Organic aerosol spatial/temporal patterns: perspectives of measurements and model

Comparison of different gas-phase mechanisms and aerosol modules for simulating particulate matter formation

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 < or =2.5 microm) particulate matter (PM2.5) 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, and low-NOx 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.

Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research

Studies of the health impacts of airborne particulates' chemical constituents typically assume spatial homogeneity and estimate exposure from ambient monitors. However, factors such as local sources may cause spatially heterogeneous pollution levels. This work examines the degree to which constituent levels vary within communities and whether exposure misclassification is introduced by spatial homogeneity assumptions. Analysis considered PM(2.5) elemental carbon (EC), organic carbon matter, ammonium, sulfate, nitrate, silicon, and sodium ion (Na(+)) for the United States, 1999-2007. Pearson correlations and coefficients of divergence were calculated and compared to distances among monitors. Linear modeling related correlations to distance between monitors, long-term constituent levels, and population density. Spatial heterogeneity was present for all constituents, yet lower for ammonium, sulfate, and nitrate. Lower correlations were associated with higher distance between monitors, especially for nitrate and sulfate, and with lower long-term levels, especially for sulfate and Na(+). Analysis of colocated monitors revealed measurement error for all constituents, especially EC and Na(+). Exposure misclassification may be introduced into epidemiological studies of PM(2.5) constituents due to spatial variability, and is affected by constituent type and level. When assessing health effects of PM constituents, new methods are needed for estimating exposure and accounting for exposure error induced by spatial variability.

Model representation of secondary organic aerosol in CMAQv4.7

Numerous scientific upgrades to the representation of secondary organic aerosol (SOA) are incorporated into the Community Multiscale Air Quality (CMAQ) modeling system. Additions include several recently identified SOA precursors: benzene, isoprene, and sesquiterpenes; and pathways: in-cloud oxidation of glyoxal and methylglyoxal, particle-phase oligomerization, and acid enhancement of isoprene SOA. NO(x)-dependent aromatic SOA yields are also added along with new empirical measurements of the enthalpies of vaporization and organic mass-to-carbon ratios. For the first time, these SOA precursors, pathways and empirical parameters are included simultaneously in an air quality model for an annual simulation spanning the continental U.S. Comparisons of CMAQ-modeled secondary organic carbon (OC(sec)) with semiempirical estimates screened from 165 routine monitoring sites across the U.S. indicate the new SOA module substantially improves model performance. The most notable improvement occurs in the central and southeastern U.S. where the regionally averaged temporal correlations (r) between modeled and semiempirical OC(sec) increase from 0.5 to 0.8 and 0.3 to 0.8, respectively, when the new SOA module is employed. Wintertime OC(sec) results improve in all regions of the continental U.S. and the seasonal and regional patterns of biogenic SOA are better represented.