Analysis of Data from International Consortium for Atmospheric Research on Transport and
4.6. Cloud Parcel Model
4.6.1. Description and Modifications
out-of-cloud, to produce increasing amounts of SO42- and oxalate. We note that although the SO2 concentrations were measured to be higher in plume compared to non-plume conditions, the concentrations of volatile organic carbon (VOC) species were essentially similar, which highlights the importance of non-VOC factors in oxalic acid production.
with H2O2 and O3 (Seinfeld and Pandis, 1998). The model does not consider the volatilization of particulate dicarboxylic acids as their vapor pressures are sufficiently low (< 10-5 mm Hg) (Saxena and Hildemann, 1996; Tong et al., 2004).
Oxalic acid has two sources and one sink in the aqueous phase; it is consumed by OH oxidation to yield CO2, and it is formed by OH oxidation of longer-chain dicarboxylic acids and glyoxylic acid (including its anion, glyoxylate). The formation route arising from the decay of longer dicarboxylic acids begins with adipic acid, which is formed primarily by the aqueous uptake of gas-phase products from cyclohexene oxidation by ozone and OH. Glutaric acid is formed by the aqueous oxidation of adipic acid and by the aqueous uptake of gas-phase products from the ozonolysis of cyclohexene. A series of oxidation steps leads, in order, from glutaric acid to succinic, malonic, and oxalic acids. The second formation route considers the oxidation of glyoxylic acid, which is formed by the OH oxidation of glyoxal, glycolate, methylglyoxal, and acetic acid. These intermediates are formed in the mechanisms associated with toluene, isoprene, and ethene. Lim et al. (2005) have proposed that methylglyoxal yields low volatility organic acids through oxidation to glyoxylic acid, via intermediate steps involving pyruvic and acetic acids, and finally to oxalic acid. The Ervens et al. (2004) aqueous-phase mechanism for isoprene oxidation has been modified to account for these reactions (Figure 4.7).
The model was applied to match the conditions encountered in the ICARTT flights. To estimate the soluble aerosol fraction (by mass), the total PILS water-soluble mass (inorganic and organic acid species) was divided by the sum of the PILS inorganic water-soluble mass and the organic mass measured by the AMS. The AMS organic
measurement includes both water-soluble and non-water soluble species. The model was also modified to incorporate measured ambient particle size distributions.
Since measurements were carried out in plumes, it is necessary to account for plume dispersion. Using measurements and estimates for various properties in the background air (including concentrations of SO2, H2O2, O3, gas-phase organic precursors, and the mixing ratio of water), the following parameterized factor, which will be called the entrainment rate, is applied to simultaneously simulate open system conditions and plume dilution,
(
c e)
c
b
t η φ φ
φ =− −
∂
∂
where φc and φe are the values of a property in the moving parcels and in the entrained air, respectively. As will be discussed later, a constant measured value representative of background air in the ambient Ohio atmosphere was used for properties represented by φe, while φc was initialized with measured values at the source of the ambient process being simulated (one simulation to be addressed in a later section begins near a power plant stack) and subsequently calculated after each time step by the model. The equation above is analogous to the plume entrainment rate presented by Squires and Turner (1962), where a value of η = 0.2is based on their laboratory experiments. The parameter b is defined as the characteristic length scale for the entrainment process at time t, and taken here as 500 m, the typical depth of the clouds sampled. It is emphasized that the entrainment rate represents a simplified ad hoc factor.
The model simulates one - hour cloud cycles that are intended to represent the trajectory of a typical air parcel in a cloudy atmosphere. Twelve cloud cycles, limited by the prescribed trajectory used, are simulated for each experiment. One cloud cycle
begins with a parcel of air ascending from near the ground up through the mixed layer until activation in cloud. The parcel continues to rise and then descends through the cloud back to the mixed layer. During each cycle, a parcel is in-cloud for approximately 800 s; the same trajectory is then repeated with the number concentration remaining the same, but with particle sizes altered due to mass addition after each cycle. The LWC time evolution in the prescribed thermodynamic trajectory represents mixing and drying by entrained air, based on the host model from which it is derived. A rough estimate of the in-cloud residence time can be estimated by calculating the volume fraction of the boundary layer (BL) occupied by cloud (Feingold et al., 1998); typical volume fractions observed were between 20 and 25% suggesting that the hourly cloud contact time was between 720 and 900 s for a well-mixed BL, a range that is consistent with the cloud contact time of 800 s assumed in the cloud cycles. Photolysis rates are calculated for 40°N on June 21 at a height of 1 km for 12 hours starting at 6 am. The photolysis rates are time dependent and uninfluenced by the presence of clouds. Gas uptake and chemical aqueous-phase processes during each cycle occur only if the LWC exceeds 1 mg/kg and for each particle size bin if the sum of the ammonium and sulfate concentrations does not exceed 1 M. The model neglects aqueous-phase chemical processes when the ionic strength exceeds 1 M. This assumption is the result of a lack of data for the estimation of rate coefficients and because the solubility of organic gases typically decreases with increasing ionic strength. The model also does not consider aerosol loss by wet and dry deposition. Predictions for SO42- and oxalate levels are presented here at the end of each cloud cycle. The comparison between field data and predictions should be viewed as semi-quantitative; for instance, the actual trajectories of air parcels are not known. Our
goal is to determine the extent to which the levels of SO42- and oxalate measured are generally consistent with those predicted by a model that is based on cloud processing and aqueous-phase chemistry.