Minako Kurisu

The trend of atmospheric deposition of bioaccessible iron (BFe) from anthropogenic sources is driven by changes in aerosol column loading of total Fe (TFe) and Fe bioaccessibility, primarily due to the evolution of human activities. Black carbon (BC) is often used as a temporal and spatial proxy for BFe emissions from anthropogenic aerosols. However, derived changes in BFe concentrations from anthropogenic processing activities (e.g., smelting and abrasion) and fuel combustion have not been explored separately because of a lack of evaluation of BFe aerosols for separate anthropogenic sources. The Fe stable isotope ratio (δ56Fe) of size-segregated aerosols is a useful tracer to disentangle anthropogenic Fe sources. Here, we evaluate the source contributions predicted by an atmospheric chemical transport model using size-segregated aerosol observations over the North Pacific. Using the improved model, we investigate the relative changes in BFe aerosols from anthropogenic processing activities and fuel combustion sources in the major continental outflow regions during 2002–2022. The model reveals a persistent increase in BFe supply to the North Pacific and Indian Oceans from anthropogenic processing activity sources from 2002 (18 Gg Fe yr−1) to 2022 (20 Gg Fe yr−1) despite a decrease from fuel combustion from 2012 (20 Gg Fe yr−1) to 2022 (15 Gg Fe yr−1). These findings may have important implications for projections of future micronutrient supply to the oceans, which regulates the magnitude and dynamics of marine primary productivity.

Atmospheric deposition of aerosol iron (Fe) can stimulate marine primary productivity by supplying dissolved Fe (d-Fe) to the surface ocean, thereby potentially influencing the global climate. Aerosol Fe solubility (Fe _sol %) is closely linked to its bioavailability, and previous studies have shown that Fe _sol % generally increases as aerosol Fe concentration decreases. However, the mechanism underlying this widely observed inverse relationship remains unresolved. In this study, aerosol observations from East Asia, the North and South Pacific, and the Atlantic were compiled, and the enrichment factor of total Fe (EF _T-Fe = (T-Fe / T-Al)aerosol/ (T-Fe / T-Al) _crust ) and dissolved Fe to dissolved Al ([d-Fe] / [d-Al]) were used to estimate the contributions of mineral-derived and anthropogenic Fe to aerosol d-Fe, as well as the Fe _sol % of each source fraction. Aerosol d-Fe was found to be derived predominantly from mineral dust in many oceanic regions. In addition, both mineral-derived Fe and anthropogenic Fe showed inverse relationships between concentration and solubility. If the inverse relationship between Fe concentration and Fe _sol % were controlled mainly by simple two-component mixing between low-solubility mineral particles and highly soluble anthropogenic Fe, the Fe _sol % of each source fraction would not be expected to vary systematically with concentration. Instead, the results suggest that atmospheric chemical processing, together with depositional removal during transport, progressively increases the solubility of Fe remaining in aerosol particles. The ability to estimate the sources and dissolution processes of aerosol Fe from such fundamental concentration data may help improve the parameterization of aerosol Fe dissolution in global climate models.

Atmospheric deposition of aerosol iron (Fe) can stimulate marine primary productivity by supplying dissolved Fe (d-Fe) to the surface ocean, thereby potentially influencing the global climate. Aerosol Fe solubility (Fe_sol%) is closely linked to its bioavailability, and previous studies have shown that Fe_sol% generally increases as aerosol Fe concentration decreases. However, the mechanism underlying this widely observed inverse relationship remains unresolved. In this study, aerosol observations from East Asia, the North and South Pacific, and the Atlantic were compiled, and the ratios of total Fe to total Al (T-Fe/T-Al) and dissolved Fe to dissolved Al ([d-Fe]/[d-Al]) were used to estimate the contributions of mineral-derived and anthropogenic Fe to aerosol d-Fe, as well as the Fe_sol% of each source fraction. Aerosol d-Fe was found to be derived predominantly from mineral dust in many oceanic regions. In addition, both mineral-derived Fe and anthropogenic Fe showed inverse relationships between concentration and solubility. If the inverse relationship between Fe concentration and Fe_sol% were controlled mainly by simple two-component mixing between low-solubility mineral particles and highly soluble anthropogenic Fe, the Fe_sol% of each source fraction would not be expected to vary systematically with concentration. Instead, the results suggest that atmospheric chemical processing, together with depositional removal during transport, progressively increases the solubility of Fe remaining in aerosol particles. The ability to estimate the sources and dissolution processes of aerosol Fe from such fundamental concentration data may help improve the parameterization of aerosol Fe dissolution in global climate models.

Atmospheric deposition of aerosol iron (Fe) can stimulate marine primary productivity by supplying dissolved Fe (d-Fe) to the surface ocean, thereby potentially influencing the global climate. Aerosol Fe solubility (Fe_sol%) is closely linked to its bioavailability, and previous studies have shown that Fe_sol% generally increases as aerosol Fe concentration decreases. However, the mechanism underlying this widely observed inverse relationship remains unresolved. In this study, aerosol observations from East Asia, the North and South Pacific, and the Atlantic were compiled, and the ratios of total Fe to total Al (T-Fe/T-Al) and dissolved Fe to dissolved Al ([d-Fe]/[d-Al]) were used to estimate the contributions of mineral-derived and anthropogenic Fe to aerosol d-Fe, as well as the Fe_sol% of each source fraction. Aerosol d-Fe was found to be derived predominantly from mineral dust in many oceanic regions. In addition, both mineral-derived Fe and anthropogenic Fe showed inverse relationships between concentration and solubility. If the inverse relationship between Fe concentration and Fe_sol% were controlled mainly by simple two-component mixing between low-solubility mineral particles and highly soluble anthropogenic Fe, the Fe_sol% of each source fraction would not be expected to vary systematically with concentration. Instead, the results suggest that atmospheric chemical processing, together with depositional removal during transport, progressively increases the solubility of Fe remaining in aerosol particles. The ability to estimate the sources and dissolution processes of aerosol Fe from such fundamental concentration data may help improve the parameterization of aerosol Fe dissolution in global climate models.

Atmospheric deposition of aerosol iron (Fe) can stimulate marine primary productivity by supplying dissolved Fe (d-Fe) to the surface ocean, thereby potentially influencing the global climate. Aerosol Fe solubility (Fe_sol%) is closely linked to its bioavailability, and previous studies have shown that Fe_sol% generally increases as aerosol Fe concentration decreases. However, the mechanism underlying this widely observed inverse relationship remains unresolved. In this study, aerosol observations from East Asia, the North and South Pacific, and the Atlantic were compiled, and the ratios of total Fe to total Al (T-Fe/T-Al) and dissolved Fe to dissolved Al ([d-Fe]/[d-Al]) were used to estimate the contributions of mineral-derived and anthropogenic Fe to aerosol d-Fe, as well as the Fe_sol% of each source fraction. Aerosol d-Fe was found to be derived predominantly from mineral dust in many oceanic regions. In addition, both mineral-derived Fe and anthropogenic Fe showed inverse relationships between concentration and solubility. If the inverse relationship between Fe concentration and Fe_sol% were controlled mainly by simple two-component mixing between low-solubility mineral particles and highly soluble anthropogenic Fe, the Fe_sol% of each source fraction would not be expected to vary systematically with concentration. Instead, the results suggest that atmospheric chemical processing, together with depositional removal during transport, progressively increases the solubility of Fe remaining in aerosol particles. The ability to estimate the sources and dissolution processes of aerosol Fe from such fundamental concentration data may help improve the parameterization of aerosol Fe dissolution in global climate models.

The supply of dissolved iron (d-Fe) can enhance marine CO₂ fixation. Aerosols are one source of d-Fe to the ocean surface, but aerosol iron solubility (Fe_sol%) depends on emission sources and atmospheric alteration processes that remain poorly reproduced by global climate and chemical transport models. Although recent advances in machine and deep learning models can capture nonlinear relationships in observational datasets, applications to environmental samples are still limited and approaches for improving interpretability require further development. This study trained XGBoost and a deep neural network (DNN) using East Asian aerosol data and tested whether Fe_sol% and d-Fe concentrations in marine aerosols can be reproduced. The effects of individual features on Fe_sol% and d-Fe were quantified using SHapley Additive exPlanations (SHAP), and independent component analysis (ICA) was applied to SHAP values to extract independent components representing dominant controlling processes of Fe_sol%. East Asian Fe_sol% was reproduced well by both XGBoost and DNN. For marine aerosols, higher reproducibility was achieved by the DNN than by XGBoost, likely because deeper relationships among features can be learned. SHAP indicated that variability in Fe_sol% and d-Fe is primarily driven by chemical alteration of Fe in mineral dust and anthropogenic aerosols. ICA further suggested that additional processes, including heavy oil combustion, influence a subset of samples. Spatial variations in process contributions were visualized by mapping the influence of each independent component. This DNN-based framework can improve interpretation of both current results and future observational datasets.

The supply of dissolved iron (d-Fe) can enhance marine CO₂ fixation. Aerosols are one source of d-Fe to the ocean surface, but aerosol iron solubility (Fe_sol%) depends on emission sources and atmospheric alteration processes that remain poorly reproduced by global climate and chemical transport models. Although recent advances in machine and deep learning models can capture nonlinear relationships in observational datasets, applications to environmental samples are still limited and approaches for improving interpretability require further development. This study trained XGBoost and a deep neural network (DNN) using East Asian aerosol data and tested whether Fe_sol% and d-Fe concentrations in marine aerosols can be reproduced. The effects of individual features on Fe_sol% and d-Fe were quantified using SHapley Additive exPlanations (SHAP), and independent component analysis (ICA) was applied to SHAP values to extract independent components representing dominant controlling processes of Fe_sol%. East Asian Fe_sol% was reproduced well by both XGBoost and DNN. For marine aerosols, higher reproducibility was achieved by the DNN than by XGBoost, likely because deeper relationships among features can be learned. SHAP indicated that variability in Fe_sol% and d-Fe is primarily driven by chemical alteration of Fe in mineral dust and anthropogenic aerosols. ICA further suggested that additional processes, including heavy oil combustion, influence a subset of samples. Spatial variations in process contributions were visualized by mapping the influence of each independent component. This DNN-based framework can improve interpretation of both current results and future observational datasets.

The supply of dissolved iron (d-Fe) can enhance marine CO₂ fixation. Aerosols are one source of d-Fe to the ocean surface, but aerosol iron solubility (Fe_sol%) depends on emission sources and atmospheric alteration processes that remain poorly reproduced by global climate and chemical transport models. Although recent advances in machine and deep learning models can capture nonlinear relationships in observational datasets, applications to environmental samples are still limited and approaches for improving interpretability require further development. This study trained XGBoost and a deep neural network (DNN) using East Asian aerosol data and tested whether Fe_sol% and d-Fe concentrations in marine aerosols can be reproduced. The effects of individual features on Fe_sol% and d-Fe were quantified using SHapley Additive exPlanations (SHAP), and independent component analysis (ICA) was applied to SHAP values to extract independent components representing dominant controlling processes of Fe_sol%. East Asian Fe_sol% was reproduced well by both XGBoost and DNN. For marine aerosols, higher reproducibility was achieved by the DNN than by XGBoost, likely because deeper relationships among features can be learned. SHAP indicated that variability in Fe_sol% and d-Fe is primarily driven by chemical alteration of Fe in mineral dust and anthropogenic aerosols. ICA further suggested that additional processes, including heavy oil combustion, influence a subset of samples. Spatial variations in process contributions were visualized by mapping the influence of each independent component. This DNN-based framework can improve interpretation of both current results and future observational datasets.