Abstract. Acidity, defined as pH, is a central component of aqueous chemistry. In the atmosphere, the acidity of condensed phases (aerosol particles, cloud water, and fog droplets) governs the phase partitioning of semivolatile gases such as HNO3, NH3, HCl, and organic acids and bases as well as chemical reaction rates. It has implications for the atmospheric lifetime of pollutants, deposition, and human health. Despite its fundamental role in atmospheric processes, only recently has this field seen a growth in the number of studies on particle acidity. Even with this growth, many fine-particle pH estimates must be based on thermodynamic model calculations since no operational techniques exist for direct measurements. Current information indicates acidic fine particles are ubiquitous, but observationally constrained pH estimates are limited in spatial and temporal coverage. Clouds and fogs are also generally acidic, but to a lesser degree than particles, and have a range of pH that is quite sensitive to anthropogenic emissions of sulfur and nitrogen oxides, as well as ambient ammonia. Historical measurements indicate that cloud and fog droplet pH has changed in recent decades in response to controls on anthropogenic emissions, while the limited trend data for aerosol particles indicate acidity may be relatively constant due to the semivolatile nature of the key acids and bases and buffering in particles. This paper reviews and synthesizes the current state of knowledge on the acidity of atmospheric condensed phases, specifically particles and cloud droplets. It includes recommendations for estimating acidity and pH, standard nomenclature, a synthesis of current pH estimates based on observations, and new model calculations on the local and global scale.

Presently only limited sets of tropospheric ammonia (NH₃) measurements in the Earth's atmosphere have been reported from satellite and surface station measurements, despite the well-documented negative impact of NH₃ on the environment and human health. Presented here is a detailed description of the satellite retrieval strategy and analysis for the Tropospheric Emission Spectrometer (TES) using simulations and measurements. These results show that: (i) the level of detectability for a representative boundary layer TES NH₃ mixing ratio value is ~0.3 ppbv, which typically corresponds to a profile that contains a maximum level value of ~1 ppbv; (ii) TES NH₃ retrievals provide at most one degree of freedom for signal (DOFS), with peak sensitivity between 700 and 900 mbar; (iii) TES NH₃ retrievals show significant spatial and seasonal variability of NH₃ globally; (iv) Initial comparisons of TES observations with GEOS-CHEM estimates show TES values being higher overall. Important differences and similarities between modeled and observed seasonal and spatial trends are noted, with discrepancies indicating areas where the timing and magnitude of modeled NH₃ emissions from agricultural sources, and to lesser extent biomass burning sources, need further study

Atmospheric ammonia (NH₃) is the primary atmospheric base and an important precursor for inorganic particulate matter and when deposited NH₃ contributes to surface water eutrophication, soil acidification and decline in species biodiversity. Flux measurements indicate that the air-surface exchange of NH₃ is bi-directional. However, the effects of bi-directional exchange, soil biogeochemistry and human activity are not parameterized in air quality models. The US Environmental Protection Agency (EPA)'s Community Multiscale Air-Quality (CMAQ) model with bi-directional NH₃ exchange has been coupled with the United States Department of Agriculture (USDA)'s Environmental Policy Integrated Climate (EPIC) agro-ecosystem model's nitrogen geochemistry algorithms. CMAQ with bi-directional NH₃ exchange coupled to EPIC connects agricultural cropping management practices to emissions and atmospheric concentrations of reduced nitrogen and models the biogeochemical feedback on NH₃ air-surface exchange. This coupled modeling system reduced the biases and error in NH_x (NH₃ + NH₄⁺) wet deposition and in ambient aerosol concentrations in an annual 2002 Continental US (CONUS) domain simulation when compared to a 2002 annual simulation of CMAQ without bi-directional exchange. Fertilizer emissions estimated in CMAQ 5.0 with bi-directional exchange exhibits markedly different seasonal dynamics than the US EPA's National Emissions Inventory (NEI), with lower emissions in the spring and fall and higher emissions in July

Abstract. Waters impounded behind dams (i.e., reservoirs) are important sources of greenhouses gases (GHGs), especially methane (CH4), but emission estimates are not well constrained due to high spatial and temporal variability, limitations in monitoring methods to characterize hot spot and hot moment emissions, and the limited number of studies that investigate diurnal, seasonal, and interannual patterns in emissions. In this study, we investigate the temporal patterns and biophysical drivers of CH4 emissions from Acton Lake, a small eutrophic reservoir, using a combination of methods: eddy covariance monitoring, continuous warm-season ebullition measurements, spatial emission surveys, and measurements of key drivers of CH4 production and emission. We used an artificial neural network to gap fill the eddy covariance time series and to explore the relative importance of biophysical drivers on the interannual timescale. We combined spatial and temporal monitoring information to estimate annual whole-reservoir emissions. Acton Lake had cumulative areal emission rates of 45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m−2 in 2017 and 2018, respectively, or 109 ± 14 and 123 ± 10 Mg CH4 in 2017 and 2018 across the whole 2.4 km2 area of the lake. The main difference between years was a period of elevated emissions lasting less than 2 weeks in the spring of 2018, which contributed 17 % of the annual emissions in the shallow region of the reservoir. The spring burst coincided with a phytoplankton bloom, which was likely driven by favorable precipitation and temperature conditions in 2018 compared to 2017. Combining spatially extensive measurements with temporally continuous monitoring enabled us to quantify aspects of the spatial and temporal variability in CH4 emission. We found that the relationships between CH4 emissions and sediment temperature depended on location within the reservoir, and we observed a clear spatiotemporal offset in maximum CH4 emissions as a function of reservoir depth. These findings suggest a strong spatial pattern in CH4 biogeochemistry within this relatively small (2.4 km2) reservoir. In addressing the need for a better understanding of GHG emissions from reservoirs, there is a trade-off in intensive measurements of one water body vs. short-term and/or spatially limited measurements in many water bodies. The insights from multi-year, continuous, spatially extensive studies like this one can be used to inform both the study design and emission upscaling from spatially or temporally limited results, specifically the importance of trophic status and intra-reservoir variability in assumptions about upscaling CH4 emissions.