Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

The effects of two gas-phase chemical kinetic mechanisms, Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and Carbon-Bond 05 (CB05), and two secondary organic aerosol (SOA) modules, the Secondary Organic Aerosoi Model (SORGAM) and AER/EPRI/Caltech model (AEC), on fine (aerodynamic diameter ≤2.5 μm) particulate matter (PM_2.5) formation is studied. The major sources of uncertainty in the chemistry of SOA formation are investigated. The use of all major SOA precursors and the treatment of SOA oligomerization are found to be the most important factors for SOA formation, leading to 66% and 60% more SOA, respectively. The explicit representation of high-NO_x and low-NO_x gas-phase chemical regimes is also important with increases in SOA of 30–120% depending on the approach used to implement the distinct SOA yields within the gas-phase chemical kinetic mechanism; further work is needed to develop gas-phase mechanisms that are fully compatible with SOA formation algorithms. The treatment of isoprene SOA as hydrophobic or hydrophilic leads to a significant difference, with more SOA being formed in the latter case. The activity coefficients may also be a major source of uncertainty, as they may differ significantly between atmospheric particles, which contain a myriad of SOA, primary organic aerosol (POA), and inorganic aerosol species, and particles formed in a smog chamber from a single precursor under dry conditions. Significant interactions exist between the uncertainties of the gas-phase chemistry and those of the SOA module.

The physics and chemistry of ultrafine particulate matter (UFPM) associated with mobile source emissions is reviewed. UFPM includes those particles that are less than 0.1 microm in diameter. Measurements of UFPM emitted from mobile sources have been conducted in the laboratory, on roadways, and downwind of roadways. In addition, UFPM formation and evolution have been modeled and the modeling results have been compared with available measurements. The results of those measurement programs and modeling studies are synthesized into a coherent description of the formation of UFPM in mobile source emissions and its subsequent evolution in the ambient atmosphere. Recommendations are provided to address the gaps in our current understanding of the processes leading to the formation of UFPM, its chemical composition, and its evolution in the atmosphere.

Beirut, the capital of Lebanon, which is located on the eastern shore of the Mediterranean basin, experiences high air pollution episodes. Annual average concentrations of coarse and fine particulate matter (PM_2.5) as well as nitrogen oxides (NO_x) often exceed the World Health Organization (WHO) guidelines. Therefore, improving air quality in this region is essential. The Polyphemus/Polair3D modeling system is used here to investigate air pollution episodes in Beirut during 2 to 18 July 2011. The modeling domain covers two nested grids of 1 and 5 km horizontal resolution over greater Beirut and Lebanon, respectively. The anthropogenic emission inventory was developed earlier (Waked et al., 2012). The Weather and Research Forecasting (WRF) model is used to generate the meteorological fields and the Model of Emissions of Gases and Aerosols from Nature (MEGAN) is used for biogenic emissions. The results of the study are compared to measurements from a field campaign conducted in the suburb of Beirut during 2–18 July 2011. The model reproduces satisfactorily the concentrations of most gaseous pollutants, the total mass of PM_2.5 as well as PM_2.5 elemental carbon (EC), organic carbon (OC), and sulfate. Ozone concentrations are overestimated and it appears that this overestimation results mainly from the boundary conditions.