Nanoplastics are ubiquitous in ecosystems and impact
planetary
health. However, our current understanding on the impacts of nanoplastics
upon terrestrial plants is fragmented. The lack of systematic approaches
to evaluating these impacts limits our ability to generalize from
existing studies and perpetuates regulatory barriers. Here, we undertook
a meta-analysis to quantify the overall strength of nanoplastic impacts
upon terrestrial plants and developed a machine learning approach
to predict adverse impacts and identify contributing features. We
show that adverse impacts are primarily associated with toxicity metrics,
followed by plant species, nanoplastic mass concentration and size,
and exposure time and medium. These results highlight that the threats
of nanoplastics depend on a diversity of reactions across molecular
to ecosystem scales. These reactions are rooted in both the spatial
and functional complexities of nanoplastics and, as such, are specific
to both the plastic characteristics and environmental conditions.
These findings demonstrate the utility of interrogating the diversity
of toxicity data in the literature to update both risk assessments
and evidence-based policy actions.