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Arctic marginal ice zone (MIZ) is a key summertime source of elevated atmospheric gaseous elemental mercury (GEM), but controls on its spatial variability remain unclear. Multiscale spatial statistics, machine learning, and quantile regression were applied to MOSAiC in situ observations (June-September 2020). GEM exhibits strong fine-scale spatial autocorrelation at 10 km (Moran’s I = 0.876). Random forest model (R2 = 0.85) with SHAP attribution identified ozone (O3), chlorophyll a (Chla), and open water fraction (OWF) as dominant GEM predictors. Quantile regression revealed that nonlinear coupling among OWF, Chla, and O3 strengthens under high GEM concentrations. By further combining spatial analysis with quantile regression modeling, a GEM-sensitive region was identified (OWF = 0.156–0.404, Chla = 1.17–1.57 μg/L, O3 = 9.2–19.7 ppb); within this region, both the O3 × Chla and O3 × OWF interactions exhibited significant positive correlations. The positive effect of O3 × Chla persists across all regions, while the O3 × OWF coupling varies dynamically with the retreat of sea ice. O3 indirectly favors GEM accumulation via regulating phytoplankton, and sea-ice melt exerts stage-dependent modulation of atmospheric oxidizing capacity. This study identifies O3 as a critical MIZ mercury regulator, and embedding O3-mediated pathways in models improves GEM projections under Arctic warming.
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戴廷安 et al. (2026) studied this question.
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