State Environmental Protection Key Laboratory of Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China; The People's Government of Dong Ping Town, Chongming County, Shanghai Municipality, China.
State Environmental Protection Key Laboratory of Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China.
Sci Total Environ. 2016 Jul 1;557-558:386-94. doi: 10.1016/j.scitotenv.2016.03.095. Epub 2016 Mar 24.
A year-long simultaneous observation of PM1 and PM2.5 were conducted at ECUST campus in Shanghai, the compositions were analyzed and compared. Results showed that PM2.5 was dominated by PM1 on clear days while the contribution of PM1-2.5 to PM2.5 increased on haze days, indicating that PM2.5 should be given priority to characterize or predict haze pollution. On haze days, accumulation of organic carbon (OC), elemental carbon (EC) and primary organic carbon (POC) in PM1-2.5 was faster than that in PM1. Humic-like substances carbon (Hulis-C) in both PM2.5 and PM1 formed faster than water soluble organic carbon (WSOC) on haze days, hence Hulis-C/WSOC increased with the intensification of haze pollution. In terms of water soluble ions, NO3(-)/SO4(2-) in PM1 increased with the aggravation of haze pollution, implying that mobile sources dominated on haze days, so is nitrogen oxidation ratio (NOR). Liquid water content (LWC) in both PM1 and PM2.5 had positive correlations with relative humidity (RH) but negative correlations with visibility, implying that hygroscopic growth might be a factor for visibility impairment, especially LWC in PM1. By comparison with multi-linear equations of LWC in PM1 and PM2.5, NO3(-) exerted a higher influence on hygroscopicity of PM1 than PM2.5, while RH, WSOC, SO4(2-) and NH4(+) had higher effects on PM2.5, especially WSOC. Source apportionment of PM2.5 was also investigated to provide reference for policy making. Cluster analysis by HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory) model showed that PM2.5 originated from marine aerosols, middle-scale transportation and large-scale transportation. Furthermore, PM2.5 on haze days was dominated by middle-scale transportation. In line with source apportionment by positive matrix factorization (PMF) model, PM2.5 was attributed to secondary inorganics, aged sea salt, combustion emissions, hygroscopic growth and secondary organics. Secondary formation was the principle source of PM2.5. Furthermore, the contribution of combustion emissions to PM2.5 increased with the intensification of haze pollution, which was just opposite to hygroscopic growth, while that of secondary formation kept quite stable on clear days and haze days.
在上海的华东理工大学校园进行了为期一年的 PM1 和 PM2.5 同步观测，对其成分进行了分析和比较。结果表明，在晴天，PM2.5 主要由 PM1 组成，而在霾天，PM1-2.5 对 PM2.5 的贡献增加，表明应优先对 PM2.5 进行特征描述或预测霾污染。在霾天，有机碳（OC）、元素碳（EC）和一次有机碳（POC）在 PM1-2.5 中的积累速度快于 PM1。在霾天，无论是在 PM2.5 还是在 PM1 中，类腐殖质碳（Hulis-C）的形成速度都快于水溶性有机碳（WSOC），因此，随着霾污染的加剧，Hulis-C/WSOC 增加。就水溶性离子而言，PM1 中的 NO3(-)/SO4(2-)随着霾污染的加剧而增加，这意味着在霾天，移动源占主导地位，氮氧化比（NOR）也是如此。PM1 和 PM2.5 中的液态水含量（LWC）与相对湿度（RH）呈正相关，与能见度呈负相关，这意味着吸湿增长可能是导致能见度下降的一个因素，尤其是 PM1 中的 LWC。与 PM1 和 PM2.5 中 LWC 的多元线性方程相比，NO3(-)对 PM1 的吸湿性影响高于 PM2.5，而 RH、WSOC、SO4(2-)和 NH4(+)对 PM2.5 的影响更高，尤其是 WSOC。还对 PM2.5 的来源进行了分配，为决策提供了参考。通过 HYSPLIT（混合单粒子拉格朗日综合轨迹）模型的聚类分析表明，PM2.5 来源于海洋气溶胶、中尺度传输和大尺度传输。此外，霾天的 PM2.5 主要由中尺度传输产生。与正矩阵因子分解（PMF）模型的源分配结果一致，PM2.5 归因于二次无机物、老化的海盐、燃烧排放、吸湿增长和二次有机物。二次形成是 PM2.5 的主要来源。此外，随着霾污染的加剧，燃烧排放对 PM2.5 的贡献增加，这与吸湿增长相反，而二次形成在晴天和霾天保持相当稳定。
