Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China
Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China
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DOI：
10.5194/acp-18-6733-2018
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发表时间：
2018-05-14
影响因子：
6.3
通讯作者：
Martin, Randall V.
中科院分区：
文献类型：
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作者：
Leung, Danny M.;Tai, Amos P. K.;Martin, Randall V.
在他的研究中，我们结合多元统计方法来了解PM2.5与中国不同地区不同时间尺度的当地气象和天气天气模式的关系。利用2014年6月至2017年5月来自类似1500个监测仪的每日总PM2.5观测数据，我们发现PM2.5与所有选定的气象变量（例如，与中国各地的温度正相关，但与海平面压力负相关；与中国北部和南部的相对湿度分别呈正相关和负相关）具有很强的相关性。空间格局表明，与个别气象变量的明显相关性可能源于与天气系统的共同关联。基于对1998-2017年气象数据的主成分分析，以诊断中国四个主要地区天气天气的不同气象模式，我们发现PM2.5与几种天气模式之间存在很强的相关性，这些模式可以解释10%至40%的PM2.5日变化。这些模态包括华北和华中地区与西伯利亚高压相关的季风气流和冷锋通道、华东地区的陆上气流以及华南地区的锋面暴雨。以京津冀（BTH）地区为例，进一步发现区域平均卫星反演年平均PM2.5与年平均相对湿度（RH；正）和西伯利亚高压春季波动频率（负）具有较强的年际相关性。我们将由此得出的PM2.5对气候敏感性应用到政府间气候变化专门委员会（IPCC）耦合模式间比较项目第5阶段（CMIP5）气候预测中，以预测到2050年代由于气候变化而导致的未来PM2.5，并发现由于RCP8.5下更频繁的冷锋通风，BTH地区的年平均PM2.5将适度减少0.5 μ g(-3)，这代表了一个小的“气候效益”。但RH引起的PM2.5变化是不确定的，因为RH预估的模式间差异很大。
In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from similar to 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998-2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40% of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing-Tianjin-Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Inter-comparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of similar to 0.5 mu g m(-3) in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small "climate benefit", but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.