Exploring correlations between microplastics, microorganisms, and water quality in an urban drinking water source

Abstract

The microplastic pollution in freshwater system is gradually becoming more severe, which has led to increasing attention on the distribution and potential harmful effects of microplastics. Moreover, microplastics may have an impact on river ecology and pose risks to ecosystems. Therefore, it is important to reveal this process. This study aimed to explore correlations between microplastics and free-living microorganisms in an urban drinking water source of Xiangjiang River by using multivariate statistical analysis. The results indicated that the abundance of microplastics (size 50 μm to 5 mm) in surface water and sediments ranged from 0.72 to 18.6 (mean ± SD: 7.32 ± 2.36) items L−1 and 26.3–302 (150 ± 75.6) items kg−1 dry weight (dw), respectively, suggesting potential microplastic pollution despite the protected status as a drinking water source. Higher microplastic abundances were observed in urban areas and the downstream of wastewater plants, with mostly granular shape, transparent and black color as well as 50–100 μm in size. The multivariate statistical analysis presented that the abundance of microplastics is not significantly correlated with water indicators, due to the complexity of the abundance data. The water indicators showed an obvious correlation with microplastics in colors of transparent and black, and smaller sizes of 50–100 μm. This is also true for microplastics and microorganisms in water and sediment. Proteobacteria was the main prokaryote in water and sediments, being positively correlated with 50–100 μm microplastics; while Chloroplastida was the dominated eukaryotes, presenting a weak correlation with smaller-size microplastics. Overall, when considering the properties of microplastics such as shape, color and size, the potential correlations with water indicators and microorganisms were more evident than abundance. This study provides new insights into the multivariate statistical analysis, explaining the potential correlations among microplastic properties, microorganisms and environmental factors in a river system.