Abstract：
There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a \"seesaw\" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.
摘要：
在细颗粒物（PM2.5）和臭氧（O3）之间的颗粒-臭氧协同关系（PO）方面存在相当大的学术兴趣。使用各种天气天气模式(SWPs)，我们定量评估了PO的变化，这与制定旨在控制空气中复杂污染的政策有关。首先,基于2018年3月至2019年2月的1年抽样数据,采用平方和技术(SS)对长江三角洲(YRD)进行SWPs分类.在YRD地区可以找到五个优势SWP，包括低于SWP1的阿留申低点(发生在今年的45%)，SWP2下的热带气旋(21%)，热带气旋和西太平洋副热带高压(WPSH)低于SWP3(15.4%)，SWP4下的WPSH（6.9%），和SWP5下的大陆高压（3.1％）。PM2.5和O3浓度之间的“跷跷板”现象表现出明显的空间异质性，受气象机制的影响。第二,采用多元线性回归（MLR）模型和偏相关（PCOR）分析来量化主要成分和气象因素对PO的影响。气象变量只能共同解释PM2.5变化的33.0%，但O3为58.0%。O3在低浓度的PM2.5中相互促进，但在高浓度的PM2.5中受到抑制。高相对湿度(RH)有利于PM2.5二次组分的生成,增强了气溶胶的辐射效应和PO的负相关。此外,应注意评估前体水平的综合影响，天气,以及对粒子-臭氧复合污染的化学反应。夏季应加强O3污染物的控制,而重点应该是减少冬季PM2.5污染物。防治措施需要反映天气状况和污染特征的差异,重点关注RH和PM2.5的次要成分。
