Particle water and pH are predicted using meteorological observations (relative humidity (RH), temperature (T)), gas/particle composition, and thermodynamic modeling (ISORROPIA-II). A comprehensive uncertainty analysis is included, and the model is validated. We investigate mass concentrations of particle water and related particle pH for ambient fine-mode aerosols sampled in a relatively remote Alabama forest during the Southern Oxidant and Aerosol Study (SOAS) in summer and at various sites in the southeastern US during different seasons, as part of the Southeastern Center for Air Pollution and Epidemiology (SCAPE) study. Particle water and pH are closely linked; pH is a measure of the particle H⁺ aqueous concentration and depends on both the presence of ions and amount of particle liquid water. Levels of particle water, in turn, are determined through water uptake by both the ionic species and organic compounds. Thermodynamic calculations based on measured ion concentrations can predict both pH and liquid water but may be biased since contributions of organic species to liquid water are not considered. In this study, contributions of both the inorganic and organic fractions to aerosol liquid water were considered, and predictions were in good agreement with measured liquid water based on differences in ambient and dry light scattering coefficients (prediction vs. measurement: slope = 0.91, intercept = 0.5 μg m⁻³, R² = 0.75). ISORROPIA-II predictions were confirmed by good agreement between predicted and measured ammonia concentrations (slope = 1.07, intercept = −0.12 μg m⁻³, R² = 0.76). Based on this study, organic species on average contributed 35% to the total water, with a substantially higher contribution (50%) at night. However, not including contributions of organic water had a minor effect on pH (changes pH by 0.15 to 0.23 units), suggesting that predicted pH without consideration of organic water could be sufficient for the purposes of aqueous secondary organic aerosol (SOA) chemistry. The mean pH predicted in the Alabama forest (SOAS) was 0.94 ± 0.59 (median 0.93). pH diurnal trends followed liquid water and were driven mainly by variability in RH; during SOAS nighttime pH was near 1.5, while daytime pH was near 0.5. pH ranged from 0.5 to 2 in summer and 1 to 3 in the winter at other sites. The systematically low pH levels in the southeast may have important ramifications, such as significantly influencing acid-catalyzed reactions, gas–aerosol partitioning, and mobilization of redox metals and minerals. Particle ion balances or molar ratios, often used to infer pH, do not consider the dissociation state of individual ions or particle liquid water levels and do not correlate with particle pH.

We develop an approach combining mass balance and four‐dimensional variational (4D‐Var) methods to facilitate inversion of decadal‐scale total nitrogen oxides (NO_x = NO + NO₂) emissions. In 7 year pseudo‐observation tests, hybrid posterior emissions have smaller normalized mean square error (NMSE) than that of mass balance when compared to true emissions in most cases and perform slightly better in detecting NO_x emission magnitudes and trends. Using this hybrid method, OMI NO₂ satellite observations and the GEOS‐Chem chemical transport model, we find more than 30% increases of emissions over most of East China at the 0.5° × 0.667° grid cell level, leading to a 16% growth of emissions over all of China from 2005 to 2012, whereas emissions in several urban centers have decreased by 10–26% in the same period. From 2010 to 2012, a decline is found in the North China Plain, Hubei Province, and Pearl River Delta area, coinciding with China's enforcement of its twelfth “Five Year Plan.” Changes in individual grid cell may be different from changes over the entire city or province, as exemplified by opposite trends in Beijing versus the Mentougou district of Beijing from 2005 to 2012. Also, NO₂ columns do not necessarily have the same trend as NO_x emissions due to their nonlinear response to emissions and the influence of meteorology, the latter alone which can cause up to 30% interannual changes in NO₂ columns. Compared to recent bottom‐up inventories, hybrid posterior emissions have the same seasonality, smaller emissions, and emission growth rate at the national scale.

Presented here is the validation of the CrIS (Cross-track Infrared Sounder) fast physical NH₃ retrieval (CFPR) column and profile measurements using ground-based Fourier transform infrared (FTIR) observations. We use the total columns and profiles from seven FTIR sites in the Network for the Detection of Atmospheric Composition Change (NDACC) to validate the satellite data products. The overall FTIR and CrIS total columns have a positive correlation of r = 0.77 (N = 218) with very little bias (a slope of 1.02). Binning the comparisons by total column amounts, for concentrations larger than 1.0 × 10¹⁶ molecules cm⁻², i.e. ranging from moderate to polluted conditions, the relative difference is on average ∼ 0–5 % with a standard deviation of 25–50 %, which is comparable to the estimated retrieval uncertainties in both CrIS and the FTIR. For the smallest total column range (< 1.0 × 10¹⁶ molecules cm⁻²) where there are a large number of observations at or near the CrIS noise level (detection limit) the absolute differences between CrIS and the FTIR total columns show a slight positive column bias. The CrIS and FTIR profile comparison differences are mostly within the range of the single-level retrieved profile values from estimated retrieval uncertainties, showing average differences in the range of ∼ 20 to 40 %. The CrIS retrievals typically show good vertical sensitivity down into the boundary layer which typically peaks at ∼ 850 hPa (∼ 1.5 km). At this level the median absolute difference is 0.87 (std = ±0.08) ppb, corresponding to a median relative difference of 39 % (std = ±2 %). Most of the absolute and relative profile comparison differences are in the range of the estimated retrieval uncertainties. At the surface, where CrIS typically has lower sensitivity, it tends to overestimate in low-concentration conditions and underestimate in higher atmospheric concentration conditions.

Bidirectional air–surface exchange of ammonia (NH₃) has been neglected in many air quality models. In this study, we implement the bidirectional exchange of NH₃ in the GEOS-Chem global chemical transport model. We also introduce an updated diurnal variability scheme for NH₃ livestock emissions and evaluate the recently developed MASAGE_NH₃ bottom-up inventory. While updated diurnal variability improves comparison of modeled-to-hourly in situ measurements in the southeastern USA, NH₃ concentrations decrease throughout the globe, up to 17 ppb in India and southeastern China, with corresponding decreases in aerosol nitrate by up to 7 μg m⁻³. The ammonium (NH₄⁺) soil pool in the bidirectional exchange model largely extends the NH₃ lifetime in the atmosphere. Including bidirectional exchange generally increases NH₃ gross emissions (7.1 %) and surface concentrations (up to 3.9 ppb) throughout the globe in July, except in India and southeastern China. In April and October, it decreases NH₃ gross emissions in the Northern Hemisphere (e.g., 43.6 % in April in China) and increases NH₃ gross emissions in the Southern Hemisphere. Bidirectional exchange does not largely impact NH₄⁺ wet deposition overall. While bidirectional exchange is fundamentally a better representation of NH₃ emissions from fertilizers, emissions from primary sources are still underestimated and thus significant model biases remain when compared to in situ measurements in the USA. The adjoint of bidirectional exchange has also been developed for the GEOS-Chem model and is used to investigate the sensitivity of NH₃ concentrations with respect to soil pH and fertilizer application rate. This study thus lays the groundwork for future inverse modeling studies to more directly constrain these physical processes rather than tuning bulk unidirectional NH₃ emissions.