The Acidity of Atmospheric Particles and Clouds

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 semi-volatile 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 indicates acidity may be relatively constant due to the semi-volatile 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.

Improving organic aerosol treatments in CESM/CAM5: Development, application, and evaluation

New treatments for organic aerosol (OA) formation have been added to a modified version of the CESM/CAM5 model (CESM-NCSU). These treatments include a volatility basis set treatment for the simulation of primary and secondary organic aerosols (SOAs), a simplified treatment for organic aerosol (OA) formation from glyoxal, and a parameterization representing the impact of new particle formation (NPF) of organic gases and sulfuric acid. With the inclusion of these new treatments, the concentration of oxygenated organic aerosol increases by 0.33 µg m-3 and that of primary organic aerosol (POA) decreases by 0.22 µg m-3 on global average. The decrease in POA leads to a reduction in the OA direct effect, while the increased OOA increases the OA indirect effects. Simulations with the new OA treatments show considerable improvement in simulated SOA, oxygenated organic aerosol (OOA), organic carbon (OC), total carbon (TC), and total organic aerosol (TOA), but degradation in the performance of HOA. In simulations of the current climate period, despite some deviations from observations, CESM-NCSU with the new OA treatments significantly improves the magnitude, spatial pattern, seasonal pattern of OC and TC, as well as, the speciation of TOA between POA and OOA. Sensitivity analysis reveals that the inclusion of the organic NPF treatment impacts the OA indirect effects by enhancing cloud properties. The simulated OA level and its impact on the climate system are most sensitive to choices in the enthalpy of vaporization and wet deposition of SVOCs, indicating that accurate representations of these parameters are critical for accurate OA-climate simulations.

Recommendations on statistics and benchmarks to assess photochemical model performance

Photochemical grid models are addressing an increasing variety of air quality related issues, yet procedures and metrics used to evaluate their performance remain inconsistent. This impacts the ability to place results in quantitative context relative to other models and applications, and to inform the user and affected community of model uncertainties and weaknesses. More consistent evaluations can serve to drive improvements in the modeling process as major weaknesses are identified and addressed. The large number of North American photochemical modeling studies published in the peer-reviewed literature over the past decade affords a rich data set from which to update previously established quantitative performance "benchmarks" for ozone and particulate matter (PM) concentrations. Here we exploit this information to develop new ozone and PM benchmarks (goals and criteria) for three well-established statistical metrics over spatial scales ranging from urban to regional and over temporal scales ranging from episodic to seasonal. We also recommend additional evaluation procedures, statistical metrics, and graphical methods for good practice. While we primarily address modeling and regulatory settings in the United States, these recommendations are relevant to any such applications of state-of-the-science photochemical models. Our primary objective is to promote quantitatively consistent evaluations across different applications, scales, models, model inputs, and configurations. The purpose of benchmarks is to understand how good or poor the results are relative to historical model applications of similar nature and to guide model performance improvements prior to using results for policy assessments. To that end, it also remains critical to evaluate all aspects of the model via diagnostic and dynamic methods. A second objective is to establish a means to assess model performance changes in the future. Statistical metrics and benchmarks need to be revisited periodically as model performance and the characteristics of air quality change in the future.

IMPLICATIONS

We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user's ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.