V
University of KwaZulu–Natal
Abstract
With an annual demand for plastic in excess of 245 million tonnes, plastic pollution is ranked as one of
the greatest threats to marine life. Marine plastic pollution consists of both macroplastic particles (> 5
mm) and microplastics. Microplastics which are manufactured to be less than 5 mm in size (primary
microplastics) are generally used for the purposes of commercial and industrial abrasives, while
secondary microplastics result from the disintegration of larger plastic particles. This may occur from
physical forces such as abrasion or UV exposure. Currently one of the most common sources of
secondary microplastic particles is the shearing of plastic textile fibres from washing machines, and
with the increase in domestic appliance reliance, microplastic pollution rates are escalating. The need
to accurately and reliably count and size microplastic particles from environmental samples or
laboratory experiments is impeded by the slow process of sifting through sand and gut contents and the
need to manually evaluate the particles with microscopy. We present here the use of a macro-enabled
counting and analysis program coded specifically for IPP (Image Pro Plus) for the automated analysis
of fluorescent microplastic fibres. To test the efficiency, accuracy and reproducibility of the above
automated counting feature, 50 microfibre images were given to 5 volunteers to manually count and
size. This data was compared to data collected from the automated counting feature for accuracy of
counts, size measurements and time taken for analysis. The macro showed no statistical differences
between the numbers of fibres counted and total length, but there was a statistical difference in fibre
width. A significant statistical difference was found for average time taken with 23.90 ± 6.86 vs 1.2 ±
0.77 mins for manual and automated analysis respectively per filter (12 images), resulting in a massive
2382.92% decrease in time.