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Multivariate Analysis of Factors Influencing the Concentration of Persistent Organic Pollutants and Microplastics in Mosses Sampled Across Germany in 2020
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Mosses growing across Germany were analyzed as living pollution sensors, and the results show that concentrations of persistent organic pollutants (POPs) in mosses are driven by a combination of large-scale atmospheric drift and proximity to local industrial or agricultural sources. Microplastics in the mosses were harder to model statistically, with only styrene-butadiene rubber and polyethylene particles showing meaningful patterns linked to nearby sources. The study reinforces the value of national moss monitoring networks for tracking atmospheric pollution, but highlights that denser sampling is needed to reliably map microplastic deposition across a country.
Mosses (Bryophyta) are well-established biomonitors of atmospheric deposition, including persistent organic pollutants (POPs) and microplastics (MPs). Using German Moss Survey 2020 data, this study identified factors influencing POPs and MPs in mosses through correlation and random forest analyses. For 10 of 11 POP groups, the models explained a variance of more than 20%. Key predictors included atmospheric deposition and the density of urban–industrial and agricultural land uses within 100–300 km. Population density and the density of extraction and dump sites within radii of <5 km (PCDD/Fs, PCDD/F TEQ values, HBCD, 23 PBDEs, BDE-209, DBDPE, PBT, and HBBz), as well as distances to residential areas and transport infrastructure (PCDD/Fs, HBCD, PBDEs, DP, and DBDPE), also proved to be highly relevant, although a direct causal relationship seems unlikely for flame retardants. These findings indicate that POP concentrations in mosses are influenced not only by large-scale atmospheric deposition but also by local emission sources near sampling sites. Vegetation parameters, particularly the leaf area index, showed additional effects. For MP, only two polymer groups (SBR and PE) yielded models with sufficient predictive strength, again dominated by proximity to local sources. Minimum sample size analysis demonstrated that a denser sampling network is required to achieve a 20% tolerance error in future monitoring campaigns.
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