科研成果

The role of clouds in the transport and transformation of tropospheric pollutants was investigated through airborne measurements made out of Cleveland, Ohio, from 21 July to 18 August 2004, as part of the International Consortium for Atmospheric Research on Transport and Transformation 2004 program. Observations of gas-phase nitrate, size-resolved particulate nitrate, cloud water nitrate, and size-distributed cloud residual nitrate are used to examine changes in the partitioning of nitrate from precloud to postcloud as a function of particle size. The [NO3-]/[SO42-] ratio was highest in the bulk cloud water and higher in the cloud droplet residuals compared with the below-cloud aerosols. Most of the nitrate entered the cloud water as HNO3, and in 30% of 43 size distributions examined, the nitrate in the cloud droplets was found in residual particle sizes smaller than those of sulfate. Simulations from a trace gas-aerosol-cloud parcel model show that this size difference results from differences in the processes by which nitrate and sulfate enter cloud water. The transfer of HNO3 to cloud droplets is governed primarily by gas-phase mass transfer to the droplets, leading to greater accumulation in the smaller, more numerous droplets with higher total surface area. In contrast, much of the sulfate in the cloud water is the result of nucleation scavenging, which distributes the sulfate mass toward slightly larger sizes. The extent of separation between nitrate and sulfate is dependent on the cloud base sulfate size distribution and the factors that govern both HNO3 and SO2 uptake, with subsequent S(IV) oxidation.

A comprehensive comparison of positive matrix factorization (PMF) and molecular marker-based chemical mass balance (CMB-MM) modeling on PM2.5 source contributions was conducted for particulate matter measurements taken at Jefferson Street, Atlanta, Georgia (JST). The datasets used in each type of receptor modeling were different: CMB-MM used data of primarily organic tracers plus a couple elements measured from 51 24-h PM2.5 samples collected in July 2001 and January 2002. While for PMF, with elements, ions, five gaseous components, and eight temperature-resolved carbon fractions as the input data, both source profiles and contributions were resolved from a total of 932 daily PM2.5 samples covering a 3-year period between January 2000 and December 2002. The model results for the overlapping periods (July 2001 and January 2002) were extracted for comparison. Seven primary sources and three secondary sources were resolved by CMB-MM, while a total of nine primary and secondary factors were resolved by PMF. On average, 107% and 85% of PM2.5 mass were explained by CMB-MM and PMF, respectively, with secondary aerosols handled differently in the above two methods. Four similar sources were resolved by both methods, with good correlation for road dust, but fair for gasoline exhaust and wood combustion. The CMB-MM diesel exhaust has very poor correlation with the PMF resolved diesel exhaust. However, the CMB-MM combined mobile source has improved correlation with the PMF result as compared with the diesel exhaust source. If only the winter data were included, the CMB-MM combined mobile source shows enhanced correlation with the PMF combined source, as compared with the single source of diesel exhaust or gasoline exhaust.