Heterogeneous graphical model for non-negative and non-Gaussian PM2.5 data
Heterogeneous graphical model for non-negative and non-Gaussian PM2.5 data
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DOI：
10.1111/rssc.12575
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发表时间：
2022-06-22
影响因子：
1.6
通讯作者：
Ma, Shuangge
中科院分区：
文献类型：
--
作者：
Zhang, Jiaqi;Fan, Xinyan;Ma, Shuangge
研究区域间PM2.5浓度的条件关系对于大气污染联防联控具有重要意义。由于大气条件的季节性变化，PM2.5的空间模式全年可能不同。此外，浓度数据是非负的和非高斯的。这些数据特征对现有方法提出了重大挑战。本文提出了一种基于分数匹配损失的非负非高斯数据的异构图模型。该方法同时聚类多个数据集，并估计每个聚类中具有复杂属性的变量的图。此外，我们的模型涉及一个网络，表明数据集之间的相似性，这个网络可以有其他的应用。在仿真研究中，所提出的方法优于竞争的替代品在聚类和边缘识别。我们还使用2019年从67个空气质量监测站获得的数据分析了台湾地区PM2.5浓度的空间相关性。将12个月分为1 - 3月、4月、5 - 9月和12 - 12月4组，对应的图分别有153、57、86和167条边。结果表明，该地区的降水具有明显的季节性，这与气象文献的结果相一致。从地理位置上看，台湾北部和南部地区的PM2.5浓度相关性更高。这些结果可以为制定联合空气质量控制策略提供有价值的信息。
Studies on the conditional relationships between PM2.5 concentrations among different regions are of great interest for the joint prevention and control of air pollution. Because of seasonal changes in atmospheric conditions, spatial patterns of PM2.5 may differ throughout the year. Additionally, concentration data are both non-negative and non-Gaussian. These data features pose significant challenges to existing methods. This study proposes a heterogeneous graphical model for non-negative and non-Gaussian data via the score matching loss. The proposed method simultaneously clusters multiple datasets and estimates a graph for variables with complex properties in each cluster. Furthermore, our model involves a network that indicate similarity among datasets, and this network can have additional applications. In simulation studies, the proposed method outperforms competing alternatives in both clustering and edge identification. We also analyse the PM2.5 concentrations' spatial correlations in Taiwan's regions using data obtained in year 2019 from 67 air-quality monitoring stations. The 12 months are clustered into four groups: January-March, April, May-September and October-December, and the corresponding graphs have 153, 57, 86 and 167 edges respectively. The results show obvious seasonality, which is consistent with the meteorological literature. Geographically, the PM2.5 concentrations of north and south Taiwan regions correlate more respectively. These results can provide valuable information for developing joint air-quality control strategies.