科研成果 by Year: 2011

New particle formation (NPF) events have been recognized as an important process contributing to the cloud condensation nuclei (CCN) formation. In this study, measurement of NPF and predicted number concentrations of CCN using kappa-Kohler theory were analyzed to assess the contribution of NPF to possible CCN. The particle growth rates of NPF events were categorized to two types: sulfur-rich (condensation and neutralization of sulfuric acid dominating net growth rate) and sulfur-poor cases. The growth rates for the sulfur-poor events were about 80% larger than those of the sulfur-rich cases on average. NPF events increased the CCN number concentrations by 0.4-6 times in the megacity area of Beijing. The enhancement ratios (the ratio of CCN number concentrations when obvious particle growth ended to that when it started during NPF events) were high for large supersaturation (S). For example, it was about 30-50% higher under S = 0.86% than under S = 0.07%. The enhancement ratios exhibited similar seasonal variation as the growth rates with a larger value in summer than other seasons, which suggested that growth rate was a key factor in the conversion of NPF to possible CCN. The enhancement ratios were higher during the sulfur-poor NPF events with larger growth rates mainly contributed by organic species, indicating that organic species were the dominant chemical contributor in facilitating the conversion of newly formed particles to possible CCN in the Beijing Megacity. (C) 2011 Elsevier Ltd. All rights reserved.

Hypoxia is a long-standing threat to the integrity of the Chesapeake Bay ecosystem. In this study, we introduce a Bayesian framework that aims to guide the parameter estimation of a Streeter-Phelps model when only hypoxic volume data are available. We present a modeling exercise that addresses a hypothetical scenario under which the only data available are hypoxic volume estimates. To address the identification problem of the model, we formulated informative priors based on available literature information and previous knowledge from the system. Our analysis shows that the use of hypoxic volume data results in reasonable predictive uncertainty, although the variances of the marginal posterior parameter distributions are usually greater than those obtained from fitting the model to dissolved oxygen (DO) profiles. Numerical experiments of joint parameter estimation were also used to facilitate the selection of more parsimonious models that effectively balance between complexity and performance. Parameters with relatively stable posterior means over time and narrow uncertainty bounds were considered as temporally constant, while those with time varying posterior patterns were used to accommodate the interannual variability by assigning year-specific values. Finally, our study offers prescriptive guidelines on how this model can be used to address the hypoxia forecasting in the Chesapeake Bay area.