Iron from coal combustion particles dissolves much faster than mineral dust under simulated atmospheric acid conditions

Abstract

Abstract. Mineral dust is the largest source of aerosol iron (Fe) to the offshore global ocean, but acidic processing of coal fly ash (CFA) in the atmosphere may result in a disproportionally higher contribution of bioavailable Fe. Here, we determined the Fe speciation and dissolution kinetics of CFA from Aberthaw (United Kingdom), Krakow (Poland), and Shandong (China) in solutions which simulate atmospheric acidic processing. In CFA-PM10 fractions, 8 %–21.5 % of the total Fe was as hematite and goethite (dithionite extracted Fe), 2 %–6.5  % as amorphous Fe (ascorbate extracted Fe), while magnetite (oxalate extracted Fe) varied from 3 %–22 %. The remaining 50 %–87  % of Fe was associated with aluminosilicates. High concentration of ammonium sulphate ((NH4)2SO4), often found in wet aerosols, increased Fe solubility of CFA up to 7 times at low pH (2–3). Our results showed a large variability in the effects of oxalate on the Fe dissolution rates at pH 2, from no impact in Shandong ash to doubled dissolution in Krakow ash. However, this enhancement was suppressed in the presence of high concentration of (NH4)2SO4. Dissolution of highly reactive Fe was insufficient to explain the high Fe solubility at low pH in CFA, and the modelled dissolution kinetics suggests that other Fe phases such as magnetite may also dissolve rapidly under acidic conditions. Overall, Fe in CFA dissolved up to 7 times faster than in Saharan dust samples at pH 2. Based on these laboratory data, we developed a new scheme for the proton- and oxalate- promoted Fe dissolution of CFA, which was implemented into the global atmospheric chemical transport model IMPACT. The revised model showed a better agreement with observations of surface concentration of dissolved Fe in aerosol particles over the Bay of Bengal, due to the rapid Fe release at the initial stage at highly acidic conditions. The improved model also enabled us to predict sensitivity to a more dynamic range of pH changes, particularly between anthropogenic combustion and biomass burning aerosols.