Ihab Abboud

Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model

Exposure to ambient fine particulate matter (PM2.5) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM2.5 to interpret globally dispersed PM2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM2.5 composition varies substantially for secondary inorganic aerosols (2.4-19.7 μg/m3), mineral dust (1.9-14.7 μg/m3), residual/organic matter (2.1-40.2 μg/m3), and black carbon (1.0-7.3 μg/m3). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 μg/m3), industry (6.5 μg/m3), and power generation (5.6 μg/m3) are leading sources of outdoor global population-weighted PM2.5 concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM2.5 mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM2.5 provides insight into sources and processes that influence the global spatial variation in PM2.5 composition.

Comparisons of a Chemical Transport Model with a Four-Year (April to September) Analysis of Fine- and Coarse-Mode Aerosol Optical Depth Retrievals Over the Canadian Arctic

ABSTRACT We compared April to September retrievals of total, fine-mode (sub-micron), and coarse-mode (super-micron) aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) with simulations from a global three-dimensional chemical transport model, the Goddard Earth Observing System (GEOS-Chem), across five Arctic stations and a four-year sampling period. It was determined that the AOD histograms of both the retrievals and the simulations were better represented by a lognormal distribution and that the successful simulation of this empirical feature as well as its consequences (including a better model versus retrieval coefficient of determination in log-log AOD space) represented a general indicator of model evaluation success. Seasonal (monthly averaged) AOD retrievals were sensitive to the way in which the averaging was performed; this was ascribed to the presence of highly variable fine-mode smoke in the western Arctic. The retrieved and modelled station-by-station fine-mode AOD averages showed a peak in April/May that decreased over the summer, while the model underestimated the fine-mode AOD by an average of about 0.004 (∼6%). Both the retrievals and simulations showed seasonal coarse-mode AOD variations with a peak in April/May that was attributed to Asian and/or Saharan dust. The models success in capturing such weak seasonal events helps to confirm the relevance of the separation of the fine and coarse modes and the general validity of model estimates in the Arctic.