全球尺度颗粒分辨气溶胶混合状态指数的无监督区域化
Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale
摘要：
气溶胶混合状态对大气气溶胶粒子的气候和健康影响有重要影响。地球系统模型中常见的简化气溶胶混合状态假设，可能会在预测这些气溶胶影响时引入误差。气溶胶混合状态指数是一种量化气溶胶混合状态的指标，是量化这些误差的方便措施。全球估计气溶胶混合状态指数最近已成为通过监督学习模型，但需要区域化，以方便时空分析。在这里，我们开发了一个简单而有效的无监督学习方法，区域化预测的全球气溶胶混合状态指数。我们使用气溶胶混合状态指数全球分布的月平均值作为输入数据。网格单元，然后聚类成区域的k-means算法没有明确的空间信息作为输入。这一方法导致全球11个区域具有特定的空间聚集模式。每个区域表现出独特的混合状态指数和气溶胶成分的分布，显示了无监督区域化方法的有效性。这项研究定义了“气溶胶混合状态区”，这可能是有用的大气科学研究。
英文摘要：
The aerosol mixing state significantly affects the climate and health impacts of atmospheric aerosol particles. Simplified aerosol mixing state assumptions, common in Earth System models, can introduce errors in the prediction of these aerosol impacts. The aerosol mixing state index, a metric to quantify aerosol mixing state, is a convenient measure for quantifying these errors. Global estimates of aerosol mixing state indices have recently become available via supervised learning models, but require regionalization to ease spatiotemporal analysis. Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices. We used the monthly average of aerosol mixing state indices global distribution as the input data. Grid cells were then clustered into regions by the k-means algorithm without explicit spatial information as input. This approach resulted in eleven regions over the globe with specific spatial aggregation patterns. Each region exhibited a unique distribution of mixing state indices and aerosol compositions, showing the effectiveness of the unsupervised regionalization approach. This study defines "aerosol mixing state zones" that could be useful for atmospheric science research.
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