• 关键词：环境气象评估指数  PM_2.5  京津冀区域  系统聚类  主成分分析

• 基金项目：天津市自然科学基金（No.19JCQNJC08000）；天津市气象局研究型业务专项（No.202119yjxywzx03）

• 姚青
• 天津市环境气象中心， 中国气象局-南开大学大气环境与健康研究联合实验室， 天津 300074

• 唐颖潇
• 天津市环境气象中心， 中国气象局-南开大学大气环境与健康研究联合实验室， 天津 300074

• 蔡子颖
• 天津市环境气象中心， 中国气象局-南开大学大气环境与健康研究联合实验室， 天津 300074

• 杨旭
• 天津市环境气象中心， 中国气象局-南开大学大气环境与健康研究联合实验室， 天津 300074

• 樊文雁
• 天津市环境气象中心， 中国气象局-南开大学大气环境与健康研究联合实验室， 天津 300074

• 韩素芹
• 天津市环境气象中心， 中国气象局-南开大学大气环境与健康研究联合实验室， 天津 300074

• 摘要：量化空气质量改善过程中气象条件和减排措施的相对贡献， 有助于科学评估减排措施的实施效果. 本文以2017—2019年京津冀区域13个城市PM_2.5质量浓度为研究对象， 采用主成分分析、系统聚类等方法客观确定各次区域的典型代表城市， 并基于环境气象评估指数（EMI）量化空气质量改善过程中气象条件和减排措施的相对贡献. 结果表明， 京津冀区域PM_2.5浓度整体呈南高北低特征， 高值区集中在河北省南部， 冬季区域PM_2.5浓度显著高于其他季节. 经旋转后的主成分分析可划分出2个主成分， 分别对应河北省中南部地区和京津冀北部地区. 系统聚类将京津冀区域分为3个次区域， 经相似性计算获得次区域典型代表城市为承德、唐山和邢台. 以2017年为基准年开展EMI评估， 结果显示2018年1月承德、唐山和邢台PM_2.5浓度下降， 减排和气象条件均有不同程度的贡献； 不利气象条件是2019年1月承德PM_2.5上涨的主要原因， 排放造成同期唐山PM_2.5浓度上升了52.8%，不利气象条件抵消了邢台减排的效果， 并造成其PM_2.5浓度小幅度增加. 京津冀区域各城市PM_2.5浓度的同步变化， 排放和气象条件对不同城市的贡献仍然存在很大差异， 在京津冀区域内划分次区域具有重要意义.

• Abstract：Quantifying the relative contribution of meteorological conditions and emission reduction measures in the air quality improvement is helpful to scientifically evaluate the implementation effect of emission control measures. According to the PM_2.5mass concentration of 13 cities in Beijing-Tianjin-Hebei region（BTH） from 2017 to 2019， the typical cities in each sub-region were objectively determined by principal component analysis（PCA） and system clustering， and the relative contribution of meteorological conditions and emission reduction measures in the air quality improvement was quantified by Environmental Meteorological Index （EMI）. The results showed that the PM_2.5mass concentration was high in the south and low in the north of BTH. The high PM_2.5 value area was concentrated in the south of Hebei Province， and the PM_2.5 mass concentration in winter was significantly higher than that in other seasons. After the rotation of the maximum variance method， the BTH could be divided into two subregions： the central and southern part of Hebei Province and the north of BTH. It was divided into three sub-regions by systematic clustering， and the similarity was obtained by calculating the correlation coefficient and normalized Euclidean distance of each sub-region. The typical cities of the three sub-regions were Chengde， Tangshan and Xingtai. Taking 2017 as the base year， the results of EMI assessment showed that the mass concentration of PM_2.5 in Chengde， Tangshan and Xingtai decreased in January 2018， and the emission reduction and meteorological conditions all contributed to varying degrees. Unfavorable weather conditions were the main reason for the rise of PM_2.5 in Chengde in January 2019. Emissions caused the concentration of PM_2.5 in Tangshan to rise by 52.8% in the same period. Unfavorable meteorological conditions offset the effect of emission reduction in Xingtai and caused a small increase in PM_2.5mass concentration. Although the PM_2.5 mass concentration of different cities in BTH region changed synchronously， the contribution of emissions and meteorological conditions for different cities was still very different， so it was of great significance to regionalize the BTH.

• Key words：Environmental Meteorological Index（EMI）  PM_2.5  Beijing-Tianjin-Hebei region（BTH）  system clustering  principal component analysis

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