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2013-2017 年华北平原逐时平均 PM 浓度与天气形势的关系。

Impact of synoptic weather patterns on 24 h-average PM concentrations in the North China Plain during 2013-2017.

机构信息

School of chemistry and chemical engineering, Southwest University, Chongqing 400715, China.

School of chemistry and chemical engineering, Southwest University, Chongqing 400715, China.

出版信息

Sci Total Environ. 2018 Jun 15;627:200-210. doi: 10.1016/j.scitotenv.2018.01.248. Epub 2018 Feb 3.

Abstract

North China Plain area (NCP) is one of the most densely populated and heavily polluted regions in the world. In the last five years, frequently happened fine particulate matter (PM) serious pollution events were one of the top environmental concerns in China. As PM concentrations are highly influenced by synoptic flow patterns and local meteorological conditions, a two-stage hierarchical clustering method based on dynamic principal component analysis (DPCA) and standard k-means clustering algorithm was employed to classify synoptic wind fields into 6 patterns over the NCP area using the data of 5 PM seasons (Sept. 15th-Apr. 15th) from 2013 to 2017. Among the six identified synoptic patterns, pattern of uniform pressure field (U) and that of zonal high pressure (Z) accounted for 78.21%, 65.55%, 63.56%, 57.11%, 59.13% and 58.27% studied heavy smog pollution events in Beijing, Tianjin, Tangshan, Baoding, Shijiazhuang and Xingtai city. The two particular patterns were associated with uniform pressure field and sparsely latitudinal isobar in 850 hPa level, respectively. They were also characterized by high relative humidity, low temperature, low-speed northerly wind in Tianjin and Tangshan, and southerly wind in the other cities. Under the continuous control of pattern Z, the values of 24 h-average PM were found to increase at a rate of 31.78 μg/m per day. To evaluate the contribution of meteorological factors and precursors to PM levels, linear mixed-effects models (LMMs) were applied to establish relations among 24 h-average PM concentrations, concentrations of main precursors, local meteorological factors and synoptic patterns. Results show that the variations of precursors, local meteorological factors and synoptic flow patterns can explain 51.67%, 19.15% and 14.01% changes of the 24 h-average PM concentrations, respectively. This study illustrates that dense precursor emissions are still the main cause for heavy haze pollution events, although meteorological conditions play almost equal roles sometimes.

摘要

华北平原地区(NCP)是世界上人口最密集、污染最严重的地区之一。在过去的五年中,频繁发生的细颗粒物(PM)严重污染事件是中国最关注的环境问题之一。由于 PM 浓度高度受天气流场和当地气象条件的影响,本研究采用基于动态主成分分析(DPCA)和标准 K-均值聚类算法的两阶段层次聚类方法,对 2013 年至 2017 年 5 个 PM 季节(9 月 15 日至 4 月 15 日)的数据,将天气风场分为 6 种类型。在确定的 6 种天气模式中,均压场模式(U)和纬向高压模式(Z)分别占北京、天津、唐山、保定、石家庄和邢台市研究重度雾霾污染事件的 78.21%、65.55%、63.56%、57.11%、59.13%和 58.27%。这两种特殊模式分别与 850 hPa 水平上的均压场和稀疏纬向等压线有关。它们的特点是相对湿度高、温度低、天津和唐山北风速度慢,其他城市南风速度快。在模式 Z 的持续控制下,发现 24 小时平均 PM 值以每天 31.78μg/m 的速度增加。为了评估气象因素和前体物对 PM 水平的贡献,采用线性混合效应模型(LMMs)建立了 24 小时平均 PM 浓度、主要前体物浓度、当地气象因素和天气流场模式之间的关系。结果表明,前体物、当地气象因素和天气流场模式的变化可以解释 24 小时平均 PM 浓度变化的 51.67%、19.15%和 14.01%。本研究表明,尽管气象条件有时也起着同等重要的作用,但密集的前体物排放仍然是重霾污染事件的主要原因。

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