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大气 PM 在北京的特征和氧化潜力:源解析和季节变化。

Characteristics and oxidative potential of atmospheric PM in Beijing: Source apportionment and seasonal variation.

机构信息

Key Laboratory for Earth Surface and Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.

State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.

出版信息

Sci Total Environ. 2019 Feb 10;650(Pt 1):277-287. doi: 10.1016/j.scitotenv.2018.09.021. Epub 2018 Sep 3.

Abstract

UNLABELLED

PM (particulate matter with the aerodynamic diameter D < 2.5 μm) was hypothesized to generate reactive oxygen species (ROS) and induce oxidative stress associated with inflammation and cardiovascular diseases. In the current study, PM concentrations, water-soluble ions and elements, carbonaceous components and ROS activity characterized by the dithiothreitol (DTT) assay were determined for the PM samples collected in Beijing, China, over a whole year. Source apportionments of PM and DTT activity were also performed. The mean ± standard deviation of PM, DTT (mass-normalized DTT activity) and DTT (volume-normalized DTT activity) were 113.8 ± 62.7 μg·m, 0.13 ± 0.10 nmol·μg·min and 12.26 ± 6.82 nmol·m·min, respectively. The seasonal averages of DTT and DTT exhibited peak values during the local summer. Organic carbon (OC), NO, SO, NH and elemental carbon (EC) were the dominant components in the constituents tested. Higher concentrations of carbonaceous components occurred in autumn and winter compared with spring and summer. Based on the positive matrix factorization model (PMF), the simulation results of source apportionment for PM in Beijing, obtained using the annual data, identified the main categories as follows: dust, coal combustion, secondary sulfate and industrial emissions, vehicle emissions and secondary nitrates. Most detected constituents exhibited significantly positive correlations with DTT (p < 0.01). The results corresponding to multiple linear regression (MLR) between DTT activity and source contribution to PM manifested the sensitivity sequence of DTT activity for the resolved sources as vehicle emissions > secondary sulfate and industrial emissions > coal combustion > dust.

CAPSULE

Based on a descending sequence of relative contribution, the diagnostic sources of DTT activity in PM from Beijing included primarily vehicle emissions, secondary sulfates and industrial emissions, coal combustion, and dust.

摘要

目的

假设 PM(空气动力学直径 D<2.5μm 的颗粒物)会产生活性氧(ROS)并诱导与炎症和心血管疾病相关的氧化应激。在本研究中,对中国北京全年采集的 PM 样本进行了 PM 浓度、水溶性离子和元素、碳质成分和 DTT(二硫苏糖醇)测定法测定的 ROS 活性分析,并进行了 PM 和 DTT 活性的源分配。PM 和 DTT 活性的源分配也进行了。PM、DTT(质量归一化 DTT 活性)和 DTT(体积归一化 DTT 活性)的平均值±标准差分别为 113.8±62.7μg·m-3、0.13±0.10nmol·μg·min-1和 12.26±6.82nmol·m·min-1。DTT 和 DTT 的季节平均值在当地夏季达到峰值。在测试的成分中,有机碳(OC)、NO、SO、NH 和元素碳(EC)是主要成分。与春季和夏季相比,秋季和冬季的碳质成分浓度较高。基于正定矩阵因子模型(PMF),使用年度数据对北京 PM 的源分配进行模拟的结果识别出的主要类别如下:灰尘、煤炭燃烧、二次硫酸盐和工业排放、车辆排放和二次硝酸盐。大多数检测到的成分与 DTT 呈显著正相关(p<0.01)。DTT 活性与 PM 源贡献之间的多元线性回归(MLR)结果表明,解析源对 DTT 活性的敏感性顺序为车辆排放>二次硫酸盐和工业排放>煤炭燃烧>灰尘。

结论

根据相对贡献的降序,北京 PM 中 DTT 活性的诊断源主要包括车辆排放、二次硫酸盐和工业排放、煤炭燃烧和灰尘。

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