用粒子分辨气溶胶模式模拟碳烟混合状态的演变
Simulating the evolution of soot mixing state with a particle-resolved aerosol model
摘要：
煤烟颗粒在大气中的混合状态对于评估其气候影响至关重要，因为它决定了它们的化学反应性、云凝结核活动和辐射特性。为了改善模型中的混合状态表示，我们提出了一种新的方法，随机粒子分辨模型PartMC-MOSAIC，它明确地解决了不同类型的气溶胶粒子在给定的人口中的单个粒子的组成。这种方法精确地跟踪由于排放、稀释、冷凝和凝结而引起的颗粒混合状态的演变。为了使这种直接的随机粒子为基础的方法实用，我们实现了一个新的多尺度随机凝聚方法。使用这种方法，我们实现了最佳的效率时，凝固内核是高度不均匀的应用程序，是许多现实的应用程序的情况。PartMC-MOSAIC被应用到一个理想化的城市羽流的情况下，代表一个大的城市地区，模拟不同类型的碳质气溶胶由于凝结和冷凝的演变。对于这个城市的烟羽的情况下，我们量化的个别过程，有助于老化的气溶胶分布，说明了我们的建模方法的能力。结果表明，第一次的多维结构的颗粒组成，这通常是失去了内部混合的截面或模态气溶胶模型。
英文摘要：
The mixing state of soot particles in the atmosphere is of crucial importance for assessing their climatic impact, since it governs their chemical reactivity, cloud condensation nuclei activity and radiative properties. To improve the mixing state representation in models, we present a new approach, the stochastic particle-resolved model PartMC-MOSAIC, which explicitly resolves the composition of individual particles in a given population of different types of aerosol particles. This approach accurately tracks the evolution of the mixing state of particles due to emission, dilution, condensation and coagulation. To make this direct stochastic particle-based method practical, we implemented a new multiscale stochastic coagulation method. With this method we achieved optimal efficiency for applications when the coagulation kernel is highly non-uniform, as is the case for many realistic applications. PartMC-MOSAIC was applied to an idealized urban plume case representative of a large urban area to simulate the evolution of carbonaceous aerosols of different types due to coagulation and condensation. For this urban plume scenario we quantified the individual processes that contribute to the aging of the aerosol distribution, illustrating the capabilities of our modeling approach. The results showed for the first time the multidimensional structure of particle composition, which is usually lost in internally-mixed sectional or modal aerosol models.
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