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Abstract. Thermodynamic models predict that sulfate aerosol (S(VI) ≡ H 2 SO 4 (aq) + HSO − 4 + SO 2− 4 ) should take up available ammonia (NH 3 ) quantitatively as ammonium (NH + 4 ) until the ammonium sulfate stoichiometry (NH 4 ) 2 SO 4 is close to being reached. This uptake of ammonia has important implications for aerosol mass, hygroscopicity, and acidity. When ammonia is in excess, the ammonium-sulfate aerosol ratio R = [NHshould approach 2, with excess ammonia remaining in the gas phase. When ammonia is in deficit, it should be fully taken up by the aerosol as ammonium and no significant ammonia should remain in the gas phase. Here we report that sulfate aerosol in the eastern US in summer has a low ammonium-sulfate ratio despite excess ammonia, and we show that this is at odds with thermodynamic models. The ammonium-sulfate ratio averages only 1.04 ± 0.21 mol mol −1 in the Southeast, even though ammonia is in large excess, as shown by the ammonium-sulfate ratio in wet deposition and by the presence of gas-phase ammonia. It further appears that the ammonium-sulfate aerosol ratio is insensitive to the supply of ammonia, remaining low even as the wet deposition ratio exceeds 6 mol mol −1 . While the ammonium-sulfate ratio in wet deposition has increased by 5.8 % yr −1 from 2003 to 2013 in the Southeast, consistent with SO 2 emission controls, the ammonium-sulfate aerosol ratio decreased by 1.4-3.0 % yr −1 . Thus, the aerosol is becoming more acidic even as SO 2 emissions decrease and ammonia emissions stay constant; this is incompatible with simple sulfate-ammonium thermodynamics. A tentative explanation is that sulfate particles are increasingly coated by organic material, retarding the uptake of ammonia. Indeed, the ratio of organic aerosol (OA) to sulfate in the Southeast increased from 1.1 to 2.4 g g −1 over the 2003-2013 period as sulfate decreased. We implement a simple kinetic mass transfer limitation for ammonia uptake to sulfate aerosols in the GEOS-Chem chemical transport model and find that we can reproduce both the observed ammonium-sulfate aerosol ratios and the concurrent presence of gas-phase ammonia. If sulfate aerosol becomes more acidic as OA / sulfate ratios increase, then controlling SO 2 emissions to decrease sulfate aerosol will not have the co-benefit of suppressing acidcatalyzed secondary organic aerosol (SOA) formation.

NO 2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO 2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO 2 model covers the entire contiguous U.S. with daily predictions on 1km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R 2 of 0.788 overall, a spatial R 2 of 0.844, and a temporal R 2 of 0.729. The relationship between daily monitored and predicted NO 2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO 2 levels. This NO 2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO 2 in unmonitored areas. We found the highest NO 2 levels along highways and in cities. We also observed that nationwide NO 2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.