Development and validation of an automated method using a FlowCAM to detect and quantify Spheroidal carbonaceous particles (SCPs) from peat samples

Abstract

The semi-quantitative exploration of Spheroidal Carbonaceous Particles (SCPs) and pollen determination in sediments has opened new avenues for studying historical atmospheric contamination trends. These particles can be found in lake, peat, ice, and other geologic archives, and have been proposed as a marker to serve as the Global Boundary Stratotype Section and Point (GSSP) for the onset of the Anthropocene in the geologic time scale, offering globally synchronous indicators of human impact. SCPs generated by high-Temperature processes, have no natural source compared to traditional methods like radiocarbon dating and lead isotope analysis, fly-ash particles present a cheaper and more accessible alternative as a simple chronomarker. Our study focuses on SCPs using FlowCAM (Fluid Imaging Technologies, FC) as an alternative tool to identify and quantitatively enumerate these particles in peat samples from purely ombrotrophic Sphagnum bogs. To investigate SCPs in peat samples, the study aims to develop a semi-automated analytical method using SCPs reference materials to create an "SCPs library" as a filter to investigate the concentration and distribution of SCPs in the Pyrenees. The research evaluates the effectiveness of FlowCAM in quantifying SCPs and demonstrates its potential as an independent method for studying historical atmospheric contamination trends in natural peat samples. FlowCAM was used to quantify SCPs from a reference material with an accuracy of 105% and a standard deviation (SD) of 34%. SCP profiles could be established in natural peat cores and allowed the reconstruction of the history of anthropogenic deposition in the Pyrenees and remote regions. All the characteristic data of SCPs from FlowCAM could also be a tool for analysis with other natural samples; this "SCPs library" could be continuously strengthened with more sample measurements to accurately refine the results from FlowCAM and inform atmospheric models based on retro-observations.