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Impact of pollution sources of microplastics and associated microbial populations in surface water
Summary
Researchers examined how pollution sources—including wastewater treatment plants, agricultural areas, urban runoff, and industrial discharge—influence microplastic abundance and associated microbial communities in the uMsunduzi River in South Africa across two seasons.
Microplastics (MPs) are ubiquitous environmental pollutants of global concern, presenting a major threat to aquatic ecosystems. The study examined the effects of potential pollution sources of MPs and associated microbial communities in riverine environments, including wastewater treatment plants (WWTPs), agricultural areas (AA), urban areas (UA), and industrial discharge (IA). The study sites were selected along the uMsunduzi River in KwaZulu-Natal, and the sampling was conducted in two seasons (summer and winter). Morphological and chemical characterization of MPs was performed using microscopy, ATRFTIR, and Pyro-GC/MS analysis. Shotgun metagenomics was used to analyze the microbial community. The potential health risks associated with selected pathogens in the biofilm were also assessed using Quantitative Microbial Risk Assessment (QMRA). Microplastics were detected in abundance from all four sites with concentrations in the IA being the highest (69 particles/L), followed by the WWTP (51 particles/L), the UA (49 particles/L), and the AA (39 particles/L). Additionally, sediment samples showed higher MP particles compared to the surface water. The most common types of MP detected were fibers, followed by pellets and fragments for both surface water and sediment samples. Furthermore, the key polymers detected via chemical characterization were polyethylene (PE), Polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and Polyvinyl Alcohol (PVA) across all sites with varying dominance. The PS, PET, and PE were predominant at the UA, while the WWTP and IA exhibited a variety of polymers, including PE, PP, PET, and PS. The AA site showed the presence of PE, PP, PS, PET, and PVA. Metagenomic data demonstrated a significant microbial diversity (p = 0.0012) and composition (PERMANOVA F = 16.386; R2 = 0.15, p < 0.001) in different sites (UA, WWTP, AA, and IA), and habitat (surface water and plastisphere). The plastisphere harbored a distinct microbial community compared to surface water. At the phylum level, Bacteroidetes were significantly higher in surface water, whereas α- and β-Proteobacteria dominated on the plastic surface (p < 0.05). In regard to the different sites, WWTP had the most different taxa (5), followed by UA (3), with AA and IA each having only 1 unique taxon. The distance decay model showed that microbial communities in the plastisphere and surrounding environments are significantly positively associated with the sources of pollution (UA: R² = 0.83, p = 0.015; WWTP: R² = 0.88, p = 0.0072; AA: R² = 0.85, p = 0.0075; IA: R² = 0.95, p = 0.0011). The study also revealed the presence of various antimicrobial resistant genes (ARGs) in both surrounding surface water and plastisphere, with MP surfaces showing higher ARGs than surrounding surface water. For instance, the plastisphere harbored 19 ARGs compared to 9 in surface water. The WWTP showed diverse ARGs, including the widely reported ARGs conferring resistance to tetracycline, fluoroquinolone, and aminoglycoside. The study also identified 17 pathogenic microbial species across different sites, with Acinetobacter baumannii being the most dominant. Furthermore, common pathogens such as Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae were detected across all sites, seasons, and habitats. The microbial risk assessment based on two dominant pathogens (Pseudomonas aeruginosa and Salmonella enterica) revealed that the risk of infection varied across different pollution sources and seasons. Notably, the highest infection risk associated with selected pathogens was found in IA and WWTP-impacted sites which is in accordance with the total number of MPs detected indicating and increase in MPs will have a significant impact on the associated health risks. Results of this study indicate that different pollution sources significantly influence MP abundance and types, as well as the structure of microbial communities, which may ultimately pose a threat to human health.
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