Microplastic type, size & dose dependence impairs Pinus massoniana growth, nutrient uptake & rhizosphere enzymes under high exposure levels

Microplastic pollution in terrestrial ecosystems is an emerging environmental concern, yet its effects on forest trees remain poorly understood. Here, we investigated how polymer type (polystyrene [PS], polypropylene [PP], polyvinyl chloride [PVC], polyethylene [PE], polyethylene terephthalate [PET]), particle size (100 nm, 5 μm, 250 μm), and concentration (0.1%, 1% w/w) of microplastics influence the growth, nutrient uptake, and rhizosphere enzyme activities of Pinus massoniana seedlings. Elevated microplastic concentrations were applied to elucidate mechanistic responses and upper-bound effect under high-exposure scenarios rather than environmentally typical forest soil conditions. Overall, microplastic exposure significantly reduced seedling growth, with dry biomass decreased by 12–45% relative to the control. Nitrogen uptake declined consistently across treatments (54–77%), with the strongest inhibition observed under the 5 μm PS treatment. Phosphorus and potassium uptake were also adversely affected by microplastics, though the responses were more variable, with significant suppression especially at the higher contamination level (1% w/w). In the rhizosphere, urease and acid phosphatase activities were generally reduced, whereas β-1,4-glucosidase activity often increased (up to a 65% rise) under microplastic stress. Three-way ANOVA revealed significant interactions among polymer type, size, and concentration (P < 0.001), indicating multifactorial control of toxicity. Among the tested polymers, PP exerted the most pronounced overall phytotoxic effects, while smaller particles and higher doses consistently intensified adverse impacts. These results demonstrate that microplastics can impair forest seedling performance and associated soil enzymatic processes under conditions of substantial accumulation, highlighting the need to assess ecological risks across environmentally realistic exposure gradients.